diff --git a/agenda/labmeetings.html b/agenda/labmeetings.html
index 83d8222..319d800 100644
--- a/agenda/labmeetings.html
+++ b/agenda/labmeetings.html
@@ -508,8 +508,8 @@ <h2 id="labmeetings_1">Labmeetings</h2>
     <li>Tuesday February 6, 2024 16-17 (live): Joint lab meeting with Eva Telzer and Ryan Tsai</li>
     <li>Tuesday February 13, 2024 11-12 (live): Felix Schreiber (external)</li>
     <li>Tuesday February 20, 2024 11-12 (live): Suzanne van de Groep</li>
-    <li>Tuesday February 27, 2024 11-12 (live)</li>
-    <li>Tuesday March 5, 2024 11-12 (live): Sterre van Riel</li>
+    <li>Tuesday February 27, 2024 11-12 (live): Sterre & Noura</li>
+    <li>Tuesday March 5, 2024 11-12 (live)</li>
     <li>Tuesday March 12, 2024 11-12 (live)</li>
     <li>Tuesday March 19, 2024 11-12 (live): Ilse van de Groep</li>
     <li>Tuesday March 26, 2024 11-12 (live)</li>
diff --git a/search/search_index.json b/search/search_index.json
index bae590e..f5feb8d 100644
--- a/search/search_index.json
+++ b/search/search_index.json
@@ -1 +1 @@
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The SYNC lab members","title":"Adding new stuff"},{"location":"about/code-of-conduct.html","text":"Contributor Covenant Code of Conduct Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Our Standards Examples of behavior that contributes to creating a positive environment include: Using welcoming and inclusive language Being respectful of differing viewpoints and experiences Gracefully accepting constructive criticism Focusing on what is best for the community Showing empathy towards other community members Examples of unacceptable behavior by participants include: The use of sexualized language or imagery and unwelcome sexual attention or advances Trolling, insulting/derogatory comments, and personal or political attacks Public or private harassment Publishing others' private information, such as a physical or electronic address, without explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team via this contact form . 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Installation Create a Github account (or log on) Install git locally (click here for instructions for RStudio ) and some Markdown editor (see below) For an introduction to Git(hub), please see the version control chapter . Markdown Markdown is a markup language that you can use to add formatting elements to plaintext text documents. When you create a Markdown-formatted file, you add Markdown syntax to the text to indicate which words and phrases should look different. Every .md (Markdown) file in this repository in fact uses Markdown for formatting! For example, to indicate a second-level header, you type: ## Title of header Advantages of Markdown: It can be used for a lot of things, e.g., for creating html pages It is platform- and operating system independent You can type markdown in any text editor and open .md files with many programs, such as Atom , Zettlr , Rstudio , Typora , online (e.g., Dillinger ) or even in Word ( Writage tool ). See this page for more tools that support Markdown. Markdown resources Markdown guide Cheatsheet More advanced Markdown tricks Contributing step-by-step A. The easy (but not recommended) way fork the repository to your own Github account by clicking the button on the upper right of the repository: Make edits to the files you want to edit in your browser by clicking the pencil at the top right of a file. All editable .md files can be found in the docs folder Write a commit message for your changes and click Commit changes . After having made all the changes you wanted, go to the tab Pull requests > New pull request . Make sure the base repository is eur-synclab/sync-manual master and head repository is your own repository, e.g., DorienHuijser/sync-manual branchinwhichyoumadechanges . Click Create pull request Your pull request will now appear in the eur-synclab repository list of pull requests . If you want, you can assign someone to review your pull request. One of the owners of the repository will review your commits, may request changes and will finally approve the pull request and merge your changes into the eur-synclab/sync-manual master branch. B. The better way fork the repository to your own Github account by clicking the button on the upper right of the repository: Create a new branch in your forked repository which you will use to make changes in (so your master branch will stay\"clean\"): clone your forked repository to your local PC ( using the command-line or Rstudio ) Make local changes. You can open a .md file in the docs folder with multiple text editors such as Typora, Atom, Zettlr, Rstudio, etc.) and, after saving your changes, commit them ( command-line : git commit -a -m \"commit message\" , RStudio . Your changes are now saved locally. Push your commits to your \u201cremote\u201d (online) repository ( using the command-line : git push origin branchname , in Rstudio ) Follow steps 4-7 explained in the Easy way Important: the next time you start working locally, first update your local version of the repository to the most recent version (Command line: git pull upstream [branchname] [be sure to set the upstream repository first], RStudio ). C. The most advanced way Follow the installation steps for mkdocs here Follow steps 1-4 explained in The better way In your prompt , navigate to your repository directory with cd C:/users/username/your/repo/directory and run mkdocs serve . This creates a URL (something like http://127.0.0.1:8000/) which you can open in your internet browser. Here, you can see all changes that you make directly \"live\". Press Cntrl+C to stop this operation. Run mkdocs build . If everything goes correctly, you can now also open the new .html files in the sync-manual/site folder to see what your changes will look like in the browser. These files have to be created in order for the website to work on others' computers. commit your newly built website (html) files, e.g., git commit -A . -m \"Build site\" Follow step 5-7 explained in The better way. Add yourself as a contributor! Go to this Github issue . Type a comment asking the all-contributors bot to add you (use template mentioned in the issue), look for appropriate emojis here . The bot will open a pull request to add you as contributor. After merging with the master branch, your face will appear in the README.md ! Issues and Projects If you would like to see a change that requires more work or input from others before you can start editing yourself, you can open an Issue . There are some great features about Issues: You can assign people to the Issue who should solve it or provide input You can add the issue to a project. In the tab Projects , you can find our Kanban board in which we have made the columns \u201cTo do\u201d, \u201cIn progress\u201d and \u201cDone\u201d. We made this so we have an overview of Issues that still need work and issues that are in progress. You can add labels to an Issue (please do so!) to specify what kind of issue you are writing After the issue has been solved, you can Close it manually. However, if you made a pull request that solves the issue, you can simply comment Closes #issuenr in the pull request. After the pull request has been merged, the issue is automatically closed!","title":"How to contribute"},{"location":"about/contribute.html#how-to-contribute","text":"This lab wiki was built using: Git : a version control system, which allows us to go back to previous versions of the web pages at all times; On Github , a platform that hosts files that use git as a version control system and on which we can collaborate on the wiki; Relying on Mkdocs , a simply static site generator which generates html pages (web pages) from .md (markdown) files.","title":"How to contribute"},{"location":"about/contribute.html#installation","text":"Create a Github account (or log on) Install git locally (click here for instructions for RStudio ) and some Markdown editor (see below) For an introduction to Git(hub), please see the version control chapter .","title":"Installation"},{"location":"about/contribute.html#markdown","text":"Markdown is a markup language that you can use to add formatting elements to plaintext text documents. 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Click Create pull request Your pull request will now appear in the eur-synclab repository list of pull requests . If you want, you can assign someone to review your pull request. One of the owners of the repository will review your commits, may request changes and will finally approve the pull request and merge your changes into the eur-synclab/sync-manual master branch.","title":"A. The easy (but not recommended) way"},{"location":"about/contribute.html#b-the-better-way","text":"fork the repository to your own Github account by clicking the button on the upper right of the repository: Create a new branch in your forked repository which you will use to make changes in (so your master branch will stay\"clean\"): clone your forked repository to your local PC ( using the command-line or Rstudio ) Make local changes. You can open a .md file in the docs folder with multiple text editors such as Typora, Atom, Zettlr, Rstudio, etc.) and, after saving your changes, commit them ( command-line : git commit -a -m \"commit message\" , RStudio . Your changes are now saved locally. Push your commits to your \u201cremote\u201d (online) repository ( using the command-line : git push origin branchname , in Rstudio ) Follow steps 4-7 explained in the Easy way Important: the next time you start working locally, first update your local version of the repository to the most recent version (Command line: git pull upstream [branchname] [be sure to set the upstream repository first], RStudio ).","title":"B. The better way"},{"location":"about/contribute.html#c-the-most-advanced-way","text":"Follow the installation steps for mkdocs here Follow steps 1-4 explained in The better way In your prompt , navigate to your repository directory with cd C:/users/username/your/repo/directory and run mkdocs serve . This creates a URL (something like http://127.0.0.1:8000/) which you can open in your internet browser. Here, you can see all changes that you make directly \"live\". Press Cntrl+C to stop this operation. Run mkdocs build . If everything goes correctly, you can now also open the new .html files in the sync-manual/site folder to see what your changes will look like in the browser. These files have to be created in order for the website to work on others' computers. commit your newly built website (html) files, e.g., git commit -A . -m \"Build site\" Follow step 5-7 explained in The better way.","title":"C. The most advanced way"},{"location":"about/contribute.html#add-yourself-as-a-contributor","text":"Go to this Github issue . Type a comment asking the all-contributors bot to add you (use template mentioned in the issue), look for appropriate emojis here . The bot will open a pull request to add you as contributor. After merging with the master branch, your face will appear in the README.md !","title":"Add yourself as a contributor!"},{"location":"about/contribute.html#issues-and-projects","text":"If you would like to see a change that requires more work or input from others before you can start editing yourself, you can open an Issue . There are some great features about Issues: You can assign people to the Issue who should solve it or provide input You can add the issue to a project. In the tab Projects , you can find our Kanban board in which we have made the columns \u201cTo do\u201d, \u201cIn progress\u201d and \u201cDone\u201d. We made this so we have an overview of Issues that still need work and issues that are in progress. You can add labels to an Issue (please do so!) to specify what kind of issue you are writing After the issue has been solved, you can Close it manually. However, if you made a pull request that solves the issue, you can simply comment Closes #issuenr in the pull request. After the pull request has been merged, the issue is automatically closed!","title":"Issues and Projects"},{"location":"about/intro-sync.html","text":"The SYNC lab SYNC stands for Society, Youth and Neuroscience Connected For more information on the SYNC lab, visit the SYNC website Mission Our mission is to bridge multiple levels of measurement to understand how young people develop into contributing members of society. The SYNC lab is firmly grounded in exciting new perspectives that have emerged from understanding the dynamic development of the adolescent brain. Vision Our vision is that science becomes better when conducted together with societal partners, including youth panels, schools, and co-creation teams. We also strongly believe in interdisciplinary research teams; therefore, we work with researchers from different scientific disciplines, including the social sciences, life sciences, and humanities. Moreover, the Erasmus SYNC lab embraces Open Science through practices such as pre-registration, data sharing and open-access publishing. Together, we hope we can work on providing the scientific building blocks needed to help shape a better future for the current and next generation of youth. Diversity and inclusivity At the Erasmus SYNC-lab we speak up against racism and strive to enable equality. The Erasmus University Rotterdam aspires to be an inclusive organization where everyone feels at home and therefore get the opportunity to excel. Talent is the basis; diversity is the added value. We believe in the enrichment that a mix of different people bring. We aim to connect all of society : regardless of origin, skin color, gender, education level, or sexual preference.","title":"The SYNC lab"},{"location":"about/intro-sync.html#the-sync-lab","text":"","title":"The SYNC lab"},{"location":"about/intro-sync.html#sync-stands-for-society-youth-and-neuroscience-connected","text":"For more information on the SYNC lab, visit the SYNC website","title":"SYNC stands for Society, Youth and Neuroscience Connected"},{"location":"about/intro-sync.html#mission","text":"Our mission is to bridge multiple levels of measurement to understand how young people develop into contributing members of society. 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Together, we hope we can work on providing the scientific building blocks needed to help shape a better future for the current and next generation of youth.","title":"Vision"},{"location":"about/intro-sync.html#diversity-and-inclusivity","text":"At the Erasmus SYNC-lab we speak up against racism and strive to enable equality. The Erasmus University Rotterdam aspires to be an inclusive organization where everyone feels at home and therefore get the opportunity to excel. Talent is the basis; diversity is the added value. We believe in the enrichment that a mix of different people bring. We aim to connect all of society : regardless of origin, skin color, gender, education level, or sexual preference.","title":"Diversity and inclusivity"},{"location":"agenda/holidays.html","text":"Holidays The link to the schedule in which you can fill in your holidays, conferences, 'writing weeks', etc. can be found here . Feel free to decide yourself whether you would like to use this.","title":"Holidays"},{"location":"agenda/holidays.html#holidays","text":"The link to the schedule in which you can fill in your holidays, conferences, 'writing weeks', etc. can be found here . Feel free to decide yourself whether you would like to use this.","title":"Holidays"},{"location":"agenda/labmeetings.html","text":"Labmeetings Currently, we meet at regular intervals for different purposes: Weekly stand-up Labmeetings Data management meetings Journal clubs Attending our meetings If you would like to attend one of our meetings as attendee or guest speaker, please feel free to send us an email via this contact form . Stand-up The weekly stand-up is meant to keep each other up to date on our work (and sometimes personal) activities and to ask for help when needed. We all shortly state what we are doing and note whether or not we have something we need help with. Labmeetings In the labmeetings, labmembers or external visitors share their latest results or ideas for new research. It is a great opportunity to get feedback in a supportive and welcoming environment. Lab meetings can also be used to practice a (conference) talk. Collaborators from outside the SYNC lab are more than welcome for labmeetings where research from the specific collaboration is discussed. So do not forget to invite your collaborators when presenting! The schedule of the labmeetings for 2023-2024 (regularly updated): Tuesday September 5, 2023 11-12 (live): Daphne van de Bongardt Tuesday September 12, 2023 11-12 (live): Noura & Nienke (qualitative data analysis) Tuesday September 19, 2023 11-12 (live): Eleni & Noura (sharepoint) and updates Thursday September 26, 2023 11-12 (live): Kayla & Yara (YX social inequality update) Tuesday October 3, 2023 11-12 (live): Lysanne te Brinke & Yolijn Aarts Tuesday October 10, 2023 11-12: cancelled because of GUTS conference Tuesday October 17, 2023 11-12 (live): Sophie Sweijen Thursday October 24, 2023 11-12 (live): Ja\u00efr van Nes Thursday October 31, 2023 11-12 (live): Elo\u00efse Geenjaar Tuesday November 7, 2023 11-12 (live): Ann Hogenhuis & Michelle Achterberg Tuesday November 14, 2023 11-12 (live): Kayla Green & Yara Toenders Tuesday November 21, 2023 11-12 (live): Yara Toenders Thursday November 28, 2023 11-12 (live): Lina van Drunen Thursday December 5, 2023 11-12 (live): Sterre van Riel & Noura Borggreven Tuesday December 12, 2023 11-12 (live): Julianna Lopez Tuesday December 19, 2023 11-12 (live) Tuesday January 9, 2024 11-12 (live) Tuesday January 16, 2024 11-12 (live): Yolijn Aarts & Kitty de Vries: GUTS Tuesday January 23, 2024 11-12 (live): Kayla Green Tuesday January 30, 2024 11-12 (live): Noura Borggreven & Sterre van Riel: Expeditie NEXT at the Hefhouse Tuesday February 6, 2024 11-12 (live): Eveline Crone & Lysanne te Brinke: on being a scientist Tuesday February 6, 2024 16-17 (live): Joint lab meeting with Eva Telzer and Ryan Tsai Tuesday February 13, 2024 11-12 (live): Felix Schreiber (external) Tuesday February 20, 2024 11-12 (live): Suzanne van de Groep Tuesday February 27, 2024 11-12 (live) Tuesday March 5, 2024 11-12 (live): Sterre van Riel Tuesday March 12, 2024 11-12 (live) Tuesday March 19, 2024 11-12 (live): Ilse van de Groep Tuesday March 26, 2024 11-12 (live) Tuesday April 2, 2024 11-12 (live) Tuesday April 9, 2024 11-12 (live) Tuesday April 16, 2024 11-12 (live) Tuesday April 23, 2024 11-12 (live) Tuesday April 30, 2024 11-12 (live) Tuesday May 7, 2024 11-12 (live) Tuesday May 14, 2024 11-12 (live) Thursday May 16 & Friday May 17, 2024 11-12 (live): Visit Gregoire Borst and lab Tuesday May 21, 2024 11-12 (live) Tuesday May 28, 2024 11-12 (live) Tuesday June 4, 2024 11-12 (live) Tuesday June 11, 2024 11-12 (live) Tuesday June 18, 2024 11-12 (live) Tuesday June 25, 2024 11-12 (live) Data management meetings In the data management meetings, updates on ongoing projects and tips on data management are shared by the data managers. The schedule of the online data management meetings for 2022-2023 (regularly updated): Monday October 31, 2022 15-16: OSF Monday November 28, 2022 15-16: Research Drive and alternatives Monday January 30, 2023 15-16: electronic logbook Monday March 27, 2023 15-16: version control Monday May 8, 2023 15-16: reserve DOI + open science journals Monday June 5, 2023, 14-15: privacy/access externals Monday June 26, 2023 15-16: sharing posters/presentations/graphics Journal clubs Starting November 2022, we have planned several journal clubs about specific topics, focused on theoretical and methodological papers. This allows us to dive deeper into theoretical aspects of our work and remain up to date with current literature. Example topics from the past are theories on social media in adolescence and the importance of effect sizes in scientific research. The schedule of the live journal clubs for 2022-2023 (regularly updated): Tuesday November 22, 2022 14-15: Lysanne and Lina Tuesday January 10, 2023 14-15: Kayla and Eloise Tuesday February 21, 2023 14-15: Sophie and Yara Tuesday April 4, 2023 14-15: Simone Tuesday May 16, 2023 14-15: Hannah and Ilse Tuesday June 27, 2023 14-15","title":"Labmeetings"},{"location":"agenda/labmeetings.html#labmeetings","text":"Currently, we meet at regular intervals for different purposes: Weekly stand-up Labmeetings Data management meetings Journal clubs","title":"Labmeetings"},{"location":"agenda/labmeetings.html#attending-our-meetings","text":"If you would like to attend one of our meetings as attendee or guest speaker, please feel free to send us an email via this contact form .","title":"Attending our meetings"},{"location":"agenda/labmeetings.html#stand-up","text":"The weekly stand-up is meant to keep each other up to date on our work (and sometimes personal) activities and to ask for help when needed. We all shortly state what we are doing and note whether or not we have something we need help with.","title":"Stand-up"},{"location":"agenda/labmeetings.html#labmeetings_1","text":"In the labmeetings, labmembers or external visitors share their latest results or ideas for new research. It is a great opportunity to get feedback in a supportive and welcoming environment. Lab meetings can also be used to practice a (conference) talk. Collaborators from outside the SYNC lab are more than welcome for labmeetings where research from the specific collaboration is discussed. So do not forget to invite your collaborators when presenting! The schedule of the labmeetings for 2023-2024 (regularly updated): Tuesday September 5, 2023 11-12 (live): Daphne van de Bongardt Tuesday September 12, 2023 11-12 (live): Noura & Nienke (qualitative data analysis) Tuesday September 19, 2023 11-12 (live): Eleni & Noura (sharepoint) and updates Thursday September 26, 2023 11-12 (live): Kayla & Yara (YX social inequality update) Tuesday October 3, 2023 11-12 (live): Lysanne te Brinke & Yolijn Aarts Tuesday October 10, 2023 11-12: cancelled because of GUTS conference Tuesday October 17, 2023 11-12 (live): Sophie Sweijen Thursday October 24, 2023 11-12 (live): Ja\u00efr van Nes Thursday October 31, 2023 11-12 (live): Elo\u00efse Geenjaar Tuesday November 7, 2023 11-12 (live): Ann Hogenhuis & Michelle Achterberg Tuesday November 14, 2023 11-12 (live): Kayla Green & Yara Toenders Tuesday November 21, 2023 11-12 (live): Yara Toenders Thursday November 28, 2023 11-12 (live): Lina van Drunen Thursday December 5, 2023 11-12 (live): Sterre van Riel & Noura Borggreven Tuesday December 12, 2023 11-12 (live): Julianna Lopez Tuesday December 19, 2023 11-12 (live) Tuesday January 9, 2024 11-12 (live) Tuesday January 16, 2024 11-12 (live): Yolijn Aarts & Kitty de Vries: GUTS Tuesday January 23, 2024 11-12 (live): Kayla Green Tuesday January 30, 2024 11-12 (live): Noura Borggreven & Sterre van Riel: Expeditie NEXT at the Hefhouse Tuesday February 6, 2024 11-12 (live): Eveline Crone & Lysanne te Brinke: on being a scientist Tuesday February 6, 2024 16-17 (live): Joint lab meeting with Eva Telzer and Ryan Tsai Tuesday February 13, 2024 11-12 (live): Felix Schreiber (external) Tuesday February 20, 2024 11-12 (live): Suzanne van de Groep Tuesday February 27, 2024 11-12 (live) Tuesday March 5, 2024 11-12 (live): Sterre van Riel Tuesday March 12, 2024 11-12 (live) Tuesday March 19, 2024 11-12 (live): Ilse van de Groep Tuesday March 26, 2024 11-12 (live) Tuesday April 2, 2024 11-12 (live) Tuesday April 9, 2024 11-12 (live) Tuesday April 16, 2024 11-12 (live) Tuesday April 23, 2024 11-12 (live) Tuesday April 30, 2024 11-12 (live) Tuesday May 7, 2024 11-12 (live) Tuesday May 14, 2024 11-12 (live) Thursday May 16 & Friday May 17, 2024 11-12 (live): Visit Gregoire Borst and lab Tuesday May 21, 2024 11-12 (live) Tuesday May 28, 2024 11-12 (live) Tuesday June 4, 2024 11-12 (live) Tuesday June 11, 2024 11-12 (live) Tuesday June 18, 2024 11-12 (live) Tuesday June 25, 2024 11-12 (live)","title":"Labmeetings"},{"location":"agenda/labmeetings.html#data-management-meetings","text":"In the data management meetings, updates on ongoing projects and tips on data management are shared by the data managers. The schedule of the online data management meetings for 2022-2023 (regularly updated): Monday October 31, 2022 15-16: OSF Monday November 28, 2022 15-16: Research Drive and alternatives Monday January 30, 2023 15-16: electronic logbook Monday March 27, 2023 15-16: version control Monday May 8, 2023 15-16: reserve DOI + open science journals Monday June 5, 2023, 14-15: privacy/access externals Monday June 26, 2023 15-16: sharing posters/presentations/graphics","title":"Data management meetings"},{"location":"agenda/labmeetings.html#journal-clubs","text":"Starting November 2022, we have planned several journal clubs about specific topics, focused on theoretical and methodological papers. This allows us to dive deeper into theoretical aspects of our work and remain up to date with current literature. Example topics from the past are theories on social media in adolescence and the importance of effect sizes in scientific research. The schedule of the live journal clubs for 2022-2023 (regularly updated): Tuesday November 22, 2022 14-15: Lysanne and Lina Tuesday January 10, 2023 14-15: Kayla and Eloise Tuesday February 21, 2023 14-15: Sophie and Yara Tuesday April 4, 2023 14-15: Simone Tuesday May 16, 2023 14-15: Hannah and Ilse Tuesday June 27, 2023 14-15","title":"Journal clubs"},{"location":"data-management/FAIR.html","text":"The FAIR principles In the data management world, making data \"FAIR\" is the ideal situation. Making your data FAIR facilitates knowledge discovery by assisting humans and machines in their discovery of and access to the data. See all FAIR principles and their explanation on the GO FAIR website . See also this FAIR data checklist to see if you have met all FAIR requirements. Findable by both humans and machines Include metadata that allow the discovery of interesting datasets: the dataset should be findable with a google datasets search. Select a data repository early on. Make sure the repository provides a persistent identifier for your data. Check the repository's data format and metadata requirements: do they provide descriptive information about the context, quality and condition, or characteristics of the data? Accessible: stored for the long term with well-defined access conditions Think about the security, legal conditions, sustainability and access conditions of the data. guarantee longevity of the data, e.g., by submitting it to a repository that has a certification like the Data Seal of Approval or an ISO certification check and describe the legal conditions under which the data can be made available establish an embargo period if necessary make sure your ICT infrastructure will keep the (meta)data available even in case of equipment failure or human error Interoperable: ready to be combined with other datasets Think about the software, documentation standards (e.g., the same labels for the same variables) and formats. This differs for different disciplines. select commonly used data formats (such as BIDS for neuroimaging data) select commonly used vocabularies (controlled vocabularies if applicable) for data items if your (meta)data relates to other datasets, indicate how Reusable Think about the licensing and provenance (can you trust this data?) of the data. make sure you keep proper provenance information: details about how and where the data was generated, including machine settings, and details about all processing steps, such as the software tools with their versions and parameters select the right minimal metadata standard and collect the necessary metadata, see link select a license for the (meta)data and the associated software tools make sure the important conclusions of your study will not only be available in a paper in narrated form, but also in a digital file (e.g., a nanopublication)","title":"FAIR data"},{"location":"data-management/FAIR.html#the-fair-principles","text":"In the data management world, making data \"FAIR\" is the ideal situation. Making your data FAIR facilitates knowledge discovery by assisting humans and machines in their discovery of and access to the data. See all FAIR principles and their explanation on the GO FAIR website . See also this FAIR data checklist to see if you have met all FAIR requirements.","title":"The FAIR principles"},{"location":"data-management/FAIR.html#findable-by-both-humans-and-machines","text":"Include metadata that allow the discovery of interesting datasets: the dataset should be findable with a google datasets search. Select a data repository early on. Make sure the repository provides a persistent identifier for your data. Check the repository's data format and metadata requirements: do they provide descriptive information about the context, quality and condition, or characteristics of the data?","title":"Findable by both humans and machines"},{"location":"data-management/FAIR.html#accessible-stored-for-the-long-term-with-well-defined-access-conditions","text":"Think about the security, legal conditions, sustainability and access conditions of the data. guarantee longevity of the data, e.g., by submitting it to a repository that has a certification like the Data Seal of Approval or an ISO certification check and describe the legal conditions under which the data can be made available establish an embargo period if necessary make sure your ICT infrastructure will keep the (meta)data available even in case of equipment failure or human error","title":"Accessible: stored for the long term with well-defined access conditions"},{"location":"data-management/FAIR.html#interoperable-ready-to-be-combined-with-other-datasets","text":"Think about the software, documentation standards (e.g., the same labels for the same variables) and formats. This differs for different disciplines. select commonly used data formats (such as BIDS for neuroimaging data) select commonly used vocabularies (controlled vocabularies if applicable) for data items if your (meta)data relates to other datasets, indicate how","title":"Interoperable: ready to be combined with other datasets"},{"location":"data-management/FAIR.html#reusable","text":"Think about the licensing and provenance (can you trust this data?) of the data. make sure you keep proper provenance information: details about how and where the data was generated, including machine settings, and details about all processing steps, such as the software tools with their versions and parameters select the right minimal metadata standard and collect the necessary metadata, see link select a license for the (meta)data and the associated software tools make sure the important conclusions of your study will not only be available in a paper in narrated form, but also in a digital file (e.g., a nanopublication)","title":"Reusable"},{"location":"data-management/codebooks.html","text":"Creating and using codebooks A codebook or data dictionary helps people understand your data, by explaining what the variable names and values in your data files (i.e., the metadata) mean. As such, a codebook is important for making your research more reproducible. Obviously, a codebook can be very beneficial for direct collaborations and your future self, but you might also consider using one if you plan to (openly) share datasets. A Primer on creating Codebooks Before you start creating a codebook, consider reading this primer on creating data dictionaries and shareable datasets: Buchanan, E. M., Crain, S. E., Cunningham, A. L., Johnson, H. R., Stash, H. E., Papadatou-Pastou, M., \u2026 Aczel, B. (2019, May 20). Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset. https://doi.org/10.31219/osf.io/vd4y3 Creating a Qualtrics Data Dictionary If you are using Qualtrics to collect questionnare data, you can use the Data Dictionary Creator to create a codebook for your dataset. Creating a Markdown Codebook from your R dataframe If you use R to analyze your data, you can use the codebook package to create a codebook based on the dataframe you are working with. Creating a Castor Data Dictionary If you are using the Castor Electronic Data Capture system to capture, process and integrate your data, you are required to build your study into the system. Before you start building your study, it is recommended to make a data dictionary, which the building process much easier. The added bonus is that you also have a data dictionary to use for your own research and collaborations. You can find out more about creating a Castor data dictionary here . If you did not make a Data Dictionary before building the study, or if you want to easily check some changes you have made later, you can also export a data dictionary for your study.","title":"Codebooks"},{"location":"data-management/codebooks.html#creating-and-using-codebooks","text":"A codebook or data dictionary helps people understand your data, by explaining what the variable names and values in your data files (i.e., the metadata) mean. As such, a codebook is important for making your research more reproducible. Obviously, a codebook can be very beneficial for direct collaborations and your future self, but you might also consider using one if you plan to (openly) share datasets.","title":"Creating and using codebooks"},{"location":"data-management/codebooks.html#a-primer-on-creating-codebooks","text":"Before you start creating a codebook, consider reading this primer on creating data dictionaries and shareable datasets: Buchanan, E. M., Crain, S. E., Cunningham, A. L., Johnson, H. R., Stash, H. E., Papadatou-Pastou, M., \u2026 Aczel, B. (2019, May 20). Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset. https://doi.org/10.31219/osf.io/vd4y3","title":"A Primer on creating Codebooks"},{"location":"data-management/codebooks.html#creating-a-qualtrics-data-dictionary","text":"If you are using Qualtrics to collect questionnare data, you can use the Data Dictionary Creator to create a codebook for your dataset.","title":"Creating a Qualtrics Data Dictionary"},{"location":"data-management/codebooks.html#creating-a-markdown-codebook-from-your-r-dataframe","text":"If you use R to analyze your data, you can use the codebook package to create a codebook based on the dataframe you are working with.","title":"Creating a Markdown Codebook from your R dataframe"},{"location":"data-management/codebooks.html#creating-a-castor-data-dictionary","text":"If you are using the Castor Electronic Data Capture system to capture, process and integrate your data, you are required to build your study into the system. Before you start building your study, it is recommended to make a data dictionary, which the building process much easier. The added bonus is that you also have a data dictionary to use for your own research and collaborations. You can find out more about creating a Castor data dictionary here . If you did not make a Data Dictionary before building the study, or if you want to easily check some changes you have made later, you can also export a data dictionary for your study.","title":"Creating a Castor Data Dictionary"},{"location":"data-management/data-security.html","text":"Data security protocol We researchers deal with a lot of data: task data, questionnaire data, MRI or EEG data, but also contact information, health information, etc. Most of these data are highly sensitive, in that the risk of identification is high. Also, most of us probably do not want people who aren't involved in your project to have access to our data without our knowledge. In order to deal with the sensitivity of the data and prevent them from being stolen, it is imperative that we ensure the highest possible data security. This document contains some tips to maximize data security. By following these tips, you can be more confident that your participants\u2019 privacy will be guarded and the university will not get sued :smile: Loss of data or other problems? Always report a data breach to the Servicedesk: servicedesk@eur.nl (phone +31 (0)10 408 8880), click here for more information . General data security principles Always report a (possible) data leak/breach Every loss of data is a potential data leak, such as: stolen or lost digital files: USBs, laptops, external hard drives, data that is not backed-up, etc. stolen or lost printed personal data, e.g, a note containing a password, lists with grades, etc. viruses on your PC or hacked accounts, including phishing mails both pseudonymized and anonymized data need to be reported Contact your supervisor and Servicedesk (servicedesk@eur.nl) Report the (possible) leak as soon as possible, so that we can adequately respond and limit the amount of damage for participants as much as possible. Keep in mind that measures taken are meant to secure the data, not to punish those involved! Anonymize your data The best way to guard the privacy of your participants is to anonymize your data, so that the data cannot be traced back to participants (not even with a key containing the name-number links). For longitudinal data, this can get complicated, since you want to be able to link the data of the same subject and may also need the participants\u2019 contact info for next waves. Choose safe passwords for your devices This concerns your EUR account, your laptop, your phone (if it has email on it), and all other devices that contain data. Use a sentence instead of a word: they are harder to crack and easier to remember (especially when they are long and contain letters, numbers and signs). Use a password manager to keep all your passwords safe, such as Lastpass . Read more about Lastpass Lastpass is a password manager that can store all your passwords safely in the cloud. You have to think of a master password - a very strong password - only once. As soon as you log in using that password, you have access to all passwords that you saved in your vault. With Lastpass you can ( all features ): Create safe passwords (no creativity required from your end) Never again have to remember passwords for all of your accounts by heart Store your passwords safely Autofill passwords on websites so that signing on will be a breeze Share passwords with others (free version: share with 1 person) Save secure notes and other details as well You can either install lastpass on your PC (or download the mobile app) or install an extension in your browser. Get started More information here User manual If possible, change your passwords or codes (e.g., to lockers) regularly Share passwords only with the people who really need it Protect mobile devices: Use a safe internet network (preferably Eduroam): never use an open network. Preferably use EduVPN , which is free for university employees and makes sure that the connection is safe. Make sure you can wipe the device and change the password(s) from a distance in case of theft or loss Make sure to have a copy of the information on the university system Always install all security updates and, if possible, antivirus and anti-malware software Do not install jailbreak/root (gaining privileged access to the operating system) Do not save confidential information, unless it is well-protected Protect files with passwords too If a file contains personal data, such as contact information, the link to a participant number or data on the MRI checklist, protect it with a password: In Word and Excel: File > Info > Protect document/Workbook structure > Encrypt with password. You can also restrict editing via these options. Save the password somewhere safe so that you can always access the document: if you lose the password, you cannot access the document anymore. Only give the password to those who really need it. Try to restrict the amount of people that have access to the document. Keep paper data (logs, questionnaires, MRI checklists) locked up Do not leave data behind in labs after testing: take them with you! When testing multiple participants in one day, do not leave data from a previous participant laying around Keep papers in a locked closet or a locker and only give access to people that need it If necessary, keep a record of who has access Do not take papers with such data home or outside, unless strictly necessary Keep a clear-desk policy Do not leave any data unattended if you leave your desk for a longer period of time. For digital data: lock your screen (Cntrl+Alt+Delete > Enter or: Windows+L) For paper data: put them in a closed closet or locker or lock your room if no one else is present When testing participants, do not leave data from a previous participant laying around and take them with you when you leave This includes all desks: your own workspace, the secretariat, computer room, the lab, etc. Email safely When emailing large amounts of people (e.g., all your participants for a project newsletter), put the email addresses in the BCC (blind carbon copy) field, so that the receivers cannot see who else got the email. Put your own email address in the \u201cTo\u201d field. Where possible, use your university email, which has a safe connection with the university servers. Avoid using Hotmail, Gmail or Yahoo. Never send research data via email (except when encrypted or using tools like SURF filesender). Print safely Use Secure printing to print confidential information via a password: The printer will only start printing when you have filled in a personal pin code. Securely printed documents will be erased from the university servers immediately after printing. The settings of the print job cannot be adjusted at the printer When throwing away confidential information on paper, use a container that can be locked (especially made for confidential paper). Storing your data Aim not to store data on local drives Use the Research Drive, which is automatically backed-up and secured through the university. If you are processing data on your local Data drive, be sure to back it up at the Research Drive. Don\u2019t use personal accounts to store data long-term: if you leave the university suddenly, your data will not be accessible for others! Do not store identifiable information on personal devices It is only permitted to work with sensitive data when this is necessary for data collection, processing or planning and, officially, only when participants have given their permission. If you do work with sensitive data on a personal computer (e.g., laptop), remove the data after the analyses. Do not store non-anonymized data in the cloud Never save documents online, just open them. You never know who will get their hands on your data when you store it in the cloud. If you want to work at home, use Owncloud to interact with the Research Drive data Or use Remote desktop: gives access to your university desktop, see this link for a manual Or use SURFdrive , a safe alternative to Google Drive that everyone with a (Dutch) university account has access to (500 GB of personal storage) N.B. You can request SURFdrive also for students or give students the link If using a local drive, laptop, USB, external hard drive, or video camera, the following rules apply: If you are processing data on your local drive, be sure to always back it up on the Research Drive If possible, protect the device, hard drive or drive with a password Do not put the passwords to laptops on the laptop itself When the data have been saved at the right location, delete the data from the device (shift + delete or empty the recycling bin) If you have to take devices onto the street, bring them to a (safe and appointed) university location as quickly as possible. Do not bring them home unless absolutely necessary. If you have to take data home, do let leave them unattended in a (semi)public place (such as a car or library). If possible, leave it in a locked room. Communication and sharing Do not share data via email attachments, Google Drive, etc. Instead of email attachments, use SURF filesender . Email attachments are saved on mail servers and on your PC, whereas this is not the case with filesender. If you are sending an internal email, use a hyperlink or the path to the relevant folder where possible If you want others to be able to edit the documents you share, use SURFdrive or share only the relevant files via Research Drive. Be sure the data shared are anonymous. Do not talk about or analyze individual data in public spaces For example in the elevator, a common room, public transport, social media, emails, etc. Coding videos and audio is only allowed where other researchers from the same project work or in special coding rooms Coding audio in a public space is only allowed when others cannot hear the audio, e.g., because you are wearing headphones Transcribe audio and video using safe websites such as uitgetypt.nl Contacting participants outside the university When contacting participants outside the university, e.g., via your own mobile phone, make sure not to send any identifiable information via your phone (incl. sms or whatsapp) Avoid coupling participant numbers with phone numbers and/or names in emails or whatsapp messages","title":"Data security protocol"},{"location":"data-management/data-security.html#data-security-protocol","text":"We researchers deal with a lot of data: task data, questionnaire data, MRI or EEG data, but also contact information, health information, etc. Most of these data are highly sensitive, in that the risk of identification is high. Also, most of us probably do not want people who aren't involved in your project to have access to our data without our knowledge. In order to deal with the sensitivity of the data and prevent them from being stolen, it is imperative that we ensure the highest possible data security. This document contains some tips to maximize data security. By following these tips, you can be more confident that your participants\u2019 privacy will be guarded and the university will not get sued :smile:","title":"Data security protocol"},{"location":"data-management/data-security.html#loss-of-data-or-other-problems","text":"Always report a data breach to the Servicedesk: servicedesk@eur.nl (phone +31 (0)10 408 8880), click here for more information .","title":"Loss of data or other problems?"},{"location":"data-management/data-security.html#general-data-security-principles","text":"","title":"General data security principles"},{"location":"data-management/data-security.html#always-report-a-possible-data-leakbreach","text":"Every loss of data is a potential data leak, such as: stolen or lost digital files: USBs, laptops, external hard drives, data that is not backed-up, etc. stolen or lost printed personal data, e.g, a note containing a password, lists with grades, etc. viruses on your PC or hacked accounts, including phishing mails both pseudonymized and anonymized data need to be reported Contact your supervisor and Servicedesk (servicedesk@eur.nl) Report the (possible) leak as soon as possible, so that we can adequately respond and limit the amount of damage for participants as much as possible. Keep in mind that measures taken are meant to secure the data, not to punish those involved!","title":"Always report a (possible) data leak/breach"},{"location":"data-management/data-security.html#anonymize-your-data","text":"The best way to guard the privacy of your participants is to anonymize your data, so that the data cannot be traced back to participants (not even with a key containing the name-number links). For longitudinal data, this can get complicated, since you want to be able to link the data of the same subject and may also need the participants\u2019 contact info for next waves.","title":"Anonymize your data"},{"location":"data-management/data-security.html#choose-safe-passwords-for-your-devices","text":"This concerns your EUR account, your laptop, your phone (if it has email on it), and all other devices that contain data. Use a sentence instead of a word: they are harder to crack and easier to remember (especially when they are long and contain letters, numbers and signs). Use a password manager to keep all your passwords safe, such as Lastpass . Read more about Lastpass Lastpass is a password manager that can store all your passwords safely in the cloud. You have to think of a master password - a very strong password - only once. As soon as you log in using that password, you have access to all passwords that you saved in your vault. With Lastpass you can ( all features ): Create safe passwords (no creativity required from your end) Never again have to remember passwords for all of your accounts by heart Store your passwords safely Autofill passwords on websites so that signing on will be a breeze Share passwords with others (free version: share with 1 person) Save secure notes and other details as well You can either install lastpass on your PC (or download the mobile app) or install an extension in your browser. Get started More information here User manual If possible, change your passwords or codes (e.g., to lockers) regularly Share passwords only with the people who really need it Protect mobile devices: Use a safe internet network (preferably Eduroam): never use an open network. Preferably use EduVPN , which is free for university employees and makes sure that the connection is safe. Make sure you can wipe the device and change the password(s) from a distance in case of theft or loss Make sure to have a copy of the information on the university system Always install all security updates and, if possible, antivirus and anti-malware software Do not install jailbreak/root (gaining privileged access to the operating system) Do not save confidential information, unless it is well-protected","title":"Choose safe passwords for your devices"},{"location":"data-management/data-security.html#protect-files-with-passwords-too","text":"If a file contains personal data, such as contact information, the link to a participant number or data on the MRI checklist, protect it with a password: In Word and Excel: File > Info > Protect document/Workbook structure > Encrypt with password. You can also restrict editing via these options. Save the password somewhere safe so that you can always access the document: if you lose the password, you cannot access the document anymore. Only give the password to those who really need it. Try to restrict the amount of people that have access to the document.","title":"Protect files with passwords too"},{"location":"data-management/data-security.html#keep-paper-data-logs-questionnaires-mri-checklists-locked-up","text":"Do not leave data behind in labs after testing: take them with you! When testing multiple participants in one day, do not leave data from a previous participant laying around Keep papers in a locked closet or a locker and only give access to people that need it If necessary, keep a record of who has access Do not take papers with such data home or outside, unless strictly necessary","title":"Keep paper data (logs, questionnaires, MRI checklists) locked up"},{"location":"data-management/data-security.html#keep-a-clear-desk-policy","text":"Do not leave any data unattended if you leave your desk for a longer period of time. For digital data: lock your screen (Cntrl+Alt+Delete > Enter or: Windows+L) For paper data: put them in a closed closet or locker or lock your room if no one else is present When testing participants, do not leave data from a previous participant laying around and take them with you when you leave This includes all desks: your own workspace, the secretariat, computer room, the lab, etc.","title":"Keep a clear-desk policy"},{"location":"data-management/data-security.html#email-safely","text":"When emailing large amounts of people (e.g., all your participants for a project newsletter), put the email addresses in the BCC (blind carbon copy) field, so that the receivers cannot see who else got the email. Put your own email address in the \u201cTo\u201d field. Where possible, use your university email, which has a safe connection with the university servers. Avoid using Hotmail, Gmail or Yahoo. Never send research data via email (except when encrypted or using tools like SURF filesender).","title":"Email safely"},{"location":"data-management/data-security.html#print-safely","text":"Use Secure printing to print confidential information via a password: The printer will only start printing when you have filled in a personal pin code. Securely printed documents will be erased from the university servers immediately after printing. The settings of the print job cannot be adjusted at the printer When throwing away confidential information on paper, use a container that can be locked (especially made for confidential paper).","title":"Print safely"},{"location":"data-management/data-security.html#storing-your-data","text":"","title":"Storing your data"},{"location":"data-management/data-security.html#aim-not-to-store-data-on-local-drives","text":"Use the Research Drive, which is automatically backed-up and secured through the university. If you are processing data on your local Data drive, be sure to back it up at the Research Drive. Don\u2019t use personal accounts to store data long-term: if you leave the university suddenly, your data will not be accessible for others!","title":"Aim not to store data on local drives"},{"location":"data-management/data-security.html#do-not-store-identifiable-information-on-personal-devices","text":"It is only permitted to work with sensitive data when this is necessary for data collection, processing or planning and, officially, only when participants have given their permission. If you do work with sensitive data on a personal computer (e.g., laptop), remove the data after the analyses.","title":"Do not store identifiable information on personal devices"},{"location":"data-management/data-security.html#do-not-store-non-anonymized-data-in-the-cloud","text":"Never save documents online, just open them. You never know who will get their hands on your data when you store it in the cloud. If you want to work at home, use Owncloud to interact with the Research Drive data Or use Remote desktop: gives access to your university desktop, see this link for a manual Or use SURFdrive , a safe alternative to Google Drive that everyone with a (Dutch) university account has access to (500 GB of personal storage) N.B. You can request SURFdrive also for students or give students the link","title":"Do not store non-anonymized data in the cloud"},{"location":"data-management/data-security.html#if-using-a-local-drive-laptop-usb-external-hard-drive-or-video-camera-the-following-rules-apply","text":"If you are processing data on your local drive, be sure to always back it up on the Research Drive If possible, protect the device, hard drive or drive with a password Do not put the passwords to laptops on the laptop itself When the data have been saved at the right location, delete the data from the device (shift + delete or empty the recycling bin) If you have to take devices onto the street, bring them to a (safe and appointed) university location as quickly as possible. Do not bring them home unless absolutely necessary. If you have to take data home, do let leave them unattended in a (semi)public place (such as a car or library). If possible, leave it in a locked room.","title":"If using a local drive, laptop, USB, external hard drive, or video camera, the following rules apply:"},{"location":"data-management/data-security.html#communication-and-sharing","text":"","title":"Communication and sharing"},{"location":"data-management/data-security.html#do-not-share-data-via-email-attachments-google-drive-etc","text":"Instead of email attachments, use SURF filesender . Email attachments are saved on mail servers and on your PC, whereas this is not the case with filesender. If you are sending an internal email, use a hyperlink or the path to the relevant folder where possible If you want others to be able to edit the documents you share, use SURFdrive or share only the relevant files via Research Drive. Be sure the data shared are anonymous.","title":"Do not share data via email attachments, Google Drive, etc."},{"location":"data-management/data-security.html#do-not-talk-about-or-analyze-individual-data-in-public-spaces","text":"For example in the elevator, a common room, public transport, social media, emails, etc. Coding videos and audio is only allowed where other researchers from the same project work or in special coding rooms Coding audio in a public space is only allowed when others cannot hear the audio, e.g., because you are wearing headphones Transcribe audio and video using safe websites such as uitgetypt.nl","title":"Do not talk about or analyze individual data in public spaces"},{"location":"data-management/data-security.html#contacting-participants-outside-the-university","text":"When contacting participants outside the university, e.g., via your own mobile phone, make sure not to send any identifiable information via your phone (incl. sms or whatsapp) Avoid coupling participant numbers with phone numbers and/or names in emails or whatsapp messages","title":"Contacting participants outside the university"},{"location":"data-management/dmp-dpia-info.html","text":"DMP & DPIA On this page, you can find more information about the Data Management Plan (DMP) and the Data Protection Impact Assessment (DPIA). Data management plan (DMP) Since 2016, all research projects must have a data management plan (DMP) before the start of the project. In a DMP, you capture which data and metadata will be collected, who is responsible for which tasks and what will happen to the (meta)data after the project has ended. The research data lifecycle Source: UK data archive Components of a DMP Fairly standard components of a DMP are: Cover information : project name, researchers, dates, ethical protocol numbers, etc. Data collection and creation How will you create your data? How will you access the data in the future? What license will you use? What kind of data will it be? E.g., how many and what type of files (see preferred file formats ), how large (search for \"file size calculator\"), data quality (resolution, quality), usefulness (versions/processed or unprocessed) Data storage and security Where will you store your data? Where and how often will you make back-ups (1 copy offsite, 3 copies, 2 different media)? What do you do to make sure that the right people can access your data? (passwords, encryption, firewalls, anonymization, aggregation, secure transport and deletion, etc.) Documentation and metadata How are your files named and structured? Are you using version control? Are you providing readme files and/or other documentation? (e.g., time, place, people involved, etc.) Data access, sharing and reuse With whom do you want to share your data? How/where? Has ownership been agreed? Who might find your data useful later? Any restrictions regarding data sharing? Reasons to opt out of sharing with others: privacy / personal data (GDPR), intellectual property rights, might jeopardize the project\u2019s main objective, commercial (working for companies), security-related, etc. Think about linking your data to your publication AND vice versa! Also think about linking your publications to you through an ORCID-ID Data preservation and archiving Which data do you want to keep for future use? Which data will you discard? Not all data necessarily need to be preserved! Where are you going to archive these data? How to organize the data so that they can still be understood in the future? Who is responsible for your data after you leave? How to write a DMP? At the EUR, you can use the DMP Online tool , which contains DMP templates of many funders, or use the EUR template. Create an account, select the (funder specific) format and simply fill out the form! If you leave the Funder field empty, you will use the general EUR-format. You can also invite colleagues to work on the DMP and leave comments Check your funder requirements: did they approve of the template you're using? E.g., if your research is funded by NWO, you have to use one of their approved templates and get feedback from an expert (see contacts below) Read more about DMPs at the EUR, EUR guidelines and the EUR template here . Need examples? See this page for a collection of publicly available DMPs. Data protection impact assessment (DPIA) Whenever you process sensitive data (or something changes regarding this), such as MRI data, names, addresses, daily diaries, etc., you are required to fill in a Data Protection Impact Assessment (DPIA) that has to be approved before starting your study by a privacy officer . In a DPIA, you identify privacy risks and formulate measures to prevent breaches. You register which data you collect and who is responsible. A DPIA touches upon: Collaborations in your project: Within your institute Between institutes: agree upon who has access to the data and which technologies are used (e.g., for saving and analyzing the data) Public-private collaborations: make contractual agreements about how to handle data Geography: outside of the EU, you need to make contractual agreements about how to handle data Types of data collected: Automatically generated (e.g., fitness watches) Own creation, e.g., interviews, pictures Re-used, e.g., multiple datasets combined (pay attention to identifiability) Resources Contacts For checking your (ESSB) DMP and/or DPIA, contact the faculty data steward For general questions about DMPs and DPIAs, contact the Erasmus Data Service Center (EDSC): edsc [at] eur [dot] nl EDSC workshop agenda For the DPIA, contact the ESSB privacy officer (privacy [at] essb [dot] eur [dot] nl) Resources Core requirements for the DMP (p.11+12) by the NWO and others More information about data management (storing, archiving, versioning, data structure, etc.) and backing up and versioning data How to name your files The costs of data management More about privacy and legal aspects Leiden University DPIA template","title":"DMP and DPIA"},{"location":"data-management/dmp-dpia-info.html#dmp-dpia","text":"On this page, you can find more information about the Data Management Plan (DMP) and the Data Protection Impact Assessment (DPIA).","title":"DMP &amp; DPIA"},{"location":"data-management/dmp-dpia-info.html#data-management-plan-dmp","text":"Since 2016, all research projects must have a data management plan (DMP) before the start of the project. In a DMP, you capture which data and metadata will be collected, who is responsible for which tasks and what will happen to the (meta)data after the project has ended. The research data lifecycle Source: UK data archive","title":"Data management plan (DMP)"},{"location":"data-management/dmp-dpia-info.html#components-of-a-dmp","text":"Fairly standard components of a DMP are: Cover information : project name, researchers, dates, ethical protocol numbers, etc. Data collection and creation How will you create your data? How will you access the data in the future? What license will you use? What kind of data will it be? E.g., how many and what type of files (see preferred file formats ), how large (search for \"file size calculator\"), data quality (resolution, quality), usefulness (versions/processed or unprocessed) Data storage and security Where will you store your data? Where and how often will you make back-ups (1 copy offsite, 3 copies, 2 different media)? What do you do to make sure that the right people can access your data? (passwords, encryption, firewalls, anonymization, aggregation, secure transport and deletion, etc.) Documentation and metadata How are your files named and structured? Are you using version control? Are you providing readme files and/or other documentation? (e.g., time, place, people involved, etc.) Data access, sharing and reuse With whom do you want to share your data? How/where? Has ownership been agreed? Who might find your data useful later? Any restrictions regarding data sharing? Reasons to opt out of sharing with others: privacy / personal data (GDPR), intellectual property rights, might jeopardize the project\u2019s main objective, commercial (working for companies), security-related, etc. Think about linking your data to your publication AND vice versa! Also think about linking your publications to you through an ORCID-ID Data preservation and archiving Which data do you want to keep for future use? Which data will you discard? Not all data necessarily need to be preserved! Where are you going to archive these data? How to organize the data so that they can still be understood in the future? Who is responsible for your data after you leave?","title":"Components of a DMP"},{"location":"data-management/dmp-dpia-info.html#how-to-write-a-dmp","text":"At the EUR, you can use the DMP Online tool , which contains DMP templates of many funders, or use the EUR template. Create an account, select the (funder specific) format and simply fill out the form! If you leave the Funder field empty, you will use the general EUR-format. You can also invite colleagues to work on the DMP and leave comments Check your funder requirements: did they approve of the template you're using? E.g., if your research is funded by NWO, you have to use one of their approved templates and get feedback from an expert (see contacts below) Read more about DMPs at the EUR, EUR guidelines and the EUR template here . Need examples? See this page for a collection of publicly available DMPs.","title":"How to write a DMP?"},{"location":"data-management/dmp-dpia-info.html#data-protection-impact-assessment-dpia","text":"Whenever you process sensitive data (or something changes regarding this), such as MRI data, names, addresses, daily diaries, etc., you are required to fill in a Data Protection Impact Assessment (DPIA) that has to be approved before starting your study by a privacy officer . In a DPIA, you identify privacy risks and formulate measures to prevent breaches. You register which data you collect and who is responsible. A DPIA touches upon: Collaborations in your project: Within your institute Between institutes: agree upon who has access to the data and which technologies are used (e.g., for saving and analyzing the data) Public-private collaborations: make contractual agreements about how to handle data Geography: outside of the EU, you need to make contractual agreements about how to handle data Types of data collected: Automatically generated (e.g., fitness watches) Own creation, e.g., interviews, pictures Re-used, e.g., multiple datasets combined (pay attention to identifiability)","title":"Data protection impact assessment (DPIA)"},{"location":"data-management/dmp-dpia-info.html#resources","text":"","title":"Resources"},{"location":"data-management/dmp-dpia-info.html#contacts","text":"For checking your (ESSB) DMP and/or DPIA, contact the faculty data steward For general questions about DMPs and DPIAs, contact the Erasmus Data Service Center (EDSC): edsc [at] eur [dot] nl EDSC workshop agenda For the DPIA, contact the ESSB privacy officer (privacy [at] essb [dot] eur [dot] nl)","title":"Contacts"},{"location":"data-management/dmp-dpia-info.html#resources_1","text":"Core requirements for the DMP (p.11+12) by the NWO and others More information about data management (storing, archiving, versioning, data structure, etc.) and backing up and versioning data How to name your files The costs of data management More about privacy and legal aspects Leiden University DPIA template","title":"Resources"},{"location":"data-management/folder-structure.html","text":"Folder structure It is important to have a folder structure that provides a good overview of your project and that maintains all data and documentation of your project logically. Important aspects of a good project structure are: Separate source and raw data from further processed data . Preferably make raw data read-only so they cannot be edited. Separate different types of data , when necessary Provide scripts or descriptions that were used to go from the raw data to all processed data > make sure your analyses from start to finish are reproducible ! Provide sufficient metadata about your project, e.g., preregistrations, study protocol, codebooks/data dictionaries, a data management plan, general information about the project, the ethics protocol, etc. Provide readme.txt files were necessary. Provide all materials and instructions used to collect the data, e.g., tasks, questionnaires and instructions Overall: use self-explanatory file and folder names or provide documentation explaining which files can be found where. Also keep in mind versioning (e.g., use the date in the filenames, the version or use a formal version control system like git) Finally, use your folders consistently , e.g., place all data in the Data folder. Example folder structure Below, you can find an example folder structure of the project \"YYYY_ProjectName\". You are encouraged to use this or a similar format, but don't feel obliged! About the project Preregistration(s) and/or study protocol (or links to them) Data dictionary/codebook Data management plan (DMP) and/or Data protection impact assessment (DPIA) Data sharing agreement (if applicable) Involved researcheres and their contact information Description of the project Overview of published manuscripts Organizational files about planning, availability, task divisioin, etc. Ethics/CME/METC Application Responses Final approved protocol Data If necessary, add another folder here for each timepoint or sub-project. Behavioral_data Task_x analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Neural_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data analysis_x raw_data participant_number anat dwi func Physiological_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Questionnaire_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros data dictionary/codebook processed_data raw_data Finances Applications participant money Overviews Receipts (kwitanties) Travel expenses Literature_resources Papers usable or used Reference manager files Measures Questionnaires Questionnaire_x papers manuals, scoreforms software scripts instructions List of questionnaires Tasks Task_x software scripts Outreach_recruitment Presentations, website texts, education, newsletters Recruitment emails, flyers, folders, contact with schools Quotes from participants Contents of the goodiebag Publication_packages Author_et_al_(year)_journal 1_Manuscript 2_Task_instructions-questionnaires-stimuli-scripts 3_Anonymized_raw_data 4_Data_processing_files-scripts 5_Processed_data 6_Ethics_protocol Readme.txt Students Year_NameStudent Data Manuscript Readme_StudentName.txt Work_documents Data_collection_protocols Subject folder documents (during data collection) Instruction protocol Forms brevethouder and portier Forms brevethouder and portier Requests for the HIX system Planning schedules Scan logs Scanning & data collection protocols Meetings Agendas Minutes Planning_of_participants Calling_and_planning Bijzonderhedenbestand (encrypted) Calling protocol Coupling subjects-numbers (encrypted) Contact information (encrypted) Emailing Confirmation and reminder emails MRI checklist Practical information and instructions (location, time, hormone collection, etc. Information letter and informed consent form","title":"File organization"},{"location":"data-management/folder-structure.html#folder-structure","text":"It is important to have a folder structure that provides a good overview of your project and that maintains all data and documentation of your project logically. Important aspects of a good project structure are: Separate source and raw data from further processed data . Preferably make raw data read-only so they cannot be edited. Separate different types of data , when necessary Provide scripts or descriptions that were used to go from the raw data to all processed data > make sure your analyses from start to finish are reproducible ! Provide sufficient metadata about your project, e.g., preregistrations, study protocol, codebooks/data dictionaries, a data management plan, general information about the project, the ethics protocol, etc. Provide readme.txt files were necessary. Provide all materials and instructions used to collect the data, e.g., tasks, questionnaires and instructions Overall: use self-explanatory file and folder names or provide documentation explaining which files can be found where. Also keep in mind versioning (e.g., use the date in the filenames, the version or use a formal version control system like git) Finally, use your folders consistently , e.g., place all data in the Data folder.","title":"Folder structure"},{"location":"data-management/folder-structure.html#example-folder-structure","text":"Below, you can find an example folder structure of the project \"YYYY_ProjectName\". You are encouraged to use this or a similar format, but don't feel obliged! About the project Preregistration(s) and/or study protocol (or links to them) Data dictionary/codebook Data management plan (DMP) and/or Data protection impact assessment (DPIA) Data sharing agreement (if applicable) Involved researcheres and their contact information Description of the project Overview of published manuscripts Organizational files about planning, availability, task divisioin, etc. Ethics/CME/METC Application Responses Final approved protocol Data If necessary, add another folder here for each timepoint or sub-project. Behavioral_data Task_x analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Neural_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data analysis_x raw_data participant_number anat dwi func Physiological_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Questionnaire_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros data dictionary/codebook processed_data raw_data Finances Applications participant money Overviews Receipts (kwitanties) Travel expenses Literature_resources Papers usable or used Reference manager files Measures Questionnaires Questionnaire_x papers manuals, scoreforms software scripts instructions List of questionnaires Tasks Task_x software scripts Outreach_recruitment Presentations, website texts, education, newsletters Recruitment emails, flyers, folders, contact with schools Quotes from participants Contents of the goodiebag Publication_packages Author_et_al_(year)_journal 1_Manuscript 2_Task_instructions-questionnaires-stimuli-scripts 3_Anonymized_raw_data 4_Data_processing_files-scripts 5_Processed_data 6_Ethics_protocol Readme.txt Students Year_NameStudent Data Manuscript Readme_StudentName.txt Work_documents Data_collection_protocols Subject folder documents (during data collection) Instruction protocol Forms brevethouder and portier Forms brevethouder and portier Requests for the HIX system Planning schedules Scan logs Scanning & data collection protocols Meetings Agendas Minutes Planning_of_participants Calling_and_planning Bijzonderhedenbestand (encrypted) Calling protocol Coupling subjects-numbers (encrypted) Contact information (encrypted) Emailing Confirmation and reminder emails MRI checklist Practical information and instructions (location, time, hormone collection, etc. Information letter and informed consent form","title":"Example folder structure"},{"location":"data-management/research-drive-how.html","text":"Research Drive protocol This page contains information on how we deal with data on the SURF Research Drive in the SYNC lab, which can be accessed by logging in here . 