--- annotations_creators: - expert-generated language_creators: - expert-generated language: - en license: - cc-by-4.0 multilinguality: - monolingual pretty_name: Elsevier OA CC-By paperswithcode_id: elsevier-oa-cc-by size_categories: - 10K, "metadata":{ "title": , "authors": [ { "first": , "initial": , "last": , "email": }, ... ], "issn": , "volume": , "firstpage": , "lastpage": , "pub_year": , "doi": , "pmid": , "openaccess": "Full", "subjareas": [], "keywords": [], "asjc": [], }, "abstract":[ { "sentence": , "startOffset": , "endOffset": }, ... ], "bib_entries":{ "BIBREF0":{ "title":, "authors":[ { "last":, "initial":, "first": }, ... ], "issn": , "volume": , "firstpage": , "lastpage": , "pub_year": , "doi": , "pmid": }, ... }, "body_text":[ { "sentence": , "secId": , "startOffset": , "endOffset": , "title": , "refoffsets": { :{ "endOffset":, "startOffset": } }, "parents": [ { "id": , "title": }, ... ] }, ... ] } ``` ***docId*** The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL for the document through the addition of `https//www.sciencedirect.com/science/pii/` ***abstract*** This is the author provided abstract for the document ***body_text*** The full text for the document. The text has been split on sentence boundaries, thus making it easier to use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article structure. References have been extracted from the sentences. The IDs of the extracted reference and their respective offset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in the “bib_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’ sentences, ***bib_entities*** All the references from within the document can be found in this section. If the meta data for the reference is available, it has been added against the key for the reference. Where possible information such as the document titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be found in the sentence where the reference is used with the start and end offset of where in the sentence that reference was used. ***metadata*** Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and PMID). Along with a number of classification schemes such as ASJC and Subject Classification. ***author_highlights*** Author highlights were included in the corpus where the author(s) have provided them. The coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with the aim of summarising the core findings and results in the article. ### Data Fields * ***title***: This is the author provided title for the document. 100% coverage. * ***abstract***: This is the author provided abstract for the document. 99.25% coverage. * ***keywords***: This is the author and publisher provided keywords for the document. 100% coverage. * ***asjc***: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage. * ***subjareas***: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage. * ***body_text***: The full text for the document. 100% coverage. * ***author_highlights***: This is the author provided highlights for the document. 61.31% coverage. ### Data Splits ***Distribution of Publication Years*** | | Train | Test | Validation | | --- | :---: | :---: | :---: | | All Articles | 32072 | 4009 | 4008 | | With Author Highlights | 19644 | 2420 | 2514 | ## Dataset Creation ### Curation Rationale [More Information Needed] ### Source Data #### Initial Data Collection and Normalization Date the data was collected: 2020-06-25T11:00:00.000Z See the [original paper](https://doi.org/10.48550/arXiv.2008.00774) for more detail on the data collection process. #### Who are the source language producers? See `3.1 Data Sampling` in the [original paper](https://doi.org/10.48550/arXiv.2008.00774). ### Annotations #### Annotation process [More Information Needed] #### Who are the annotators? [More Information Needed] ### Personal and Sensitive Information [More Information Needed] ## Considerations for Using the Data ### Social Impact of Dataset [More Information Needed] ### Discussion of Biases [More Information Needed] ### Other Known Limitations [More Information Needed] ## Additional Information ### Dataset Curators [More Information Needed] ### Licensing Information [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) ### Citation Information ``` @article{Kershaw2020ElsevierOC, title = {Elsevier OA CC-By Corpus}, author = {Daniel James Kershaw and R. Koeling}, journal = {ArXiv}, year = {2020}, volume = {abs/2008.00774}, doi = {https://doi.org/10.48550/arXiv.2008.00774}, url = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs}, keywords = {Science, Natural Language Processing, Machine Learning, Open Dataset}, abstract = {We introduce the Elsevier OA CC-BY corpus. This is the first open corpus of Scientific Research papers which has a representative sample from across scientific disciplines. This corpus not only includes the full text of the article, but also the metadata of the documents, along with the bibliographic information for each reference.} } ``` ``` @dataset{https://10.17632/zm33cdndxs.3, doi = {10.17632/zm33cdndxs.2}, url = {https://data.mendeley.com/datasets/zm33cdndxs/3}, author = "Daniel Kershaw and Rob Koeling", keywords = {Science, Natural Language Processing, Machine Learning, Open Dataset}, title = {Elsevier OA CC-BY Corpus}, publisher = {Mendeley}, year = {2020}, month = {sep} } ``` ### Contributions Thanks to [@orieg](https://github.com/orieg) for adding this dataset.