This GitHub repository includes a JupyterLab notebook to introduce the Elsevier Scopus Search and Abstract Retrieval Search APIs. It was created as a supplemental document for the conference paper and presentation:
Hennesy, C., McBurney, J., & Gyendina, M. (2023). "Leveraging scholarly APIs to analyze publication trends." LibPMC 2023: International Conference on Performance Measurement in Libraries. July 11, 2023.
If you are new to Jupyter and/or Python, we recommend following the directions on the Software Carpentry site to download and install Anaconda on your own machine, which includes both Jupyter and Python 3, as well as many of the most common scientific computing libraries for Python.
Note: Users without institutional access to Scopus will likely not be able to utilize much of this code to work with Scopus API metadata.
To run and modify the code in the notebook file in this repository:
- Download the code using the Download ZIP option from the <> Code dropdown on this page.
- Unzip the folder with the code and use the command line to navigate inside of the elsevier_apis folder. See instructions for Mac or Windows 10 or 11.
- Use Anaconda to launch a JupyterLab notebook on your laptop by typing
jupyter labon the command line. - From here you can open the Scopus_APIs.ipynb file in Jupyter from your browser and follow along with the code.
- You'll need to register for your own Elsevier API key to make API calls.
- Also make sure to run this code from a campus computer or using an institutional VPN so you can access Scopus metadata that is licensed by your institution.