| Summary: | Curating institutional bibliographies with the ADS web interface is currently a manual process that scales with the number of search terms. Long author lists and institutions with multiple sub-organizations or name variations increase the workload. Review work is monotonous and can take significant time depending on the size of the institution and the frequency of reviews. Consequently, bibliographies generated in this way are costly and may suffer from human error. We propose a semi-automated workflow that uses an iterative approach to discovery with ADS’s new search engine and a recently developed Google Sheets add on. First, affiliation strings from a user created spreadsheet are searched with the ADS API and for each result the matched affiliation and the paired author are retrieved. Next, each author name string is searched and items where that author is paired with an empty affiliation field are retrieved. The results from both queries are then compiled into output sheets with pertinent information for manual review. Finally, the selected items can be added to an ADS library from the Google Sheets interface. The tool can also use previously rejected affiliation strings to flag false positives in subsequent queries. Curators do not need to have extensive technical skills in order to use the workflow and they can help improve the ADS by opting to share ORCIDs, author synonyms, and affiliation synonyms.
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