The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central
Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2022-01-01
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| Series: | PeerJ Computer Science |
| Subjects: | |
| Online Access: | https://peerj.com/articles/cs-835.pdf |
| _version_ | 1818753074493128704 |
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| author | David Schindler Felix Bensmann Stefan Dietze Frank Krüger |
| author_facet | David Schindler Felix Bensmann Stefan Dietze Frank Krüger |
| author_sort | David Schindler |
| collection | DOAJ |
| description | Science across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software. |
| first_indexed | 2024-12-18T05:01:35Z |
| format | Article |
| id | doaj.art-ed7377d6da684583b6cc48f84ef93bc9 |
| institution | Directory Open Access Journal |
| issn | 2376-5992 |
| language | English |
| last_indexed | 2024-12-18T05:01:35Z |
| publishDate | 2022-01-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj.art-ed7377d6da684583b6cc48f84ef93bc92022-12-21T21:20:07ZengPeerJ Inc.PeerJ Computer Science2376-59922022-01-018e83510.7717/peerj-cs.835The role of software in science: a knowledge graph-based analysis of software mentions in PubMed CentralDavid Schindler0Felix Bensmann1Stefan Dietze2Frank Krüger3Institute of Communications Engineering, University of Rostock, Rostock, GermanyGESIS - Leibniz Institute for the Social Sciences, Cologne, GermanyGESIS - Leibniz Institute for the Social Sciences, Cologne, GermanyInstitute of Communications Engineering, University of Rostock, Rostock, GermanyScience across all disciplines has become increasingly data-driven, leading to additional needs with respect to software for collecting, processing and analysing data. Thus, transparency about software used as part of the scientific process is crucial to understand provenance of individual research data and insights, is a prerequisite for reproducibility and can enable macro-analysis of the evolution of scientific methods over time. However, missing rigor in software citation practices renders the automated detection and disambiguation of software mentions a challenging problem. In this work, we provide a large-scale analysis of software usage and citation practices facilitated through an unprecedented knowledge graph of software mentions and affiliated metadata generated through supervised information extraction models trained on a unique gold standard corpus and applied to more than 3 million scientific articles. Our information extraction approach distinguishes different types of software and mentions, disambiguates mentions and outperforms the state-of-the-art significantly, leading to the most comprehensive corpus of 11.8 M software mentions that are described through a knowledge graph consisting of more than 300 M triples. Our analysis provides insights into the evolution of software usage and citation patterns across various fields, ranks of journals, and impact of publications. Whereas, to the best of our knowledge, this is the most comprehensive analysis of software use and citation at the time, all data and models are shared publicly to facilitate further research into scientific use and citation of software.https://peerj.com/articles/cs-835.pdfKnowledge graphSoftware mentionNamed entity recognitionSoftware citation |
| spellingShingle | David Schindler Felix Bensmann Stefan Dietze Frank Krüger The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central PeerJ Computer Science Knowledge graph Software mention Named entity recognition Software citation |
| title | The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central |
| title_full | The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central |
| title_fullStr | The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central |
| title_full_unstemmed | The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central |
| title_short | The role of software in science: a knowledge graph-based analysis of software mentions in PubMed Central |
| title_sort | role of software in science a knowledge graph based analysis of software mentions in pubmed central |
| topic | Knowledge graph Software mention Named entity recognition Software citation |
| url | https://peerj.com/articles/cs-835.pdf |
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