The Engineering Library at the University of Iowa conducted a project which consisted of reviewing metadata of engineering faculty publications in the Academic and Professional Records (APR), which is a locally branded faculty profile system. The challenge of the project was that there are thousands of records with erroneous or missing metadata, making it difficult to manually check Digital Object Identifier (DOI) and ISSN. Our strategy was to analyze the complete dataset, break it down into subsets with some common patterns and then focus on those subsets. The processes were conducted using Python. As a result, we prioritized records that have almost complete metadata but missing DOI and/or ISSN, retrieved DOI from PubMed and CrossRef online queries separately and added ISSN by matching journal titles or conference names with authorities. The implementation of Python can not only make the review process effective and efficient but also expand library services to the APR project.
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