  Reproducibility and replicability of research findings are central to the
scientific integrity of epidemiology. In addition, many research questions
require combiningdata from multiple sources to achieve adequate statistical
power. However, barriers related to confidentiality, costs, and incentives
often limit the extent and speed of sharing resources, both data and code.
Epidemiological practices that follow FAIR principles can address these
barriers by making resources (F)indable with the necessary metadata ,
(A)ccessible to authorized users and (I)nteroperable with other data, to
optimize the (R)e-use of resources with appropriate credit to its creators. We
provide an overview of these principles and describe approaches for
implementation in epidemiology. Increasing degrees of FAIRness can be achieved
by moving data and code from on-site locations to the Cloud, using
machine-readable and non-proprietary files, and developing open-source code.
Adoption of these practices will improve daily work and collaborative analyses,
and facilitate compliance with data sharing policies from funders and
scientific journals. Achieving a high degree of FAIRness will require funding,
training, organizational support, recognition, and incentives for sharing
resources. But these costs are amply outweighed by the benefits of making
research more reproducible, impactful, and equitable by facilitating the re-use
of precious research resources by the scientific community.
