Developing a data sharing community for spinal cord injury research

Abstract

The rapid growth in data sharing presents new opportunities across the spectrum of biomedical research. Global efforts are underway to develop practical guidance for implementation of data sharing and open data resources. These include the recent recommendation of 'FAIR Data Principles', which assert that if data is to have broad scientific value, then digital representations of that data should be Findable, Accessible, Interoperable and Reusable (FAIR). The spinal cord injury (SCI) research field has a long history of collaborative initiatives that include sharing of preclinical research models and outcome measures. In addition, new tools and resources are being developed by the SCI research community to enhance opportunities for data sharing and access. With this in mind, the National Institute of Neurological Disorders and Stroke (NINDS) at the National Institutes of Health (NIH) hosted a workshop on October 5-6, 2016 in Bethesda, MD, in collaboration with the Open Data Commons for Spinal Cord Injury (ODC-SCI) titled "Preclinical SCI Data: Creating a FAIR Share Community". Workshop invitees were nominated by the workshop steering committee (co-chairs: ARF and VPL; members: AC, KDA, MSB, KF, LBJ, PGP, JMS), to bring together junior and senior level experts including preclinical and basic SCI researchers from academia and industry, data science and bioinformatics experts, investigators with expertise in other neurological disease fields, clinical researchers, members of the SCI community, and program staff representing federal and private funding agencies. The workshop and ODC-SCI efforts were sponsored by the International Spinal Research Trust (ISRT), the Rick Hansen Institute, Wings for Life, the Craig H. Neilsen Foundation and NINDS. The number of attendees was limited to ensure active participation and feedback in small groups. The goals were to examine the current landscape for data sharing in SCI research and provide a path to its future. Below are highlights from the workshop, including perspectives on the value of data sharing in SCI research, workshop participant perspectives and concerns, descriptions of existing resources and actionable directions for further engaging the SCI research community in a model that may be applicable to many other areas of neuroscience. This manuscript is intended to share these initial findings with the broader research community, and to provide talking points for continued feedback from the SCI field, as it continues to move forward in the age of data sharing.

Keywords: FAIR data principles; Informatics; Neuroscience; Open Data Commons; Reproducibility; Workshop proceedings.

Figure 1.

Workshop participant areas of expertise. Some participants indicated more than one.

Figure 2.

Workshop survey responses to the question “Who do you share experimental data generated by your lab with?”. All respondents share data with co-authors and lab members; fewer than half share their data with anyone who requests it, and only 2 of 19 respondents share any data publicly online.

Figure 3.

Workshop survey responses to the question “How do you store experimental data generated in your lab?”. Most respondents use paper notebooks and spreadsheets to store data; close to half use manuscript drafts, and few use electronic notebooks and version tracking systems.

Figure 4.

Workshop survey responses to the question “How do you rank possible incentives for data sharing?”. ‘Compliance with journal requirements’, ‘scientific discovery’ and ‘compliance with funder requirements’ were the incentives ranked most important on average. Citations were ranked least important on average.