Datasets:
Languages:
English
Multilinguality:
monolingual
Size Categories:
10K<n<100K
Language Creators:
expert-generated
Annotations Creators:
expert-generated
Source Datasets:
original
ArXiv:
License:
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<n<100K | |
source_datasets: | |
- original | |
task_categories: | |
- fill-mask | |
- summarization | |
- text-classification | |
task_ids: | |
- masked-language-modeling | |
- news-articles-summarization | |
- news-articles-headline-generation | |
# Dataset Card for Elsevier OA CC-By | |
## Table of Contents | |
- [Dataset Card for Elsevier OA CC-By](#dataset-card-for-elsevier-oa-cc-by) | |
- [Table of Contents](#table-of-contents) | |
- [Dataset Description](#dataset-description) | |
- [Dataset Summary](#dataset-summary) | |
- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards) | |
- [Languages](#languages) | |
- [Dataset Structure](#dataset-structure) | |
- [Data Instances](#data-instances) | |
- [Data Fields](#data-fields) | |
- [Data Splits](#data-splits) | |
- [Dataset Creation](#dataset-creation) | |
- [Curation Rationale](#curation-rationale) | |
- [Source Data](#source-data) | |
- [Initial Data Collection and Normalization](#initial-data-collection-and-normalization) | |
- [Who are the source language producers?](#who-are-the-source-language-producers) | |
- [Annotations](#annotations) | |
- [Annotation process](#annotation-process) | |
- [Who are the annotators?](#who-are-the-annotators) | |
- [Personal and Sensitive Information](#personal-and-sensitive-information) | |
- [Considerations for Using the Data](#considerations-for-using-the-data) | |
- [Social Impact of Dataset](#social-impact-of-dataset) | |
- [Discussion of Biases](#discussion-of-biases) | |
- [Other Known Limitations](#other-known-limitations) | |
- [Additional Information](#additional-information) | |
- [Dataset Curators](#dataset-curators) | |
- [Licensing Information](#licensing-information) | |
- [Citation Information](#citation-information) | |
- [Contributions](#contributions) | |
## Dataset Description | |
- **Homepage:** https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs | |
- **Repository:** https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs | |
- **Paper:** https://arxiv.org/abs/2008.00774 | |
- **Leaderboard:** | |
- **Point of Contact:** [@orieg](https://huggingface.co/orieg) | |
### Dataset Summary | |
Elsevier OA CC-By: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals | |
representing a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text | |
articles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification). | |
***Distribution of Publication Years*** | |
| Publication Year | Number of Articles | | |
| :---: | :---: | | |
| 2014 | 3018 | | |
| 2015 | 4438 | | |
| 2016 | 5913 | | |
| 2017 | 6419 | | |
| 2018 | 8016 | | |
| 2019 | 10135 | | |
| 2020 | 2159 | | |
***Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.*** | |
| Discipline | Count | | |
| --- | ---: | | |
| General | 3847 | | |
| Agricultural and Biological Sciences | 4840 | | |
| Arts and Humanities | 982 | | |
| Biochemistry, Genetics and Molecular Biology | 8356 | | |
| Business, Management and Accounting | 937 | | |
| Chemical Engineering | 1878 | | |
| Chemistry | 2490 | | |
| Computer Science | 2039 | | |
| Decision Sciences | 406 | | |
| Earth and Planetary Sciences | 2393 | | |
| Economics, Econometrics and Finance | 976 | | |
| Energy | 2730 | | |
| Engineering | 4778 | | |
| Environmental Science | 6049 | | |
| Immunology and Microbiology | 3211 | | |
| Materials Science | 3477 | | |
| Mathematics | 538 | | |
| Medicine | 7273 | | |
| Neuroscience | 3669 | | |
| Nursing | 308 | | |
| Pharmacology, Toxicology and Pharmaceutics | 2405 | | |
| Physics and Astronomy | 2404 | | |
| Psychology | 1760 | | |
| Social Sciences | 3540 | | |
| Veterinary | 991 | | |
| Dentistry | 40 | | |
| Health Professions | 821 | | |
### Supported Tasks and Leaderboards | |
[More Information Needed] | |
### Languages | |
English (`en`). | |
## Dataset Structure | |
### Data Instances | |
The original dataset was published with the following json structure: | |
``` | |
{ | |
"docId": <str>, | |
"metadata":{ | |
"title": <str>, | |
"authors": [ | |
{ | |
"first": <str>, | |
"initial": <str>, | |
"last": <str>, | |
"email": <str> | |
}, | |
... | |
], | |
"issn": <str>, | |
"volume": <str>, | |
"firstpage": <str>, | |
"lastpage": <str>, | |
"pub_year": <int>, | |
"doi": <str>, | |
"pmid": <str>, | |
"openaccess": "Full", | |
"subjareas": [<str>], | |
"keywords": [<str>], | |
"asjc": [<int>], | |
}, | |
"abstract":[ | |
{ | |
"sentence": <str>, | |
"startOffset": <int>, | |
"endOffset": <int> | |
}, | |
... | |
], | |
"bib_entries":{ | |
"BIBREF0":{ | |
"title":<str>, | |
"authors":[ | |
{ | |
"last":<str>, | |
"initial":<str>, | |
"first":<str> | |
}, | |
... | |
], | |
"issn": <str>, | |
"volume": <str>, | |
"firstpage": <str>, | |
"lastpage": <str>, | |
"pub_year": <int>, | |
"doi": <str>, | |
"pmid": <str> | |
}, | |
... | |
}, | |
"body_text":[ | |
{ | |
"sentence": <str>, | |
"secId": <str>, | |
"startOffset": <int>, | |
"endOffset": <int>, | |
"title": <str>, | |
"refoffsets": { | |
<str>:{ | |
"endOffset":<int>, | |
"startOffset":<int> | |
} | |
}, | |
"parents": [ | |
{ | |
"id": <str>, | |
"title": <str> | |
}, | |
... | |
] | |
}, | |
... | |
] | |
} | |
``` | |
***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/<docId>` | |
***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. |