-
-
-
-
-
-
-
-
+
+
+
+
+
+
+
Onboarding
++ Onboarding +
- Coral offers onboarding tooltips to guide new users through the application. The tooltips indicate what elements represent and what roles they play in Coral. - A general workflow will be supported with these onboarding tooltips to explain the iterative workflow, cohort creation and how cohorts can be characterized. + Coral offers onboarding tooltips to guide new users through the application. The tooltips indicate what elements represent and what roles they + play in Coral. A general workflow will be supported with these onboarding tooltips to explain the iterative workflow, cohort creation and how + cohorts can be characterized.
Cohort Tracking
++ Cohort Tracking +
- The Cohort Evolution View (upper panel) presents how all cohorts were generated as well as their relationships as a graph. - Operations and cohorts are encoded as nodes connected by edges to represent the analysis flow. - The first cohort includes all items of the loaded dataset and is created automatically. - When the user selects a cohort in the graph, it is assigned a color that is used consistently in all visualizations. + The Cohort Evolution View (upper panel) presents how all cohorts were generated as well as their relationships as a graph. Operations + and cohorts are encoded as nodes connected by edges to represent the analysis flow. The first cohort includes all items of the loaded dataset + and is created automatically. When the user selects a cohort in the graph, it is assigned a color that is used consistently in all + visualizations.
Cohort Creation
++ Cohort Creation +
- Selected cohorts are loaded into the Action View (lower panel), which allows users to perform cohort characterization and creation operations. - New cohorts created by these operations are also added to the graph, which results in an iterative cohort definition and analysis workflow. - The Action View is divided into three areas: the Input Area, the Operation Area, and the optional Output Area, which is shown if the operation results in new cohorts. - The Operation Area provides the several operations that can be applied to the input cohorts. + Selected cohorts are loaded into the Action View (lower panel), which allows users to perform cohort characterization and creation + operations. New cohorts created by these operations are also added to the graph, which results in an iterative cohort definition and analysis + workflow. The Action View is divided into three areas: the Input Area, the Operation Area, and the optional Output Area, which is shown if the + operation results in new cohorts. The Operation Area provides the several operations that can be applied to the input cohorts.
Filter & Split
- The Filter & Split operation is used to create cohorts from the loaded dataset. - Filtering works by selecting the values of interest to create a new cohort. In contrast, the Split operation can be used to divide a cohort into mulitple sub-cohorst. + The Filter & Split operation is used to create cohorts from the loaded dataset. Filtering works by selecting the values of interest to + create a new cohort. In contrast, the Split operation can be used to divide a cohort into mulitple sub-cohorst.
View
++ View +
- The View operation is the main route to exploring the dataset and investigating how the values of one or more attributes are distributed across cohorts. -
-- Coral offers different visualizations, based on the number and type of attributes: + The View operation is the main route to exploring the dataset and investigating how the values of one or more attributes are distributed + across cohorts.
+Coral offers different visualizations, based on the number and type of attributes:
- A categorical attribute will be shown with a bar chart, which shows the distribution of values for each category. - If more than one cohort is selected, a grouped bar chart will be shown. + A categorical attribute will be shown with a bar chart, which shows the distribution of values for each category. If more than one cohort is + selected, a grouped bar chart will be shown.
A quantitative attribute will be displayed with a density plot and will superimpose the different curves for multiple selected cohorts.
- The survival plot is only used for quantitative attributes related to the survival, if multiple cohorts are selected the multiple curves will be superimposed. + The survival plot is only used for quantitative attributes related to the survival, if multiple cohorts are selected the multiple curves will + be superimposed.
- Selecting a categorical and a quantitative attribute will show a boxplot, with a box for each category. - For multiple cohorts a box plot with mutliple boxes for each category will be shown, each representing a cohort. + Selecting a categorical and a quantitative attribute will show a boxplot, with a box for each category. For multiple cohorts a box plot with + mutliple boxes for each category will be shown, each representing a cohort.
- A scatterplot will be used to display two quantitative attributes. - In case of multiple cohorts, all datapoints of each cohort will be plotted corresponding with the color of the cohort. + A scatterplot will be used to display two quantitative attributes. In case of multiple cohorts, all datapoints of each cohort will be plotted + corresponding with the color of the cohort.
