  The broad sharing of research data is widely viewed as of critical importance
for the speed, quality, accessibility, and integrity of science. Despite
increasing efforts to encourage data sharing, both the quality of shared data,
and the frequency of data reuse, remain stubbornly low. We argue here that a
major reason for this unfortunate state of affairs is that the organization of
research results in the findable, accessible, interoperable, and reusable
(FAIR) form required for reuse is too often deferred to the end of a research
project, when preparing publications, by which time essential details are no
longer accessible. Thus, we propose an approach to research informatics that
applies FAIR principles continuously, from the very inception of a research
project, and ubiquitously, to every data asset produced by experiment or
computation. We suggest that this seemingly challenging task can be made
feasible by the adoption of simple tools, such as lightweight identifiers (to
ensure that every data asset is findable), packaging methods (to facilitate
understanding of data contents), data access methods, and metadata organization
and structuring tools (to support schema development and evolution). We use an
example from experimental neuroscience to illustrate how these methods can work
in practice.
