  There is a growing acknowledgement in the scientific community of the
importance of making experimental data machine findable, accessible,
interoperable, and reusable (FAIR). Recognizing that high quality metadata are
essential to make datasets FAIR, members of the GO FAIR Initiative and the
Research Data Alliance (RDA) have initiated a series of workshops to encourage
the creation of Metadata for Machines (M4M), enabling any self-identified
stakeholder to define and promote the reuse of standardized, comprehensive
machine-actionable metadata. The funders of scientific research recognize that
they have an important role to play in ensuring that experimental results are
FAIR, and that high quality metadata and careful planning for FAIR data
stewardship are central to these goals. We describe the outcome of a recent M4M
workshop that has led to a pilot programme involving two national science
funders, the Health Research Board of Ireland (HRB) and the Netherlands
Organisation for Health Research and Development (ZonMW). These funding
organizations will explore new technologies to define at the time that a
request for proposals is issued the minimal set of machine-actionable metadata
that they would like investigators to use to annotate their datasets, to enable
investigators to create such metadata to help make their data FAIR, and to
develop data-stewardship plans that ensure that experimental data will be
managed appropriately abiding by the FAIR principles. The FAIR Funders design
envisions a data-management workflow having seven essential stages, where
solution providers are openly invited to participate. The initial pilot
programme will launch using existing computer-based tools of those who attended
the M4M Workshop.
