Research area schemes, alongside baselines, are important to place bibliometric data into context. A citation count of a paper in isolation is a relatively meaningless number. But by looking at it in the context of peer publications, one can understand the performance, see if it is above or below average and by how much. Through benchmarking, data becomes actionable knowledge.

It is necessary to understand performance within the context of research areas because publication rates and citation behavior can vary considerably from discipline to discipline, document type and over time. For example, mathematics papers are usually cited at a relatively low rate but the citation rate can persist over a long period of time. Whereas molecular biology papers are typically cited more frequently and the citations tail off after a few years as the research is superseded. By understanding the underlying trends and comparing the publications of interest to publications in the same research area, year and document type will have more meaningful results.

Twelve different research area schemes are available in InCites. Three are exclusive to InCites and are described below.

A further eight are based on mapping Clarivate data to external subject classification systems. These schemes are designed to enable the use of bibliometric indicators in the context of a regional research evaluation program, for example the Research Excellence Framework in the United Kingdom. Alternatively, the Organization for Economic Cooperation and Development (OECD) subject classification scheme is a valuable tool for looking at national level bibliometric indicators in the context of demographical and financial data provided by the OECD. Typically, schemes based on external subject classifications are developed in partnership with research evaluation bodies in that region. They may be based on journal classifications or the mapping of Web of Science categories. Please see the Appendix (Regional Subject Schemes) for details of these schemes.

Which scheme to use will depend on the objectives of the analysis. Typically if looking at small sets of publications, such as the output of a single department or individual author, it is advisable to use the higher precision of a narrow subject classification such as the Web of Science scheme. This approach may be useful to overcome differences between things such as applied and theoretical research of the same topic.

However, if you wish to understand the overall subject mix of an organization or a country/region, using a broader scheme may be more appropriate.

Web of Science

Essential Science Indicators

GIPP Research Areas

Research Area Scheme Selection and Total Results