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8 results for “full-text”
Automated Literature Screening for Systematic Reviews: Dataset for Evaluation Against Human Title and Abstract and Full-Text Screening Decisions
<p>This Zenodo entry contains the supplementary material associated with the manuscript titled <em>Automated Literature Screening for Systematic Reviews: A 5-Tier Prompting Approach Meeting Cochrane’s Sensitivity Requirement of Greater Than 0.99.</em> The paper will be presented at <a href="https://dbis.rwth-aachen.de/LLMs4MI2024/">LLMsMI 2024</a> in November 2024.</p> <p>A script is provided for replicating the executed experiments, along with a comprehensive evaluation file that reports all the experiment results. Provided data files represent an extension to the original datasets as provided by [1]. For associated systematic review manuscripts and eligibility criteria, please refer to [1] as well. </p> <p>[1] Guo, Eddie; Gupta, Mehul; Deng, Jiawen; Park, Ye-Jean; Paget, Mike; Naugler, Christopher (2023). "Automated Paper Screening for Clinical Reviews Using Large Language Models." <em>Mendeley Data</em>, V1, doi: 10.17632/np79tmhkh5.1. Accessed from: <a href="https://data.mendeley.com/datasets/np79tmhkh5/1" target="_new" rel="noopener">https://data.mendeley.com/datasets/np79tmhkh5/1</a>.</p>
Distribution of trial registry numbers within full-text PubMed Central - full dataset of discovered links
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unarXive: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network (open subset)
<h2><strong>Description</strong></h2><p>unarXive is a scholarly data set containing publications' structured full-text, annotated in-text citations, linked non-text content (mathematical notation, figure/table captions) and a citation network.</p><p>The data is generated from all LaTeX sources on <a href="https://arxiv.org/">arXiv</a> and therefore of higher quality than data generated from PDF files.</p><p>Typical uses are</p><ul><li>Training of ML models (citation recommendation, summarization, LLMs)</li><li>Citation context analysis</li><li>Bibliographic analyses</li></ul><h2><strong>Access</strong></h2><p>┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓<br>┃ <a href="https://github.com/IllDepence/unarXive/raw/master/doc/unarXive_data_sample.tar.gz"><strong>D O W N L O A D S A M P L E</strong></a> ┃<br>┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛</p><p>Regarding the full data set, please note the following:</p><blockquote><p><strong>Note</strong>: this Zenodo record is the "open subset" of unarXive, which contains all permissively licensed papers from arXiv.org. You can find the <a href="https://doi.org/10.5281/zenodo.7752754">full version here</a>.</p></blockquote><p>The code used for generating the data set is <a href="https://github.com/IllDepence/unarXive">publicly available</a>.</p>
NLMChem a new resource for chemical entity recognition in PubMed full-text literature
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Appendix II - Studies ineligible following full-text review
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Full-texts of articles in "GERONTOLOGIE CH. Praxis + Forschung" published by members of Swiss universities
<p>The swissuniversities-funded project GOAL (Unlocking the Green Open Access PotentiaL in scholarly and professional journals in Switzerland) aims to develop case scenarios for the semi-automatic inclusion of full-text articles from professional journals in Open Access repositories. These articles originate from journals with which the project has successfully negotiated self-archiving rights. To this end, the project is testing semi-automated workflows that process and enrich bibliographic metadata and full-texts provided by two professional journals. The dataset contains all full-texts of articles mentioned in the CSV/Excel for the journal “GERONTOLOGIE CH. Praxis + Forschung”. All issues provided on the publisher’s website “Gerontologie CH.” were split into single PDFs/A (one PDF per article).</p>
unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata
