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New release v1.12
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CITATION.cff

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@@ -2,8 +2,8 @@ cff-version: 1.2.0
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title: "Ricgraph - Research in context graph"
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message: "If you use Ricgraph, please cite it."
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type: software
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version: "v1.11"
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date-released: "2023-11-08"
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version: "v1.12"
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date-released: "2023-12-19"
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authors:
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- given-names: "Rik D.T."
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family-names: Janssen
@@ -52,35 +52,45 @@ identifiers:
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- description: "Ricgraph DOI v1.11"
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type: doi
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value: "10.5281/zenodo.10083972"
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- description: "Ricgraph DOI v1.12"
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type: doi
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value: "10.5281/zenodo.XXXXXXXX"
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abstract: >-
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Ricgraph (Research in context graph) is a graph
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(https://en.wikipedia.org/wiki/Graph_theory) with nodes (sometimes called
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vertices) and edges (sometimes called links) to represent objects and their
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relations. It can be used to store, manipulate and read metadata of any
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object that has a relation to another object, as long as every object can be
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'represented' by at least a 'name' and a 'value'. In Ricgraph, one node
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represents one object, and an edge represents the relation between two
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objects. It is written in Python and uses Neo4j as graph database engine
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(https://en.wikipedia.org/wiki/Graph_database).
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With Ricgraph (Research in context graph), you can create a graph from
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research information that is stored in various source systems. This can be
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done for multiple organizations. You can explore this graph and discover
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relations you were not aware of.
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Metadata of an object are stored as 'properties' in a node, i.e. as
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information associated with a node. For example, a node may store two
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properties, 'name = PET' and 'value = cat'. Another node may store 'name =
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FULL_NAME' and 'value = John Doe'. Then the edge between those two nodes
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means that the person with FULL_NAME John Doe has a PET which is a cat.
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What if... we look at research information as a graph? We would have
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relations between objects, we would be able to "walk" from one object to
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another, and related objects would be neighbors. For example, starting with a
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researcher, the publications of this person are only one step away by
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following one edge, and other contributors to that publication are again one
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step (edge) away.
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The philosophy of Ricgraph is that it stores metadata, not the objects the
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metadata refer to. To access an object, a node has a link to that object in
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the system it was obtained from. The objective is to get metadata from
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objects from a source system in a process called 'harvesting'. All
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information harvested from several source systems will be combined into one
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graph. Modification of metadata of an object is done in the source system the
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object was harvested from, and then reharvesting of that source system.
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Ricgraph has been developed because our university had a need to explore and
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visualize our researchers, their research results (e.g. publications, data
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sets, software packages), the teams, their collaborations, skills, projects,
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and their relations. By combining this information in one graph, it is
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possible to show research in context (hence the name).
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Ricgraph is a graph that uses nodes and edges to represent objects and their
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relations. It can be used to store, manipulate and read metadata of any
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object that has a relation to another object. Metadata from objects can be
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obtained from any source system you'd like. You run the harvest script for
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that system, and data will be imported in Ricgraph and will be combined
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automatically with data which is already there. Ricgraph provides several
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harvest scripts, and scripts for other sources can be written easily.
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keywords:
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- ricgraph
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- "research in context graph"
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- graph
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- "graph database"
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- "Ricgraph"
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- "Ricgraph Explorer"
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- "Research in context graph"
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- "Graph"
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- "Graph database"
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- "Data harvesting"
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- "Data enrichment"
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- "Linked data"
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- "Knowledge graph"
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- "Utrecht University"
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license: MIT
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repository-code: "https://github.com/UtrechtUniversity/ricgraph"

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