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There is a growing acknowledgement in the scientific community of theimportance of making experimental data machine findable, accessible,interoperable, and reusable (FAIR). Recognizing that high quality metadata areessential to make datasets FAIR, members of the GO FAIR Initiative and theResearch Data Alliance (RDA) have initiated a series of workshops to encouragethe creation of Metadata for Machines (M4M), enabling any self-identifiedstakeholder to define and promote the reuse of standardized, comprehensivemachine-actionable metadata. The funders of scientific research recognize thatthey have an important role to play in ensuring that experimental results areFAIR, and that high quality metadata and careful planning for FAIR datastewardship are central to these goals. We describe the outcome of a recent M4Mworkshop that has led to a pilot programme involving two national sciencefunders, the Health Research Board of Ireland (HRB) and the NetherlandsOrganisation for Health Research and Development (ZonMW). These fundingorganizations will explore new technologies to define at the time that arequest for proposals is issued the minimal set of machine-actionable metadatathat they would like investigators to use to annotate their datasets, to enableinvestigators to create such metadata to help make their data FAIR, and todevelop data-stewardship plans that ensure that experimental data will bemanaged appropriately abiding by the FAIR principles. The FAIR Funders designenvisions a data-management workflow having seven essential stages, wheresolution providers are openly invited to participate. The initial pilotprogramme will launch using existing computer-based tools of those who attendedthe M4M Workshop.

The FAIR Funder pilot programme to make it easy for funders to require and for grantees to produce FAIR Data