A research data sharing game

While reusing research data has evident benefits for the scientific community as a whole, decisions to archive and share these data are primarily made by individual researchers. For individuals, it is less obvious that the benefits of sharing data outweigh the associated costs, for example time and money. In this sense the problem of data sharing resembles a typical game in interactive decision theory, more commonly known as game theory. Within this framework we analyse how measures to promote sharing and reuse of research data affect individuals who do and do not share data. We find that the scientific community can benefit from top-down policies to enhance sharing data even when the act of sharing itself implies a cost. Namely, if (almost) everyone shares, many individuals receive benefits, as datasets in our model can be reused to achieve a higher efficiency (i.e. more publications, higher quality papers). Surprisingly, as sharing implies a cost, even sharing individuals themselves, in a community in which sharing is common, can gain a higher efficiency than individuals who do not share, in a community in which sharing is not common. In addition to these findings, we find that measures to ensure better data retrieval and quality can compensate for sharing costs by further enabling reuse. Nevertheless, an individual researcher who decides not to share omits the costs of sharing. Assuming that the natural tendency will be to use a strategy that will lead to maximisation of individual efficiency, we see the average scientific community efficiency in our model steadily drop as more individuals decide not to share. With this in mind, we conclude that the key to motivate the researcher to share data lies in reducing the costs associated with sharing, or even better, turning it into a benefit.