As sustainability becomes an increasing priority throughout global society, academic and research institutions are assessed on their contribution to relevant research publications. This study compares four methods of identifying research publications related to United Nations Sustainable Development Goal 13—Climate Action (SDG 13). The four methods (Elsevier, STRINGS, SIRIS, and Dimensions) have each developed search strings with the help of subject matter experts, which are then enhanced through distinct methods to produce a final set of publications. Our analysis showed that the methods produced comparable quantities of publications but with little overlap between them. We visualized some difference in topic focus between the methods and drew links with the search strategies used. Differences between publications retrieved are likely to come from subjective interpretation of the goals, keyword selection, operationalizing search strategies, AI enhancements, and selection of bibliographic database. Each of the elements warrants deeper investigation to understand their role in identifying SDG-related research. Before choosing any method to assess the research contribution to SDGs, end users of SDG data should carefully consider their interpretation of the goal and determine which of the available methods produces the closest data set. Meanwhile, data providers might customize their methods for varying interpretations of the SDGs.

Research managers benchmarking universities against international peers face the problem of affiliation disambiguation. Different databases have taken separate approaches to this problem and discrepancies exist between them. Bibliometric data sources typically conduct a disambiguation process that unifies variant institutional names and those of its subunits so that researchers can then search all records from that institution using a single unified name. This study examined affiliation discrepancies between Scopus, Web of Science (WoS), Dimensions, and Microsoft Academic for 18 Arab universities over a 5-year period. We confirmed that digital object identifiers (DOIs) are suitable for extracting comparable scholarly material across databases and quantified the affiliation discrepancies between them. A substantial share of records assigned to the selected universities in any one database were not assigned to the same university in another. The share of discrepancy was higher in the larger databases (Dimensions and Microsoft Academic). The smaller, more selective databases (Scopus and especially WoS) tended to agree to a greater degree with affiliations in the other databases. Manual examination of affiliation discrepancies showed that they were caused by a mixture of missing affiliations, unification differences, and assignation of records to the wrong institution.