The National Library of Medicine indexer assignment dataset: A new large‐scale dataset for reviewer assignment research

• Published in: Journal of the American Society for Information Science and Technology Vol. 74; no. 2; pp. 205 - 218
• Main Authors: Rae, Alastair R., Mork, James G., Demner‐Fushman, Dina
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2023; Wiley Periodicals Inc
• Subjects: Algorithms; Citation indexes; Datasets; Government Libraries; Indexing; Libraries; Medical Subject Headings-MeSH; Medicine; National libraries
• DOI: 10.1002/asi.24722
• ISSN: 2330-1635

MEDLINE is the National Library of Medicine's (NLM) journal citation database. It contains over 28 million references to biomedical and life science journal articles, and a key feature of the database is that all articles are indexed with NLM Medical Subject Headings (MeSH). The library employs a team of MeSH indexers, and in recent years they have been asked to index close to 1 million articles per year in order to keep MEDLINE up to date. An important part of the MEDLINE indexing process is the assignment of articles to indexers. High quality and timely indexing is only possible when articles are assigned to indexers with suitable expertise. This article introduces the NLM indexer assignment dataset: a large dataset of 4.2 million indexer article assignments for articles indexed between 2011 and 2019. The dataset is shown to be a valuable testbed for expert matching and assignment algorithms, and indexer article assignment is also found to be useful domain‐adaptive pre‐training for the closely related task of reviewer assignment.