We are very happy to announce that the latest Analytics API release (1.21) includes the capability of searching GRID – the Global Research Identifiers Database – as well as matching free-text affiliation strings against it, so it will return valid GRID identifiers.
What is GRID?
GRID is an openly accessible database of educational and research organizations worldwide, created and maintained by Digital Science. Each organization is assigned a unique GRID ID and its curated record contains useful information like the institution’s type, geo-coordinates, official website, and Wikipedia page. Name variations of institutions are included, as well. In December 2016, Digital Science released GRID under a Creative Commons CC0 license — without restriction under copyright or database law.
GRID has been broadly adopted in the Digital Science portfolio companies and beyond to facilitate data exchange, increase functionality, and support novel features. We think these benefits should be shared more widely in the scientific community to foster innovation and increase interoperability. Recently, Digital Science supported the creation of ROR, where the GRID data set served as a base data set – and the ROR identifiers have also been integrated back into GRID – basically given the Dimensions API users the possibility to match and resolve their affiliations to the identifier system of their choice.
That’s why we are now making it available as a new structured data source in the context of the Dimensions Search Language.
What can I do with the API?
The data for over 97 thousand research-related organizations are searchable either using a simple full-text query (e.g. matching organizations names or locations) or using a more sophisticated fielded search (e.g. search by organization type, country, external IDs, etc.. or any combination of the available fields).
This new Dimensions API feature makes it easier to perform a number of scholarly analytics and also opens up new opportunities, for example:
- Retrieving organizations programmatically using full-text search, combined with country/territory filters.
- Selecting organizations of a specific type (e.g. government, academic or nonprofit) and then analyzing the aggregate publications, grants or patents output.
- Leveraging the various external identifiers available in GRID (eg Wikidata, Fundref, UKPRN, UCAS, etc..) so to create applications that combine heterogeneous datasets.
- Enriching private datasets with non-disambiguated organizations data with Dimensions IDs, so to then take advantage of the wealth of linked data available in Dimensions.
If you want to see the API in action, do check out this resource too:
- Jupyter Notebook: Searching GRID organizational data using the Dimensions API.
Want to stay up to date with the latest Dimension API developments? Please take a look at these resources :