Benefits of Data Sharing

Sharing research is critical for scientific progress. Current approaches to data sharing in scientific communities primarily include direct sharing (i.e., via email) between individuals, upload of data as supplementary materials in a publication, or minimally-regulated sharing through personal websites or social media platforms. However, these options do not make the shared data FAIR, as it is not readily findable, not broadly accessible, and almost never interoperable and reusable.

The best way to publish data is using data repositories which offer capabilities specifically oriented to the goal of sharing data. The ODC is currently the only community-driven data repository for SCI and TBI research. This specific focus allows us to align the repository with FAIR data principles and the needs of the SCI and TBI communities. Sharing data through the ODC unlocks latent potential in research data, enabling the SCI and TBI communities to better tackle many challenges.

Benefits to Researchers

Makes data findable, reusable, and limits knowledge loss

• All of your lab data is stored in a known, central location (the ODC) vs. with individual researchers

  • If someone leaves your lab, the data is in a known location and is accompanied by a data dictionary file that explains how to interpret the data (i.e., makes data reusable)

• Your data will always be organized in the same format and be easily accessible and interpretable for you and your lab for years to come

  • Reduces the need to reformat/reorganize data → facilitates and speeds up analysis and/or re-analysis (e.g., manuscript revisions)

Helps meet data sharing mandates from funders and/or publishers

• Data will be ready to share/publish to meet mandates set by funders (i.e., in compliance with your Data Management and Sharing Plan)

• Data published on the ODC is citable - meaning you can cite datasets within your manuscript or supply a citation to a dataset as part of a manuscript's data availability statement

Helps inform decision making for experimental planning and resource allocation (time, human, financial)

• Allows you to compare or validate your findings to those from other labs (or your past work)

• Shared data from other labs can be used to guide your experiments

Benefits to the Research Community

Contributes towards reducing data bias and improving research transparency

• Results can be validated across multiple laboratories

• "Dark data" (i.e., negative/null data, pilot work, etc.) doesn't go to waste

• Shared data from other labs can be used to inform decision making for resource allocation (time, human, financial)

Accelerates discovery

• Promotes data reuse

  • Enables retrospective analysis or subject-level meta-analysis

• Interoperable data makes harmonization and integration of related datasets easier, allowing researchers to harness big data approaches to solving problems

• Facilitates collaboration

Benefits to the Public

Makes the products of the scientific process accessible to the general public

• Greater accountability to those who fund research

  • i.e., taxpayers and funding agencies

• Improved transparency makes it easier to validate results

  • Helps minimize duplication of effort and resources to investment in science more efficient

• Accelerated discovery can lead to better health or treatment outcomes - quicker!