D F Sittig1. 1. Clinical Systems Research and Development, Partners Healthcare System, Chestnut Hill, MA 02167, USA. dfsittig@bics.bwh.harvard.edu
Abstract
OBJECTIVE: Experts disagree on the parameters to use to identify the "best" serials within a scientific field. The author set out to develop an extension to Dhawan's journal selection model for ranking serials in any scientific field. METHODS: Comparison of three different instantiations of Dhawan's model were used to rank thirty-four biomedical informatics serials. RESULTS: The first instantiation of Dhawan's model identified seven serials and divided them into two groups. The second instantiation of Dhawan's model identified twelve serials and separated them into two groups. Using fuzzy set theory the new extended model produced a rank ordered list of the top twelve biomedical informatics serials. CONCLUSIONS: Use of fuzzy set theory to assign set membership and combine data in Dhawan's journal selection model allows one to: (1) eliminate the need to determine arbitrary cutoff points for inclusion of serials within each of Dhawan's evaluation criteria categories, (2) combine data from disparate sources, and (3) obtain a rank-ordered list of the biomedical informatics serials rather than simply identifying a set of the "top" serials. Such a ranked list provides librarians and researchers alike with the information necessary to help them make their biomedical informatics serial selection decisions based on objective, quantifiable data.
OBJECTIVE: Experts disagree on the parameters to use to identify the "best" serials within a scientific field. The author set out to develop an extension to Dhawan's journal selection model for ranking serials in any scientific field. METHODS: Comparison of three different instantiations of Dhawan's model were used to rank thirty-four biomedical informatics serials. RESULTS: The first instantiation of Dhawan's model identified seven serials and divided them into two groups. The second instantiation of Dhawan's model identified twelve serials and separated them into two groups. Using fuzzy set theory the new extended model produced a rank ordered list of the top twelve biomedical informatics serials. CONCLUSIONS: Use of fuzzy set theory to assign set membership and combine data in Dhawan's journal selection model allows one to: (1) eliminate the need to determine arbitrary cutoff points for inclusion of serials within each of Dhawan's evaluation criteria categories, (2) combine data from disparate sources, and (3) obtain a rank-ordered list of the biomedical informatics serials rather than simply identifying a set of the "top" serials. Such a ranked list provides librarians and researchers alike with the information necessary to help them make their biomedical informatics serial selection decisions based on objective, quantifiable data.