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Learn about the Dimensions Analytics API via code examples and Jupyter notebooks
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README.md

Getting Started

This GitHub repository contains code samples and reusable Jupyter notebooks for scholarly data analytics using the Dimensions API.

The Dimensions Analytics API enables users to perform complex searches in the Dimensions database. For more information about the API language, see also the official documentation.

A companion website including HTML versions of these tutorials is also available: https://digital-science.github.io/dimensions-api-lab/

License: CC BY-NC-SA 4.0

What is Dimensions?

Digital Science's Dimensions is a dynamic, easy to use, linked-research data platform that re-imagines the way research can be discovered, accessed and analyzed. Within Dimensions, users can explore the connections between grants, publications, clinical trials, patents and policy documents.

For more information, see https://www.dimensions.ai/

What are Jupyter Notebooks?

The Jupyter Notebook is an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text. Uses include: data cleaning and transformation, numerical simulation, statistical modeling, data visualization, machine learning, and much more.

For more information, see https://jupyter.org/

Running the examples

If you are already familiar with Python and Jupyter, then you probably know what to do already. Download this repository and run it locally. Feel free to modify and adapt these examples so to match your project needs.

You can also run these examples online in your browser, thanks to Binder and Gigantum.

Using Binder

mybinder.org is a free service that transforms a github repository into a JupyterHub server hosting the repository's contents. Click on the link below for launching it with the Dimensions API Lab repository.

Binder

Using Gigantum

Gigantum is an open-source platform for developing, executing, and sharing analysis and computations using JupyterLab. Gigantum provides a full-featured environment where you You can easily install packages with apt, pip and conda, as well as add Docker snippets for more customized packages.

  • download the zipped dimensions-api-examples image
  • go to https://try.gigantum.com/, click on login, then sign up in order to create an account
  • once you are logged in and in the main 'projects' page, click on 'import existing'
  • drag the zipped image to the project import window
  • load the project and click on launch: jupyterlab

Comments, bug reports

This project lives on Github. You can file issues or ask questions there. Suggestions, pull requests and improvements welcome!

See also

https://docs.dimensions.ai/dsl/resources.html

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