Beyond Licences and APIs: A Human-Centred Decision Framework for Finance Data in Research Libraries

In today’s unpredictable environment, decisions about selecting materials in research libraries go far beyond issues of coverage and cost. These choices influence who gains access to knowledge, how reliably research can be replicated, and whether scholars can conduct their work with dignity and thoughtful support. The banking and finance sectors pose an especially complex challenge, given their rapidly evolving markets, multilayered regulations, and a patchwork of data sources that include proprietary databases, official statistics, academic indexes, and repositories. This talk presents insights from a comprehensive scoping review of finance data sources conducted between 2019 and 2025 (with the latest update in September 2025). It introduces a human-centred framework to guide decisions in collection development and library service design. The review examines four types of sources: (1) global financial databases (like Bloomberg, Refinitiv, S&P Capital IQ, and FactSet); (2) official and regulatory statistics (such as those from the ECB, IMF, Eurostat, and national statistics agencies and central banks); (3) academic discovery platforms (including Scopus and Web of Science); and (4) repositories from institutions, nations, and central banks. The sources were evaluated through a mix of document analysis and comparative review, using weighted criteria like source authority, thematic and geographical scope, how often they’re updated, quality of data and metadata, discovery tools, and licensing terms. Moving past just technical assessments, this framework also incorporates metrics rooted in care: the burden of ensuring reproducibility, the mental load required to use the data, the stress caused by restrictive licenses and use conditions, and the wider benefits to communities through open standards and machine-readable licenses. These care-based metrics are not arbitrary. They’re drawn from established research in reproducibility, human-computer interaction, care ethics, and open science, and have been translated into practical tools for library collection decisions. The findings highlight that while proprietary platforms are often timely and integrate well into workflows, they tend to obstruct transparency and reuse. Official statistics are authoritative but still struggle with integration across systems, and repositories bring valuable grey literature to light but remain poorly connected to broader systems. Ongoing gaps in FAIR data principles,especially regarding versioning, persistent IDs, and citation standards, continue to threaten research integrity. The proposed framework brings together a scored evaluation rubric and a decision tree based on typical finance research scenarios. It helps libraries maintain a healthy balance between open and licensed resources, make hidden risks like vendor lock-in visible, and provide built-in support for user training and data literacy. Ultimately, it reimagines collection development as a form of scholarly care, blending human values with strong evidence to enhance equity, reproducibility, and researcher well-being.