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A collection of Jupyter notebooks, each walking you through a common example of bibliometric analysis using scholarly data from the OpenAlex API.

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OpenAlex API tutorials

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A collection of Jupyter notebooks, each walking you through a common example of bibliometric analysis using scholarly data from the OpenAlex API. (:warning: Work In Progress).

💡 What is OpenAlex?

OpenAlex is a fully-open index of scholarly works, authors, venues, institutions, and concepts — along with all the ways they're connected to one another. It's named after the ancient Library of Alexandria and made by the nonprofit OurResearch.

What makes OpenAlex stand out as a bibliographic data source is its Openness:

  • The data is made available under the CC0 license. That means it's in the public domain, and free to use in any way you like.

  • The primary way to access the data, is the API. It is free and requires no authentication.

📓 What are Jupyter notebooks?

Jupyter notebooks are documents that let you combine executable code snippets with explanatory text, formulas and visualizations. Weaving both of them together allows to craft a narrative around the How? and Why? of one's programming work which makes them especially useful for writing up documentation and tutorials. But not only that: you can also dive right in by modifying and re-running code snippets as needed. Therefore a notebook may serve as a starting point to prototype your own idea!

🚀 How do I run the notebooks?

Note: You can browse through and read the notebooks right here on GitHub. However, the code snippets won't be executable.

The easiest way to run Jupyter notebooks is via cloud services like Binder, Google’s Colaboratory or Deepnote. They provide you with a free execution environment that you can access directly in your browser - no setup needed. Just click on one of the badges at the top of this README and it will take you to the selected service.

Alternatively you can set up a Jupyter server on your computer (for instructions please refer to the official Jupyter docs).
Many IDEs also support running Jupyter notebooks out of the box or via a plugin. If you have one installed, it may be a good idea to consult its docs or marketplace. If you go local, though, please remember to install the Python packages specified in the requirements.txt file.

📖 Citation

If you use OpenAlex in your research, please cite this paper:

Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. ArXiv. https://arxiv.org/abs/2205.01833

and don't forget to tell us about your project. We love to hear what you come up with using data from OpenAlex!

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A collection of Jupyter notebooks, each walking you through a common example of bibliometric analysis using scholarly data from the OpenAlex API.

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