D1.6 Characterisation of datasets from previous research projects

This deliverable presents the methodology defined within the AGRICORE project to characterise datasets resulting from previous research that could be useful for performing agricultural research analysis in the future. This methodology has been developed as part of the first of the work packages defined in the AGRICORE project. AGRICORE is a research project proposing an innovative way to apply agent-based modelling to improve the capacities of policymakers to evaluate the impact of agricultural-related measurements under and outside the framework of the Common Agricultural Policy. This project was funded by the European Commission as a result of the RUR-04-2018 call, part of the H2020 programme. In the introduction, the basis on which the methodology has been developed is presented, i.e., the ARDIT tool and the AGRICORE DCAT-AP 2.0 ontology. Then, the proposed methodology is detailed including also the definition of all the fields to be filled while characterising a dataset in ARDIT. As part of this methodology, also all the main challenges encountered during the characterisation of the datasets have been explained, illustrating the proposed solutions. After that, also the methodology proposed to guarantee that the tool ARDIT remains efficiently working is described, relying on a continuous upgrade and maintenance of the data. Finally, some conclusions regarding the characterisation process and the need for a governance strategy to ensure the survival of the ARDIT after the project are provided. It is important to remark that although this deliverable has been developed in the framework of the AGRICORE project, the participating partners have aimed for broader usage of the proposed methodology. As the final goal of this work package, the proposed EU Index Tool (now renamed as Agricultural Research Data Index Tool (ARDIT) aims to serve as a central entry point for locating useful datasets useful for agricultural research.