Software, and in particular source code, plays an important role in science: it is used in all research fields to produce, transform and analyse research data, and is sometimes itself an object of research and/or an output of research.
Unlike research data and scientific articles, though, software source code has only very recently been recognised as important subject matter in a few initiatives related to scholarly publication and archiving. These initiatives are now working on a variety of plans for handling the identification of software artifacts.
The aim of this Working Group (WG) is to develop synergies between existing education and training activities and agricultural science needs by performing a landscape assessment to identify existing gaps and training requirements within the Interest Group on Agricultural Data (IGAD) WGs related. A particular focus will be on sharing knowledge about training initiatives and technologies, reducing digital divides so that researchers and practitioners in developing countries can also benefit.
Research data is increasingly recognized as an important output of scholarly research, however, there are not yet standardized or comprehensive research data metrics as there are with articles. While data citations standards are being constructed and gaining user adoption, there are not yet usage (views, downloads) metrics for data. In addition, data citations are currently not yet counted and aggregated into clear metrics, as it is done for articles.