For the last couple of months I’ve been working on a new open source Python project. This is called DimCli  and it’s a library aimed at making it simpler to work with the Dimensions Analytics API.

The project is available on Github. In a nutshell, DimCli helps people becoming productive with the powerful scholarly analytics API from Dimensions. See the video below for a quick taster of the functionalities available.

Background

I recenlty joined the Dimensions team, so needed a way to get to grips with their feature-rich API (official docs). So, building DimCli has been a fun way for me to dig into the logic of the Dimensions Search Language (DSL).

Plus, this project gave me a chance to learn more about two awesome Python technologies: JupyterLab and its magic commands, as well as the Python Prompt Toolkit library.

Features

In a nutshell, this is what DimCli has to offer:

It’s an interactive query console for the Dimensions Analytics API (ps: Dimensions is a world-class research-data platform including information about millions of documents like publications, patents, grants, clinical trials and policy documents.

It helps learning the Dimensions Search Language (DSL) thanks to a built-in autocomplete and documentation search mechanism.

It handles authentication transparently either via a global user-specific credentials file, or by passing credentials manually (e.g. when used within shared environments).

It allows to export results to CSV, JSON and pandas dataframes, hence making it easier to integrate with other data analysis tools.

It is compatible with Jupyter, e.g. it includes various magic commands that make it super simple to interrogate Dimensions (various examples here).

Feedback

DimCli lives on Github, so for any feedback or bug reports, feel free to open an issue there.