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A large dataset of scientific text reuse in Open-Access publications
We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains 91 million cases of reused text passages found in 4.2 million unique open-access publications. Cases range from overlap of as few as eight words to near-duplicate publicatio...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879940/ https://www.ncbi.nlm.nih.gov/pubmed/36702840 http://dx.doi.org/10.1038/s41597-022-01908-z |
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author | Gienapp, Lukas Kircheis, Wolfgang Sievers, Bjarne Stein, Benno Potthast, Martin |
author_facet | Gienapp, Lukas Kircheis, Wolfgang Sievers, Bjarne Stein, Benno Potthast, Martin |
author_sort | Gienapp, Lukas |
collection | PubMed |
description | We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains 91 million cases of reused text passages found in 4.2 million unique open-access publications. Cases range from overlap of as few as eight words to near-duplicate publications and include a variety of reuse types, ranging from boilerplate text to verbatim copying to quotations and paraphrases. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. The Webis-STEREO-21 does not indicate if a reuse case is legitimate or not, as its focus is on the general study of text reuse in science, which is legitimate in the vast majority of cases. It allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications. |
format | Online Article Text |
id | pubmed-9879940 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98799402023-01-28 A large dataset of scientific text reuse in Open-Access publications Gienapp, Lukas Kircheis, Wolfgang Sievers, Bjarne Stein, Benno Potthast, Martin Sci Data Data Descriptor We present the Webis-STEREO-21 dataset, a massive collection of Scientific Text Reuse in Open-access publications. It contains 91 million cases of reused text passages found in 4.2 million unique open-access publications. Cases range from overlap of as few as eight words to near-duplicate publications and include a variety of reuse types, ranging from boilerplate text to verbatim copying to quotations and paraphrases. Featuring a high coverage of scientific disciplines and varieties of reuse, as well as comprehensive metadata to contextualize each case, our dataset addresses the most salient shortcomings of previous ones on scientific writing. The Webis-STEREO-21 does not indicate if a reuse case is legitimate or not, as its focus is on the general study of text reuse in science, which is legitimate in the vast majority of cases. It allows for tackling a wide range of research questions from different scientific backgrounds, facilitating both qualitative and quantitative analysis of the phenomenon as well as a first-time grounding on the base rate of text reuse in scientific publications. Nature Publishing Group UK 2023-01-26 /pmc/articles/PMC9879940/ /pubmed/36702840 http://dx.doi.org/10.1038/s41597-022-01908-z Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Data Descriptor Gienapp, Lukas Kircheis, Wolfgang Sievers, Bjarne Stein, Benno Potthast, Martin A large dataset of scientific text reuse in Open-Access publications |
title | A large dataset of scientific text reuse in Open-Access publications |
title_full | A large dataset of scientific text reuse in Open-Access publications |
title_fullStr | A large dataset of scientific text reuse in Open-Access publications |
title_full_unstemmed | A large dataset of scientific text reuse in Open-Access publications |
title_short | A large dataset of scientific text reuse in Open-Access publications |
title_sort | large dataset of scientific text reuse in open-access publications |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9879940/ https://www.ncbi.nlm.nih.gov/pubmed/36702840 http://dx.doi.org/10.1038/s41597-022-01908-z |
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