Spurred by the National Institute of Health mandating a data management and sharing plan as a requirement of grant funding, research data management has exploded in importance for librarians supporting researchers and research institutions. This editorial examines the role and direction of libraries in this process from several viewpoints. Key markers of success include collaboration, establishing new relationships, leveraging existing relationships, accessing multiple avenues of communication, and building niche expertise and cachè as a valued and trustworthy partner.

Translational research requires data management skills, which, however, are noticeably lacking in the medical education curriculum. With the expanding number of data types involved, research teams need to become more interdisciplinary and incorporate data management responsibilities. Adoption of more organized practices, workflows, and tools can help maximize insights and knowledge transfer from bench to bedside. Data management in clinical and translational research is essential for research reproducibility and replicability. Researchers are expected to plan for data collection, manage research responsibilities, ensure data quality, and comply with data sharing regulations. Software and tools play a critical role in managing the integration, quality assurance, analysis, and sharing of research data. For example, Research Electronic Data Capture is a widely used electronic data capture system for storing research data. However, there are still gaps in data management education in the clinical research setting. This chapter will recommend how to safely and effectively manage data, with an emphasis on translational surgery research activities, including recommended practices for working with data, challenges for this discipline, and tools to help streamline workflows.

The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes learner reported data and course generated analytics from March 2020 through June 2021 for the course. This analysis focuses on three subsets of participant data during the pandemic to understand global learner demographics and interest in biomedical research data management. Related preprint: 10.31219/osf.io/9qbg4

This article reports on the development and evaluation of a massive open online course (MOOC) that provides instruction on best practices in research data management (RDM). The course was developed in response to the growing need for data management professional development for LIS professionals and to promote data management to researchers. In just 18 months of the course launch, the course reached more than 1,000 people from across the world and was effective in building student competency in RDM. The success of this course illustrates the value and utility of free online professional development as a tool for both library and research staff.

The Harvard Medical School Countway Library’s Massive Open Online Course (MOOC) Best Practices for Biomedical Research Data Management launched on Canvas in January 2018. This report analyzes student reported data and course generated analytics from January 2018, through July 8, 2020, for the course Best Practices for Biomedical Research Data Management. By comparing the findings from the enrollment period through March 8, 2020 (pre-pandemic) to the period through July 8, 2020 (during-pandemic), the main goal is to investigate potential shifts due to the COVID-19 pandemic. Related preprint: 10.31219/osf.io/gcqb8

In 2017 a group of academic library and information technology staff from institutions across New England piloted a process of joining The Carpentries, an organization developed to train researchers in essential computing skills and practices for automating and improving their handling of data, as a consortium. The New England Software Carpentry Library Consortium (NESCLiC) shared a gold-level tier membership to become a Carpentries member organization. NESCLiC members attended a Software Carpentry workshop together and then participated in instructor training as a cohort, collaborating on learning the material, practicing, and beginning to host and teach workshops as a group.

This article describes both the successes and challenges of forming this new consortium, suggests good practices for those who might wish to form similar collaborations, and discusses the future of this program and other efforts to help researchers improve their computing and data handling skills.

Objectives: To meet the changing needs of our campuses, librarians responsible for research data services are often tasked with starting new endeavors with new populations without much support. This paper reports on a collaborative effort to build a community of practice of librarians tasked with addressing the research data needs of their campuses, describes how this effort was evaluated, and presents future opportunities.

Methods: In March of 2015, three librarians found themselves in a situation of serendipitous professional development: one was seeking to provide a new method of mentorship, and two more were working on an event, hoping to broadcast it to a wider community. From these two disparate goals, the Research Data Management (RDM) Roundtables were created. The RDM Roundtables planning committee developed a low-cost professional development day divided into two parts: a morning session that detailed an idea or solution relevant to our practice, and an afternoon roundtable discussion on practical aspects of research data services. Evaluations from these events were coded in NVivo and we report on the common themes.

Results: Participants returned sixty-one evaluations from four events. Five themes emerged from the evaluations: learning, sharing, format, networking, and empathy.

Conclusion: The events provide a valuable professional development experience for attendees, and the authors hope that by providing a description of the events’ development, others will establish their own local communities of practice.

Research in many academic fields today generates large amounts of data. These data not only must be processed and analyzed by the researchers, but also managed throughout the data life cycle. Recently, some academic libraries have begun to offer research data management (RDM) services to their communities. Often, this service starts with helping faculty write data management plans, now required by many federal granting agencies. Libraries with more developed services may work with researchers as they decide how to archive and share data once the grant work is complete.

Objective: To understand how New England medical libraries are addressing scientific research data management and providing services to their communities.

Setting: The National Network of Libraries of Medicine, New England Region (NN/LM NER) contains 17 Resource Libraries. The University of Massachusetts Medical School serves as the New England Regional Medical Library (RML). Sixteen of the NER Resource Libraries completed this survey.

Methods: A 40-question online survey assessed libraries’ services and programs for providing research data management education and support. Libraries shared their current plans and institutional challenges associated with developing data services.

Results: This study shows few NER Resource Libraries currently integrate scientific research data management into their services and programs, and highlights the region’s use of resources provided by the NN/LM NER RML at the University of Massachusetts Medical School.

Conclusions: Understanding the types of data services being delivered at NER libraries helps to inform the NN/LM NER about the eScience learning needs of New England medical librarians and helps in the planning of professional development programs that foster effective biomedical research data services.