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Without funding for metadata standards, data-sharing mandates will be ineffective

25 October 2022

Mark Alan Musen, professor of Biomedical Informatics and of Biomedical Data Science at Stanford University points out in Nature that without appropriate metadata, shared data cannot be reused and data-sharing mandates will be pointless. In fact, he says, in the majority of fields, the metadata standards needed to make data FAIR don’t even exist. While this is still mostly true, INCF is working to change that for the neuroscience field.

What makes a good community standard? Its usefulness to the community, and its ability to adapt to changing community needs - two important factors that are hard to judge without access to detailed knowledge of the field and its problems. 

This knowledge exists among the INCF community, and to leverage their expertise, we set up a process for endorsement of neuroscience community standards. Central to the process is our Standards and Best Practices committee, whose members are senior researchers with strong expertise in different neuroscience subfields. 

Prospective standards that go through the vetting process are evaluated on their contributions to FAIR research, but also on the clarity of their formal specification and documentation, their flexibility and extensibility and on how well their governance allows users to influence development and decision making. When the reviewers and the committee members have summarized their conclusions, a call goes out to the wider neuroscience community for 60 days of community review, where users can point out problems, suggest improvements or just express their support. Without a strong show of community support, the committee will not endorse the standard.

Developing a potential standard to the point where a community can use it is a resource-demanding and intensely collaborative process. The lack of established community standards is likely a consequence of how most research funding is allocated and structured:

  • Lack of merit is a barrier. Developing standards or tools is often considered less meritable than acquiring new data, and is generally harder to get funding and recognition for. 

  • Inflexible funding is a barrier. The type of open international collaboration that is needed to develop community standards is often hard to get dedicated funding for, since most research funding is earmarked for a specific country or project. 

  • Lack of sustainable funding mechanisms is a barrier. Once in use, community standards also need long-term maintenance and regular updates; however, scientific funding is time-limited and runs in cycles.

The lack of sustained funding means that even well established community standards are vulnerable - governance, maintenance and sustainability of community standards are under-recognized and largely unsolved problems. If we want FAIR research outputs to stay FAIR, community standards must be made more sustainable. 

A standard or best practice can be endorsed by INCF if it enables or facilitates FAIR research practice. But it must also have:

  • A clear specification that can be implemented by others
  • Clear and extensive documentation
  • A reference implementation in a widely used programming language
  • An API or CLI for programmatic access in at least one common programming language (e.g. Python)
  • Transparent and sustainable governance open to community input
  • Strong user support/engaged user community 

In addition to the standards and best practices endorsement process, INCF has many other activities and resources aiming to support development and adoption of community standards in neuroscience. We offer a large range of courses on data science and FAIR data management in neuroscience (TrainingSpace) and have collaboratively developed a search engine for finding openly available neuroscience data across many of the world’s leading neuroscience repositories (KnowledgeSpace). Our working groups develop and implement a range of different established and prospective neuroscience community standards.