ΑΙhub.org
 

COVID-19 Open Research Dataset (CORD-19) now available for researchers

by
17 March 2020



share this:
CORD-19 dataset

On 16 March the COVID-19 Open Research Dataset (CORD-19) was released. This comprises an open-source, machine-readable collection of scholarly literature covering COVID-19, SARS-CoV-2, and the Coronavirus group. This free resource contains over 29,000 relevant scholarly articles, including over 13,000 with full text.

The release of the dataset is a result of a collaborate effort between the Allen Institute for AI, Chan Zuckerberg Initiative, Georgetown University, Microsoft, and the US National Library of Medicine (NLM). This resource 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 CORD-19 dataset is available on the Allen Institute’s SemanticScholar.org website and will continue to be updated as new research is published in archival services and peer-reviewed publications.

Kaggle is hosting a challenge using this dataset and at present there are 10 initial tasks for people to work on. These key scientific questions have been drawn from the National Academies of Sciences, Engineering, and Medicine’s research topics and the World Health Organization’s R&D Blueprint for COVID-19.

Links:

You can access the official webpage for CORD-19 here .
Find the kaggle challenge page here.




Lucy Smith , Managing Editor for AIhub.
Lucy Smith , Managing Editor for AIhub.




            AIhub is supported by:


Related posts :



#AAAI2024 workshops round-up 4: eXplainable AI approaches for deep reinforcement learning, and responsible language models

We hear from the organisers of two workshops at AAAI2024 and find out the key takeaways from their events.
12 April 2024, by

Deep learning-powered system maps corals in 3D

A system developed at EPFL can produce 3D maps of coral reefs from camera footage in just a few minutes.
11 April 2024, by

Is compute the binding constraint on AI research? Interview with Rebecca Gelles and Ronnie Kinoshita

We hear from authors of work presented at AAAI 2024 studying access to compute and the impact this has on AI research and researchers.
10 April 2024, by

Forthcoming machine learning and AI seminars: April 2024 edition

A list of free-to-attend AI-related seminars that are scheduled to take place between 9 April and 31 May 2024.
09 April 2024, by

Modeling extremely large images with xT

Introducing a new framework to model large images on contemporary GPUs while aggregating global context with local details.
08 April 2024, by

Going top shelf with AI to better track hockey data

Waterloo researchers get an assist from AI in identifying hockey players with greater accuracy and speed.
05 April 2024, by




AIhub is supported by:






©2024 - Association for the Understanding of Artificial Intelligence


 












©2021 - ROBOTS Association