Practical considerations for a library's research data management services: the case of the National Institutes of Health Library.

Soojung Kim,Sue Yeon Syn

doi:10.5195/jmla.2021.995

Abstract

Objective:This study investigates research data management (RDM) services using a crosstab framework with the National Institutes of Health (NIH) Library as a case study to provide practical considerations for libraries seeking to improve their RDM services.Methods:We conducted semistructured interviews with four librarians who provide data services at the NIH Library regarding library user characteristics, RDM services provided, RDM infrastructure, and collaboration experiences. Through the analysis of interview transcripts, we identified and analyzed the NIH Library's RDM services according to Online Computer Library Center (OCLC)'s three categories of RDM services and the six stages of the data lifecycle.Results:The findings show that the two models' crosstab framework can provide an overview of an institution's current RDM services and identify service gaps. The NIH Library tends to take more responsibility in providing education and expertise services while relying more on information technology departments for curation services. The library provides significant support for data creation, analysis, and sharing stages to meet biomedical researchers' needs, suggesting areas for potential expansion of RDM services in the less supported stages of data description, storage, and preservation. Based on these findings, we recommend three key considerations for libraries: identify gaps in current services, identify services that can be supported via partnerships, and get regular feedback from users.Conclusion:These findings provide a deeper understanding of RDM support on the basis of RDM service categories and the data lifecycle and promote discussion of issues to be considered for future improvements in RDM services.

Highlights

In recent decades, the increasing volume of data-driven research and changing funding agency policies for research data management (RDM) have posed significant challenges for researchers who are not well versed in RDM practices and sharing [1, 2]
This study investigates RDM services using the National Institutes of Health (NIH) Library as a case study and provides practical considerations for libraries seeking to extend their RDM services
Because NIH comprises twenty-seven separate institutes and centers [23], each of which has its own information technology (IT) infrastructure, these institutes and centers handle curation services. This leads to some educational services not being able to be provided to all members of the NIH, the library’s primary RDM services include training classes

Summary

Introduction

The increasing volume of data-driven research and changing funding agency policies for research data management (RDM) have posed significant challenges for researchers who are not well versed in RDM practices and sharing [1, 2]. Health science libraries have developed a wide range of RDM services: instruction and consultation for data management plans (DMPs) [7]; training on data jmla.mlanet.org. I just want to use the tool.’” This remark shows the task-oriented nature of researchers’ goals in taking classes and explains why they are application specific. While their training mainly addresses data analysis needs, librarians have recently begun to emphasize data sharing. In an “Openness and Reproducibility Workshop: A Day of Open Science” in 2019, participants first learned about general issues in data sharing and how to use the Open Science Framework (OSF), an open-source Web platform that helps researchers create, store, collaborate, and share projects.

Methods

Results

Conclusion