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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
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OpCitance: Citation contexts identified from the PubMed Central open access articles.
Tzu-Kun Hsiao1, Vetle I Torvik2
1School of Information Sciences, University of Illinois at Urbana-Champaign, 501 E. Daniel Street, Champaign, IL, 61820, USA. tkhsiao2@illinois.edu.
Scientific Data
|April 28, 2023
Summary
OpCitance annotates 137 million inline citations from open-access articles, improving citation context studies. This dataset offers a high identification rate and accurate PubMed ID mapping for text-mining research.
Area of Science:
- Bibliometrics
- Computational Biology
- Bioinformatics
Background:
- Extracting citation contexts from scientific literature is challenging due to diverse referencing styles.
- Previous methods had limitations in accurately linking inline citations to references.
Purpose of the Study:
- To present OpCitance, a comprehensive dataset of annotated citation contexts from PubMed Central open-access articles.
- To improve the accuracy of citation mapping and facilitate citation context analysis.
Main Methods:
- Parsed 2 million PubMed Central open-access (PMCOA) articles to identify 137 million inline citations and their contexts.
- Developed and utilized an in-house citation matcher, Patci, to correct and supplement PubMed IDs (PMIDs).
- Compared citation data with NCBI E-Utilities and addressed discrepancies.
Main Results:
- Achieved a high inline citation identification rate, with only 0.5% of contexts remaining unidentified.
- Corrected and supplemented 6.84% of referenced PMIDs using the Patci matcher.
- OpCitance demonstrates superior accuracy in reference mapping to PMIDs compared to other corpora.
Conclusions:
- OpCitance provides a valuable resource for citation context studies and text-mining research.
- The dataset's high accuracy and coverage enhance the utility of open-access scientific literature for computational analysis.
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