1. Folders A project folder (at the root of the Research Drive) has to be created by the contract administrator (currently: Mark). This means that each project is created under the contract. Information needed includes: the name of the folder (preferably Year_ProjectName, e.g., \"2018_Brainlinks\" the data steward of the project folder the amount of storage needed (can be changed later) Storage : our contract, \u201cESSB Brain and Development\u201d, currently has 16 TB of storage capacity which is divided among the existing project folders. More storage capacity has to be requested with Research Data Management/Jeroen Rombouts (who in turn requests this at SURF) by the contract administrator 2. Requesting and providing access Requesting access (users) Request access to a folder with the data steward of the relevant project. You can find who is the data steward for which project in the file \u201c ResearchDrive_overview_Projects_Access_Groups \u201d . Giving access (data stewards) Select the folder and click the sharing icon. In the area that appears, you can add individual users or groups and alternatively, create sharing links (URLs) with non-Research Drive users. Giving access to a folder means providing that selected access for all subfolders below the shared folder too. Preferably never give anyone sharing rights (unless strictly necessary), because that person can give others more rights than they themselves have. This causes you to lose overview of who can access the data. Custom groups Give Research Drive users access via personal access groups (Settings > Custom groups). Members in such a group all receive the same rights when added to a folder. The group owner is the only one who can add and remove access group members and see the members of the group. However, all access group names are visible for the entire Research Drive, so aptly name the groups according to the following format: SYNC_ProjectName_Accesslevel , e.g., \"SYNC_Brainlinks_edit\". If a new person needs access, they can be added to the relevant access group and automatically gains access to the same files/folders as the other group members. After you have made any changes in access groups, please update the access document . Individual users Add users to folders individually if they should not have the same permissions as the custom groups. This could for example be outside researchers . Please note that users of SURFdrive do not necessarily need a new Research Drive account, you can simply share the relevant files/folders with their Federated cloud ID . Students and other outside users Generally, when sharing data with students or outsiders, stick to the following guidelines: Share data only when the sharing serves a scientific goal Keep the amount of data shared to a minimum Pseudonymize or, if possible, anonymize data before sharing Students should sign a non-disclosure agreement and stick to the working guidelines for students Give students and outside users access via access links that allow Download/View/Upload (edit only if it concerns a separate (Students) folder). Make use of the password and end-date functions: students and outside users should not have access to the data after their internships / the agreement have/has ended and the password will give extra protection against non-authorized use. Alternatively, you could add the external users or students as new Research Drive users (Dashboard > Add user), although for large amounts of users, this is not recommended. 3. Uploading data to Research Drive Rclone Rclone is a command-line tool to upload data to any location. It can run easily in the background and is relatively simple. Setting up Download rclone: https://rclone.org/downloads/ (no admin rights needed) Once downloaded, open your Command prompt (search for cmd ) Your command prompt shows a drive which it uses, e.g., P:/ . Move the unwrapped rclone files (at least rclone.exe ) to that drive. In your command prompt, type rclone config In your Research Drive account, create a new WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cRclone\u201d. Copy the password that was just created to a temporary file/your clipboard. Follow the instructions listed here . Use the following link for step 3: https://eur.data.surfsara.nl/remote.php/nonshib-webdav/ . Your username is yourERNAid@eur.nl and the password is the WebDAV password you just created (you will probably not see the password being pasted, but it is!). When you are asked for a bearer token , just press Enter Example summary of the config: Uploading data with Rclone Upload files from your command window (type cmd in your search bar if you don't know where it is), using the following general format: rclone copy [flags] \"source\" RD:\"destination\" Example command rclone copy -v -P --ignore-existing \u201cJ:\\ResearchData\\FSW\\Brain and Development - Projects\\2018_Brainlinks\\Ouderstudie\\\u201d RD:\u201c2018_Brainlinks (Projectfolder)/Brainlinks_Parentstudy\u201d copy the source contents to the destination folder print progress continuously ( -v and -P ) skip already existing files ( --ignore-existing ) Other Rclone commands List the files in the specified folder: rclone ls RD:\u201c2016_Zelfbeeld (Projectfolder)/Zelfbeeld_Data/Zelfbeeld_ProcessedData/\u201d Show configuration details: rclone config show Edit the configuration details: rclone config e More information on Rclone On the Research Drive wiki Flags to add to commands Rclone alternative: Cyberduck Cyberduck is a graphical user interface for uploading data. It also has built-in encryption software (cryptomator), which allows simultaneously encrypting and uploading data. Setting up Download and install Cyberduck (the program is free, select $0 of donation) - you do need admin access for this tool. Read the wiki page of SURF Research Drive and from there download the Research Drive cyberduck profile . Save the Research Drive cyberduck profile in the \u201cProfiles\u201d folder within your Cyberduck program files folder (e.g., \u201cC:\\Program Files\\Cyberduck\\profiles\u201d) In your Research Drive account, create a WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cCyberduck\u201d. Copy the password that was just created to a temporary file/your clipboard. (Re)Start Cyberduck Click \u201cNew connection\u201d (Nieuwe verbinding). In the pop-up window that appears, select the SURF Research Drive profile, change the server to eur.data.surfsara.nl and fill out your username (ERNA ID) and the WebDAV password that you just created. Do not forget to select \u201c Save password \u201d (Bewaar wachtwoord)! Then, select \u201cConnect\u201d (Verbind). After having made the connection, you should be able to navigate your (project) folders like in Research Drive. Every time Cyberduck restarts, this connection will be made, except when you explicitly disconnect (then you have to re-connect again). Uploading data in Cyberduck Turn on Checksum (Edit > Preferences > Wachtrij (Queue) > Checksum) Select a folder in which you want to upload files Click Upload and select the files to be uploaded. Once your files have been uploaded, they should appear in your Research Drive! 4. Uploading sensitive data to Research Drive Encryption Research Drive cannot contain special types of personal data (bijzondere persoonsgegevens). These comprise of: Race, ethnicity, political views, religion Sexual life/preferences Genetic or biometric data with the purpose of unique identification Health information, among which medical data (MRI data!) Criminal past Therefore, the following data need to be encrypted before uploading to Research Drive: Participant databases with contact information - using a password on Microsoft Office documents is sufficient MRI checklist information files (\u201cBijzonderhedenbestand\u201d) containing information about past surgeries and other health information - using a password on Microsoft Office documents is sufficient Files containing demographic data, responses about race, political views, religion, sexual life, criminal past and other potential health information. This type of file can be stored on Research Drive without encryption only when they are pseudonymized / not directly traceable to individuals. Raw, non-defaced MRI images In general, it is best to avoid having to use encryption , because passwords can be lost and software can deprecate. Data are much more durable if they can instead be anonymized or pseudonymized. For example, upload defaced MRI data and anonymized/pseudonymized health information to avoid having to encrypt them. How to encrypt? Keep it local : don't upload them to Research Drive. If this means you may lose data, don't do this Passwords : put a password on a Microsoft Office document (e.g., Excel, Word) and keep the password at a safe location . If you lose the password, the data is not accessible anymore Use encryption software : we use Cryptomator to encrypt non-defaced MRI data. Using Cryptomator to encrypt data Use Cryptomator when you want to encrypt folders containing multiple sensitive files before uploading that folder to Research Drive: Download and install the most recent version of Cryptomator Create an encrypted folder (vault) . Be sure to create both a password and a recovery key that can be used in case the password gets lost. Save both at a safe location ! If you lose them, you cannot access the data anymore. Open (decrypt) the vault . In Cryptomator, select a Vault and click \"Open vault\". Select the cryptomator masterkey file. You will be prompted to fill out the password and afterwards, the folder will open. Note that you need the Cryptomator software to see the files in a normal way. In your file explorer, you will probably only see nonsense files in a folder called 'd'. Work with vault contents : after opening a vault, the decrypted files will appear in a separate path on your computer (e.g., \"Z://\"). You can simply copy the path to tools (Matlab, R) or open files from here to work with them. After usage, remember to lock the vault again. Uploading encrypted folders with rclone works the same way as uploading regular folders! Encryption within Cyberduck Cyberduck has in-built functionality to encrypt files using Cryptomator : In Cyberduck, select the folder in which you want to create the encrypted folder Right click and select \u201cNew locked vault\u201d (Nieuwe versleutelde safe) Give the vault a name (remember to put the Project name in there, e.g., \u201cBrainlinks_Neural_data_raw\u201d) and a password. Store the password somewhere safe immediately. You can now upload folders into this encrypted folder as with normal folders. In the background, Cyberduck will decrypt your encrypted folders automatically (because it knows the password), which is why it looks no different than a normal folder in the Cyberduck environment. Saving and sharing passwords A few safe options are: Lastpass stores your passwords in a vault in the cloud (behind 1 master password). It can also store secure notes (such as Cryptomator recovery keys) and allows sharing passwords with others (premium version: Network center > Share item). Network drive : make sure that the drive is secured, backed up and only accessible to those who are allowed to decrypt the data Locally : keep in mind that when your PC is hacked, hackers may have access to the passwords and when your PC / drive crashes, the passwords may be lost. You can share passwords via SURFfilesender : per password and recovery key, create a txt file. Send it/them via SURF filesender and make sure a password is required to download the file(s). Send the password to download to files to the receiver(s) via another way (e.g., text or slack message). 5. Working with data: editing and analysis Editing documents The easiest way to edit documents is in your internet browser, because it allows collaborative editing (with OnlyOffice) and changes are automatically saved. If you mount Research Drive to your file explorer and then edit the document at the same time someone else does, there can be merging conflicts and the version with the last edit \u201cwins\u201d. Mounting Research Drive to your file explorer You can mount your Research Drive account to your file explorer, so that the Research Drive files can be accessed on your local PC. Note that collaborative editing is not possible this way, and merging conflicts may emerge when multiple people are working on the same files. OwnCloud is the recommended tool that is useful for working with small and few files. However, it is not suitable for synchronizing large (numbers of) files . Download the OwnCloud via this link or in Research Drive, go Settings. Scroll all the way down until you see something like this. Click on \"Desktop app\": Choose the installation location wisely: if you are going to work with large amounts of data, install Owncloud on a hard disk with sufficient storage space. See the Research Drive wiki page for how to configure OwnCloud Use the link eur.data.surfsara.nl to connect with and authorize the share by logging in to your Research Drive account Choose Selective synchronization and select only the folders you need to work on from your local machine. All synced files are stored and synced on your local machine. If your Research Drive storage is really high, you should not sync them all with your PC! Alternatively, choose Virtual file support , which makes sure that only files that are being worked on are downloaded To work with encrypted folders: synchronize the encrypted folder to your PC (somewhere with enough disk space) - this will probably take some time depending on the size of the folder open Cryptomator and select Open vault open the cryptomator masterkey file and fill out the password you should now be able to see your files and work with them. Bonus: your work will be automatically synchronized with Research Drive as long as you work in the synchronized folder Analyzing data from Research Drive There are multiple ways that you can analyze data that are stored on Research Drive: Use OwnCloud and run analyses on data that are stored in Research Drive as if the data were stored on your local PC. Advantage: cloud synchronization Disadvantage: requires sufficient disk space, synchronization may take a long time Use a cloud computing service , such as Jupyter Hub (built into Research Drive) or the LISA cluster Advantage: no local copies needed, fast analysis Disadvantage: mostly meant for large data analysis, may take some getting used to Locally : download the data to your local PC and analyze them there Advantage: no dependencies on your internet connection Disadvantage: not great for a lot of data, no cloud synchronization, requires manual upload to Research Drive afterwards Working with MRI data: recommended method Deface the MRI data before uploading them to Research Drive. If this is not possible (anymore), encrypt the folder that contains the relevant data as high as possible in the hierarchy, so that you only have to decrypt one folder for analyses. When installing Owncloud, choose a location with sufficient disk space (e.g., an external hard disk) Synchronize only the folder(s) that you need on your local PC via OwnCloud: your PC needs to have enough disk memory to save the data, also after processing! Note that syncing may take a while. After synchronizing, if needed, use Cryptomator to decrypt the folder (enter the password) The folder is now shown as a separate directory on your local PC (e.g., \"Z:\"). You can add this directory in SPM or Matlab for your analysis.","title":"How to"},{"location":"data-management/research-drive-how.html#research-drive-protocol","text":"This page contains information on how we deal with data on the SURF Research Drive in the SYNC lab, which can be accessed by logging in here .","title":"Research Drive protocol"},{"location":"data-management/research-drive-how.html#1-folders","text":"A project folder (at the root of the Research Drive) has to be created by the contract administrator (currently: Mark). This means that each project is created under the contract. Information needed includes: the name of the folder (preferably Year_ProjectName, e.g., \"2018_Brainlinks\" the data steward of the project folder the amount of storage needed (can be changed later) Storage : our contract, \u201cESSB Brain and Development\u201d, currently has 16 TB of storage capacity which is divided among the existing project folders. More storage capacity has to be requested with Research Data Management/Jeroen Rombouts (who in turn requests this at SURF) by the contract administrator","title":"1. Folders"},{"location":"data-management/research-drive-how.html#2-requesting-and-providing-access","text":"","title":"2. Requesting and providing access"},{"location":"data-management/research-drive-how.html#requesting-access-users","text":"Request access to a folder with the data steward of the relevant project. You can find who is the data steward for which project in the file \u201c ResearchDrive_overview_Projects_Access_Groups \u201d .","title":"Requesting access (users)"},{"location":"data-management/research-drive-how.html#giving-access-data-stewards","text":"Select the folder and click the sharing icon. In the area that appears, you can add individual users or groups and alternatively, create sharing links (URLs) with non-Research Drive users. Giving access to a folder means providing that selected access for all subfolders below the shared folder too. Preferably never give anyone sharing rights (unless strictly necessary), because that person can give others more rights than they themselves have. This causes you to lose overview of who can access the data.","title":"Giving access (data stewards)"},{"location":"data-management/research-drive-how.html#custom-groups","text":"Give Research Drive users access via personal access groups (Settings > Custom groups). Members in such a group all receive the same rights when added to a folder. The group owner is the only one who can add and remove access group members and see the members of the group. However, all access group names are visible for the entire Research Drive, so aptly name the groups according to the following format: SYNC_ProjectName_Accesslevel , e.g., \"SYNC_Brainlinks_edit\". If a new person needs access, they can be added to the relevant access group and automatically gains access to the same files/folders as the other group members. After you have made any changes in access groups, please update the access document .","title":"Custom groups"},{"location":"data-management/research-drive-how.html#individual-users","text":"Add users to folders individually if they should not have the same permissions as the custom groups. This could for example be outside researchers . Please note that users of SURFdrive do not necessarily need a new Research Drive account, you can simply share the relevant files/folders with their Federated cloud ID .","title":"Individual users"},{"location":"data-management/research-drive-how.html#students-and-other-outside-users","text":"Generally, when sharing data with students or outsiders, stick to the following guidelines: Share data only when the sharing serves a scientific goal Keep the amount of data shared to a minimum Pseudonymize or, if possible, anonymize data before sharing Students should sign a non-disclosure agreement and stick to the working guidelines for students Give students and outside users access via access links that allow Download/View/Upload (edit only if it concerns a separate (Students) folder). Make use of the password and end-date functions: students and outside users should not have access to the data after their internships / the agreement have/has ended and the password will give extra protection against non-authorized use. Alternatively, you could add the external users or students as new Research Drive users (Dashboard > Add user), although for large amounts of users, this is not recommended.","title":"Students and other outside users"},{"location":"data-management/research-drive-how.html#3-uploading-data-to-research-drive","text":"","title":"3. Uploading data to Research Drive"},{"location":"data-management/research-drive-how.html#rclone","text":"Rclone is a command-line tool to upload data to any location. It can run easily in the background and is relatively simple.","title":"Rclone"},{"location":"data-management/research-drive-how.html#setting-up","text":"Download rclone: https://rclone.org/downloads/ (no admin rights needed) Once downloaded, open your Command prompt (search for cmd ) Your command prompt shows a drive which it uses, e.g., P:/ . Move the unwrapped rclone files (at least rclone.exe ) to that drive. In your command prompt, type rclone config In your Research Drive account, create a new WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cRclone\u201d. Copy the password that was just created to a temporary file/your clipboard. Follow the instructions listed here . Use the following link for step 3: https://eur.data.surfsara.nl/remote.php/nonshib-webdav/ . Your username is yourERNAid@eur.nl and the password is the WebDAV password you just created (you will probably not see the password being pasted, but it is!). When you are asked for a bearer token , just press Enter Example summary of the config:","title":"Setting up"},{"location":"data-management/research-drive-how.html#uploading-data-with-rclone","text":"Upload files from your command window (type cmd in your search bar if you don't know where it is), using the following general format: rclone copy [flags] \"source\" RD:\"destination\"","title":"Uploading data with Rclone"},{"location":"data-management/research-drive-how.html#example-command","text":"rclone copy -v -P --ignore-existing \u201cJ:\\ResearchData\\FSW\\Brain and Development - Projects\\2018_Brainlinks\\Ouderstudie\\\u201d RD:\u201c2018_Brainlinks (Projectfolder)/Brainlinks_Parentstudy\u201d copy the source contents to the destination folder print progress continuously ( -v and -P ) skip already existing files ( --ignore-existing )","title":"Example command"},{"location":"data-management/research-drive-how.html#other-rclone-commands","text":"List the files in the specified folder: rclone ls RD:\u201c2016_Zelfbeeld (Projectfolder)/Zelfbeeld_Data/Zelfbeeld_ProcessedData/\u201d Show configuration details: rclone config show Edit the configuration details: rclone config e","title":"Other Rclone commands"},{"location":"data-management/research-drive-how.html#more-information-on-rclone","text":"On the Research Drive wiki Flags to add to commands","title":"More information on Rclone"},{"location":"data-management/research-drive-how.html#rclone-alternative-cyberduck","text":"Cyberduck is a graphical user interface for uploading data. It also has built-in encryption software (cryptomator), which allows simultaneously encrypting and uploading data.","title":"Rclone alternative: Cyberduck"},{"location":"data-management/research-drive-how.html#setting-up_1","text":"Download and install Cyberduck (the program is free, select $0 of donation) - you do need admin access for this tool. Read the wiki page of SURF Research Drive and from there download the Research Drive cyberduck profile . Save the Research Drive cyberduck profile in the \u201cProfiles\u201d folder within your Cyberduck program files folder (e.g., \u201cC:\\Program Files\\Cyberduck\\profiles\u201d) In your Research Drive account, create a WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cCyberduck\u201d. Copy the password that was just created to a temporary file/your clipboard. (Re)Start Cyberduck Click \u201cNew connection\u201d (Nieuwe verbinding). In the pop-up window that appears, select the SURF Research Drive profile, change the server to eur.data.surfsara.nl and fill out your username (ERNA ID) and the WebDAV password that you just created. Do not forget to select \u201c Save password \u201d (Bewaar wachtwoord)! Then, select \u201cConnect\u201d (Verbind). After having made the connection, you should be able to navigate your (project) folders like in Research Drive. Every time Cyberduck restarts, this connection will be made, except when you explicitly disconnect (then you have to re-connect again).","title":"Setting up"},{"location":"data-management/research-drive-how.html#uploading-data-in-cyberduck","text":"Turn on Checksum (Edit > Preferences > Wachtrij (Queue) > Checksum) Select a folder in which you want to upload files Click Upload and select the files to be uploaded. Once your files have been uploaded, they should appear in your Research Drive!","title":"Uploading data in Cyberduck"},{"location":"data-management/research-drive-how.html#4-uploading-sensitive-data-to-research-drive","text":"","title":"4. Uploading sensitive data to Research Drive"},{"location":"data-management/research-drive-how.html#encryption","text":"Research Drive cannot contain special types of personal data (bijzondere persoonsgegevens). These comprise of: Race, ethnicity, political views, religion Sexual life/preferences Genetic or biometric data with the purpose of unique identification Health information, among which medical data (MRI data!) Criminal past Therefore, the following data need to be encrypted before uploading to Research Drive: Participant databases with contact information - using a password on Microsoft Office documents is sufficient MRI checklist information files (\u201cBijzonderhedenbestand\u201d) containing information about past surgeries and other health information - using a password on Microsoft Office documents is sufficient Files containing demographic data, responses about race, political views, religion, sexual life, criminal past and other potential health information. This type of file can be stored on Research Drive without encryption only when they are pseudonymized / not directly traceable to individuals. Raw, non-defaced MRI images In general, it is best to avoid having to use encryption , because passwords can be lost and software can deprecate. Data are much more durable if they can instead be anonymized or pseudonymized. For example, upload defaced MRI data and anonymized/pseudonymized health information to avoid having to encrypt them.","title":"Encryption"},{"location":"data-management/research-drive-how.html#how-to-encrypt","text":"Keep it local : don't upload them to Research Drive. If this means you may lose data, don't do this Passwords : put a password on a Microsoft Office document (e.g., Excel, Word) and keep the password at a safe location . If you lose the password, the data is not accessible anymore Use encryption software : we use Cryptomator to encrypt non-defaced MRI data.","title":"How to encrypt?"},{"location":"data-management/research-drive-how.html#using-cryptomator-to-encrypt-data","text":"Use Cryptomator when you want to encrypt folders containing multiple sensitive files before uploading that folder to Research Drive: Download and install the most recent version of Cryptomator Create an encrypted folder (vault) . Be sure to create both a password and a recovery key that can be used in case the password gets lost. Save both at a safe location ! If you lose them, you cannot access the data anymore. Open (decrypt) the vault . In Cryptomator, select a Vault and click \"Open vault\". Select the cryptomator masterkey file. You will be prompted to fill out the password and afterwards, the folder will open. Note that you need the Cryptomator software to see the files in a normal way. In your file explorer, you will probably only see nonsense files in a folder called 'd'. Work with vault contents : after opening a vault, the decrypted files will appear in a separate path on your computer (e.g., \"Z://\"). You can simply copy the path to tools (Matlab, R) or open files from here to work with them. After usage, remember to lock the vault again. Uploading encrypted folders with rclone works the same way as uploading regular folders!","title":"Using Cryptomator to encrypt data"},{"location":"data-management/research-drive-how.html#encryption-within-cyberduck","text":"Cyberduck has in-built functionality to encrypt files using Cryptomator : In Cyberduck, select the folder in which you want to create the encrypted folder Right click and select \u201cNew locked vault\u201d (Nieuwe versleutelde safe) Give the vault a name (remember to put the Project name in there, e.g., \u201cBrainlinks_Neural_data_raw\u201d) and a password. Store the password somewhere safe immediately. You can now upload folders into this encrypted folder as with normal folders. In the background, Cyberduck will decrypt your encrypted folders automatically (because it knows the password), which is why it looks no different than a normal folder in the Cyberduck environment.","title":"Encryption within Cyberduck"},{"location":"data-management/research-drive-how.html#saving-and-sharing-passwords","text":"A few safe options are: Lastpass stores your passwords in a vault in the cloud (behind 1 master password). It can also store secure notes (such as Cryptomator recovery keys) and allows sharing passwords with others (premium version: Network center > Share item). Network drive : make sure that the drive is secured, backed up and only accessible to those who are allowed to decrypt the data Locally : keep in mind that when your PC is hacked, hackers may have access to the passwords and when your PC / drive crashes, the passwords may be lost. You can share passwords via SURFfilesender : per password and recovery key, create a txt file. Send it/them via SURF filesender and make sure a password is required to download the file(s). Send the password to download to files to the receiver(s) via another way (e.g., text or slack message).","title":"Saving and sharing passwords"},{"location":"data-management/research-drive-how.html#5-working-with-data-editing-and-analysis","text":"","title":"5. Working with data: editing and analysis"},{"location":"data-management/research-drive-how.html#editing-documents","text":"The easiest way to edit documents is in your internet browser, because it allows collaborative editing (with OnlyOffice) and changes are automatically saved. If you mount Research Drive to your file explorer and then edit the document at the same time someone else does, there can be merging conflicts and the version with the last edit \u201cwins\u201d.","title":"Editing documents"},{"location":"data-management/research-drive-how.html#mounting-research-drive-to-your-file-explorer","text":"You can mount your Research Drive account to your file explorer, so that the Research Drive files can be accessed on your local PC. Note that collaborative editing is not possible this way, and merging conflicts may emerge when multiple people are working on the same files. OwnCloud is the recommended tool that is useful for working with small and few files. However, it is not suitable for synchronizing large (numbers of) files . Download the OwnCloud via this link or in Research Drive, go Settings. Scroll all the way down until you see something like this. Click on \"Desktop app\": Choose the installation location wisely: if you are going to work with large amounts of data, install Owncloud on a hard disk with sufficient storage space. See the Research Drive wiki page for how to configure OwnCloud Use the link eur.data.surfsara.nl to connect with and authorize the share by logging in to your Research Drive account Choose Selective synchronization and select only the folders you need to work on from your local machine. All synced files are stored and synced on your local machine. If your Research Drive storage is really high, you should not sync them all with your PC! Alternatively, choose Virtual file support , which makes sure that only files that are being worked on are downloaded To work with encrypted folders: synchronize the encrypted folder to your PC (somewhere with enough disk space) - this will probably take some time depending on the size of the folder open Cryptomator and select Open vault open the cryptomator masterkey file and fill out the password you should now be able to see your files and work with them. Bonus: your work will be automatically synchronized with Research Drive as long as you work in the synchronized folder","title":"Mounting Research Drive to your file explorer"},{"location":"data-management/research-drive-how.html#analyzing-data-from-research-drive","text":"There are multiple ways that you can analyze data that are stored on Research Drive: Use OwnCloud and run analyses on data that are stored in Research Drive as if the data were stored on your local PC. Advantage: cloud synchronization Disadvantage: requires sufficient disk space, synchronization may take a long time Use a cloud computing service , such as Jupyter Hub (built into Research Drive) or the LISA cluster Advantage: no local copies needed, fast analysis Disadvantage: mostly meant for large data analysis, may take some getting used to Locally : download the data to your local PC and analyze them there Advantage: no dependencies on your internet connection Disadvantage: not great for a lot of data, no cloud synchronization, requires manual upload to Research Drive afterwards","title":"Analyzing data from Research Drive"},{"location":"data-management/research-drive-how.html#working-with-mri-data-recommended-method","text":"Deface the MRI data before uploading them to Research Drive. If this is not possible (anymore), encrypt the folder that contains the relevant data as high as possible in the hierarchy, so that you only have to decrypt one folder for analyses. When installing Owncloud, choose a location with sufficient disk space (e.g., an external hard disk) Synchronize only the folder(s) that you need on your local PC via OwnCloud: your PC needs to have enough disk memory to save the data, also after processing! Note that syncing may take a while. After synchronizing, if needed, use Cryptomator to decrypt the folder (enter the password) The folder is now shown as a separate directory on your local PC (e.g., \"Z:\"). You can add this directory in SPM or Matlab for your analysis.","title":"Working with MRI data: recommended method"},{"location":"data-management/research-drive-info.html","text":"SURF Research Drive: General information Note: this is a general introduction to Research Drive. For more specific instructions, to go the next chapter or visit the official Research Drive wiki . What is SURF Research Drive? Research Drive is a cloud environment offered by SURF and used by Erasmus University Rotterdam to store research data during the active research phase. It is not meant for long-term archiving, data publishing or data analysis! You can compare it to Google Drive, but for teams: files are stored in the cloud , can be shared and collaboratively edited data are stored in the Netherlands (backed-up weekly) there is no personal storage , only team storage (storage per project) - data will always be part of a project and remain accessible, even when users leave or permissions are changed there are several integrations with other applications and environments (LISA cluster, Jupyter Hub, HPC cloud, OwnCloud, OnlyOffice, etc.). Roles Each project gets a storage quotum and a data steward. The data steward gives rights to end-users. If permitted, end-users can upload data in the folders they received rights to. Roles within Research Drive: Site administrator : manager of the entire (EUR-)instance > usually someone from SURF Dashboard administrator : manages all contracts, can add new contracts and assigns contract administrators > someone from EUR Contract administrator/owner : manages a contract that has a specific amount of storage available to divide over the projects under the contract: can create project folders including a storage quotum and data steward does not automatically have access to the created projects Data steward : responsible for specific project(s): gives rights to members or groups by permissions on folder level can invite new members can always see all project contents Member : normal end-user, anyone can add new members by sending invitations (but data stewards have to give them permissions) Sharing files and folders Data stewards can give the following permissions to users: Read-only : keep in mind that copying data to a local machine is always possible Write : Create: create and add new items and rename existing folders Change: upload and replace existing items in the folder Delete: delete existingg items in the folder Share : re-share the item or a child item. If users have this right, they can set (perhaps broader) permissions for other users. This is not recommended, since you can quickly lose overview of who has access to which data. This way, the data steward remains in control of the data. Folders and/or files can be shared in the following ways: Existing users (search for the email address or user name) A custom group (Settings > Custom groups): you can add users to a custom group and then set permissions for the entire group. All users in that group then have the same permissions. Others cannot see who is in the group, but Anyone in Research Drive can find a group name, so make a well-defined, distinguishing group name, e.g., \"SYNC_Brainlinks_edit\" A SURFdrive user : if someone uses SURFdrive via their institution, they don't need a new Research Drive account. You can simply share the files or folders with their federated cloud ID , click here for how to do this. A new user : Dashboard > New user. The new user can choose 2 types of accounts: Organization: recommended if the user has a SURFconext account (e.g., because they work at a different university) Local: if the user does not have a SURFconext account, choose this option After the user has an account, the data stewards needs to add the new user to the relevant folder(s). Non users: public link : everyone with a link gets the specified permissions. You can set an expiry date, password and permissions. A great option here is the drop file function : partners can put files in the project folder but cannot see or download the contents of the other partners (\u201cwrite-only\u201d). Important notes on sharing Permissions are inherited from parent folders, unless they are specifically changed (e.g., during re-sharing): giving access to a folder means giving access to all subfolders as well! A user with whom subfolders are shared cannot see the parent folders. Make understandable folder names if you plan to share subfolders (e.g., not \"Students\", but \"Brainlinks_Students\") Specific subfolder permissions overrule higher-level permissions A share name can be renamed individually . This is invisible to original sharer (data steward). However, the contents of the folder remain the same and visible to all. Interface When you log into Research Drive, you will automatically see an overview of Projects / folders of which you are data steward or member. These files can be accessed in the Menu in the upper left corner, as are Applications and the Dashboard: Dashboard The Dashboard is accessible to anyone, but only relevant data will be shown. Users can only invite new users. Under \"User accounts\", click \"Invite user\". For each user, you can view their Project membership, contract details, storage overview and service overview (available apps can be added here too). After account removal, the account will exist for 30 days. Data stewards can invite new users and additionally have an overview of project folders and user accounts Contract administrators can invite new users and have overview of project folders, but they can also add new and edit existing project folders and see the contract details. Settings Settings can be found on the top right of the screen: The most important settings are: General : here, you can find your account details, federated cloud ID and links to external apps Security : contains, among others, your saved encryption keys (if any) and Webdav passwords Custom groups : here, you can make new custom groups and add users to them. Note that every EUR instance Research Drive user can find this group, so aptly name them! Version control New versions of single files are automatically stored: any version older than two weeks will be removed When you are working on the same file simultaneously and not in your internet browser, the last saved file will be the version stored. However, you can restore previous versions via the Versions tab. Deleted files are moved to the trash bin . They will be removed after 30 days and can be restored from here during this period. Retention periods can be configured. To prevent syncing issues, make a copy first or work on local files (only copy what you need) Further reading Official Research Drive wiki Slides for end-users Hands-on exercises for end-users Slides for data stewards Link for logging on in the demo environment","title":"About Research Drive"},{"location":"data-management/research-drive-info.html#surf-research-drive-general-information","text":"Note: this is a general introduction to Research Drive. For more specific instructions, to go the next chapter or visit the official Research Drive wiki .","title":"SURF Research Drive: General information"},{"location":"data-management/research-drive-info.html#what-is-surf-research-drive","text":"Research Drive is a cloud environment offered by SURF and used by Erasmus University Rotterdam to store research data during the active research phase. It is not meant for long-term archiving, data publishing or data analysis! You can compare it to Google Drive, but for teams: files are stored in the cloud , can be shared and collaboratively edited data are stored in the Netherlands (backed-up weekly) there is no personal storage , only team storage (storage per project) - data will always be part of a project and remain accessible, even when users leave or permissions are changed there are several integrations with other applications and environments (LISA cluster, Jupyter Hub, HPC cloud, OwnCloud, OnlyOffice, etc.).","title":"What is SURF Research Drive?"},{"location":"data-management/research-drive-info.html#roles","text":"Each project gets a storage quotum and a data steward. The data steward gives rights to end-users. If permitted, end-users can upload data in the folders they received rights to. Roles within Research Drive: Site administrator : manager of the entire (EUR-)instance > usually someone from SURF Dashboard administrator : manages all contracts, can add new contracts and assigns contract administrators > someone from EUR Contract administrator/owner : manages a contract that has a specific amount of storage available to divide over the projects under the contract: can create project folders including a storage quotum and data steward does not automatically have access to the created projects Data steward : responsible for specific project(s): gives rights to members or groups by permissions on folder level can invite new members can always see all project contents Member : normal end-user, anyone can add new members by sending invitations (but data stewards have to give them permissions)","title":"Roles"},{"location":"data-management/research-drive-info.html#sharing-files-and-folders","text":"Data stewards can give the following permissions to users: Read-only : keep in mind that copying data to a local machine is always possible Write : Create: create and add new items and rename existing folders Change: upload and replace existing items in the folder Delete: delete existingg items in the folder Share : re-share the item or a child item. If users have this right, they can set (perhaps broader) permissions for other users. This is not recommended, since you can quickly lose overview of who has access to which data. This way, the data steward remains in control of the data. Folders and/or files can be shared in the following ways: Existing users (search for the email address or user name) A custom group (Settings > Custom groups): you can add users to a custom group and then set permissions for the entire group. All users in that group then have the same permissions. Others cannot see who is in the group, but Anyone in Research Drive can find a group name, so make a well-defined, distinguishing group name, e.g., \"SYNC_Brainlinks_edit\" A SURFdrive user : if someone uses SURFdrive via their institution, they don't need a new Research Drive account. You can simply share the files or folders with their federated cloud ID , click here for how to do this. A new user : Dashboard > New user. The new user can choose 2 types of accounts: Organization: recommended if the user has a SURFconext account (e.g., because they work at a different university) Local: if the user does not have a SURFconext account, choose this option After the user has an account, the data stewards needs to add the new user to the relevant folder(s). Non users: public link : everyone with a link gets the specified permissions. You can set an expiry date, password and permissions. A great option here is the drop file function : partners can put files in the project folder but cannot see or download the contents of the other partners (\u201cwrite-only\u201d). Important notes on sharing Permissions are inherited from parent folders, unless they are specifically changed (e.g., during re-sharing): giving access to a folder means giving access to all subfolders as well! A user with whom subfolders are shared cannot see the parent folders. Make understandable folder names if you plan to share subfolders (e.g., not \"Students\", but \"Brainlinks_Students\") Specific subfolder permissions overrule higher-level permissions A share name can be renamed individually . This is invisible to original sharer (data steward). However, the contents of the folder remain the same and visible to all.","title":"Sharing files and folders"},{"location":"data-management/research-drive-info.html#interface","text":"When you log into Research Drive, you will automatically see an overview of Projects / folders of which you are data steward or member. These files can be accessed in the Menu in the upper left corner, as are Applications and the Dashboard:","title":"Interface"},{"location":"data-management/research-drive-info.html#dashboard","text":"The Dashboard is accessible to anyone, but only relevant data will be shown. Users can only invite new users. Under \"User accounts\", click \"Invite user\". For each user, you can view their Project membership, contract details, storage overview and service overview (available apps can be added here too). After account removal, the account will exist for 30 days. Data stewards can invite new users and additionally have an overview of project folders and user accounts Contract administrators can invite new users and have overview of project folders, but they can also add new and edit existing project folders and see the contract details.","title":"Dashboard"},{"location":"data-management/research-drive-info.html#settings","text":"Settings can be found on the top right of the screen: The most important settings are: General : here, you can find your account details, federated cloud ID and links to external apps Security : contains, among others, your saved encryption keys (if any) and Webdav passwords Custom groups : here, you can make new custom groups and add users to them. Note that every EUR instance Research Drive user can find this group, so aptly name them!","title":"Settings"},{"location":"data-management/research-drive-info.html#version-control","text":"New versions of single files are automatically stored: any version older than two weeks will be removed When you are working on the same file simultaneously and not in your internet browser, the last saved file will be the version stored. However, you can restore previous versions via the Versions tab. Deleted files are moved to the trash bin . They will be removed after 30 days and can be restored from here during this period. Retention periods can be configured. To prevent syncing issues, make a copy first or work on local files (only copy what you need)","title":"Version control"},{"location":"data-management/research-drive-info.html#further-reading","text":"Official Research Drive wiki Slides for end-users Hands-on exercises for end-users Slides for data stewards Link for logging on in the demo environment","title":"Further reading"},{"location":"data-management/types-metadata.html","text":"Metadata Metadata is data about data : in a broad sense, metadata is all the information that you provide about your project, dataset, variables, code, etc. Read some nice examples here . Providing metadata is incredibly important, since metadata makes data: Findable Readable Interpretable Manageable Without metadata, a lot of data are just numbers that cannot be interpreted. Example research metadata A project readme containing the information below. Often in a readme.txt . Find an example template here or use the information below: Creator (PI): name and affiliation of PI Title : project title Funding sources : names of funders, incl. grant numbers and related acknowledgements Data collector/producer : who is responsible for data collection + date and location of data production Description : project description, incl. relevant publications Sample and sampling procedures : target population and methods to sample it (or link to document describing this), retention rates for longitudinal studies Coverage : topics, time period and location covered Source : if relevant, citations to original source from which data were obtained Metadata for a specific data file, containing, for example, file description, data format, relationship with other files, date of creation and versioning information, etc. This can be a readme.txt or other filetypes, such as nameofdatafile.json or nameofdatafile.xml A codebook (data dictionary), which specifies what all variables in your dataset mean. See the codebook chapter for more information. Question wording or meaning Variable text : question text or item number Respondent : who was asked the question? Meaning of codes : interpretation of the codes assigned to each variable Missing data codes , e.g., 999 Summary statistics for both valid and missing cases Imputation and editing : identify data that have been estimated or extensively edited Constructed and weight variables : how were they constructed Location in the data file : field or column location, if relevant Variable groupings : if you categorize variables into conceptual groupings Metadata in systems, such as a data repository. This type of metadata is often enforced and interoperable so that you don't have to manually create this type of metadata. Interoperable metadata Metadata standards Metadata standards are frameworks for metadata fields. They describe how metadata fields should be formatted, so that they will become machine-readable and therefore interoperable. An enormous amount of metadata standards is available which all differ per discipline , but the best known metadata standards for the social sciences are: Dublin Core: this is a set of basic elements to describe a wide range of networked resources, among which Title, Creator, Subject, Description, Publisher, Contributor, Date, Type, Format, Identifier, etc. (see readme information above). Note this Dublin Core metadata file generator to see the elements. Data Documentation Initiative (DDI) is a standard often used in the social, behavioral, economic, and health sciences. It knows several sub-profiles that are based on DDI, but may be more extensive. One of those is CESSDA (Consortium of European Social Science Data Archives). CESSDA's metadata model can be found here As an individual researcher, you are often not directly confronted with these standards. It is just good to know that different repositories can use different standards. See more standards here . Controlled vocabularies Where metadata standards tell us what to call the metadata fields, controlled vocabularies come in handy when we have to fill in those fields. Using controlled vocabularies enables machines to identify identical values, instead of everyone using a different term for the same thing. Whereas some fields have very extensive controlled vocabularies, psychology does not have many. A few links: Controlled vocabularies from the DDI CESSDA vocabularies (large overlap with DDI) ELSST \u2013 European Language Social Science Thesaurus","title":"Types of metadata"},{"location":"data-management/types-metadata.html#metadata","text":"Metadata is data about data : in a broad sense, metadata is all the information that you provide about your project, dataset, variables, code, etc. Read some nice examples here . Providing metadata is incredibly important, since metadata makes data: Findable Readable Interpretable Manageable Without metadata, a lot of data are just numbers that cannot be interpreted.","title":"Metadata"},{"location":"data-management/types-metadata.html#example-research-metadata","text":"A project readme containing the information below. Often in a readme.txt . Find an example template here or use the information below: Creator (PI): name and affiliation of PI Title : project title Funding sources : names of funders, incl. grant numbers and related acknowledgements Data collector/producer : who is responsible for data collection + date and location of data production Description : project description, incl. relevant publications Sample and sampling procedures : target population and methods to sample it (or link to document describing this), retention rates for longitudinal studies Coverage : topics, time period and location covered Source : if relevant, citations to original source from which data were obtained Metadata for a specific data file, containing, for example, file description, data format, relationship with other files, date of creation and versioning information, etc. This can be a readme.txt or other filetypes, such as nameofdatafile.json or nameofdatafile.xml A codebook (data dictionary), which specifies what all variables in your dataset mean. See the codebook chapter for more information. Question wording or meaning Variable text : question text or item number Respondent : who was asked the question? Meaning of codes : interpretation of the codes assigned to each variable Missing data codes , e.g., 999 Summary statistics for both valid and missing cases Imputation and editing : identify data that have been estimated or extensively edited Constructed and weight variables : how were they constructed Location in the data file : field or column location, if relevant Variable groupings : if you categorize variables into conceptual groupings Metadata in systems, such as a data repository. This type of metadata is often enforced and interoperable so that you don't have to manually create this type of metadata.","title":"Example research metadata"},{"location":"data-management/types-metadata.html#interoperable-metadata","text":"","title":"Interoperable metadata"},{"location":"data-management/types-metadata.html#metadata-standards","text":"Metadata standards are frameworks for metadata fields. They describe how metadata fields should be formatted, so that they will become machine-readable and therefore interoperable. An enormous amount of metadata standards is available which all differ per discipline , but the best known metadata standards for the social sciences are: Dublin Core: this is a set of basic elements to describe a wide range of networked resources, among which Title, Creator, Subject, Description, Publisher, Contributor, Date, Type, Format, Identifier, etc. (see readme information above). Note this Dublin Core metadata file generator to see the elements. Data Documentation Initiative (DDI) is a standard often used in the social, behavioral, economic, and health sciences. It knows several sub-profiles that are based on DDI, but may be more extensive. One of those is CESSDA (Consortium of European Social Science Data Archives). CESSDA's metadata model can be found here As an individual researcher, you are often not directly confronted with these standards. It is just good to know that different repositories can use different standards. See more standards here .","title":"Metadata standards"},{"location":"data-management/types-metadata.html#controlled-vocabularies","text":"Where metadata standards tell us what to call the metadata fields, controlled vocabularies come in handy when we have to fill in those fields. Using controlled vocabularies enables machines to identify identical values, instead of everyone using a different term for the same thing. Whereas some fields have very extensive controlled vocabularies, psychology does not have many. A few links: Controlled vocabularies from the DDI CESSDA vocabularies (large overlap with DDI) ELSST \u2013 European Language Social Science Thesaurus","title":"Controlled vocabularies"},{"location":"data-management/vc-datalad.html","text":"Version control for datasets Version control for data is equally important for being able to reproduce results as it is for software/code and other documentation. Git, however, is very bad at handling (a) large (amount of) data files, because every version of every file is stored in the repository. So how can we formally version our data? git-annex git-annex is a command line-based version control system that can manage all file content is a separate directory in the repository called the annex ( .git/annex/objects ). Only the files names and some metadata are placed into git version control. When you push a git repository with an annex to Github, the annex is not uploaded, but can be stored in a web-hosting service. Thus, a copy (clone) of the github repository only contains the version histories and not the data files themselves. Any file content can be downloaded from the external storage with git-annex get . DataLad DataLad is a great version control system for datasets, independently of its size. It is based on git and git-annex and is relatively simple to use. It also has many more functionalities and a great and comprehensive handbook . Note that DataLad is a command line tool , so some previous experience with command line git is advantageous. If you want to learn how to use DataLad, please go to the handbook via http://handbook.datalad.org/en/latest/ If you find anything unclear about the handbook or want to contribute, head over to the handbook repository on github and open an issue or a pull request (see their how to contribute )","title":"Version control for data"},{"location":"data-management/vc-datalad.html#version-control-for-datasets","text":"Version control for data is equally important for being able to reproduce results as it is for software/code and other documentation. Git, however, is very bad at handling (a) large (amount of) data files, because every version of every file is stored in the repository. So how can we formally version our data?","title":"Version control for datasets"},{"location":"data-management/vc-datalad.html#git-annex","text":"git-annex is a command line-based version control system that can manage all file content is a separate directory in the repository called the annex ( .git/annex/objects ). Only the files names and some metadata are placed into git version control. When you push a git repository with an annex to Github, the annex is not uploaded, but can be stored in a web-hosting service. Thus, a copy (clone) of the github repository only contains the version histories and not the data files themselves. Any file content can be downloaded from the external storage with git-annex get .","title":"git-annex"},{"location":"data-management/vc-datalad.html#datalad","text":"DataLad is a great version control system for datasets, independently of its size. It is based on git and git-annex and is relatively simple to use. It also has many more functionalities and a great and comprehensive handbook . Note that DataLad is a command line tool , so some previous experience with command line git is advantageous. If you want to learn how to use DataLad, please go to the handbook via http://handbook.datalad.org/en/latest/ If you find anything unclear about the handbook or want to contribute, head over to the handbook repository on github and open an issue or a pull request (see their how to contribute )","title":"DataLad"},{"location":"data-management/vc-github.html","text":"Version control with git(hub) What are git & github? Git is a version control system: it tracks the history as you change files. More specifically, it tracks who made which changes and when. It allows reverting files to a previous state. Note that it is possible to work on git projects locally without ever using github. Github is a platform that you can use to collaborate on projects that use git. It additionally allows for threaded discussions (issues), pull requests (see below) and several great apps. Please note that there are also other platforms that work similarly, such as GitLab , BitBucket or SourceFourge . Git and Github are most suitable for working with relatively small files . While originally used for code/software, you can use it for other types of small files as well, such as documentation. Why should I use git(hub)? Git is used a lot all over the globe and is free to download and use via several interfaces You will always be able to revert your errors - or those of someone else You can report which version of the files you have used for which publication. Even better, github allows exporting a snapshot (version) of your github repository (folder with files) to Zenodo, meaning you can publish your version used and give it a citable DOI. Github also has several other great functions, such as making a website out of your repository (such as this lab wiki!) Installation Create a Github account Install git locally . If you don't want to use the command line, also download a GUI such as RStudio or GitKraken ) Note: if you want to work with git in the command line on Windows, I can highly recommend using the Ubuntu app (the Linux Subsystem for Windows, downloadable via the Windows store), which may cause fewer Windows-related errors. The git workflow When working on a git project (within a folder called a git repository ), you will always perform the following steps: Make changes to some file and save them like you normally would Stage the changes: select which files you want to make a snapshot of (this step is most explicit if you work in the command line) Commit the changes: make a snapshot of the changes made so far. A commit (snapshot) is always accompanied by a commit message explaining what changes were made Any commit gets a specific identifier that can be used to reverse (undo) the commit. Some stage- and commit-related commands Check which files are changed but not yet staged or committed: git status Stage a file (tip: use the tab to use autocompletion): git add filename Stage multiple files: git add filename1 filename2 filename3 Stage all unstaged files in the workspace: git add -A . Commit the change(s) you staged: git commit -m \"Change x and y to z\" Commit all (staged and unstaged) change(s) made in the workspace: git commit -a -m \"Change x and y and z\" Branches A git repository can exist in multiple \u201cversions\u201d which are called branches . There is always a \u201cmaster\u201d branch, which you should consider the clean branch. Besides that, you can create other branches that are meant to make your own changes, or try something different without dirtying the clean (master) version. After you have made changes in your own branch and you think they should be incorporated in the master branch, you can then merge your branch with the master branch. Some branch-related commands Check which branch you are working on now (and list which branches there are): git branch -v Change branches: git checkout branchname Create a new branch: git checkout -b newbranchname Workflow on github On Github, the workflow is a bit more extensive, because often you are collaborating and do not want others to just start editing the master branch right away. There are multiple methods to collaborate on a project, but we recommend the following, assuming that there is already a repository for the project and you want to contribute: On the repository page on Github, fork the repository: this creates a copy of the repository on your own Github account that you have full access to. In your forked (copied) Github repository, create a new branch for the changes you are about to make with a short but comprehensible name, e.g. \u201cdorienchanges\u201d. If you want to edit files locally, clone your repository to your local PC, creating a folder in your file explorer (the contents of which can change according to which branch you are on!). Via the command line: git clone https://github.com/UserName/RepositoryName.git Via Rstudio, see this link Edit the files you want to edit and commit the changes (making a snapshot; include a comprehensible commit message!) You have now committed changes locally, but they are not yet visible in your remote repository, i.e., the online github repository on your account. In order to get the commits you made locally to be visible online, you need to push them to your remote repository on Github. Via the command line, note that the repository on your account is usually called \"origin\": git push origin branchnameonwhichyouworked Via Rstudio, see this link Now the changes are visible in your own account, but not in the main repository. In order to get your changes into the main repository, you need to do a pull request on Github. This is a request to the owners of the original repository to merge your branch with (one of) theirs . Once merged by the owners, you are often prompted to remove your own branch (which is not necessary if you are planning to make more changes later). Keeping your local copy (clone) up to date If you are working on a project with many collaborators making changes, the odds are that your own fork (online copy) and/or clone (local copy) are becoming out-of-date quite fast. Therefore, it is recommended to update those copies each time before you start making changes yourself, so you are working on the most recent versions of the files. In your clone (offline), you can set up the owner\u2019s repository as the \"upstream\" repository and then pull all commits from the upstream repository to your local PC Setting up the original repository as the upstream: git remote add upstream https://github.com/ownername/repositoryname.git Pulling changes from the upstream repository: git pull upstream branchname See this page when you use RStudio To update your online version of the repository, simply push the changes (e.g., push origin master after pulling from the upstream Resources For every piece of software, remember that google is your best friend . Or use one of the following other resources: Also a very comprehensive git guide by The Turing Way More info on the Git workflow (especially useful if you are going to use git via the command line) Github guide: git handbook (duration ca. 1 hour) Using Git(hub) with Rstudio: https://happygitwithr.com/ Introduction on Github by Ana Martinovici Git terminology: https://git-scm.com/docs/gitglossary More terminology: https://the-turing-way.netlify.app/reproducible-research/vcs/vcs-resources.html#definitions-glossary If you want to use Gitlab instead, here are the materials of a comprehensive course (ironically, on GitHub)","title":"Git(hub)"},{"location":"data-management/vc-github.html#version-control-with-github","text":"","title":"Version control with git(hub)"},{"location":"data-management/vc-github.html#what-are-git-github","text":"Git is a version control system: it tracks the history as you change files. More specifically, it tracks who made which changes and when. It allows reverting files to a previous state. Note that it is possible to work on git projects locally without ever using github. Github is a platform that you can use to collaborate on projects that use git. It additionally allows for threaded discussions (issues), pull requests (see below) and several great apps. Please note that there are also other platforms that work similarly, such as GitLab , BitBucket or SourceFourge . Git and Github are most suitable for working with relatively small files . While originally used for code/software, you can use it for other types of small files as well, such as documentation.","title":"What are git &amp; github?"},{"location":"data-management/vc-github.html#why-should-i-use-github","text":"Git is used a lot all over the globe and is free to download and use via several interfaces You will always be able to revert your errors - or those of someone else You can report which version of the files you have used for which publication. Even better, github allows exporting a snapshot (version) of your github repository (folder with files) to Zenodo, meaning you can publish your version used and give it a citable DOI. Github also has several other great functions, such as making a website out of your repository (such as this lab wiki!)","title":"Why should I use git(hub)?"