Datasets:
Tasks:
Other
Languages:
English
Multilinguality:
monolingual
Size Categories:
100K<n<1M
Language Creators:
found
Annotations Creators:
no-annotation
Source Datasets:
original
ArXiv:
License:
| annotations_creators: | |
| - no-annotation | |
| language_creators: | |
| - found | |
| language: | |
| - en | |
| license: | |
| - cc-by-nd-4.0 | |
| - cc-by-sa-4.0 | |
| - other | |
| multilinguality: | |
| - monolingual | |
| size_categories: | |
| - 100K<n<1M | |
| source_datasets: | |
| - original | |
| task_categories: | |
| - other | |
| task_ids: [] | |
| paperswithcode_id: cord-19 | |
| pretty_name: CORD-19 | |
| dataset_info: | |
| - config_name: metadata | |
| features: | |
| - name: cord_uid | |
| dtype: string | |
| - name: sha | |
| dtype: string | |
| - name: source_x | |
| dtype: string | |
| - name: title | |
| dtype: string | |
| - name: doi | |
| dtype: string | |
| - name: abstract | |
| dtype: string | |
| - name: publish_time | |
| dtype: string | |
| - name: authors | |
| dtype: string | |
| - name: journal | |
| dtype: string | |
| - name: url | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 496247275 | |
| num_examples: 368618 | |
| download_size: 6142360818 | |
| dataset_size: 496247275 | |
| - config_name: fulltext | |
| features: | |
| - name: cord_uid | |
| dtype: string | |
| - name: sha | |
| dtype: string | |
| - name: source_x | |
| dtype: string | |
| - name: title | |
| dtype: string | |
| - name: doi | |
| dtype: string | |
| - name: abstract | |
| dtype: string | |
| - name: publish_time | |
| dtype: string | |
| - name: authors | |
| dtype: string | |
| - name: journal | |
| dtype: string | |
| - name: url | |
| dtype: string | |
| - name: fulltext | |
| dtype: string | |
| splits: | |
| - name: train | |
| num_bytes: 3718245736 | |
| num_examples: 368618 | |
| download_size: 6142360818 | |
| dataset_size: 3718245736 | |
| - config_name: embeddings | |
| features: | |
| - name: cord_uid | |
| dtype: string | |
| - name: sha | |
| dtype: string | |
| - name: source_x | |
| dtype: string | |
| - name: title | |
| dtype: string | |
| - name: doi | |
| dtype: string | |
| - name: abstract | |
| dtype: string | |
| - name: publish_time | |
| dtype: string | |
| - name: authors | |
| dtype: string | |
| - name: journal | |
| dtype: string | |
| - name: url | |
| dtype: string | |
| - name: doc_embeddings | |
| sequence: float64 | |
| splits: | |
| - name: train | |
| num_bytes: 2759561943 | |
| num_examples: 368618 | |
| download_size: 6142360818 | |
| dataset_size: 2759561943 | |
| # Dataset Card for CORD-19 | |
| ## 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) | |
| - [Annotations](#annotations) | |
| - [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://www.semanticscholar.org/cord19](https://www.semanticscholar.org/cord19) | |
| - **Repository:** [https://github.com/allenai/cord19](https://github.com/allenai/cord19) | |
| - **Paper:** [CORD-19: The COVID-19 Open Research Dataset](https://www.aclweb.org/anthology/2020.nlpcovid19-acl.1/) | |
| - **Leaderboard:** [Kaggle challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) | |
| ### Dataset Summary | |
| CORD-19 is a corpus of academic papers about COVID-19 and related coronavirus research. It's curated and maintained by the Semantic Scholar team at the Allen Institute for AI to support text mining and NLP research. | |
| ### Supported Tasks and Leaderboards | |
| See tasks defined in the related [Kaggle challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge/tasks) | |
| ### Languages | |
| The dataset is in english (en). | |
| ## Dataset Structure | |
| ### Data Instances | |
| The following code block present an overview of a sample in json-like syntax (abbreviated since some fields are very long): | |
