Linked data for cross-disciplinary collaboration cohort discovery

Cross-disciplinary collaborations potentially offer the diversity of understanding required to answer complex problems. However, barriers to cohort discovery exist because content about people is predominantly only in human-readable form on websites and/or in disparate databases. Notably, many cross-disciplinary collaborations never form due to a lack of awareness of cross-boundary synergies. This project applies semantic technologies to automate linkages to reveal hidden connections between people from metadata parameters about data, rather than from publication products. The information in metadata, commonly used for data discovery, can be used to link researchers for potential partnerships. The proposed system combines pre-existing and custom ontologies, populated from a number of accessible repositories, to describe the relationships between researchers based on metadata parameters. The system was tested from the researcher's perspective where significant alignments with potential partners were found based on transitive relationships, similar interests (e.g., research fields) and/or other commonalities (e.g., location/time of research)