},{"location":"data-management/vc-github.html#installation","text":"Create a Github account Install git locally . If you don't want to use the command line, also download a GUI such as RStudio or GitKraken ) Note: if you want to work with git in the command line on Windows, I can highly recommend using the Ubuntu app (the Linux Subsystem for Windows, downloadable via the Windows store), which may cause fewer Windows-related errors.","title":"Installation"},{"location":"data-management/vc-github.html#the-git-workflow","text":"When working on a git project (within a folder called a git repository ), you will always perform the following steps: Make changes to some file and save them like you normally would Stage the changes: select which files you want to make a snapshot of (this step is most explicit if you work in the command line) Commit the changes: make a snapshot of the changes made so far. A commit (snapshot) is always accompanied by a commit message explaining what changes were made Any commit gets a specific identifier that can be used to reverse (undo) the commit.","title":"The git workflow"},{"location":"data-management/vc-github.html#some-stage-and-commit-related-commands","text":"Check which files are changed but not yet staged or committed: git status Stage a file (tip: use the tab to use autocompletion): git add filename Stage multiple files: git add filename1 filename2 filename3 Stage all unstaged files in the workspace: git add -A . Commit the change(s) you staged: git commit -m \"Change x and y to z\" Commit all (staged and unstaged) change(s) made in the workspace: git commit -a -m \"Change x and y and z\"","title":"Some stage- and commit-related commands"},{"location":"data-management/vc-github.html#branches","text":"A git repository can exist in multiple \u201cversions\u201d which are called branches . There is always a \u201cmaster\u201d branch, which you should consider the clean branch. Besides that, you can create other branches that are meant to make your own changes, or try something different without dirtying the clean (master) version. After you have made changes in your own branch and you think they should be incorporated in the master branch, you can then merge your branch with the master branch.","title":"Branches"},{"location":"data-management/vc-github.html#some-branch-related-commands","text":"Check which branch you are working on now (and list which branches there are): git branch -v Change branches: git checkout branchname Create a new branch: git checkout -b newbranchname","title":"Some branch-related commands"},{"location":"data-management/vc-github.html#workflow-on-github","text":"On Github, the workflow is a bit more extensive, because often you are collaborating and do not want others to just start editing the master branch right away. There are multiple methods to collaborate on a project, but we recommend the following, assuming that there is already a repository for the project and you want to contribute: On the repository page on Github, fork the repository: this creates a copy of the repository on your own Github account that you have full access to. In your forked (copied) Github repository, create a new branch for the changes you are about to make with a short but comprehensible name, e.g. \u201cdorienchanges\u201d. If you want to edit files locally, clone your repository to your local PC, creating a folder in your file explorer (the contents of which can change according to which branch you are on!). Via the command line: git clone https://github.com/UserName/RepositoryName.git Via Rstudio, see this link Edit the files you want to edit and commit the changes (making a snapshot; include a comprehensible commit message!) You have now committed changes locally, but they are not yet visible in your remote repository, i.e., the online github repository on your account. In order to get the commits you made locally to be visible online, you need to push them to your remote repository on Github. Via the command line, note that the repository on your account is usually called \"origin\": git push origin branchnameonwhichyouworked Via Rstudio, see this link Now the changes are visible in your own account, but not in the main repository. In order to get your changes into the main repository, you need to do a pull request on Github. This is a request to the owners of the original repository to merge your branch with (one of) theirs . Once merged by the owners, you are often prompted to remove your own branch (which is not necessary if you are planning to make more changes later).","title":"Workflow on github"},{"location":"data-management/vc-github.html#keeping-your-local-copy-clone-up-to-date","text":"If you are working on a project with many collaborators making changes, the odds are that your own fork (online copy) and/or clone (local copy) are becoming out-of-date quite fast. Therefore, it is recommended to update those copies each time before you start making changes yourself, so you are working on the most recent versions of the files. In your clone (offline), you can set up the owner\u2019s repository as the \"upstream\" repository and then pull all commits from the upstream repository to your local PC Setting up the original repository as the upstream: git remote add upstream https://github.com/ownername/repositoryname.git Pulling changes from the upstream repository: git pull upstream branchname See this page when you use RStudio To update your online version of the repository, simply push the changes (e.g., push origin master after pulling from the upstream","title":"Keeping your local copy (clone) up to date"},{"location":"data-management/vc-github.html#resources","text":"For every piece of software, remember that google is your best friend . Or use one of the following other resources: Also a very comprehensive git guide by The Turing Way More info on the Git workflow (especially useful if you are going to use git via the command line) Github guide: git handbook (duration ca. 1 hour) Using Git(hub) with Rstudio: https://happygitwithr.com/ Introduction on Github by Ana Martinovici Git terminology: https://git-scm.com/docs/gitglossary More terminology: https://the-turing-way.netlify.app/reproducible-research/vcs/vcs-resources.html#definitions-glossary If you want to use Gitlab instead, here are the materials of a comprehensive course (ironically, on GitHub)","title":"Resources"},{"location":"data-management/vc-principles.html","text":"Version control principles Version control is a way to track changes made to a file, creating a history of the file that can be reviewed. It is important to keep different versions separated in almost all situations , because: it keeps different stages of processing/editing separated it allows you to go back to previous versions if something went wrong ideally, it allows tracking who did what and when it prevents a lot of confusion in this type of situation: There are both informal and formal ways of using version control for your files. However, for both ways, the typical procedure is as follows: Do something with a file (create, edit, remove) Save the file Register the change by making a snapshot of the file status, i.e., a version In informal ways, step 2 and 3 cannot always be separated. Sometimes, step 3 is even left out completely, which, from personal experience, I cannot recommend you to do! \u200b In formal version control systems, however, step 3 is a necessary step that cannot be skipped, forcing you to create a new version each time you make a change. Informal ways to use version control It is highly recommended to make a habit out of at least one, but preferably more of the following practices if you do not (want to) use a formal version control system: Keep raw data separately from any processed data and document which steps have been taken to go from the former to the latter Rename a file every time you make a sizable change Use dates in the filename in the format YYYYMMDD Append the filename with a version number, e.g., document_v1.0, document_v1.2, etc. See this link for a helper document for coming up with a good file naming convention Include a versioning history within the document, e.g., on the first page, explaining what changed in which version Use services like Google drive and Dropbox, which allow collaborative editing but also reverting to previous versions Formal version control systems There are also formal version control systems, such as: Git Mercurial SVN These actually need to be installed and worked with while or after you are editing files. They require some knowledge and skills of the systems, but they also reward you with perfect file histories and reverting possibilities. The next chapter will go into the most often used version control system: git.","title":"Basic principles"},{"location":"data-management/vc-principles.html#version-control-principles","text":"Version control is a way to track changes made to a file, creating a history of the file that can be reviewed. It is important to keep different versions separated in almost all situations , because: it keeps different stages of processing/editing separated it allows you to go back to previous versions if something went wrong ideally, it allows tracking who did what and when it prevents a lot of confusion in this type of situation: There are both informal and formal ways of using version control for your files. However, for both ways, the typical procedure is as follows: Do something with a file (create, edit, remove) Save the file Register the change by making a snapshot of the file status, i.e., a version In informal ways, step 2 and 3 cannot always be separated. Sometimes, step 3 is even left out completely, which, from personal experience, I cannot recommend you to do! \u200b In formal version control systems, however, step 3 is a necessary step that cannot be skipped, forcing you to create a new version each time you make a change.","title":"Version control principles"},{"location":"data-management/vc-principles.html#informal-ways-to-use-version-control","text":"It is highly recommended to make a habit out of at least one, but preferably more of the following practices if you do not (want to) use a formal version control system: Keep raw data separately from any processed data and document which steps have been taken to go from the former to the latter Rename a file every time you make a sizable change Use dates in the filename in the format YYYYMMDD Append the filename with a version number, e.g., document_v1.0, document_v1.2, etc. See this link for a helper document for coming up with a good file naming convention Include a versioning history within the document, e.g., on the first page, explaining what changed in which version Use services like Google drive and Dropbox, which allow collaborative editing but also reverting to previous versions","title":"Informal ways to use version control"},{"location":"data-management/vc-principles.html#formal-version-control-systems","text":"There are also formal version control systems, such as: Git Mercurial SVN These actually need to be installed and worked with while or after you are editing files. They require some knowledge and skills of the systems, but they also reward you with perfect file histories and reverting possibilities. The next chapter will go into the most often used version control system: git.","title":"Formal version control systems"},{"location":"getting-started-eur/email-signature.html","text":"The SYNC email signature Setting your email signature in Outlook Log in via Outlook using your ERNA-ID At the top right of your screen, click the \"Settings\" wheel > \u201cView all Outlook settings\u201d In the Settings, choose \"Mail\" > \u201cCompose and reply\u201d In the box under \u201cEmail signature\u201d, make your email signature. Configure when to use the signature: in original emails only or also when replying to emails? Click \"Save\" For EUR-wide email templates, see this link . For the EUR-specific color codes, see this webpage (the ESSB HEX code is #ff9e00). Individual email signature Name, title Function SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: youremailaddress@eur.nl / yoursecondemailaddress@eur.nl W: http://erasmus-synclab.nl/ A: Mandeville building, T13 room x / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands Present: Days present Lab email signature SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: synclab@essb.eur.nl W: http://erasmus-synclab.nl/ T: https://twitter.com/SYNClabEUR A: Mandeville building, T13 / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands","title":"Email signature"},{"location":"getting-started-eur/email-signature.html#the-sync-email-signature","text":"","title":"The SYNC email signature"},{"location":"getting-started-eur/email-signature.html#setting-your-email-signature-in-outlook","text":"Log in via Outlook using your ERNA-ID At the top right of your screen, click the \"Settings\" wheel > \u201cView all Outlook settings\u201d In the Settings, choose \"Mail\" > \u201cCompose and reply\u201d In the box under \u201cEmail signature\u201d, make your email signature. Configure when to use the signature: in original emails only or also when replying to emails? Click \"Save\" For EUR-wide email templates, see this link . For the EUR-specific color codes, see this webpage (the ESSB HEX code is #ff9e00).","title":"Setting your email signature in Outlook"},{"location":"getting-started-eur/email-signature.html#individual-email-signature","text":"Name, title Function SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: youremailaddress@eur.nl / yoursecondemailaddress@eur.nl W: http://erasmus-synclab.nl/ A: Mandeville building, T13 room x / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands Present: Days present","title":"Individual email signature"},{"location":"getting-started-eur/email-signature.html#lab-email-signature","text":"SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: synclab@essb.eur.nl W: http://erasmus-synclab.nl/ T: https://twitter.com/SYNClabEUR A: Mandeville building, T13 / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands","title":"Lab email signature"},{"location":"getting-started-eur/finances.html","text":"Finances On this page, you can find practical information on declaring payments for different purposes. The WBS numbers to declare on can be found in this Research Drive document . Paying a large group of participants The procedure for letting large groups of participants get paid is currently as followed: Ask participants to fill in a receipt (kwitantie) with their contact details and signature (see an example format in this Research Drive file ). Keep the receipt in a safe place, because you have to be able to show it in case of checks. Fill in the form \" Form_for_paying_participants_explained.xls \" (on Research Drive) after copying it (only sheet \"1\", remove the example line). Follow the instructions in the document closely. Save the form as .xls \u00e1nd as .pdf with a recognizable name, e.g., \"20200421_Brainlinks_payment_T2.5_corona\". Have the relevant budget keeper (procuratiehouder) sign the pdf (see the Research Drive document for who to ask, usually Eveline is the one to sign) Send both the .xsl and the .pdf to Patricia Engelbrecht ( patricia.engelbrecht@eur.nl ), preferably via SURF filesender , considering there are personal data in the documents. Patricia will feed the .xls form to the system. If something doesn't work, it has been filled in wrongly and she will return the document to you. When it does work, all participants from the file will receive payment simultaneously. Notes: It is undesirable to imburse participants with the money with your own money and then declaring it via the ESS portal. The ESS portal is not suitable for this kind of declaration, since it asks for very specific proof of payment. Depending on the amount of money that needs to be paid, there may be a different budget keeper (e.g., someone for amounts below \u20ac1000, etc.). Paying small numbers of individuals If the amount of participants or other persons to imburse is not that large, there is another way. This is for example the case when reimbursing travel costs or paying student assistants for extra jobs. Copy the form \" Example_payment_natural_persons.xlsx \" or, if the individuals do not work at EUR, the form \" Declaration_form_non-EUR_individuals_NL.pdf \" Fill in the relevant data and correct the Kostenplaats/WBS element Send the form to invoice [dot] fin [at] eur [dot] nl . It will then land in the portal and be processed by finance Invoices If an external company requires payment using an invoice, let them address the invoice to: \u2003Erasmus University Rotterdam \u2003Erasmus School of Social and Behavioural Sciences \u2003PO Box 1738 \u20033000 DR Rotterdam Email the invoice to invoice [dot] fin [at] eur [dot] nl and mention the WBS element or Kostenplaats under which it should fall. Other declarations Travel costs (home-work) EUR automatically gives you travel cost reimbursement via your paycheck based on the distance you have to travel to work (read more here ). However, if you use public transport, there is an extra regulation that can reimburse a higher amount if needed (up to \u20ac250 for full-time employment). You can request this regulation via the ESS portal, read more about it here . Conference and other research-related costs If you have made costs that do not relate to home-work travel costs, invoices or paying participants, such as conference fees or research materials, you can declare them via the ESS portal.","title":"Finances"},{"location":"getting-started-eur/finances.html#finances","text":"On this page, you can find practical information on declaring payments for different purposes. The WBS numbers to declare on can be found in this Research Drive document .","title":"Finances"},{"location":"getting-started-eur/finances.html#paying-a-large-group-of-participants","text":"The procedure for letting large groups of participants get paid is currently as followed: Ask participants to fill in a receipt (kwitantie) with their contact details and signature (see an example format in this Research Drive file ). Keep the receipt in a safe place, because you have to be able to show it in case of checks. Fill in the form \" Form_for_paying_participants_explained.xls \" (on Research Drive) after copying it (only sheet \"1\", remove the example line). Follow the instructions in the document closely. Save the form as .xls \u00e1nd as .pdf with a recognizable name, e.g., \"20200421_Brainlinks_payment_T2.5_corona\". Have the relevant budget keeper (procuratiehouder) sign the pdf (see the Research Drive document for who to ask, usually Eveline is the one to sign) Send both the .xsl and the .pdf to Patricia Engelbrecht ( patricia.engelbrecht@eur.nl ), preferably via SURF filesender , considering there are personal data in the documents. Patricia will feed the .xls form to the system. If something doesn't work, it has been filled in wrongly and she will return the document to you. When it does work, all participants from the file will receive payment simultaneously. Notes: It is undesirable to imburse participants with the money with your own money and then declaring it via the ESS portal. The ESS portal is not suitable for this kind of declaration, since it asks for very specific proof of payment. Depending on the amount of money that needs to be paid, there may be a different budget keeper (e.g., someone for amounts below \u20ac1000, etc.).","title":"Paying a large group of participants"},{"location":"getting-started-eur/finances.html#paying-small-numbers-of-individuals","text":"If the amount of participants or other persons to imburse is not that large, there is another way. This is for example the case when reimbursing travel costs or paying student assistants for extra jobs. Copy the form \" Example_payment_natural_persons.xlsx \" or, if the individuals do not work at EUR, the form \" Declaration_form_non-EUR_individuals_NL.pdf \" Fill in the relevant data and correct the Kostenplaats/WBS element Send the form to invoice [dot] fin [at] eur [dot] nl . It will then land in the portal and be processed by finance","title":"Paying small numbers of individuals"},{"location":"getting-started-eur/finances.html#invoices","text":"If an external company requires payment using an invoice, let them address the invoice to: \u2003Erasmus University Rotterdam \u2003Erasmus School of Social and Behavioural Sciences \u2003PO Box 1738 \u20033000 DR Rotterdam Email the invoice to invoice [dot] fin [at] eur [dot] nl and mention the WBS element or Kostenplaats under which it should fall.","title":"Invoices"},{"location":"getting-started-eur/finances.html#other-declarations","text":"","title":"Other declarations"},{"location":"getting-started-eur/finances.html#travel-costs-home-work","text":"EUR automatically gives you travel cost reimbursement via your paycheck based on the distance you have to travel to work (read more here ). However, if you use public transport, there is an extra regulation that can reimburse a higher amount if needed (up to \u20ac250 for full-time employment). You can request this regulation via the ESS portal, read more about it here .","title":"Travel costs (home-work)"},{"location":"getting-started-eur/finances.html#conference-and-other-research-related-costs","text":"If you have made costs that do not relate to home-work travel costs, invoices or paying participants, such as conference fees or research materials, you can declare them via the ESS portal.","title":"Conference and other research-related costs"},{"location":"getting-started-eur/time-registration.html","text":"Time registration What is time registration? The hours you work for a project should be registered daily or weekly in the correct WBS element (kostenplaats). The hours that you cannot register on a project will go on the general kostenplaats (or on a blank line). For administration purposes it is important that you register your hours before the 4th day of the next month If you are late, you will receive reminders Sick days will be automatically registered by HR National holidays and requested vacation days will also be automatically processed after your request is granted Why time registration? Both for justifying costs for funders and for the EUR's administration, the correct costs should be linked to the correct project, even if you are only working on (being paid by) one project. How? Go to the ESS portal (Employee Self Service) Click \"Tijdschrijven\" (Time registration) Within the Tijdschrijven menu, navigate to the week in which you want to register worked hours. Choose an empty line Fill in the WBS number or Kostenplaats (i.e., the source of your paycheck), check them here (link to Research Drive) Your labor contract (arbeidsovereenkomst) will automatically appear if you only work on one Fill in the hours you worked each day Save your changes If you always work the same hours during the week, you can also make a template : Fill in the week as you would want to save it Save your week as template (Sjabloon /Template > Opslaan als sjabloon/Save as template) To apply the template to a new week, navigate to a new week and click \"Werkvoorraad\" to copy the template from the previous period Important notes You can only navigate 6 weeks in the past. Hours that have not been justified before that time can only be registered by Project Control. The first line in the ESS portal contains your norm hours. You have to justify all those hours to prevent errors If you log on twice or try to open the window twice you will get the error: \"Your personnel number is blocked at the moment\"","title":"Time registration"},{"location":"getting-started-eur/time-registration.html#time-registration","text":"","title":"Time registration"},{"location":"getting-started-eur/time-registration.html#what-is-time-registration","text":"The hours you work for a project should be registered daily or weekly in the correct WBS element (kostenplaats). The hours that you cannot register on a project will go on the general kostenplaats (or on a blank line). For administration purposes it is important that you register your hours before the 4th day of the next month If you are late, you will receive reminders Sick days will be automatically registered by HR National holidays and requested vacation days will also be automatically processed after your request is granted","title":"What is time registration?"},{"location":"getting-started-eur/time-registration.html#why-time-registration","text":"Both for justifying costs for funders and for the EUR's administration, the correct costs should be linked to the correct project, even if you are only working on (being paid by) one project.","title":"Why time registration?"},{"location":"getting-started-eur/time-registration.html#how","text":"Go to the ESS portal (Employee Self Service) Click \"Tijdschrijven\" (Time registration) Within the Tijdschrijven menu, navigate to the week in which you want to register worked hours. Choose an empty line Fill in the WBS number or Kostenplaats (i.e., the source of your paycheck), check them here (link to Research Drive) Your labor contract (arbeidsovereenkomst) will automatically appear if you only work on one Fill in the hours you worked each day Save your changes If you always work the same hours during the week, you can also make a template : Fill in the week as you would want to save it Save your week as template (Sjabloon /Template > Opslaan als sjabloon/Save as template) To apply the template to a new week, navigate to a new week and click \"Werkvoorraad\" to copy the template from the previous period","title":"How?"},{"location":"getting-started-eur/time-registration.html#important-notes","text":"You can only navigate 6 weeks in the past. Hours that have not been justified before that time can only be registered by Project Control. The first line in the ESS portal contains your norm hours. You have to justify all those hours to prevent errors If you log on twice or try to open the window twice you will get the error: \"Your personnel number is blocked at the moment\"","title":"Important notes"},{"location":"getting-started-eur/welcome-eur.html","text":"Welcome at the EUR! On this page, you can find some information to get you started at the EUR. As soon as you get started, make sure to read the new employees page as it contains a lot of practical information to get started! Important EUR portals As soon as your account is activated you can access the following portals using your ERNAid@eur.nl and password . Mailbox and agenda Link: https://outlook.office365.com/mail/inbox You can automatically create Teams meetings in the agenda. Personal IDM Link: https://personal.idm.eur.nl/user/login.jsp Change your personal information (name, contact information, etc.) here. MyEUR Link: https://my.eur.nl/ Employee portal. Change your profile picture here, request an employee pass (for parking and getting into buildings) or search for information and news. You can also make an annual leave agreement here . MyApps Since 2021, the EUR requires that you use their network to browse at least the following platforms: ESS portal : Self service portal, see manuals here . This portal is for, a.o. requesting vacation days, requesting extra travel cost reimbursement (My administration > Extra travel reimbursement), making declarations, time registration , seeing paychecks, performance and development (R&O) interview documents. R&O : Go to \"Mijn R&O\" and create a progress form for the interview. You can already fill in this form before the interview; your changes are immediately visible to your employer. Academic staff have to complement this form with additional documents. After the interview and after you have agreed on the terms, send the form to HR. Read more here . Pure , the system used to register academic output and to change your EUR profile. Please see here for relevant manuals. MyApps - How to To access these platforms, you can use \"MyApps\" . MyApps is a type of Remote desktop connection that can be set up between your PC and the Erasmus University network. If you (used to) work at Leiden University, it is comparable to Citrix in that you can access the network, some software and the files, but you are not working directly on a PC and can only access the files on the server (not on a local disk). Read FAQs about MyApps here . To set up a connection, user this user guide . Within MyApps, it is possible to start a Remote desktop session with a physical PC at the EUR (and thus you'll be able to access a local disk). To do so: Ask IT servicedesk to enable the Remote desktop connection for your PC, because this is currently not automatically enabled for all EUR PCs When enabled, open a MyApps session Within the MyApps session , click the magnifying class at the bottom of your screen to search for \"Remote Desktop Connection\" In the screen that opens, type the complete network name from the PC in the format CL#########.campus.eur.nl. If entered correctly, you can now log in with your ERNA ID and use your local PC from home! ICT Read all about your ICT workplace here . You can install software via the Software Center (locally or via MyApps). There is more software available than is visible here, see this page for more information. If you have a question or request, go to the ICT Self Service Desk or contact the Service desk via servicedesk [at] eur [dot] nl (phone number: +31 010 408 8880) How to print? Go to https://eur.mycampusprint.nl/Login/Login Login and upload your documents Useful contacts Below are some useful support email addresses. See an overview of ESSB support staff here . General procedural questions: office.strategy@essb.eur.nl (managed by Carina Schlosser) Secretariat of the Dean's Office, see this link HR-related questions: see this link Financial questions (project control), see this link ICT: servicedesk@eur.nl or it.servicedesk@eur.nl Research Data Management, open science & privacy Jeroen Rombouts, head RDM University Library Data Management Team: datarepository@eur. nl Research Data Management, see this link Privacy officer ESSB: privacy@essb.eur.nl Open science community Rotterdam: Antonio Schettino Ethical review, see this link Communications department Marjolein Kooistra: media relations and internal communication Britt Boeddha van Dongen: communications advisor of the strategy group ESSB and Vital cities and citizens Kristel Segeren: senior editor ESSB website Ivy van Regteren Altena, communications advisor ESSB","title":"Intro to the EUR!"},{"location":"getting-started-eur/welcome-eur.html#welcome-at-the-eur","text":"On this page, you can find some information to get you started at the EUR. As soon as you get started, make sure to read the new employees page as it contains a lot of practical information to get started!","title":"Welcome at the EUR!"},{"location":"getting-started-eur/welcome-eur.html#important-eur-portals","text":"As soon as your account is activated you can access the following portals using your ERNAid@eur.nl and password .","title":"Important EUR portals"},{"location":"getting-started-eur/welcome-eur.html#mailbox-and-agenda","text":"Link: https://outlook.office365.com/mail/inbox You can automatically create Teams meetings in the agenda.","title":"Mailbox and agenda"},{"location":"getting-started-eur/welcome-eur.html#personal-idm","text":"Link: https://personal.idm.eur.nl/user/login.jsp Change your personal information (name, contact information, etc.) here.","title":"Personal IDM"},{"location":"getting-started-eur/welcome-eur.html#myeur","text":"Link: https://my.eur.nl/ Employee portal. Change your profile picture here, request an employee pass (for parking and getting into buildings) or search for information and news. You can also make an annual leave agreement here .","title":"MyEUR"},{"location":"getting-started-eur/welcome-eur.html#myapps","text":"Since 2021, the EUR requires that you use their network to browse at least the following platforms: ESS portal : Self service portal, see manuals here . This portal is for, a.o. requesting vacation days, requesting extra travel cost reimbursement (My administration > Extra travel reimbursement), making declarations, time registration , seeing paychecks, performance and development (R&O) interview documents. R&O : Go to \"Mijn R&O\" and create a progress form for the interview. You can already fill in this form before the interview; your changes are immediately visible to your employer. Academic staff have to complement this form with additional documents. After the interview and after you have agreed on the terms, send the form to HR. Read more here . Pure , the system used to register academic output and to change your EUR profile. Please see here for relevant manuals.","title":"MyApps"},{"location":"getting-started-eur/welcome-eur.html#myapps-how-to","text":"To access these platforms, you can use \"MyApps\" . MyApps is a type of Remote desktop connection that can be set up between your PC and the Erasmus University network. If you (used to) work at Leiden University, it is comparable to Citrix in that you can access the network, some software and the files, but you are not working directly on a PC and can only access the files on the server (not on a local disk). Read FAQs about MyApps here . To set up a connection, user this user guide . Within MyApps, it is possible to start a Remote desktop session with a physical PC at the EUR (and thus you'll be able to access a local disk). To do so: Ask IT servicedesk to enable the Remote desktop connection for your PC, because this is currently not automatically enabled for all EUR PCs When enabled, open a MyApps session Within the MyApps session , click the magnifying class at the bottom of your screen to search for \"Remote Desktop Connection\" In the screen that opens, type the complete network name from the PC in the format CL#########.campus.eur.nl. If entered correctly, you can now log in with your ERNA ID and use your local PC from home!","title":"MyApps - How to"},{"location":"getting-started-eur/welcome-eur.html#ict","text":"Read all about your ICT workplace here . You can install software via the Software Center (locally or via MyApps). There is more software available than is visible here, see this page for more information. If you have a question or request, go to the ICT Self Service Desk or contact the Service desk via servicedesk [at] eur [dot] nl (phone number: +31 010 408 8880)","title":"ICT"},{"location":"getting-started-eur/welcome-eur.html#how-to-print","text":"Go to https://eur.mycampusprint.nl/Login/Login Login and upload your documents","title":"How to print?"},{"location":"getting-started-eur/welcome-eur.html#useful-contacts","text":"Below are some useful support email addresses. See an overview of ESSB support staff here . General procedural questions: office.strategy@essb.eur.nl (managed by Carina Schlosser) Secretariat of the Dean's Office, see this link HR-related questions: see this link Financial questions (project control), see this link ICT: servicedesk@eur.nl or it.servicedesk@eur.nl Research Data Management, open science & privacy Jeroen Rombouts, head RDM University Library Data Management Team: datarepository@eur. nl Research Data Management, see this link Privacy officer ESSB: privacy@essb.eur.nl Open science community Rotterdam: Antonio Schettino Ethical review, see this link Communications department Marjolein Kooistra: media relations and internal communication Britt Boeddha van Dongen: communications advisor of the strategy group ESSB and Vital cities and citizens Kristel Segeren: senior editor ESSB website Ivy van Regteren Altena, communications advisor ESSB","title":"Useful contacts"},{"location":"open-science/data-sharing-how.html","text":"Sharing research data: how? Sharing data is becoming the golden standard in science. It enables others to reproduce your results and prevent fraud and honest mistakes in data analysis. Moreover, it enables reuse of your data in new analyses, increasing the impact of your work. Short guide: When to share what data? If data are completely anonymous , you can share them publicly in a dedicated repository, see step 1 or 2 If data cannot be completely anonymized, they are personal. You need a legal basis to share these data: Informed consent : what you can do with the data depends on the contents of the consent form. If participants consented to public data sharing and their data are not very sensitive (e.g., not from children or clinical groups), publish them in a repository or datapaper . If participants consented to sharing with restrictions, use a repository that allows access restrictions or use a data use agreement to share data case by case. If participants did not consent to any personal data sharing, share characteristics or aggregated data . Public interest : In theory, most research is publicly funded, and therefore we should be able to use this as legal basis for data sharing. However, it is still unclear when we are allowed to use it. The minimal prerequities are: the personal data sharing should rely on the principles of lawfulness, fairness and transparency informed consent was impossible to obtain, e.g., because the study took place a long time ago and consent cannot be obtained retroactively. Participants not consenting to data sharing is not a valid reason! When sharing personal data using the Public interest basis, you are encouraged to share data with access restrictions, especially if your data are sensitive or highly identifiable (e.g., data from minors or clinical groups, special categories of sensitive data, etc.) If you share data with a similar purpose as the original research project (such as for collaborating with other researchers on a related topic), a data use agreement suffices (not strictly necessary for EUR collaborators as they are from the same institution). Such agreement should lay out the conditions of storing, sharing and publishing the data. This falls under the scope of processing that is \"compatible with the original purpose\", which does not require a new/separate legal basis (GDPR Articles 5(1)(b) , 6(4) and 89(1) ). Ways of sharing data Publishing data can go roughly in the following ways: 1. Publish in a data repository For example (or find one here ): The EUR Data Repository : for publication packages at the EUR All data and materials accompanying a publication Only suitable for anonymous data See the publication packages page for more information. DANS DataverseNL : for publication packages at Leiden University ( instructions ) All data and materials accompanying a publication Not suitable for large or publication-independent datasets (max zip file size 10GB) or non-anonymous data Only accessibly via institutes that use DataverseNL DANS EASY (Dutch) For data and materials, not necessarily accompanying a publication Has deals with the university but still some limitations to the size of the data (max. 100 GB) Is aimed more at archiving than sharing data Also has a dark archive for non-anonymous data 4TU Research data International data repository for science, engineering and design Enables open or restricted access, private links, embargoes or even metadata-only records Up to 1 TB of storage for affiliated researchers, 10 GB for non-affiliated researchers Open Science Framework max 5 GB for private, 50 GB for public projects Choose storage location in EU: Germany Keep your data close to all other relevant files in your OSF project OSF is more aimed at project management than dissemination Other general-purpose repositories, such as: Zenodo (free up to 50GB) Dryad (not free) Non-EUR Figshare (free, max 20GB private space and 5GB per file) In all cases, make your data FAIR and take privacy considerations into account. 2. Publish a datapaper In a datapaper, you describe the data and the methods of collecting them, without the need to analyze them. This will get you a publication out of your data, irrespective of whether or not you publish results. This often requires that you make all described data public, because the aim of such publications is to provide access to high quality datasets and to facilitate reuse. Also, most journals have some policy in which repository you should deposit the data accompanying the datapaper. Note that a datapaper will be peer-reviewed just as well as a regular article. See this link for a list of data journals. 3. Share case-by-case For data that cannot be shared publicly, you can sometimes still share the data case-by-case. This can be the case: For MRI-data for which you have a legal basis to share them, but you may not want to publish publicly because of the sensitivity. For this type of data, you may want to consider using a data use agreement as well For data that has not been published about For data that does not belong to a publication, data that is too large to share in another way or some other reason Please note that this is only a FAIR solution if your metadata and access options are publicly findable and available (e.g., consider creating a metadata-only record in a repository). 4. Share only characteristics of the data If you do not want to or you can't share any real data, you can still make your data valuable: Aggregated data If your data are privacy-sensitive and you cannot share them, you can still share aggregated data, for example: Share first- and second level MRI data in NeuroVault . You can also link this to your manuscript (and the other way around). NeuroVault allowes meta-analyses of fMRI studies, making it worthwhile to share your group MRI data there. See the NeuroVault page for more information Share summary details of your data, such as averages and variation measures. Or make a shinyapp that allows exploring the data without accessing it! Synthetic data Creating a synthetic dataset can be useful to capture the statistical idiosyncrasies of your real dataset. This synthetic dataset can be used to reproduce the results of your analysis, without violating any privacy or intellectual property regulations. Read more: Review of synthetic generation methods synthpop : an R package for creating synthetic data ( paper , blog how to ) For MRI-data, see brainpower . Federated learning Federated learning arises from the field of Artificial Intelligence and relies \u201con the principle of remote execution\u2014that is, distributing copies of a machine learning algorithm to the sites or devices where the data is kept (nodes), performing training iterations locally, and returning the results of the computation (for example, updated neural network weights) to a central repository to update the main algorithm.\u201d ( Kaissis et al., 2020). This means that you do not move your data, while still providing valuable information about it. Some federated learning tools and projects: COINSTAC PySyft ENIGMA consortium: Consortium with several working groups. Share pre- and post-processing analysis scripts, the leading site will conduct meta-analysis OHDSI (Observational Health Data Sciences and Informatics): collaborative to bring out the value of health data through large-scale analytics Personal Health Train , part of Health-RI (official website here ) Licensing data With licenses , you specify what others are permitted to do with your product. You can see it as some kind of agreement: if someone violates the license, you have the right to sue them, just like a regular lawful agreement. For anonymous data, it is recommended to choose a CC0 (public domain) or CC-BY 4.0 license. These open licenses both allow others to use the data without restrictions. For non-anonymous data, use a more restrictive license (but please don't use non-derivate (ND) or non-commercial (NC) licenses, read why here ) or formulate your own terms of use, for example in a data use agreement . Don't know which license to choose? Use a license selector ! Resources Data management and sharing tools (list compiled by the Leiden University Library) The Turing Way - open data Utrecht University information about data sharing FAQ about data sharing (Donders Institute) Decision aid choosing a repository (not exhaustive)","title":"How to"},{"location":"open-science/data-sharing-how.html#sharing-research-data-how","text":"Sharing data is becoming the golden standard in science. It enables others to reproduce your results and prevent fraud and honest mistakes in data analysis. Moreover, it enables reuse of your data in new analyses, increasing the impact of your work.","title":"Sharing research data: how?"},{"location":"open-science/data-sharing-how.html#short-guide-when-to-share-what-data","text":"If data are completely anonymous , you can share them publicly in a dedicated repository, see step 1 or 2 If data cannot be completely anonymized, they are personal. You need a legal basis to share these data: Informed consent : what you can do with the data depends on the contents of the consent form. If participants consented to public data sharing and their data are not very sensitive (e.g., not from children or clinical groups), publish them in a repository or datapaper . If participants consented to sharing with restrictions, use a repository that allows access restrictions or use a data use agreement to share data case by case. If participants did not consent to any personal data sharing, share characteristics or aggregated data . Public interest : In theory, most research is publicly funded, and therefore we should be able to use this as legal basis for data sharing. However, it is still unclear when we are allowed to use it. The minimal prerequities are: the personal data sharing should rely on the principles of lawfulness, fairness and transparency informed consent was impossible to obtain, e.g., because the study took place a long time ago and consent cannot be obtained retroactively. Participants not consenting to data sharing is not a valid reason! When sharing personal data using the Public interest basis, you are encouraged to share data with access restrictions, especially if your data are sensitive or highly identifiable (e.g., data from minors or clinical groups, special categories of sensitive data, etc.) If you share data with a similar purpose as the original research project (such as for collaborating with other researchers on a related topic), a data use agreement suffices (not strictly necessary for EUR collaborators as they are from the same institution). Such agreement should lay out the conditions of storing, sharing and publishing the data. This falls under the scope of processing that is \"compatible with the original purpose\", which does not require a new/separate legal basis (GDPR Articles 5(1)(b) , 6(4) and 89(1) ).","title":"Short guide: When to share what data?"},{"location":"open-science/data-sharing-how.html#ways-of-sharing-data","text":"Publishing data can go roughly in the following ways:","title":"Ways of sharing data"},{"location":"open-science/data-sharing-how.html#licensing-data","text":"With licenses , you specify what others are permitted to do with your product. You can see it as some kind of agreement: if someone violates the license, you have the right to sue them, just like a regular lawful agreement. For anonymous data, it is recommended to choose a CC0 (public domain) or CC-BY 4.0 license. These open licenses both allow others to use the data without restrictions. For non-anonymous data, use a more restrictive license (but please don't use non-derivate (ND) or non-commercial (NC) licenses, read why here ) or formulate your own terms of use, for example in a data use agreement . Don't know which license to choose? Use a license selector !","title":"Licensing data"},{"location":"open-science/data-sharing-how.html#resources","text":"Data management and sharing tools (list compiled by the Leiden University Library) The Turing Way - open data Utrecht University information about data sharing FAQ about data sharing (Donders Institute) Decision aid choosing a repository (not exhaustive)","title":"Resources"},{"location":"open-science/dsa-template.html","text":"Data sharing agreement What is a data sharing agreement? A data sharing agreement can be set up between the owner of research data and someone with whom data is shared or who will process the data further. Important components of a data sharing agreement are who remains responsible in which role (i.e., are parties joint controllers or independent controllers of the data), about the use of the data (e.g., for scientific purposes) the use of intellectual property (if any) and confidentiality (incl. privacy-sensitive data). Using a data sharing agreement to share data leaves a high degree of control for the data owner concerning who has access to the data. However, please note that doing so can slow the process of data sharing tremendously . Therefore, always first consider whether there are more efficient ways of data sharing, e.g., using a data repository that has access restriction options. When to use a data sharing agreement? When the data cannot be made entirely anonymous and you want to take additional measures to protect data subjects' privacy AND When the data subject has given explicit informed consent to share their personal data with such parties OR you have confirmation that you can use a different legal basis to share personal data When data can be made entirely anonymous, you do not strictly need a data sharing agreement, except when the data include intellectual property rights. When the participant has NOT given explicit informed consent to share their personal data (non-anonymous data) and there is no other legal basis you can use, you are not allowed to share the data at all . Try to find a way to anonymize the data or do not share the data at all. How to make use of a data sharing agreement? Consider whether you really need to use an agreement to share your data. For example, it is not necessary when the data is not confidential, the data does not include intellectual property rights, or if you want to share with researchers within the same university. If you do need an agreement, try to make use of a template. The template that we can use at EUR (if sharing EUR-data) can be downloaded here . Another possible template from the Open Brain Consent can be found here . Edit the template to fit your specific situation. Send the agreement to erslegal [at] eur [dot] nl. They will check the agreement. If you use one of their templates, this process should go relatively quickly. Send the agreement to the other party to discuss with their legal department. Once both parties are good with the agreement, have someone with legal permission to decide sign the agreement. At EUR, this is usually the faculty dean (if the agreement spans less than 4 years, otherwise it is College van Bestuur). To do this, send the agreement to the dean's secretariat (office [dot] dean [at] essb [dot] eur [dot] nl). Send the signed version to legal services , so that the agreement is registered in the system. Important notes If the agreement is signed by someone without legal jurisdiction to sign, you won't have the law on your side in case of breach of agreement terms! When using the EUR template, please note: 5b. Data Receiver shall, to safeguard any potential intellectual property right and protect confidential information (if any), send papers intended for publication to EUR at least fourteen working days prior to submission. This article was added by legal services to be able to check for violations of intellectual property and privacy issues before a manuscript using \"our\" data is published. This checking is basically our (the researcher's) responsibility. However, if you need legal support with this, ask legal services for help!","title":"Data sharing agreement"},{"location":"open-science/dsa-template.html#data-sharing-agreement","text":"","title":"Data sharing agreement"},{"location":"open-science/dsa-template.html#what-is-a-data-sharing-agreement","text":"A data sharing agreement can be set up between the owner of research data and someone with whom data is shared or who will process the data further. Important components of a data sharing agreement are who remains responsible in which role (i.e., are parties joint controllers or independent controllers of the data), about the use of the data (e.g., for scientific purposes) the use of intellectual property (if any) and confidentiality (incl. privacy-sensitive data). Using a data sharing agreement to share data leaves a high degree of control for the data owner concerning who has access to the data. However, please note that doing so can slow the process of data sharing tremendously . Therefore, always first consider whether there are more efficient ways of data sharing, e.g., using a data repository that has access restriction options.","title":"What is a data sharing agreement?"},{"location":"open-science/dsa-template.html#when-to-use-a-data-sharing-agreement","text":"When the data cannot be made entirely anonymous and you want to take additional measures to protect data subjects' privacy AND When the data subject has given explicit informed consent to share their personal data with such parties OR you have confirmation that you can use a different legal basis to share personal data When data can be made entirely anonymous, you do not strictly need a data sharing agreement, except when the data include intellectual property rights. When the participant has NOT given explicit informed consent to share their personal data (non-anonymous data) and there is no other legal basis you can use, you are not allowed to share the data at all . Try to find a way to anonymize the data or do not share the data at all.","title":"When to use a data sharing agreement?"},{"location":"open-science/dsa-template.html#how-to-make-use-of-a-data-sharing-agreement","text":"Consider whether you really need to use an agreement to share your data. For example, it is not necessary when the data is not confidential, the data does not include intellectual property rights, or if you want to share with researchers within the same university. If you do need an agreement, try to make use of a template. The template that we can use at EUR (if sharing EUR-data) can be downloaded here . Another possible template from the Open Brain Consent can be found here . Edit the template to fit your specific situation. Send the agreement to erslegal [at] eur [dot] nl. They will check the agreement. If you use one of their templates, this process should go relatively quickly. Send the agreement to the other party to discuss with their legal department. Once both parties are good with the agreement, have someone with legal permission to decide sign the agreement. At EUR, this is usually the faculty dean (if the agreement spans less than 4 years, otherwise it is College van Bestuur). To do this, send the agreement to the dean's secretariat (office [dot] dean [at] essb [dot] eur [dot] nl). Send the signed version to legal services , so that the agreement is registered in the system.","title":"How to make use of a data sharing agreement?"},{"location":"open-science/dsa-template.html#important-notes","text":"If the agreement is signed by someone without legal jurisdiction to sign, you won't have the law on your side in case of breach of agreement terms! When using the EUR template, please note: 5b. Data Receiver shall, to safeguard any potential intellectual property right and protect confidential information (if any), send papers intended for publication to EUR at least fourteen working days prior to submission. This article was added by legal services to be able to check for violations of intellectual property and privacy issues before a manuscript using \"our\" data is published. This checking is basically our (the researcher's) responsibility. However, if you need legal support with this, ask legal services for help!","title":"Important notes"},{"location":"open-science/fair-software.html","text":"Open source software Let's say I have an experiment or analysis code that I want to share with the world. However, I want to get acknowledgement and I don't want to reply to all separate emails asking for it. How to go about it? Create an open source project! See also: FAIR software recommendations Open source guide 1. Create a github repository If you want to know more about how github works, check out the github chapter . Choose a recognizable name (check for projects with a similar name!) Choose a public repository Initialize a readme.md file 2. Include information files Write a comprehensive readme file : what does the repository contain? What is the background? Can people contribute and how can people use your software? (You can also write one online ) Write contributing guidelines , if you are open to people contributing Write a code of conduct Choose a license for your project (see Github docs ): this is important, because it specifies how people can use your software. MIT , Apache 2.0 , and GPLv3 are the most popular open source licenses, but there are other options . You can use this license selector as well. Read about all these steps on this website . 3. Fill up the repository with your software use consistent code conventions and clear function/method/variable names comment your code! remove sensitive materials in the revision history, issues, or pull requests use logical file names and structure check whether your software is of sufficient quality 4. Make your software citable Despite you having specified how people can reuse your software (through the license), your software is not yet citable using a persistent identifier. Unfortunately, Github does not offer the possibility to create a persistent identifier for a repository directly. However , it is possible to make a release (a snapshot of the repository at a certain point in time) on Github that you can then publish on Zenodo , which will create a DOI. Click here to see how to do this. 5. Register your software in a community registry This allows others to easily find and reuse your software or code. Find a registry here .","title":"FAIR software"},{"location":"open-science/fair-software.html#open-source-software","text":"Let's say I have an experiment or analysis code that I want to share with the world. However, I want to get acknowledgement and I don't want to reply to all separate emails asking for it. How to go about it? Create an open source project! See also: FAIR software recommendations Open source guide","title":"Open source software"},{"location":"open-science/fair-software.html#1-create-a-github-repository","text":"If you want to know more about how github works, check out the github chapter . Choose a recognizable name (check for projects with a similar name!) Choose a public repository Initialize a readme.md file","title":"1. Create a github repository"},{"location":"open-science/fair-software.html#2-include-information-files","text":"Write a comprehensive readme file : what does the repository contain? What is the background? Can people contribute and how can people use your software? (You can also write one online ) Write contributing guidelines , if you are open to people contributing Write a code of conduct Choose a license for your project (see Github docs ): this is important, because it specifies how people can use your software. MIT , Apache 2.0 , and GPLv3 are the most popular open source licenses, but there are other options . You can use this license selector as well. Read about all these steps on this website .","title":"2. Include information files"},{"location":"open-science/fair-software.html#3-fill-up-the-repository-with-your-software","text":"use consistent code conventions and clear function/method/variable names comment your code! remove sensitive materials in the revision history, issues, or pull requests use logical file names and structure check whether your software is of sufficient quality","title":"3. Fill up the repository with your software"},{"location":"open-science/fair-software.html#4-make-your-software-citable","text":"Despite you having specified how people can reuse your software (through the license), your software is not yet citable using a persistent identifier. Unfortunately, Github does not offer the possibility to create a persistent identifier for a repository directly. However , it is possible to make a release (a snapshot of the repository at a certain point in time) on Github that you can then publish on Zenodo , which will create a DOI. Click here to see how to do this.","title":"4. Make your software citable"},{"location":"open-science/fair-software.html#5-register-your-software-in-a-community-registry","text":"This allows others to easily find and reuse your software or code. Find a registry here .","title":"5. Register your software in a community registry"},{"location":"open-science/gdpr.html","text":"When can I share my data and with whom? Whether you can share your research data with others depends on: 1. The anonymity of your data 2. Who owns your data 3. The infrastructure available to share the data In this chapter, we will go into nr. 1 and talk about the EU privacy law: the General Data Protection Regulation. The GDPR Since May 2018, the General Data Protection Regulation (Dutch: Algemene Verordening Gegevensbescherming [AVG]) has been in place to better protect personal data. The most important aspects of the GDPR are: Privacy by Design : build privacy-increasing measures into your study design Privacy by Default : make sure your default settings already improve your participants' privacy Data minimization : Only collect and use personal data necessary for your research goal Legal basis : Make sure there is a legal basis (6 possible) to process (and share) the personal data you collect (e.g., informed consent or public interest more info (Dutch) DPIA : Conduct a Data Protection Impact Assessment whenever you collect (highly) sensitive data, such as names, addresses, race or health data. Inform participants about the goal of the personal data collection and which data you collect. What is personal data? Data is personal when you can identify someone by it, either directly (e.g., name, address) or indirectly (e.g., height, job, income, education). Indirect indicators are personal data if they can identify someone: when it concerns an extreme case (e.g., someone 2.20m tall) when combining data so that they can only be applicable to one person (NB. this can also concern publicly available data) when re-identification is still possible (e.g., with a name-number key conversion file) By law, data is considered identifiable when identification can occur with reasonable (proportionate) effort. Thus, it is not about the hypothetical possibility that data can be linked or combined. Because not everyone has access to the same data, the definition of \"identifiable\" may differ per situation. Important types of data Pseudonymous data: Data that is only identifiable with a key (that still exists). This is the case when after encryption, it is still possible to identify someone, e.g., because the key or the source data still exist. Pseudonymous data are still considered personal data , because the encryption is reversible , thus requiring a legal basis for processing. Special personal data: special sensitive categories of personal data that may be difficult to anonymize, they require additional measures: race of ethnic descent political views religion union membership genetic or biometric data aimed at unique identification health data sexual life and preference criminal records in the Netherlands: burgerservicenummer (BSN) Anonymous data: Data that are not (re)identifiable anymore: neiher by a name-number key, nor by combining with other publicly available data. Anonymous data are not considered personal data , so processing and sharing this kind of data do not require a legal basis. Sharing data under the GDPR Anonymous data can be shared without restriction if they are really anonymous. You may share non-anonymous data only when: You have attained explicit informed consent from the participant to do so (most used legal basis). For special personal data, this consent should be very explicit (\"I agree to share x, y and z\" with A, B and C): there cannot be any doubt about this. See some example sentences and a GDPR version of the Open Brain Consent initiative You reduce the amount of personal data shared to a minimum (data minimization principle) You take the necessary measures to protect your participants' privacy Always write a Data Management Plan (DMP) and Data Protection Impact Assessment (DPIA) before starting a project with personal data When sharing data with researchers outside of the EU, Norway, Liechtenstein and Iceland (no GDPR present), make sure that country has an adequacy decision . If the country does not have one, you need to take extra protection measures, such as standard contractual clauses or agreements. In case your data are not anonymous, but you have attained consent and still want to protect your participants' privacy better, you may always use a data sharing agreement . This document contains what users can and cannot do with your data, for how long and if you will get credit if the user publishes about your data. A good example is the agreement used by the Donders repository . The Open Brain Consent initiative is also working on a template agreement , or find an example template in the template chapter . Anonymizing data General tips Remove identifiers (name, address) Replace identifiers (e.g., date of birth by age or age groups) Use pseudonyms (e.g., participant numbers) Randomize the pseudonyms (participant numbers) Use only the middle range of the data: extreme cases may lead to identification because by definition, there are only few of them Remove the name-participant number key Plan how to anonymize the data up front and keep a log of your procedures Store original data in a safe location Determine whether different measures combined could lead to identification. If needed, consult a privacy officer. Deidentifying MRI-data There is some debate as to whether or not MRI data can be anonymized. One paper, for example, found that brain morphology, although preprocessed, was personally identifiable ( Takao, Hayashi, & Ohtomo, 2015 ). Moreover, it could be argued that, when combining multiple databases, the data may be identifiable in that way as well. Therefore, we do not speak of anonymizing MRI-data, but deidentifying it: MRI-data will always remain pseudonymous at best and therefore require a legal basis before sharing. Anonymize the filenames: replace names with codes Remove the header information (when using hdr and img files, not for nifti files) Deface the MRI-scans if your software does not do that automatically already. We recommend using pydeface . If you are uncertain whether your data are anonymous, please don't hesitate to contact a privacy officer. Have a look at this MRI data sharing guide for more info! GDPR resources Open Brain Consent initiative , a bottom-up initiative to make sense of the GDPR in sharing MRI data A great overview of the GDPR and its practical implications (by Enrico Glerean, 2020) Course about privacy in research Privacy dos and donts Guide for sensitive data UU guides for handling personal data and informed consent Legal instruments protecting data (agreements) Erasmus University contacts Privacy office ESSB: privacy [at] essb [dot] eur [dot] nl, or see this page Legal counsel: see this page Research support, e.g., data stewards: see this page IT-related questions: it [dot] servicedesk [at] eur [dot] nl See all support staff here","title":"The GDPR"},{"location":"open-science/gdpr.html#when-can-i-share-my-data-and-with-whom","text":"Whether you can share your research data with others depends on: 1. The anonymity of your data 2. Who owns your data 3. The infrastructure available to share the data In this chapter, we will go into nr. 1 and talk about the EU privacy law: the General Data Protection Regulation.","title":"When can I share my data and with whom?"},{"location":"open-science/gdpr.html#the-gdpr","text":"Since May 2018, the General Data Protection Regulation (Dutch: Algemene Verordening Gegevensbescherming [AVG]) has been in place to better protect personal data. The most important aspects of the GDPR are: Privacy by Design : build privacy-increasing measures into your study design Privacy by Default : make sure your default settings already improve your participants' privacy Data minimization : Only collect and use personal data necessary for your research goal Legal basis : Make sure there is a legal basis (6 possible) to process (and share) the personal data you collect (e.g., informed consent or public interest more info (Dutch) DPIA : Conduct a Data Protection Impact Assessment whenever you collect (highly) sensitive data, such as names, addresses, race or health data. Inform participants about the goal of the personal data collection and which data you collect.","title":"The GDPR"},{"location":"open-science/gdpr.html#what-is-personal-data","text":"Data is personal when you can identify someone by it, either directly (e.g., name, address) or indirectly (e.g., height, job, income, education). Indirect indicators are personal data if they can identify someone: when it concerns an extreme case (e.g., someone 2.20m tall) when combining data so that they can only be applicable to one person (NB. this can also concern publicly available data) when re-identification is still possible (e.g., with a name-number key conversion file) By law, data is considered identifiable when identification can occur with reasonable (proportionate) effort. Thus, it is not about the hypothetical possibility that data can be linked or combined. Because not everyone has access to the same data, the definition of \"identifiable\" may differ per situation.","title":"What is personal data?"},{"location":"open-science/gdpr.html#important-types-of-data","text":"Pseudonymous data: Data that is only identifiable with a key (that still exists). This is the case when after encryption, it is still possible to identify someone, e.g., because the key or the source data still exist. Pseudonymous data are still considered personal data , because the encryption is reversible , thus requiring a legal basis for processing. Special personal data: special sensitive categories of personal data that may be difficult to anonymize, they require additional measures: race of ethnic descent political views religion union membership genetic or biometric data aimed at unique identification health data sexual life and preference criminal records in the Netherlands: burgerservicenummer (BSN) Anonymous data: Data that are not (re)identifiable anymore: neiher by a name-number key, nor by combining with other publicly available data. Anonymous data are not considered personal data , so processing and sharing this kind of data do not require a legal basis.","title":"Important types of data"},{"location":"open-science/gdpr.html#sharing-data-under-the-gdpr","text":"Anonymous data can be shared without restriction if they are really anonymous. You may share non-anonymous data only when: You have attained explicit informed consent from the participant to do so (most used legal basis). For special personal data, this consent should be very explicit (\"I agree to share x, y and z\" with A, B and C): there cannot be any doubt about this. See some example sentences and a GDPR version of the Open Brain Consent initiative You reduce the amount of personal data shared to a minimum (data minimization principle) You take the necessary measures to protect your participants' privacy Always write a Data Management Plan (DMP) and Data Protection Impact Assessment (DPIA) before starting a project with personal data When sharing data with researchers outside of the EU, Norway, Liechtenstein and Iceland (no GDPR present), make sure that country has an adequacy decision . If the country does not have one, you need to take extra protection measures, such as standard contractual clauses or agreements. In case your data are not anonymous, but you have attained consent and still want to protect your participants' privacy better, you may always use a data sharing agreement . This document contains what users can and cannot do with your data, for how long and if you will get credit if the user publishes about your data. A good example is the agreement used by the Donders repository . The Open Brain Consent initiative is also working on a template agreement , or find an example template in the template chapter .","title":"Sharing data under the GDPR"},{"location":"open-science/gdpr.html#anonymizing-data","text":"","title":"Anonymizing data"},{"location":"open-science/gdpr.html#general-tips","text":"Remove identifiers (name, address) Replace identifiers (e.g., date of birth by age or age groups) Use pseudonyms (e.g., participant numbers) Randomize the pseudonyms (participant numbers) Use only the middle range of the data: extreme cases may lead to identification because by definition, there are only few of them Remove the name-participant number key Plan how to anonymize the data up front and keep a log of your procedures Store original data in a safe location Determine whether different measures combined could lead to identification. If needed, consult a privacy officer.","title":"General tips"},{"location":"open-science/gdpr.html#deidentifying-mri-data","text":"There is some debate as to whether or not MRI data can be anonymized. One paper, for example, found that brain morphology, although preprocessed, was personally identifiable ( Takao, Hayashi, & Ohtomo, 2015 ). Moreover, it could be argued that, when combining multiple databases, the data may be identifiable in that way as well. Therefore, we do not speak of anonymizing MRI-data, but deidentifying it: MRI-data will always remain pseudonymous at best and therefore require a legal basis before sharing. Anonymize the filenames: replace names with codes Remove the header information (when using hdr and img files, not for nifti files) Deface the MRI-scans if your software does not do that automatically already. We recommend using pydeface . If you are uncertain whether your data are anonymous, please don't hesitate to contact a privacy officer. Have a look at this MRI data sharing guide for more info!","title":"Deidentifying MRI-data"},{"location":"open-science/gdpr.html#gdpr-resources","text":"Open Brain Consent initiative , a bottom-up initiative to make sense of the GDPR in sharing MRI data A great overview of the GDPR and its practical implications (by Enrico Glerean, 2020) Course about privacy in research Privacy dos and donts Guide for sensitive data UU guides for handling personal data and informed consent Legal instruments protecting data (agreements)","title":"GDPR resources"},{"location":"open-science/gdpr.html#erasmus-university-contacts","text":"Privacy office ESSB: privacy [at] essb [dot] eur [dot] nl, or see this page Legal counsel: see this page Research support, e.g., data stewards: see this page IT-related questions: it [dot] servicedesk [at] eur [dot] nl See all support staff here","title":"Erasmus University contacts"},{"location":"open-science/inclusivity-goals.html","text":"Diversity & inclusion at the SYNC lab: SMART goals (SMART: specific, measurable, achievable, realistic, timely) Open Science also means inclusive science, and we formulated the following goals in July 2020 (Descriptions copied from our blogpost ): 1. Inclusive research samples Description Although we have made some steps to diversify our research samples, we recognize that we still have much to learn and improve. Therefore, we want to make an effort to make our samples more diverse and representative of the whole population. For this purpose, we will (1) pay more attention to two facets of diversity, namely educational background and ethnic-cultural background and (2) align our recruitment strategy accordingly \u2013 choosing the appropriate channels to reach a diverse group of participants and minimize issues that may prevent certain sub-groups from participating. SMART goals For each future research project, we aim to have at least 30% of our participants who go to a vmbo-school or mbo-school (depending on the targeted age group). In the Netherlands almost 50% of the secondary school students are enrolled in a vmbo education . For each future research project, we aim to have at least 20% of our participants who have a migration background or who identify with other ethnicities besides Dutch. According to recent findings 27% of the Dutch youth has a migration background, in Rotterdam this is even 60% 2. Inclusive education and work environment Description Another essential aspect of our work and responsibility involves education \u2013 whether it pertains to teaching our knowledge and skills to university students or to society more broadly, in the form of outreach or policy recommendations. We need to consider how to be more inclusive and supportive in the opportunities we provide to a wide arrange of students, what the most appropriate channels are for sharing our findings to reach all of society, and how we can make sure that policy recommendations do not exclude important sub-groups of our population. We are developing SMART rules to get there. SMART goals For each future research project, we aim to reach out to at least vmbo schools, ideally in a non-textual way (e.g., via a vlog, information video or in person meeting). Depending on the targeted age group this could also be mbo-schools. As from the new academic year onwards (i.e. September, 2020) we aim to broaden our internship possibilities, by offering at least two internships to non-university students . We want to reach out to at least two youth workers and two secondary (i.e. vmbo/havo/vwo) and/or mbo-schools to inform them about our internship and youth panel possibilities. 