Computational access to digital collections

GLAM institutions have explored new ways of publishing digitised and born-digital collections in order to apply computational methods. The initiative Always Already Computational: Collections as Data guides institutions and researchers to provide collections as data. This section introduces several relevant projects and innovative initiatives that reuse digital collections and explore the application of computational methods by means of Jupyter Notebooks.

The information describing the Jupyter Notebook projects and the datasets reused has been introduced into Wikidata, a collaborative edition platform, to provide a clickable graphical visualization that is shown below. The list of projects introduced in this section can be retrieved from Wikidata by clicking on the following link.

In addition, an approach to assess the quality of Jupyter projects published by GLAM institutions have been recently published in the form of a research article in the Journal of the Association for Information Science and Technology.

Data Foundry - Jupyter Notebooks

These Jupyter Notebooks provide initial, exploratory analysis of some of the National Library of Scotland datasets. No prior programming experience is needed to access and use these Notebooks.

GLAM Workbench

A collection of tools, tutorials, examples, and hacks to help you work with data from GLAM institutions. While the primary focus is Australia and New Zealand, new collections are being added all the time.

LC for Robots

The data-exploration repository includes Jupyter Notebooks and example scripts using openly available Library of Congress Digital Collections or records.

Jupyter Notebooks using the British Library’s Digital Collections and Data

A list of Jupyter Notebook' projects using the British Library’s digital collections and data.

GLAM Jupyter Notebooks

A collection of Jupyter Notebooks based on GLAM institutions provided by Biblioteca Virtual Miguel de Cervantes. Additional notebooks are provided by ONB Labs.