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14 results for “Software Mention”
Softcite software mention extraction from the CORD-19 publications
<p><strong>Softcite software mention extraction from the CORD-19 publications </strong></p> <p>This dataset is the result of the extraction of software mentions from the set of publications of the CORD-19 corpus (<a href="https://allenai.org/data/cord-19">https://allenai.org/data/cord-19</a>) by the Softcite software recognizer (SciBERT-CRF fine-tuned model), see <a href="https://github.com/ourresearch/software-mentions">https://github.com/ourresearch/software-mentions</a>.</p> <p>The CORD-19 version used for this dataset is the one dated <strong>2021-07-26,</strong> using the <em>metadata.csv</em> file only. We re-harvested the PDF with <a href="https://github.com/kermitt2/article-dataset-builder">https://github.com/kermitt2/article-dataset-builder</a> in order to also extract coordinates of software mentions in the PDF and to take advantage of the latest version of GROBID to produce better full text extraction from PDF. We also harvested 61,230 full-texts more than the standard CORD-19 distribution.</p> <p>Note that this is the third version of this dataset (version 0.3.0). The previous Softcite software mention extraction from the CORD-19 was based on 2020-09-11 and 2021-03-22 versions. The new version cover a larger set of documents and is using an improved version of the extraction tools.</p> <p><strong>Data format </strong></p> <p>The extraction consists of 3 JSON files:</p> <p><strong>annotations.jsonl</strong> contains the individual software annotations including <em>software name</em> and possible attached attributes (<em>publisher</em>, <em>URL</em> and <em>version</em>). Each annotation is associated with coordinates expressed as bounding boxes in the original PDF. See <a href="https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/">Coordinates of structures in the original PDF</a> for more details on the coordinate format.</p> <p>The context of citation is the sentence where the software name and its attributes are extracted. It is added to the JSON structure (field <em>context</em>), as well as the identifier of the document where the annotation belongs (field <em>document</em>, pointing to entries available in <em>documents.json</em>) and a list of bibliographical references attached to the software name (field <em>references</em>, pointing to entries available in <em>references.json</em>, with the used reference marker string). See <a href="https://github.com/ourresearch/software-mentions">https://github.com/ourresearch/software-mentions</a> for more details on the extracted attributes.</p> <p>If the software name was sucessfully disambiguated against WikiData ("entity linking"), it appears in the field <em>wikidataId</em> as Wikidata entity identifier and in the field <em>wikipediaExternalRef</em> as a Wikipedia PageID from the English Wikipedia. Entity linking is realized with <a href="https://github.com/kermitt2/entity-fishing">entity-fishing</a>.</p> <p><strong>documents.jsonl</strong> contains the metadata of the all the CORD-19 documents containing at least one software annotation. The metadata are given as a CrossRef JSON structure. The abstract should be included in the metadata most of the time, as well as some complements extracted by GROBID directly from the PDF. In addition, the size of the pages and the unique file path to the PDF can be found to allow annotations directly on the PDF (see <a href="https://grobid.readthedocs.io/en/latest/Coordinates-in-PDF/">Coordinates of structures in the original PDF</a> for more details on the PDF annotation display mechanism).</p> <p><strong>references.jsonl </strong>contains the parsed reference entries associated to software mentions. These references are given in the field <em>tei</em> encoded in the XML TEI format of GROBID extraction. The extracted raw references have been matched against CrossRef to get a DOI and more complete metadata with <a href="https://github.com/kermitt2/biblio-glutton">biblio-glutton</a>.</p> <p><strong>Statistics</strong></p> <p>CORD-19 version: 2021-07-26</p> <p>- total Open Access full texts: 296,686<br> - with at least one software mention: 115,073</p> <p>- total software name annotations: 652,518<br> - with linked Wikidata ID: 231,599</p> <p>- associated field <br> - publisher: 107,421<br> - version: 188,724<br> - URL: 59,366<br> - references: 230,145</p> <p>- associated bibliographical references: 92,573<br> - references with matched DOI: 49,350<br> - references with matched PMID: 32,895<br> - references with matched PMC ID: 18,741</p> <p><strong>License and acknowledgements</strong></p> <p>This dataset is licensed under a Creative Commons Attribution 4.0 International License.</p> <p>We thank the Alfred P. Sloan Foundation and of the Gordon and Betty Moore Foundation for supporting this work.</p>
Softcite Dataset: A dataset of software mentions in research publications
