Health sciences librarians' engagement in open science: a scoping review.

OBJECTIVES: To identify the engagement of health sciences librarians (HSLs) in open science (OS) through the delivery of library services, support, and programs for researchers. METHODS: We performed a scoping review guided by Arksey and O'Malley's framework and Joanna Briggs' Manual for Scoping Reviews. Our search methods consisted of searching five bibliographic databases (MEDLINE, Embase, CINAHL, LISTA, and Web of Science Core Collection), reference harvesting, and targeted website and journal searching. To determine study eligibility, we applied predetermined inclusion and exclusion criteria and reached consensus when there was disagreement. We extracted data in duplicate and performed qualitative analysis to map key themes. RESULTS: We included fifty-four studies. Research methods included descriptive or narrative approaches (76%); surveys, questionnaires, and interviews (15%); or mixed methods (9%). We labeled studies with one or more of FOSTER's six OS themes: open access (54%), open data (43%), open science (24%), open education (6%), open source (6%), and citizen science (6%). Key drivers in OS were scientific integrity and transparency, openness as a guiding principle in research, and funder mandates making research publicly accessible. CONCLUSIONS: HSLs play key roles in advancing OS worldwide. Formal studies are needed to assess the impact of HSLs' engagement in OS. HSLs should promote adoption of OS within their research communities and develop strategic plans aligned with institutional partners. HSLs can promote OS by adopting more rigorous and transparent research practices of their own. Future research should examine HSLs' engagement in OS through social justice and equity perspectives.

Assessing the impact of introductory programming workshops on the computational reproducibility of biomedical workflows.

INTRODUCTION: As biomedical research becomes more data-intensive, computational reproducibility is a growing area of importance. Unfortunately, many biomedical researchers have not received formal computational training and often struggle to produce results that can be reproduced using the same data, code, and methods. Programming workshops can be a tool to teach new computational methods, but it is not always clear whether researchers are able to use their new skills to make their work more computationally reproducible. METHODS: This mixed methods study consisted of in-depth interviews with 14 biomedical researchers before and after participation in an introductory programming workshop. During the interviews, participants described their research workflows and responded to a quantitative checklist measuring reproducible behaviors. The interview data was analyzed using a thematic analysis approach, and the pre and post workshop checklist scores were compared to assess the impact of the workshop on the computational reproducibility of the researchers' workflows. RESULTS: Pre and post scores on a checklist of reproducible behaviors did not change in a statistically significant manner. The qualitative interviews revealed that several participants had made small changes to their workflows including switching to open source programming languages for their data cleaning, analysis, and visualization. Overall many of the participants indicated higher levels of programming literacy, and an interest in further training. Factors that enabled change included supportive environments and an immediate research need, while barriers included collaborators that were resistant to new tools, and a lack of time. CONCLUSION: While none of the workshop participants completely changed their workflows, many of them did incorporate new practices, tools, or methods that helped make their work more reproducible and transparent to other researchers. This indicates that programming workshops now offered by libraries and other organizations contribute to computational reproducibility training for researchers.

Why do biomedical researchers learn to program? An exploratory investigation.

OBJECTIVE: As computer programming becomes increasingly important in the biomedical sciences and more libraries offer programming classes, it is crucial for librarians to understand how researchers use programming in their work. The goal of this study was to understand why biomedical researchers who enrolled in a library-sponsored workshop wanted to learn to program in R and Python. METHODS: Semi-structured in-depth interviews were performed with fourteen researchers registered for beginning R and Python programming workshops at the University of California, San Francisco Library. A thematic analysis approach was used to extract the top reasons that researchers learned to program. RESULTS: Four major themes emerged from the interviews. Researchers wanted to learn R and Python programming in order to perform their data analysis independently, to be an informed collaborator, to engage with new forms of big data research, and to have more flexibility in the tools that they used for their research. CONCLUSIONS: Librarians designing programming workshops should remember that most researchers are hoping to apply their new skills to a specific research task such as data cleaning, data analysis, and statistics and that language preferences can vary based on research community as well as personal preferences. Understanding the programming goals of researchers will make it easier for librarians to partner effectively and offer services that are critically needed in the biomedical community.

Assessing the National Library of Medicine's Informationist Awards.

OBJECTIVE: To understand the experience of the informationist recipients of NLM-funded Administrative Supplements for Informationist Services and gather evidence for their impact on NIH-funded biomedical research. METHODS: A mixed methods approach consisting of a survey of principal investigators and a focus group of informationists. RESULTS: Informationists appeared to have a positive impact on their team's research, especially in the areas of data storage, data management planning, data organization, and literature searching. In addition, many informationists felt that their involvement had increased their research skills and made them true research partners. Assessing their own impact was a challenge for the award recipients, and questions remain about the best evaluation methods. The overall experience of the informationists and researchers was mixed but largely positive. CONCLUSION: The NLM-funded informationist supplement award appears to be a successful mechanism for immersing informationists into research teams and improving data management in the supported projects.