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Author: Caitlin Cricco

SN SciGraph is the P站视频 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 P站视频 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

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. , a conference, e.g. , a concept, e.g. , or an institute, e.g. the .

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.

Exploring SciGraph by following links

This approach is open-ended and exploratory in nature, and does not require any technical expertise (apart from some basic understanding of  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:

Search

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. .

Ontology

Alternately one can select the  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 ) better.

Web link

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. .

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. 

Getting SN SciGraph data via the API

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  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.

Further information: tutorials, learning and resources

This section contains links to tutorials, learning materials and other resources related to SN SciGraph.

• The blog post  provides a step by step tutorial on how to download these datasets and start interrogating them using free-download software.
• The  (June 2017) generated several ideas and suggestions of what do to with our Linked Open Data offering in areas such as Data Visualization, Information Retrieval, Linked Data Browsing and Predictive Analytics.
• The  (November 2017) focused on ways to combine and mine existing publication-related datasets and metadata with external knowledge structures.

SN SciGraph data has been loaded into various applications already, for example SemSpect. . Read his full article explaining how to run through SN SciGraph with SemSpect without knowledge of the data or query languages.