SN SciGraph is the Springer Nature linked data platform that collates information from across the research landscape, i.e. the things, documents, people, places and relations of importance to the science and scholarly domain. SN SciGraph includes data on all Springer Nature publications including journals and books from almost two hundred years (1830s to present), as well as funders, research projects, conferences, affiliations and institutions. The SN SciGraph Data Explorer is a website which allows users to explore the contents of SN SciGraph. It is aimed both at linked data specialists, who can find machine-readable representations of SN SciGraph resources, and at the more general research community, as it provides a visually appealing and intuitive overview on the variety of data in SN SciGraph, and how the data is structured.
The Data Explorer has two main functionalities targeted at different audiences:
General users can explore the SN SciGraph data landscape in an interactive manner by clicking on a node in the graph, viewing the information associated with it, and then following on to the next node. For example, try opening the page for a journal, e.g. Nature, a conference, e.g. ISWC, a concept, e.g. DNA Nanotechnology, or an institute, e.g. the Francis Crick Institute.
Metadata specialists and data scientists
Metadata specialists and data scientists can get rich data descriptions for SN SciGraph things by using the Linked Data API. Developers can also use the Redirect API to look up SN SciGraph things by e.g. DOI or ISBN.
This approach is open-ended and exploratory in nature, and does not require any technical expertise (apart from some basic understanding of linked data principles) and thus is suitable for all audiences who want to discover the range of things available in SN SciGraph, and how they are related.
There are a number of ways to begin a journey through SN SciGraph. We describe a few of them here:
The simplest way to start a new journey is to use the Data Explorer's search bar and input a simple text string. For example, try searching for a topic, e.g. 'machine learning'.
Alternately one can select the Ontology tab and browse the SN SciGraph class and property hierarchies. This is a useful means to understand the data structure of SN SciGraph (see our GitHub repository for the ontology source code in RDF Turtle) better.
Lastly one can simply enter a SN Scigraph URL into a browser search bar and this will open the Data Explorer centered on that resource. For example, try opening the page for a journal, e.g. Nature.
A quick note about URLs. SN SciGraph URLs reference SN SciGraph things and these can be expanded to reveal information about the things by clicking on the node. Generally, other URLs reference external resources and these have not been loaded into SciGraph and as such cannot be expanded to reveal further information.
This second approach allows metadata specialists, developers and data scientists building linked data applications to retrieve the data by the SN SciGraph API.
For more details about this approach see the API page.
The Linked Data API allows users to dereference a SN SciGraph URL and obtain linked data directly from it, either as JSON-LD (content-type application/ld+json), as N-Triples (content-type application/n-triples), as Turtle (content-type text/turtle), or as RDF/XML (content-type application/rdf+xml). By default an HTML representation is returned, which is what you would see by accessing the SN SciGraph URL with a browser.
Developers can also use the Redirect API to look up SN SciGraph things by e.g. DOI or ISBN.
This section contains links to tutorials, learning materials and other resources related to SN SciGraph.
SN SciGraph data has been loaded into various applications already, for example SemSpect. Have a look at this inspiring blog post by derivo's Thorsten Liebig, the visionary behind SemSpect. Read his full article explaining how to run through SN SciGraph with SemSpect without knowledge of the data or query languages.