Full-Length Paper

Use of Optional Data Curation Features by Users of Harvard Dataverse Repository

  • Ceilyn Boyd (Simmons University)


Objective: Investigate how different groups of depositors vary in their use of optional data curation features that provide support for FAIR research data in the Harvard Dataverse repository.

Methods: A numerical score based upon the presence or absence of characteristics associated with the use of optional features was assigned to each of the 29,295 datasets deposited in Harvard Dataverse between 2007 and 2019. Statistical analyses were performed to investigate patterns of optional feature use amongst different groups of depositors and their relationship to other dataset characteristics.

Results: Members of groups make greater use of Harvard Dataverse's optional features than individual researchers. Datasets that undergo a data curation review before submission to Harvard Dataverse, are associated with a publication, or contain restricted files also make greater use of optional features.

Conclusions: Individual researchers might benefit from increased outreach and improved documentation about the benefits and use of optional features to improve their datasets' level of curation beyond the FAIR-informed support that the Harvard Dataverse repository provides by default. Platform designers, developers, and managers may also use the numerical scoring approach to explore how different user groups use optional application features.

Keywords: research data, optional feature use, application users, research data curation, research data repositories, Harvard Dataverse, Dataverse Project

How to Cite:

Boyd, C., (2021) “Use of Optional Data Curation Features by Users of Harvard Dataverse Repository”, Journal of eScience Librarianship 10(2): 1. doi: https://doi.org/10.7191/jeslib.2021.1191

Rights: © 2021 Boyd. This is an open access article licensed under the terms of the Creative Commons Attribution License.

Download PDF



Published on
01 Mar 2021