  The data underlying scientific papers should be accessible to researchers
both now and in the future, but how best can we ensure that these data are
available? Here we examine the effectiveness of four approaches to data
archiving: no stated archiving policy, recommending (but not requiring)
archiving, and two versions of mandating data deposition at acceptance. We
control for differences between data types by trying to obtain data from papers
that use a single, widespread population genetic analysis, STRUCTURE. At one
extreme, we found that mandated data archiving policies that require the
inclusion of a data availability statement in the manuscript improve the odds
of finding the data online almost a thousand-fold compared to having no policy.
However, archiving rates at journals with less stringent policies were only
very slightly higher than those with no policy at all. At one extreme, we found
that mandated data archiving policies that require the inclusion of a data
availability statement in the manuscript improve the odds of finding the data
online almost a thousand fold compared to having no policy. However, archiving
rates at journals with less stringent policies were only very slightly higher
than those with no policy at all. We also assessed the effectiveness of asking
for data directly from authors and obtained over half of the requested
datasets, albeit with about 8 days delay and some disagreement with authors.
Given the long term benefits of data accessibility to the academic community,
we believe that journal based mandatory data archiving policies and mandatory
data availability statements should be more widely adopted.