| ``` | |
| { | |
| "abstract": "OBJECTIVE: This retrospective chart review describes the epidemiology and clinical features of 40 patients with culture-proven Mycoplasma pneumoniae infections at King Abdulaziz University Hospital, Jeddah, Saudi Arabia. METHODS: Patients with positive M. pneumoniae cultures from respiratory specimens from January 1997 through December 1998 were identified through the Microbiology records. Charts of patients were reviewed. RESULTS: 40 patients were identified [...]", | |
| "authors": "Madani, Tariq A; Al-Ghamdi, Aisha A", | |
| "cord_uid": "ug7v899j", | |
| "doc_embeddings": [ | |
| -2.939983606338501, | |
| -6.312200546264648, | |
| -1.0459030866622925, | |
| [...] 766 values in total [...] | |
| -4.107113361358643, | |
| -3.8174145221710205, | |
| 1.8976187705993652, | |
| 5.811529159545898, | |
| -2.9323840141296387 | |
| ], | |
| "doi": "10.1186/1471-2334-1-6", | |
| "journal": "BMC Infect Dis", | |
| "publish_time": "2001-07-04", | |
| "sha": "d1aafb70c066a2068b02786f8929fd9c900897fb", | |
| "source_x": "PMC", | |
| "title": "Clinical features of culture-proven Mycoplasma pneumoniae infections at King Abdulaziz University Hospital, Jeddah, Saudi Arabia", | |
| "url": "https: //www.ncbi.nlm.nih.gov/pmc/articles/PMC35282/" | |
| } | |
| ``` | |
| ### Data Fields | |
| Currently only the following fields are integrated: `cord_uid`, `sha`,`source_x`, `title`, `doi`, `abstract`, `publish_time`, `authors`, `journal`. With `fulltext` configuration, the sections transcribed in `pdf_json_files` are converted in `fulltext` feature. | |
| - `cord_uid`: A `str`-valued field that assigns a unique identifier to each CORD-19 paper. This is not necessariy unique per row, which is explained in the FAQs. | |
| - `sha`: A `List[str]`-valued field that is the SHA1 of all PDFs associated with the CORD-19 paper. Most papers will have either zero or one value here (since we either have a PDF or we don't), but some papers will have multiple. For example, the main paper might have supplemental information saved in a separate PDF. Or we might have two separate PDF copies of the same paper. If multiple PDFs exist, their SHA1 will be semicolon-separated (e.g. `'4eb6e165ee705e2ae2a24ed2d4e67da42831ff4a; d4f0247db5e916c20eae3f6d772e8572eb828236'`) | |
| - `source_x`: A `List[str]`-valued field that is the names of sources from which we received this paper. Also semicolon-separated. For example, `'ArXiv; Elsevier; PMC; WHO'`. There should always be at least one source listed. | |
| - `title`: A `str`-valued field for the paper title | |
| - `doi`: A `str`-valued field for the paper DOI | |
| - `pmcid`: A `str`-valued field for the paper's ID on PubMed Central. Should begin with `PMC` followed by an integer. | |
| - `pubmed_id`: An `int`-valued field for the paper's ID on PubMed. | |
| - `license`: A `str`-valued field with the most permissive license we've found associated with this paper. Possible values include: `'cc0', 'hybrid-oa', 'els-covid', 'no-cc', 'cc-by-nc-sa', 'cc-by', 'gold-oa', 'biorxiv', 'green-oa', 'bronze-oa', 'cc-by-nc', 'medrxiv', 'cc-by-nd', 'arxiv', 'unk', 'cc-by-sa', 'cc-by-nc-nd'` | |
| - `abstract`: A `str`-valued field for the paper's abstract | |
| - `publish_time`: A `str`-valued field for the published date of the paper. This is in `yyyy-mm-dd` format. Not always accurate as some publishers will denote unknown dates with future dates like `yyyy-12-31` | |
| - `authors`: A `List[str]`-valued field for the authors of the paper. Each author name is in `Last, First Middle` format and semicolon-separated. | |
| - `journal`: A `str`-valued field for the paper journal. Strings are not normalized (e.g. `BMJ` and `British Medical Journal` can both exist). Empty string if unknown. | |
