Datasets:
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README.md
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### Dataset Summary
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Elsevier OA CC-By Corpus: This is a corpus of 40k (40,
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representing a large scale, cross-discipline set of research data to support NLP and ML research.
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***docId*** The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL
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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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structure. References have been extracted from the sentences. The IDs of the extracted reference and their respective
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offset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in
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the “bib_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’
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sentences,
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***bib_entities*** All the references from within the document can be found in this section. If the meta data for the
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reference is available, it has been added against the key for the reference. Where possible information such as the
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document titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be
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found in the sentence where the reference is used with the start and end offset of where in the sentence that reference
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was used.
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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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### Supported Tasks and Leaderboards
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[More Information Needed]
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English (`en`).
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## Dataset Structure
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* ***title***:This is the author provided title for the document. 100% coverage.
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* ***abstract***: This is the author provided abstract for the document. 99.25% coverage.
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* ***keywords***: This is the author and publisher provided keywords for the document. 100% coverage.
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* ***asjc***: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.
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* ***subjareas***: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.
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* ***body_text***: The full text for the document. 100% coverage.
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* ***author_highlights***: This is the author provided highlights for the document. 61.31% coverage.
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### Data Instances
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}
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```
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### Data Fields
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### Data Splits
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### Curation Rationale
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See `3.1 Data Sampling` in the [original paper](https://doi.org/10.48550/arXiv.2008.00774).
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[More Information Needed]
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### Source Data
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Date the data was collected: 2020-06-25T11:00:00.000Z
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[
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#### Who are the source language producers?
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[
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### Annotations
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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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[More Information Needed]
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English (`en`).
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## Dataset Structure
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### Data Instances
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}
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```
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***docId*** The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL
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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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structure. References have been extracted from the sentences. The IDs of the extracted reference and their respective
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+
offset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in
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the “bib_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’
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sentences,
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+
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***bib_entities*** All the references from within the document can be found in this section. If the meta data for the
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reference is available, it has been added against the key for the reference. Where possible information such as the
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+
document titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be
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found in the sentence where the reference is used with the start and end offset of where in the sentence that reference
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was used.
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+
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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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***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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+
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### Data Fields
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* ***title***: This is the author provided title for the document. 100% coverage.
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+
* ***abstract***: This is the author provided abstract for the document. 99.25% coverage.
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+
* ***keywords***: This is the author and publisher provided keywords for the document. 100% coverage.
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+
* ***asjc***: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.
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+
* ***subjareas***: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.
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* ***body_text***: The full text for the document. 100% coverage.
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+
* ***author_highlights***: This is the author provided highlights for the document. 61.31% coverage.
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### Data Splits
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### Curation Rationale
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[More Information Needed]
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### Source Data
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Date the data was collected: 2020-06-25T11:00:00.000Z
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See the [original paper](https://doi.org/10.48550/arXiv.2008.00774) for more detail on the data collection process.
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#### Who are the source language producers?
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See `3.1 Data Sampling` in the [original paper](https://doi.org/10.48550/arXiv.2008.00774).
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### Annotations
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