Conference Agenda

This study explores how digital repositories approach qualitative research data curation through an ethics of care lens, particularly when handling data containing identifiable participants. Through 44 semi-structured interviews with educational researchers and teacher-educators who produce and reuse video records of practice (VROP), the research examines perceptions of care in repository practices and the relationships between repositories and their designated communities.

Our findings indicate that (1) data producers and reusers in education view repositories as sites of care, (2) they view data curation as a form of care, and (3) they expect repositories to act in the best interest of the participants represented in research data, thereby enacting an ethics of care.

Interviewees emphasized that repositories must extend beyond technical compliance to embrace ethical commitments that preserve participant dignity throughout the data lifecycle. They sought repositories whose values aligned with their own ethics of care, particularly regarding protection of vulnerable populations. The study identifies care as both a relational process that develops over time and a framework that should inform repository policies from data selection through access decisions.

These findings extend current understanding of designated communities beyond consumers of data to include groups whose ethical frameworks should inform repository practices, with implications for qualitative data repositories containing data with identifiable participants.

In academic research, data sharing, particularly secondary data reuse, relies heavily on informal networking. ‘Grey’ transfers of data motivated by research purposes are common. In this paper, working through use cases presented by professionals in digital health, research governance and sensitive data management and publication, we explore the compliance challenges of informal data sharing, its detection, policy challenges such as penalties and associated risks such as accidental data breach and scientific impact. We highlight challenges of maintaining researcher awareness of best practice, given the fast-moving UK legal and regulatory landscape and the need to maintain compliance with standards required by key research partners in the EU. We then explore how good data privacy practices, privacy impact assessments, principles of privacy by design and existing frameworks might be used to support the process of engineering systems that provide the needed flexibility to researchers while minimising the risks.

Much has been written about artificial intelligence, with astonishingly rapid progress in computer sciences. In the social sciences, concerns have been raised that artificial intelligence may impact the actual production of scientific output. Most of the discussion has been about the writing of texts, and estimates suggests that the number of articles created and possibly submitted with the help of AI systems is non-trivial. Less interest has been devoted to the use of AI as part of the legitimate scientific production process. Yet use of AI methods in legitimate scientific work is also increasing. With the earlier “replication crisis” still in mind, the question for curators is whether and how to curate AI-supported research tools, input data, and outputs. This article will approach the topic from the perspective of a “data editor”, responsible for verifying reproducibility and supporting curation of research compendia for a prominent learned society in economics.