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  4. Developing a data sharing community for spinal cord injury research

Developing a data sharing community for spinal cord injury research

Exp Neurol, 2017 · DOI: 10.1016/j.expneurol.2017.05.012 · Published: September 1, 2017

Spinal Cord InjuryHealthcareNeurology

Simple Explanation

The spinal cord injury (SCI) research field has a history of collaborative initiatives that include sharing of preclinical research models and outcome measures. A workshop was hosted to examine the current landscape for data sharing in SCI research and provide a path to its future. This manuscript shares initial findings from the workshop with the broader research community, providing talking points for continued feedback as the field moves forward with data sharing.

Study Duration
Not specified
Participants
Workshop invitees nominated by the workshop steering committee
Evidence Level
Not specified

Key Findings

  • 1
    Preclinical spinal cord injury researchers are open to data sharing, but only about 10% of workshop attendees currently share data online.
  • 2
    Data collection and storage logistics, along with personnel training, pose challenges to data sharing within the SCI research community.
  • 3
    Researchers prioritize scientific discovery and compliance with journal/funder requirements as incentives for data sharing, more so than increased citations of papers and datasets.

Research Summary

Global efforts are underway to develop practical guidance for implementation of data sharing and open data resources. The spinal cord injury (SCI) research field has a long history of collaborative initiatives tat include sharing of preclinical research models and outcome measures. 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.

Practical Implications

Develop Guidelines and Training

Develop guidelines and training resources for researchers to enable FAIR sharing practices.

Implement Tools for Data Collection

Implement tools for data collection and create mechanisms for dataset citation, quality evaluation, and annotation.

Foster Cultural Changes

Engage the community broadly to facilitate essential cultural changes around data sharing.

Study Limitations

  • 1
    Data collection and organization schemes vary across laboratories
  • 2
    Training laboratory members requires additional time and expertise
  • 3
    Infrastructure for data storage and upload to repositories is expensive

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