Lisa Federer

Biomedical Data Sharing and Reuse: Attitudes and Practices of Clinical and Scientific Research Staff

Background Significant efforts are underway within the biomedical research community to encourage sharing and reuse of research data in order to enhance research reproducibility and enable scientific discovery. While some technological challenges do exist, many of the barriers to sharing and reuse are social in nature, arising from researchers’ concerns about and attitudes toward sharing their data. In addition, clinical and basic science researchers face their own unique sets of challenges to sharing data within their communities. This study investigates these differences in experiences with and perceptions about sharing data, as well as barriers to sharing among clinical and basic science researchers. Methods Clinical and basic science researchers in the Intramural Research Program at the National Institutes of Health were surveyed about their attitudes toward and experiences with sharing and reusing research data. Of 190 respondents to the survey, the 135 respondents who identified themselves as clinical or basic science researchers were included in this analysis. Odds ratio and Fisher’s exact tests were the primary methods to examine potential relationships between variables. Worst-case scenario sensitivity tests were conducted when necessary. Results and Discussion While most respondents considered data sharing and reuse important to their work, they generally rated their expertise as low. Sharing data directly with other researchers was common, but most respondents did not have experience with uploading data to a repository. A number of significant differences exist between the attitudes and practices of clinical and basic science researchers, including their motivations for sharing, their reasons for not sharing, and the amount of work required to prepare their data. Conclusions Even within the scope of biomedical research, addressing the unique concerns of diverse research communities is important to encouraging researchers to share and reuse data. Efforts at promoting data sharing and reuse should be aimed at solving not only technological problems, but also addressing researchers’ concerns about sharing their data. Given the varied practices of individual researchers and research communities, standardizing data practices like data citation and repository upload could make sharing and reuse easier.

The librarian as research informationist: a case study

QUESTION How can an embedded research informationist add value to the scientific output of research teams? SETTING The University of California-Los Angeles (UCLA) Louise M. Darling Biomedical Library is an academic health sciences library serving the clinical, educational, and research needs of the UCLA community. METHODS A grant from the National Library of Medicine funded a librarian to join a UCLA research team as an informationist. The informationist meets regularly with the research team and provides guidance related to data management, preservation, and other information-related issues. MAIN RESULTS Early results suggest that the informationists involvement has influenced the teams data gathering, storage, and curation methods. The UCLA Library has also changed the librarians title to research informationist to reflect the new activities that she performs. CONCLUSION The research informationist role provides an opportunity for librarians to become effective members of research teams and improve research output.

Closing gaps between open software and public data in a hackathon setting: User-centered software prototyping

In genomics, bioinformatics and other areas of data science, gaps exist between extant public datasets and the open-source software tools built by the community to analyze similar data types. The purpose of biological data science hackathons is to assemble groups of genomics or bioinformatics professionals and software developers to rapidly prototype software to address these gaps. The only two rules for the NCBI-assisted hackathons run so far are that 1) data either must be housed in public data repositories or be deposited to such repositories shortly after the hackathon’s conclusion, and 2) all software comprising the final pipeline must be open-source or open-use. Proposed topics, as well as suggested tools and approaches, are distributed to participants at the beginning of each hackathon and refined during the event. Software, scripts, and pipelines are developed and published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development. The code resulting from each hackathon is published at https://github.com/NCBI-Hackathons/ with separate directories or repositories for each team.

Building Genomic Analysis Pipelines in a Hackathon Setting with Bioinformatician Teams: DNA-seq, Epigenomics, Metagenomics and RNA-seq

We assembled teams of genomics professionals to assess whether we could rapidly develop pipelines to answer biological questions commonly asked by biologists and others new to bioinformatics by facilitating analysis of high-throughput sequencing data. In January 2015, teams were assembled on the National Institutes of Health (NIH) campus to address questions in the DNA-seq, epigenomics, metagenomics and RNA-seq subfields of genomics. The only two rules for this hackathon were that either the data used were housed at the National Center for Biotechnology Information (NCBI) or would be submitted there by a participant in the next six months, and that all software going into the pipeline was open-source or open-use. Questions proposed by organizers, as well as suggested tools and approaches, were distributed to participants a few days before the event and were refined during the event. Pipelines were published on GitHub, a web service providing publicly available, free-usage tiers for collaborative software development (https://github.com/features/). The code was published at https://github.com/DCGenomics/ with separate repositories for each team, starting with hackathon_v001.

Data sharing in PLOS ONE: An analysis of Data Availability Statements

A number of publishers and funders, including PLOS, have recently adopted policies requiring researchers to share the data underlying their results and publications. Such policies help increase the reproducibility of the published literature, as well as make a larger body of data available for reuse and re-analysis. In this study, we evaluate the extent to which authors have complied with this policy by analyzing Data Availability Statements from 47,593 papers published in PLOS ONE between March 2014 (when the policy went into effect) and May 2016. Our analysis shows that compliance with the policy has increased, with a significant decline over time in papers that did not include a Data Availability Statement. However, only about 20% of statements indicate that data are deposited in a repository, which the PLOS policy states is the preferred method. More commonly, authors state that their data are in the paper itself or in the supplemental information, though it is unclear whether these data meet the level of sharing required in the PLOS policy. These findings suggest that additional review of Data Availability Statements or more stringent policies may be needed to increase data sharing.

Providing meaningful information: Part C—Data management and visualization

Abstract This chapter provides an introduction to how informationists can support one of the biggest challenges that researchers and medical professionals now face: how to deal with the rapidly increasing data deluge. With the size of research and clinical data growing exponentially, and with new policies from funders and journals, researchers and clinicians need help to ensure that they are able to work effectively with their data in ways that comply with requirements but do not present an undue burden. Specifically, this chapter considers how informationists can provide support for data management and visualization. Many of the skills that informationists already have are applicable to these problems, and this chapter also provides suggestions for how informationists can get started with gaining new skills and designing data services.

Practicing what we preach: developing a data sharing policy for the Journal of the Medical Library Association

Providing access to the data underlying research results in published literature allows others to reproduce those results or analyze the data in new ways. Health sciences librarians and information professionals have long been advocates of data sharing. It is time for us to practice what we preach and share the data associated with our published research. This editorial describes the activity of a working group charged with developing a research data sharing policy for the Journal of the Medical Library Association.

Defining data librarianship: a survey of competencies, skills, and training

Objectives Many librarians are taking on new roles in research data services. However, the emerging field of data librarianship, including specific roles and competencies, has not been clearly established. This study aims to better define data librarianship by exploring the skills and knowledge that data librarians utilize and the training that they need to succeed. Methods Librarians who do data-related work were surveyed about their work and educational backgrounds and asked to rate the relevance of a set of data-related skills and knowledge to their work. Results Respondents considered a broad range of skills and knowledge important to their work, especially “soft skills” and personal characteristics, like communication skills and the ability to develop relationships with researchers. Traditional library skills like cataloging and collection development were considered less important. A cluster analysis of the responses revealed two types of data librarians: data generalists, who tend to provide data services across a variety of fields, and subject specialists, who tend to provide more specialized services to a distinct discipline. Discussion The findings of this study suggest that data librarians provide a broad range of services to their users and, therefore, need a variety of skills and expertise. Libraries hiring a data librarian may wish to consider whether their communities will be best served by a data generalist or a subject specialist and write their job postings accordingly. These findings also have implications for library schools, which could consider adjusting their curricula to better prepare their students for data librarian roles.