Transitive Credit and JSON-LD

Science and engineering research increasingly relies on activities that facilitate research but are not currently rewarded or recognized. This includes the sharing of data; development of common data resources, software and methodologies; and annotation of data and publications. This situation has been documented in a number of recent reports [1, 2] that focus on changing needs and mechanisms for attribution and citation of digital products, from the use of alternative metrics [3] that track reports of research impact apart from research publications, to work on data [4]. About half of the articles in many recent issues of Science describe research that depended on software, and a larger fraction analyze data. Indeed, the US National Science Foundation recently updated its guide to proposers to instruct them to provide a list of their “products”— objects that are “citable and accessible including but not limited to publications, data sets, software, patents, and copyrights”—rather than publications [5]. To promote and advance pursuit of activities that facilitate research, we must develop mechanisms for assigning credit, facilitate the appropriate attribution of research outcomes, devise incentives for activities that facilitate research, and allocate funds to maximize return on investment. In this article, we explore how the idea of transitive credit [6, 7], which would credit both direct and indirect contributions, can be implemented. Note that this article is an extended version of an earlier paper [8].