Clarivate Plc, a global leader in providing trusted information and insights to accelerate the pace of innovation, has released a report that examines the organisation of information in the global scientific community and introduces a flexible new data-driven approach to citation-based classification. The report highlights new technology, developed in collaboration with the leading academic scientometrics team at the Centre for Science and Technology Studies (CWTS) at Leiden University in the Netherlands.
Entitled ‘Data categorization: understanding choices and outcomes’, the report from the Institute for Scientific Information (ISI)™ outlines existing research categorisation systems from around the world and the analytical consequences of applying them to national and institutional data. It introduces a new and highly innovative approach to data aggregation based on trusted research data in the Web of Science™ citation network. The report also enhances the need for good practice in data management to improve knowledge, competency and confidence and to ensure the responsible use of research metrics.
The team’s research found that a categorisation scheme informed by article metadata is stronger than one arranged by human concepts, e.g., those which are journal-based, top-down and use expert input to split domains into related sub-categories. Instead, a citation-based classification of articles and reviews progressively links individual elements into larger units with shared characteristics based on features in the underlying data. This innovative approach demonstrated in InCites™ Citation Topics more accurately represents microclusters, or specialties, provides more uniform content and improves citation normalisation. It also gives opportunity for novel groups to appear that were not previously possible with journal-based schemes.
Apart from its wide range of data selections, tests and visualisations, InCites provides multiple choices of top-down data classifications and now also offers Citation Topics as a bottom-up citation-based classification. The current implementation of Citation Topics is composed of 10 macro topics, 326 meso topics and 2,444 micro topics, with monthly and annual updating built in which will allow it to evolve over time.
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