3. Include all voices Description In all (the stages of) our activities, we need to hear other voices, to help us expand our view and understand what is important for all of society. That is the ultimate way to truly get society, youth and neuroscience connected. SMART goals We aim to have yearly diverse youth panels in which we can discuss our research and how we can improve it. As from the new academic year onwards (i.e. September, 2020) we aim to pop-up, in terms of a living lab, in a diverse community within Rotterdam city where we can come together as a lab and work there once a week for at least three months . We aim to have yearly focus group meetings on diversity and inclusion in which representative individuals from society can join. At the beginning of the new academic year (i.e. September, 2020) we aim to reach out in a non-textual way (via a vlog, person-meetings or event) to the community in which our living lab will be located, in order to increase our visibility as a lab, introduce our work and science in general, and to stimulate co-creation. Evaluation of SMART goals Every two months we aim to schedule a meeting in which we can discuss the progress of our SMART goals. Some of the goals are more long-term goals and depend for instance on new projects being started. Other goals can be accomplished in the more near future. We think it is important that we realize that all these goals are a process in itself and that we will learn along the way. This could mean that we might change and adapt our goals according to new experiences and new knowledge.","title":"Our goals for inclusive science"},{"location":"open-science/inclusivity-goals.html#diversity-inclusion-at-the-sync-lab-smart-goals","text":"","title":"Diversity &amp; inclusion at the SYNC lab: SMART goals"},{"location":"open-science/inclusivity-goals.html#smart-specific-measurable-achievable-realistic-timely","text":"Open Science also means inclusive science, and we formulated the following goals in July 2020 (Descriptions copied from our blogpost ):","title":"(SMART: specific, measurable, achievable, realistic, timely)"},{"location":"open-science/inclusivity-goals.html#1-inclusive-research-samples","text":"Description Although we have made some steps to diversify our research samples, we recognize that we still have much to learn and improve. Therefore, we want to make an effort to make our samples more diverse and representative of the whole population. For this purpose, we will (1) pay more attention to two facets of diversity, namely educational background and ethnic-cultural background and (2) align our recruitment strategy accordingly \u2013 choosing the appropriate channels to reach a diverse group of participants and minimize issues that may prevent certain sub-groups from participating.","title":"1. Inclusive research samples"},{"location":"open-science/inclusivity-goals.html#smart-goals","text":"For each future research project, we aim to have at least 30% of our participants who go to a vmbo-school or mbo-school (depending on the targeted age group). In the Netherlands almost 50% of the secondary school students are enrolled in a vmbo education . For each future research project, we aim to have at least 20% of our participants who have a migration background or who identify with other ethnicities besides Dutch. According to recent findings 27% of the Dutch youth has a migration background, in Rotterdam this is even 60%","title":"SMART goals"},{"location":"open-science/inclusivity-goals.html#2-inclusive-education-and-work-environment","text":"Description Another essential aspect of our work and responsibility involves education \u2013 whether it pertains to teaching our knowledge and skills to university students or to society more broadly, in the form of outreach or policy recommendations. We need to consider how to be more inclusive and supportive in the opportunities we provide to a wide arrange of students, what the most appropriate channels are for sharing our findings to reach all of society, and how we can make sure that policy recommendations do not exclude important sub-groups of our population. We are developing SMART rules to get there.","title":"2. Inclusive education and work environment"},{"location":"open-science/inclusivity-goals.html#smart-goals_1","text":"For each future research project, we aim to reach out to at least vmbo schools, ideally in a non-textual way (e.g., via a vlog, information video or in person meeting). Depending on the targeted age group this could also be mbo-schools. As from the new academic year onwards (i.e. September, 2020) we aim to broaden our internship possibilities, by offering at least two internships to non-university students . We want to reach out to at least two youth workers and two secondary (i.e. vmbo/havo/vwo) and/or mbo-schools to inform them about our internship and youth panel possibilities.","title":"SMART goals"},{"location":"open-science/inclusivity-goals.html#3-include-all-voices","text":"Description In all (the stages of) our activities, we need to hear other voices, to help us expand our view and understand what is important for all of society. That is the ultimate way to truly get society, youth and neuroscience connected.","title":"3. Include all voices"},{"location":"open-science/inclusivity-goals.html#smart-goals_2","text":"We aim to have yearly diverse youth panels in which we can discuss our research and how we can improve it. As from the new academic year onwards (i.e. September, 2020) we aim to pop-up, in terms of a living lab, in a diverse community within Rotterdam city where we can come together as a lab and work there once a week for at least three months . We aim to have yearly focus group meetings on diversity and inclusion in which representative individuals from society can join. At the beginning of the new academic year (i.e. September, 2020) we aim to reach out in a non-textual way (via a vlog, person-meetings or event) to the community in which our living lab will be located, in order to increase our visibility as a lab, introduce our work and science in general, and to stimulate co-creation.","title":"SMART goals"},{"location":"open-science/inclusivity-goals.html#evaluation-of-smart-goals","text":"Every two months we aim to schedule a meeting in which we can discuss the progress of our SMART goals. Some of the goals are more long-term goals and depend for instance on new projects being started. Other goals can be accomplished in the more near future. We think it is important that we realize that all these goals are a process in itself and that we will learn along the way. This could mean that we might change and adapt our goals according to new experiences and new knowledge.","title":"Evaluation of SMART goals"},{"location":"open-science/neurovault.html","text":"NeuroVault: instructions and tips NeuroVault is a repository for processed neuroimaging data (you can upload first and second level processed data). This type of data is anonymous, so it can be shared without any restrictions. We encourage you to do this! Why NeuroVault? Share data without sending files around Visualize your MRI contrasts and give the dataset a persistent identifier Make meta-analyses a lot easier Provide transparency to reviewers asking about your data Refer to the original paper and from the paper to your NeuroVault collection Tips: Before uploading: Keep a good documentation of your contrasts during your data analysis so that it is easier to look up which contrast is which (since SPM is not good at file naming) Upload the images before you send in the first version of your paper. This way you can show the reviewers the data from the start (with the NeuroVault link) You can upload both t-maps and ROIs from many different modalities (see below) Include all analyses published in the paper and the main effects even if they are not included in the publication. This makes your analysis more transparent to the reviewers Which data can be uploaded into NeuroVault? Map types: t, z, F, chi-squared, p (given null hypothesis), 1-p (\"inverted\" probability), univariate beta map, multivariable beta map, ROI/mask, parcellation, anatomical, variance Modalities: fMRI BOLD, fMRI-CBF, fMRI-CBV, diffusion MRI, structural MRI, PET FDP, PET [15O]-water, PET other, MEG, EEG, Other Example datasets: Renske van der Cruijsen , Michelle Achterberg Instructions Log in at https://neurovault.org/. If you have never used NeuroVault before, create an account (or use your Google account). Create a dataset, click \u201cGet started and upload an image!\u201d or \u201cAdd new collection\u201d under the \u201cCollections\u201d tab. Fill in the following information (metadata): Essentials Name of the collection: title of your article DOI of your article (if already present: always!!) Developmental neuroscience community Full dataset URL: for example a link to an OSF project, dataverseNL publication package, or Openneuro dataset, if applicable Contributors: add the last author of your paper (i.e., the NeuroVault username). In case you lose access to your account, the contributor can still make adaptations Accessibility: public, unless you are still in the reviewing process and only want the reviewers to see the data (with a view-only link) Subjects: Mean, min and max age of the sample (for easier meta-analysis) Design: type of design Acquisition, registration, preprocessing, first level and second level: these details should be included in your paper. You can include them here as well but not necessarily. Click \"Add image\" Name: short & as clear as possible which map / contrast you are referring to (otherwise add a description) Map type (often t-map), modality (often fMRI BOLD) and template image (often MNI) Cognitive Atlas Paradigm: choose the task that resembles yours the best. This may not always be possible, however this field is mandatory You can upload .nii, .nii.gz and .hdr/.img files. Make sure to select the correct contrast (i.e., have good data documentation)! Cognitive paradigm description: if you have a task that is not well-known or widely used, e.g., the SNAT, you can refer to a document about the task in this field. Analysis level: often group (if single-subject, upload each contrast for each subject) No. of subjects Corresponding figure: not necessary but very insightful for reviewers","title":"NeuroVault"},{"location":"open-science/neurovault.html#neurovault-instructions-and-tips","text":"NeuroVault is a repository for processed neuroimaging data (you can upload first and second level processed data). This type of data is anonymous, so it can be shared without any restrictions. We encourage you to do this!","title":"NeuroVault: instructions and tips"},{"location":"open-science/neurovault.html#why-neurovault","text":"Share data without sending files around Visualize your MRI contrasts and give the dataset a persistent identifier Make meta-analyses a lot easier Provide transparency to reviewers asking about your data Refer to the original paper and from the paper to your NeuroVault collection Tips: Before uploading: Keep a good documentation of your contrasts during your data analysis so that it is easier to look up which contrast is which (since SPM is not good at file naming) Upload the images before you send in the first version of your paper. This way you can show the reviewers the data from the start (with the NeuroVault link) You can upload both t-maps and ROIs from many different modalities (see below) Include all analyses published in the paper and the main effects even if they are not included in the publication. This makes your analysis more transparent to the reviewers","title":"Why NeuroVault?"},{"location":"open-science/neurovault.html#which-data-can-be-uploaded-into-neurovault","text":"Map types: t, z, F, chi-squared, p (given null hypothesis), 1-p (\"inverted\" probability), univariate beta map, multivariable beta map, ROI/mask, parcellation, anatomical, variance Modalities: fMRI BOLD, fMRI-CBF, fMRI-CBV, diffusion MRI, structural MRI, PET FDP, PET [15O]-water, PET other, MEG, EEG, Other Example datasets: Renske van der Cruijsen , Michelle Achterberg","title":"Which data can be uploaded into NeuroVault?"},{"location":"open-science/neurovault.html#instructions","text":"Log in at https://neurovault.org/. If you have never used NeuroVault before, create an account (or use your Google account). Create a dataset, click \u201cGet started and upload an image!\u201d or \u201cAdd new collection\u201d under the \u201cCollections\u201d tab. Fill in the following information (metadata): Essentials Name of the collection: title of your article DOI of your article (if already present: always!!) Developmental neuroscience community Full dataset URL: for example a link to an OSF project, dataverseNL publication package, or Openneuro dataset, if applicable Contributors: add the last author of your paper (i.e., the NeuroVault username). In case you lose access to your account, the contributor can still make adaptations Accessibility: public, unless you are still in the reviewing process and only want the reviewers to see the data (with a view-only link) Subjects: Mean, min and max age of the sample (for easier meta-analysis) Design: type of design Acquisition, registration, preprocessing, first level and second level: these details should be included in your paper. You can include them here as well but not necessarily. Click \"Add image\" Name: short & as clear as possible which map / contrast you are referring to (otherwise add a description) Map type (often t-map), modality (often fMRI BOLD) and template image (often MNI) Cognitive Atlas Paradigm: choose the task that resembles yours the best. This may not always be possible, however this field is mandatory You can upload .nii, .nii.gz and .hdr/.img files. Make sure to select the correct contrast (i.e., have good data documentation)! Cognitive paradigm description: if you have a task that is not well-known or widely used, e.g., the SNAT, you can refer to a document about the task in this field. Analysis level: often group (if single-subject, upload each contrast for each subject) No. of subjects Corresponding figure: not necessary but very insightful for reviewers","title":"Instructions"},{"location":"open-science/open-access-how.html","text":"How to publish open access? Publishing & green open access at the EUR You have to register your articles yourself in Pure . Read how to do this on this webpage . In short: Go to Pure and log in with your ERNA id and password Register your article in Pure within 6 months after publication Include all relevant details such as Title, Author(s), DOI, Email address of corresponding author, Journal, etc. Upload the accepted version of your publication (final author version, without publisher formatting) to RePub (Green route) Gold open access To check for publisher deals (APC reimbursement) see the journal browser on https://www.openaccess.nl/ or use the EUR journal browser To check whether the combination of your journal, funder and institution is compliant with Plan S , use the Journal Checker tool . In case a journal is not on this list and there is no funding from a project to cover the open access fees, there is an Erasmus Open Access Fund that can cover the fee. For more information please have a look here Use an open license: CC-BY Journals often offer multiple licensing possibilities. It is best to choose the most open license, preferably CC-BY 4.0 . Try to avoid using non-derivative (ND) and non-commercial (NC) licenses. For more information, check out this documentation on choosing a license. Use your ORCID When publishing anything, always use your ORCID . This allows all your works to be associated with you , not someone with accidentally the same name, independent of your work or email address. Read all about ORCID in this EUR libguide . Some nice-to-knows: Institutions have organization identifiers . For EUR, use \"Erasmus University Rotterdam, Zuid-Holland, NL\". Automatic syncing : Under \"Works\" > \"Search and link\", allow parties like Crossref and DataCite access to your ORCID, so all your works linked to them will appear in your record automatically ! The peer review category appears only if your publisher indicates that or when you use Publons. This will make explicit how much work you spend reviewing. Check the quality of an (open access) journal How do you know whether or not you found a trustworthy (open access) journal for your publication? Please pay attention to the following requirements: The journal has an ISSN (International Standard Serial Number) The publisher is a member of the Open Access Scholarly Publisher Association and the journal is included in the DOAJ The journal is connected to or is sponsored by a scientific institute or society All articles have a DOI The journal is not part of this list of dubious publishers The content area of the journal is clearly described and the articles are in line with this description The target group of the journal are researchers and scientific teachers The editorial board consists of renowned/well-known researchers from the discipline The costs of publishing open access are clearly mentioned The user licenses are clearly mentioned in each article You can also check the scoring of the journal here . Resources Transpose , a database of journal policies on peer review, co-reviewing and preprinting A repository for open access books can be found in OAPEN and via DOAB (Directory for Open Access Books) Read more about open access in The Turing Way","title":"How to"},{"location":"open-science/open-access-how.html#how-to-publish-open-access","text":"","title":"How to publish open access?"},{"location":"open-science/open-access-how.html#publishing-green-open-access-at-the-eur","text":"You have to register your articles yourself in Pure . Read how to do this on this webpage . In short: Go to Pure and log in with your ERNA id and password Register your article in Pure within 6 months after publication Include all relevant details such as Title, Author(s), DOI, Email address of corresponding author, Journal, etc. Upload the accepted version of your publication (final author version, without publisher formatting) to RePub (Green route)","title":"Publishing &amp; green open access at the EUR"},{"location":"open-science/open-access-how.html#gold-open-access","text":"To check for publisher deals (APC reimbursement) see the journal browser on https://www.openaccess.nl/ or use the EUR journal browser To check whether the combination of your journal, funder and institution is compliant with Plan S , use the Journal Checker tool . In case a journal is not on this list and there is no funding from a project to cover the open access fees, there is an Erasmus Open Access Fund that can cover the fee. For more information please have a look here","title":"Gold open access"},{"location":"open-science/open-access-how.html#use-an-open-license-cc-by","text":"Journals often offer multiple licensing possibilities. It is best to choose the most open license, preferably CC-BY 4.0 . Try to avoid using non-derivative (ND) and non-commercial (NC) licenses. For more information, check out this documentation on choosing a license.","title":"Use an open license: CC-BY"},{"location":"open-science/open-access-how.html#use-your-orcid","text":"When publishing anything, always use your ORCID . This allows all your works to be associated with you , not someone with accidentally the same name, independent of your work or email address. Read all about ORCID in this EUR libguide . Some nice-to-knows: Institutions have organization identifiers . For EUR, use \"Erasmus University Rotterdam, Zuid-Holland, NL\". Automatic syncing : Under \"Works\" > \"Search and link\", allow parties like Crossref and DataCite access to your ORCID, so all your works linked to them will appear in your record automatically ! The peer review category appears only if your publisher indicates that or when you use Publons. This will make explicit how much work you spend reviewing.","title":"Use your ORCID"},{"location":"open-science/open-access-how.html#check-the-quality-of-an-open-access-journal","text":"How do you know whether or not you found a trustworthy (open access) journal for your publication? Please pay attention to the following requirements: The journal has an ISSN (International Standard Serial Number) The publisher is a member of the Open Access Scholarly Publisher Association and the journal is included in the DOAJ The journal is connected to or is sponsored by a scientific institute or society All articles have a DOI The journal is not part of this list of dubious publishers The content area of the journal is clearly described and the articles are in line with this description The target group of the journal are researchers and scientific teachers The editorial board consists of renowned/well-known researchers from the discipline The costs of publishing open access are clearly mentioned The user licenses are clearly mentioned in each article You can also check the scoring of the journal here .","title":"Check the quality of an (open access) journal"},{"location":"open-science/open-access-how.html#resources","text":"Transpose , a database of journal policies on peer review, co-reviewing and preprinting A repository for open access books can be found in OAPEN and via DOAB (Directory for Open Access Books) Read more about open access in The Turing Way","title":"Resources"},{"location":"open-science/open-access-info.html","text":"Open access publishing What is open access publishing? Why publish open access? Ethical argument: science is often financed by public funds and therefore should be accessible to all Impact: increase visibility, use, citations and therefore impact Required: most funders nowadays require articles to be published open access Routes to open access Publishing open access is possible via the gold and the green routes: Gold route : publish open access via a journal, which often requires paying an Article Processing Charge (APC). APCs can sometimes be reimbursed because of Big Deals made between publishers and university libraries. According to Plan S, the open access journals have to fulfill several requirements that you can find here \". Green route (self-archiving): publish a version of your article in an institutional repository ( RePub for the EUR) after publishing it via a journal. Each repository should be registered in the Directory of Open Access Repositories ( OpenDOAR ) You can check which version of your article (submitted, accepted or published) you can archive in a repository in Sherpa Romeo Read more about open access at the EUR open access website . Preprints A preprint is the submitted, non-peer reviewed version of your article. An increasing number of researchers publish preprints in Preprint servers in order to get their results out there quicker. Read more below and in this preprint FAQ . Why publish preprints? Speed : Preprints are almost immediately publicly visible, besides some checks on content and ethics Visibility : Because preprints are open access and many preprint servers are indexed by search engines (e.g., Google Scholar), you can reach more people with your work Feedback : Some preprint servers allow collecting feedback on preprints, which can make your work so much better Prevent scooping : preprints are timestamped, so by posting it, you have established precedence Individual gain : such as showing productivity, openness to feedback, etc. Where to publish preprints? Preferably a preprint server that provides a persistent identifier. For example: OSF Preprints : you can choose many preprint servers and can also share supplementary files. A list of preprint servers hosted via OSF Preprints can be found here . Directly via a preprint server, such as BioRxiv or PsyArXiv You can even add a preprint on ResearchGate ! Feedback and updating There are different ways researchers can give/receive feedback on preprints: - In OSF preprints, you can use their tool Hypothes.is to annotate preprints, see their help guide - Some servers offer comment functionalities (e.g., when logged in) - Use (academic) twitter What you do with feedback is completely up to you. If you want, you can update your preprint to a new version. However, note that all versions are timestamped and retained . Often new versions get a new identifier (DOI) and old versions cannot be removed! If your work gets published by a publisher, many preprint servers also offer the possibility to refer to the identifier (DOI) of your published work, so that readers of the preprint get a notification that they are not reading the most up-to-date version.","title":"About open access"},{"location":"open-science/open-access-info.html#open-access-publishing","text":"","title":"Open access publishing"},{"location":"open-science/open-access-info.html#what-is-open-access-publishing","text":"","title":"What is open access publishing?"},{"location":"open-science/open-access-info.html#why-publish-open-access","text":"Ethical argument: science is often financed by public funds and therefore should be accessible to all Impact: increase visibility, use, citations and therefore impact Required: most funders nowadays require articles to be published open access","title":"Why publish open access?"},{"location":"open-science/open-access-info.html#routes-to-open-access","text":"Publishing open access is possible via the gold and the green routes: Gold route : publish open access via a journal, which often requires paying an Article Processing Charge (APC). APCs can sometimes be reimbursed because of Big Deals made between publishers and university libraries. According to Plan S, the open access journals have to fulfill several requirements that you can find here \". Green route (self-archiving): publish a version of your article in an institutional repository ( RePub for the EUR) after publishing it via a journal. Each repository should be registered in the Directory of Open Access Repositories ( OpenDOAR ) You can check which version of your article (submitted, accepted or published) you can archive in a repository in Sherpa Romeo Read more about open access at the EUR open access website .","title":"Routes to open access"},{"location":"open-science/open-access-info.html#preprints","text":"A preprint is the submitted, non-peer reviewed version of your article. An increasing number of researchers publish preprints in Preprint servers in order to get their results out there quicker. Read more below and in this preprint FAQ .","title":"Preprints"},{"location":"open-science/open-access-info.html#why-publish-preprints","text":"Speed : Preprints are almost immediately publicly visible, besides some checks on content and ethics Visibility : Because preprints are open access and many preprint servers are indexed by search engines (e.g., Google Scholar), you can reach more people with your work Feedback : Some preprint servers allow collecting feedback on preprints, which can make your work so much better Prevent scooping : preprints are timestamped, so by posting it, you have established precedence Individual gain : such as showing productivity, openness to feedback, etc.","title":"Why publish preprints?"},{"location":"open-science/open-access-info.html#where-to-publish-preprints","text":"Preferably a preprint server that provides a persistent identifier. For example: OSF Preprints : you can choose many preprint servers and can also share supplementary files. A list of preprint servers hosted via OSF Preprints can be found here . Directly via a preprint server, such as BioRxiv or PsyArXiv You can even add a preprint on ResearchGate !","title":"Where to publish preprints?"},{"location":"open-science/open-access-info.html#feedback-and-updating","text":"There are different ways researchers can give/receive feedback on preprints: - In OSF preprints, you can use their tool Hypothes.is to annotate preprints, see their help guide - Some servers offer comment functionalities (e.g., when logged in) - Use (academic) twitter What you do with feedback is completely up to you. If you want, you can update your preprint to a new version. However, note that all versions are timestamped and retained . Often new versions get a new identifier (DOI) and old versions cannot be removed! If your work gets published by a publisher, many preprint servers also offer the possibility to refer to the identifier (DOI) of your published work, so that readers of the preprint get a notification that they are not reading the most up-to-date version.","title":"Feedback and updating"},{"location":"open-science/open-code.html","text":"Sharing analysis code \u201cAn article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.\u201d - Buckheit & Donoho, 1995 (read the full article here ) This quote nicely summarizes the importance of sharing data, methods and code so that others can evaluate the actual work that was done in a research paper. Luckily, web-based technologies make it very easy to share these materials (and even to share the complete software environment using Docker and Singularity containers). However, just releasing your code without annotation is not very informative because others (and future you!) can't make a lot of sense of it. Two helpful tools to annotate your code are RMarkdown and Jupyter notebooks. How do tools like R Markdown and Jupyter notebooks make research more reproducible? R Markdown files and Jupyter notebooks contain 1) the code that others need to reproduce your work along with 2) the narration that a reader needs to understand your work R and Python code can be run within these documents, meaning that in principle others can check whether your work is reproducible R Markdown Especially when you do your data analysis in R / RStudio, R Markdown is a very useful tool to put your text and analysis together in one place. It is basically R + Markdown (a markup language to format text). It can be used to write a whole paper, including code to generate figures. This code can be outputted in many formats such as html, pdf and Word. For full documentation see also the R Markdown documentation and this neat cheatsheet (pdf) . Example of RMarkdown chunk in RStudio with associated html output (from RMarkdown docs) Installing R Markdown have R and RStudio installed (avalaible for free) install R Markdown install.packages(\"rmarkdown\") Reference lists using Zotero in R Markdown When writing papers, it is also very useful to connect RStudio with Zotero. Zotero is a free and open source reference manager with a very handy browser plugin . If you have never used a reference manager before: it is a great way to keep a library of all your literature (including pdf's) together and will help you to cite papers in the right way and produce automatic reference lists in the right format for you. This can be done in a word processor like Microsoft Word, but also in R Markdown. The basic steps you need to make this work: install Zotero and import references (e.g., using the browser plugin) install the Better BibTex plugin for Zotero by clicking Tools > Add-ons within Zotero and follow these instructions install the citr R package Now when writing text in an RMarkdown file in RStudio: within RStudio in the toolbar click Addins > Insert citations here you can search through your references and select the ones you want to enter you should also edit the YAML header (the upper part of the Rmd file with title, author, output et cetera): here the bibtex file (eg: bibliography: references.bib ) should be listed; if you also add # References at the end of the main text, your bibliography will be created automatically there the format of the bibliography can be defined by pointing to a csl file in the YAML header (e.g, csl: ./apa.csl ). All csl (citation style language) files can be downloaded from the internet, see also https://citationstyles.org/ Jupyter notebooks For analyses that are conducted using Python, Jupyter notebooks are a great way to keep executable code and annotation in one place (note that many other programming languages are also supported by Jupyter notebooks: the name is reference to the 3 core languages Julia, Python, and R). For full documentation see the Jupyter Notebook docs and https://jupyter.org/ for more information about the larger Project Jupyter ecosystem. When opening a Jupyter Notebook, you are opening an interactive session. Here you can add different sort of cells: code cells that can be executed (after execution the results will be displayed in the notebook), and Markdown cells that can be used to add descriptive text that can be marked up using the Markdown language. Example GIF of a Jupyter notebook for the Qoala-T tool. See notebook here Installing Jupyter notebooks you might want to try Jupyter online first, which can be done here If you want to install Jupyter notebooks, instructions for the different ways to install it can be found here Python is a requirement for installation, and the recommended way to install both Python and Jupyter notebooks is to install Anaconda (available for free) Once anaconda is installed you can launch Jupyter Notebooks from the Anaconda Navigator or by typing jupyter notebook in the command line","title":"Reproducible code"},{"location":"open-science/open-code.html#sharing-analysis-code","text":"\u201cAn article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.\u201d - Buckheit & Donoho, 1995 (read the full article here ) This quote nicely summarizes the importance of sharing data, methods and code so that others can evaluate the actual work that was done in a research paper. Luckily, web-based technologies make it very easy to share these materials (and even to share the complete software environment using Docker and Singularity containers). However, just releasing your code without annotation is not very informative because others (and future you!) can't make a lot of sense of it. Two helpful tools to annotate your code are RMarkdown and Jupyter notebooks.","title":"Sharing analysis code"},{"location":"open-science/open-code.html#how-do-tools-like-r-markdown-and-jupyter-notebooks-make-research-more-reproducible","text":"R Markdown files and Jupyter notebooks contain 1) the code that others need to reproduce your work along with 2) the narration that a reader needs to understand your work R and Python code can be run within these documents, meaning that in principle others can check whether your work is reproducible","title":"How do tools like R Markdown and Jupyter notebooks make research more reproducible?"},{"location":"open-science/open-code.html#r-markdown","text":"Especially when you do your data analysis in R / RStudio, R Markdown is a very useful tool to put your text and analysis together in one place. It is basically R + Markdown (a markup language to format text). It can be used to write a whole paper, including code to generate figures. This code can be outputted in many formats such as html, pdf and Word. For full documentation see also the R Markdown documentation and this neat cheatsheet (pdf) . Example of RMarkdown chunk in RStudio with associated html output (from RMarkdown docs)","title":"R Markdown"},{"location":"open-science/open-code.html#installing-r-markdown","text":"have R and RStudio installed (avalaible for free) install R Markdown install.packages(\"rmarkdown\")","title":"Installing R Markdown"},{"location":"open-science/open-code.html#reference-lists-using-zotero-in-r-markdown","text":"When writing papers, it is also very useful to connect RStudio with Zotero. Zotero is a free and open source reference manager with a very handy browser plugin . If you have never used a reference manager before: it is a great way to keep a library of all your literature (including pdf's) together and will help you to cite papers in the right way and produce automatic reference lists in the right format for you. This can be done in a word processor like Microsoft Word, but also in R Markdown.","title":"Reference lists using Zotero in R Markdown"},{"location":"open-science/open-code.html#the-basic-steps-you-need-to-make-this-work","text":"install Zotero and import references (e.g., using the browser plugin) install the Better BibTex plugin for Zotero by clicking Tools > Add-ons within Zotero and follow these instructions install the citr R package","title":"The basic steps you need to make this work:"},{"location":"open-science/open-code.html#now-when-writing-text-in-an-rmarkdown-file-in-rstudio","text":"within RStudio in the toolbar click Addins > Insert citations here you can search through your references and select the ones you want to enter you should also edit the YAML header (the upper part of the Rmd file with title, author, output et cetera): here the bibtex file (eg: bibliography: references.bib ) should be listed; if you also add # References at the end of the main text, your bibliography will be created automatically there the format of the bibliography can be defined by pointing to a csl file in the YAML header (e.g, csl: ./apa.csl ). All csl (citation style language) files can be downloaded from the internet, see also https://citationstyles.org/","title":"Now when writing text in an RMarkdown file in RStudio:"},{"location":"open-science/open-code.html#jupyter-notebooks","text":"For analyses that are conducted using Python, Jupyter notebooks are a great way to keep executable code and annotation in one place (note that many other programming languages are also supported by Jupyter notebooks: the name is reference to the 3 core languages Julia, Python, and R). For full documentation see the Jupyter Notebook docs and https://jupyter.org/ for more information about the larger Project Jupyter ecosystem. When opening a Jupyter Notebook, you are opening an interactive session. Here you can add different sort of cells: code cells that can be executed (after execution the results will be displayed in the notebook), and Markdown cells that can be used to add descriptive text that can be marked up using the Markdown language. Example GIF of a Jupyter notebook for the Qoala-T tool. See notebook here","title":"Jupyter notebooks"},{"location":"open-science/open-code.html#installing-jupyter-notebooks","text":"you might want to try Jupyter online first, which can be done here If you want to install Jupyter notebooks, instructions for the different ways to install it can be found here Python is a requirement for installation, and the recommended way to install both Python and Jupyter notebooks is to install Anaconda (available for free) Once anaconda is installed you can launch Jupyter Notebooks from the Anaconda Navigator or by typing jupyter notebook in the command line","title":"Installing Jupyter notebooks"},{"location":"open-science/oscr.html","text":"Open Science Community Rotterdam Interested in open science? Have a look at the Open Science Community Rotterdam (OSCR) . OSCR workshops For Open Science related topics, it is also an option to reach a broader audience and organise a workshop for the Open Science Community Rotterdam. All lab members can take part in these workshops as well, and other researchers can then also enjoy our expertise and experience. See the OSCR website for a list of previous and upcoming workshops. Two lab members have already given a workshop: Eduard: Introduction to the Brain Imaging Data Structure (BIDS), workshop description Dorien: Introduction to sharing brain MRI data, workshop description","title":"Open Science Community Rotterdam"},{"location":"open-science/oscr.html#open-science-community-rotterdam","text":"Interested in open science? Have a look at the Open Science Community Rotterdam (OSCR) .","title":"Open Science Community Rotterdam"},{"location":"open-science/oscr.html#oscr-workshops","text":"For Open Science related topics, it is also an option to reach a broader audience and organise a workshop for the Open Science Community Rotterdam. All lab members can take part in these workshops as well, and other researchers can then also enjoy our expertise and experience. See the OSCR website for a list of previous and upcoming workshops. Two lab members have already given a workshop: Eduard: Introduction to the Brain Imaging Data Structure (BIDS), workshop description Dorien: Introduction to sharing brain MRI data, workshop description","title":"OSCR workshops"},{"location":"open-science/osf.html","text":"The Open Science Framework The Open Science Framework (OSF) is a project management, storage and collaboration platform that is used by many scientists. In an OSF project, you can: - Register a preregistration - Register a preprint - Store and share project documentation - Link external services, such as git(hub) repositories, publication packages, Research Drive (owncloud) folders, etc. - ... and much more, such as obtaining Open science badges! . See this link for more information on the functionality of OSF and the OSF guides for many FAQs and technical documentation. Recommended use of the OSF We recommend using the OSF as a central place for your project, especially if you will produce multiple publications in a project. Link your OSF project to the SYNC lab OSF page (Log in > Components > Link Projects). No worries, the SYNC lab collaborators cannot automatically edit your linked project! Register preregistrations (Registrations > New registration) and Preprints in the relevant components. If your preregistration concerns one manuscript, we recommend creating a separate component in which you link all relevant materials (publication package, preregistration, code, preprint if applicable, etc.) belonging to that manuscript. A project can contain multiple Registrations and Preprints. Be sure to provide good metadata, preferably choose a CC BY 4.0 license and create a DOI so that your preregistration is citable. Data : you can link OSF with external storage (see below) or store data on the OSF itself (choose the German storage site). On the OSF itself, there is now a storage limit of 5 GB for private and 50 GB for public projects. If you store on OSF directly, please take privacy into account at all times Code and software : you can link OSF with a Github repository in case your preprocessing pipeline, processing steps or experimental files are stored there (see below). If your code is however not code-worthy, we recommend storing the code in the publication package or on OSF itself. Link your OSF profile to your ORCID account so that there cannot be any confusion as to who you are. Connecting the OSF to external services Via the Add-on tab, you can connect the OSF with several external services if OSF itself does not provide enough storage. Note that this will not store data on the OSF itself! SURF Research Drive (via Owncloud) Connecting with an Owncloud service like the Research Drive goes via a WebDAV connection. All changes made in the selected Research Drive folder will become visible in the OSF component, but the data are still stored at the EUR. Note that you can only make 1 Research Drive connection per OSF component (multiple connections per project are possible). Please note the following: Make sure the access level is read-only, unless you want your collaborators to be able to adjust the Research Drive contents Think about anonymity: if you make the project/component public, data that are shared have to be anonymous or consented by participants to share publicly Linking an Owncloud folder is not a FAIR solution: there is no curated metadata and no license. Also, the Research Drive is not meant to archive data after the project is finished, so this will always be a temporary solution. How to link a Research Drive folder to OSF Create a WebDAV password in Research Drive (Account > Settings > Security > WebDAV passwords). Copy the WebDAV password you created and click \"done\" Go to the component in your OSF project in which you want to add the Research Drive folder (if you are not in the right component, the Research Drive folder will be linked to the incorrect location) Select \u201cAdd-on\u201d to add the ownCloud Add-on in OSF Configure the add-on in OSF: URL: https://eur.data.surfsara.nl Username: erna-id@eur.nl Password: WebDAV password you just made Figshare or a publication package in the EDR You can link 1 publication package (1 Figshare link) per OSF component, so that the publication package appears in your OSF project. This way, you can license your data (not possible in OSF) and provide metadata but still link it to your OSF project for more context. Click here for a tutorial. Github repository If you stored code on Github (for re-use, think about licensing!), you can link your repository to an OSF component. Again, you can only link 1 repository per OSF component. See this link for how to do this.","title":"Open Science Framework"},{"location":"open-science/osf.html#the-open-science-framework","text":"The Open Science Framework (OSF) is a project management, storage and collaboration platform that is used by many scientists. In an OSF project, you can: - Register a preregistration - Register a preprint - Store and share project documentation - Link external services, such as git(hub) repositories, publication packages, Research Drive (owncloud) folders, etc. - ... and much more, such as obtaining Open science badges! . See this link for more information on the functionality of OSF and the OSF guides for many FAQs and technical documentation.","title":"The Open Science Framework"},{"location":"open-science/osf.html#recommended-use-of-the-osf","text":"We recommend using the OSF as a central place for your project, especially if you will produce multiple publications in a project. Link your OSF project to the SYNC lab OSF page (Log in > Components > Link Projects). No worries, the SYNC lab collaborators cannot automatically edit your linked project! Register preregistrations (Registrations > New registration) and Preprints in the relevant components. If your preregistration concerns one manuscript, we recommend creating a separate component in which you link all relevant materials (publication package, preregistration, code, preprint if applicable, etc.) belonging to that manuscript. A project can contain multiple Registrations and Preprints. Be sure to provide good metadata, preferably choose a CC BY 4.0 license and create a DOI so that your preregistration is citable. Data : you can link OSF with external storage (see below) or store data on the OSF itself (choose the German storage site). On the OSF itself, there is now a storage limit of 5 GB for private and 50 GB for public projects. If you store on OSF directly, please take privacy into account at all times Code and software : you can link OSF with a Github repository in case your preprocessing pipeline, processing steps or experimental files are stored there (see below). If your code is however not code-worthy, we recommend storing the code in the publication package or on OSF itself. Link your OSF profile to your ORCID account so that there cannot be any confusion as to who you are.","title":"Recommended use of the OSF"},{"location":"open-science/osf.html#connecting-the-osf-to-external-services","text":"Via the Add-on tab, you can connect the OSF with several external services if OSF itself does not provide enough storage. Note that this will not store data on the OSF itself!","title":"Connecting the OSF to external services"},{"location":"open-science/osf.html#surf-research-drive-via-owncloud","text":"Connecting with an Owncloud service like the Research Drive goes via a WebDAV connection. All changes made in the selected Research Drive folder will become visible in the OSF component, but the data are still stored at the EUR. Note that you can only make 1 Research Drive connection per OSF component (multiple connections per project are possible). Please note the following: Make sure the access level is read-only, unless you want your collaborators to be able to adjust the Research Drive contents Think about anonymity: if you make the project/component public, data that are shared have to be anonymous or consented by participants to share publicly Linking an Owncloud folder is not a FAIR solution: there is no curated metadata and no license. Also, the Research Drive is not meant to archive data after the project is finished, so this will always be a temporary solution. How to link a Research Drive folder to OSF Create a WebDAV password in Research Drive (Account > Settings > Security > WebDAV passwords). Copy the WebDAV password you created and click \"done\" Go to the component in your OSF project in which you want to add the Research Drive folder (if you are not in the right component, the Research Drive folder will be linked to the incorrect location) Select \u201cAdd-on\u201d to add the ownCloud Add-on in OSF Configure the add-on in OSF: URL: https://eur.data.surfsara.nl Username: erna-id@eur.nl Password: WebDAV password you just made","title":"SURF Research Drive (via Owncloud)"},{"location":"open-science/osf.html#figshare-or-a-publication-package-in-the-edr","text":"You can link 1 publication package (1 Figshare link) per OSF component, so that the publication package appears in your OSF project. This way, you can license your data (not possible in OSF) and provide metadata but still link it to your OSF project for more context. Click here for a tutorial.","title":"Figshare or a publication package in the EDR"},{"location":"open-science/osf.html#github-repository","text":"If you stored code on Github (for re-use, think about licensing!), you can link your repository to an OSF component. Again, you can only link 1 repository per OSF component. See this link for how to do this.","title":"Github repository"},{"location":"open-science/preregistration.html","text":"Preregistration What is a preregistration? A preregistration is basically a time-stamped plan of your research before you have seen your research data (either before data collection or before data analysis), \"the introduction and methods sections of your paper\". It is a document containing at least: Your hypotheses Your methodology and variables (design, sample, stopping rule, exclusion criteria, procedure, variables, etc.) Your analysis plan to test the hypotheses (statistical tests, transformations, assumption tests, etc.) Inference criteria: when do you consider your hypothesis rejected or confirmed? Some examples can be found here . Why should I preregister my research? clearly distinguish between confirmatory and exploratory analysis and therefore prevent presenting exploration as hypothesized result maintain transparency, preventing selective reporting and p-hacking contribute to decreasing the file drawer problem and publication bias function as a safety net for your future self: you don't have to remember what exactly you were going to do and how, and, once registered, you only have to execute your plan (and, if deviating from it, report this) Read this article for more selfish reasons to preregister Preregistration dilemmas \"Preregistration costs way too much time\" > Actually, the time you would normally spend after data collection is now spent before. It will likely even save you time, because you are not pointlessly trying multiple analyses and because you basically already wrote your introduction and methods sections. \"What to do when my research doesn't go according to plan?\" > Before the analysis, you can add an addendum to your preregistration explaining what went differently. Afterwards, simply report the deviation in your manuscript! The goal is to be transparent. \"No one will ever look at my preregistration\" > You can use the preregistration already as a reminder for yourself, use it as a justification to reviewers of your manuscript and inspire colleagues and interested researchers with your amazing open attitude. Go for it! See this page for more information about preregistration and dilemmas in preregistering fMRI studies. How can I preregister? See this link for an easy tutorial on how to preregister on the Open Science Framework (OSF). Don't forget to include your collaborators and to include the link to your preregistration in your manuscript. What are Registered Reports? Registered Reports are preregistrations that are peer reviewed by journals. This highly eliminates publication bias, because at Stage 1 peer review, no results are known yet, so manuscripts cannot be accepted or rejected basead on results. Below the process of Registered Reports is visualized: After your preregistration has received an In Principle Acceptance (Stage 1), you can start collecting data and writing up your results. Most journals that get through Stage 1 will also get accepted in Stage 2, because the study design has already been reviewed. Resources A simple preregistration template All OSF templates A list of resources on preregistration Information about Registered Reports Overview of all journals doing Registered Reports A preregistration tutorial and template for secondary data analysis Preregistration: dream vs. reality","title":"Preregistration"},{"location":"open-science/preregistration.html#preregistration","text":"","title":"Preregistration"},{"location":"open-science/preregistration.html#what-is-a-preregistration","text":"A preregistration is basically a time-stamped plan of your research before you have seen your research data (either before data collection or before data analysis), \"the introduction and methods sections of your paper\". It is a document containing at least: Your hypotheses Your methodology and variables (design, sample, stopping rule, exclusion criteria, procedure, variables, etc.) Your analysis plan to test the hypotheses (statistical tests, transformations, assumption tests, etc.) Inference criteria: when do you consider your hypothesis rejected or confirmed? Some examples can be found here .","title":"What is a preregistration?"},{"location":"open-science/preregistration.html#why-should-i-preregister-my-research","text":"clearly distinguish between confirmatory and exploratory analysis and therefore prevent presenting exploration as hypothesized result maintain transparency, preventing selective reporting and p-hacking contribute to decreasing the file drawer problem and publication bias function as a safety net for your future self: you don't have to remember what exactly you were going to do and how, and, once registered, you only have to execute your plan (and, if deviating from it, report this) Read this article for more selfish reasons to preregister","title":"Why should I preregister my research?"},{"location":"open-science/preregistration.html#preregistration-dilemmas","text":"\"Preregistration costs way too much time\" > Actually, the time you would normally spend after data collection is now spent before. It will likely even save you time, because you are not pointlessly trying multiple analyses and because you basically already wrote your introduction and methods sections. \"What to do when my research doesn't go according to plan?\" > Before the analysis, you can add an addendum to your preregistration explaining what went differently. Afterwards, simply report the deviation in your manuscript! The goal is to be transparent. \"No one will ever look at my preregistration\" > You can use the preregistration already as a reminder for yourself, use it as a justification to reviewers of your manuscript and inspire colleagues and interested researchers with your amazing open attitude. Go for it! See this page for more information about preregistration and dilemmas in preregistering fMRI studies.","title":"Preregistration dilemmas"},{"location":"open-science/preregistration.html#how-can-i-preregister","text":"See this link for an easy tutorial on how to preregister on the Open Science Framework (OSF). Don't forget to include your collaborators and to include the link to your preregistration in your manuscript.","title":"How can I preregister?"},{"location":"open-science/preregistration.html#what-are-registered-reports","text":"Registered Reports are preregistrations that are peer reviewed by journals. This highly eliminates publication bias, because at Stage 1 peer review, no results are known yet, so manuscripts cannot be accepted or rejected basead on results. Below the process of Registered Reports is visualized: After your preregistration has received an In Principle Acceptance (Stage 1), you can start collecting data and writing up your results. Most journals that get through Stage 1 will also get accepted in Stage 2, because the study design has already been reviewed.","title":"What are Registered Reports?"},{"location":"open-science/preregistration.html#resources","text":"A simple preregistration template All OSF templates A list of resources on preregistration Information about Registered Reports Overview of all journals doing Registered Reports A preregistration tutorial and template for secondary data analysis Preregistration: dream vs. reality","title":"Resources"},{"location":"open-science/pub-packages.html","text":"Publication packages What are publication packages? Publication packages are bundles of all materials necessary to reproduce the results from a scientific article. They contain (following the national publication package guidelines ): The manuscript or a link to the open access manuscript Experimental files, such as Eprime or scripts that run the experiment, task instructions, questionnaires, etc. Anonymized raw research data or a link to the repository where the data are stored Anonymized processed research data or a link to the repository where the data are stored Processing and statistical analysis scripts Readme file with all relevant metadata, including links to the article and other relevant information, e.g., the preregistration, the dataset on NeuroVault, contact information of involved researchers, information on excluded participants, information on methods used, etc. The more metadata, the better! Approved ethics protocol How to upload a publication package? Log into the EUR data repository (EDR) (instance of Figshare) with your EUR credentials. Link your EDR profile to your ORCID. This will make sure that your publication package appears in your ORCID profile too. Under my data, create a new item. In this item, you can upload all publication package files (preferably without zipping), so that your files are linked under 1 DOI. Fill out as many fields in the form as possible, including all authors on your manuscript and links to external resources. This will make sure your data will be Findable. For license, preferably choose CC-BY 4.0, which enables maximum reuse of your data, besides being acknowledged for your data. If your manuscript is not accepted for publication yet, 1) Reserve a DOI that you can use in your publication, 2) Generate a private link for reviewers to see your data and 3) Save changes (do not Publish yet). Once your manuscript is accepted for publication and your data (analysis) will not change anymore, you can Publish it. After saving or publishing, your package will be looked at by a data curator from the EUR. They will contact you if they have any questions about your package or tips to improve it even more. More information on how to use the EUR data repository can be found in this Youtube playlist . Tips Be sure to create the publication package (reserve a DOI) before your manuscript is published, so that the manuscript links to the publication package and vice versa Prevent uploading files multiple times. If you have already uploaded data or code on OSF or Github, link those uploads to the publication package, instead of re-uploading in the publication package. If you have uploaded elsewhere (e.g., NeuroVault), provide a readme with the relevant links to make sure everything is findable! If applicable, create links with an OSF project, a Github repository and your ORCID, click here and here for how to do this. Only publish data as confidential when that is absolutely necessary. The reuse of your data is significantly reduced when they are behind a confidentiality wall. Moreover, when the owner of the publication package leaves the EUR, they cannot provide access to the confidential files anymore. For questions about the EUR data repository, please contact the university library (datarepository [at] eur [dot] nl). For the ESSB policy implementation, contact the ESSB data steward .","title":"Publication packages"},{"location":"open-science/pub-packages.html#publication-packages","text":"","title":"Publication packages"},{"location":"open-science/pub-packages.html#what-are-publication-packages","text":"Publication packages are bundles of all materials necessary to reproduce the results from a scientific article. They contain (following the national publication package guidelines ): The manuscript or a link to the open access manuscript Experimental files, such as Eprime or scripts that run the experiment, task instructions, questionnaires, etc. Anonymized raw research data or a link to the repository where the data are stored Anonymized processed research data or a link to the repository where the data are stored Processing and statistical analysis scripts Readme file with all relevant metadata, including links to the article and other relevant information, e.g., the preregistration, the dataset on NeuroVault, contact information of involved researchers, information on excluded participants, information on methods used, etc. The more metadata, the better! Approved ethics protocol","title":"What are publication packages?"},{"location":"open-science/pub-packages.html#how-to-upload-a-publication-package","text":"Log into the EUR data repository (EDR) (instance of Figshare) with your EUR credentials. Link your EDR profile to your ORCID. This will make sure that your publication package appears in your ORCID profile too. Under my data, create a new item. In this item, you can upload all publication package files (preferably without zipping), so that your files are linked under 1 DOI. Fill out as many fields in the form as possible, including all authors on your manuscript and links to external resources. This will make sure your data will be Findable. For license, preferably choose CC-BY 4.0, which enables maximum reuse of your data, besides being acknowledged for your data. If your manuscript is not accepted for publication yet, 1) Reserve a DOI that you can use in your publication, 2) Generate a private link for reviewers to see your data and 3) Save changes (do not Publish yet). Once your manuscript is accepted for publication and your data (analysis) will not change anymore, you can Publish it. After saving or publishing, your package will be looked at by a data curator from the EUR. They will contact you if they have any questions about your package or tips to improve it even more. More information on how to use the EUR data repository can be found in this Youtube playlist .","title":"How to upload a publication package?"},{"location":"open-science/pub-packages.html#tips","text":"Be sure to create the publication package (reserve a DOI) before your manuscript is published, so that the manuscript links to the publication package and vice versa Prevent uploading files multiple times. If you have already uploaded data or code on OSF or Github, link those uploads to the publication package, instead of re-uploading in the publication package. If you have uploaded elsewhere (e.g., NeuroVault), provide a readme with the relevant links to make sure everything is findable! If applicable, create links with an OSF project, a Github repository and your ORCID, click here and here for how to do this. Only publish data as confidential when that is absolutely necessary. The reuse of your data is significantly reduced when they are behind a confidentiality wall. Moreover, when the owner of the publication package leaves the EUR, they cannot provide access to the confidential files anymore. For questions about the EUR data repository, please contact the university library (datarepository [at] eur [dot] nl). For the ESSB policy implementation, contact the ESSB data steward .","title":"Tips"},{"location":"open-science/stempelkaart.html","text":"Open Science stamp card (stempelkaart) In order to make all the different facets of Open Science easy to implement for our lab, we developed an Open Science stamp card. It provides oversight and resources for making your research as openly available as possible in four major phases of research: Preparation of the paper Conducting the study Writing the manuscript After publication Each phase has its own steps that can be checked off, with some steps (in red) being labeled as \u2018must-do\u2019 (e.g., making a publication package). The other, nice-to-do, steps are those that are not required, but are in line with the vision of SYNC (e.g., uploading a preprint or writing a blog on your research). Our Open Science core-team, with input from the entire lab, made the following template which can be downloaded here . (Work in progress, special thanks to Dorien Huijser for helping with the setup.)","title":"Open Science stempelkaart"},{"location":"open-science/stempelkaart.html#open-science-stamp-card-stempelkaart","text":"In order to make all the different facets of Open Science easy to implement for our lab, we developed an Open Science stamp card. It provides oversight and resources for making your research as openly available as possible in four major phases of research: Preparation of the paper Conducting the study Writing the manuscript After publication Each phase has its own steps that can be checked off, with some steps (in red) being labeled as \u2018must-do\u2019 (e.g., making a publication package). The other, nice-to-do, steps are those that are not required, but are in line with the vision of SYNC (e.g., uploading a preprint or writing a blog on your research). Our Open Science core-team, with input from the entire lab, made the following template which can be downloaded here . (Work in progress, special thanks to Dorien Huijser for helping with the setup.)","title":"Open Science stamp card (stempelkaart)"},{"location":"open-science/transparency-checklists.html","text":"Transparency checklists On this page, you will find more resources aimed at increasing transparency in your manuscript. Open Project Checklist Find the Open Project checklist here . Note that this is a work in progress . The final version will be published on Zenodo. You can use this checklist for your project to: See where in your research project you can improve your openness Check how open you are: an \u2714\ufe0f apprentice, \ud83d\udcaa master or \ud83c\udfc6 champion Transparency checklist (Aczel et al., 2020) How transparent are you with respect to the process of and around your manuscript? This is what you can use this checklist for. Note that this checklist is not necessarily for your entire project. Paper: https://doi.org/10.1038/s41562-019-0772-6 Online checklist: http://www.shinyapps.org/apps/TransparencyChecklist/ Short transparency checklist: http://www.shinyapps.org/apps/ShortTransparencyChecklist/ (e)COBIDAS: reproducible methods reporting A few years ago, in order to improve reproducibility in (f)MRI research, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to promote best practices for methods and results reporting . This was recently followed by a similar initiative for EEG and MEG . The goal of eCOBIDAS is to develop an online version of the checklist to increase the implementation of both reports. See the first version of the online checklist here (see also the OSF page and Github repo ) Other resources (please add more if you run into them) Klapwijk et al., 2019: Opportunities for increased reproducibility and replicability of developmental cognitive neuroscience, click here for the preprint . Pickering, Topor et al., 2020: Non-Interventional, Reproducible, and Open (NIRO) Systematic Review guidelines v0.1, click here for the OSF page and here for the website .","title":"Open project checklists"},{"location":"open-science/transparency-checklists.html#transparency-checklists","text":"On this page, you will find more resources aimed at increasing transparency in your manuscript.","title":"Transparency checklists"},{"location":"open-science/transparency-checklists.html#open-project-checklist","text":"Find the Open Project checklist here . Note that this is a work in progress . The final version will be published on Zenodo. You can use this checklist for your project to: See where in your research project you can improve your openness Check how open you are: an \u2714\ufe0f apprentice, \ud83d\udcaa master or \ud83c\udfc6 champion","title":"Open Project Checklist"},{"location":"open-science/transparency-checklists.html#transparency-checklist-aczel-et-al-2020","text":"How transparent are you with respect to the process of and around your manuscript? This is what you can use this checklist for. Note that this checklist is not necessarily for your entire project. Paper: https://doi.org/10.1038/s41562-019-0772-6 Online checklist: http://www.shinyapps.org/apps/TransparencyChecklist/ Short transparency checklist: http://www.shinyapps.org/apps/ShortTransparencyChecklist/","title":"Transparency checklist (Aczel et al., 2020)"},{"location":"open-science/transparency-checklists.html#ecobidas-reproducible-methods-reporting","text":"A few years ago, in order to improve reproducibility in (f)MRI research, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to promote best practices for methods and results reporting . This was recently followed by a similar initiative for EEG and MEG . The goal of eCOBIDAS is to develop an online version of the checklist to increase the implementation of both reports. See the first version of the online checklist here (see also the OSF page and Github repo )","title":"(e)COBIDAS: reproducible methods reporting"},{"location":"open-science/transparency-checklists.html#other-resources-please-add-more-if-you-run-into-them","text":"Klapwijk et al., 2019: Opportunities for increased reproducibility and replicability of developmental cognitive neuroscience, click here for the preprint . Pickering, Topor et al., 2020: Non-Interventional, Reproducible, and Open (NIRO) Systematic Review guidelines v0.1, click here for the OSF page and here for the website .","title":"Other resources (please add more if you run into them)"},{"location":"reaching-out/scicom.html","text":"Science communication On this page, we can collect sources on science communication and citizen science. Resources Blog on how to start a podcast and wiki with additional materials. Here you can find slides for presentations, lectures, etc. made and used by SYNC lab members. On the YoungXperts website you can find more resources and tools on citizen science. Here you can find SYNC logos that can be used for outreach. EUR logos can be found here . Tips Tip for science communication in practice: Check synonyms for difficult Dutch words: https://www.ishetb1.nl/","title":"Science communication"},{"location":"reaching-out/scicom.html#science-communication","text":"On this page, we can collect sources on science communication and citizen science.","title":"Science communication"},{"location":"reaching-out/scicom.html#resources","text":"Blog on how to start a podcast and wiki with additional materials. Here you can find slides for presentations, lectures, etc. made and used by SYNC lab members. On the YoungXperts website you can find more resources and tools on citizen science. Here you can find SYNC logos that can be used for outreach. EUR logos can be found here .","title":"Resources"},{"location":"reaching-out/scicom.html#tips","text":"Tip for science communication in practice: Check synonyms for difficult Dutch words: https://www.ishetb1.nl/","title":"Tips"}]}
\ No newline at end of file
