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Board 96 : Leveraging Python to Improve Quality of Metadata of Engineering Faculty Publication Records
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhang, Qianjin |
| Copyright Year | 2018 |
| 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. Background Faculty profile systems that capture and showcase faculty scholarly activities and accomplishments are emerging in many institutions. The platforms of faculty profile systems include commercial platforms such as Activity Insight’s Digital Measures, Elsevier’s Pure and Symplectic Elements and open-source platforms such as Profiles and VIVO [1]. During the trend, some university libraries have become actively involved in the implementation of faculty profile systems and expanded their roles in university leadership and stakeholders. For example, librarians from Duke University, Emory University and Georgia Institute of Technology recently reported use cases of implementation of Symplectic Elements at their home institutions and highlighted libraries’ significant roles in the system adoption [1]. Like many other institutions, the University of Iowa has started migrating faculty information to Activity Insight’s Digital Measures, locally branded as Academic and Professional Records (APR). The APR project is a collaborative initiative of the Office of the Provost, Information Technology Services and the University colleges to capture faculty information on teaching, research, grants, service, as well as records on professional accomplishments and interests. Since a record of publication would make a strong case for faculty excellence in scholarship especially for promotion and tenure, accuracy of publication records is significantly important. Since early 2017, the College of Engineering has steadily migrated their faculty data to the APR. The College of Engineering has 95 faculty members in five departments, including Biomedical Engineering, Chemical and Biochemical Engineering, Civil and Environmental Engineering, Electrical and Computer Engineering, and Industrial and Mechanical Engineering. Upon request by the College of Engineering and the APR project leader, we were to review engineering faculty publication records shown in Figure 1 [2] to improve the quality of metadata, especially focusing on Digital Object Identifier (DOI), ISSN, PubMed ID (PMID) and PubMed Central ID (PMCID). Based on a rough evaluation of metadata quality, we realized that thousands of yet-to-beidentified records with erroneous and missing metadata would make a routine manual review time-consuming and costly. However, some libraries have implemented Python scripts in managing metadata for library resources. For example, the University of Minnesota Libraries used Python scripts to evaluate MARC record completeness for electronic books [3] and librarians at the University of Virginia utilized Python to perform quality control on MODS records for digital collections [4]. Both examples indicate that Python would increase efficiency in quality control of metadata. In consideration of the challenges we are facing, scripting with Python would be an appropriate approach over the manual approach. Figure 1: Faculty Publication Record User Interface in the APR |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://ir.uiowa.edu/cgi/viewcontent.cgi?article=1334&context=lib_pubs |
| Alternate Webpage(s) | https://peer.asee.org/board-96-leveraging-python-to-improve-quality-of-metadata-of-engineering-faculty-publication-records.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