- Choosing two categorical attributes will result in an area chart. - For multiple cohorts each category combination will have an area representing one cohort. + Choosing two categorical attributes will result in an area chart. For multiple cohorts each category combination will have an area + representing one cohort.
Estimate the Prevalence
++ Estimate the Prevalence +
- Prevalence is the proportion of items with a certain characteristic in a cohort. An exmaple would be the proportion of patients with a gene mutation among female Asian patients with NSCLC. - Coral provides a dedicated analysis view to assess prevalence estimates. After selecting the sample cohort with items that have the characteristic of interest, - the user can flexibly define the reference cohort by applying or skipping Filter & Split operations used to create the sample cohort. - The cohorts’ sizes and the resulting prevalences are then displayed in a bar chart. + Prevalence is the proportion of items with a certain characteristic in a cohort. An exmaple would be the proportion of patients with a gene + mutation among female Asian patients with NSCLC. Coral provides a dedicated analysis view to assess prevalence estimates. After selecting the + sample cohort with items that have the characteristic of interest, the user can flexibly define the reference cohort by applying or skipping + Filter & Split operations used to create the sample cohort. The cohorts’ sizes and the resulting prevalences are then displayed in a bar + chart.
Perform Basic Statistical Analyses
++ Perform Basic Statistical Analyses +
- Seeking relationships and patterns in tabular data is a common data exploration task. To confirm hypotheses that are based on visual patterns observed during exploratory data analysis, - users need to be able to quickly compare data subsets, and get further information on the significance of the result and the statistical test applied.
+ Seeking relationships and patterns in tabular data is a common data exploration task. To confirm hypotheses that are based on visual patterns + observed during exploratory data analysis, users need to be able to quickly compare data subsets, and get further information on the + significance of the result and the statistical test applied. +- The Comparison operation enables users who are not experts in statistics to verify generated hypotheses and confirm insights gained during the exploration of tabular data. - Concretely, it presents an overview of the statistical significance of various cohort comparisons. On demand, it shows further details, including the test score, a textual description, and a detail visualization explaining the results. + The Comparison operation enables users who are not experts in statistics to verify generated hypotheses and confirm insights gained + during the exploration of tabular data. Concretely, it presents an overview of the statistical significance of various cohort comparisons. On + demand, it shows further details, including the test score, a textual description, and a detail visualization explaining + the results.
Inspect the Items in a Cohort
++ Inspect the Items in a Cohort +
- Coral uses the tabular visualization technique LineUp for visualizing the items of the cohorts.
- This makes it possible to identify outliers or to assess single data points.
+ Coral uses the tabular visualization technique{' '}
+
+ LineUp
+ {' '}
+ for visualizing the items of the cohorts. This makes it possible to identify outliers or to assess single data points.
Attributes can be selected to display their data in the table, and sort, filter, and group the data.
-
+ This update contains several style changes, bugfixes, and structural changes of the application. The most important changes are: +
+Remove grid lines from visualizations
+The grid lines in the visualizations have been removed to improve the readability of the visualizations.
+Fix colors assignment in plots
++ Previously, the color assignment for cohorts with the same name (e.g., after filtering two cohorts by Gender: Female) in plots was wrong. We + have fixed this issue and the correct color of the cohort should be assigned now. +
+ +Select root cohort automatically after onboarding
+The onboarding is only displayed when users launch the application for the first time. Afterwards, the root cohort is selected automatically.
+ + ), + }, { id: 'v1-2', name: 'Version 1.2', @@ -20,16 +52,13 @@ const sections = [Export Cohorts
-
+
- It is now possible to export cohorts as a CSV-file from Coral and continue the analysis elsewhere.
- - It is now possible to export cohorts as a CSV-file from Coral and continue the analysis elsewhere. - -
- - How to: Open the Inspect Items operation and add any attributes you would like to include in the export. - As soon as the data is loaded the export button will be displayed in the top right of the operation area. - The exported data will match the displayed table. + How to: Open the Inspect Items operation and add any attributes you would like to include in the export. As soon as the data is loaded + the export button will be displayed in the top right of the operation area. The exported data will match the displayed table.
Start a New Session
-
@@ -37,7 +66,7 @@ const sections = [
You can now start a new session by clicking the Start New Session button next to the available datasets.
