Ricgraph - Research in context graph

With Ricgraph (Research in context graph), you can create a graph from research information that is stored in various source systems. You can explore this graph and discover relations you were not aware of.


Cite this software

What Ricgraph - Research in context graph can do for you

What is Ricgraph?

What if... we look at research information as a graph? We would have relations between objects, we would be able to “walk” from one object to another, and related objects would be neighbors. For example, starting with a researcher, the publications of this person are only one step away by following one edge, and other contributors to that publication are again one step (edge) away.

With Ricgraph, you can create a graph from research information that is stored in various source systems. You can explore this graph and discover relations you were not aware of. We have developed Ricgraph (Research in context graph) because our university had a need to be able to show our researchers, their skills, (child) organizations (e.g. unit, department, faculty, university), projects and research outputs (e.g. publications, datasets, software packages) in relation to each other. This information is stored in different systems, such as Pure, OpenAlex, Yoda, the Research Software Directory, and our organization’s employee pages.

By combining this information in one graph, it is possible to show research in context (hence the name). Ricgraph is a graph that uses nodes and edges to represent objects and their relations. It can be used to store, manipulate and read metadata of any object that has a relation to another object.

Why Ricgraph?

Ricgraph can answer questions like:

  • Which researcher has contributed to which publication, dataset, software package, project, etc.?
  • Given e.g. a dataset, software package, or project, who has contributed to it?
  • What identifiers does a researcher have (e.g. ORCID, ISNI, organization employee ID, email address)?
  • What skills does a researcher have?
  • Show a network of researchers who have worked together?
  • Which organizations have worked together?

Also, more elaborate information can be found using Ricgraph and Ricgraph explorer:

  • You can find information about persons or their results in a (child) organization (unit, department, faculty, university). For example, you can find out what data sets or software are produced in your faculty. Or the skills of all persons in your department. Of course this is only possible in case you have harvested them.
  • You can find out with whom a person shares research output types. For example, you can find out with whom someone shares software or data sets.
  • You can get tables showing how you can enrich a source system based on other systems you have harvested. For example, suppose you have harvested both Pure and OpenAlex, using this feature you can find out which publications in OpenAlex are not in Pure. You might want to add those to Pure.
  • You can get a table that shows the overlap in harvests from different source systems. For example, after a query to show all ORCID nodes, the table summarizes the number of ORCID nodes which were only found in one source, and which were found in multiple sources. Another table gives a detailed overview how many nodes originate from which different source systems. Then, you can drill down by clicking on a number in one of these two tables to find the nodes corresponding to that number.

With Ricgraph, you can get metadata from objects from any source system you’d like. You run the harvest script for that system, and data will be imported in Ricgraph and will be combined automatically with data which is already there. Ricgraph provides harvest scripts for the systems mentioned above. Scripts for other sources can be written easily.

More information

Read more at Ricgraph - Research in context graph, Rik D.T. Janssen (2023). Extensive documentation, publications, videos and source code can be found in the Github repository.

Logo of Ricgraph - Research in context graph
Programming languages
  • Python 93%
  • JavaScript 4%
  • CSS 3%
  • MIT
  • Open Access
</>Source code

Participating organisations

Utrecht University



Rik D.T. Janssen