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It is a searchable guide for all members of the SYNC lab It can be updated by all lab members It is easier to keep up to date than a zillion different Word documents It gives transparent insight in how we work as a lab How to use this wiki? This wiki is primarily meant for employees of the SYNC-lab, but can be used by other researchers too, if desired. Click on a chapter in the menu bar that you would like to read more about, or use the search bar! Adding new stuff Are you missing information or should current information be updated? Use our Github repository . On the How to contribute page, you can find more details on how to use git, Github and Markdown to make contributions. If you are missing information here, and you think someone else could write a page about that topic, suggest it as an Issue on the Github repository or just ask someone by mail / Slack / in real life! The SYNC lab members","title":"Home"},{"location":"index.html#welcome-to-the-sync-lab-wiki","text":"Welcome! 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The SYNC lab members","title":"Adding new stuff"},{"location":"about/code-of-conduct.html","text":"Contributor Covenant Code of Conduct Our Pledge In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation. Our Standards Examples of behavior that contributes to creating a positive environment include: Using welcoming and inclusive language Being respectful of differing viewpoints and experiences Gracefully accepting constructive criticism Focusing on what is best for the community Showing empathy towards other community members Examples of unacceptable behavior by participants include: The use of sexualized language or imagery and unwelcome sexual attention or advances Trolling, insulting/derogatory comments, and personal or political attacks Public or private harassment Publishing others' private information, such as a physical or electronic address, without explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting Our Responsibilities Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful. Scope This Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers. Enforcement Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team via this contact form . All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately. Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership. Attribution This Code of Conduct is adapted from the Contributor Covenant , version 1.4, available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html For answers to common questions about this code of conduct, see https://www.contributor-covenant.org/faq","title":"Code of conduct"},{"location":"about/code-of-conduct.html#contributor-covenant-code-of-conduct","text":"","title":"Contributor Covenant Code of Conduct"},{"location":"about/code-of-conduct.html#our-pledge","text":"In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.","title":"Our Pledge"},{"location":"about/code-of-conduct.html#our-standards","text":"Examples of behavior that contributes to creating a positive environment include: Using welcoming and inclusive language Being respectful of differing viewpoints and experiences Gracefully accepting constructive criticism Focusing on what is best for the community Showing empathy towards other community members Examples of unacceptable behavior by participants include: The use of sexualized language or imagery and unwelcome sexual attention or advances Trolling, insulting/derogatory comments, and personal or political attacks Public or private harassment Publishing others' private information, such as a physical or electronic address, without explicit permission Other conduct which could reasonably be considered inappropriate in a professional setting","title":"Our Standards"},{"location":"about/code-of-conduct.html#our-responsibilities","text":"Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior. 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Installation Create a Github account (or log on) Install git locally (click here for instructions for RStudio ) and some Markdown editor (see below) For an introduction to Git(hub), please see the version control chapter . Markdown Markdown is a markup language that you can use to add formatting elements to plaintext text documents. When you create a Markdown-formatted file, you add Markdown syntax to the text to indicate which words and phrases should look different. Every .md (Markdown) file in this repository in fact uses Markdown for formatting! For example, to indicate a second-level header, you type: ## Title of header Advantages of Markdown: It can be used for a lot of things, e.g., for creating html pages It is platform- and operating system independent You can type markdown in any text editor and open .md files with many programs, such as Atom , Zettlr , Rstudio , Typora , online (e.g., Dillinger ) or even in Word ( Writage tool ). See this page for more tools that support Markdown. Markdown resources Markdown guide Cheatsheet More advanced Markdown tricks Contributing step-by-step A. The easy (but not recommended) way fork the repository to your own Github account by clicking the button on the upper right of the repository: Make edits to the files you want to edit in your browser by clicking the pencil at the top right of a file. All editable .md files can be found in the docs folder Write a commit message for your changes and click Commit changes . After having made all the changes you wanted, go to the tab Pull requests > New pull request . Make sure the base repository is eur-synclab/sync-manual master and head repository is your own repository, e.g., DorienHuijser/sync-manual branchinwhichyoumadechanges . Click Create pull request Your pull request will now appear in the eur-synclab repository list of pull requests . If you want, you can assign someone to review your pull request. One of the owners of the repository will review your commits, may request changes and will finally approve the pull request and merge your changes into the eur-synclab/sync-manual master branch. B. The better way fork the repository to your own Github account by clicking the button on the upper right of the repository: Create a new branch in your forked repository which you will use to make changes in (so your master branch will stay\"clean\"): clone your forked repository to your local PC ( using the command-line or Rstudio ) Make local changes. You can open a .md file in the docs folder with multiple text editors such as Typora, Atom, Zettlr, Rstudio, etc.) and, after saving your changes, commit them ( command-line : git commit -a -m \"commit message\" , RStudio . Your changes are now saved locally. Push your commits to your \u201cremote\u201d (online) repository ( using the command-line : git push origin branchname , in Rstudio ) Follow steps 4-7 explained in the Easy way Important: the next time you start working locally, first update your local version of the repository to the most recent version (Command line: git pull upstream [branchname] [be sure to set the upstream repository first], RStudio ). C. The most advanced way Follow the installation steps for mkdocs here Follow steps 1-4 explained in The better way In your prompt , navigate to your repository directory with cd C:/users/username/your/repo/directory and run mkdocs serve . This creates a URL (something like http://127.0.0.1:8000/) which you can open in your internet browser. Here, you can see all changes that you make directly \"live\". Press Cntrl+C to stop this operation. Run mkdocs build . If everything goes correctly, you can now also open the new .html files in the sync-manual/site folder to see what your changes will look like in the browser. These files have to be created in order for the website to work on others' computers. commit your newly built website (html) files, e.g., git commit -A . -m \"Build site\" Follow step 5-7 explained in The better way. Add yourself as a contributor! Go to this Github issue . Type a comment asking the all-contributors bot to add you (use template mentioned in the issue), look for appropriate emojis here . The bot will open a pull request to add you as contributor. After merging with the master branch, your face will appear in the README.md ! Issues and Projects If you would like to see a change that requires more work or input from others before you can start editing yourself, you can open an Issue . There are some great features about Issues: You can assign people to the Issue who should solve it or provide input You can add the issue to a project. In the tab Projects , you can find our Kanban board in which we have made the columns \u201cTo do\u201d, \u201cIn progress\u201d and \u201cDone\u201d. We made this so we have an overview of Issues that still need work and issues that are in progress. You can add labels to an Issue (please do so!) to specify what kind of issue you are writing After the issue has been solved, you can Close it manually. However, if you made a pull request that solves the issue, you can simply comment Closes #issuenr in the pull request. After the pull request has been merged, the issue is automatically closed!","title":"How to contribute"},{"location":"about/contribute.html#how-to-contribute","text":"This lab wiki was built using: Git : a version control system, which allows us to go back to previous versions of the web pages at all times; On Github , a platform that hosts files that use git as a version control system and on which we can collaborate on the wiki; Relying on Mkdocs , a simply static site generator which generates html pages (web pages) from .md (markdown) files.","title":"How to contribute"},{"location":"about/contribute.html#installation","text":"Create a Github account (or log on) Install git locally (click here for instructions for RStudio ) and some Markdown editor (see below) For an introduction to Git(hub), please see the version control chapter .","title":"Installation"},{"location":"about/contribute.html#markdown","text":"Markdown is a markup language that you can use to add formatting elements to plaintext text documents. 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Markdown resources Markdown guide Cheatsheet More advanced Markdown tricks","title":"Markdown"},{"location":"about/contribute.html#contributing-step-by-step","text":"","title":"Contributing step-by-step"},{"location":"about/contribute.html#a-the-easy-but-not-recommended-way","text":"fork the repository to your own Github account by clicking the button on the upper right of the repository: Make edits to the files you want to edit in your browser by clicking the pencil at the top right of a file. All editable .md files can be found in the docs folder Write a commit message for your changes and click Commit changes . After having made all the changes you wanted, go to the tab Pull requests > New pull request . Make sure the base repository is eur-synclab/sync-manual master and head repository is your own repository, e.g., DorienHuijser/sync-manual branchinwhichyoumadechanges . Click Create pull request Your pull request will now appear in the eur-synclab repository list of pull requests . If you want, you can assign someone to review your pull request. One of the owners of the repository will review your commits, may request changes and will finally approve the pull request and merge your changes into the eur-synclab/sync-manual master branch.","title":"A. The easy (but not recommended) way"},{"location":"about/contribute.html#b-the-better-way","text":"fork the repository to your own Github account by clicking the button on the upper right of the repository: Create a new branch in your forked repository which you will use to make changes in (so your master branch will stay\"clean\"): clone your forked repository to your local PC ( using the command-line or Rstudio ) Make local changes. You can open a .md file in the docs folder with multiple text editors such as Typora, Atom, Zettlr, Rstudio, etc.) and, after saving your changes, commit them ( command-line : git commit -a -m \"commit message\" , RStudio . Your changes are now saved locally. Push your commits to your \u201cremote\u201d (online) repository ( using the command-line : git push origin branchname , in Rstudio ) Follow steps 4-7 explained in the Easy way Important: the next time you start working locally, first update your local version of the repository to the most recent version (Command line: git pull upstream [branchname] [be sure to set the upstream repository first], RStudio ).","title":"B. The better way"},{"location":"about/contribute.html#c-the-most-advanced-way","text":"Follow the installation steps for mkdocs here Follow steps 1-4 explained in The better way In your prompt , navigate to your repository directory with cd C:/users/username/your/repo/directory and run mkdocs serve . This creates a URL (something like http://127.0.0.1:8000/) which you can open in your internet browser. Here, you can see all changes that you make directly \"live\". Press Cntrl+C to stop this operation. Run mkdocs build . If everything goes correctly, you can now also open the new .html files in the sync-manual/site folder to see what your changes will look like in the browser. These files have to be created in order for the website to work on others' computers. commit your newly built website (html) files, e.g., git commit -A . -m \"Build site\" Follow step 5-7 explained in The better way.","title":"C. The most advanced way"},{"location":"about/contribute.html#add-yourself-as-a-contributor","text":"Go to this Github issue . Type a comment asking the all-contributors bot to add you (use template mentioned in the issue), look for appropriate emojis here . The bot will open a pull request to add you as contributor. After merging with the master branch, your face will appear in the README.md !","title":"Add yourself as a contributor!"},{"location":"about/contribute.html#issues-and-projects","text":"If you would like to see a change that requires more work or input from others before you can start editing yourself, you can open an Issue . There are some great features about Issues: You can assign people to the Issue who should solve it or provide input You can add the issue to a project. In the tab Projects , you can find our Kanban board in which we have made the columns \u201cTo do\u201d, \u201cIn progress\u201d and \u201cDone\u201d. We made this so we have an overview of Issues that still need work and issues that are in progress. You can add labels to an Issue (please do so!) to specify what kind of issue you are writing After the issue has been solved, you can Close it manually. However, if you made a pull request that solves the issue, you can simply comment Closes #issuenr in the pull request. After the pull request has been merged, the issue is automatically closed!","title":"Issues and Projects"},{"location":"about/intro-sync.html","text":"The SYNC lab SYNC stands for Society, Youth and Neuroscience Connected For more information on the SYNC lab, visit the SYNC website Mission Our mission is to bridge multiple levels of measurement to understand how young people develop into contributing members of society. The SYNC lab is firmly grounded in exciting new perspectives that have emerged from understanding the dynamic development of the adolescent brain. Vision Our vision is that science becomes better when conducted together with societal partners, including youth panels, schools, and co-creation teams. We also strongly believe in interdisciplinary research teams; therefore, we work with researchers from different scientific disciplines, including the social sciences, life sciences, and humanities. Moreover, the Erasmus SYNC lab embraces Open Science through practices such as pre-registration, data sharing and open-access publishing. Together, we hope we can work on providing the scientific building blocks needed to help shape a better future for the current and next generation of youth. Diversity and inclusivity At the Erasmus SYNC-lab we speak up against racism and strive to enable equality. The Erasmus University Rotterdam aspires to be an inclusive organization where everyone feels at home and therefore get the opportunity to excel. Talent is the basis; diversity is the added value. We believe in the enrichment that a mix of different people bring. We aim to connect all of society : regardless of origin, skin color, gender, education level, or sexual preference.","title":"The SYNC lab"},{"location":"about/intro-sync.html#the-sync-lab","text":"","title":"The SYNC lab"},{"location":"about/intro-sync.html#sync-stands-for-society-youth-and-neuroscience-connected","text":"For more information on the SYNC lab, visit the SYNC website","title":"SYNC stands for Society, Youth and Neuroscience Connected"},{"location":"about/intro-sync.html#mission","text":"Our mission is to bridge multiple levels of measurement to understand how young people develop into contributing members of society. The SYNC lab is firmly grounded in exciting new perspectives that have emerged from understanding the dynamic development of the adolescent brain.","title":"Mission"},{"location":"about/intro-sync.html#vision","text":"Our vision is that science becomes better when conducted together with societal partners, including youth panels, schools, and co-creation teams. We also strongly believe in interdisciplinary research teams; therefore, we work with researchers from different scientific disciplines, including the social sciences, life sciences, and humanities. Moreover, the Erasmus SYNC lab embraces Open Science through practices such as pre-registration, data sharing and open-access publishing. Together, we hope we can work on providing the scientific building blocks needed to help shape a better future for the current and next generation of youth.","title":"Vision"},{"location":"about/intro-sync.html#diversity-and-inclusivity","text":"At the Erasmus SYNC-lab we speak up against racism and strive to enable equality. The Erasmus University Rotterdam aspires to be an inclusive organization where everyone feels at home and therefore get the opportunity to excel. Talent is the basis; diversity is the added value. We believe in the enrichment that a mix of different people bring. We aim to connect all of society : regardless of origin, skin color, gender, education level, or sexual preference.","title":"Diversity and inclusivity"},{"location":"agenda/holidays.html","text":"Holidays The link to the schedule in which you can fill in your holidays, conferences, 'writing weeks', etc. can be found here . Feel free to decide yourself whether you would like to use this.","title":"Holidays"},{"location":"agenda/holidays.html#holidays","text":"The link to the schedule in which you can fill in your holidays, conferences, 'writing weeks', etc. can be found here . Feel free to decide yourself whether you would like to use this.","title":"Holidays"},{"location":"agenda/labmeetings.html","text":"Labmeetings Currently, we meet at regular intervals for different purposes: Weekly stand-up Labmeetings Data management meetings Journal clubs Attending our meetings If you would like to attend one of our meetings as attendee or guest speaker, please feel free to send us an email via this contact form . Stand-up The weekly stand-up is meant to keep each other up to date on our work (and sometimes personal) activities and to ask for help when needed. We all shortly state what we are doing and note whether or not we have something we need help with. Labmeetings In the labmeetings, labmembers or external visitors share their latest results or ideas for new research. It is a great opportunity to get feedback in a supportive and welcoming environment. Lab meetings can also be used to practice a (conference) talk. Collaborators from outside the SYNC lab are more than welcome for labmeetings where research from the specific collaboration is discussed. So do not forget to invite your collaborators when presenting! The schedule of the labmeetings for 2023-2024 (regularly updated): Tuesday September 5, 2023 11-12 (live): Daphne van de Bongardt Tuesday September 12, 2023 11-12 (live): Noura & Nienke (qualitative data analysis) Tuesday September 19, 2023 11-12 (live): Eleni & Noura (sharepoint) and updates Thursday September 26, 2023 11-12 (live): Kayla & Yara (YX social inequality update) Tuesday October 3, 2023 11-12 (live): Lysanne te Brinke & Yolijn Aarts Tuesday October 10, 2023 11-12: cancelled because of GUTS conference Tuesday October 17, 2023 11-12 (live): Sophie Sweijen Thursday October 24, 2023 11-12 (live): Ja\u00efr van Nes Thursday October 31, 2023 11-12 (live): Elo\u00efse Geenjaar Tuesday November 7, 2023 11-12 (live): Ann Hogenhuis & Michelle Achterberg Tuesday November 14, 2023 11-12 (live): Kayla Green & Yara Toenders Tuesday November 21, 2023 11-12 (live): Yara Toenders Thursday November 28, 2023 11-12 (live): Lina van Drunen Thursday December 5, 2023 11-12 (live): Sterre van Riel & Noura Borggreven Tuesday December 12, 2023 11-12 (live): Julianna Lopez Tuesday December 19, 2023 11-12 (live) Tuesday January 9, 2024 11-12 (live) Tuesday January 16, 2024 11-12 (live): Yolijn Aarts & Kitty de Vries: GUTS Tuesday January 23, 2024 11-12 (live): Kayla Green Tuesday January 30, 2024 11-12 (live): Noura Borggreven & Sterre van Riel: Expeditie NEXT at the Hefhouse Tuesday February 6, 2024 11-12 (live): Eveline Crone & Lysanne te Brinke: on being a scientist Tuesday February 6, 2024 16-17 (live): Joint lab meeting with Eva Telzer and Ryan Tsai Tuesday February 13, 2024 11-12 (live): Felix Schreiber (external) Tuesday February 20, 2024 11-12 (live): Suzanne van de Groep Tuesday February 27, 2024 11-12 (live): Sterre & Noura Tuesday March 5, 2024 11-12 (live) Tuesday March 12, 2024 11-12 (live) Tuesday March 19, 2024 11-12 (live): Ilse van de Groep Tuesday March 26, 2024 11-12 (live) Tuesday April 2, 2024 11-12 (live) Tuesday April 9, 2024 11-12 (live) Tuesday April 16, 2024 11-12 (live) Tuesday April 23, 2024 11-12 (live) Tuesday April 30, 2024 11-12 (live) Tuesday May 7, 2024 11-12 (live) Tuesday May 14, 2024 11-12 (live) Thursday May 16 & Friday May 17, 2024 11-12 (live): Visit Gregoire Borst and lab Tuesday May 21, 2024 11-12 (live) Tuesday May 28, 2024 11-12 (live) Tuesday June 4, 2024 11-12 (live) Tuesday June 11, 2024 11-12 (live) Tuesday June 18, 2024 11-12 (live) Tuesday June 25, 2024 11-12 (live) Data management meetings In the data management meetings, updates on ongoing projects and tips on data management are shared by the data managers. The schedule of the online data management meetings for 2022-2023 (regularly updated): Monday October 31, 2022 15-16: OSF Monday November 28, 2022 15-16: Research Drive and alternatives Monday January 30, 2023 15-16: electronic logbook Monday March 27, 2023 15-16: version control Monday May 8, 2023 15-16: reserve DOI + open science journals Monday June 5, 2023, 14-15: privacy/access externals Monday June 26, 2023 15-16: sharing posters/presentations/graphics Journal clubs Starting November 2022, we have planned several journal clubs about specific topics, focused on theoretical and methodological papers. This allows us to dive deeper into theoretical aspects of our work and remain up to date with current literature. Example topics from the past are theories on social media in adolescence and the importance of effect sizes in scientific research. The schedule of the live journal clubs for 2022-2023 (regularly updated): Tuesday November 22, 2022 14-15: Lysanne and Lina Tuesday January 10, 2023 14-15: Kayla and Eloise Tuesday February 21, 2023 14-15: Sophie and Yara Tuesday April 4, 2023 14-15: Simone Tuesday May 16, 2023 14-15: Hannah and Ilse Tuesday June 27, 2023 14-15","title":"Labmeetings"},{"location":"agenda/labmeetings.html#labmeetings","text":"Currently, we meet at regular intervals for different purposes: Weekly stand-up Labmeetings Data management meetings Journal clubs","title":"Labmeetings"},{"location":"agenda/labmeetings.html#attending-our-meetings","text":"If you would like to attend one of our meetings as attendee or guest speaker, please feel free to send us an email via this contact form .","title":"Attending our meetings"},{"location":"agenda/labmeetings.html#stand-up","text":"The weekly stand-up is meant to keep each other up to date on our work (and sometimes personal) activities and to ask for help when needed. We all shortly state what we are doing and note whether or not we have something we need help with.","title":"Stand-up"},{"location":"agenda/labmeetings.html#labmeetings_1","text":"In the labmeetings, labmembers or external visitors share their latest results or ideas for new research. It is a great opportunity to get feedback in a supportive and welcoming environment. Lab meetings can also be used to practice a (conference) talk. Collaborators from outside the SYNC lab are more than welcome for labmeetings where research from the specific collaboration is discussed. So do not forget to invite your collaborators when presenting! The schedule of the labmeetings for 2023-2024 (regularly updated): Tuesday September 5, 2023 11-12 (live): Daphne van de Bongardt Tuesday September 12, 2023 11-12 (live): Noura & Nienke (qualitative data analysis) Tuesday September 19, 2023 11-12 (live): Eleni & Noura (sharepoint) and updates Thursday September 26, 2023 11-12 (live): Kayla & Yara (YX social inequality update) Tuesday October 3, 2023 11-12 (live): Lysanne te Brinke & Yolijn Aarts Tuesday October 10, 2023 11-12: cancelled because of GUTS conference Tuesday October 17, 2023 11-12 (live): Sophie Sweijen Thursday October 24, 2023 11-12 (live): Ja\u00efr van Nes Thursday October 31, 2023 11-12 (live): Elo\u00efse Geenjaar Tuesday November 7, 2023 11-12 (live): Ann Hogenhuis & Michelle Achterberg Tuesday November 14, 2023 11-12 (live): Kayla Green & Yara Toenders Tuesday November 21, 2023 11-12 (live): Yara Toenders Thursday November 28, 2023 11-12 (live): Lina van Drunen Thursday December 5, 2023 11-12 (live): Sterre van Riel & Noura Borggreven Tuesday December 12, 2023 11-12 (live): Julianna Lopez Tuesday December 19, 2023 11-12 (live) Tuesday January 9, 2024 11-12 (live) Tuesday January 16, 2024 11-12 (live): Yolijn Aarts & Kitty de Vries: GUTS Tuesday January 23, 2024 11-12 (live): Kayla Green Tuesday January 30, 2024 11-12 (live): Noura Borggreven & Sterre van Riel: Expeditie NEXT at the Hefhouse Tuesday February 6, 2024 11-12 (live): Eveline Crone & Lysanne te Brinke: on being a scientist Tuesday February 6, 2024 16-17 (live): Joint lab meeting with Eva Telzer and Ryan Tsai Tuesday February 13, 2024 11-12 (live): Felix Schreiber (external) Tuesday February 20, 2024 11-12 (live): Suzanne van de Groep Tuesday February 27, 2024 11-12 (live): Sterre & Noura Tuesday March 5, 2024 11-12 (live) Tuesday March 12, 2024 11-12 (live) Tuesday March 19, 2024 11-12 (live): Ilse van de Groep Tuesday March 26, 2024 11-12 (live) Tuesday April 2, 2024 11-12 (live) Tuesday April 9, 2024 11-12 (live) Tuesday April 16, 2024 11-12 (live) Tuesday April 23, 2024 11-12 (live) Tuesday April 30, 2024 11-12 (live) Tuesday May 7, 2024 11-12 (live) Tuesday May 14, 2024 11-12 (live) Thursday May 16 & Friday May 17, 2024 11-12 (live): Visit Gregoire Borst and lab Tuesday May 21, 2024 11-12 (live) Tuesday May 28, 2024 11-12 (live) Tuesday June 4, 2024 11-12 (live) Tuesday June 11, 2024 11-12 (live) Tuesday June 18, 2024 11-12 (live) Tuesday June 25, 2024 11-12 (live)","title":"Labmeetings"},{"location":"agenda/labmeetings.html#data-management-meetings","text":"In the data management meetings, updates on ongoing projects and tips on data management are shared by the data managers. The schedule of the online data management meetings for 2022-2023 (regularly updated): Monday October 31, 2022 15-16: OSF Monday November 28, 2022 15-16: Research Drive and alternatives Monday January 30, 2023 15-16: electronic logbook Monday March 27, 2023 15-16: version control Monday May 8, 2023 15-16: reserve DOI + open science journals Monday June 5, 2023, 14-15: privacy/access externals Monday June 26, 2023 15-16: sharing posters/presentations/graphics","title":"Data management meetings"},{"location":"agenda/labmeetings.html#journal-clubs","text":"Starting November 2022, we have planned several journal clubs about specific topics, focused on theoretical and methodological papers. This allows us to dive deeper into theoretical aspects of our work and remain up to date with current literature. Example topics from the past are theories on social media in adolescence and the importance of effect sizes in scientific research. The schedule of the live journal clubs for 2022-2023 (regularly updated): Tuesday November 22, 2022 14-15: Lysanne and Lina Tuesday January 10, 2023 14-15: Kayla and Eloise Tuesday February 21, 2023 14-15: Sophie and Yara Tuesday April 4, 2023 14-15: Simone Tuesday May 16, 2023 14-15: Hannah and Ilse Tuesday June 27, 2023 14-15","title":"Journal clubs"},{"location":"data-management/FAIR.html","text":"The FAIR principles In the data management world, making data \"FAIR\" is the ideal situation. Making your data FAIR facilitates knowledge discovery by assisting humans and machines in their discovery of and access to the data. See all FAIR principles and their explanation on the GO FAIR website . See also this FAIR data checklist to see if you have met all FAIR requirements. Findable by both humans and machines Include metadata that allow the discovery of interesting datasets: the dataset should be findable with a google datasets search. Select a data repository early on. Make sure the repository provides a persistent identifier for your data. Check the repository's data format and metadata requirements: do they provide descriptive information about the context, quality and condition, or characteristics of the data? Accessible: stored for the long term with well-defined access conditions Think about the security, legal conditions, sustainability and access conditions of the data. guarantee longevity of the data, e.g., by submitting it to a repository that has a certification like the Data Seal of Approval or an ISO certification check and describe the legal conditions under which the data can be made available establish an embargo period if necessary make sure your ICT infrastructure will keep the (meta)data available even in case of equipment failure or human error Interoperable: ready to be combined with other datasets Think about the software, documentation standards (e.g., the same labels for the same variables) and formats. This differs for different disciplines. select commonly used data formats (such as BIDS for neuroimaging data) select commonly used vocabularies (controlled vocabularies if applicable) for data items if your (meta)data relates to other datasets, indicate how Reusable Think about the licensing and provenance (can you trust this data?) of the data. make sure you keep proper provenance information: details about how and where the data was generated, including machine settings, and details about all processing steps, such as the software tools with their versions and parameters select the right minimal metadata standard and collect the necessary metadata, see link select a license for the (meta)data and the associated software tools make sure the important conclusions of your study will not only be available in a paper in narrated form, but also in a digital file (e.g., a nanopublication)","title":"FAIR data"},{"location":"data-management/FAIR.html#the-fair-principles","text":"In the data management world, making data \"FAIR\" is the ideal situation. Making your data FAIR facilitates knowledge discovery by assisting humans and machines in their discovery of and access to the data. See all FAIR principles and their explanation on the GO FAIR website . See also this FAIR data checklist to see if you have met all FAIR requirements.","title":"The FAIR principles"},{"location":"data-management/FAIR.html#findable-by-both-humans-and-machines","text":"Include metadata that allow the discovery of interesting datasets: the dataset should be findable with a google datasets search. Select a data repository early on. Make sure the repository provides a persistent identifier for your data. Check the repository's data format and metadata requirements: do they provide descriptive information about the context, quality and condition, or characteristics of the data?","title":"Findable by both humans and machines"},{"location":"data-management/FAIR.html#accessible-stored-for-the-long-term-with-well-defined-access-conditions","text":"Think about the security, legal conditions, sustainability and access conditions of the data. guarantee longevity of the data, e.g., by submitting it to a repository that has a certification like the Data Seal of Approval or an ISO certification check and describe the legal conditions under which the data can be made available establish an embargo period if necessary make sure your ICT infrastructure will keep the (meta)data available even in case of equipment failure or human error","title":"Accessible: stored for the long term with well-defined access conditions"},{"location":"data-management/FAIR.html#interoperable-ready-to-be-combined-with-other-datasets","text":"Think about the software, documentation standards (e.g., the same labels for the same variables) and formats. This differs for different disciplines. select commonly used data formats (such as BIDS for neuroimaging data) select commonly used vocabularies (controlled vocabularies if applicable) for data items if your (meta)data relates to other datasets, indicate how","title":"Interoperable: ready to be combined with other datasets"},{"location":"data-management/FAIR.html#reusable","text":"Think about the licensing and provenance (can you trust this data?) of the data. make sure you keep proper provenance information: details about how and where the data was generated, including machine settings, and details about all processing steps, such as the software tools with their versions and parameters select the right minimal metadata standard and collect the necessary metadata, see link select a license for the (meta)data and the associated software tools make sure the important conclusions of your study will not only be available in a paper in narrated form, but also in a digital file (e.g., a nanopublication)","title":"Reusable"},{"location":"data-management/codebooks.html","text":"Creating and using codebooks A codebook or data dictionary helps people understand your data, by explaining what the variable names and values in your data files (i.e., the metadata) mean. As such, a codebook is important for making your research more reproducible. Obviously, a codebook can be very beneficial for direct collaborations and your future self, but you might also consider using one if you plan to (openly) share datasets. A Primer on creating Codebooks Before you start creating a codebook, consider reading this primer on creating data dictionaries and shareable datasets: Buchanan, E. M., Crain, S. E., Cunningham, A. L., Johnson, H. R., Stash, H. E., Papadatou-Pastou, M., \u2026 Aczel, B. (2019, May 20). Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset. https://doi.org/10.31219/osf.io/vd4y3 Creating a Qualtrics Data Dictionary If you are using Qualtrics to collect questionnare data, you can use the Data Dictionary Creator to create a codebook for your dataset. Creating a Markdown Codebook from your R dataframe If you use R to analyze your data, you can use the codebook package to create a codebook based on the dataframe you are working with. Creating a Castor Data Dictionary If you are using the Castor Electronic Data Capture system to capture, process and integrate your data, you are required to build your study into the system. Before you start building your study, it is recommended to make a data dictionary, which the building process much easier. The added bonus is that you also have a data dictionary to use for your own research and collaborations. You can find out more about creating a Castor data dictionary here . If you did not make a Data Dictionary before building the study, or if you want to easily check some changes you have made later, you can also export a data dictionary for your study.","title":"Codebooks"},{"location":"data-management/codebooks.html#creating-and-using-codebooks","text":"A codebook or data dictionary helps people understand your data, by explaining what the variable names and values in your data files (i.e., the metadata) mean. As such, a codebook is important for making your research more reproducible. Obviously, a codebook can be very beneficial for direct collaborations and your future self, but you might also consider using one if you plan to (openly) share datasets.","title":"Creating and using codebooks"},{"location":"data-management/codebooks.html#a-primer-on-creating-codebooks","text":"Before you start creating a codebook, consider reading this primer on creating data dictionaries and shareable datasets: Buchanan, E. M., Crain, S. E., Cunningham, A. L., Johnson, H. R., Stash, H. E., Papadatou-Pastou, M., \u2026 Aczel, B. (2019, May 20). Getting Started Creating Data Dictionaries: How to Create a Shareable Dataset. https://doi.org/10.31219/osf.io/vd4y3","title":"A Primer on creating Codebooks"},{"location":"data-management/codebooks.html#creating-a-qualtrics-data-dictionary","text":"If you are using Qualtrics to collect questionnare data, you can use the Data Dictionary Creator to create a codebook for your dataset.","title":"Creating a Qualtrics Data Dictionary"},{"location":"data-management/codebooks.html#creating-a-markdown-codebook-from-your-r-dataframe","text":"If you use R to analyze your data, you can use the codebook package to create a codebook based on the dataframe you are working with.","title":"Creating a Markdown Codebook from your R dataframe"},{"location":"data-management/codebooks.html#creating-a-castor-data-dictionary","text":"If you are using the Castor Electronic Data Capture system to capture, process and integrate your data, you are required to build your study into the system. Before you start building your study, it is recommended to make a data dictionary, which the building process much easier. The added bonus is that you also have a data dictionary to use for your own research and collaborations. You can find out more about creating a Castor data dictionary here . If you did not make a Data Dictionary before building the study, or if you want to easily check some changes you have made later, you can also export a data dictionary for your study.","title":"Creating a Castor Data Dictionary"},{"location":"data-management/data-security.html","text":"Data security protocol We researchers deal with a lot of data: task data, questionnaire data, MRI or EEG data, but also contact information, health information, etc. Most of these data are highly sensitive, in that the risk of identification is high. Also, most of us probably do not want people who aren't involved in your project to have access to our data without our knowledge. In order to deal with the sensitivity of the data and prevent them from being stolen, it is imperative that we ensure the highest possible data security. This document contains some tips to maximize data security. By following these tips, you can be more confident that your participants\u2019 privacy will be guarded and the university will not get sued :smile: Loss of data or other problems? Always report a data breach to the Servicedesk: servicedesk@eur.nl (phone +31 (0)10 408 8880), click here for more information . General data security principles Always report a (possible) data leak/breach Every loss of data is a potential data leak, such as: stolen or lost digital files: USBs, laptops, external hard drives, data that is not backed-up, etc. stolen or lost printed personal data, e.g, a note containing a password, lists with grades, etc. viruses on your PC or hacked accounts, including phishing mails both pseudonymized and anonymized data need to be reported Contact your supervisor and Servicedesk (servicedesk@eur.nl) Report the (possible) leak as soon as possible, so that we can adequately respond and limit the amount of damage for participants as much as possible. Keep in mind that measures taken are meant to secure the data, not to punish those involved! Anonymize your data The best way to guard the privacy of your participants is to anonymize your data, so that the data cannot be traced back to participants (not even with a key containing the name-number links). For longitudinal data, this can get complicated, since you want to be able to link the data of the same subject and may also need the participants\u2019 contact info for next waves. Choose safe passwords for your devices This concerns your EUR account, your laptop, your phone (if it has email on it), and all other devices that contain data. Use a sentence instead of a word: they are harder to crack and easier to remember (especially when they are long and contain letters, numbers and signs). Use a password manager to keep all your passwords safe, such as Lastpass . Read more about Lastpass Lastpass is a password manager that can store all your passwords safely in the cloud. You have to think of a master password - a very strong password - only once. As soon as you log in using that password, you have access to all passwords that you saved in your vault. With Lastpass you can ( all features ): Create safe passwords (no creativity required from your end) Never again have to remember passwords for all of your accounts by heart Store your passwords safely Autofill passwords on websites so that signing on will be a breeze Share passwords with others (free version: share with 1 person) Save secure notes and other details as well You can either install lastpass on your PC (or download the mobile app) or install an extension in your browser. Get started More information here User manual If possible, change your passwords or codes (e.g., to lockers) regularly Share passwords only with the people who really need it Protect mobile devices: Use a safe internet network (preferably Eduroam): never use an open network. Preferably use EduVPN , which is free for university employees and makes sure that the connection is safe. Make sure you can wipe the device and change the password(s) from a distance in case of theft or loss Make sure to have a copy of the information on the university system Always install all security updates and, if possible, antivirus and anti-malware software Do not install jailbreak/root (gaining privileged access to the operating system) Do not save confidential information, unless it is well-protected Protect files with passwords too If a file contains personal data, such as contact information, the link to a participant number or data on the MRI checklist, protect it with a password: In Word and Excel: File > Info > Protect document/Workbook structure > Encrypt with password. You can also restrict editing via these options. Save the password somewhere safe so that you can always access the document: if you lose the password, you cannot access the document anymore. Only give the password to those who really need it. Try to restrict the amount of people that have access to the document. Keep paper data (logs, questionnaires, MRI checklists) locked up Do not leave data behind in labs after testing: take them with you! When testing multiple participants in one day, do not leave data from a previous participant laying around Keep papers in a locked closet or a locker and only give access to people that need it If necessary, keep a record of who has access Do not take papers with such data home or outside, unless strictly necessary Keep a clear-desk policy Do not leave any data unattended if you leave your desk for a longer period of time. For digital data: lock your screen (Cntrl+Alt+Delete > Enter or: Windows+L) For paper data: put them in a closed closet or locker or lock your room if no one else is present When testing participants, do not leave data from a previous participant laying around and take them with you when you leave This includes all desks: your own workspace, the secretariat, computer room, the lab, etc. Email safely When emailing large amounts of people (e.g., all your participants for a project newsletter), put the email addresses in the BCC (blind carbon copy) field, so that the receivers cannot see who else got the email. Put your own email address in the \u201cTo\u201d field. Where possible, use your university email, which has a safe connection with the university servers. Avoid using Hotmail, Gmail or Yahoo. Never send research data via email (except when encrypted or using tools like SURF filesender). Print safely Use Secure printing to print confidential information via a password: The printer will only start printing when you have filled in a personal pin code. Securely printed documents will be erased from the university servers immediately after printing. The settings of the print job cannot be adjusted at the printer When throwing away confidential information on paper, use a container that can be locked (especially made for confidential paper). Storing your data Aim not to store data on local drives Use the Research Drive, which is automatically backed-up and secured through the university. If you are processing data on your local Data drive, be sure to back it up at the Research Drive. Don\u2019t use personal accounts to store data long-term: if you leave the university suddenly, your data will not be accessible for others! Do not store identifiable information on personal devices It is only permitted to work with sensitive data when this is necessary for data collection, processing or planning and, officially, only when participants have given their permission. If you do work with sensitive data on a personal computer (e.g., laptop), remove the data after the analyses. Do not store non-anonymized data in the cloud Never save documents online, just open them. You never know who will get their hands on your data when you store it in the cloud. If you want to work at home, use Owncloud to interact with the Research Drive data Or use Remote desktop: gives access to your university desktop, see this link for a manual Or use SURFdrive , a safe alternative to Google Drive that everyone with a (Dutch) university account has access to (500 GB of personal storage) N.B. You can request SURFdrive also for students or give students the link If using a local drive, laptop, USB, external hard drive, or video camera, the following rules apply: If you are processing data on your local drive, be sure to always back it up on the Research Drive If possible, protect the device, hard drive or drive with a password Do not put the passwords to laptops on the laptop itself When the data have been saved at the right location, delete the data from the device (shift + delete or empty the recycling bin) If you have to take devices onto the street, bring them to a (safe and appointed) university location as quickly as possible. Do not bring them home unless absolutely necessary. If you have to take data home, do let leave them unattended in a (semi)public place (such as a car or library). If possible, leave it in a locked room. Communication and sharing Do not share data via email attachments, Google Drive, etc. Instead of email attachments, use SURF filesender . Email attachments are saved on mail servers and on your PC, whereas this is not the case with filesender. If you are sending an internal email, use a hyperlink or the path to the relevant folder where possible If you want others to be able to edit the documents you share, use SURFdrive or share only the relevant files via Research Drive. Be sure the data shared are anonymous. Do not talk about or analyze individual data in public spaces For example in the elevator, a common room, public transport, social media, emails, etc. Coding videos and audio is only allowed where other researchers from the same project work or in special coding rooms Coding audio in a public space is only allowed when others cannot hear the audio, e.g., because you are wearing headphones Transcribe audio and video using safe websites such as uitgetypt.nl Contacting participants outside the university When contacting participants outside the university, e.g., via your own mobile phone, make sure not to send any identifiable information via your phone (incl. sms or whatsapp) Avoid coupling participant numbers with phone numbers and/or names in emails or whatsapp messages","title":"Data security protocol"},{"location":"data-management/data-security.html#data-security-protocol","text":"We researchers deal with a lot of data: task data, questionnaire data, MRI or EEG data, but also contact information, health information, etc. Most of these data are highly sensitive, in that the risk of identification is high. Also, most of us probably do not want people who aren't involved in your project to have access to our data without our knowledge. In order to deal with the sensitivity of the data and prevent them from being stolen, it is imperative that we ensure the highest possible data security. This document contains some tips to maximize data security. By following these tips, you can be more confident that your participants\u2019 privacy will be guarded and the university will not get sued :smile:","title":"Data security protocol"},{"location":"data-management/data-security.html#loss-of-data-or-other-problems","text":"Always report a data breach to the Servicedesk: servicedesk@eur.nl (phone +31 (0)10 408 8880), click here for more information .","title":"Loss of data or other problems?"},{"location":"data-management/data-security.html#general-data-security-principles","text":"","title":"General data security principles"},{"location":"data-management/data-security.html#always-report-a-possible-data-leakbreach","text":"Every loss of data is a potential data leak, such as: stolen or lost digital files: USBs, laptops, external hard drives, data that is not backed-up, etc. stolen or lost printed personal data, e.g, a note containing a password, lists with grades, etc. viruses on your PC or hacked accounts, including phishing mails both pseudonymized and anonymized data need to be reported Contact your supervisor and Servicedesk (servicedesk@eur.nl) Report the (possible) leak as soon as possible, so that we can adequately respond and limit the amount of damage for participants as much as possible. Keep in mind that measures taken are meant to secure the data, not to punish those involved!","title":"Always report a (possible) data leak/breach"},{"location":"data-management/data-security.html#anonymize-your-data","text":"The best way to guard the privacy of your participants is to anonymize your data, so that the data cannot be traced back to participants (not even with a key containing the name-number links). For longitudinal data, this can get complicated, since you want to be able to link the data of the same subject and may also need the participants\u2019 contact info for next waves.","title":"Anonymize your data"},{"location":"data-management/data-security.html#choose-safe-passwords-for-your-devices","text":"This concerns your EUR account, your laptop, your phone (if it has email on it), and all other devices that contain data. Use a sentence instead of a word: they are harder to crack and easier to remember (especially when they are long and contain letters, numbers and signs). Use a password manager to keep all your passwords safe, such as Lastpass . Read more about Lastpass Lastpass is a password manager that can store all your passwords safely in the cloud. You have to think of a master password - a very strong password - only once. As soon as you log in using that password, you have access to all passwords that you saved in your vault. With Lastpass you can ( all features ): Create safe passwords (no creativity required from your end) Never again have to remember passwords for all of your accounts by heart Store your passwords safely Autofill passwords on websites so that signing on will be a breeze Share passwords with others (free version: share with 1 person) Save secure notes and other details as well You can either install lastpass on your PC (or download the mobile app) or install an extension in your browser. Get started More information here User manual If possible, change your passwords or codes (e.g., to lockers) regularly Share passwords only with the people who really need it Protect mobile devices: Use a safe internet network (preferably Eduroam): never use an open network. Preferably use EduVPN , which is free for university employees and makes sure that the connection is safe. Make sure you can wipe the device and change the password(s) from a distance in case of theft or loss Make sure to have a copy of the information on the university system Always install all security updates and, if possible, antivirus and anti-malware software Do not install jailbreak/root (gaining privileged access to the operating system) Do not save confidential information, unless it is well-protected","title":"Choose safe passwords for your devices"},{"location":"data-management/data-security.html#protect-files-with-passwords-too","text":"If a file contains personal data, such as contact information, the link to a participant number or data on the MRI checklist, protect it with a password: In Word and Excel: File > Info > Protect document/Workbook structure > Encrypt with password. You can also restrict editing via these options. Save the password somewhere safe so that you can always access the document: if you lose the password, you cannot access the document anymore. Only give the password to those who really need it. Try to restrict the amount of people that have access to the document.","title":"Protect files with passwords too"},{"location":"data-management/data-security.html#keep-paper-data-logs-questionnaires-mri-checklists-locked-up","text":"Do not leave data behind in labs after testing: take them with you! When testing multiple participants in one day, do not leave data from a previous participant laying around Keep papers in a locked closet or a locker and only give access to people that need it If necessary, keep a record of who has access Do not take papers with such data home or outside, unless strictly necessary","title":"Keep paper data (logs, questionnaires, MRI checklists) locked up"},{"location":"data-management/data-security.html#keep-a-clear-desk-policy","text":"Do not leave any data unattended if you leave your desk for a longer period of time. For digital data: lock your screen (Cntrl+Alt+Delete > Enter or: Windows+L) For paper data: put them in a closed closet or locker or lock your room if no one else is present When testing participants, do not leave data from a previous participant laying around and take them with you when you leave This includes all desks: your own workspace, the secretariat, computer room, the lab, etc.","title":"Keep a clear-desk policy"},{"location":"data-management/data-security.html#email-safely","text":"When emailing large amounts of people (e.g., all your participants for a project newsletter), put the email addresses in the BCC (blind carbon copy) field, so that the receivers cannot see who else got the email. Put your own email address in the \u201cTo\u201d field. Where possible, use your university email, which has a safe connection with the university servers. Avoid using Hotmail, Gmail or Yahoo. Never send research data via email (except when encrypted or using tools like SURF filesender).","title":"Email safely"},{"location":"data-management/data-security.html#print-safely","text":"Use Secure printing to print confidential information via a password: The printer will only start printing when you have filled in a personal pin code. Securely printed documents will be erased from the university servers immediately after printing. The settings of the print job cannot be adjusted at the printer When throwing away confidential information on paper, use a container that can be locked (especially made for confidential paper).","title":"Print safely"},{"location":"data-management/data-security.html#storing-your-data","text":"","title":"Storing your data"},{"location":"data-management/data-security.html#aim-not-to-store-data-on-local-drives","text":"Use the Research Drive, which is automatically backed-up and secured through the university. If you are processing data on your local Data drive, be sure to back it up at the Research Drive. Don\u2019t use personal accounts to store data long-term: if you leave the university suddenly, your data will not be accessible for others!","title":"Aim not to store data on local drives"},{"location":"data-management/data-security.html#do-not-store-identifiable-information-on-personal-devices","text":"It is only permitted to work with sensitive data when this is necessary for data collection, processing or planning and, officially, only when participants have given their permission. If you do work with sensitive data on a personal computer (e.g., laptop), remove the data after the analyses.","title":"Do not store identifiable information on personal devices"},{"location":"data-management/data-security.html#do-not-store-non-anonymized-data-in-the-cloud","text":"Never save documents online, just open them. You never know who will get their hands on your data when you store it in the cloud. If you want to work at home, use Owncloud to interact with the Research Drive data Or use Remote desktop: gives access to your university desktop, see this link for a manual Or use SURFdrive , a safe alternative to Google Drive that everyone with a (Dutch) university account has access to (500 GB of personal storage) N.B. You can request SURFdrive also for students or give students the link","title":"Do not store non-anonymized data in the cloud"},{"location":"data-management/data-security.html#if-using-a-local-drive-laptop-usb-external-hard-drive-or-video-camera-the-following-rules-apply","text":"If you are processing data on your local drive, be sure to always back it up on the Research Drive If possible, protect the device, hard drive or drive with a password Do not put the passwords to laptops on the laptop itself When the data have been saved at the right location, delete the data from the device (shift + delete or empty the recycling bin) If you have to take devices onto the street, bring them to a (safe and appointed) university location as quickly as possible. Do not bring them home unless absolutely necessary. If you have to take data home, do let leave them unattended in a (semi)public place (such as a car or library). If possible, leave it in a locked room.","title":"If using a local drive, laptop, USB, external hard drive, or video camera, the following rules apply:"},{"location":"data-management/data-security.html#communication-and-sharing","text":"","title":"Communication and sharing"},{"location":"data-management/data-security.html#do-not-share-data-via-email-attachments-google-drive-etc","text":"Instead of email attachments, use SURF filesender . Email attachments are saved on mail servers and on your PC, whereas this is not the case with filesender. If you are sending an internal email, use a hyperlink or the path to the relevant folder where possible If you want others to be able to edit the documents you share, use SURFdrive or share only the relevant files via Research Drive. Be sure the data shared are anonymous.","title":"Do not share data via email attachments, Google Drive, etc."},{"location":"data-management/data-security.html#do-not-talk-about-or-analyze-individual-data-in-public-spaces","text":"For example in the elevator, a common room, public transport, social media, emails, etc. Coding videos and audio is only allowed where other researchers from the same project work or in special coding rooms Coding audio in a public space is only allowed when others cannot hear the audio, e.g., because you are wearing headphones Transcribe audio and video using safe websites such as uitgetypt.nl","title":"Do not talk about or analyze individual data in public spaces"},{"location":"data-management/data-security.html#contacting-participants-outside-the-university","text":"When contacting participants outside the university, e.g., via your own mobile phone, make sure not to send any identifiable information via your phone (incl. sms or whatsapp) Avoid coupling participant numbers with phone numbers and/or names in emails or whatsapp messages","title":"Contacting participants outside the university"},{"location":"data-management/dmp-dpia-info.html","text":"DMP & DPIA On this page, you can find more information about the Data Management Plan (DMP) and the Data Protection Impact Assessment (DPIA). Data management plan (DMP) Since 2016, all research projects must have a data management plan (DMP) before the start of the project. In a DMP, you capture which data and metadata will be collected, who is responsible for which tasks and what will happen to the (meta)data after the project has ended. The research data lifecycle Source: UK data archive Components of a DMP Fairly standard components of a DMP are: Cover information : project name, researchers, dates, ethical protocol numbers, etc. Data collection and creation How will you create your data? How will you access the data in the future? What license will you use? What kind of data will it be? E.g., how many and what type of files (see preferred file formats ), how large (search for \"file size calculator\"), data quality (resolution, quality), usefulness (versions/processed or unprocessed) Data storage and security Where will you store your data? Where and how often will you make back-ups (1 copy offsite, 3 copies, 2 different media)? What do you do to make sure that the right people can access your data? (passwords, encryption, firewalls, anonymization, aggregation, secure transport and deletion, etc.) Documentation and metadata How are your files named and structured? Are you using version control? Are you providing readme files and/or other documentation? (e.g., time, place, people involved, etc.) Data access, sharing and reuse With whom do you want to share your data? How/where? Has ownership been agreed? Who might find your data useful later? Any restrictions regarding data sharing? Reasons to opt out of sharing with others: privacy / personal data (GDPR), intellectual property rights, might jeopardize the project\u2019s main objective, commercial (working for companies), security-related, etc. Think about linking your data to your publication AND vice versa! Also think about linking your publications to you through an ORCID-ID Data preservation and archiving Which data do you want to keep for future use? Which data will you discard? Not all data necessarily need to be preserved! Where are you going to archive these data? How to organize the data so that they can still be understood in the future? Who is responsible for your data after you leave? How to write a DMP? At the EUR, you can use the DMP Online tool , which contains DMP templates of many funders, or use the EUR template. Create an account, select the (funder specific) format and simply fill out the form! If you leave the Funder field empty, you will use the general EUR-format. You can also invite colleagues to work on the DMP and leave comments Check your funder requirements: did they approve of the template you're using? E.g., if your research is funded by NWO, you have to use one of their approved templates and get feedback from an expert (see contacts below) Read more about DMPs at the EUR, EUR guidelines and the EUR template here . Need examples? See this page for a collection of publicly available DMPs. Data protection impact assessment (DPIA) Whenever you process sensitive data (or something changes regarding this), such as MRI data, names, addresses, daily diaries, etc., you are required to fill in a Data Protection Impact Assessment (DPIA) that has to be approved before starting your study by a privacy officer . In a DPIA, you identify privacy risks and formulate measures to prevent breaches. You register which data you collect and who is responsible. A DPIA touches upon: Collaborations in your project: Within your institute Between institutes: agree upon who has access to the data and which technologies are used (e.g., for saving and analyzing the data) Public-private collaborations: make contractual agreements about how to handle data Geography: outside of the EU, you need to make contractual agreements about how to handle data Types of data collected: Automatically generated (e.g., fitness watches) Own creation, e.g., interviews, pictures Re-used, e.g., multiple datasets combined (pay attention to identifiability) Resources Contacts For checking your (ESSB) DMP and/or DPIA, contact the faculty data steward For general questions about DMPs and DPIAs, contact the Erasmus Data Service Center (EDSC): edsc [at] eur [dot] nl EDSC workshop agenda For the DPIA, contact the ESSB privacy officer (privacy [at] essb [dot] eur [dot] nl) Resources Core requirements for the DMP (p.11+12) by the NWO and others More information about data management (storing, archiving, versioning, data structure, etc.) and backing up and versioning data How to name your files The costs of data management More about privacy and legal aspects Leiden University DPIA template","title":"DMP and DPIA"},{"location":"data-management/dmp-dpia-info.html#dmp-dpia","text":"On this page, you can find more information about the Data Management Plan (DMP) and the Data Protection Impact Assessment (DPIA).","title":"DMP &amp; DPIA"},{"location":"data-management/dmp-dpia-info.html#data-management-plan-dmp","text":"Since 2016, all research projects must have a data management plan (DMP) before the start of the project. In a DMP, you capture which data and metadata will be collected, who is responsible for which tasks and what will happen to the (meta)data after the project has ended. The research data lifecycle Source: UK data archive","title":"Data management plan (DMP)"},{"location":"data-management/dmp-dpia-info.html#components-of-a-dmp","text":"Fairly standard components of a DMP are: Cover information : project name, researchers, dates, ethical protocol numbers, etc. Data collection and creation How will you create your data? How will you access the data in the future? What license will you use? What kind of data will it be? E.g., how many and what type of files (see preferred file formats ), how large (search for \"file size calculator\"), data quality (resolution, quality), usefulness (versions/processed or unprocessed) Data storage and security Where will you store your data? Where and how often will you make back-ups (1 copy offsite, 3 copies, 2 different media)? What do you do to make sure that the right people can access your data? (passwords, encryption, firewalls, anonymization, aggregation, secure transport and deletion, etc.) Documentation and metadata How are your files named and structured? Are you using version control? Are you providing readme files and/or other documentation? (e.g., time, place, people involved, etc.) Data access, sharing and reuse With whom do you want to share your data? How/where? Has ownership been agreed? Who might find your data useful later? Any restrictions regarding data sharing? Reasons to opt out of sharing with others: privacy / personal data (GDPR), intellectual property rights, might jeopardize the project\u2019s main objective, commercial (working for companies), security-related, etc. Think about linking your data to your publication AND vice versa! Also think about linking your publications to you through an ORCID-ID Data preservation and archiving Which data do you want to keep for future use? Which data will you discard? Not all data necessarily need to be preserved! Where are you going to archive these data? How to organize the data so that they can still be understood in the future? Who is responsible for your data after you leave?","title":"Components of a DMP"},{"location":"data-management/dmp-dpia-info.html#how-to-write-a-dmp","text":"At the EUR, you can use the DMP Online tool , which contains DMP templates of many funders, or use the EUR template. Create an account, select the (funder specific) format and simply fill out the form! If you leave the Funder field empty, you will use the general EUR-format. You can also invite colleagues to work on the DMP and leave comments Check your funder requirements: did they approve of the template you're using? E.g., if your research is funded by NWO, you have to use one of their approved templates and get feedback from an expert (see contacts below) Read more about DMPs at the EUR, EUR guidelines and the EUR template here . Need examples? See this page for a collection of publicly available DMPs.","title":"How to write a DMP?"},{"location":"data-management/dmp-dpia-info.html#data-protection-impact-assessment-dpia","text":"Whenever you process sensitive data (or something changes regarding this), such as MRI data, names, addresses, daily diaries, etc., you are required to fill in a Data Protection Impact Assessment (DPIA) that has to be approved before starting your study by a privacy officer . In a DPIA, you identify privacy risks and formulate measures to prevent breaches. You register which data you collect and who is responsible. A DPIA touches upon: Collaborations in your project: Within your institute Between institutes: agree upon who has access to the data and which technologies are used (e.g., for saving and analyzing the data) Public-private collaborations: make contractual agreements about how to handle data Geography: outside of the EU, you need to make contractual agreements about how to handle data Types of data collected: Automatically generated (e.g., fitness watches) Own creation, e.g., interviews, pictures Re-used, e.g., multiple datasets combined (pay attention to identifiability)","title":"Data protection impact assessment (DPIA)"},{"location":"data-management/dmp-dpia-info.html#resources","text":"","title":"Resources"},{"location":"data-management/dmp-dpia-info.html#contacts","text":"For checking your (ESSB) DMP and/or DPIA, contact the faculty data steward For general questions about DMPs and DPIAs, contact the Erasmus Data Service Center (EDSC): edsc [at] eur [dot] nl EDSC workshop agenda For the DPIA, contact the ESSB privacy officer (privacy [at] essb [dot] eur [dot] nl)","title":"Contacts"},{"location":"data-management/dmp-dpia-info.html#resources_1","text":"Core requirements for the DMP (p.11+12) by the NWO and others More information about data management (storing, archiving, versioning, data structure, etc.) and backing up and versioning data How to name your files The costs of data management More about privacy and legal aspects Leiden University DPIA template","title":"Resources"},{"location":"data-management/folder-structure.html","text":"Folder structure It is important to have a folder structure that provides a good overview of your project and that maintains all data and documentation of your project logically. Important aspects of a good project structure are: Separate source and raw data from further processed data . Preferably make raw data read-only so they cannot be edited. Separate different types of data , when necessary Provide scripts or descriptions that were used to go from the raw data to all processed data > make sure your analyses from start to finish are reproducible ! Provide sufficient metadata about your project, e.g., preregistrations, study protocol, codebooks/data dictionaries, a data management plan, general information about the project, the ethics protocol, etc. Provide readme.txt files were necessary. Provide all materials and instructions used to collect the data, e.g., tasks, questionnaires and instructions Overall: use self-explanatory file and folder names or provide documentation explaining which files can be found where. Also keep in mind versioning (e.g., use the date in the filenames, the version or use a formal version control system like git) Finally, use your folders consistently , e.g., place all data in the Data folder. Example folder structure Below, you can find an example folder structure of the project \"YYYY_ProjectName\". You are encouraged to use this or a similar format, but don't feel obliged! About the project Preregistration(s) and/or study protocol (or links to them) Data dictionary/codebook Data management plan (DMP) and/or Data protection impact assessment (DPIA) Data sharing agreement (if applicable) Involved researcheres and their contact information Description of the project Overview of published manuscripts Organizational files about planning, availability, task divisioin, etc. Ethics/CME/METC Application Responses Final approved protocol Data If necessary, add another folder here for each timepoint or sub-project. Behavioral_data Task_x analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Neural_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data analysis_x raw_data participant_number anat dwi func Physiological_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Questionnaire_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros data dictionary/codebook processed_data raw_data Finances Applications participant money Overviews Receipts (kwitanties) Travel expenses Literature_resources Papers usable or used Reference manager files Measures Questionnaires Questionnaire_x papers manuals, scoreforms software scripts instructions List of questionnaires Tasks Task_x software scripts Outreach_recruitment Presentations, website texts, education, newsletters Recruitment emails, flyers, folders, contact with schools Quotes from participants Contents of the goodiebag Publication_packages Author_et_al_(year)_journal 1_Manuscript 2_Task_instructions-questionnaires-stimuli-scripts 3_Anonymized_raw_data 4_Data_processing_files-scripts 5_Processed_data 6_Ethics_protocol Readme.txt Students Year_NameStudent Data Manuscript Readme_StudentName.txt Work_documents Data_collection_protocols Subject folder documents (during data collection) Instruction protocol Forms brevethouder and portier Forms brevethouder and portier Requests for the HIX system Planning schedules Scan logs Scanning & data collection protocols Meetings Agendas Minutes Planning_of_participants Calling_and_planning Bijzonderhedenbestand (encrypted) Calling protocol Coupling subjects-numbers (encrypted) Contact information (encrypted) Emailing Confirmation and reminder emails MRI checklist Practical information and instructions (location, time, hormone collection, etc. Information letter and informed consent form","title":"File organization"},{"location":"data-management/folder-structure.html#folder-structure","text":"It is important to have a folder structure that provides a good overview of your project and that maintains all data and documentation of your project logically. Important aspects of a good project structure are: Separate source and raw data from further processed data . Preferably make raw data read-only so they cannot be edited. Separate different types of data , when necessary Provide scripts or descriptions that were used to go from the raw data to all processed data > make sure your analyses from start to finish are reproducible ! Provide sufficient metadata about your project, e.g., preregistrations, study protocol, codebooks/data dictionaries, a data management plan, general information about the project, the ethics protocol, etc. Provide readme.txt files were necessary. Provide all materials and instructions used to collect the data, e.g., tasks, questionnaires and instructions Overall: use self-explanatory file and folder names or provide documentation explaining which files can be found where. Also keep in mind versioning (e.g., use the date in the filenames, the version or use a formal version control system like git) Finally, use your folders consistently , e.g., place all data in the Data folder.","title":"Folder structure"},{"location":"data-management/folder-structure.html#example-folder-structure","text":"Below, you can find an example folder structure of the project \"YYYY_ProjectName\". You are encouraged to use this or a similar format, but don't feel obliged! About the project Preregistration(s) and/or study protocol (or links to them) Data dictionary/codebook Data management plan (DMP) and/or Data protection impact assessment (DPIA) Data sharing agreement (if applicable) Involved researcheres and their contact information Description of the project Overview of published manuscripts Organizational files about planning, availability, task divisioin, etc. Ethics/CME/METC Application Responses Final approved protocol Data If necessary, add another folder here for each timepoint or sub-project. Behavioral_data Task_x analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Neural_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data analysis_x raw_data participant_number anat dwi func Physiological_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros quality check document processed_data raw_data Questionnaire_data analysis (link to) pipeline/manual used for processing analysis log scripts/macros data dictionary/codebook processed_data raw_data Finances Applications participant money Overviews Receipts (kwitanties) Travel expenses Literature_resources Papers usable or used Reference manager files Measures Questionnaires Questionnaire_x papers manuals, scoreforms software scripts instructions List of questionnaires Tasks Task_x software scripts Outreach_recruitment Presentations, website texts, education, newsletters Recruitment emails, flyers, folders, contact with schools Quotes from participants Contents of the goodiebag Publication_packages Author_et_al_(year)_journal 1_Manuscript 2_Task_instructions-questionnaires-stimuli-scripts 3_Anonymized_raw_data 4_Data_processing_files-scripts 5_Processed_data 6_Ethics_protocol Readme.txt Students Year_NameStudent Data Manuscript Readme_StudentName.txt Work_documents Data_collection_protocols Subject folder documents (during data collection) Instruction protocol Forms brevethouder and portier Forms brevethouder and portier Requests for the HIX system Planning schedules Scan logs Scanning & data collection protocols Meetings Agendas Minutes Planning_of_participants Calling_and_planning Bijzonderhedenbestand (encrypted) Calling protocol Coupling subjects-numbers (encrypted) Contact information (encrypted) Emailing Confirmation and reminder emails MRI checklist Practical information and instructions (location, time, hormone collection, etc. Information letter and informed consent form","title":"Example folder structure"},{"location":"data-management/research-drive-how.html","text":"Research Drive protocol This page contains information on how we deal with data on the SURF Research Drive in the SYNC lab, which can be accessed by logging in here . 