IASSIST GVC 2021: Openness (2021-05-19)
Channel: iassistdata
Date: 18/06/2021

0:00 Mathematics, Risk, and Messy Survey Data
20:28 Optimizing Openness in Human Subjects Research: Balancing Transparency and Protection
43:15 How to guide anonymisation?

Mathematics, Risk, and Messy Survey Data
Data de-identification is a fraught and somewhat poorly understood area beset with intimidating ethical implications and oddly named mathematical algorithms. In addition, data needing assessment may be poorly documented and difficult to work with. Finally, data librarians are often not trained in data risk assessment, yet find themselves in the position of deciding whether a dataset can or should be shared openly. This session will introduce key topics in data de-identification and risk assessment, including various population heuristics, k-anonymity, and related concepts. Using these concepts the authors cane up with a workflow to clean, assess, and eventually publish some high-risk, neglected government survey datasets.
Kristi Thompson, Western University
Carolyn Sullivan, Western University

Optimizing Openness in Human Subjects Research: Balancing Transparency and Protection
Different institutional stakeholders play different roles in guiding the sharing of research data.  Funders and publishers/journals, for instance, are both sources and “enforcers” of data management and sharing mandates. Repositories are “transparency facilitators”, offering workflows, tools and technologies for data preservation and access. Finally, ethics boards (in the United States, often university-based Institutional Review Boards [IRBs]) are charged with ensuring that researchers comply with regulations and rules designed to protect from harm the human participants they engage in their work. Given that role, some assume that IRBs’ natural position is to stand in the way of openness. Our research tests that assumption: we investigate whether and how IRB professionals understand the challenges and opportunities presented by the renewed emphasis on data sharing in the social sciences. Specifically, we undertook a qualitative study of the US-based IRB community, conducting interviews and focus groups with representatives of 30 institutions that receive high levels of funding from the National Science Foundation’s Directorate for Social Behavioral and Educational Sciences. Using a structured questionnaire, we asked respondents about the advice they give scholars concerning data sharing; their familiarity with and views on repositories’ processes and technology; and their coordination with other campus units on these issues. While many respondents reported awareness of evolution in data-sharing mandates, few had made systematic changes to their policies or procedures to accommodate that evolution. Crucially, however, we found that when approached as partners in supporting robust scholarly research, IRBs are open – in fact eager – to engage with other actors who can provide different expertise. Integrating IRBs into ongoing conversations about sharing social science research data ethically and legally will help IRB professionals to understand, and enable them to encourage researchers to see, how potential conflicts between human participant protection and research transparency can be prevented or mitigated.
Dessislava Kirilova, Qualitative Data Repository
Diana Kapiszewski, Georgetown University / Qualitative Data Repository
Colin Elman, Syracuse University / Qualitative Data Repository

How to guide anonymisation?
The presentation focuses on anonymisation guidance that FSD offers to its customers for both quantitative and qualitative research. Anonymity of the datasets makes data dissemination much easier for researchers since it removes the need to apply the GDPR. Anonymisation is also one of the safeguards mentioned in the GDPR. In practice, FSD also mainly archives anonymous research data with some exceptions and requires that researchers do the anonymization themselves. The challenge is that researchers often lack skills of anonymisation. This presentation discusses the guidance given in the FSD´s Data Management Guide and practices used in anonymisation training and workshops held for researchers. The focus is not in anonymisation techniques or statistical anonymisation methods but rather in anonymisation planning, decision making and practical tips for anonymisation process. The presentation may help other archives to enhance their anonymisation guidance and offers an opportunity to think together about anonymisation and anonymisation practices in more detail. 
Annika Valaranta, Finnish Social Science Data Archive FSD