Addressing barriers in FAIR data practices for biomedical data

TY - JOUR

T1 - Addressing barriers in FAIR data practices for biomedical data

AU - the NIAID Systems Biology Data Dissemination Working Group

AU - Hughes, Laura D.

AU - Tsueng, Ginger

AU - DiGiovanna, Jack

AU - Horvath, Thomas D.

AU - Rasmussen, Luke V.

AU - Savidge, Tor C.

AU - Stoeger, Thomas

AU - Turkarslan, Serdar

AU - Wu, Qinglong

AU - Wu, Chunlei

AU - Su, Andrew I.

AU - Pache, Lars

N1 - Funding Information: This work was supported in part by the National Institute of Allergy and Infectious Diseases (NIAID), National Institutes of Health (NIH) grants U19 AI135995 (Scripps Research: LDH, AIS, GT, CW), U19 AI135972 (Sanford Burnham Prebys: LP), U01 AI124290 (Baylor College of Medicine: TDH, TCS, QW), P01 AI152999 (Baylor College of Medicine: TDH, TCS, QW), U19 AI135964 (Northwestern University: LVR, TS), U19 AI135976 (Institute for Systems Biology: ST), U19 AI106761 (Institute for Systems Biology: ST), and 75N91019D00024 (Seven Bridges: JD, Scripps Research: GT, LDH, AS, CW). We acknowledge the NIAID/DMID Systems Biology Consortium for Infectious Diseases Data Dissemination Working Group and Sai Lakshmi Subramanian for providing critical feedback on the manuscript. We thank Reed Shabman for his leadership within the Data Dissemination Working Group and for helpful comments and careful revisions of the paper. We additionally thank Liliana Brown for the support of the Program this paper originated from and Ishwar Chandramouliswaran for helpful discussions in preparing this manuscript. Funding Information: Solution 3.3: Provide training and guidance to adapt policies within the context of how a lab operates . Even for researchers keen to disseminate their data, it is overwhelming and time-consuming to figure out where to start. It’s easy for people to agree they want to make their data FAIR; it’s harder to figure out what exactly is meant by “FAIR” or how to make data FAIR. The barrier to entry is large; for instance, when we wanted to develop the NIAID SyBio schemas, we could find no model or guidance on how to navigate this process, and instead implemented our own solution with little guidance to the best practices. Researchers are not educated on how to do data management properly from the start of the project, resulting in wasted effort at the end of a project to retrofit previously collected data. Researchers are not often aware of the best practices with data sharing, and even if they are, there are few guides for how to implement them in a biological context. Easy-to-implement, practical guidance, accompanied by training sessions, will be critical to ensure that researchers and staff take advantage of standards (Solution 2.1) and tools (Solution 2.3) that are developed to improve the sharing, discovery, and reuse of data. The NIH has begun to develop such guidance in response to their data sharing policy ( https://sharing.nih.gov/data-management-and-sharing-policy/about-data-management-and-sharing-policy/data-management-and-sharing-policy-overview ); such education, training, and guidance is essential to ensure the success of this policy. As a complement to this guidance, we also recommend integrating data and metadata curation and management as a core component of graduate training, supported by postdoctoral fellowship opportunities to research these topics.

PY - 2023/12

Y1 - 2023/12

UR - https://www.scopus.com/pages/publications/85148844335

U2 - 10.1038/s41597-023-01969-8

DO - 10.1038/s41597-023-01969-8

M3 - Comment/debate

C2 - 36823198

AN - SCOPUS:85148844335

SN - 2052-4463

VL - 10

JO - Scientific Data

JF - Scientific Data

IS - 1

M1 - 98

ER -