Document Type

Poster

Peer Reviewed

1

Publication Date

6-23-2018

Journal/Book/Conference Title

2018 ASEE Annual Conference & Exposition

Conference Location

Salt Lake City, Utah

Abstract

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.

Keywords

Faculty Profile System, Academic & Professional Record, APR, Engineering Faculty Publication Records, Metadata, Quality Control, Python, Jupyter Notebook

Journal Article Version

Author's Original

Published Article/Book Citation

Zhang, Q. (2018, June), Board 96 : Leveraging Python to Improve Quality of Metadata of Engineering Faculty Publication Records Paper presented at 2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah. https://peer.asee.org/30145

Rights

Copyright © 2018 Qianjin Zhang

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License

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URL

https://ir.uiowa.edu/lib_pubs/238