Briefly noted
-
@@ -46,7 +75,7 @@ const sections = [
Hello Coral! 🐣
- This is the first release of Coral. - Coral is a cohort analysis tool to interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. + This is the first release of Coral. Coral is a cohort analysis tool to interactively create and refine cohorts, which can then be compared, + characterized, and inspected down to the level of single items.
- Coral comes with this dedicated homepage to welcome new users, providing an overview of the features, available datasets, and publications. - For an overview of Coral's features, we also provide an introductory video to get to know Coral. + Coral comes with this dedicated homepage to welcome new users, providing an overview of the features,{' '} + available datasets, and publications. For an overview of Coral's features, we also + provide an introductory video to get to know Coral.
In the future, we will also present the most recent changes and developments here.
- You can skip this welcome page and start the analysis in Coral directly, by going to the /app subsite.
+ You can skip this welcome page and start the analysis in Coral directly, by going to the{' '}
+
+ /app
+ {' '}
+ subsite.
{item.name} ({item.date})
++ {item.name} ({item.date}) +
Abstract
-- A main task in computational cancer analysis is the identification of patient subgroups (i.e., cohorts) based on metadata attributes (patient stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users can interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. We visualize the evolution of cohorts and also provide intuitive access to prevalence information. Coral can be utilized to explore any type of cohort and sample set. Here, we focus on the analysis of genomic data from cancer patients and therefore included in the public version data from the AACR Project GENIE, The Cancer Genome Atlas, and the Cell Line Encyclopedia. -
+ return ( +Abstract
++ A main task in computational cancer analysis is the identification of patient subgroups (i.e., cohorts) based on metadata attributes (patient + stratification) or genomic markers of response (biomarkers). Coral is a web-based cohort analysis tool that is designed to support this task: Users + can interactively create and refine cohorts, which can then be compared, characterized, and inspected down to the level of single items. We + visualize the evolution of cohorts and also provide intuitive access to prevalence information. Coral can be utilized to explore any type of cohort + and sample set. Here, we focus on the analysis of genomic data from cancer patients and therefore included in the public version data from the AACR + Project GENIE, The Cancer Genome Atlas, and the Cell Line Encyclopedia. +
-Citation
-
- Patrick Adelberger, Klaus Eckelt, Markus J. Bauer, Marc Streit, Christian Haslinger, Thomas Zichner.
- Coral: a web-based visual analysis tool for creating and characterizing cohorts.
- Bioinformatics, doi:10.1093/bioinformatics/btab695, 2021.
-
Citation
+
+ Patrick Adelberger, Klaus Eckelt, Markus J. Bauer, Marc Streit, Christian Haslinger, Thomas Zichner.
+
+ Coral: a web-based visual analysis tool for creating and characterizing cohorts.
+
+ Bioinformatics, doi:10.1093/bioinformatics/btab695, 2021.
+
Abstract
-- Ordino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, - filter and explore genes, cell lines and tissue samples based on pre-loaded data, - including The Cancer Genome Atlas, the Cancer Cell Line Encyclopedia and manually - uploaded information. Interactive tabular data visualization that facilitates the - user-driven prioritization process forms a core component of Ordino. Detail views - of selected items complement the exploration. Findings can be stored, shared and - reproduced via the integrated session management. -
+Abstract
++ Ordino is a web-based analysis tool for cancer genomics that allows users to flexibly rank, filter and explore genes, cell lines and tissue samples + based on pre-loaded data, including The Cancer Genome Atlas, the Cancer Cell Line Encyclopedia and manually uploaded information. Interactive + tabular data visualization that facilitates the user-driven prioritization process forms a core component of Ordino. Detail views of selected items + complement the exploration. Findings can be stored, shared and reproduced via the integrated session management. +
-Citation
-
- Marc Streit, Samuel Gratzl, Holger Stitz, Andreas Wernitznig, Thomas Zichner, Christian Haslinger.
- Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples.
- Bioinformatics, 35(17): 3140-3142, 2019.
-
Citation
+
+ Marc Streit, Samuel Gratzl, Holger Stitz, Andreas Wernitznig, Thomas Zichner, Christian Haslinger.
+
+ Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples.
+
+ Bioinformatics, 35(17): 3140-3142, 2019.