1. Folders A project folder (at the root of the Research Drive) has to be created by the contract administrator (currently: Mark). This means that each project is created under the contract. Information needed includes: the name of the folder (preferably Year_ProjectName, e.g., \"2018_Brainlinks\" the data steward of the project folder the amount of storage needed (can be changed later) Storage : our contract, \u201cESSB Brain and Development\u201d, currently has 16 TB of storage capacity which is divided among the existing project folders. More storage capacity has to be requested with Research Data Management/Jeroen Rombouts (who in turn requests this at SURF) by the contract administrator 2. Requesting and providing access Requesting access (users) Request access to a folder with the data steward of the relevant project. You can find who is the data steward for which project in the file \u201c ResearchDrive_overview_Projects_Access_Groups \u201d . Giving access (data stewards) Select the folder and click the sharing icon. In the area that appears, you can add individual users or groups and alternatively, create sharing links (URLs) with non-Research Drive users. Giving access to a folder means providing that selected access for all subfolders below the shared folder too. Preferably never give anyone sharing rights (unless strictly necessary), because that person can give others more rights than they themselves have. This causes you to lose overview of who can access the data. Custom groups Give Research Drive users access via personal access groups (Settings > Custom groups). Members in such a group all receive the same rights when added to a folder. The group owner is the only one who can add and remove access group members and see the members of the group. However, all access group names are visible for the entire Research Drive, so aptly name the groups according to the following format: SYNC_ProjectName_Accesslevel , e.g., \"SYNC_Brainlinks_edit\". If a new person needs access, they can be added to the relevant access group and automatically gains access to the same files/folders as the other group members. After you have made any changes in access groups, please update the access document . Individual users Add users to folders individually if they should not have the same permissions as the custom groups. This could for example be outside researchers . Please note that users of SURFdrive do not necessarily need a new Research Drive account, you can simply share the relevant files/folders with their Federated cloud ID . Students and other outside users Generally, when sharing data with students or outsiders, stick to the following guidelines: Share data only when the sharing serves a scientific goal Keep the amount of data shared to a minimum Pseudonymize or, if possible, anonymize data before sharing Students should sign a non-disclosure agreement and stick to the working guidelines for students Give students and outside users access via access links that allow Download/View/Upload (edit only if it concerns a separate (Students) folder). Make use of the password and end-date functions: students and outside users should not have access to the data after their internships / the agreement have/has ended and the password will give extra protection against non-authorized use. Alternatively, you could add the external users or students as new Research Drive users (Dashboard > Add user), although for large amounts of users, this is not recommended. 3. Uploading data to Research Drive Rclone Rclone is a command-line tool to upload data to any location. It can run easily in the background and is relatively simple. Setting up Download rclone: https://rclone.org/downloads/ (no admin rights needed) Once downloaded, open your Command prompt (search for cmd ) Your command prompt shows a drive which it uses, e.g., P:/ . Move the unwrapped rclone files (at least rclone.exe ) to that drive. In your command prompt, type rclone config In your Research Drive account, create a new WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cRclone\u201d. Copy the password that was just created to a temporary file/your clipboard. Follow the instructions listed here . Use the following link for step 3: https://eur.data.surfsara.nl/remote.php/nonshib-webdav/ . Your username is yourERNAid@eur.nl and the password is the WebDAV password you just created (you will probably not see the password being pasted, but it is!). When you are asked for a bearer token , just press Enter Example summary of the config: Uploading data with Rclone Upload files from your command window (type cmd in your search bar if you don't know where it is), using the following general format: rclone copy [flags] \"source\" RD:\"destination\" Example command rclone copy -v -P --ignore-existing \u201cJ:\\ResearchData\\FSW\\Brain and Development - Projects\\2018_Brainlinks\\Ouderstudie\\\u201d RD:\u201c2018_Brainlinks (Projectfolder)/Brainlinks_Parentstudy\u201d copy the source contents to the destination folder print progress continuously ( -v and -P ) skip already existing files ( --ignore-existing ) Other Rclone commands List the files in the specified folder: rclone ls RD:\u201c2016_Zelfbeeld (Projectfolder)/Zelfbeeld_Data/Zelfbeeld_ProcessedData/\u201d Show configuration details: rclone config show Edit the configuration details: rclone config e More information on Rclone On the Research Drive wiki Flags to add to commands Rclone alternative: Cyberduck Cyberduck is a graphical user interface for uploading data. It also has built-in encryption software (cryptomator), which allows simultaneously encrypting and uploading data. Setting up Download and install Cyberduck (the program is free, select $0 of donation) - you do need admin access for this tool. Read the wiki page of SURF Research Drive and from there download the Research Drive cyberduck profile . Save the Research Drive cyberduck profile in the \u201cProfiles\u201d folder within your Cyberduck program files folder (e.g., \u201cC:\\Program Files\\Cyberduck\\profiles\u201d) In your Research Drive account, create a WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cCyberduck\u201d. Copy the password that was just created to a temporary file/your clipboard. (Re)Start Cyberduck Click \u201cNew connection\u201d (Nieuwe verbinding). In the pop-up window that appears, select the SURF Research Drive profile, change the server to eur.data.surfsara.nl and fill out your username (ERNA ID) and the WebDAV password that you just created. Do not forget to select \u201c Save password \u201d (Bewaar wachtwoord)! Then, select \u201cConnect\u201d (Verbind). After having made the connection, you should be able to navigate your (project) folders like in Research Drive. Every time Cyberduck restarts, this connection will be made, except when you explicitly disconnect (then you have to re-connect again). Uploading data in Cyberduck Turn on Checksum (Edit > Preferences > Wachtrij (Queue) > Checksum) Select a folder in which you want to upload files Click Upload and select the files to be uploaded. Once your files have been uploaded, they should appear in your Research Drive! 4. Uploading sensitive data to Research Drive Encryption Research Drive cannot contain special types of personal data (bijzondere persoonsgegevens). These comprise of: Race, ethnicity, political views, religion Sexual life/preferences Genetic or biometric data with the purpose of unique identification Health information, among which medical data (MRI data!) Criminal past Therefore, the following data need to be encrypted before uploading to Research Drive: Participant databases with contact information - using a password on Microsoft Office documents is sufficient MRI checklist information files (\u201cBijzonderhedenbestand\u201d) containing information about past surgeries and other health information - using a password on Microsoft Office documents is sufficient Files containing demographic data, responses about race, political views, religion, sexual life, criminal past and other potential health information. This type of file can be stored on Research Drive without encryption only when they are pseudonymized / not directly traceable to individuals. Raw, non-defaced MRI images In general, it is best to avoid having to use encryption , because passwords can be lost and software can deprecate. Data are much more durable if they can instead be anonymized or pseudonymized. For example, upload defaced MRI data and anonymized/pseudonymized health information to avoid having to encrypt them. How to encrypt? Keep it local : don't upload them to Research Drive. If this means you may lose data, don't do this Passwords : put a password on a Microsoft Office document (e.g., Excel, Word) and keep the password at a safe location . If you lose the password, the data is not accessible anymore Use encryption software : we use Cryptomator to encrypt non-defaced MRI data. Using Cryptomator to encrypt data Use Cryptomator when you want to encrypt folders containing multiple sensitive files before uploading that folder to Research Drive: Download and install the most recent version of Cryptomator Create an encrypted folder (vault) . Be sure to create both a password and a recovery key that can be used in case the password gets lost. Save both at a safe location ! If you lose them, you cannot access the data anymore. Open (decrypt) the vault . In Cryptomator, select a Vault and click \"Open vault\". Select the cryptomator masterkey file. You will be prompted to fill out the password and afterwards, the folder will open. Note that you need the Cryptomator software to see the files in a normal way. In your file explorer, you will probably only see nonsense files in a folder called 'd'. Work with vault contents : after opening a vault, the decrypted files will appear in a separate path on your computer (e.g., \"Z://\"). You can simply copy the path to tools (Matlab, R) or open files from here to work with them. After usage, remember to lock the vault again. Uploading encrypted folders with rclone works the same way as uploading regular folders! Encryption within Cyberduck Cyberduck has in-built functionality to encrypt files using Cryptomator : In Cyberduck, select the folder in which you want to create the encrypted folder Right click and select \u201cNew locked vault\u201d (Nieuwe versleutelde safe) Give the vault a name (remember to put the Project name in there, e.g., \u201cBrainlinks_Neural_data_raw\u201d) and a password. Store the password somewhere safe immediately. You can now upload folders into this encrypted folder as with normal folders. In the background, Cyberduck will decrypt your encrypted folders automatically (because it knows the password), which is why it looks no different than a normal folder in the Cyberduck environment. Saving and sharing passwords A few safe options are: Lastpass stores your passwords in a vault in the cloud (behind 1 master password). It can also store secure notes (such as Cryptomator recovery keys) and allows sharing passwords with others (premium version: Network center > Share item). Network drive : make sure that the drive is secured, backed up and only accessible to those who are allowed to decrypt the data Locally : keep in mind that when your PC is hacked, hackers may have access to the passwords and when your PC / drive crashes, the passwords may be lost. You can share passwords via SURFfilesender : per password and recovery key, create a txt file. Send it/them via SURF filesender and make sure a password is required to download the file(s). Send the password to download to files to the receiver(s) via another way (e.g., text or slack message). 5. Working with data: editing and analysis Editing documents The easiest way to edit documents is in your internet browser, because it allows collaborative editing (with OnlyOffice) and changes are automatically saved. If you mount Research Drive to your file explorer and then edit the document at the same time someone else does, there can be merging conflicts and the version with the last edit \u201cwins\u201d. Mounting Research Drive to your file explorer You can mount your Research Drive account to your file explorer, so that the Research Drive files can be accessed on your local PC. Note that collaborative editing is not possible this way, and merging conflicts may emerge when multiple people are working on the same files. OwnCloud is the recommended tool that is useful for working with small and few files. However, it is not suitable for synchronizing large (numbers of) files . Download the OwnCloud via this link or in Research Drive, go Settings. Scroll all the way down until you see something like this. Click on \"Desktop app\": Choose the installation location wisely: if you are going to work with large amounts of data, install Owncloud on a hard disk with sufficient storage space. See the Research Drive wiki page for how to configure OwnCloud Use the link eur.data.surfsara.nl to connect with and authorize the share by logging in to your Research Drive account Choose Selective synchronization and select only the folders you need to work on from your local machine. All synced files are stored and synced on your local machine. If your Research Drive storage is really high, you should not sync them all with your PC! Alternatively, choose Virtual file support , which makes sure that only files that are being worked on are downloaded To work with encrypted folders: synchronize the encrypted folder to your PC (somewhere with enough disk space) - this will probably take some time depending on the size of the folder open Cryptomator and select Open vault open the cryptomator masterkey file and fill out the password you should now be able to see your files and work with them. Bonus: your work will be automatically synchronized with Research Drive as long as you work in the synchronized folder Analyzing data from Research Drive There are multiple ways that you can analyze data that are stored on Research Drive: Use OwnCloud and run analyses on data that are stored in Research Drive as if the data were stored on your local PC. Advantage: cloud synchronization Disadvantage: requires sufficient disk space, synchronization may take a long time Use a cloud computing service , such as Jupyter Hub (built into Research Drive) or the LISA cluster Advantage: no local copies needed, fast analysis Disadvantage: mostly meant for large data analysis, may take some getting used to Locally : download the data to your local PC and analyze them there Advantage: no dependencies on your internet connection Disadvantage: not great for a lot of data, no cloud synchronization, requires manual upload to Research Drive afterwards Working with MRI data: recommended method Deface the MRI data before uploading them to Research Drive. If this is not possible (anymore), encrypt the folder that contains the relevant data as high as possible in the hierarchy, so that you only have to decrypt one folder for analyses. When installing Owncloud, choose a location with sufficient disk space (e.g., an external hard disk) Synchronize only the folder(s) that you need on your local PC via OwnCloud: your PC needs to have enough disk memory to save the data, also after processing! Note that syncing may take a while. After synchronizing, if needed, use Cryptomator to decrypt the folder (enter the password) The folder is now shown as a separate directory on your local PC (e.g., \"Z:\"). You can add this directory in SPM or Matlab for your analysis.","title":"How to"},{"location":"data-management/research-drive-how.html#research-drive-protocol","text":"This page contains information on how we deal with data on the SURF Research Drive in the SYNC lab, which can be accessed by logging in here .","title":"Research Drive protocol"},{"location":"data-management/research-drive-how.html#1-folders","text":"A project folder (at the root of the Research Drive) has to be created by the contract administrator (currently: Mark). This means that each project is created under the contract. Information needed includes: the name of the folder (preferably Year_ProjectName, e.g., \"2018_Brainlinks\" the data steward of the project folder the amount of storage needed (can be changed later) Storage : our contract, \u201cESSB Brain and Development\u201d, currently has 16 TB of storage capacity which is divided among the existing project folders. More storage capacity has to be requested with Research Data Management/Jeroen Rombouts (who in turn requests this at SURF) by the contract administrator","title":"1. Folders"},{"location":"data-management/research-drive-how.html#2-requesting-and-providing-access","text":"","title":"2. Requesting and providing access"},{"location":"data-management/research-drive-how.html#requesting-access-users","text":"Request access to a folder with the data steward of the relevant project. You can find who is the data steward for which project in the file \u201c ResearchDrive_overview_Projects_Access_Groups \u201d .","title":"Requesting access (users)"},{"location":"data-management/research-drive-how.html#giving-access-data-stewards","text":"Select the folder and click the sharing icon. In the area that appears, you can add individual users or groups and alternatively, create sharing links (URLs) with non-Research Drive users. Giving access to a folder means providing that selected access for all subfolders below the shared folder too. Preferably never give anyone sharing rights (unless strictly necessary), because that person can give others more rights than they themselves have. This causes you to lose overview of who can access the data.","title":"Giving access (data stewards)"},{"location":"data-management/research-drive-how.html#custom-groups","text":"Give Research Drive users access via personal access groups (Settings > Custom groups). Members in such a group all receive the same rights when added to a folder. The group owner is the only one who can add and remove access group members and see the members of the group. However, all access group names are visible for the entire Research Drive, so aptly name the groups according to the following format: SYNC_ProjectName_Accesslevel , e.g., \"SYNC_Brainlinks_edit\". If a new person needs access, they can be added to the relevant access group and automatically gains access to the same files/folders as the other group members. After you have made any changes in access groups, please update the access document .","title":"Custom groups"},{"location":"data-management/research-drive-how.html#individual-users","text":"Add users to folders individually if they should not have the same permissions as the custom groups. This could for example be outside researchers . Please note that users of SURFdrive do not necessarily need a new Research Drive account, you can simply share the relevant files/folders with their Federated cloud ID .","title":"Individual users"},{"location":"data-management/research-drive-how.html#students-and-other-outside-users","text":"Generally, when sharing data with students or outsiders, stick to the following guidelines: Share data only when the sharing serves a scientific goal Keep the amount of data shared to a minimum Pseudonymize or, if possible, anonymize data before sharing Students should sign a non-disclosure agreement and stick to the working guidelines for students Give students and outside users access via access links that allow Download/View/Upload (edit only if it concerns a separate (Students) folder). Make use of the password and end-date functions: students and outside users should not have access to the data after their internships / the agreement have/has ended and the password will give extra protection against non-authorized use. Alternatively, you could add the external users or students as new Research Drive users (Dashboard > Add user), although for large amounts of users, this is not recommended.","title":"Students and other outside users"},{"location":"data-management/research-drive-how.html#3-uploading-data-to-research-drive","text":"","title":"3. Uploading data to Research Drive"},{"location":"data-management/research-drive-how.html#rclone","text":"Rclone is a command-line tool to upload data to any location. It can run easily in the background and is relatively simple.","title":"Rclone"},{"location":"data-management/research-drive-how.html#setting-up","text":"Download rclone: https://rclone.org/downloads/ (no admin rights needed) Once downloaded, open your Command prompt (search for cmd ) Your command prompt shows a drive which it uses, e.g., P:/ . Move the unwrapped rclone files (at least rclone.exe ) to that drive. In your command prompt, type rclone config In your Research Drive account, create a new WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cRclone\u201d. Copy the password that was just created to a temporary file/your clipboard. Follow the instructions listed here . Use the following link for step 3: https://eur.data.surfsara.nl/remote.php/nonshib-webdav/ . Your username is yourERNAid@eur.nl and the password is the WebDAV password you just created (you will probably not see the password being pasted, but it is!). When you are asked for a bearer token , just press Enter Example summary of the config:","title":"Setting up"},{"location":"data-management/research-drive-how.html#uploading-data-with-rclone","text":"Upload files from your command window (type cmd in your search bar if you don't know where it is), using the following general format: rclone copy [flags] \"source\" RD:\"destination\"","title":"Uploading data with Rclone"},{"location":"data-management/research-drive-how.html#example-command","text":"rclone copy -v -P --ignore-existing \u201cJ:\\ResearchData\\FSW\\Brain and Development - Projects\\2018_Brainlinks\\Ouderstudie\\\u201d RD:\u201c2018_Brainlinks (Projectfolder)/Brainlinks_Parentstudy\u201d copy the source contents to the destination folder print progress continuously ( -v and -P ) skip already existing files ( --ignore-existing )","title":"Example command"},{"location":"data-management/research-drive-how.html#other-rclone-commands","text":"List the files in the specified folder: rclone ls RD:\u201c2016_Zelfbeeld (Projectfolder)/Zelfbeeld_Data/Zelfbeeld_ProcessedData/\u201d Show configuration details: rclone config show Edit the configuration details: rclone config e","title":"Other Rclone commands"},{"location":"data-management/research-drive-how.html#more-information-on-rclone","text":"On the Research Drive wiki Flags to add to commands","title":"More information on Rclone"},{"location":"data-management/research-drive-how.html#rclone-alternative-cyberduck","text":"Cyberduck is a graphical user interface for uploading data. It also has built-in encryption software (cryptomator), which allows simultaneously encrypting and uploading data.","title":"Rclone alternative: Cyberduck"},{"location":"data-management/research-drive-how.html#setting-up_1","text":"Download and install Cyberduck (the program is free, select $0 of donation) - you do need admin access for this tool. Read the wiki page of SURF Research Drive and from there download the Research Drive cyberduck profile . Save the Research Drive cyberduck profile in the \u201cProfiles\u201d folder within your Cyberduck program files folder (e.g., \u201cC:\\Program Files\\Cyberduck\\profiles\u201d) In your Research Drive account, create a WebDAV password: Settings > Security > WebDAV passwords. Create a new password by filling in a name, e.g., \u201cCyberduck\u201d. Copy the password that was just created to a temporary file/your clipboard. (Re)Start Cyberduck Click \u201cNew connection\u201d (Nieuwe verbinding). In the pop-up window that appears, select the SURF Research Drive profile, change the server to eur.data.surfsara.nl and fill out your username (ERNA ID) and the WebDAV password that you just created. Do not forget to select \u201c Save password \u201d (Bewaar wachtwoord)! Then, select \u201cConnect\u201d (Verbind). After having made the connection, you should be able to navigate your (project) folders like in Research Drive. Every time Cyberduck restarts, this connection will be made, except when you explicitly disconnect (then you have to re-connect again).","title":"Setting up"},{"location":"data-management/research-drive-how.html#uploading-data-in-cyberduck","text":"Turn on Checksum (Edit > Preferences > Wachtrij (Queue) > Checksum) Select a folder in which you want to upload files Click Upload and select the files to be uploaded. Once your files have been uploaded, they should appear in your Research Drive!","title":"Uploading data in Cyberduck"},{"location":"data-management/research-drive-how.html#4-uploading-sensitive-data-to-research-drive","text":"","title":"4. Uploading sensitive data to Research Drive"},{"location":"data-management/research-drive-how.html#encryption","text":"Research Drive cannot contain special types of personal data (bijzondere persoonsgegevens). These comprise of: Race, ethnicity, political views, religion Sexual life/preferences Genetic or biometric data with the purpose of unique identification Health information, among which medical data (MRI data!) Criminal past Therefore, the following data need to be encrypted before uploading to Research Drive: Participant databases with contact information - using a password on Microsoft Office documents is sufficient MRI checklist information files (\u201cBijzonderhedenbestand\u201d) containing information about past surgeries and other health information - using a password on Microsoft Office documents is sufficient Files containing demographic data, responses about race, political views, religion, sexual life, criminal past and other potential health information. This type of file can be stored on Research Drive without encryption only when they are pseudonymized / not directly traceable to individuals. Raw, non-defaced MRI images In general, it is best to avoid having to use encryption , because passwords can be lost and software can deprecate. Data are much more durable if they can instead be anonymized or pseudonymized. For example, upload defaced MRI data and anonymized/pseudonymized health information to avoid having to encrypt them.","title":"Encryption"},{"location":"data-management/research-drive-how.html#how-to-encrypt","text":"Keep it local : don't upload them to Research Drive. If this means you may lose data, don't do this Passwords : put a password on a Microsoft Office document (e.g., Excel, Word) and keep the password at a safe location . If you lose the password, the data is not accessible anymore Use encryption software : we use Cryptomator to encrypt non-defaced MRI data.","title":"How to encrypt?"},{"location":"data-management/research-drive-how.html#using-cryptomator-to-encrypt-data","text":"Use Cryptomator when you want to encrypt folders containing multiple sensitive files before uploading that folder to Research Drive: Download and install the most recent version of Cryptomator Create an encrypted folder (vault) . Be sure to create both a password and a recovery key that can be used in case the password gets lost. Save both at a safe location ! If you lose them, you cannot access the data anymore. Open (decrypt) the vault . In Cryptomator, select a Vault and click \"Open vault\". Select the cryptomator masterkey file. You will be prompted to fill out the password and afterwards, the folder will open. Note that you need the Cryptomator software to see the files in a normal way. In your file explorer, you will probably only see nonsense files in a folder called 'd'. Work with vault contents : after opening a vault, the decrypted files will appear in a separate path on your computer (e.g., \"Z://\"). You can simply copy the path to tools (Matlab, R) or open files from here to work with them. After usage, remember to lock the vault again. Uploading encrypted folders with rclone works the same way as uploading regular folders!","title":"Using Cryptomator to encrypt data"},{"location":"data-management/research-drive-how.html#encryption-within-cyberduck","text":"Cyberduck has in-built functionality to encrypt files using Cryptomator : In Cyberduck, select the folder in which you want to create the encrypted folder Right click and select \u201cNew locked vault\u201d (Nieuwe versleutelde safe) Give the vault a name (remember to put the Project name in there, e.g., \u201cBrainlinks_Neural_data_raw\u201d) and a password. Store the password somewhere safe immediately. You can now upload folders into this encrypted folder as with normal folders. In the background, Cyberduck will decrypt your encrypted folders automatically (because it knows the password), which is why it looks no different than a normal folder in the Cyberduck environment.","title":"Encryption within Cyberduck"},{"location":"data-management/research-drive-how.html#saving-and-sharing-passwords","text":"A few safe options are: Lastpass stores your passwords in a vault in the cloud (behind 1 master password). It can also store secure notes (such as Cryptomator recovery keys) and allows sharing passwords with others (premium version: Network center > Share item). Network drive : make sure that the drive is secured, backed up and only accessible to those who are allowed to decrypt the data Locally : keep in mind that when your PC is hacked, hackers may have access to the passwords and when your PC / drive crashes, the passwords may be lost. You can share passwords via SURFfilesender : per password and recovery key, create a txt file. Send it/them via SURF filesender and make sure a password is required to download the file(s). Send the password to download to files to the receiver(s) via another way (e.g., text or slack message).","title":"Saving and sharing passwords"},{"location":"data-management/research-drive-how.html#5-working-with-data-editing-and-analysis","text":"","title":"5. Working with data: editing and analysis"},{"location":"data-management/research-drive-how.html#editing-documents","text":"The easiest way to edit documents is in your internet browser, because it allows collaborative editing (with OnlyOffice) and changes are automatically saved. If you mount Research Drive to your file explorer and then edit the document at the same time someone else does, there can be merging conflicts and the version with the last edit \u201cwins\u201d.","title":"Editing documents"},{"location":"data-management/research-drive-how.html#mounting-research-drive-to-your-file-explorer","text":"You can mount your Research Drive account to your file explorer, so that the Research Drive files can be accessed on your local PC. Note that collaborative editing is not possible this way, and merging conflicts may emerge when multiple people are working on the same files. OwnCloud is the recommended tool that is useful for working with small and few files. However, it is not suitable for synchronizing large (numbers of) files . Download the OwnCloud via this link or in Research Drive, go Settings. Scroll all the way down until you see something like this. Click on \"Desktop app\": Choose the installation location wisely: if you are going to work with large amounts of data, install Owncloud on a hard disk with sufficient storage space. See the Research Drive wiki page for how to configure OwnCloud Use the link eur.data.surfsara.nl to connect with and authorize the share by logging in to your Research Drive account Choose Selective synchronization and select only the folders you need to work on from your local machine. All synced files are stored and synced on your local machine. If your Research Drive storage is really high, you should not sync them all with your PC! Alternatively, choose Virtual file support , which makes sure that only files that are being worked on are downloaded To work with encrypted folders: synchronize the encrypted folder to your PC (somewhere with enough disk space) - this will probably take some time depending on the size of the folder open Cryptomator and select Open vault open the cryptomator masterkey file and fill out the password you should now be able to see your files and work with them. Bonus: your work will be automatically synchronized with Research Drive as long as you work in the synchronized folder","title":"Mounting Research Drive to your file explorer"},{"location":"data-management/research-drive-how.html#analyzing-data-from-research-drive","text":"There are multiple ways that you can analyze data that are stored on Research Drive: Use OwnCloud and run analyses on data that are stored in Research Drive as if the data were stored on your local PC. Advantage: cloud synchronization Disadvantage: requires sufficient disk space, synchronization may take a long time Use a cloud computing service , such as Jupyter Hub (built into Research Drive) or the LISA cluster Advantage: no local copies needed, fast analysis Disadvantage: mostly meant for large data analysis, may take some getting used to Locally : download the data to your local PC and analyze them there Advantage: no dependencies on your internet connection Disadvantage: not great for a lot of data, no cloud synchronization, requires manual upload to Research Drive afterwards","title":"Analyzing data from Research Drive"},{"location":"data-management/research-drive-how.html#working-with-mri-data-recommended-method","text":"Deface the MRI data before uploading them to Research Drive. If this is not possible (anymore), encrypt the folder that contains the relevant data as high as possible in the hierarchy, so that you only have to decrypt one folder for analyses. When installing Owncloud, choose a location with sufficient disk space (e.g., an external hard disk) Synchronize only the folder(s) that you need on your local PC via OwnCloud: your PC needs to have enough disk memory to save the data, also after processing! Note that syncing may take a while. After synchronizing, if needed, use Cryptomator to decrypt the folder (enter the password) The folder is now shown as a separate directory on your local PC (e.g., \"Z:\"). You can add this directory in SPM or Matlab for your analysis.","title":"Working with MRI data: recommended method"},{"location":"data-management/research-drive-info.html","text":"SURF Research Drive: General information Note: this is a general introduction to Research Drive. For more specific instructions, to go the next chapter or visit the official Research Drive wiki . What is SURF Research Drive? Research Drive is a cloud environment offered by SURF and used by Erasmus University Rotterdam to store research data during the active research phase. It is not meant for long-term archiving, data publishing or data analysis! You can compare it to Google Drive, but for teams: files are stored in the cloud , can be shared and collaboratively edited data are stored in the Netherlands (backed-up weekly) there is no personal storage , only team storage (storage per project) - data will always be part of a project and remain accessible, even when users leave or permissions are changed there are several integrations with other applications and environments (LISA cluster, Jupyter Hub, HPC cloud, OwnCloud, OnlyOffice, etc.). Roles Each project gets a storage quotum and a data steward. The data steward gives rights to end-users. If permitted, end-users can upload data in the folders they received rights to. Roles within Research Drive: Site administrator : manager of the entire (EUR-)instance > usually someone from SURF Dashboard administrator : manages all contracts, can add new contracts and assigns contract administrators > someone from EUR Contract administrator/owner : manages a contract that has a specific amount of storage available to divide over the projects under the contract: can create project folders including a storage quotum and data steward does not automatically have access to the created projects Data steward : responsible for specific project(s): gives rights to members or groups by permissions on folder level can invite new members can always see all project contents Member : normal end-user, anyone can add new members by sending invitations (but data stewards have to give them permissions) Sharing files and folders Data stewards can give the following permissions to users: Read-only : keep in mind that copying data to a local machine is always possible Write : Create: create and add new items and rename existing folders Change: upload and replace existing items in the folder Delete: delete existingg items in the folder Share : re-share the item or a child item. If users have this right, they can set (perhaps broader) permissions for other users. This is not recommended, since you can quickly lose overview of who has access to which data. This way, the data steward remains in control of the data. Folders and/or files can be shared in the following ways: Existing users (search for the email address or user name) A custom group (Settings > Custom groups): you can add users to a custom group and then set permissions for the entire group. All users in that group then have the same permissions. Others cannot see who is in the group, but Anyone in Research Drive can find a group name, so make a well-defined, distinguishing group name, e.g., \"SYNC_Brainlinks_edit\" A SURFdrive user : if someone uses SURFdrive via their institution, they don't need a new Research Drive account. You can simply share the files or folders with their federated cloud ID , click here for how to do this. A new user : Dashboard > New user. The new user can choose 2 types of accounts: Organization: recommended if the user has a SURFconext account (e.g., because they work at a different university) Local: if the user does not have a SURFconext account, choose this option After the user has an account, the data stewards needs to add the new user to the relevant folder(s). Non users: public link : everyone with a link gets the specified permissions. You can set an expiry date, password and permissions. A great option here is the drop file function : partners can put files in the project folder but cannot see or download the contents of the other partners (\u201cwrite-only\u201d). Important notes on sharing Permissions are inherited from parent folders, unless they are specifically changed (e.g., during re-sharing): giving access to a folder means giving access to all subfolders as well! A user with whom subfolders are shared cannot see the parent folders. Make understandable folder names if you plan to share subfolders (e.g., not \"Students\", but \"Brainlinks_Students\") Specific subfolder permissions overrule higher-level permissions A share name can be renamed individually . This is invisible to original sharer (data steward). However, the contents of the folder remain the same and visible to all. Interface When you log into Research Drive, you will automatically see an overview of Projects / folders of which you are data steward or member. These files can be accessed in the Menu in the upper left corner, as are Applications and the Dashboard: Dashboard The Dashboard is accessible to anyone, but only relevant data will be shown. Users can only invite new users. Under \"User accounts\", click \"Invite user\". For each user, you can view their Project membership, contract details, storage overview and service overview (available apps can be added here too). After account removal, the account will exist for 30 days. Data stewards can invite new users and additionally have an overview of project folders and user accounts Contract administrators can invite new users and have overview of project folders, but they can also add new and edit existing project folders and see the contract details. Settings Settings can be found on the top right of the screen: The most important settings are: General : here, you can find your account details, federated cloud ID and links to external apps Security : contains, among others, your saved encryption keys (if any) and Webdav passwords Custom groups : here, you can make new custom groups and add users to them. Note that every EUR instance Research Drive user can find this group, so aptly name them! Version control New versions of single files are automatically stored: any version older than two weeks will be removed When you are working on the same file simultaneously and not in your internet browser, the last saved file will be the version stored. However, you can restore previous versions via the Versions tab. Deleted files are moved to the trash bin . They will be removed after 30 days and can be restored from here during this period. Retention periods can be configured. To prevent syncing issues, make a copy first or work on local files (only copy what you need) Further reading Official Research Drive wiki Slides for end-users Hands-on exercises for end-users Slides for data stewards Link for logging on in the demo environment","title":"About Research Drive"},{"location":"data-management/research-drive-info.html#surf-research-drive-general-information","text":"Note: this is a general introduction to Research Drive. For more specific instructions, to go the next chapter or visit the official Research Drive wiki .","title":"SURF Research Drive: General information"},{"location":"data-management/research-drive-info.html#what-is-surf-research-drive","text":"Research Drive is a cloud environment offered by SURF and used by Erasmus University Rotterdam to store research data during the active research phase. It is not meant for long-term archiving, data publishing or data analysis! You can compare it to Google Drive, but for teams: files are stored in the cloud , can be shared and collaboratively edited data are stored in the Netherlands (backed-up weekly) there is no personal storage , only team storage (storage per project) - data will always be part of a project and remain accessible, even when users leave or permissions are changed there are several integrations with other applications and environments (LISA cluster, Jupyter Hub, HPC cloud, OwnCloud, OnlyOffice, etc.).","title":"What is SURF Research Drive?"},{"location":"data-management/research-drive-info.html#roles","text":"Each project gets a storage quotum and a data steward. The data steward gives rights to end-users. If permitted, end-users can upload data in the folders they received rights to. Roles within Research Drive: Site administrator : manager of the entire (EUR-)instance > usually someone from SURF Dashboard administrator : manages all contracts, can add new contracts and assigns contract administrators > someone from EUR Contract administrator/owner : manages a contract that has a specific amount of storage available to divide over the projects under the contract: can create project folders including a storage quotum and data steward does not automatically have access to the created projects Data steward : responsible for specific project(s): gives rights to members or groups by permissions on folder level can invite new members can always see all project contents Member : normal end-user, anyone can add new members by sending invitations (but data stewards have to give them permissions)","title":"Roles"},{"location":"data-management/research-drive-info.html#sharing-files-and-folders","text":"Data stewards can give the following permissions to users: Read-only : keep in mind that copying data to a local machine is always possible Write : Create: create and add new items and rename existing folders Change: upload and replace existing items in the folder Delete: delete existingg items in the folder Share : re-share the item or a child item. If users have this right, they can set (perhaps broader) permissions for other users. This is not recommended, since you can quickly lose overview of who has access to which data. This way, the data steward remains in control of the data. Folders and/or files can be shared in the following ways: Existing users (search for the email address or user name) A custom group (Settings > Custom groups): you can add users to a custom group and then set permissions for the entire group. All users in that group then have the same permissions. Others cannot see who is in the group, but Anyone in Research Drive can find a group name, so make a well-defined, distinguishing group name, e.g., \"SYNC_Brainlinks_edit\" A SURFdrive user : if someone uses SURFdrive via their institution, they don't need a new Research Drive account. You can simply share the files or folders with their federated cloud ID , click here for how to do this. A new user : Dashboard > New user. The new user can choose 2 types of accounts: Organization: recommended if the user has a SURFconext account (e.g., because they work at a different university) Local: if the user does not have a SURFconext account, choose this option After the user has an account, the data stewards needs to add the new user to the relevant folder(s). Non users: public link : everyone with a link gets the specified permissions. You can set an expiry date, password and permissions. A great option here is the drop file function : partners can put files in the project folder but cannot see or download the contents of the other partners (\u201cwrite-only\u201d). Important notes on sharing Permissions are inherited from parent folders, unless they are specifically changed (e.g., during re-sharing): giving access to a folder means giving access to all subfolders as well! A user with whom subfolders are shared cannot see the parent folders. Make understandable folder names if you plan to share subfolders (e.g., not \"Students\", but \"Brainlinks_Students\") Specific subfolder permissions overrule higher-level permissions A share name can be renamed individually . This is invisible to original sharer (data steward). However, the contents of the folder remain the same and visible to all.","title":"Sharing files and folders"},{"location":"data-management/research-drive-info.html#interface","text":"When you log into Research Drive, you will automatically see an overview of Projects / folders of which you are data steward or member. These files can be accessed in the Menu in the upper left corner, as are Applications and the Dashboard:","title":"Interface"},{"location":"data-management/research-drive-info.html#dashboard","text":"The Dashboard is accessible to anyone, but only relevant data will be shown. Users can only invite new users. Under \"User accounts\", click \"Invite user\". For each user, you can view their Project membership, contract details, storage overview and service overview (available apps can be added here too). After account removal, the account will exist for 30 days. Data stewards can invite new users and additionally have an overview of project folders and user accounts Contract administrators can invite new users and have overview of project folders, but they can also add new and edit existing project folders and see the contract details.","title":"Dashboard"},{"location":"data-management/research-drive-info.html#settings","text":"Settings can be found on the top right of the screen: The most important settings are: General : here, you can find your account details, federated cloud ID and links to external apps Security : contains, among others, your saved encryption keys (if any) and Webdav passwords Custom groups : here, you can make new custom groups and add users to them. Note that every EUR instance Research Drive user can find this group, so aptly name them!","title":"Settings"},{"location":"data-management/research-drive-info.html#version-control","text":"New versions of single files are automatically stored: any version older than two weeks will be removed When you are working on the same file simultaneously and not in your internet browser, the last saved file will be the version stored. However, you can restore previous versions via the Versions tab. Deleted files are moved to the trash bin . They will be removed after 30 days and can be restored from here during this period. Retention periods can be configured. To prevent syncing issues, make a copy first or work on local files (only copy what you need)","title":"Version control"},{"location":"data-management/research-drive-info.html#further-reading","text":"Official Research Drive wiki Slides for end-users Hands-on exercises for end-users Slides for data stewards Link for logging on in the demo environment","title":"Further reading"},{"location":"data-management/types-metadata.html","text":"Metadata Metadata is data about data : in a broad sense, metadata is all the information that you provide about your project, dataset, variables, code, etc. Read some nice examples here . Providing metadata is incredibly important, since metadata makes data: Findable Readable Interpretable Manageable Without metadata, a lot of data are just numbers that cannot be interpreted. Example research metadata A project readme containing the information below. Often in a readme.txt . Find an example template here or use the information below: Creator (PI): name and affiliation of PI Title : project title Funding sources : names of funders, incl. grant numbers and related acknowledgements Data collector/producer : who is responsible for data collection + date and location of data production Description : project description, incl. relevant publications Sample and sampling procedures : target population and methods to sample it (or link to document describing this), retention rates for longitudinal studies Coverage : topics, time period and location covered Source : if relevant, citations to original source from which data were obtained Metadata for a specific data file, containing, for example, file description, data format, relationship with other files, date of creation and versioning information, etc. This can be a readme.txt or other filetypes, such as nameofdatafile.json or nameofdatafile.xml A codebook (data dictionary), which specifies what all variables in your dataset mean. See the codebook chapter for more information. Question wording or meaning Variable text : question text or item number Respondent : who was asked the question? Meaning of codes : interpretation of the codes assigned to each variable Missing data codes , e.g., 999 Summary statistics for both valid and missing cases Imputation and editing : identify data that have been estimated or extensively edited Constructed and weight variables : how were they constructed Location in the data file : field or column location, if relevant Variable groupings : if you categorize variables into conceptual groupings Metadata in systems, such as a data repository. This type of metadata is often enforced and interoperable so that you don't have to manually create this type of metadata. Interoperable metadata Metadata standards Metadata standards are frameworks for metadata fields. They describe how metadata fields should be formatted, so that they will become machine-readable and therefore interoperable. An enormous amount of metadata standards is available which all differ per discipline , but the best known metadata standards for the social sciences are: Dublin Core: this is a set of basic elements to describe a wide range of networked resources, among which Title, Creator, Subject, Description, Publisher, Contributor, Date, Type, Format, Identifier, etc. (see readme information above). Note this Dublin Core metadata file generator to see the elements. Data Documentation Initiative (DDI) is a standard often used in the social, behavioral, economic, and health sciences. It knows several sub-profiles that are based on DDI, but may be more extensive. One of those is CESSDA (Consortium of European Social Science Data Archives). CESSDA's metadata model can be found here As an individual researcher, you are often not directly confronted with these standards. It is just good to know that different repositories can use different standards. See more standards here . Controlled vocabularies Where metadata standards tell us what to call the metadata fields, controlled vocabularies come in handy when we have to fill in those fields. Using controlled vocabularies enables machines to identify identical values, instead of everyone using a different term for the same thing. Whereas some fields have very extensive controlled vocabularies, psychology does not have many. A few links: Controlled vocabularies from the DDI CESSDA vocabularies (large overlap with DDI) ELSST \u2013 European Language Social Science Thesaurus","title":"Types of metadata"},{"location":"data-management/types-metadata.html#metadata","text":"Metadata is data about data : in a broad sense, metadata is all the information that you provide about your project, dataset, variables, code, etc. Read some nice examples here . Providing metadata is incredibly important, since metadata makes data: Findable Readable Interpretable Manageable Without metadata, a lot of data are just numbers that cannot be interpreted.","title":"Metadata"},{"location":"data-management/types-metadata.html#example-research-metadata","text":"A project readme containing the information below. Often in a readme.txt . Find an example template here or use the information below: Creator (PI): name and affiliation of PI Title : project title Funding sources : names of funders, incl. grant numbers and related acknowledgements Data collector/producer : who is responsible for data collection + date and location of data production Description : project description, incl. relevant publications Sample and sampling procedures : target population and methods to sample it (or link to document describing this), retention rates for longitudinal studies Coverage : topics, time period and location covered Source : if relevant, citations to original source from which data were obtained Metadata for a specific data file, containing, for example, file description, data format, relationship with other files, date of creation and versioning information, etc. This can be a readme.txt or other filetypes, such as nameofdatafile.json or nameofdatafile.xml A codebook (data dictionary), which specifies what all variables in your dataset mean. See the codebook chapter for more information. Question wording or meaning Variable text : question text or item number Respondent : who was asked the question? Meaning of codes : interpretation of the codes assigned to each variable Missing data codes , e.g., 999 Summary statistics for both valid and missing cases Imputation and editing : identify data that have been estimated or extensively edited Constructed and weight variables : how were they constructed Location in the data file : field or column location, if relevant Variable groupings : if you categorize variables into conceptual groupings Metadata in systems, such as a data repository. This type of metadata is often enforced and interoperable so that you don't have to manually create this type of metadata.","title":"Example research metadata"},{"location":"data-management/types-metadata.html#interoperable-metadata","text":"","title":"Interoperable metadata"},{"location":"data-management/types-metadata.html#metadata-standards","text":"Metadata standards are frameworks for metadata fields. They describe how metadata fields should be formatted, so that they will become machine-readable and therefore interoperable. An enormous amount of metadata standards is available which all differ per discipline , but the best known metadata standards for the social sciences are: Dublin Core: this is a set of basic elements to describe a wide range of networked resources, among which Title, Creator, Subject, Description, Publisher, Contributor, Date, Type, Format, Identifier, etc. (see readme information above). Note this Dublin Core metadata file generator to see the elements. Data Documentation Initiative (DDI) is a standard often used in the social, behavioral, economic, and health sciences. It knows several sub-profiles that are based on DDI, but may be more extensive. One of those is CESSDA (Consortium of European Social Science Data Archives). CESSDA's metadata model can be found here As an individual researcher, you are often not directly confronted with these standards. It is just good to know that different repositories can use different standards. See more standards here .","title":"Metadata standards"},{"location":"data-management/types-metadata.html#controlled-vocabularies","text":"Where metadata standards tell us what to call the metadata fields, controlled vocabularies come in handy when we have to fill in those fields. Using controlled vocabularies enables machines to identify identical values, instead of everyone using a different term for the same thing. Whereas some fields have very extensive controlled vocabularies, psychology does not have many. A few links: Controlled vocabularies from the DDI CESSDA vocabularies (large overlap with DDI) ELSST \u2013 European Language Social Science Thesaurus","title":"Controlled vocabularies"},{"location":"data-management/vc-datalad.html","text":"Version control for datasets Version control for data is equally important for being able to reproduce results as it is for software/code and other documentation. Git, however, is very bad at handling (a) large (amount of) data files, because every version of every file is stored in the repository. So how can we formally version our data? git-annex git-annex is a command line-based version control system that can manage all file content is a separate directory in the repository called the annex ( .git/annex/objects ). Only the files names and some metadata are placed into git version control. When you push a git repository with an annex to Github, the annex is not uploaded, but can be stored in a web-hosting service. Thus, a copy (clone) of the github repository only contains the version histories and not the data files themselves. Any file content can be downloaded from the external storage with git-annex get . DataLad DataLad is a great version control system for datasets, independently of its size. It is based on git and git-annex and is relatively simple to use. It also has many more functionalities and a great and comprehensive handbook . Note that DataLad is a command line tool , so some previous experience with command line git is advantageous. If you want to learn how to use DataLad, please go to the handbook via http://handbook.datalad.org/en/latest/ If you find anything unclear about the handbook or want to contribute, head over to the handbook repository on github and open an issue or a pull request (see their how to contribute )","title":"Version control for data"},{"location":"data-management/vc-datalad.html#version-control-for-datasets","text":"Version control for data is equally important for being able to reproduce results as it is for software/code and other documentation. Git, however, is very bad at handling (a) large (amount of) data files, because every version of every file is stored in the repository. So how can we formally version our data?","title":"Version control for datasets"},{"location":"data-management/vc-datalad.html#git-annex","text":"git-annex is a command line-based version control system that can manage all file content is a separate directory in the repository called the annex ( .git/annex/objects ). Only the files names and some metadata are placed into git version control. When you push a git repository with an annex to Github, the annex is not uploaded, but can be stored in a web-hosting service. Thus, a copy (clone) of the github repository only contains the version histories and not the data files themselves. Any file content can be downloaded from the external storage with git-annex get .","title":"git-annex"},{"location":"data-management/vc-datalad.html#datalad","text":"DataLad is a great version control system for datasets, independently of its size. It is based on git and git-annex and is relatively simple to use. It also has many more functionalities and a great and comprehensive handbook . Note that DataLad is a command line tool , so some previous experience with command line git is advantageous. If you want to learn how to use DataLad, please go to the handbook via http://handbook.datalad.org/en/latest/ If you find anything unclear about the handbook or want to contribute, head over to the handbook repository on github and open an issue or a pull request (see their how to contribute )","title":"DataLad"},{"location":"data-management/vc-github.html","text":"Version control with git(hub) What are git & github? Git is a version control system: it tracks the history as you change files. More specifically, it tracks who made which changes and when. It allows reverting files to a previous state. Note that it is possible to work on git projects locally without ever using github. Github is a platform that you can use to collaborate on projects that use git. It additionally allows for threaded discussions (issues), pull requests (see below) and several great apps. Please note that there are also other platforms that work similarly, such as GitLab , BitBucket or SourceFourge . Git and Github are most suitable for working with relatively small files . While originally used for code/software, you can use it for other types of small files as well, such as documentation. Why should I use git(hub)? Git is used a lot all over the globe and is free to download and use via several interfaces You will always be able to revert your errors - or those of someone else You can report which version of the files you have used for which publication. Even better, github allows exporting a snapshot (version) of your github repository (folder with files) to Zenodo, meaning you can publish your version used and give it a citable DOI. Github also has several other great functions, such as making a website out of your repository (such as this lab wiki!) Installation Create a Github account Install git locally . If you don't want to use the command line, also download a GUI such as RStudio or GitKraken ) Note: if you want to work with git in the command line on Windows, I can highly recommend using the Ubuntu app (the Linux Subsystem for Windows, downloadable via the Windows store), which may cause fewer Windows-related errors. The git workflow When working on a git project (within a folder called a git repository ), you will always perform the following steps: Make changes to some file and save them like you normally would Stage the changes: select which files you want to make a snapshot of (this step is most explicit if you work in the command line) Commit the changes: make a snapshot of the changes made so far. A commit (snapshot) is always accompanied by a commit message explaining what changes were made Any commit gets a specific identifier that can be used to reverse (undo) the commit. Some stage- and commit-related commands Check which files are changed but not yet staged or committed: git status Stage a file (tip: use the tab to use autocompletion): git add filename Stage multiple files: git add filename1 filename2 filename3 Stage all unstaged files in the workspace: git add -A . Commit the change(s) you staged: git commit -m \"Change x and y to z\" Commit all (staged and unstaged) change(s) made in the workspace: git commit -a -m \"Change x and y and z\" Branches A git repository can exist in multiple \u201cversions\u201d which are called branches . There is always a \u201cmaster\u201d branch, which you should consider the clean branch. Besides that, you can create other branches that are meant to make your own changes, or try something different without dirtying the clean (master) version. After you have made changes in your own branch and you think they should be incorporated in the master branch, you can then merge your branch with the master branch. Some branch-related commands Check which branch you are working on now (and list which branches there are): git branch -v Change branches: git checkout branchname Create a new branch: git checkout -b newbranchname Workflow on github On Github, the workflow is a bit more extensive, because often you are collaborating and do not want others to just start editing the master branch right away. There are multiple methods to collaborate on a project, but we recommend the following, assuming that there is already a repository for the project and you want to contribute: On the repository page on Github, fork the repository: this creates a copy of the repository on your own Github account that you have full access to. In your forked (copied) Github repository, create a new branch for the changes you are about to make with a short but comprehensible name, e.g. \u201cdorienchanges\u201d. If you want to edit files locally, clone your repository to your local PC, creating a folder in your file explorer (the contents of which can change according to which branch you are on!). Via the command line: git clone https://github.com/UserName/RepositoryName.git Via Rstudio, see this link Edit the files you want to edit and commit the changes (making a snapshot; include a comprehensible commit message!) You have now committed changes locally, but they are not yet visible in your remote repository, i.e., the online github repository on your account. In order to get the commits you made locally to be visible online, you need to push them to your remote repository on Github. Via the command line, note that the repository on your account is usually called \"origin\": git push origin branchnameonwhichyouworked Via Rstudio, see this link Now the changes are visible in your own account, but not in the main repository. In order to get your changes into the main repository, you need to do a pull request on Github. This is a request to the owners of the original repository to merge your branch with (one of) theirs . Once merged by the owners, you are often prompted to remove your own branch (which is not necessary if you are planning to make more changes later). Keeping your local copy (clone) up to date If you are working on a project with many collaborators making changes, the odds are that your own fork (online copy) and/or clone (local copy) are becoming out-of-date quite fast. Therefore, it is recommended to update those copies each time before you start making changes yourself, so you are working on the most recent versions of the files. In your clone (offline), you can set up the owner\u2019s repository as the \"upstream\" repository and then pull all commits from the upstream repository to your local PC Setting up the original repository as the upstream: git remote add upstream https://github.com/ownername/repositoryname.git Pulling changes from the upstream repository: git pull upstream branchname See this page when you use RStudio To update your online version of the repository, simply push the changes (e.g., push origin master after pulling from the upstream Resources For every piece of software, remember that google is your best friend . Or use one of the following other resources: Also a very comprehensive git guide by The Turing Way More info on the Git workflow (especially useful if you are going to use git via the command line) Github guide: git handbook (duration ca. 1 hour) Using Git(hub) with Rstudio: https://happygitwithr.com/ Introduction on Github by Ana Martinovici Git terminology: https://git-scm.com/docs/gitglossary More terminology: https://the-turing-way.netlify.app/reproducible-research/vcs/vcs-resources.html#definitions-glossary If you want to use Gitlab instead, here are the materials of a comprehensive course (ironically, on GitHub)","title":"Git(hub)"},{"location":"data-management/vc-github.html#version-control-with-github","text":"","title":"Version control with git(hub)"},{"location":"data-management/vc-github.html#what-are-git-github","text":"Git is a version control system: it tracks the history as you change files. More specifically, it tracks who made which changes and when. It allows reverting files to a previous state. Note that it is possible to work on git projects locally without ever using github. Github is a platform that you can use to collaborate on projects that use git. It additionally allows for threaded discussions (issues), pull requests (see below) and several great apps. Please note that there are also other platforms that work similarly, such as GitLab , BitBucket or SourceFourge . Git and Github are most suitable for working with relatively small files . While originally used for code/software, you can use it for other types of small files as well, such as documentation.","title":"What are git &amp; github?"},{"location":"data-management/vc-github.html#why-should-i-use-github","text":"Git is used a lot all over the globe and is free to download and use via several interfaces You will always be able to revert your errors - or those of someone else You can report which version of the files you have used for which publication. Even better, github allows exporting a snapshot (version) of your github repository (folder with files) to Zenodo, meaning you can publish your version used and give it a citable DOI. Github also has several other great functions, such as making a website out of your repository (such as this lab wiki!)","title":"Why should I use git(hub)?"