Special Issue: 2023 Research Data Access and Preservation (RDAP) Summit

  • Alicia Cappello orcid logo (Queen's University)
  • Marla Hertz orcid logo (University of Alabama at Birmingham)
  • Tina Griffin orcid logo (University of Illinois Chicago)
  • Jamaica Jones orcid logo (University of Pittsburgh)


The 2023 Research Data Access and Preservation (RDAP) Summit, Building on Experience: Centering Communities in Data Creation and Access, focused on engagement with and building communities within data environments, including how data is being made more accessible for a wider range of communities. The 2023 RDAP Summit was a natural extension from the prior year’s theme of Envisioning an Inclusive Data Future, which highlighted the ways data service providers tailor their services to address specific needs. A selection of presentations from this year’s Summit were expanded into articles for this special issue covering topics on developing and maintaining communities that address aspects across the research data life cycle.

Keywords: RDAP, conference proceedings, community

How to Cite:

Cappello, Alicia, Marla Hertz, Tina Griffin, and Jamaica Jones. 2023. "Special Issue: 2023 Research Data Access and Preservation Summit." Journal of eScience Librarianship 12(3): e862. https://doi.org/10.7191/jeslib.862.

Rights: Copyright © 2023 The Author(s). This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.



Published on
20 Dec 2023

In 2023, the Research Data Access and Preservation (RDAP) association’s third virtual Summit theme was Building on Experience: Centering Communities in Data Creation and Access . Community has always been crucial to RDAP members. As a new (-ish) professional association, we strive to create our own community while also seeking to support the data communities to which we partner. The articles included in this special issue showcase these ambitions. This year's issue includes one commentary, four eScience in action pieces, and four full papers that demonstrate a range of engagement and dedication to community work with data. Within these articles, we see connections to community through strategies to counter research misconduct (Goben), the development of collaborations (Hedgepeth; Hertz), understanding and providing better storage and preservation options (Haberman; Key; Magle), as well as the continuation of teaching data management principles (Marchant; Badger) and providing data management services (Newman).

The commentary, “ What if It Didn’t Happen: Data Management and Avoiding Research Misconduct ,” reviews three case studies that seek to better understand how the lack of proper data management could allow for research misconduct. The article examines the relationship between research misconduct, the power dynamics that exist within research teams, and effective data management. It recommends activities that could improve data management practices, reduce power imbalances, and avoid misconduct, thus contributing to the rigor and reproducibility of research.

Two articles centered around the theme of building collaborations and communities. One article, “ ‘What About Your Friends?’: How a Collaborative Transdisciplinary Training Approach Supports FAIR Data Sharing Principles in Federally Funded Research ” by Hedgepeth et al., details the efforts at the USDA Agricultural Research Services (ARS) to build connections with agricultural researchers. The authors conducted surveys to identify the needs of such researchers, and then used the results to create discipline specific resources and training sessions. The authors found that this customized approach resulted in more effective data management plans from ARS researchers. The other article, “ A Problem Shared Is a Community Created: Recommendations for Cross-Institutional Collaborations ” by Hertz et al., describes how a volunteer working group of over 30 librarians and data professionals produced a variety of tools to help their community address the new NIH Data Management and Sharing Policy requirements. The article provides recommendations on developing, organizing, communicating, and disseminating practical outputs from similar volunteer groups.

Three articles focused on the theme of reviewing and analyzing the various aspects of data curation and preservation. “ Connecting Repositories to the Global Research Community: A Re-Curation Process ” by Haberman provides strategies on how to update dataset records in data repositories. The article focuses on re-curation, an important process that is required when repository records need to be updated for example, when a new persistent identifier is created. The article “ Building a Trustworthy Data Repository: CoreTrustSeal Certification as a Lens for Service Improvements ” by Key et al., describes the authors’ experience with and rationale for pursuing CoreTrustSeal certification for their institutional repository. The article outlines the purpose, value, and options available for repository certifications and why their institution opted to pursue CoreTrustSeal specifically. The article also provides lessons learned through the certification process and how the endeavour improved repository services overall. Finally, “ Why Can’t I just use Dropbox? A comparison of cloud file storage platforms used for research ” by Magle and McCafferey compares user access functionality, storage capacity, and sharing constraints of four common cloud storage platforms. This article outlines the benefits and limitations of these platforms and serves as a reminder that such storage solutions will have a downstream impact on research data management.

Another three articles concentrated on the theme of improving data management instruction and services. One, “ Teaching by Example: Evidence of Data Literacy Competencies and Practices in Top Economics Journal Articles ” by Marchant, is an exploration of data literacy in economics. The author performed a content analysis of journal articles in economics and analyzed the use of data-related terminology in five key areas of data literacy. These analyses identified several problem areas, namely the lack of data sharing and incorrect data citations. The article concludes with suggestions for librarians that would aid economics professors in teaching data literacy concepts to students. The article “ Towards a Shared Framework: A Classificatory Matrix for Teaching Data Standards ” by Badger, is written with the intention of sparking cross-disciplinary conversations to advance the practice of data standards, which are identified as a common pain-point for many researchers. Data standards, a deceptively simple term, have a wide range of parameters that vary in their purpose and in the type of information they contain. The article compares several approaches to data standards used by librarians and researchers to describe data and provides a classification model that organizes types of data standards based on their purpose and type of information. The article “ Using Data Interviews to Map Research Data Services (RDS) Needs at a Health Science Institution ” by Newman, details a quality improvement project implemented to provide better data management services at the author’s institution. From interviews with ten faculty members, the author was able to map the feedback to Harvard’s Biomedical Data Lifecycle in order to identify both potential data service partners and areas for improvement or creation of library services.

The 2023 RDAP Summit showcased the power of building and maintaining communities to improve research data practices through training and instruction, tailoring service options, and advancing data repositories. Looking forward to the 2024 theme, Bridging Boundaries: Interoperability in the Data Community , next year’s Summit will center on the vitally important, but often overlooked, “I”, in making data FAIR. This theme encompasses many areas, including interoperability within the data itself, between different systems, between data communities, and across institutions, as well as the social components associated with making systems interoperable.