Getting Started

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

A companion website including browsable HTML versions of these tutorials is also available.

Note: please use the notebooks in the cookbook folder for up-to-date examples and templates (see below for more details).

Background

If you've never heard of Dimensions.ai or Jupyter notebooks, then this section if for you.

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/

For a detailed breakdown of the Dimensions API language, see the API documentation

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.

There are two main folders:

• The cookbooks folder contains the latest version of notebooks providing reusable, simple solutions to common scholarly analytics problems. The notebooks in this folder are regularly tested and kept up to date. If any of these do not work, please open an issue to let us know.
• The archive folder contains historical copies of notebooks used for workshops and tutorials. Most often, these are customized versions of the generic notebook templates (in the cookbooks folder), adapted for a specific institution or audience. Please note that the notebooks in this folder are simply archived for future reference and not maintained afterwards. So use the materials in the cookbooks folder for up to date examples and reusable templates.

Using Binder

mybinder.org is a free service that transforms a github repository into a Jupyter server hosting the repository's contents.

With Binder, you can run most of the Jupyter notebooks directly from your web browser without installing anything. Just click on the launch binder button below. A temporary Jupyter Notebook server with all dependencies will be automatically launched in the cloud. It is not persistent: all your changes will be lost after some time.

Using Google Colab

Google Colaboratory is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud.

With Colaboratory you can write and execute code, save and share your analyses, and access powerful computing resources, all for free from your browser.

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