},{"location":"data-management/vc-github.html#installation","text":"Create a Github account Install git locally . If you don't want to use the command line, also download a GUI such as RStudio or GitKraken ) Note: if you want to work with git in the command line on Windows, I can highly recommend using the Ubuntu app (the Linux Subsystem for Windows, downloadable via the Windows store), which may cause fewer Windows-related errors.","title":"Installation"},{"location":"data-management/vc-github.html#the-git-workflow","text":"When working on a git project (within a folder called a git repository ), you will always perform the following steps: Make changes to some file and save them like you normally would Stage the changes: select which files you want to make a snapshot of (this step is most explicit if you work in the command line) Commit the changes: make a snapshot of the changes made so far. A commit (snapshot) is always accompanied by a commit message explaining what changes were made Any commit gets a specific identifier that can be used to reverse (undo) the commit.","title":"The git workflow"},{"location":"data-management/vc-github.html#some-stage-and-commit-related-commands","text":"Check which files are changed but not yet staged or committed: git status Stage a file (tip: use the tab to use autocompletion): git add filename Stage multiple files: git add filename1 filename2 filename3 Stage all unstaged files in the workspace: git add -A . Commit the change(s) you staged: git commit -m \"Change x and y to z\" Commit all (staged and unstaged) change(s) made in the workspace: git commit -a -m \"Change x and y and z\"","title":"Some stage- and commit-related commands"},{"location":"data-management/vc-github.html#branches","text":"A git repository can exist in multiple \u201cversions\u201d which are called branches . There is always a \u201cmaster\u201d branch, which you should consider the clean branch. Besides that, you can create other branches that are meant to make your own changes, or try something different without dirtying the clean (master) version. After you have made changes in your own branch and you think they should be incorporated in the master branch, you can then merge your branch with the master branch.","title":"Branches"},{"location":"data-management/vc-github.html#some-branch-related-commands","text":"Check which branch you are working on now (and list which branches there are): git branch -v Change branches: git checkout branchname Create a new branch: git checkout -b newbranchname","title":"Some branch-related commands"},{"location":"data-management/vc-github.html#workflow-on-github","text":"On Github, the workflow is a bit more extensive, because often you are collaborating and do not want others to just start editing the master branch right away. There are multiple methods to collaborate on a project, but we recommend the following, assuming that there is already a repository for the project and you want to contribute: On the repository page on Github, fork the repository: this creates a copy of the repository on your own Github account that you have full access to. In your forked (copied) Github repository, create a new branch for the changes you are about to make with a short but comprehensible name, e.g. \u201cdorienchanges\u201d. If you want to edit files locally, clone your repository to your local PC, creating a folder in your file explorer (the contents of which can change according to which branch you are on!). Via the command line: git clone https://github.com/UserName/RepositoryName.git Via Rstudio, see this link Edit the files you want to edit and commit the changes (making a snapshot; include a comprehensible commit message!) You have now committed changes locally, but they are not yet visible in your remote repository, i.e., the online github repository on your account. In order to get the commits you made locally to be visible online, you need to push them to your remote repository on Github. Via the command line, note that the repository on your account is usually called \"origin\": git push origin branchnameonwhichyouworked Via Rstudio, see this link Now the changes are visible in your own account, but not in the main repository. In order to get your changes into the main repository, you need to do a pull request on Github. This is a request to the owners of the original repository to merge your branch with (one of) theirs . Once merged by the owners, you are often prompted to remove your own branch (which is not necessary if you are planning to make more changes later).","title":"Workflow on github"},{"location":"data-management/vc-github.html#keeping-your-local-copy-clone-up-to-date","text":"If you are working on a project with many collaborators making changes, the odds are that your own fork (online copy) and/or clone (local copy) are becoming out-of-date quite fast. Therefore, it is recommended to update those copies each time before you start making changes yourself, so you are working on the most recent versions of the files. In your clone (offline), you can set up the owner\u2019s repository as the \"upstream\" repository and then pull all commits from the upstream repository to your local PC Setting up the original repository as the upstream: git remote add upstream https://github.com/ownername/repositoryname.git Pulling changes from the upstream repository: git pull upstream branchname See this page when you use RStudio To update your online version of the repository, simply push the changes (e.g., push origin master after pulling from the upstream","title":"Keeping your local copy (clone) up to date"},{"location":"data-management/vc-github.html#resources","text":"For every piece of software, remember that google is your best friend . Or use one of the following other resources: Also a very comprehensive git guide by The Turing Way More info on the Git workflow (especially useful if you are going to use git via the command line) Github guide: git handbook (duration ca. 1 hour) Using Git(hub) with Rstudio: https://happygitwithr.com/ Introduction on Github by Ana Martinovici Git terminology: https://git-scm.com/docs/gitglossary More terminology: https://the-turing-way.netlify.app/reproducible-research/vcs/vcs-resources.html#definitions-glossary If you want to use Gitlab instead, here are the materials of a comprehensive course (ironically, on GitHub)","title":"Resources"},{"location":"data-management/vc-principles.html","text":"Version control principles Version control is a way to track changes made to a file, creating a history of the file that can be reviewed. It is important to keep different versions separated in almost all situations , because: it keeps different stages of processing/editing separated it allows you to go back to previous versions if something went wrong ideally, it allows tracking who did what and when it prevents a lot of confusion in this type of situation: There are both informal and formal ways of using version control for your files. However, for both ways, the typical procedure is as follows: Do something with a file (create, edit, remove) Save the file Register the change by making a snapshot of the file status, i.e., a version In informal ways, step 2 and 3 cannot always be separated. Sometimes, step 3 is even left out completely, which, from personal experience, I cannot recommend you to do! \u200b In formal version control systems, however, step 3 is a necessary step that cannot be skipped, forcing you to create a new version each time you make a change. Informal ways to use version control It is highly recommended to make a habit out of at least one, but preferably more of the following practices if you do not (want to) use a formal version control system: Keep raw data separately from any processed data and document which steps have been taken to go from the former to the latter Rename a file every time you make a sizable change Use dates in the filename in the format YYYYMMDD Append the filename with a version number, e.g., document_v1.0, document_v1.2, etc. See this link for a helper document for coming up with a good file naming convention Include a versioning history within the document, e.g., on the first page, explaining what changed in which version Use services like Google drive and Dropbox, which allow collaborative editing but also reverting to previous versions Formal version control systems There are also formal version control systems, such as: Git Mercurial SVN These actually need to be installed and worked with while or after you are editing files. They require some knowledge and skills of the systems, but they also reward you with perfect file histories and reverting possibilities. The next chapter will go into the most often used version control system: git.","title":"Basic principles"},{"location":"data-management/vc-principles.html#version-control-principles","text":"Version control is a way to track changes made to a file, creating a history of the file that can be reviewed. It is important to keep different versions separated in almost all situations , because: it keeps different stages of processing/editing separated it allows you to go back to previous versions if something went wrong ideally, it allows tracking who did what and when it prevents a lot of confusion in this type of situation: There are both informal and formal ways of using version control for your files. However, for both ways, the typical procedure is as follows: Do something with a file (create, edit, remove) Save the file Register the change by making a snapshot of the file status, i.e., a version In informal ways, step 2 and 3 cannot always be separated. Sometimes, step 3 is even left out completely, which, from personal experience, I cannot recommend you to do! \u200b In formal version control systems, however, step 3 is a necessary step that cannot be skipped, forcing you to create a new version each time you make a change.","title":"Version control principles"},{"location":"data-management/vc-principles.html#informal-ways-to-use-version-control","text":"It is highly recommended to make a habit out of at least one, but preferably more of the following practices if you do not (want to) use a formal version control system: Keep raw data separately from any processed data and document which steps have been taken to go from the former to the latter Rename a file every time you make a sizable change Use dates in the filename in the format YYYYMMDD Append the filename with a version number, e.g., document_v1.0, document_v1.2, etc. See this link for a helper document for coming up with a good file naming convention Include a versioning history within the document, e.g., on the first page, explaining what changed in which version Use services like Google drive and Dropbox, which allow collaborative editing but also reverting to previous versions","title":"Informal ways to use version control"},{"location":"data-management/vc-principles.html#formal-version-control-systems","text":"There are also formal version control systems, such as: Git Mercurial SVN These actually need to be installed and worked with while or after you are editing files. They require some knowledge and skills of the systems, but they also reward you with perfect file histories and reverting possibilities. The next chapter will go into the most often used version control system: git.","title":"Formal version control systems"},{"location":"getting-started-eur/email-signature.html","text":"The SYNC email signature Setting your email signature in Outlook Log in via Outlook using your ERNA-ID At the top right of your screen, click the \"Settings\" wheel > \u201cView all Outlook settings\u201d In the Settings, choose \"Mail\" > \u201cCompose and reply\u201d In the box under \u201cEmail signature\u201d, make your email signature. Configure when to use the signature: in original emails only or also when replying to emails? Click \"Save\" For EUR-wide email templates, see this link . For the EUR-specific color codes, see this webpage (the ESSB HEX code is #ff9e00). Individual email signature Name, title Function SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: youremailaddress@eur.nl / yoursecondemailaddress@eur.nl W: http://erasmus-synclab.nl/ A: Mandeville building, T13 room x / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands Present: Days present Lab email signature SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: synclab@essb.eur.nl W: http://erasmus-synclab.nl/ T: https://twitter.com/SYNClabEUR A: Mandeville building, T13 / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands","title":"Email signature"},{"location":"getting-started-eur/email-signature.html#the-sync-email-signature","text":"","title":"The SYNC email signature"},{"location":"getting-started-eur/email-signature.html#setting-your-email-signature-in-outlook","text":"Log in via Outlook using your ERNA-ID At the top right of your screen, click the \"Settings\" wheel > \u201cView all Outlook settings\u201d In the Settings, choose \"Mail\" > \u201cCompose and reply\u201d In the box under \u201cEmail signature\u201d, make your email signature. Configure when to use the signature: in original emails only or also when replying to emails? Click \"Save\" For EUR-wide email templates, see this link . For the EUR-specific color codes, see this webpage (the ESSB HEX code is #ff9e00).","title":"Setting your email signature in Outlook"},{"location":"getting-started-eur/email-signature.html#individual-email-signature","text":"Name, title Function SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: youremailaddress@eur.nl / yoursecondemailaddress@eur.nl W: http://erasmus-synclab.nl/ A: Mandeville building, T13 room x / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands Present: Days present","title":"Individual email signature"},{"location":"getting-started-eur/email-signature.html#lab-email-signature","text":"SYNC lab: Society, Youth and Neuroscience Connected Erasmus School of Social and Behavioral Sciences E: synclab@essb.eur.nl W: http://erasmus-synclab.nl/ T: https://twitter.com/SYNClabEUR A: Mandeville building, T13 / Burgemeester Oudlaan 50 / 3062 PA Rotterdam, the Netherlands","title":"Lab email signature"},{"location":"getting-started-eur/finances.html","text":"Finances On this page, you can find practical information on declaring payments for different purposes. The WBS numbers to declare on can be found in this Research Drive document . Paying a large group of participants The procedure for letting large groups of participants get paid is currently as followed: Ask participants to fill in a receipt (kwitantie) with their contact details and signature (see an example format in this Research Drive file ). Keep the receipt in a safe place, because you have to be able to show it in case of checks. Fill in the form \" Form_for_paying_participants_explained.xls \" (on Research Drive) after copying it (only sheet \"1\", remove the example line). Follow the instructions in the document closely. Save the form as .xls \u00e1nd as .pdf with a recognizable name, e.g., \"20200421_Brainlinks_payment_T2.5_corona\". Have the relevant budget keeper (procuratiehouder) sign the pdf (see the Research Drive document for who to ask, usually Eveline is the one to sign) Send both the .xsl and the .pdf to Patricia Engelbrecht ( patricia.engelbrecht@eur.nl ), preferably via SURF filesender , considering there are personal data in the documents. Patricia will feed the .xls form to the system. If something doesn't work, it has been filled in wrongly and she will return the document to you. When it does work, all participants from the file will receive payment simultaneously. Notes: It is undesirable to imburse participants with the money with your own money and then declaring it via the ESS portal. The ESS portal is not suitable for this kind of declaration, since it asks for very specific proof of payment. Depending on the amount of money that needs to be paid, there may be a different budget keeper (e.g., someone for amounts below \u20ac1000, etc.). Paying small numbers of individuals If the amount of participants or other persons to imburse is not that large, there is another way. This is for example the case when reimbursing travel costs or paying student assistants for extra jobs. Copy the form \" Example_payment_natural_persons.xlsx \" or, if the individuals do not work at EUR, the form \" Declaration_form_non-EUR_individuals_NL.pdf \" Fill in the relevant data and correct the Kostenplaats/WBS element Send the form to invoice [dot] fin [at] eur [dot] nl . It will then land in the portal and be processed by finance Invoices If an external company requires payment using an invoice, let them address the invoice to: \u2003Erasmus University Rotterdam \u2003Erasmus School of Social and Behavioural Sciences \u2003PO Box 1738 \u20033000 DR Rotterdam Email the invoice to invoice [dot] fin [at] eur [dot] nl and mention the WBS element or Kostenplaats under which it should fall. Other declarations Travel costs (home-work) EUR automatically gives you travel cost reimbursement via your paycheck based on the distance you have to travel to work (read more here ). However, if you use public transport, there is an extra regulation that can reimburse a higher amount if needed (up to \u20ac250 for full-time employment). You can request this regulation via the ESS portal, read more about it here . Conference and other research-related costs If you have made costs that do not relate to home-work travel costs, invoices or paying participants, such as conference fees or research materials, you can declare them via the ESS portal.","title":"Finances"},{"location":"getting-started-eur/finances.html#finances","text":"On this page, you can find practical information on declaring payments for different purposes. The WBS numbers to declare on can be found in this Research Drive document .","title":"Finances"},{"location":"getting-started-eur/finances.html#paying-a-large-group-of-participants","text":"The procedure for letting large groups of participants get paid is currently as followed: Ask participants to fill in a receipt (kwitantie) with their contact details and signature (see an example format in this Research Drive file ). Keep the receipt in a safe place, because you have to be able to show it in case of checks. Fill in the form \" Form_for_paying_participants_explained.xls \" (on Research Drive) after copying it (only sheet \"1\", remove the example line). Follow the instructions in the document closely. Save the form as .xls \u00e1nd as .pdf with a recognizable name, e.g., \"20200421_Brainlinks_payment_T2.5_corona\". Have the relevant budget keeper (procuratiehouder) sign the pdf (see the Research Drive document for who to ask, usually Eveline is the one to sign) Send both the .xsl and the .pdf to Patricia Engelbrecht ( patricia.engelbrecht@eur.nl ), preferably via SURF filesender , considering there are personal data in the documents. Patricia will feed the .xls form to the system. If something doesn't work, it has been filled in wrongly and she will return the document to you. When it does work, all participants from the file will receive payment simultaneously. Notes: It is undesirable to imburse participants with the money with your own money and then declaring it via the ESS portal. The ESS portal is not suitable for this kind of declaration, since it asks for very specific proof of payment. Depending on the amount of money that needs to be paid, there may be a different budget keeper (e.g., someone for amounts below \u20ac1000, etc.).","title":"Paying a large group of participants"},{"location":"getting-started-eur/finances.html#paying-small-numbers-of-individuals","text":"If the amount of participants or other persons to imburse is not that large, there is another way. This is for example the case when reimbursing travel costs or paying student assistants for extra jobs. Copy the form \" Example_payment_natural_persons.xlsx \" or, if the individuals do not work at EUR, the form \" Declaration_form_non-EUR_individuals_NL.pdf \" Fill in the relevant data and correct the Kostenplaats/WBS element Send the form to invoice [dot] fin [at] eur [dot] nl . It will then land in the portal and be processed by finance","title":"Paying small numbers of individuals"},{"location":"getting-started-eur/finances.html#invoices","text":"If an external company requires payment using an invoice, let them address the invoice to: \u2003Erasmus University Rotterdam \u2003Erasmus School of Social and Behavioural Sciences \u2003PO Box 1738 \u20033000 DR Rotterdam Email the invoice to invoice [dot] fin [at] eur [dot] nl and mention the WBS element or Kostenplaats under which it should fall.","title":"Invoices"},{"location":"getting-started-eur/finances.html#other-declarations","text":"","title":"Other declarations"},{"location":"getting-started-eur/finances.html#travel-costs-home-work","text":"EUR automatically gives you travel cost reimbursement via your paycheck based on the distance you have to travel to work (read more here ). However, if you use public transport, there is an extra regulation that can reimburse a higher amount if needed (up to \u20ac250 for full-time employment). You can request this regulation via the ESS portal, read more about it here .","title":"Travel costs (home-work)"},{"location":"getting-started-eur/finances.html#conference-and-other-research-related-costs","text":"If you have made costs that do not relate to home-work travel costs, invoices or paying participants, such as conference fees or research materials, you can declare them via the ESS portal.","title":"Conference and other research-related costs"},{"location":"getting-started-eur/time-registration.html","text":"Time registration What is time registration? The hours you work for a project should be registered daily or weekly in the correct WBS element (kostenplaats). The hours that you cannot register on a project will go on the general kostenplaats (or on a blank line). For administration purposes it is important that you register your hours before the 4th day of the next month If you are late, you will receive reminders Sick days will be automatically registered by HR National holidays and requested vacation days will also be automatically processed after your request is granted Why time registration? Both for justifying costs for funders and for the EUR's administration, the correct costs should be linked to the correct project, even if you are only working on (being paid by) one project. How? Go to the ESS portal (Employee Self Service) Click \"Tijdschrijven\" (Time registration) Within the Tijdschrijven menu, navigate to the week in which you want to register worked hours. Choose an empty line Fill in the WBS number or Kostenplaats (i.e., the source of your paycheck), check them here (link to Research Drive) Your labor contract (arbeidsovereenkomst) will automatically appear if you only work on one Fill in the hours you worked each day Save your changes If you always work the same hours during the week, you can also make a template : Fill in the week as you would want to save it Save your week as template (Sjabloon /Template > Opslaan als sjabloon/Save as template) To apply the template to a new week, navigate to a new week and click \"Werkvoorraad\" to copy the template from the previous period Important notes You can only navigate 6 weeks in the past. Hours that have not been justified before that time can only be registered by Project Control. The first line in the ESS portal contains your norm hours. You have to justify all those hours to prevent errors If you log on twice or try to open the window twice you will get the error: \"Your personnel number is blocked at the moment\"","title":"Time registration"},{"location":"getting-started-eur/time-registration.html#time-registration","text":"","title":"Time registration"},{"location":"getting-started-eur/time-registration.html#what-is-time-registration","text":"The hours you work for a project should be registered daily or weekly in the correct WBS element (kostenplaats). The hours that you cannot register on a project will go on the general kostenplaats (or on a blank line). For administration purposes it is important that you register your hours before the 4th day of the next month If you are late, you will receive reminders Sick days will be automatically registered by HR National holidays and requested vacation days will also be automatically processed after your request is granted","title":"What is time registration?"},{"location":"getting-started-eur/time-registration.html#why-time-registration","text":"Both for justifying costs for funders and for the EUR's administration, the correct costs should be linked to the correct project, even if you are only working on (being paid by) one project.","title":"Why time registration?"},{"location":"getting-started-eur/time-registration.html#how","text":"Go to the ESS portal (Employee Self Service) Click \"Tijdschrijven\" (Time registration) Within the Tijdschrijven menu, navigate to the week in which you want to register worked hours. Choose an empty line Fill in the WBS number or Kostenplaats (i.e., the source of your paycheck), check them here (link to Research Drive) Your labor contract (arbeidsovereenkomst) will automatically appear if you only work on one Fill in the hours you worked each day Save your changes If you always work the same hours during the week, you can also make a template : Fill in the week as you would want to save it Save your week as template (Sjabloon /Template > Opslaan als sjabloon/Save as template) To apply the template to a new week, navigate to a new week and click \"Werkvoorraad\" to copy the template from the previous period","title":"How?"},{"location":"getting-started-eur/time-registration.html#important-notes","text":"You can only navigate 6 weeks in the past. Hours that have not been justified before that time can only be registered by Project Control. The first line in the ESS portal contains your norm hours. You have to justify all those hours to prevent errors If you log on twice or try to open the window twice you will get the error: \"Your personnel number is blocked at the moment\"","title":"Important notes"},{"location":"getting-started-eur/welcome-eur.html","text":"Welcome at the EUR! On this page, you can find some information to get you started at the EUR. As soon as you get started, make sure to read the new employees page as it contains a lot of practical information to get started! Important EUR portals As soon as your account is activated you can access the following portals using your ERNAid@eur.nl and password . Mailbox and agenda Link: https://outlook.office365.com/mail/inbox You can automatically create Teams meetings in the agenda. Personal IDM Link: https://personal.idm.eur.nl/user/login.jsp Change your personal information (name, contact information, etc.) here. MyEUR Link: https://my.eur.nl/ Employee portal. Change your profile picture here, request an employee pass (for parking and getting into buildings) or search for information and news. You can also make an annual leave agreement here . MyApps Since 2021, the EUR requires that you use their network to browse at least the following platforms: ESS portal : Self service portal, see manuals here . This portal is for, a.o. requesting vacation days, requesting extra travel cost reimbursement (My administration > Extra travel reimbursement), making declarations, time registration , seeing paychecks, performance and development (R&O) interview documents. R&O : Go to \"Mijn R&O\" and create a progress form for the interview. You can already fill in this form before the interview; your changes are immediately visible to your employer. Academic staff have to complement this form with additional documents. After the interview and after you have agreed on the terms, send the form to HR. Read more here . Pure , the system used to register academic output and to change your EUR profile. Please see here for relevant manuals. MyApps - How to To access these platforms, you can use \"MyApps\" . MyApps is a type of Remote desktop connection that can be set up between your PC and the Erasmus University network. If you (used to) work at Leiden University, it is comparable to Citrix in that you can access the network, some software and the files, but you are not working directly on a PC and can only access the files on the server (not on a local disk). Read FAQs about MyApps here . To set up a connection, user this user guide . Within MyApps, it is possible to start a Remote desktop session with a physical PC at the EUR (and thus you'll be able to access a local disk). To do so: Ask IT servicedesk to enable the Remote desktop connection for your PC, because this is currently not automatically enabled for all EUR PCs When enabled, open a MyApps session Within the MyApps session , click the magnifying class at the bottom of your screen to search for \"Remote Desktop Connection\" In the screen that opens, type the complete network name from the PC in the format CL#########.campus.eur.nl. If entered correctly, you can now log in with your ERNA ID and use your local PC from home! ICT Read all about your ICT workplace here . You can install software via the Software Center (locally or via MyApps). There is more software available than is visible here, see this page for more information. If you have a question or request, go to the ICT Self Service Desk or contact the Service desk via servicedesk [at] eur [dot] nl (phone number: +31 010 408 8880) How to print? Go to https://eur.mycampusprint.nl/Login/Login Login and upload your documents Useful contacts Below are some useful support email addresses. See an overview of ESSB support staff here . General procedural questions: office.strategy@essb.eur.nl (managed by Carina Schlosser) Secretariat of the Dean's Office, see this link HR-related questions: see this link Financial questions (project control), see this link ICT: servicedesk@eur.nl or it.servicedesk@eur.nl Research Data Management, open science & privacy Jeroen Rombouts, head RDM University Library Data Management Team: datarepository@eur. nl Research Data Management, see this link Privacy officer ESSB: privacy@essb.eur.nl Open science community Rotterdam: Antonio Schettino Ethical review, see this link Communications department Marjolein Kooistra: media relations and internal communication Britt Boeddha van Dongen: communications advisor of the strategy group ESSB and Vital cities and citizens Kristel Segeren: senior editor ESSB website Ivy van Regteren Altena, communications advisor ESSB","title":"Intro to the EUR!"},{"location":"getting-started-eur/welcome-eur.html#welcome-at-the-eur","text":"On this page, you can find some information to get you started at the EUR. As soon as you get started, make sure to read the new employees page as it contains a lot of practical information to get started!","title":"Welcome at the EUR!"},{"location":"getting-started-eur/welcome-eur.html#important-eur-portals","text":"As soon as your account is activated you can access the following portals using your ERNAid@eur.nl and password .","title":"Important EUR portals"},{"location":"getting-started-eur/welcome-eur.html#mailbox-and-agenda","text":"Link: https://outlook.office365.com/mail/inbox You can automatically create Teams meetings in the agenda.","title":"Mailbox and agenda"},{"location":"getting-started-eur/welcome-eur.html#personal-idm","text":"Link: https://personal.idm.eur.nl/user/login.jsp Change your personal information (name, contact information, etc.) here.","title":"Personal IDM"},{"location":"getting-started-eur/welcome-eur.html#myeur","text":"Link: https://my.eur.nl/ Employee portal. Change your profile picture here, request an employee pass (for parking and getting into buildings) or search for information and news. You can also make an annual leave agreement here .","title":"MyEUR"},{"location":"getting-started-eur/welcome-eur.html#myapps","text":"Since 2021, the EUR requires that you use their network to browse at least the following platforms: ESS portal : Self service portal, see manuals here . This portal is for, a.o. requesting vacation days, requesting extra travel cost reimbursement (My administration > Extra travel reimbursement), making declarations, time registration , seeing paychecks, performance and development (R&O) interview documents. R&O : Go to \"Mijn R&O\" and create a progress form for the interview. You can already fill in this form before the interview; your changes are immediately visible to your employer. Academic staff have to complement this form with additional documents. After the interview and after you have agreed on the terms, send the form to HR. Read more here . Pure , the system used to register academic output and to change your EUR profile. Please see here for relevant manuals.","title":"MyApps"},{"location":"getting-started-eur/welcome-eur.html#myapps-how-to","text":"To access these platforms, you can use \"MyApps\" . MyApps is a type of Remote desktop connection that can be set up between your PC and the Erasmus University network. If you (used to) work at Leiden University, it is comparable to Citrix in that you can access the network, some software and the files, but you are not working directly on a PC and can only access the files on the server (not on a local disk). Read FAQs about MyApps here . To set up a connection, user this user guide . Within MyApps, it is possible to start a Remote desktop session with a physical PC at the EUR (and thus you'll be able to access a local disk). To do so: Ask IT servicedesk to enable the Remote desktop connection for your PC, because this is currently not automatically enabled for all EUR PCs When enabled, open a MyApps session Within the MyApps session , click the magnifying class at the bottom of your screen to search for \"Remote Desktop Connection\" In the screen that opens, type the complete network name from the PC in the format CL#########.campus.eur.nl. If entered correctly, you can now log in with your ERNA ID and use your local PC from home!","title":"MyApps - How to"},{"location":"getting-started-eur/welcome-eur.html#ict","text":"Read all about your ICT workplace here . You can install software via the Software Center (locally or via MyApps). There is more software available than is visible here, see this page for more information. If you have a question or request, go to the ICT Self Service Desk or contact the Service desk via servicedesk [at] eur [dot] nl (phone number: +31 010 408 8880)","title":"ICT"},{"location":"getting-started-eur/welcome-eur.html#how-to-print","text":"Go to https://eur.mycampusprint.nl/Login/Login Login and upload your documents","title":"How to print?"},{"location":"getting-started-eur/welcome-eur.html#useful-contacts","text":"Below are some useful support email addresses. See an overview of ESSB support staff here . General procedural questions: office.strategy@essb.eur.nl (managed by Carina Schlosser) Secretariat of the Dean's Office, see this link HR-related questions: see this link Financial questions (project control), see this link ICT: servicedesk@eur.nl or it.servicedesk@eur.nl Research Data Management, open science & privacy Jeroen Rombouts, head RDM University Library Data Management Team: datarepository@eur. nl Research Data Management, see this link Privacy officer ESSB: privacy@essb.eur.nl Open science community Rotterdam: Antonio Schettino Ethical review, see this link Communications department Marjolein Kooistra: media relations and internal communication Britt Boeddha van Dongen: communications advisor of the strategy group ESSB and Vital cities and citizens Kristel Segeren: senior editor ESSB website Ivy van Regteren Altena, communications advisor ESSB","title":"Useful contacts"},{"location":"open-science/data-sharing-how.html","text":"Sharing research data: how? Sharing data is becoming the golden standard in science. It enables others to reproduce your results and prevent fraud and honest mistakes in data analysis. Moreover, it enables reuse of your data in new analyses, increasing the impact of your work. Short guide: When to share what data? If data are completely anonymous , you can share them publicly in a dedicated repository, see step 1 or 2 If data cannot be completely anonymized, they are personal. You need a legal basis to share these data: Informed consent : what you can do with the data depends on the contents of the consent form. If participants consented to public data sharing and their data are not very sensitive (e.g., not from children or clinical groups), publish them in a repository or datapaper . If participants consented to sharing with restrictions, use a repository that allows access restrictions or use a data use agreement to share data case by case. If participants did not consent to any personal data sharing, share characteristics or aggregated data . Public interest : In theory, most research is publicly funded, and therefore we should be able to use this as legal basis for data sharing. However, it is still unclear when we are allowed to use it. The minimal prerequities are: the personal data sharing should rely on the principles of lawfulness, fairness and transparency informed consent was impossible to obtain, e.g., because the study took place a long time ago and consent cannot be obtained retroactively. Participants not consenting to data sharing is not a valid reason! When sharing personal data using the Public interest basis, you are encouraged to share data with access restrictions, especially if your data are sensitive or highly identifiable (e.g., data from minors or clinical groups, special categories of sensitive data, etc.) If you share data with a similar purpose as the original research project (such as for collaborating with other researchers on a related topic), a data use agreement suffices (not strictly necessary for EUR collaborators as they are from the same institution). Such agreement should lay out the conditions of storing, sharing and publishing the data. This falls under the scope of processing that is \"compatible with the original purpose\", which does not require a new/separate legal basis (GDPR Articles 5(1)(b) , 6(4) and 89(1) ). Ways of sharing data Publishing data can go roughly in the following ways: 1. Publish in a data repository For example (or find one here ): The EUR Data Repository : for publication packages at the EUR All data and materials accompanying a publication Only suitable for anonymous data See the publication packages page for more information. DANS DataverseNL : for publication packages at Leiden University ( instructions ) All data and materials accompanying a publication Not suitable for large or publication-independent datasets (max zip file size 10GB) or non-anonymous data Only accessibly via institutes that use DataverseNL DANS EASY (Dutch) For data and materials, not necessarily accompanying a publication Has deals with the university but still some limitations to the size of the data (max. 100 GB) Is aimed more at archiving than sharing data Also has a dark archive for non-anonymous data 4TU Research data International data repository for science, engineering and design Enables open or restricted access, private links, embargoes or even metadata-only records Up to 1 TB of storage for affiliated researchers, 10 GB for non-affiliated researchers Open Science Framework max 5 GB for private, 50 GB for public projects Choose storage location in EU: Germany Keep your data close to all other relevant files in your OSF project OSF is more aimed at project management than dissemination Other general-purpose repositories, such as: Zenodo (free up to 50GB) Dryad (not free) Non-EUR Figshare (free, max 20GB private space and 5GB per file) In all cases, make your data FAIR and take privacy considerations into account. 2. Publish a datapaper In a datapaper, you describe the data and the methods of collecting them, without the need to analyze them. This will get you a publication out of your data, irrespective of whether or not you publish results. This often requires that you make all described data public, because the aim of such publications is to provide access to high quality datasets and to facilitate reuse. Also, most journals have some policy in which repository you should deposit the data accompanying the datapaper. Note that a datapaper will be peer-reviewed just as well as a regular article. See this link for a list of data journals. 3. Share case-by-case For data that cannot be shared publicly, you can sometimes still share the data case-by-case. This can be the case: For MRI-data for which you have a legal basis to share them, but you may not want to publish publicly because of the sensitivity. For this type of data, you may want to consider using a data use agreement as well For data that has not been published about For data that does not belong to a publication, data that is too large to share in another way or some other reason Please note that this is only a FAIR solution if your metadata and access options are publicly findable and available (e.g., consider creating a metadata-only record in a repository). 4. Share only characteristics of the data If you do not want to or you can't share any real data, you can still make your data valuable: Aggregated data If your data are privacy-sensitive and you cannot share them, you can still share aggregated data, for example: Share first- and second level MRI data in NeuroVault . You can also link this to your manuscript (and the other way around). NeuroVault allowes meta-analyses of fMRI studies, making it worthwhile to share your group MRI data there. See the NeuroVault page for more information Share summary details of your data, such as averages and variation measures. Or make a shinyapp that allows exploring the data without accessing it! Synthetic data Creating a synthetic dataset can be useful to capture the statistical idiosyncrasies of your real dataset. This synthetic dataset can be used to reproduce the results of your analysis, without violating any privacy or intellectual property regulations. Read more: Review of synthetic generation methods synthpop : an R package for creating synthetic data ( paper , blog how to ) For MRI-data, see brainpower . Federated learning Federated learning arises from the field of Artificial Intelligence and relies \u201con the principle of remote execution\u2014that is, distributing copies of a machine learning algorithm to the sites or devices where the data is kept (nodes), performing training iterations locally, and returning the results of the computation (for example, updated neural network weights) to a central repository to update the main algorithm.\u201d ( Kaissis et al., 2020). This means that you do not move your data, while still providing valuable information about it. Some federated learning tools and projects: COINSTAC PySyft ENIGMA consortium: Consortium with several working groups. Share pre- and post-processing analysis scripts, the leading site will conduct meta-analysis OHDSI (Observational Health Data Sciences and Informatics): collaborative to bring out the value of health data through large-scale analytics Personal Health Train , part of Health-RI (official website here ) Licensing data With licenses , you specify what others are permitted to do with your product. You can see it as some kind of agreement: if someone violates the license, you have the right to sue them, just like a regular lawful agreement. For anonymous data, it is recommended to choose a CC0 (public domain) or CC-BY 4.0 license. These open licenses both allow others to use the data without restrictions. For non-anonymous data, use a more restrictive license (but please don't use non-derivate (ND) or non-commercial (NC) licenses, read why here ) or formulate your own terms of use, for example in a data use agreement . Don't know which license to choose? Use a license selector ! Resources Data management and sharing tools (list compiled by the Leiden University Library) The Turing Way - open data Utrecht University information about data sharing FAQ about data sharing (Donders Institute) Decision aid choosing a repository (not exhaustive)","title":"How to"},{"location":"open-science/data-sharing-how.html#sharing-research-data-how","text":"Sharing data is becoming the golden standard in science. It enables others to reproduce your results and prevent fraud and honest mistakes in data analysis. Moreover, it enables reuse of your data in new analyses, increasing the impact of your work.","title":"Sharing research data: how?"},{"location":"open-science/data-sharing-how.html#short-guide-when-to-share-what-data","text":"If data are completely anonymous , you can share them publicly in a dedicated repository, see step 1 or 2 If data cannot be completely anonymized, they are personal. You need a legal basis to share these data: Informed consent : what you can do with the data depends on the contents of the consent form. If participants consented to public data sharing and their data are not very sensitive (e.g., not from children or clinical groups), publish them in a repository or datapaper . If participants consented to sharing with restrictions, use a repository that allows access restrictions or use a data use agreement to share data case by case. If participants did not consent to any personal data sharing, share characteristics or aggregated data . Public interest : In theory, most research is publicly funded, and therefore we should be able to use this as legal basis for data sharing. However, it is still unclear when we are allowed to use it. The minimal prerequities are: the personal data sharing should rely on the principles of lawfulness, fairness and transparency informed consent was impossible to obtain, e.g., because the study took place a long time ago and consent cannot be obtained retroactively. Participants not consenting to data sharing is not a valid reason! When sharing personal data using the Public interest basis, you are encouraged to share data with access restrictions, especially if your data are sensitive or highly identifiable (e.g., data from minors or clinical groups, special categories of sensitive data, etc.) If you share data with a similar purpose as the original research project (such as for collaborating with other researchers on a related topic), a data use agreement suffices (not strictly necessary for EUR collaborators as they are from the same institution). Such agreement should lay out the conditions of storing, sharing and publishing the data. This falls under the scope of processing that is \"compatible with the original purpose\", which does not require a new/separate legal basis (GDPR Articles 5(1)(b) , 6(4) and 89(1) ).","title":"Short guide: When to share what data?"},{"location":"open-science/data-sharing-how.html#ways-of-sharing-data","text":"Publishing data can go roughly in the following ways:","title":"Ways of sharing data"},{"location":"open-science/data-sharing-how.html#licensing-data","text":"With licenses , you specify what others are permitted to do with your product. You can see it as some kind of agreement: if someone violates the license, you have the right to sue them, just like a regular lawful agreement. For anonymous data, it is recommended to choose a CC0 (public domain) or CC-BY 4.0 license. These open licenses both allow others to use the data without restrictions. For non-anonymous data, use a more restrictive license (but please don't use non-derivate (ND) or non-commercial (NC) licenses, read why here ) or formulate your own terms of use, for example in a data use agreement . Don't know which license to choose? Use a license selector !","title":"Licensing data"},{"location":"open-science/data-sharing-how.html#resources","text":"Data management and sharing tools (list compiled by the Leiden University Library) The Turing Way - open data Utrecht University information about data sharing FAQ about data sharing (Donders Institute) Decision aid choosing a repository (not exhaustive)","title":"Resources"},{"location":"open-science/dsa-template.html","text":"Data sharing agreement What is a data sharing agreement? A data sharing agreement can be set up between the owner of research data and someone with whom data is shared or who will process the data further. Important components of a data sharing agreement are who remains responsible in which role (i.e., are parties joint controllers or independent controllers of the data), about the use of the data (e.g., for scientific purposes) the use of intellectual property (if any) and confidentiality (incl. privacy-sensitive data). Using a data sharing agreement to share data leaves a high degree of control for the data owner concerning who has access to the data. However, please note that doing so can slow the process of data sharing tremendously . Therefore, always first consider whether there are more efficient ways of data sharing, e.g., using a data repository that has access restriction options. When to use a data sharing agreement? When the data cannot be made entirely anonymous and you want to take additional measures to protect data subjects' privacy AND When the data subject has given explicit informed consent to share their personal data with such parties OR you have confirmation that you can use a different legal basis to share personal data When data can be made entirely anonymous, you do not strictly need a data sharing agreement, except when the data include intellectual property rights. When the participant has NOT given explicit informed consent to share their personal data (non-anonymous data) and there is no other legal basis you can use, you are not allowed to share the data at all . Try to find a way to anonymize the data or do not share the data at all. How to make use of a data sharing agreement? Consider whether you really need to use an agreement to share your data. For example, it is not necessary when the data is not confidential, the data does not include intellectual property rights, or if you want to share with researchers within the same university. If you do need an agreement, try to make use of a template. The template that we can use at EUR (if sharing EUR-data) can be downloaded here . Another possible template from the Open Brain Consent can be found here . Edit the template to fit your specific situation. Send the agreement to erslegal [at] eur [dot] nl. They will check the agreement. If you use one of their templates, this process should go relatively quickly. Send the agreement to the other party to discuss with their legal department. Once both parties are good with the agreement, have someone with legal permission to decide sign the agreement. At EUR, this is usually the faculty dean (if the agreement spans less than 4 years, otherwise it is College van Bestuur). To do this, send the agreement to the dean's secretariat (office [dot] dean [at] essb [dot] eur [dot] nl). Send the signed version to legal services , so that the agreement is registered in the system. Important notes If the agreement is signed by someone without legal jurisdiction to sign, you won't have the law on your side in case of breach of agreement terms! When using the EUR template, please note: 5b. Data Receiver shall, to safeguard any potential intellectual property right and protect confidential information (if any), send papers intended for publication to EUR at least fourteen working days prior to submission. This article was added by legal services to be able to check for violations of intellectual property and privacy issues before a manuscript using \"our\" data is published. This checking is basically our (the researcher's) responsibility. However, if you need legal support with this, ask legal services for help!","title":"Data sharing agreement"},{"location":"open-science/dsa-template.html#data-sharing-agreement","text":"","title":"Data sharing agreement"},{"location":"open-science/dsa-template.html#what-is-a-data-sharing-agreement","text":"A data sharing agreement can be set up between the owner of research data and someone with whom data is shared or who will process the data further. Important components of a data sharing agreement are who remains responsible in which role (i.e., are parties joint controllers or independent controllers of the data), about the use of the data (e.g., for scientific purposes) the use of intellectual property (if any) and confidentiality (incl. privacy-sensitive data). Using a data sharing agreement to share data leaves a high degree of control for the data owner concerning who has access to the data. However, please note that doing so can slow the process of data sharing tremendously . Therefore, always first consider whether there are more efficient ways of data sharing, e.g., using a data repository that has access restriction options.","title":"What is a data sharing agreement?"},{"location":"open-science/dsa-template.html#when-to-use-a-data-sharing-agreement","text":"When the data cannot be made entirely anonymous and you want to take additional measures to protect data subjects' privacy AND When the data subject has given explicit informed consent to share their personal data with such parties OR you have confirmation that you can use a different legal basis to share personal data When data can be made entirely anonymous, you do not strictly need a data sharing agreement, except when the data include intellectual property rights. When the participant has NOT given explicit informed consent to share their personal data (non-anonymous data) and there is no other legal basis you can use, you are not allowed to share the data at all . Try to find a way to anonymize the data or do not share the data at all.","title":"When to use a data sharing agreement?"},{"location":"open-science/dsa-template.html#how-to-make-use-of-a-data-sharing-agreement","text":"Consider whether you really need to use an agreement to share your data. For example, it is not necessary when the data is not confidential, the data does not include intellectual property rights, or if you want to share with researchers within the same university. If you do need an agreement, try to make use of a template. The template that we can use at EUR (if sharing EUR-data) can be downloaded here . Another possible template from the Open Brain Consent can be found here . Edit the template to fit your specific situation. Send the agreement to erslegal [at] eur [dot] nl. They will check the agreement. If you use one of their templates, this process should go relatively quickly. Send the agreement to the other party to discuss with their legal department. Once both parties are good with the agreement, have someone with legal permission to decide sign the agreement. At EUR, this is usually the faculty dean (if the agreement spans less than 4 years, otherwise it is College van Bestuur). To do this, send the agreement to the dean's secretariat (office [dot] dean [at] essb [dot] eur [dot] nl). Send the signed version to legal services , so that the agreement is registered in the system.","title":"How to make use of a data sharing agreement?"},{"location":"open-science/dsa-template.html#important-notes","text":"If the agreement is signed by someone without legal jurisdiction to sign, you won't have the law on your side in case of breach of agreement terms! When using the EUR template, please note: 5b. Data Receiver shall, to safeguard any potential intellectual property right and protect confidential information (if any), send papers intended for publication to EUR at least fourteen working days prior to submission. This article was added by legal services to be able to check for violations of intellectual property and privacy issues before a manuscript using \"our\" data is published. This checking is basically our (the researcher's) responsibility. However, if you need legal support with this, ask legal services for help!","title":"Important notes"},{"location":"open-science/fair-software.html","text":"Open source software Let's say I have an experiment or analysis code that I want to share with the world. However, I want to get acknowledgement and I don't want to reply to all separate emails asking for it. How to go about it? Create an open source project! See also: FAIR software recommendations Open source guide 1. Create a github repository If you want to know more about how github works, check out the github chapter . Choose a recognizable name (check for projects with a similar name!) Choose a public repository Initialize a readme.md file 2. Include information files Write a comprehensive readme file : what does the repository contain? What is the background? Can people contribute and how can people use your software? (You can also write one online ) Write contributing guidelines , if you are open to people contributing Write a code of conduct Choose a license for your project (see Github docs ): this is important, because it specifies how people can use your software. MIT , Apache 2.0 , and GPLv3 are the most popular open source licenses, but there are other options . You can use this license selector as well. Read about all these steps on this website . 3. Fill up the repository with your software use consistent code conventions and clear function/method/variable names comment your code! remove sensitive materials in the revision history, issues, or pull requests use logical file names and structure check whether your software is of sufficient quality 4. Make your software citable Despite you having specified how people can reuse your software (through the license), your software is not yet citable using a persistent identifier. Unfortunately, Github does not offer the possibility to create a persistent identifier for a repository directly. However , it is possible to make a release (a snapshot of the repository at a certain point in time) on Github that you can then publish on Zenodo , which will create a DOI. Click here to see how to do this. 5. Register your software in a community registry This allows others to easily find and reuse your software or code. Find a registry here .","title":"FAIR software"},{"location":"open-science/fair-software.html#open-source-software","text":"Let's say I have an experiment or analysis code that I want to share with the world. However, I want to get acknowledgement and I don't want to reply to all separate emails asking for it. How to go about it? Create an open source project! See also: FAIR software recommendations Open source guide","title":"Open source software"},{"location":"open-science/fair-software.html#1-create-a-github-repository","text":"If you want to know more about how github works, check out the github chapter . Choose a recognizable name (check for projects with a similar name!) Choose a public repository Initialize a readme.md file","title":"1. Create a github repository"},{"location":"open-science/fair-software.html#2-include-information-files","text":"Write a comprehensive readme file : what does the repository contain? What is the background? Can people contribute and how can people use your software? (You can also write one online ) Write contributing guidelines , if you are open to people contributing Write a code of conduct Choose a license for your project (see Github docs ): this is important, because it specifies how people can use your software. MIT , Apache 2.0 , and GPLv3 are the most popular open source licenses, but there are other options . You can use this license selector as well. Read about all these steps on this website .","title":"2. Include information files"},{"location":"open-science/fair-software.html#3-fill-up-the-repository-with-your-software","text":"use consistent code conventions and clear function/method/variable names comment your code! remove sensitive materials in the revision history, issues, or pull requests use logical file names and structure check whether your software is of sufficient quality","title":"3. Fill up the repository with your software"},{"location":"open-science/fair-software.html#4-make-your-software-citable","text":"Despite you having specified how people can reuse your software (through the license), your software is not yet citable using a persistent identifier. Unfortunately, Github does not offer the possibility to create a persistent identifier for a repository directly. However , it is possible to make a release (a snapshot of the repository at a certain point in time) on Github that you can then publish on Zenodo , which will create a DOI. Click here to see how to do this.","title":"4. Make your software citable"},{"location":"open-science/fair-software.html#5-register-your-software-in-a-community-registry","text":"This allows others to easily find and reuse your software or code. Find a registry here .","title":"5. Register your software in a community registry"},{"location":"open-science/gdpr.html","text":"When can I share my data and with whom? Whether you can share your research data with others depends on: 1. The anonymity of your data 2. Who owns your data 3. The infrastructure available to share the data In this chapter, we will go into nr. 1 and talk about the EU privacy law: the General Data Protection Regulation. The GDPR Since May 2018, the General Data Protection Regulation (Dutch: Algemene Verordening Gegevensbescherming [AVG]) has been in place to better protect personal data. The most important aspects of the GDPR are: Privacy by Design : build privacy-increasing measures into your study design Privacy by Default : make sure your default settings already improve your participants' privacy Data minimization : Only collect and use personal data necessary for your research goal Legal basis : Make sure there is a legal basis (6 possible) to process (and share) the personal data you collect (e.g., informed consent or public interest more info (Dutch) DPIA : Conduct a Data Protection Impact Assessment whenever you collect (highly) sensitive data, such as names, addresses, race or health data. Inform participants about the goal of the personal data collection and which data you collect. What is personal data? Data is personal when you can identify someone by it, either directly (e.g., name, address) or indirectly (e.g., height, job, income, education). Indirect indicators are personal data if they can identify someone: when it concerns an extreme case (e.g., someone 2.20m tall) when combining data so that they can only be applicable to one person (NB. this can also concern publicly available data) when re-identification is still possible (e.g., with a name-number key conversion file) By law, data is considered identifiable when identification can occur with reasonable (proportionate) effort. Thus, it is not about the hypothetical possibility that data can be linked or combined. Because not everyone has access to the same data, the definition of \"identifiable\" may differ per situation. Important types of data Pseudonymous data: Data that is only identifiable with a key (that still exists). This is the case when after encryption, it is still possible to identify someone, e.g., because the key or the source data still exist. Pseudonymous data are still considered personal data , because the encryption is reversible , thus requiring a legal basis for processing. Special personal data: special sensitive categories of personal data that may be difficult to anonymize, they require additional measures: race of ethnic descent political views religion union membership genetic or biometric data aimed at unique identification health data sexual life and preference criminal records in the Netherlands: burgerservicenummer (BSN) Anonymous data: Data that are not (re)identifiable anymore: neiher by a name-number key, nor by combining with other publicly available data. Anonymous data are not considered personal data , so processing and sharing this kind of data do not require a legal basis. Sharing data under the GDPR Anonymous data can be shared without restriction if they are really anonymous. You may share non-anonymous data only when: You have attained explicit informed consent from the participant to do so (most used legal basis). For special personal data, this consent should be very explicit (\"I agree to share x, y and z\" with A, B and C): there cannot be any doubt about this. See some example sentences and a GDPR version of the Open Brain Consent initiative You reduce the amount of personal data shared to a minimum (data minimization principle) You take the necessary measures to protect your participants' privacy Always write a Data Management Plan (DMP) and Data Protection Impact Assessment (DPIA) before starting a project with personal data When sharing data with researchers outside of the EU, Norway, Liechtenstein and Iceland (no GDPR present), make sure that country has an adequacy decision . If the country does not have one, you need to take extra protection measures, such as standard contractual clauses or agreements. In case your data are not anonymous, but you have attained consent and still want to protect your participants' privacy better, you may always use a data sharing agreement . This document contains what users can and cannot do with your data, for how long and if you will get credit if the user publishes about your data. A good example is the agreement used by the Donders repository . The Open Brain Consent initiative is also working on a template agreement , or find an example template in the template chapter . Anonymizing data General tips Remove identifiers (name, address) Replace identifiers (e.g., date of birth by age or age groups) Use pseudonyms (e.g., participant numbers) Randomize the pseudonyms (participant numbers) Use only the middle range of the data: extreme cases may lead to identification because by definition, there are only few of them Remove the name-participant number key Plan how to anonymize the data up front and keep a log of your procedures Store original data in a safe location Determine whether different measures combined could lead to identification. If needed, consult a privacy officer. Deidentifying MRI-data There is some debate as to whether or not MRI data can be anonymized. One paper, for example, found that brain morphology, although preprocessed, was personally identifiable ( Takao, Hayashi, & Ohtomo, 2015 ). Moreover, it could be argued that, when combining multiple databases, the data may be identifiable in that way as well. Therefore, we do not speak of anonymizing MRI-data, but deidentifying it: MRI-data will always remain pseudonymous at best and therefore require a legal basis before sharing. Anonymize the filenames: replace names with codes Remove the header information (when using hdr and img files, not for nifti files) Deface the MRI-scans if your software does not do that automatically already. We recommend using pydeface . If you are uncertain whether your data are anonymous, please don't hesitate to contact a privacy officer. Have a look at this MRI data sharing guide for more info! GDPR resources Open Brain Consent initiative , a bottom-up initiative to make sense of the GDPR in sharing MRI data A great overview of the GDPR and its practical implications (by Enrico Glerean, 2020) Course about privacy in research Privacy dos and donts Guide for sensitive data UU guides for handling personal data and informed consent Legal instruments protecting data (agreements) Erasmus University contacts Privacy office ESSB: privacy [at] essb [dot] eur [dot] nl, or see this page Legal counsel: see this page Research support, e.g., data stewards: see this page IT-related questions: it [dot] servicedesk [at] eur [dot] nl See all support staff here","title":"The GDPR"},{"location":"open-science/gdpr.html#when-can-i-share-my-data-and-with-whom","text":"Whether you can share your research data with others depends on: 1. The anonymity of your data 2. Who owns your data 3. The infrastructure available to share the data In this chapter, we will go into nr. 1 and talk about the EU privacy law: the General Data Protection Regulation.","title":"When can I share my data and with whom?"},{"location":"open-science/gdpr.html#the-gdpr","text":"Since May 2018, the General Data Protection Regulation (Dutch: Algemene Verordening Gegevensbescherming [AVG]) has been in place to better protect personal data. The most important aspects of the GDPR are: Privacy by Design : build privacy-increasing measures into your study design Privacy by Default : make sure your default settings already improve your participants' privacy Data minimization : Only collect and use personal data necessary for your research goal Legal basis : Make sure there is a legal basis (6 possible) to process (and share) the personal data you collect (e.g., informed consent or public interest more info (Dutch) DPIA : Conduct a Data Protection Impact Assessment whenever you collect (highly) sensitive data, such as names, addresses, race or health data. Inform participants about the goal of the personal data collection and which data you collect.","title":"The GDPR"},{"location":"open-science/gdpr.html#what-is-personal-data","text":"Data is personal when you can identify someone by it, either directly (e.g., name, address) or indirectly (e.g., height, job, income, education). Indirect indicators are personal data if they can identify someone: when it concerns an extreme case (e.g., someone 2.20m tall) when combining data so that they can only be applicable to one person (NB. this can also concern publicly available data) when re-identification is still possible (e.g., with a name-number key conversion file) By law, data is considered identifiable when identification can occur with reasonable (proportionate) effort. Thus, it is not about the hypothetical possibility that data can be linked or combined. Because not everyone has access to the same data, the definition of \"identifiable\" may differ per situation.","title":"What is personal data?"},{"location":"open-science/gdpr.html#important-types-of-data","text":"Pseudonymous data: Data that is only identifiable with a key (that still exists). This is the case when after encryption, it is still possible to identify someone, e.g., because the key or the source data still exist. Pseudonymous data are still considered personal data , because the encryption is reversible , thus requiring a legal basis for processing. Special personal data: special sensitive categories of personal data that may be difficult to anonymize, they require additional measures: race of ethnic descent political views religion union membership genetic or biometric data aimed at unique identification health data sexual life and preference criminal records in the Netherlands: burgerservicenummer (BSN) Anonymous data: Data that are not (re)identifiable anymore: neiher by a name-number key, nor by combining with other publicly available data. Anonymous data are not considered personal data , so processing and sharing this kind of data do not require a legal basis.","title":"Important types of data"},{"location":"open-science/gdpr.html#sharing-data-under-the-gdpr","text":"Anonymous data can be shared without restriction if they are really anonymous. You may share non-anonymous data only when: You have attained explicit informed consent from the participant to do so (most used legal basis). For special personal data, this consent should be very explicit (\"I agree to share x, y and z\" with A, B and C): there cannot be any doubt about this. See some example sentences and a GDPR version of the Open Brain Consent initiative You reduce the amount of personal data shared to a minimum (data minimization principle) You take the necessary measures to protect your participants' privacy Always write a Data Management Plan (DMP) and Data Protection Impact Assessment (DPIA) before starting a project with personal data When sharing data with researchers outside of the EU, Norway, Liechtenstein and Iceland (no GDPR present), make sure that country has an adequacy decision . If the country does not have one, you need to take extra protection measures, such as standard contractual clauses or agreements. In case your data are not anonymous, but you have attained consent and still want to protect your participants' privacy better, you may always use a data sharing agreement . This document contains what users can and cannot do with your data, for how long and if you will get credit if the user publishes about your data. A good example is the agreement used by the Donders repository . The Open Brain Consent initiative is also working on a template agreement , or find an example template in the template chapter .","title":"Sharing data under the GDPR"},{"location":"open-science/gdpr.html#anonymizing-data","text":"","title":"Anonymizing data"},{"location":"open-science/gdpr.html#general-tips","text":"Remove identifiers (name, address) Replace identifiers (e.g., date of birth by age or age groups) Use pseudonyms (e.g., participant numbers) Randomize the pseudonyms (participant numbers) Use only the middle range of the data: extreme cases may lead to identification because by definition, there are only few of them Remove the name-participant number key Plan how to anonymize the data up front and keep a log of your procedures Store original data in a safe location Determine whether different measures combined could lead to identification. If needed, consult a privacy officer.","title":"General tips"},{"location":"open-science/gdpr.html#deidentifying-mri-data","text":"There is some debate as to whether or not MRI data can be anonymized. One paper, for example, found that brain morphology, although preprocessed, was personally identifiable ( Takao, Hayashi, & Ohtomo, 2015 ). Moreover, it could be argued that, when combining multiple databases, the data may be identifiable in that way as well. Therefore, we do not speak of anonymizing MRI-data, but deidentifying it: MRI-data will always remain pseudonymous at best and therefore require a legal basis before sharing. Anonymize the filenames: replace names with codes Remove the header information (when using hdr and img files, not for nifti files) Deface the MRI-scans if your software does not do that automatically already. We recommend using pydeface . If you are uncertain whether your data are anonymous, please don't hesitate to contact a privacy officer. Have a look at this MRI data sharing guide for more info!","title":"Deidentifying MRI-data"},{"location":"open-science/gdpr.html#gdpr-resources","text":"Open Brain Consent initiative , a bottom-up initiative to make sense of the GDPR in sharing MRI data A great overview of the GDPR and its practical implications (by Enrico Glerean, 2020) Course about privacy in research Privacy dos and donts Guide for sensitive data UU guides for handling personal data and informed consent Legal instruments protecting data (agreements)","title":"GDPR resources"},{"location":"open-science/gdpr.html#erasmus-university-contacts","text":"Privacy office ESSB: privacy [at] essb [dot] eur [dot] nl, or see this page Legal counsel: see this page Research support, e.g., data stewards: see this page IT-related questions: it [dot] servicedesk [at] eur [dot] nl See all support staff here","title":"Erasmus University contacts"},{"location":"open-science/inclusivity-goals.html","text":"Diversity & inclusion at the SYNC lab: SMART goals (SMART: specific, measurable, achievable, realistic, timely) Open Science also means inclusive science, and we formulated the following goals in July 2020 (Descriptions copied from our blogpost ): 1. Inclusive research samples Description Although we have made some steps to diversify our research samples, we recognize that we still have much to learn and improve. Therefore, we want to make an effort to make our samples more diverse and representative of the whole population. For this purpose, we will (1) pay more attention to two facets of diversity, namely educational background and ethnic-cultural background and (2) align our recruitment strategy accordingly \u2013 choosing the appropriate channels to reach a diverse group of participants and minimize issues that may prevent certain sub-groups from participating. SMART goals For each future research project, we aim to have at least 30% of our participants who go to a vmbo-school or mbo-school (depending on the targeted age group). In the Netherlands almost 50% of the secondary school students are enrolled in a vmbo education . For each future research project, we aim to have at least 20% of our participants who have a migration background or who identify with other ethnicities besides Dutch. According to recent findings 27% of the Dutch youth has a migration background, in Rotterdam this is even 60% 2. Inclusive education and work environment Description Another essential aspect of our work and responsibility involves education \u2013 whether it pertains to teaching our knowledge and skills to university students or to society more broadly, in the form of outreach or policy recommendations. We need to consider how to be more inclusive and supportive in the opportunities we provide to a wide arrange of students, what the most appropriate channels are for sharing our findings to reach all of society, and how we can make sure that policy recommendations do not exclude important sub-groups of our population. We are developing SMART rules to get there. SMART goals For each future research project, we aim to reach out to at least vmbo schools, ideally in a non-textual way (e.g., via a vlog, information video or in person meeting). Depending on the targeted age group this could also be mbo-schools. As from the new academic year onwards (i.e. September, 2020) we aim to broaden our internship possibilities, by offering at least two internships to non-university students . We want to reach out to at least two youth workers and two secondary (i.e. vmbo/havo/vwo) and/or mbo-schools to inform them about our internship and youth panel possibilities. 