  Sharing research data is complex, labor-intensive, expensive, and requires
infrastructure investments by multiple stakeholders. Open science policies
focus on data release rather than on data reuse, yet reuse is also difficult,
expensive, and may never occur. Investments in data management could be made
more wisely by considering who might reuse data, how, why, for what purposes,
and when. Data creators cannot anticipate all possible reuses or reusers; our
goal is to identify factors that may aid stakeholders in deciding how to invest
in research data, how to identify potential reuses and reusers, and how to
improve data exchange processes. Drawing upon empirical studies of data sharing
and reuse, we develop the theoretical construct of distance between data
creator and data reuser, identifying six distance dimensions that influence the
ability to transfer knowledge effectively: domain, methods, collaboration,
curation, purposes, and time and temporality. These dimensions are primarily
social in character, with associated technical aspects that can decrease - or
increase - distances between creators and reusers. We identify the order of
expected influence on data reuse and ways in which the six dimensions are
interdependent. Our theoretical framing of the distance between data creators
and prospective reusers leads to recommendations to four categories of
stakeholders on how to make data sharing and reuse more effective: data
creators, data reusers, data archivists, and funding agencies.
