
Ricgraph, also known as Research in context graph, enables the exploration of researchers, teams, their results, collaborations, skills, projects, and the relations between these items. Ricgraph can store many types of items into a single graph. These items can be obtained from various systems and from multiple organizations. Ricgraph facilitates reasoning about these items because it infers new relations between items, relations that are not present in any of the separate source systems. It is flexible and extensible, and can be adapted to new application areas. The presentation below is from July 4, 2024. It explains how to use Ricgraph and BackToPure to enrich Pure data. Enriching is a process to improve or enhance information in source system A based on information in other source systems, not present in system A. For a gentle introduction in Ricgraph, read the reference publication: Rik D.T. Janssen (2024). Ricgraph: A flexible and extensible graph to explore research in context from various systems. SoftwareX, 26(101736). https://doi.org/10.1016/j.softx.2024.101736. Extensive documentation, publications, videos and source code can be found in the GitHub repository https://github.com/UtrechtUniversity/ricgraph. The website for Ricgraph can be found at https://www.ricgraph.eu. BackToPure can be found in the GitHub repository https://github.com/UtrechtUniversity/BackToPure.
Knowledge graph, Metadata, Data harvesting, Utrecht University, Linked data, Research in context graph, Data enrichment, Data linking, Ricgraph Explorer, Ricgraph, Ricgraph REST API, Graph, Graph database
Knowledge graph, Metadata, Data harvesting, Utrecht University, Linked data, Research in context graph, Data enrichment, Data linking, Ricgraph Explorer, Ricgraph, Ricgraph REST API, Graph, Graph database
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