| - `mag_id`: Deprecated, but originally an `int`-valued field for the paper as represented in the Microsoft Academic Graph. | |
| - `who_covidence_id`: A `str`-valued field for the ID assigned by the WHO for this paper. Format looks like `#72306`. | |
| - `arxiv_id`: A `str`-valued field for the arXiv ID of this paper. | |
| - `pdf_json_files`: A `List[str]`-valued field containing paths from the root of the current data dump version to the parses of the paper PDFs into JSON format. Multiple paths are semicolon-separated. Example: `document_parses/pdf_json/4eb6e165ee705e2ae2a24ed2d4e67da42831ff4a.json; document_parses/pdf_json/d4f0247db5e916c20eae3f6d772e8572eb828236.json` | |
| - `pmc_json_files`: A `List[str]`-valued field. Same as above, but corresponding to the full text XML files downloaded from PMC, parsed into the same JSON format as above. | |
| - `url`: A `List[str]`-valued field containing all URLs associated with this paper. Semicolon-separated. | |
| - `s2_id`: A `str`-valued field containing the Semantic Scholar ID for this paper. Can be used with the Semantic Scholar API (e.g. `s2_id=9445722` corresponds to `http://api.semanticscholar.org/corpusid:9445722`) | |
| Extra fields based on selected configuration during loading: | |
| - `fulltext`: A `str`-valued field containing the concatenation of all text sections from json (itself extracted from pdf) | |
| - `doc_embeddings`: A `sequence` of float-valued elements containing document embeddings as a vector of floats (parsed from string of values separated by ','). Details on the system used to extract the embeddings are available in: [SPECTER: Document-level Representation Learning using Citation-informed Transformers](https://arxiv.org/abs/2004.07180). TL;DR: it's relying on a BERT model pre-trained on document-level relatedness using the citation graph. The system can be queried through REST (see [public API documentation](https://github.com/allenai/paper-embedding-public-apis)). | |
| ### Data Splits | |
| No annotation provided in this dataset so all instances are provided in training split. | |
| The sizes of each configuration are: | |
| | | train | | |
| |------------|-------:| | |
| | metadata | 368618 | | |
| | fulltext | 368618 | | |
| | embeddings | 368618 | | |
| ## Dataset Creation | |
| ### Curation Rationale | |
| See [official readme](https://github.com/allenai/cord19/blob/master/README.md) | |
| ### Source Data | |
| See [official readme](https://github.com/allenai/cord19/blob/master/README.md) | |
| #### Initial Data Collection and Normalization | |
| See [official readme](https://github.com/allenai/cord19/blob/master/README.md) | |
| #### Who are the source language producers? | |
| See [official readme](https://github.com/allenai/cord19/blob/master/README.md) | |
| ### Annotations | |
| No annotations | |
| #### Annotation process | |
| N/A | |
| #### Who are the annotators? | |
| N/A | |
| ### 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 | |
| [More Information Needed] | |
| ### Citation Information | |
| ``` | |
| @article{Wang2020CORD19TC, | |
| title={CORD-19: The Covid-19 Open Research Dataset}, | |
| author={Lucy Lu Wang and Kyle Lo and Yoganand Chandrasekhar and Russell Reas and Jiangjiang Yang and Darrin Eide and | |
| K. Funk and Rodney Michael Kinney and Ziyang Liu and W. Merrill and P. Mooney and D. Murdick and Devvret Rishi and | |
| Jerry Sheehan and Zhihong Shen and B. Stilson and A. Wade and K. Wang and Christopher Wilhelm and Boya Xie and | |
| D. Raymond and Daniel S. Weld and Oren Etzioni and Sebastian Kohlmeier}, | |
| journal={ArXiv}, | |
| year={2020} | |
| } | |
| ``` | |
| ### Contributions | |
| Thanks to [@ggdupont](https://github.com/ggdupont) for adding this dataset. |