To address developments in data analysis, particularly text analysis, using Machine Learning (ML) and Artificial Intelligence (AI), the University Library (UL) has set up an interdisciplinary team, the Future Library Lab (FLL). This team comprises experts with diverse yet overlapping expertise in software architecture, AI and ML, scientific information and data science. This enables the UL to experiment with the applying ML and AI techniques, both supervised and unsupervised, to library data and other datasets.
Services and collaborations
AI-assisted literature review via topic identification
The FLL supports researchers by leveraging Machine Learning (ML) and Artificial Intelligence (AI) to identify key topics during the literature review process. This approach accelerates the (systematic) literature review and can enhance and broaden the research scope.
Patient stories
The Library has a large, mostly Dutch-language collection of patient experience stories on physical disabilities and diseases such as cancer, Alzheimer’s, and psychosis. This collection is a rich resource for researchers, active in the field of health policy and citizen science. Currently, the FLL is working with a team of researchers from the Erasmus School of Health Policy and Management (ESHPM) to explore the potential of this collection for their research.
Summa
Summa is a searchable multi-tenant platform that can ingest, transform, merge, and enhance data from different sources via so-called pipelines. This platform allows the FLL to present a comprehensive overview of publications, enriched with an additional layer of AI-generated metadata. Summa is designed to handle other text files, such as patient experience stories.
Machine-generated metadata
FLL aims to generate additional layers of metadata for academic publications that improve the discoverability of the publications and provide additional information to the users about the content of the documents.
Improving the readability of academic articles
With the application of Large Language Models (LLMs), the FLL can provide simplified versions of academic publications without compromising the content of the publication. This service can help lecturers present course material that is otherwise difficult (or less) accessible due to the use of jargon and complex language. Improving the readability of articles to make them comprehensible to scientists from other disciplines will also foster interdisciplinary collaboration.
Research trend analysis
The FLL seeks to provide insight into trends in scientific research, inside and outside EUR, by merging databases such as Pure, OpenAlex and RePEc, and by using Natural Language Programming (NLP) and AI.
Research sprints
The FLL helps researchers compile data via application programming interfaces (APIs) or web scraping. It also provides support for working with datasets, including exploratory data analysis (EDA), cleansing, analysis, visualisation, and text mining.
Workshops
- Programming with Python (introduction).
- Programming with Python (intermediate).
- Introduction ‘Machine Learning’ (ML).
Other collaborations
The FLL is also supporting a team of researchers from the Erasmus School of History, Culture and Communication (ESHCC) who are building a EUR large language model (LLM) in collaboration with TU Delft as part of the Convergence project.
Staff members of Erasmus University Rotterdam
Want to know more about the Library's services on this topic? Visit the Research Support Portal for practical and detailed information. This portal is accessible to EUR staff only (via MyEUR).
Contact
Future Library Lab
University Library
- Email address
- fll.library@eur.nl
For questions and more information, contact the Future Library Lab via email. A team member will respond to your email as soon as possible.
Team members
- Nick Jelicic
- Jasper Op de Coul
- Farzane Zahra Zarepour