<p>The Softcite dataset is a gold-standard dataset of software mentions in research publications, a free resource primarily for software entity recognition in scholarly text. This is the first release of this dataset.</p> <p><strong>What's in the dataset</strong></p> <p>With the aim of facilitating software entity recognition efforts at scale and eventually increased visibility of research software for the due credit of software contributions to scholarly research, a team of trained annotators from Howison Lab at the University of Texas at Austin annotated 4,093 software mentions in 4,971 open access research publications in biomedicine (from PubMed Central Open Access collection) and economics (from Unpaywall open access services). The annotated software mentions, along with their <em>publisher</em>, <em>version</em>, and access <em>URL</em>, if mentioned in the text, as well as those publications annotated as containing no software mentions, are all included in the released dataset as a TEI/XML corpus file.</p> <p>For understanding the schema of the Softcite corpus, its design considerations, and provenance, please refer to our paper included in this release (preprint version).</p> <p><strong>Use scenarios</strong></p> <p>The release of the Softcite dataset is intended to encourage researchers and stakeholders to make research software more visible in science, especially to academic databases and systems of information retrieval; and facilitate interoperability and collaboration among similar and relevant efforts in software entity recognition and building utilities for software information retrieval. This dataset can also be useful for researchers investigating software use in academic research.</p> <p><strong>Current release content</strong></p> <p><em>softcite-dataset v1.0</em><strong> </strong>release includes<strong>:</strong></p> <ul> <li>The Softcite dataset corpus file: softcite_corpus-full.tei.xml</li> <li><em>Softcite Dataset: A Dataset of Software Mentions in Biomedical and Economic Research Publications</em>, our paper that describes the design consideration and creation process of the dataset: Softcite_Dataset_Description_RC.pdf. (This is a preprint version of our forthcoming publication in the Journal of the Association for Information Science and Technology.)</li> </ul> <p>The Softcite dataset is licensed under a <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</p> <p>If you have questions, please start a discussion or issue in the <a href="https://github.com/howisonlab/softcite-dataset">howisonlab/softcite-dataset Github repository</a>.</p>
CZ Software Mentions: A large dataset of software mentions in the biomedical literature
<p>We describe the CZ Software Mentions dataset, a new dataset of software mentions in biomedical papers. Plain-text software mentions are extracted with a trained SciBERT model from several sources: the NIH PubMed Central collection and from papers provided by various publishers to the Chan Zuckerberg Initiative. The dataset provides sources, context and metadata, and, for a number of mentions, the disambiguated software entities and links. We extract 1.12 million unique string software mentions from 2.4 million papers in the NIH PMC-OA Commercial subset, 481k unique mentions from the NIH PMC-OA Non-Commercial subset (both gathered in October 2021) and 934k unique mentions from 4 million papers in the Publishers' collection. There is variation in how software is mentioned in papers and extracted by the NER algorithm. We propose a clustering-based disambiguation algorithm to map plain-text software mentions into distinct software entities and apply it on the NIH PubMed Central Commercial collection. Through this methodology, we disambiguate 1.12 million unique strings extracted by the NER model into ~97000 unique software entities, covering 78% of all links. We link 185 000 of the mentions to a repository, covering about 55% of all software-paper links. We make all data and code publicly available as a new resource to help assess the impact of software (in particular scientific open source projects) on science.</p>
Manual classification of dataset and software mention contexts as used, created or shared
<p>This dataset consists of two JSON files containing:</p> <p>- software-contexts.json: 49195 sentences from the SoMeSci and Softcite datasets with 3162 software mentions and with added manual indication if the mentioned software is used, created and/or shared,</p> <p>- dataset-software-extra-contexts.json: an additional set of 481 sentences with dataset or software mentions and with added manual indication if the mentioned software is used, created and/or shared.</p> <p>Softcite dataset: <a href="https://zenodo.org/record/7995565">https://zenodo.org/record/7995565</a></p> <p>SoMeSci dataset: <a href="https://zenodo.org/record/4968738">https://zenodo.org/record/4968738</a></p>
Don't mention it: challenges to using software mentions to investigate citation and discoverability - Data and Notebooks
<p>This deposit contains the data and Jupyter notebooks used for sampling and annotation analysis of our submission to the PeerJ Computer Science special issue <em>Software Citation, Indexing, and Discoverability </em>(<a href="https://peerj.com/collections/84-software">https://peerj.com/collections/84-software</a>):</p> <blockquote> <p>Stephan Druskat, Neil P. Chue Hong, Sammie Buzzard, Olexandr Konovalov, and Patrick Kornek. Don’t mention it: challenges to using software mentions to investigate citation and discoverability.</p> </blockquote> <p>See the README in this deposit.</p> <p>The contents of this deposit can also be browsed at <a href="https://github.com/softwaresaved/habeas-corpus/tree/main/replication-package">https://github.com/softwaresaved/habeas-corpus/tree/main/replication-package</a>.</p>
