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. 10.18260/1-2--30145

\bibitem{asee_peer_30145}
  Zhang, Q. (2018, June), \emph{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.
  10.18260/1-2--30145

Qianjin Zhang.  "Board 96 : Leveraging Python to Improve Quality of Metadata of Engineering Faculty Publication Records".  2018 ASEE Annual Conference & Exposition , Salt Lake City, Utah, 2018, June.  ASEE Conferences, 2018.  https://peer.asee.org/30145 Internet.  14 Dec, 2024

\bibitem{asee_peer_30145}
  Qianjin Zhang.
  "Board 96 : Leveraging Python to Improve Quality of Metadata of Engineering Faculty Publication Records".
  \emph{2018 ASEE Annual Conference \& Exposition , Salt Lake City, Utah, 2018, June}.
  ASEE Conferences, 2018.
  https://peer.asee.org/30145 Internet.  14 Dec, 2024

@INPROCEEDINGS{asee_peer_30145
author = "Qianjin Zhang"
title = "Board 96 : Leveraging Python to Improve Quality of Metadata of Engineering Faculty Publication Records"
booktitle = "2018 ASEE Annual Conference \& Exposition "
year = "2018"
month = "June"
address = "Salt Lake City, Utah"
publisher = "ASEE Conferences"
note = {https://peer.asee.org/30145}
number = {10.18260/1-2--30145}
}

TY  - CPAPER
AB  - 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.  
AU  - Qianjin Zhang
CY  - Salt Lake City, Utah
DA  - 2018/06/23
PB  - ASEE Conferences
TI  - Board 96 : Leveraging Python to Improve Quality of Metadata of Engineering Faculty Publication Records
UR  - https://peer.asee.org/30145
DO  - 10.18260/1-2--30145
ER  -