+
Abstract
-- Seeking relationships and patterns in tabular data is a common data exploration task. - To confirm hypotheses that are based on visual patterns observed during exploratory data analysis, - users need to be able to quickly compare data subsets, and get further information on the significance of the result and the statistical test applied. - Existing tools, however, either focus on the comparison of a single data type, such as comparing numerical attributes only, or provide little or no statistical evaluation to assess a hypothesis. To fill this gap, - we present TourDino, a support view that helps users who are not experts in statistics to verify generated hypotheses and confirm insights gained during the exploration of tabular data. - In TourDino we present an overview of the statistical significance of various row or column comparisons. On demand, we show further details, including the test score, a textual description, and a detail visualization explaining the results. - To demonstrate the efficacy of our approach, we have integrated TourDino in the Ordino drug discovery platform for the purpose of identifying new drug targets. -
+Abstract
++ Seeking relationships and patterns in tabular data is a common data exploration task. To confirm hypotheses that are based on visual patterns + observed during exploratory data analysis, users need to be able to quickly compare data subsets, and get further information on the significance of + the result and the statistical test applied. Existing tools, however, either focus on the comparison of a single data type, such as comparing + numerical attributes only, or provide little or no statistical evaluation to assess a hypothesis. To fill this gap, we present TourDino, a support + view that helps users who are not experts in statistics to verify generated hypotheses and confirm insights gained during the exploration of tabular + data. In TourDino we present an overview of the statistical significance of various row or column comparisons. On demand, we show further details, + including the test score, a textual description, and a detail visualization explaining the results. To demonstrate the efficacy of our approach, we + have integrated TourDino in the Ordino drug discovery platform for the purpose of identifying new drug targets. +
-Citation
-
- Klaus Eckelt, Patrick Adelberger, Thomas Zichner, Andreas Wernitznig, Marc Streit.
- TourDino: A Support View for Confirming Patterns in Tabular Data.
- EuroVis Workshop on Visual Analytics (EuroVA '19), 2019.
-
Citation
+
+ Klaus Eckelt, Patrick Adelberger, Thomas Zichner, Andreas Wernitznig, Marc Streit.
+
+ TourDino: A Support View for Confirming Patterns in Tabular Data.
+
+ EuroVis Workshop on Visual Analytics (EuroVA '19), 2019.
+
Abstract
-- Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items of interest. - At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on multiple columns and rich data selection and filtering capabilities. - We demonstrate Taggle using a case study conducted by a domain expert on complex genomics data analysis for the purpose of drug discovery. -
+Abstract
++ Most tabular data visualization techniques focus on overviews, yet many practical analysis tasks are concerned with investigating individual items + of interest. At the same time, relating an item to the rest of a potentially large table is important. In this work we present Taggle, a tabular + visualization technique for exploring and presenting large and complex tables. Taggle takes an item-centric, spreadsheet-like approach, visualizing + each row in the source data individually using visual encodings for the cells. At the same time, Taggle introduces data-driven aggregation of data + subsets. The aggregation strategy is complemented by interaction methods tailored to answer specific analysis questions, such as sorting based on + multiple columns and rich data selection and filtering capabilities. We demonstrate Taggle using a case study conducted by a domain expert on + complex genomics data analysis for the purpose of drug discovery. +
-Citation
-
- Katarina Furmanova, Samuel Gratzl, Holger Stitz, Thomas Zichner, Miroslava Jaresova, Martin Ennemoser, Alexander Lex, Marc Streit.
- Taggle: Combining Overview and Details in Tabular Data Visualizations.
- Information Visualization, 19(2): 114-136, 2019.
-
Citation
+
+ Katarina Furmanova, Samuel Gratzl, Holger Stitz, Thomas Zichner, Miroslava Jaresova, Martin Ennemoser, Alexander Lex, Marc Streit.
+
+ Taggle: Combining Overview and Details in Tabular Data Visualizations.
+
+ Information Visualization, 19(2): 114-136, 2019.
+
- Coral and its components have been described in the following scientific publications.
+ Coral and its components have been described in the following scientific publications.
+
Please cite at least the first article when using Coral and publishing your results.