CZ Software Mentions: A large dataset of software mentions in the biomedical literature
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CZ Software Mentions: A large dataset of software mentions in the biomedical literature - Expanded 2024
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A Dependency Graph for 460,000 Papers and Their Software Mentions from the CZI Software Mentions Dataset
<p>Using the CZI Software Mentions Dataset and <a href="https://ecosyste.ms">ecosyste.ms</a> we create a graph of papers, their mentioned software, and recursive dependencies of each piece of software across 466,000 papers and three software registries (PyPI, CRAN, and BioConductor).</p>
Corpus of correspondences between software mentions and papers
<p>This corpus has been created using a collection of 148 papers extracted from the repository of Papers With Code. It represents the bidirectional corresponde between software mentions and papers. It means, if a software is mentioned in a paper, and the paper is mentioned in the software repository.</p>
SOMD - SOftware Mention Detection
<p>The dataset contains the training and test data for the SOftware Mention Detection challenge. The data is derived from the SoMeSci Knowledge Graph of software mentions.</p> <ul> <li>Subtask 1 deals with the recognition of software mentions and the classification of mention (e.g. Usage, Creation,...) and software types (e.g. Application, PlugIn,...) at the same time </li> <li>Subtask 2 requires the recognition of additional meta data of software mentions (e.g. Version, Developer, URL,...)</li> <li>Subtask 3 deals with extracting the relations between the different entities of interest (e.g. Version_of, License_of,...)</li> </ul> <p>A detailed description of the dataset including the creation and a baseline for the different subtasks can be found in the following article </p> <p>D. Schindler, F. Bensmann, S. Dietze, and F. Krüger, “SoMeSci—A 5 Star Open Data Gold Standard Knowledge Graph of Software Mentions in Scientific Articles,” in Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM ’21), Virtual Event, QLD, Australia: Association for Computing Machinery, Nov. 2021. doi: <a href="https://doi.org/10.1145/3459637.3482017">10.1145/3459637.3482017</a>.</p>
Software Mentions Knowledge Graph
<p>Software plays an essential role in modern society. Its growth and evolution have given rise to a wide variety of pseudonyms to refer to the same software. This multiplicity of names can be confusing, complicating the accurate identification of software and its relationship to other programs and applications.<br> It is necessary to identify mentions of software in texts because they provide key information about its use and application in different contexts. Such identification makes it possible to establish a clearer link between programs, applications and their usefulness in various spheres. In addition, being able to group the different pseudonyms of a software under a single name facilitates its search and study, simplifying the acquisition of knowledge and the exchange of information between users and developers.<br> The term "Alias" refers to the name chosen to represent a group of pseudonyms that identify the same software tool. This alias acts as a common denominator, unifying the different ways in which a software tool can be mentioned in the scientific articles under analysis. From now on, we will refer to this unifying term as "alias" or "group", while "pseudonym" will designate the various forms of mentions of the same software that have appeared in the scientific literature.<br> Therefore, the main objective of this project is the construction of a knowledge graph [1] that groups the pseudonyms of scientific software tools into a common alias or group. This grouping will be done by analyzing and classifying the mentions of such software in academic publications provided by the article "CZ Software Mentions", published by the Chan Zuckerberg Initiative.</p>
Corpus of software mentions in Papers With Code
<p>To build this corpus, we have downloaded a list of publications from the <a href="https://paperswithcode.com/">Papers with Code</a> site. We have selected texts from the publications based on the appearance of the github repo reported as metadata in papers with code.</p>
SoMeSci - Software Mentions in Science
<p>"SoMeSci - Software Mentions in Science" is a gold standard knowledge graph of software mentions in scholarly articles created by manual annotation. The annotated articles are also available from https://github.com/dave-s477/SoMeSci/</p> <p>Beside the original SoMeSci gold standard data, this version contains annotations of all software related citations. Annotated citations are also available from Github: <a href="https://github.com/dave-s477/SoMeSci_Citation">https://github.com/dave-s477/SoMeSci_Citation</a></p>
CORD-19 Software Mentions
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