The most prevalent data source of institutional references is author affiliations found on scientific articles. Typically, creators of original research articles and conference proceedings annotate papers with their department, organisation and address. Not only does this allow readers to recognise the origin of the research, but it also serves as a mechanism to aggregate and assess scientific output in order to provide science metrics.
However, when acquiring and processing large quantities of author affiliations, it becomes apparent that significant variation in the format and structure prevents effective aggregation and reporting. This, coupled with changes in name and institutional structure over time, makes large-scale integration of such data prohibitively expensive given the manual effort required to properly disambiguate each affiliation.
GRID provides an automatic disambiguation service to overcome these challenges. By exploiting the wealth of data we have acquired during the processing of award and publication data, coupled with extensive database of institutions, we are able to provide algorithmic matching of author affiliation strings to institutions.
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Location aware disambiguation
Our disambiguation algorithm recognises geographic locations in the affiliation string to select the correct institution even if the name is ambiguous.
GRID has the ability to process affiliations in languages other than English.
An extensive hand curated list of mappings from real datasets ensures GRID has many name varations to match on.