Promoting Computational Access to Digital Collections in the Nordic and Baltic Countries: An Icelandic Use Case - Gustavo Candela; Olga Holownia; Max Odsbjerg; Mirjam Cuper; Nele Gabriëls; Katrine Hofmann; Edward J. Gray; Sally Chambers; Mahendra Mahey (Journal of Open Humanities Data, 2025)
Over the last decade, GLAM (Galleries, Libraries, Archives and Museums) institutions have been exploring new ways to make their content available. Recent initiatives such as GLAM Labs and Collections as data promote the reuse of and computational access to digital collections. Low barrier-to-entry tools, such as the web-based interactive computing platform Jupyter Notebooks have been gradually gaining popularity among users who want to explore digital collections and GLAM institutions that want to provide access to them. This paper is an extension of the workshop focusing on computational access at GLAM institutions delivered at the 2024 edition of Digital Humanities in the Nordic and Baltic Countries (DHNB) Conference.1 This paper aims to provide a framework for creating a collection of Jupyter Notebooks using GLAM digital collections following best practices and guidelines and as part of a Collections as data publication workflow. The intended audience is Digital Humanities researchers and GLAM professionals interested in exploring the computational reuse of digital collections. The main contributions of this work are: (a) a framework for creating a collection of Jupyter Notebooks in the GLAM domain, (b) the reproducible code and examples of use, and (c) the results obtained after its application. These contributions are intended to foster responsible computational access to collections in the GLAM sector. The framework was applied to a relevant dataset derived from Timarit.is, a digitised collection of newspapers and periodicals from Iceland, the Faroe Islands and Greenland. We identified common features and best practices and we provide a detailed analysis that can be useful for organisations interested in publishing reproducible code combined with documentation such as Jupyter Notebooks. We also outline open issues that require further work.