3. Include all voices Description In all (the stages of) our activities, we need to hear other voices, to help us expand our view and understand what is important for all of society. That is the ultimate way to truly get society, youth and neuroscience connected. SMART goals We aim to have yearly diverse youth panels in which we can discuss our research and how we can improve it. As from the new academic year onwards (i.e. September, 2020) we aim to pop-up, in terms of a living lab, in a diverse community within Rotterdam city where we can come together as a lab and work there once a week for at least three months . We aim to have yearly focus group meetings on diversity and inclusion in which representative individuals from society can join. At the beginning of the new academic year (i.e. September, 2020) we aim to reach out in a non-textual way (via a vlog, person-meetings or event) to the community in which our living lab will be located, in order to increase our visibility as a lab, introduce our work and science in general, and to stimulate co-creation. Evaluation of SMART goals Every two months we aim to schedule a meeting in which we can discuss the progress of our SMART goals. Some of the goals are more long-term goals and depend for instance on new projects being started. Other goals can be accomplished in the more near future. We think it is important that we realize that all these goals are a process in itself and that we will learn along the way. This could mean that we might change and adapt our goals according to new experiences and new knowledge.","title":"Our goals for inclusive science"},{"location":"open-science/inclusivity-goals.html#diversity-inclusion-at-the-sync-lab-smart-goals","text":"","title":"Diversity &amp; inclusion at the SYNC lab: SMART goals"},{"location":"open-science/inclusivity-goals.html#smart-specific-measurable-achievable-realistic-timely","text":"Open Science also means inclusive science, and we formulated the following goals in July 2020 (Descriptions copied from our blogpost ):","title":"(SMART: specific, measurable, achievable, realistic, timely)"},{"location":"open-science/inclusivity-goals.html#1-inclusive-research-samples","text":"Description Although we have made some steps to diversify our research samples, we recognize that we still have much to learn and improve. Therefore, we want to make an effort to make our samples more diverse and representative of the whole population. For this purpose, we will (1) pay more attention to two facets of diversity, namely educational background and ethnic-cultural background and (2) align our recruitment strategy accordingly \u2013 choosing the appropriate channels to reach a diverse group of participants and minimize issues that may prevent certain sub-groups from participating.","title":"1. Inclusive research samples"},{"location":"open-science/inclusivity-goals.html#smart-goals","text":"For each future research project, we aim to have at least 30% of our participants who go to a vmbo-school or mbo-school (depending on the targeted age group). In the Netherlands almost 50% of the secondary school students are enrolled in a vmbo education . For each future research project, we aim to have at least 20% of our participants who have a migration background or who identify with other ethnicities besides Dutch. According to recent findings 27% of the Dutch youth has a migration background, in Rotterdam this is even 60%","title":"SMART goals"},{"location":"open-science/inclusivity-goals.html#2-inclusive-education-and-work-environment","text":"Description Another essential aspect of our work and responsibility involves education \u2013 whether it pertains to teaching our knowledge and skills to university students or to society more broadly, in the form of outreach or policy recommendations. We need to consider how to be more inclusive and supportive in the opportunities we provide to a wide arrange of students, what the most appropriate channels are for sharing our findings to reach all of society, and how we can make sure that policy recommendations do not exclude important sub-groups of our population. We are developing SMART rules to get there.","title":"2. Inclusive education and work environment"},{"location":"open-science/inclusivity-goals.html#smart-goals_1","text":"For each future research project, we aim to reach out to at least vmbo schools, ideally in a non-textual way (e.g., via a vlog, information video or in person meeting). Depending on the targeted age group this could also be mbo-schools. As from the new academic year onwards (i.e. September, 2020) we aim to broaden our internship possibilities, by offering at least two internships to non-university students . We want to reach out to at least two youth workers and two secondary (i.e. vmbo/havo/vwo) and/or mbo-schools to inform them about our internship and youth panel possibilities.","title":"SMART goals"},{"location":"open-science/inclusivity-goals.html#3-include-all-voices","text":"Description In all (the stages of) our activities, we need to hear other voices, to help us expand our view and understand what is important for all of society. That is the ultimate way to truly get society, youth and neuroscience connected.","title":"3. Include all voices"},{"location":"open-science/inclusivity-goals.html#smart-goals_2","text":"We aim to have yearly diverse youth panels in which we can discuss our research and how we can improve it. As from the new academic year onwards (i.e. September, 2020) we aim to pop-up, in terms of a living lab, in a diverse community within Rotterdam city where we can come together as a lab and work there once a week for at least three months . We aim to have yearly focus group meetings on diversity and inclusion in which representative individuals from society can join. At the beginning of the new academic year (i.e. September, 2020) we aim to reach out in a non-textual way (via a vlog, person-meetings or event) to the community in which our living lab will be located, in order to increase our visibility as a lab, introduce our work and science in general, and to stimulate co-creation.","title":"SMART goals"},{"location":"open-science/inclusivity-goals.html#evaluation-of-smart-goals","text":"Every two months we aim to schedule a meeting in which we can discuss the progress of our SMART goals. Some of the goals are more long-term goals and depend for instance on new projects being started. Other goals can be accomplished in the more near future. We think it is important that we realize that all these goals are a process in itself and that we will learn along the way. This could mean that we might change and adapt our goals according to new experiences and new knowledge.","title":"Evaluation of SMART goals"},{"location":"open-science/neurovault.html","text":"NeuroVault: instructions and tips NeuroVault is a repository for processed neuroimaging data (you can upload first and second level processed data). This type of data is anonymous, so it can be shared without any restrictions. We encourage you to do this! Why NeuroVault? Share data without sending files around Visualize your MRI contrasts and give the dataset a persistent identifier Make meta-analyses a lot easier Provide transparency to reviewers asking about your data Refer to the original paper and from the paper to your NeuroVault collection Tips: Before uploading: Keep a good documentation of your contrasts during your data analysis so that it is easier to look up which contrast is which (since SPM is not good at file naming) Upload the images before you send in the first version of your paper. This way you can show the reviewers the data from the start (with the NeuroVault link) You can upload both t-maps and ROIs from many different modalities (see below) Include all analyses published in the paper and the main effects even if they are not included in the publication. This makes your analysis more transparent to the reviewers Which data can be uploaded into NeuroVault? Map types: t, z, F, chi-squared, p (given null hypothesis), 1-p (\"inverted\" probability), univariate beta map, multivariable beta map, ROI/mask, parcellation, anatomical, variance Modalities: fMRI BOLD, fMRI-CBF, fMRI-CBV, diffusion MRI, structural MRI, PET FDP, PET [15O]-water, PET other, MEG, EEG, Other Example datasets: Renske van der Cruijsen , Michelle Achterberg Instructions Log in at https://neurovault.org/. If you have never used NeuroVault before, create an account (or use your Google account). Create a dataset, click \u201cGet started and upload an image!\u201d or \u201cAdd new collection\u201d under the \u201cCollections\u201d tab. Fill in the following information (metadata): Essentials Name of the collection: title of your article DOI of your article (if already present: always!!) Developmental neuroscience community Full dataset URL: for example a link to an OSF project, dataverseNL publication package, or Openneuro dataset, if applicable Contributors: add the last author of your paper (i.e., the NeuroVault username). In case you lose access to your account, the contributor can still make adaptations Accessibility: public, unless you are still in the reviewing process and only want the reviewers to see the data (with a view-only link) Subjects: Mean, min and max age of the sample (for easier meta-analysis) Design: type of design Acquisition, registration, preprocessing, first level and second level: these details should be included in your paper. You can include them here as well but not necessarily. Click \"Add image\" Name: short & as clear as possible which map / contrast you are referring to (otherwise add a description) Map type (often t-map), modality (often fMRI BOLD) and template image (often MNI) Cognitive Atlas Paradigm: choose the task that resembles yours the best. This may not always be possible, however this field is mandatory You can upload .nii, .nii.gz and .hdr/.img files. Make sure to select the correct contrast (i.e., have good data documentation)! Cognitive paradigm description: if you have a task that is not well-known or widely used, e.g., the SNAT, you can refer to a document about the task in this field. Analysis level: often group (if single-subject, upload each contrast for each subject) No. of subjects Corresponding figure: not necessary but very insightful for reviewers","title":"NeuroVault"},{"location":"open-science/neurovault.html#neurovault-instructions-and-tips","text":"NeuroVault is a repository for processed neuroimaging data (you can upload first and second level processed data). This type of data is anonymous, so it can be shared without any restrictions. We encourage you to do this!","title":"NeuroVault: instructions and tips"},{"location":"open-science/neurovault.html#why-neurovault","text":"Share data without sending files around Visualize your MRI contrasts and give the dataset a persistent identifier Make meta-analyses a lot easier Provide transparency to reviewers asking about your data Refer to the original paper and from the paper to your NeuroVault collection Tips: Before uploading: Keep a good documentation of your contrasts during your data analysis so that it is easier to look up which contrast is which (since SPM is not good at file naming) Upload the images before you send in the first version of your paper. This way you can show the reviewers the data from the start (with the NeuroVault link) You can upload both t-maps and ROIs from many different modalities (see below) Include all analyses published in the paper and the main effects even if they are not included in the publication. This makes your analysis more transparent to the reviewers","title":"Why NeuroVault?"},{"location":"open-science/neurovault.html#which-data-can-be-uploaded-into-neurovault","text":"Map types: t, z, F, chi-squared, p (given null hypothesis), 1-p (\"inverted\" probability), univariate beta map, multivariable beta map, ROI/mask, parcellation, anatomical, variance Modalities: fMRI BOLD, fMRI-CBF, fMRI-CBV, diffusion MRI, structural MRI, PET FDP, PET [15O]-water, PET other, MEG, EEG, Other Example datasets: Renske van der Cruijsen , Michelle Achterberg","title":"Which data can be uploaded into NeuroVault?"},{"location":"open-science/neurovault.html#instructions","text":"Log in at https://neurovault.org/. If you have never used NeuroVault before, create an account (or use your Google account). Create a dataset, click \u201cGet started and upload an image!\u201d or \u201cAdd new collection\u201d under the \u201cCollections\u201d tab. Fill in the following information (metadata): Essentials Name of the collection: title of your article DOI of your article (if already present: always!!) Developmental neuroscience community Full dataset URL: for example a link to an OSF project, dataverseNL publication package, or Openneuro dataset, if applicable Contributors: add the last author of your paper (i.e., the NeuroVault username). In case you lose access to your account, the contributor can still make adaptations Accessibility: public, unless you are still in the reviewing process and only want the reviewers to see the data (with a view-only link) Subjects: Mean, min and max age of the sample (for easier meta-analysis) Design: type of design Acquisition, registration, preprocessing, first level and second level: these details should be included in your paper. You can include them here as well but not necessarily. Click \"Add image\" Name: short & as clear as possible which map / contrast you are referring to (otherwise add a description) Map type (often t-map), modality (often fMRI BOLD) and template image (often MNI) Cognitive Atlas Paradigm: choose the task that resembles yours the best. This may not always be possible, however this field is mandatory You can upload .nii, .nii.gz and .hdr/.img files. Make sure to select the correct contrast (i.e., have good data documentation)! Cognitive paradigm description: if you have a task that is not well-known or widely used, e.g., the SNAT, you can refer to a document about the task in this field. Analysis level: often group (if single-subject, upload each contrast for each subject) No. of subjects Corresponding figure: not necessary but very insightful for reviewers","title":"Instructions"},{"location":"open-science/open-access-how.html","text":"How to publish open access? Publishing & green open access at the EUR You have to register your articles yourself in Pure . Read how to do this on this webpage . In short: Go to Pure and log in with your ERNA id and password Register your article in Pure within 6 months after publication Include all relevant details such as Title, Author(s), DOI, Email address of corresponding author, Journal, etc. Upload the accepted version of your publication (final author version, without publisher formatting) to RePub (Green route) Gold open access To check for publisher deals (APC reimbursement) see the journal browser on https://www.openaccess.nl/ or use the EUR journal browser To check whether the combination of your journal, funder and institution is compliant with Plan S , use the Journal Checker tool . In case a journal is not on this list and there is no funding from a project to cover the open access fees, there is an Erasmus Open Access Fund that can cover the fee. For more information please have a look here Use an open license: CC-BY Journals often offer multiple licensing possibilities. It is best to choose the most open license, preferably CC-BY 4.0 . Try to avoid using non-derivative (ND) and non-commercial (NC) licenses. For more information, check out this documentation on choosing a license. Use your ORCID When publishing anything, always use your ORCID . This allows all your works to be associated with you , not someone with accidentally the same name, independent of your work or email address. Read all about ORCID in this EUR libguide . Some nice-to-knows: Institutions have organization identifiers . For EUR, use \"Erasmus University Rotterdam, Zuid-Holland, NL\". Automatic syncing : Under \"Works\" > \"Search and link\", allow parties like Crossref and DataCite access to your ORCID, so all your works linked to them will appear in your record automatically ! The peer review category appears only if your publisher indicates that or when you use Publons. This will make explicit how much work you spend reviewing. Check the quality of an (open access) journal How do you know whether or not you found a trustworthy (open access) journal for your publication? Please pay attention to the following requirements: The journal has an ISSN (International Standard Serial Number) The publisher is a member of the Open Access Scholarly Publisher Association and the journal is included in the DOAJ The journal is connected to or is sponsored by a scientific institute or society All articles have a DOI The journal is not part of this list of dubious publishers The content area of the journal is clearly described and the articles are in line with this description The target group of the journal are researchers and scientific teachers The editorial board consists of renowned/well-known researchers from the discipline The costs of publishing open access are clearly mentioned The user licenses are clearly mentioned in each article You can also check the scoring of the journal here . Resources Transpose , a database of journal policies on peer review, co-reviewing and preprinting A repository for open access books can be found in OAPEN and via DOAB (Directory for Open Access Books) Read more about open access in The Turing Way","title":"How to"},{"location":"open-science/open-access-how.html#how-to-publish-open-access","text":"","title":"How to publish open access?"},{"location":"open-science/open-access-how.html#publishing-green-open-access-at-the-eur","text":"You have to register your articles yourself in Pure . Read how to do this on this webpage . In short: Go to Pure and log in with your ERNA id and password Register your article in Pure within 6 months after publication Include all relevant details such as Title, Author(s), DOI, Email address of corresponding author, Journal, etc. Upload the accepted version of your publication (final author version, without publisher formatting) to RePub (Green route)","title":"Publishing &amp; green open access at the EUR"},{"location":"open-science/open-access-how.html#gold-open-access","text":"To check for publisher deals (APC reimbursement) see the journal browser on https://www.openaccess.nl/ or use the EUR journal browser To check whether the combination of your journal, funder and institution is compliant with Plan S , use the Journal Checker tool . In case a journal is not on this list and there is no funding from a project to cover the open access fees, there is an Erasmus Open Access Fund that can cover the fee. For more information please have a look here","title":"Gold open access"},{"location":"open-science/open-access-how.html#use-an-open-license-cc-by","text":"Journals often offer multiple licensing possibilities. It is best to choose the most open license, preferably CC-BY 4.0 . Try to avoid using non-derivative (ND) and non-commercial (NC) licenses. For more information, check out this documentation on choosing a license.","title":"Use an open license: CC-BY"},{"location":"open-science/open-access-how.html#use-your-orcid","text":"When publishing anything, always use your ORCID . This allows all your works to be associated with you , not someone with accidentally the same name, independent of your work or email address. Read all about ORCID in this EUR libguide . Some nice-to-knows: Institutions have organization identifiers . For EUR, use \"Erasmus University Rotterdam, Zuid-Holland, NL\". Automatic syncing : Under \"Works\" > \"Search and link\", allow parties like Crossref and DataCite access to your ORCID, so all your works linked to them will appear in your record automatically ! The peer review category appears only if your publisher indicates that or when you use Publons. This will make explicit how much work you spend reviewing.","title":"Use your ORCID"},{"location":"open-science/open-access-how.html#check-the-quality-of-an-open-access-journal","text":"How do you know whether or not you found a trustworthy (open access) journal for your publication? Please pay attention to the following requirements: The journal has an ISSN (International Standard Serial Number) The publisher is a member of the Open Access Scholarly Publisher Association and the journal is included in the DOAJ The journal is connected to or is sponsored by a scientific institute or society All articles have a DOI The journal is not part of this list of dubious publishers The content area of the journal is clearly described and the articles are in line with this description The target group of the journal are researchers and scientific teachers The editorial board consists of renowned/well-known researchers from the discipline The costs of publishing open access are clearly mentioned The user licenses are clearly mentioned in each article You can also check the scoring of the journal here .","title":"Check the quality of an (open access) journal"},{"location":"open-science/open-access-how.html#resources","text":"Transpose , a database of journal policies on peer review, co-reviewing and preprinting A repository for open access books can be found in OAPEN and via DOAB (Directory for Open Access Books) Read more about open access in The Turing Way","title":"Resources"},{"location":"open-science/open-access-info.html","text":"Open access publishing What is open access publishing? Why publish open access? Ethical argument: science is often financed by public funds and therefore should be accessible to all Impact: increase visibility, use, citations and therefore impact Required: most funders nowadays require articles to be published open access Routes to open access Publishing open access is possible via the gold and the green routes: Gold route : publish open access via a journal, which often requires paying an Article Processing Charge (APC). APCs can sometimes be reimbursed because of Big Deals made between publishers and university libraries. According to Plan S, the open access journals have to fulfill several requirements that you can find here \". Green route (self-archiving): publish a version of your article in an institutional repository ( RePub for the EUR) after publishing it via a journal. Each repository should be registered in the Directory of Open Access Repositories ( OpenDOAR ) You can check which version of your article (submitted, accepted or published) you can archive in a repository in Sherpa Romeo Read more about open access at the EUR open access website . Preprints A preprint is the submitted, non-peer reviewed version of your article. An increasing number of researchers publish preprints in Preprint servers in order to get their results out there quicker. Read more below and in this preprint FAQ . Why publish preprints? Speed : Preprints are almost immediately publicly visible, besides some checks on content and ethics Visibility : Because preprints are open access and many preprint servers are indexed by search engines (e.g., Google Scholar), you can reach more people with your work Feedback : Some preprint servers allow collecting feedback on preprints, which can make your work so much better Prevent scooping : preprints are timestamped, so by posting it, you have established precedence Individual gain : such as showing productivity, openness to feedback, etc. Where to publish preprints? Preferably a preprint server that provides a persistent identifier. For example: OSF Preprints : you can choose many preprint servers and can also share supplementary files. A list of preprint servers hosted via OSF Preprints can be found here . Directly via a preprint server, such as BioRxiv or PsyArXiv You can even add a preprint on ResearchGate ! Feedback and updating There are different ways researchers can give/receive feedback on preprints: - In OSF preprints, you can use their tool Hypothes.is to annotate preprints, see their help guide - Some servers offer comment functionalities (e.g., when logged in) - Use (academic) twitter What you do with feedback is completely up to you. If you want, you can update your preprint to a new version. However, note that all versions are timestamped and retained . Often new versions get a new identifier (DOI) and old versions cannot be removed! If your work gets published by a publisher, many preprint servers also offer the possibility to refer to the identifier (DOI) of your published work, so that readers of the preprint get a notification that they are not reading the most up-to-date version.","title":"About open access"},{"location":"open-science/open-access-info.html#open-access-publishing","text":"","title":"Open access publishing"},{"location":"open-science/open-access-info.html#what-is-open-access-publishing","text":"","title":"What is open access publishing?"},{"location":"open-science/open-access-info.html#why-publish-open-access","text":"Ethical argument: science is often financed by public funds and therefore should be accessible to all Impact: increase visibility, use, citations and therefore impact Required: most funders nowadays require articles to be published open access","title":"Why publish open access?"},{"location":"open-science/open-access-info.html#routes-to-open-access","text":"Publishing open access is possible via the gold and the green routes: Gold route : publish open access via a journal, which often requires paying an Article Processing Charge (APC). APCs can sometimes be reimbursed because of Big Deals made between publishers and university libraries. According to Plan S, the open access journals have to fulfill several requirements that you can find here \". Green route (self-archiving): publish a version of your article in an institutional repository ( RePub for the EUR) after publishing it via a journal. Each repository should be registered in the Directory of Open Access Repositories ( OpenDOAR ) You can check which version of your article (submitted, accepted or published) you can archive in a repository in Sherpa Romeo Read more about open access at the EUR open access website .","title":"Routes to open access"},{"location":"open-science/open-access-info.html#preprints","text":"A preprint is the submitted, non-peer reviewed version of your article. An increasing number of researchers publish preprints in Preprint servers in order to get their results out there quicker. Read more below and in this preprint FAQ .","title":"Preprints"},{"location":"open-science/open-access-info.html#why-publish-preprints","text":"Speed : Preprints are almost immediately publicly visible, besides some checks on content and ethics Visibility : Because preprints are open access and many preprint servers are indexed by search engines (e.g., Google Scholar), you can reach more people with your work Feedback : Some preprint servers allow collecting feedback on preprints, which can make your work so much better Prevent scooping : preprints are timestamped, so by posting it, you have established precedence Individual gain : such as showing productivity, openness to feedback, etc.","title":"Why publish preprints?"},{"location":"open-science/open-access-info.html#where-to-publish-preprints","text":"Preferably a preprint server that provides a persistent identifier. For example: OSF Preprints : you can choose many preprint servers and can also share supplementary files. A list of preprint servers hosted via OSF Preprints can be found here . Directly via a preprint server, such as BioRxiv or PsyArXiv You can even add a preprint on ResearchGate !","title":"Where to publish preprints?"},{"location":"open-science/open-access-info.html#feedback-and-updating","text":"There are different ways researchers can give/receive feedback on preprints: - In OSF preprints, you can use their tool Hypothes.is to annotate preprints, see their help guide - Some servers offer comment functionalities (e.g., when logged in) - Use (academic) twitter What you do with feedback is completely up to you. If you want, you can update your preprint to a new version. However, note that all versions are timestamped and retained . Often new versions get a new identifier (DOI) and old versions cannot be removed! If your work gets published by a publisher, many preprint servers also offer the possibility to refer to the identifier (DOI) of your published work, so that readers of the preprint get a notification that they are not reading the most up-to-date version.","title":"Feedback and updating"},{"location":"open-science/open-code.html","text":"Sharing analysis code \u201cAn article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.\u201d - Buckheit & Donoho, 1995 (read the full article here ) This quote nicely summarizes the importance of sharing data, methods and code so that others can evaluate the actual work that was done in a research paper. Luckily, web-based technologies make it very easy to share these materials (and even to share the complete software environment using Docker and Singularity containers). However, just releasing your code without annotation is not very informative because others (and future you!) can't make a lot of sense of it. Two helpful tools to annotate your code are RMarkdown and Jupyter notebooks. How do tools like R Markdown and Jupyter notebooks make research more reproducible? R Markdown files and Jupyter notebooks contain 1) the code that others need to reproduce your work along with 2) the narration that a reader needs to understand your work R and Python code can be run within these documents, meaning that in principle others can check whether your work is reproducible R Markdown Especially when you do your data analysis in R / RStudio, R Markdown is a very useful tool to put your text and analysis together in one place. It is basically R + Markdown (a markup language to format text). It can be used to write a whole paper, including code to generate figures. This code can be outputted in many formats such as html, pdf and Word. For full documentation see also the R Markdown documentation and this neat cheatsheet (pdf) . Example of RMarkdown chunk in RStudio with associated html output (from RMarkdown docs) Installing R Markdown have R and RStudio installed (avalaible for free) install R Markdown install.packages(\"rmarkdown\") Reference lists using Zotero in R Markdown When writing papers, it is also very useful to connect RStudio with Zotero. Zotero is a free and open source reference manager with a very handy browser plugin . If you have never used a reference manager before: it is a great way to keep a library of all your literature (including pdf's) together and will help you to cite papers in the right way and produce automatic reference lists in the right format for you. This can be done in a word processor like Microsoft Word, but also in R Markdown. The basic steps you need to make this work: install Zotero and import references (e.g., using the browser plugin) install the Better BibTex plugin for Zotero by clicking Tools > Add-ons within Zotero and follow these instructions install the citr R package Now when writing text in an RMarkdown file in RStudio: within RStudio in the toolbar click Addins > Insert citations here you can search through your references and select the ones you want to enter you should also edit the YAML header (the upper part of the Rmd file with title, author, output et cetera): here the bibtex file (eg: bibliography: references.bib ) should be listed; if you also add # References at the end of the main text, your bibliography will be created automatically there the format of the bibliography can be defined by pointing to a csl file in the YAML header (e.g, csl: ./apa.csl ). All csl (citation style language) files can be downloaded from the internet, see also https://citationstyles.org/ Jupyter notebooks For analyses that are conducted using Python, Jupyter notebooks are a great way to keep executable code and annotation in one place (note that many other programming languages are also supported by Jupyter notebooks: the name is reference to the 3 core languages Julia, Python, and R). For full documentation see the Jupyter Notebook docs and https://jupyter.org/ for more information about the larger Project Jupyter ecosystem. When opening a Jupyter Notebook, you are opening an interactive session. Here you can add different sort of cells: code cells that can be executed (after execution the results will be displayed in the notebook), and Markdown cells that can be used to add descriptive text that can be marked up using the Markdown language. Example GIF of a Jupyter notebook for the Qoala-T tool. See notebook here Installing Jupyter notebooks you might want to try Jupyter online first, which can be done here If you want to install Jupyter notebooks, instructions for the different ways to install it can be found here Python is a requirement for installation, and the recommended way to install both Python and Jupyter notebooks is to install Anaconda (available for free) Once anaconda is installed you can launch Jupyter Notebooks from the Anaconda Navigator or by typing jupyter notebook in the command line","title":"Reproducible code"},{"location":"open-science/open-code.html#sharing-analysis-code","text":"\u201cAn article about computational science in a scientific publication is not the scholarship itself, it is merely advertising of the scholarship. The actual scholarship is the complete software development environment and the complete set of instructions which generated the figures.\u201d - Buckheit & Donoho, 1995 (read the full article here ) This quote nicely summarizes the importance of sharing data, methods and code so that others can evaluate the actual work that was done in a research paper. Luckily, web-based technologies make it very easy to share these materials (and even to share the complete software environment using Docker and Singularity containers). However, just releasing your code without annotation is not very informative because others (and future you!) can't make a lot of sense of it. Two helpful tools to annotate your code are RMarkdown and Jupyter notebooks.","title":"Sharing analysis code"},{"location":"open-science/open-code.html#how-do-tools-like-r-markdown-and-jupyter-notebooks-make-research-more-reproducible","text":"R Markdown files and Jupyter notebooks contain 1) the code that others need to reproduce your work along with 2) the narration that a reader needs to understand your work R and Python code can be run within these documents, meaning that in principle others can check whether your work is reproducible","title":"How do tools like R Markdown and Jupyter notebooks make research more reproducible?"},{"location":"open-science/open-code.html#r-markdown","text":"Especially when you do your data analysis in R / RStudio, R Markdown is a very useful tool to put your text and analysis together in one place. It is basically R + Markdown (a markup language to format text). It can be used to write a whole paper, including code to generate figures. This code can be outputted in many formats such as html, pdf and Word. For full documentation see also the R Markdown documentation and this neat cheatsheet (pdf) . Example of RMarkdown chunk in RStudio with associated html output (from RMarkdown docs)","title":"R Markdown"},{"location":"open-science/open-code.html#installing-r-markdown","text":"have R and RStudio installed (avalaible for free) install R Markdown install.packages(\"rmarkdown\")","title":"Installing R Markdown"},{"location":"open-science/open-code.html#reference-lists-using-zotero-in-r-markdown","text":"When writing papers, it is also very useful to connect RStudio with Zotero. Zotero is a free and open source reference manager with a very handy browser plugin . If you have never used a reference manager before: it is a great way to keep a library of all your literature (including pdf's) together and will help you to cite papers in the right way and produce automatic reference lists in the right format for you. This can be done in a word processor like Microsoft Word, but also in R Markdown.","title":"Reference lists using Zotero in R Markdown"},{"location":"open-science/open-code.html#the-basic-steps-you-need-to-make-this-work","text":"install Zotero and import references (e.g., using the browser plugin) install the Better BibTex plugin for Zotero by clicking Tools > Add-ons within Zotero and follow these instructions install the citr R package","title":"The basic steps you need to make this work:"},{"location":"open-science/open-code.html#now-when-writing-text-in-an-rmarkdown-file-in-rstudio","text":"within RStudio in the toolbar click Addins > Insert citations here you can search through your references and select the ones you want to enter you should also edit the YAML header (the upper part of the Rmd file with title, author, output et cetera): here the bibtex file (eg: bibliography: references.bib ) should be listed; if you also add # References at the end of the main text, your bibliography will be created automatically there the format of the bibliography can be defined by pointing to a csl file in the YAML header (e.g, csl: ./apa.csl ). All csl (citation style language) files can be downloaded from the internet, see also https://citationstyles.org/","title":"Now when writing text in an RMarkdown file in RStudio:"},{"location":"open-science/open-code.html#jupyter-notebooks","text":"For analyses that are conducted using Python, Jupyter notebooks are a great way to keep executable code and annotation in one place (note that many other programming languages are also supported by Jupyter notebooks: the name is reference to the 3 core languages Julia, Python, and R). For full documentation see the Jupyter Notebook docs and https://jupyter.org/ for more information about the larger Project Jupyter ecosystem. When opening a Jupyter Notebook, you are opening an interactive session. Here you can add different sort of cells: code cells that can be executed (after execution the results will be displayed in the notebook), and Markdown cells that can be used to add descriptive text that can be marked up using the Markdown language. Example GIF of a Jupyter notebook for the Qoala-T tool. See notebook here","title":"Jupyter notebooks"},{"location":"open-science/open-code.html#installing-jupyter-notebooks","text":"you might want to try Jupyter online first, which can be done here If you want to install Jupyter notebooks, instructions for the different ways to install it can be found here Python is a requirement for installation, and the recommended way to install both Python and Jupyter notebooks is to install Anaconda (available for free) Once anaconda is installed you can launch Jupyter Notebooks from the Anaconda Navigator or by typing jupyter notebook in the command line","title":"Installing Jupyter notebooks"},{"location":"open-science/oscr.html","text":"Open Science Community Rotterdam Interested in open science? Have a look at the Open Science Community Rotterdam (OSCR) . OSCR workshops For Open Science related topics, it is also an option to reach a broader audience and organise a workshop for the Open Science Community Rotterdam. All lab members can take part in these workshops as well, and other researchers can then also enjoy our expertise and experience. See the OSCR website for a list of previous and upcoming workshops. Two lab members have already given a workshop: Eduard: Introduction to the Brain Imaging Data Structure (BIDS), workshop description Dorien: Introduction to sharing brain MRI data, workshop description","title":"Open Science Community Rotterdam"},{"location":"open-science/oscr.html#open-science-community-rotterdam","text":"Interested in open science? Have a look at the Open Science Community Rotterdam (OSCR) .","title":"Open Science Community Rotterdam"},{"location":"open-science/oscr.html#oscr-workshops","text":"For Open Science related topics, it is also an option to reach a broader audience and organise a workshop for the Open Science Community Rotterdam. All lab members can take part in these workshops as well, and other researchers can then also enjoy our expertise and experience. See the OSCR website for a list of previous and upcoming workshops. Two lab members have already given a workshop: Eduard: Introduction to the Brain Imaging Data Structure (BIDS), workshop description Dorien: Introduction to sharing brain MRI data, workshop description","title":"OSCR workshops"},{"location":"open-science/osf.html","text":"The Open Science Framework The Open Science Framework (OSF) is a project management, storage and collaboration platform that is used by many scientists. In an OSF project, you can: - Register a preregistration - Register a preprint - Store and share project documentation - Link external services, such as git(hub) repositories, publication packages, Research Drive (owncloud) folders, etc. - ... and much more, such as obtaining Open science badges! . See this link for more information on the functionality of OSF and the OSF guides for many FAQs and technical documentation. Recommended use of the OSF We recommend using the OSF as a central place for your project, especially if you will produce multiple publications in a project. Link your OSF project to the SYNC lab OSF page (Log in > Components > Link Projects). No worries, the SYNC lab collaborators cannot automatically edit your linked project! Register preregistrations (Registrations > New registration) and Preprints in the relevant components. If your preregistration concerns one manuscript, we recommend creating a separate component in which you link all relevant materials (publication package, preregistration, code, preprint if applicable, etc.) belonging to that manuscript. A project can contain multiple Registrations and Preprints. Be sure to provide good metadata, preferably choose a CC BY 4.0 license and create a DOI so that your preregistration is citable. Data : you can link OSF with external storage (see below) or store data on the OSF itself (choose the German storage site). On the OSF itself, there is now a storage limit of 5 GB for private and 50 GB for public projects. If you store on OSF directly, please take privacy into account at all times Code and software : you can link OSF with a Github repository in case your preprocessing pipeline, processing steps or experimental files are stored there (see below). If your code is however not code-worthy, we recommend storing the code in the publication package or on OSF itself. Link your OSF profile to your ORCID account so that there cannot be any confusion as to who you are. Connecting the OSF to external services Via the Add-on tab, you can connect the OSF with several external services if OSF itself does not provide enough storage. Note that this will not store data on the OSF itself! SURF Research Drive (via Owncloud) Connecting with an Owncloud service like the Research Drive goes via a WebDAV connection. All changes made in the selected Research Drive folder will become visible in the OSF component, but the data are still stored at the EUR. Note that you can only make 1 Research Drive connection per OSF component (multiple connections per project are possible). Please note the following: Make sure the access level is read-only, unless you want your collaborators to be able to adjust the Research Drive contents Think about anonymity: if you make the project/component public, data that are shared have to be anonymous or consented by participants to share publicly Linking an Owncloud folder is not a FAIR solution: there is no curated metadata and no license. Also, the Research Drive is not meant to archive data after the project is finished, so this will always be a temporary solution. How to link a Research Drive folder to OSF Create a WebDAV password in Research Drive (Account > Settings > Security > WebDAV passwords). Copy the WebDAV password you created and click \"done\" Go to the component in your OSF project in which you want to add the Research Drive folder (if you are not in the right component, the Research Drive folder will be linked to the incorrect location) Select \u201cAdd-on\u201d to add the ownCloud Add-on in OSF Configure the add-on in OSF: URL: https://eur.data.surfsara.nl Username: erna-id@eur.nl Password: WebDAV password you just made Figshare or a publication package in the EDR You can link 1 publication package (1 Figshare link) per OSF component, so that the publication package appears in your OSF project. This way, you can license your data (not possible in OSF) and provide metadata but still link it to your OSF project for more context. Click here for a tutorial. Github repository If you stored code on Github (for re-use, think about licensing!), you can link your repository to an OSF component. Again, you can only link 1 repository per OSF component. See this link for how to do this.","title":"Open Science Framework"},{"location":"open-science/osf.html#the-open-science-framework","text":"The Open Science Framework (OSF) is a project management, storage and collaboration platform that is used by many scientists. In an OSF project, you can: - Register a preregistration - Register a preprint - Store and share project documentation - Link external services, such as git(hub) repositories, publication packages, Research Drive (owncloud) folders, etc. - ... and much more, such as obtaining Open science badges! . See this link for more information on the functionality of OSF and the OSF guides for many FAQs and technical documentation.","title":"The Open Science Framework"},{"location":"open-science/osf.html#recommended-use-of-the-osf","text":"We recommend using the OSF as a central place for your project, especially if you will produce multiple publications in a project. Link your OSF project to the SYNC lab OSF page (Log in > Components > Link Projects). No worries, the SYNC lab collaborators cannot automatically edit your linked project! Register preregistrations (Registrations > New registration) and Preprints in the relevant components. If your preregistration concerns one manuscript, we recommend creating a separate component in which you link all relevant materials (publication package, preregistration, code, preprint if applicable, etc.) belonging to that manuscript. A project can contain multiple Registrations and Preprints. Be sure to provide good metadata, preferably choose a CC BY 4.0 license and create a DOI so that your preregistration is citable. Data : you can link OSF with external storage (see below) or store data on the OSF itself (choose the German storage site). On the OSF itself, there is now a storage limit of 5 GB for private and 50 GB for public projects. If you store on OSF directly, please take privacy into account at all times Code and software : you can link OSF with a Github repository in case your preprocessing pipeline, processing steps or experimental files are stored there (see below). If your code is however not code-worthy, we recommend storing the code in the publication package or on OSF itself. Link your OSF profile to your ORCID account so that there cannot be any confusion as to who you are.","title":"Recommended use of the OSF"},{"location":"open-science/osf.html#connecting-the-osf-to-external-services","text":"Via the Add-on tab, you can connect the OSF with several external services if OSF itself does not provide enough storage. Note that this will not store data on the OSF itself!","title":"Connecting the OSF to external services"},{"location":"open-science/osf.html#surf-research-drive-via-owncloud","text":"Connecting with an Owncloud service like the Research Drive goes via a WebDAV connection. All changes made in the selected Research Drive folder will become visible in the OSF component, but the data are still stored at the EUR. Note that you can only make 1 Research Drive connection per OSF component (multiple connections per project are possible). Please note the following: Make sure the access level is read-only, unless you want your collaborators to be able to adjust the Research Drive contents Think about anonymity: if you make the project/component public, data that are shared have to be anonymous or consented by participants to share publicly Linking an Owncloud folder is not a FAIR solution: there is no curated metadata and no license. Also, the Research Drive is not meant to archive data after the project is finished, so this will always be a temporary solution. How to link a Research Drive folder to OSF Create a WebDAV password in Research Drive (Account > Settings > Security > WebDAV passwords). Copy the WebDAV password you created and click \"done\" Go to the component in your OSF project in which you want to add the Research Drive folder (if you are not in the right component, the Research Drive folder will be linked to the incorrect location) Select \u201cAdd-on\u201d to add the ownCloud Add-on in OSF Configure the add-on in OSF: URL: https://eur.data.surfsara.nl Username: erna-id@eur.nl Password: WebDAV password you just made","title":"SURF Research Drive (via Owncloud)"},{"location":"open-science/osf.html#figshare-or-a-publication-package-in-the-edr","text":"You can link 1 publication package (1 Figshare link) per OSF component, so that the publication package appears in your OSF project. This way, you can license your data (not possible in OSF) and provide metadata but still link it to your OSF project for more context. Click here for a tutorial.","title":"Figshare or a publication package in the EDR"},{"location":"open-science/osf.html#github-repository","text":"If you stored code on Github (for re-use, think about licensing!), you can link your repository to an OSF component. Again, you can only link 1 repository per OSF component. See this link for how to do this.","title":"Github repository"},{"location":"open-science/preregistration.html","text":"Preregistration What is a preregistration? A preregistration is basically a time-stamped plan of your research before you have seen your research data (either before data collection or before data analysis), \"the introduction and methods sections of your paper\". It is a document containing at least: Your hypotheses Your methodology and variables (design, sample, stopping rule, exclusion criteria, procedure, variables, etc.) Your analysis plan to test the hypotheses (statistical tests, transformations, assumption tests, etc.) Inference criteria: when do you consider your hypothesis rejected or confirmed? Some examples can be found here . Why should I preregister my research? clearly distinguish between confirmatory and exploratory analysis and therefore prevent presenting exploration as hypothesized result maintain transparency, preventing selective reporting and p-hacking contribute to decreasing the file drawer problem and publication bias function as a safety net for your future self: you don't have to remember what exactly you were going to do and how, and, once registered, you only have to execute your plan (and, if deviating from it, report this) Read this article for more selfish reasons to preregister Preregistration dilemmas \"Preregistration costs way too much time\" > Actually, the time you would normally spend after data collection is now spent before. It will likely even save you time, because you are not pointlessly trying multiple analyses and because you basically already wrote your introduction and methods sections. \"What to do when my research doesn't go according to plan?\" > Before the analysis, you can add an addendum to your preregistration explaining what went differently. Afterwards, simply report the deviation in your manuscript! The goal is to be transparent. \"No one will ever look at my preregistration\" > You can use the preregistration already as a reminder for yourself, use it as a justification to reviewers of your manuscript and inspire colleagues and interested researchers with your amazing open attitude. Go for it! See this page for more information about preregistration and dilemmas in preregistering fMRI studies. How can I preregister? See this link for an easy tutorial on how to preregister on the Open Science Framework (OSF). Don't forget to include your collaborators and to include the link to your preregistration in your manuscript. What are Registered Reports? Registered Reports are preregistrations that are peer reviewed by journals. This highly eliminates publication bias, because at Stage 1 peer review, no results are known yet, so manuscripts cannot be accepted or rejected basead on results. Below the process of Registered Reports is visualized: After your preregistration has received an In Principle Acceptance (Stage 1), you can start collecting data and writing up your results. Most journals that get through Stage 1 will also get accepted in Stage 2, because the study design has already been reviewed. Resources A simple preregistration template All OSF templates A list of resources on preregistration Information about Registered Reports Overview of all journals doing Registered Reports A preregistration tutorial and template for secondary data analysis Preregistration: dream vs. reality","title":"Preregistration"},{"location":"open-science/preregistration.html#preregistration","text":"","title":"Preregistration"},{"location":"open-science/preregistration.html#what-is-a-preregistration","text":"A preregistration is basically a time-stamped plan of your research before you have seen your research data (either before data collection or before data analysis), \"the introduction and methods sections of your paper\". It is a document containing at least: Your hypotheses Your methodology and variables (design, sample, stopping rule, exclusion criteria, procedure, variables, etc.) Your analysis plan to test the hypotheses (statistical tests, transformations, assumption tests, etc.) Inference criteria: when do you consider your hypothesis rejected or confirmed? Some examples can be found here .","title":"What is a preregistration?"},{"location":"open-science/preregistration.html#why-should-i-preregister-my-research","text":"clearly distinguish between confirmatory and exploratory analysis and therefore prevent presenting exploration as hypothesized result maintain transparency, preventing selective reporting and p-hacking contribute to decreasing the file drawer problem and publication bias function as a safety net for your future self: you don't have to remember what exactly you were going to do and how, and, once registered, you only have to execute your plan (and, if deviating from it, report this) Read this article for more selfish reasons to preregister","title":"Why should I preregister my research?"},{"location":"open-science/preregistration.html#preregistration-dilemmas","text":"\"Preregistration costs way too much time\" > Actually, the time you would normally spend after data collection is now spent before. It will likely even save you time, because you are not pointlessly trying multiple analyses and because you basically already wrote your introduction and methods sections. \"What to do when my research doesn't go according to plan?\" > Before the analysis, you can add an addendum to your preregistration explaining what went differently. Afterwards, simply report the deviation in your manuscript! The goal is to be transparent. \"No one will ever look at my preregistration\" > You can use the preregistration already as a reminder for yourself, use it as a justification to reviewers of your manuscript and inspire colleagues and interested researchers with your amazing open attitude. Go for it! See this page for more information about preregistration and dilemmas in preregistering fMRI studies.","title":"Preregistration dilemmas"},{"location":"open-science/preregistration.html#how-can-i-preregister","text":"See this link for an easy tutorial on how to preregister on the Open Science Framework (OSF). Don't forget to include your collaborators and to include the link to your preregistration in your manuscript.","title":"How can I preregister?"},{"location":"open-science/preregistration.html#what-are-registered-reports","text":"Registered Reports are preregistrations that are peer reviewed by journals. This highly eliminates publication bias, because at Stage 1 peer review, no results are known yet, so manuscripts cannot be accepted or rejected basead on results. Below the process of Registered Reports is visualized: After your preregistration has received an In Principle Acceptance (Stage 1), you can start collecting data and writing up your results. Most journals that get through Stage 1 will also get accepted in Stage 2, because the study design has already been reviewed.","title":"What are Registered Reports?"},{"location":"open-science/preregistration.html#resources","text":"A simple preregistration template All OSF templates A list of resources on preregistration Information about Registered Reports Overview of all journals doing Registered Reports A preregistration tutorial and template for secondary data analysis Preregistration: dream vs. reality","title":"Resources"},{"location":"open-science/pub-packages.html","text":"Publication packages What are publication packages? Publication packages are bundles of all materials necessary to reproduce the results from a scientific article. They contain (following the national publication package guidelines ): The manuscript or a link to the open access manuscript Experimental files, such as Eprime or scripts that run the experiment, task instructions, questionnaires, etc. Anonymized raw research data or a link to the repository where the data are stored Anonymized processed research data or a link to the repository where the data are stored Processing and statistical analysis scripts Readme file with all relevant metadata, including links to the article and other relevant information, e.g., the preregistration, the dataset on NeuroVault, contact information of involved researchers, information on excluded participants, information on methods used, etc. The more metadata, the better! Approved ethics protocol How to upload a publication package? Log into the EUR data repository (EDR) (instance of Figshare) with your EUR credentials. Link your EDR profile to your ORCID. This will make sure that your publication package appears in your ORCID profile too. Under my data, create a new item. In this item, you can upload all publication package files (preferably without zipping), so that your files are linked under 1 DOI. Fill out as many fields in the form as possible, including all authors on your manuscript and links to external resources. This will make sure your data will be Findable. For license, preferably choose CC-BY 4.0, which enables maximum reuse of your data, besides being acknowledged for your data. If your manuscript is not accepted for publication yet, 1) Reserve a DOI that you can use in your publication, 2) Generate a private link for reviewers to see your data and 3) Save changes (do not Publish yet). Once your manuscript is accepted for publication and your data (analysis) will not change anymore, you can Publish it. After saving or publishing, your package will be looked at by a data curator from the EUR. They will contact you if they have any questions about your package or tips to improve it even more. More information on how to use the EUR data repository can be found in this Youtube playlist . Tips Be sure to create the publication package (reserve a DOI) before your manuscript is published, so that the manuscript links to the publication package and vice versa Prevent uploading files multiple times. If you have already uploaded data or code on OSF or Github, link those uploads to the publication package, instead of re-uploading in the publication package. If you have uploaded elsewhere (e.g., NeuroVault), provide a readme with the relevant links to make sure everything is findable! If applicable, create links with an OSF project, a Github repository and your ORCID, click here and here for how to do this. Only publish data as confidential when that is absolutely necessary. The reuse of your data is significantly reduced when they are behind a confidentiality wall. Moreover, when the owner of the publication package leaves the EUR, they cannot provide access to the confidential files anymore. For questions about the EUR data repository, please contact the university library (datarepository [at] eur [dot] nl). For the ESSB policy implementation, contact the ESSB data steward .","title":"Publication packages"},{"location":"open-science/pub-packages.html#publication-packages","text":"","title":"Publication packages"},{"location":"open-science/pub-packages.html#what-are-publication-packages","text":"Publication packages are bundles of all materials necessary to reproduce the results from a scientific article. They contain (following the national publication package guidelines ): The manuscript or a link to the open access manuscript Experimental files, such as Eprime or scripts that run the experiment, task instructions, questionnaires, etc. Anonymized raw research data or a link to the repository where the data are stored Anonymized processed research data or a link to the repository where the data are stored Processing and statistical analysis scripts Readme file with all relevant metadata, including links to the article and other relevant information, e.g., the preregistration, the dataset on NeuroVault, contact information of involved researchers, information on excluded participants, information on methods used, etc. The more metadata, the better! Approved ethics protocol","title":"What are publication packages?"},{"location":"open-science/pub-packages.html#how-to-upload-a-publication-package","text":"Log into the EUR data repository (EDR) (instance of Figshare) with your EUR credentials. Link your EDR profile to your ORCID. This will make sure that your publication package appears in your ORCID profile too. Under my data, create a new item. In this item, you can upload all publication package files (preferably without zipping), so that your files are linked under 1 DOI. Fill out as many fields in the form as possible, including all authors on your manuscript and links to external resources. This will make sure your data will be Findable. For license, preferably choose CC-BY 4.0, which enables maximum reuse of your data, besides being acknowledged for your data. If your manuscript is not accepted for publication yet, 1) Reserve a DOI that you can use in your publication, 2) Generate a private link for reviewers to see your data and 3) Save changes (do not Publish yet). Once your manuscript is accepted for publication and your data (analysis) will not change anymore, you can Publish it. After saving or publishing, your package will be looked at by a data curator from the EUR. They will contact you if they have any questions about your package or tips to improve it even more. More information on how to use the EUR data repository can be found in this Youtube playlist .","title":"How to upload a publication package?"},{"location":"open-science/pub-packages.html#tips","text":"Be sure to create the publication package (reserve a DOI) before your manuscript is published, so that the manuscript links to the publication package and vice versa Prevent uploading files multiple times. If you have already uploaded data or code on OSF or Github, link those uploads to the publication package, instead of re-uploading in the publication package. If you have uploaded elsewhere (e.g., NeuroVault), provide a readme with the relevant links to make sure everything is findable! If applicable, create links with an OSF project, a Github repository and your ORCID, click here and here for how to do this. Only publish data as confidential when that is absolutely necessary. The reuse of your data is significantly reduced when they are behind a confidentiality wall. Moreover, when the owner of the publication package leaves the EUR, they cannot provide access to the confidential files anymore. For questions about the EUR data repository, please contact the university library (datarepository [at] eur [dot] nl). For the ESSB policy implementation, contact the ESSB data steward .","title":"Tips"},{"location":"open-science/stempelkaart.html","text":"Open Science stamp card (stempelkaart) In order to make all the different facets of Open Science easy to implement for our lab, we developed an Open Science stamp card. It provides oversight and resources for making your research as openly available as possible in four major phases of research: Preparation of the paper Conducting the study Writing the manuscript After publication Each phase has its own steps that can be checked off, with some steps (in red) being labeled as \u2018must-do\u2019 (e.g., making a publication package). The other, nice-to-do, steps are those that are not required, but are in line with the vision of SYNC (e.g., uploading a preprint or writing a blog on your research). Our Open Science core-team, with input from the entire lab, made the following template which can be downloaded here . (Work in progress, special thanks to Dorien Huijser for helping with the setup.)","title":"Open Science stempelkaart"},{"location":"open-science/stempelkaart.html#open-science-stamp-card-stempelkaart","text":"In order to make all the different facets of Open Science easy to implement for our lab, we developed an Open Science stamp card. It provides oversight and resources for making your research as openly available as possible in four major phases of research: Preparation of the paper Conducting the study Writing the manuscript After publication Each phase has its own steps that can be checked off, with some steps (in red) being labeled as \u2018must-do\u2019 (e.g., making a publication package). The other, nice-to-do, steps are those that are not required, but are in line with the vision of SYNC (e.g., uploading a preprint or writing a blog on your research). Our Open Science core-team, with input from the entire lab, made the following template which can be downloaded here . (Work in progress, special thanks to Dorien Huijser for helping with the setup.)","title":"Open Science stamp card (stempelkaart)"},{"location":"open-science/transparency-checklists.html","text":"Transparency checklists On this page, you will find more resources aimed at increasing transparency in your manuscript. Open Project Checklist Find the Open Project checklist here . Note that this is a work in progress . The final version will be published on Zenodo. You can use this checklist for your project to: See where in your research project you can improve your openness Check how open you are: an \u2714\ufe0f apprentice, \ud83d\udcaa master or \ud83c\udfc6 champion Transparency checklist (Aczel et al., 2020) How transparent are you with respect to the process of and around your manuscript? This is what you can use this checklist for. Note that this checklist is not necessarily for your entire project. Paper: https://doi.org/10.1038/s41562-019-0772-6 Online checklist: http://www.shinyapps.org/apps/TransparencyChecklist/ Short transparency checklist: http://www.shinyapps.org/apps/ShortTransparencyChecklist/ (e)COBIDAS: reproducible methods reporting A few years ago, in order to improve reproducibility in (f)MRI research, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to promote best practices for methods and results reporting . This was recently followed by a similar initiative for EEG and MEG . The goal of eCOBIDAS is to develop an online version of the checklist to increase the implementation of both reports. See the first version of the online checklist here (see also the OSF page and Github repo ) Other resources (please add more if you run into them) Klapwijk et al., 2019: Opportunities for increased reproducibility and replicability of developmental cognitive neuroscience, click here for the preprint . Pickering, Topor et al., 2020: Non-Interventional, Reproducible, and Open (NIRO) Systematic Review guidelines v0.1, click here for the OSF page and here for the website .","title":"Open project checklists"},{"location":"open-science/transparency-checklists.html#transparency-checklists","text":"On this page, you will find more resources aimed at increasing transparency in your manuscript.","title":"Transparency checklists"},{"location":"open-science/transparency-checklists.html#open-project-checklist","text":"Find the Open Project checklist here . Note that this is a work in progress . The final version will be published on Zenodo. You can use this checklist for your project to: See where in your research project you can improve your openness Check how open you are: an \u2714\ufe0f apprentice, \ud83d\udcaa master or \ud83c\udfc6 champion","title":"Open Project Checklist"},{"location":"open-science/transparency-checklists.html#transparency-checklist-aczel-et-al-2020","text":"How transparent are you with respect to the process of and around your manuscript? This is what you can use this checklist for. Note that this checklist is not necessarily for your entire project. Paper: https://doi.org/10.1038/s41562-019-0772-6 Online checklist: http://www.shinyapps.org/apps/TransparencyChecklist/ Short transparency checklist: http://www.shinyapps.org/apps/ShortTransparencyChecklist/","title":"Transparency checklist (Aczel et al., 2020)"},{"location":"open-science/transparency-checklists.html#ecobidas-reproducible-methods-reporting","text":"A few years ago, in order to improve reproducibility in (f)MRI research, the Committee on Best Practices in Data Analysis and Sharing (COBIDAS) of OHBM released a report to promote best practices for methods and results reporting . This was recently followed by a similar initiative for EEG and MEG . The goal of eCOBIDAS is to develop an online version of the checklist to increase the implementation of both reports. See the first version of the online checklist here (see also the OSF page and Github repo )","title":"(e)COBIDAS: reproducible methods reporting"},{"location":"open-science/transparency-checklists.html#other-resources-please-add-more-if-you-run-into-them","text":"Klapwijk et al., 2019: Opportunities for increased reproducibility and replicability of developmental cognitive neuroscience, click here for the preprint . Pickering, Topor et al., 2020: Non-Interventional, Reproducible, and Open (NIRO) Systematic Review guidelines v0.1, click here for the OSF page and here for the website .","title":"Other resources (please add more if you run into them)"},{"location":"reaching-out/scicom.html","text":"Science communication On this page, we can collect sources on science communication and citizen science. Resources Blog on how to start a podcast and wiki with additional materials. Here you can find slides for presentations, lectures, etc. made and used by SYNC lab members. On the YoungXperts website you can find more resources and tools on citizen science. Here you can find SYNC logos that can be used for outreach. EUR logos can be found here . Tips Tip for science communication in practice: Check synonyms for difficult Dutch words: https://www.ishetb1.nl/","title":"Science communication"},{"location":"reaching-out/scicom.html#science-communication","text":"On this page, we can collect sources on science communication and citizen science.","title":"Science communication"},{"location":"reaching-out/scicom.html#resources","text":"Blog on how to start a podcast and wiki with additional materials. Here you can find slides for presentations, lectures, etc. made and used by SYNC lab members. On the YoungXperts website you can find more resources and tools on citizen science. Here you can find SYNC logos that can be used for outreach. EUR logos can be found here .","title":"Resources"},{"location":"reaching-out/scicom.html#tips","text":"Tip for science communication in practice: Check synonyms for difficult Dutch words: https://www.ishetb1.nl/","title":"Tips"}]}
\ No newline at end of file
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index ff0f759..f72c770 100644
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