<h2><strong>Description</strong></h2> <p><strong>unarXive</strong> is a scholarly data set containing <strong>publications' full-text</strong>, annotated <strong>in-text citations</strong>, and a <strong>citation network</strong>.</p> <p>The data is <strong>generated from all LaTeX sources on </strong><a href="https://arxiv.org/"><strong>arXiv</strong></a> and therefore of higher quality than data generated from PDF files.</p> <p>Typical <strong>use cases</strong> are</p> <ul> <li>Citation recommendation</li> <li>Citation context analysis</li> <li>Bibliographic analyses</li> <li>Reference string parsing</li> </ul> <p>This version (v3) of our data set is based on all arXiv publications until 2020-07-31 and on the Microsoft Academic Graph as of 2020-08-18. As additional contribution, we included a table with the publication date and the scientific discipline for each paper for easier filtering.</p> <p><strong>Note:</strong> This Zenodo record is an old version of unarXive. You can find the <strong>most recent version</strong> at <a href="../record/7752754">https://zenodo.org/record/7752754</a> and <a href="../record/7752615">https://zenodo.org/record/7752615</a></p> <h2><strong>Access</strong></h2> <p>┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓<br>┃ <a href="https://github.com/IllDepence/unarXive/blob/legacy_2020/doc/unarXive_sample.tar.bz2"><strong>D O W N L O A D S A M P L E</strong></a>  ┃<br>┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛</p> <p>To download the whole data set send an access request and note the following:</p> <blockquote> <p><strong>Note</strong>: this Zenodo record is a "full" version of unarXive, which was generated from all of arXiv.org <em>including non-permissively licensed papers</em>. Make sure that your use of the data is compliant with the paper's licensing terms.¹</p> <p>¹ For information on papers' licenses use <a href="https://info.arxiv.org/help/bulk_data/index.html">arXiv's bulk metadata access</a>.</p> </blockquote> <p>The <strong>code</strong> used for generating the data set is <a href="https://github.com/IllDepence/unarXive/tree/legacy_2020/">publicly available</a>.</p> <p><strong>Usage examples</strong> for our data set are provided at <a href="https://github.com/IllDepence/unarXive/tree/legacy_2020/#usage-examples">here on GitHub</a>.</p> <h2><strong>Citing</strong></h2> <p>This initial version of unarXive is described in the following journal article.</p> <p><em>Tarek Saier, Michael Färber: "</em><a href="http://dx.doi.org/10.1007/s11192-020-03382-z"><em>unarXive: A Large Scholarly Data Set with Publications' Full-Text, Annotated In-Text Citations, and Links to Metadata</em></a><em>", Scientometrics, 2020,</em><br>[<a href="https://www.aifb.kit.edu/images/f/f9/UnarXive_Scientometrics2020.pdf">link to an author copy]</a></p> <p>The <strong>updated version</strong> is described in the following conference paper.</p> <p><em>Tarek Saier, Michael Färber. "</em><a href="10.1109/JCDL57899.2023.00020"><em>unarXive 2022: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network</em></a><em>", JCDL 2023.</em><br>[<a href="https://doi.org/10.48550/arXiv.2303.14957">link to an author copy</a>]</p>
unarXive: All arXiv Publications Pre-Processed for NLP, Including Structured Full-Text and Citation Network (full)
<h2><strong>Description</strong></h2><p>unarXive is a scholarly data set containing publications' structured full-text, annotated in-text citations, linked non-text content (mathematical notation, figure/table captions) and a citation network.</p><p>The data is generated from all LaTeX sources on <a href="https://arxiv.org/">arXiv</a> and therefore of higher quality than data generated from PDF files.</p><p>Typical uses are</p><ul><li>Training of ML models (citation recommendation, summarization, LLMs)</li><li>Citation context analysis</li><li>Bibliographic analyses</li></ul><h2><strong>Access</strong></h2><p>┏━━━━━━━━━━━━━━━━━━━━━━━━━━┓<br>┃ <a href="https://github.com/IllDepence/unarXive/raw/master/doc/unarXive_data_sample.tar.gz"><strong>D O W N L O A D S A M P L E</strong></a> ┃<br>┗━━━━━━━━━━━━━━━━━━━━━━━━━━┛</p><p>To download the whole data set send an access request and note the following:</p><blockquote><p><strong>Note</strong>: this Zenodo record is the "full" version of unarXive, which was generated from all of arXiv.org <i>including non-permissively licensed papers</i>. Make sure that your use of the data is compliant with the paper's licensing terms.¹<br>Alternatively you can use the <a href="https://doi.org/10.5281/zenodo.7752615">unarXive open subset</a>.</p><p>¹ For information on papers' licenses use <a href="https://info.arxiv.org/help/bulk_data/index.html">arXiv's bulk metadata access</a>.</p></blockquote><p>The code for generating the data set is <a href="https://github.com/IllDepence/unarXive">publicly available</a>.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.