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Overview and details of the sessions of this conference. Please select a date or location to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

 
 
Session Overview
Session
Paper Session 10: Research Data Management
Time:
Monday, 01/Nov/2021:
8:00am - 9:30am

Session Chair: Yi-Yun Cheng, University of Illinois at Urbana-Champaign, USA
Location: Salon J, Lobby Level, Marriott


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Presentations
8:00am - 8:30am
ID: 260 / PS-10: 1
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Archives; Data Curation; and Preservation
Keywords: Data Curation; Sensitive Data; Collective Harms; Data Sharing; Open Science

Collective Harms and Contextual Integrity for Sensitive Data

Nicholas Weber

University of Washington, USA

Privacy protections for human subject data are often focused on reducing individual harms that result from improper disclosure of personally identifiable information. However, in a networked environment where information infrastructures enable rapid sharing and linking of different datasets there exist numerous harms which abstract to group or collective levels. In this paper we discuss how privacy protections aimed at individual harms, as opposed to collective or group harms, results in an incompatible notion of privacy protections for social science research that synthesizes multiple data sources. Using the framework of Contextual Integrity, we present empirical scenarios drawn from 17 in-depth interviews with researchers conducting synthetic research using one or more privacy sensitive data sources. We use these scenarios to identify ways that digital infrastructure providers can help social scientists manage collective harms over time through specific, targeted privacy engineering of supporting research infrastructures and data curation.



8:30am - 9:00am
ID: 172 / PS-10: 2
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Library and Information Science
Keywords: Research data management, metadata application profiles, DCAT, FAIR principles

MetaFAIR: A Metadata Application Profile for Managing Research Data

Vivian Tompkins, Brendan Honick, Katherine Polley, Jian Qin

Syracuse University, USA

This paper reports on the development of a metadata application profile (AP), MetaFAIR, designed to support research data management (RDM) to make research data findable, accessible, interoperable, and reusable. The development of MetaFAIR followed a three-step process that included learning about the characteristics of datasets from researchers to establish their context and requirements, as well as iterative design and testing with researchers’ feedback. Guided by the FAIR principles, MetaFAIR focuses on accommodating description needs particular to computational social science datasets while seeking to provide general enough elements to describe data collections across many different domains. In this paper, MetaFAIR is placed in the context of historical and recent developments in the areas of RDM and application profile creation; following this contextualization, the paper describes the central considerations and challenges of the MetaFAIR development process and discusses its significance for future work in RDM.



9:00am - 9:30am
ID: 178 / PS-10: 3
Long Papers
Confirmation 1: I/we agree if this paper/presentation is accepted, all authors/panelists listed as “presenters” will present during the Annual Meeting and will pay and register at least for the day of the presentation.
Confirmation 2: I/we further agree presenting authors/panelists who have not registered on or before the early bird registration deadline will be removed from the conference program, and their paper will be removed from the Proceedings.
Confirmation 3: I/we acknowledge that all session authors/presenters have read and agree to the ASIS&T Annual Meeting Policies found at https://www.asist.org/am21/submission-types-instructions/
Topics: Library and Information Science
Keywords: Research data management, metadata, data description, data documentation

Toward Best Practices for Unstructured Descriptions of Research Data

Dan Phillips, Michael Smit

Dalhousie University, Canada

Achieving the potential of widespread sharing of open research data requires that sharing data is straightforward, supported, and well-understood; and that data is discoverable by researchers. Our literature review and environment scan suggest that while substantial effort is dedicated to structured descriptions of research data, unstructured fields are commonly available (title, description) yet poorly understood. There is no clear description of what information should be included, in what level of detail, and in what order. These human-readable fields, routinely used in indexing and search features and reliably federated, are essential to the research data user experience. We propose a set of high-level best practices for unstructured description of datasets, to serve as the essential starting point for more granular, discipline-specific guidance. We based these practices on extensive review of literature on research article abstracts; archival practice; experience in supporting research data management; and grey literature on data documentation. They were iteratively refined based on comments received in a webinar series with researchers, data curators, data repository managers, and librarians in Canada. We demonstrate the need for information research to more closely examine these unstructured fields and provide a foundation for a more detailed conversation.