{item.name}
++ {item.name} +
- {'Do you have questions or found a bug, do not hesitate to contact us using the contact form below. You can also contact us by writing an email to '} - coral@caleydo.org. We are glad to help you. -
-{item.name}
-+ {item.name} +
+Coral is a cohort analysis tool to interactively create and refine patient cohorts,
while visualizing their provenance in the Cohort Evolution Graph. The resulting cohorts can then
be compared, characterized, and inspected down to the level of single entities.
+ Coral is a cohort analysis tool to interactively create and refine patient cohorts,
+ while visualizing their provenance in the Cohort Evolution Graph. The resulting cohorts can then
+ be compared, characterized, and inspected down to the level of single entities.
+
The video was produced with an earlier Coral version and shows a slightly different user interface compared to the current app.
*/} - {!modalIsClosed && } + {/*The video was produced with an earlier Coral version and shows a slightly different user interface compared to the current app.
*/} + {!modalIsClosed && ( + + )}What's new?
++ What's new? +
- 🎉 It's the 2-year anniversary of Coral and we're happy to launch Coral v1.0! 🚀 + Coral 2.0 is available! 🚀
- This websites introduces Coral's features, the available datasets, and publications. - We also provide an introductory video to get to know Coral. -
-- In the upcoming releases, we will focus improving the usability and interactions with Coral. + The latest Coral release contains several style changes, bugfixes, and structural changes of the application.
Publication
++ Publication +
- Coral and its components have been published in multiple scientific articles. Please cite the following article when using Coral and publishing your results. + Coral and its components have been published in multiple scientific articles. Please cite the following article when using Coral and publishing + your results.
- Patrick Adelberger, Klaus Eckelt, Markus J. Bauer, Marc Streit, Christian Haslinger, Thomas Zichner.
- Coral: a web-based visual analysis tool for creating and characterizing cohorts.
- Bioinformatics, doi:10.1093/bioinformatics/btab695, 2021.
+ Patrick Adelberger, Klaus Eckelt, Markus J. Bauer, Marc Streit, Christian Haslinger, Thomas Zichner.
+
+ Coral: a web-based visual analysis tool for creating and characterizing cohorts.
+
+
+ Bioinformatics, doi:
+
+ 10.1093/bioinformatics/btab695
+
+ , 2021.
+
- {/*
+ {/*
{title}
-
- Number of Attribtues: {config.numbAttr}
-
- Type of Attribtues: {config.attribute}
-
{title}
+
+ Number of Attribtues: {config.numbAttr}
+
+ Type of Attribtues: {config.attribute}
+
Getting Started
-
- The workflow of Coral consists of two steps: creating cohorts, and characterizing them. - Operations from these two categories are carried out in an iterative workflow. -
++ The workflow of Coral consists of two steps: creating cohorts, and characterizing them. Operations from these two categories are carried out in an + iterative workflow. +
Cohort Creation
- An initial cohort that contains all items of the selected dataset is created automatically. - Creation operations allow users to create new sub-cohorts based on different attributes and attribute combinations. - Cohorts are refined with the Filter operation, or divided into multiple cohorts with the Split operation. + An initial cohort that contains all items of the selected dataset is created automatically. Creation operations allow users to create new + sub-cohorts based on different attributes and attribute combinations. Cohorts are refined with the Filter operation, or divided into multiple + cohorts with the Split operation.
Cohort Characterization
- Characterization operations give insights into the cohorts. - Similarities and differences between cohorts can be checked visually with the View operation, and statistically with the Compare operation. - Additional operations give access to prevalence information and the data of individual items. + Characterization operations give insights into the cohorts. Similarities and differences between cohorts can be checked visually with the{' '} + View operation, and statistically with the Compare operation. Additional operations give access to prevalence information and the data + of individual items.
Cohort Tracking
@@ -40,12 +39,13 @@ export function GettingStarted() { Coral also assigns a color to each selected cohort that is used throughout the application to visualize the cohort's data.
- The source code of Coral is released at GitHub. -
-- This application is part of Phovea, a platform for developing - web-based visualization applications. For tutorials, API docs, and - more information about the build and deployment process, see the - documentation page. -
-Version: {(status === 'success') ? value.version : 'Fetching current version ...'}
-+ The source code of Coral is released at{' '} + + GitHub + + . +
++ This application is part of Phovea, a platform for developing web-based visualization applications. For tutorials, API docs, and more information + about the build and deployment process, see the documentation page. +
++ Version: {status === 'success' ? value.version : 'Fetching current version ...'} +
- Coral is a cohort analysis tool to interactively create and refine patient cohorts,
- while visualizing their provenance in the Cohort Evolution Graph.
