For science funders, ORCID provides a persistent identifier that distinguishes one researcher from the others, and can facilitate workflows in grant submission, career tracking, and research impact(s). It makes life easier for the researcher – they can update their information in ORCID and make his/her past publications available to a funder as an ongoing service by just allowing this access as a one-time agreement. With these newly launched persistent tokens, researchers can grant a funder the right to update their grant record on ORCID once awarded – the metadata goes on an automatic roundtrip – effortless for the researcher, but the researcher stays in control, and can remove this right at any stage. Having and sharing data is one aspect – but being able to understand true researcher activity is another – and even more challenging is to understand research activity in the aggregate. What are hundreds or thousands of researchers doing? Often a standard search will only answer or provide insights into a slice of the data. Research classification systems - like the Fields of Research (FOR) - provide sufficient aggregation, but these normally require manual tagging and curation of all the documents in a dataset. However, by using machine learning to automate tagging, it becomes possible to answer the ‘what’ question easily. This ‘article-based classification’ is realized using Natural Language Processing (NLP) technology. With Dimensions, a portfolio analysis tool for research funders these capabilities are combined for research funders: allowing the researcher to provide controlled access to their ORCID profile and a solution environment for flexible article based classification, providing immediate access to analytical information on the researcher and institutional level – answering the questions ‘who is who’ and ‘what are they doing’?
Herzog, C., & Radford, G. (2015). ORCID for funders: Who’s who - and what are they doing? - ORCID IDs as identifiers for researchers and flexible article based classifications to understand the collective researcher portfolio. F1000Research, 4, 122. https://doi.org/10.12688/f1000research.6504.1