Datasets:
update dataset card with tables and better summary
Browse files
README.md
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### Dataset Summary
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Elsevier OA CC-By Corpus: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals
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representing a large scale, cross-discipline set of research data to support NLP and ML research.
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### Supported Tasks and Leaderboards
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for the document through the addition of `https//www.sciencedirect.com/science/pii/<docId>`
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***abstract*** This is the author provided abstract for the document
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use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and
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IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the
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title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article
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***metadata*** Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and
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PMID). Along with a number of classification schemes such as ASJC and Subject Classification.
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***
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coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with
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the aim of summarising the core findings and results in the article.
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### Dataset Summary
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Elsevier OA CC-By Corpus: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals
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representing a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text
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articles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification).
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***Distribution of Publication Years***
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| Publication Year | Number of Articles |
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| :---: | :---: |
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| 2014 | 3018 |
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| 2015 | 4438 |
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| 2016 | 5913 |
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| 2017 | 6419 |
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| 2018 | 8016 |
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| 2019 | 10135 |
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| 2020 | 2159 |
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***Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.***
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| Discipline | Count |
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| --- | ---: |
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| General | 3847 |
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| Agricultural and Biological Sciences | 4840 |
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| Arts and Humanities | 982 |
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| Biochemistry, Genetics and Molecular Biology | 8356 |
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| Business, Management and Accounting | 937 |
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| Chemical Engineering | 1878 |
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| Chemistry | 2490 |
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| Computer Science | 2039 |
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| Decision Sciences | 406 |
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| Earth and Planetary Sciences | 2393 |
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| Economics, Econometrics and Finance | 976 |
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| Energy | 2730 |
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| Engineering | 4778 |
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| Environmental Science | 6049 |
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| Immunology and Microbiology | 3211 |
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| Materials Science | 3477 |
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| Mathematics | 538 |
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| Medicine | 7273 |
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| Neuroscience | 3669 |
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| Nursing | 308 |
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| Pharmacology, Toxicology and Pharmaceutics | 2405 |
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| Physics and Astronomy | 2404 |
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| Psychology | 1760 |
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| Social Sciences | 3540 |
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| Veterinary | 991 |
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| Dentistry | 40 |
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| Health Professions | 821 |
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### Supported Tasks and Leaderboards
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for the document through the addition of `https//www.sciencedirect.com/science/pii/<docId>`
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***abstract*** This is the author provided abstract for the document
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***body_text*** The full text for the document. The text has been split on sentence boundaries, thus making it easier to
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use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and
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IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the
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title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article
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***metadata*** Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and
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PMID). Along with a number of classification schemes such as ASJC and Subject Classification.
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***author_highlights*** Author highlights were included in the corpus where the author(s) have provided them. The
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coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with
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the aim of summarising the core findings and results in the article.
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