+ Coral is a cohort analysis tool to interactively create and refine patient cohorts, while visualizing their provenance in the Cohort Evolution Graph.{' '}
+
The resulting cohorts can then be compared, characterized, and inspected down to the level of single entities.
- Workflow and Features + + Workflow and Features
- The workflow of Coral consists of two steps: creating cohorts, and characterizing them.
- Operations from these two categories are carried out in an iterative workflow.
+ The workflow of Coral consists of two steps: creating cohorts, and characterizing them. Operations from these two categories are carried out in an
+ iterative workflow.
+
Creation operations allow users to create new sub-cohorts based on different attributes and attribute combinations.
Characterization operations give insights into the cohorts.
- You can find more details on Coral's workflow and features in the Features section. + You can find more details on Coral's workflow and features in the{' '} + + Features + {' '} + section.
- Datasets + + Datasets
- Coral's database contains metadata as well as mutation data from the AACR Project GENIE, - mRNA expression, DNA copy number, and mutation data from The Cancer Genome Atlas (TCGA) and the Cell Line Encyclopedia (CCLE). - Furthermore, two CRISPR / RNAi loss-of-function screen data sets (DRIVE and Avana) are included. + Coral's database contains metadata as well as mutation data from the AACR Project GENIE, mRNA expression, DNA copy number, and mutation data from + The Cancer Genome Atlas (TCGA) and the Cell Line Encyclopedia (CCLE). Furthermore, two CRISPR / RNAi loss-of-function screen data sets (DRIVE and + Avana) are included.
- You can find more detailed information about the datasets here. + You can find more detailed information about the datasets{' '} + + here + + .
- Publications + + Publications
-- Coral and its components have been described in the following scientific publications. -
+Coral and its components have been described in the following scientific publications.
- Patrick Adelberger, Klaus Eckelt, Markus J. Bauer, Marc Streit, Christian Haslinger, Thomas Zichner.
- Coral: a web-based visual analysis tool for creating and characterizing cohorts.
+ Patrick Adelberger, Klaus Eckelt, Markus J. Bauer, Marc Streit, Christian Haslinger, Thomas Zichner.
+
+ Coral: a web-based visual analysis tool for creating and characterizing cohorts.
+
Bioinformatics, doi:10.1093/bioinformatics/btab695, 2021.
- Marc Streit, Samuel Gratzl, Holger Stitz, Andreas Wernitznig, Thomas Zichner, Christian Haslinger.
- Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples.
+ Marc Streit, Samuel Gratzl, Holger Stitz, Andreas Wernitznig, Thomas Zichner, Christian Haslinger.
+
+ Ordino: visual analysis tool for ranking and exploring genes, cell lines, and tissue samples.
+
Bioinformatics, 35(17): 3140-3142, 2019.
- Klaus Eckelt, Patrick Adelberger, Thomas Zichner, Andreas Wernitznig, Marc Streit.
- TourDino: A Support View for Confirming Patterns in Tabular Data.
- EuroVis Workshop on Visual Analytics (EuroVA '19), 2019.
+ Klaus Eckelt, Patrick Adelberger, Thomas Zichner, Andreas Wernitznig, Marc Streit.
+
+ TourDino: A Support View for Confirming Patterns in Tabular Data.
+
+ EuroVis Workshop on Visual Analytics (EuroVA '19), 2019.
- Katarina Furmanova, Samuel Gratzl, Holger Stitz, Thomas Zichner, Miroslava Jaresova, Martin Ennemoser, Alexander Lex, Marc Streit.
- Taggle: Combining Overview and Details in Tabular Data Visualizations.
+ Katarina Furmanova, Samuel Gratzl, Holger Stitz, Thomas Zichner, Miroslava Jaresova, Martin Ennemoser, Alexander Lex, Marc Streit.
+
+ Taggle: Combining Overview and Details in Tabular Data Visualizations.
+
Information Visualization, 19(2): 114-136, 2019.
Please cite the first article when using Coral and publishing your results.
- Please cite the first article when using Coral and publishing your results. -
-- You can find more information about the publications here. + You can find more information about the publications{' '} + + here + + .