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Dataset Open Access

COVID-19 Open Research Dataset (CORD-19)

Sebastian Kohlmeier; Kyle Lo; Lucy Lu Wang; JJ Yang


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  "description": "<p>A full description of this dataset along with updated information can be found <a href=\"https://pages.semanticscholar.org/coronavirus-research\">here</a>.</p>\n\n<p>In response to the COVID-19 pandemic, the&nbsp;<a href=\"https://allenai.org/\">Allen Institute for AI</a>&nbsp;has partnered with leading research groups to prepare and distribute the COVID-19 Open Research Dataset (CORD-19), a free resource of scholarly articles, including full text content, about COVID-19 and the coronavirus family of viruses for use by the global research community.</p>\n\n<p>This dataset is intended to mobilize researchers to apply recent advances in natural language processing to generate new insights in support of the fight against this infectious disease. The corpus will be updated weekly as new research is published in peer-reviewed publications and archival services like&nbsp;<a href=\"https://www.biorxiv.org/\">bioRxiv</a>,&nbsp;<a href=\"https://www.medrxiv.org/\">medRxiv</a>, and others.</p>\n\n<p>By downloading this dataset you are agreeing to the Dataset license. Specific licensing information for individual articles in the dataset is available in the metadata file.</p>\n\n<p>Additional licensing information is available on the&nbsp;<a href=\"https://www.ncbi.nlm.nih.gov/pmc/tools/openftlist/\">PMC website</a>,&nbsp;<a href=\"https://www.medrxiv.org/submit-a-manuscript\">medRxiv website</a>&nbsp;and&nbsp;<a href=\"https://www.biorxiv.org/about-biorxiv\">bioRxiv website</a>.</p>\n\n<p><strong>Dataset content:</strong></p>\n\n<ul>\n\t<li>Commercial use subset</li>\n\t<li>Non-commercial use subset</li>\n\t<li>PMC custom license subset</li>\n\t<li>bioRxiv/medRxiv subset&nbsp;(pre-prints that are not peer reviewed)</li>\n\t<li>Metadata file</li>\n\t<li>Readme</li>\n</ul>\n\n<p>Each paper is represented as a single JSON object (see schema file for details).</p>\n\n<p><strong>Description:</strong></p>\n\n<p>The dataset contains all COVID-19 and coronavirus-related research (e.g. SARS, MERS, etc.) from the following sources:</p>\n\n<ul>\n\t<li>PubMed&#39;s PMC open access corpus using this&nbsp;<a href=\"https://www.ncbi.nlm.nih.gov/pmc/?term=%22COVID-19%22+OR+Coronavirus+OR+%22Corona+virus%22+OR+%222019-nCoV%22+OR+%22SARS-CoV%22+OR+%22MERS-CoV%22+OR+%E2%80%9CSevere+Acute+Respiratory+Syndrome%E2%80%9D+OR+%E2%80%9CMiddle+East+Respiratory+Syndrome%E2%80%9D\">query</a>&nbsp;(COVID-19 and coronavirus research)</li>\n\t<li>Additional COVID-19 research articles from a corpus maintained by the&nbsp;<a href=\"https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov\">WHO</a></li>\n\t<li>bioRxiv and medRxiv pre-prints using the same query as PMC (COVID-19 and coronavirus research)</li>\n</ul>\n\n<p>We also provide a comprehensive metadata file of coronavirus and COVID-19 research articles with links to&nbsp;<a href=\"https://www.ncbi.nlm.nih.gov/pmc/?term=%22COVID-19%22+OR+Coronavirus+OR+%22Corona+virus%22+OR+%222019-nCoV%22+OR+%22SARS-CoV%22+OR+%22MERS-CoV%22+OR+%E2%80%9CSevere+Acute+Respiratory+Syndrome%E2%80%9D+OR+%E2%80%9CMiddle+East+Respiratory+Syndrome%E2%80%9D\">PubMed</a>,&nbsp;<a href=\"https://aka.ms/AA7q3eb\">Microsoft Academic</a>&nbsp;and the&nbsp;<a href=\"https://www.who.int/emergencies/diseases/novel-coronavirus-2019/global-research-on-novel-coronavirus-2019-ncov\">WHO COVID-19 database of publications</a>&nbsp;(includes articles without open access full text).</p>\n\n<p>We recommend using metadata from the comprehensive file when available, instead of parsed metadata in the dataset. Please note the dataset may contain multiple entries for individual PMC IDs in cases when supplementary materials are available.</p>\n\n<p>This repository is linked to the WHO database of publications on coronavirus disease and other resources, such as Microsoft Academic Graph, PubMed, and Semantic Scholar. A coalition including the&nbsp;<a href=\"https://chanzuckerberg.com/\">Chan Zuckerberg Initiative</a>, Georgetown University&rsquo;s&nbsp;<a href=\"https://cset.georgetown.edu/\">Center for Security and Emerging Technology</a>,&nbsp;<a href=\"https://www.microsoft.com/en-us/research/\">Microsoft Research</a>, and the&nbsp;<a href=\"https://www.nlm.nih.gov/\">National Library of Medicine</a>&nbsp;of the National Institutes of Health came together to provide this service.</p>\n\n<p><strong>Citation:</strong></p>\n\n<p>When including CORD-19 data in a publication or redistribution, please cite the dataset as follows:</p>\n\n<p>In bibliography:</p>\n\n<pre><code>COVID-19 Open Research Dataset (CORD-19). 2020. Version 2020-MM-DD. Retrieved from https://pages.semanticscholar.org/coronavirus-research. Accessed YYYY-MM-DD. 10.5281/zenodo.3715505</code></pre>\n\n<p>In text:</p>\n\n<pre><code>(CORD-19, 2020)</code></pre>\n\n<p>The&nbsp;<a href=\"https://allenai.org/\">Allen Institute for AI</a>&nbsp;and particularly the Semantic Scholar team will continue to provide updates to this dataset as the situation evolves and new research is released.</p>", 
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      "affiliation": "Allen Institute for AI", 
      "@type": "Person", 
      "name": "Sebastian Kohlmeier"
    }, 
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      "affiliation": "Allen Institute for AI", 
      "@type": "Person", 
      "name": "Kyle Lo"
    }, 
    {
      "affiliation": "Allen Institute for AI", 
      "@type": "Person", 
      "name": "Lucy Lu Wang"
    }, 
    {
      "affiliation": "Allen Institute for AI", 
      "@type": "Person", 
      "name": "JJ Yang"
    }
  ], 
  "url": "https://zenodo.org/record/3727291", 
  "datePublished": "2020-03-16", 
  "version": "2020-03-20", 
  "keywords": [
    "COVID-19", 
    "Coronavirus", 
    "2019-nCoV", 
    "SARS-CoV", 
    "MERS-CoV", 
    "Severe Acute Respiratory Syndrome", 
    "Middle East Respiratory Syndrome"
  ], 
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  "@type": "Dataset", 
  "name": "COVID-19 Open Research Dataset (CORD-19)"
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