COVID-19
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- Kaggle
- Kaggle Competitions
- Competitions | Kaggle
- Covid Tracking Project
- Fighting COVID-19 with Epidemiology: A Johns Hopkins Teach-Out This free Teach-Out is for anyone who has been curious about how we identify and measure outbreaks like the COVID-19 epidemic and wants to understand the epidemiology of these infections.
- COVID-19 Open Research Dataset (CORD-19)
Kaggle
The primary goal of Kaggle’s COVID-19 effort is to find factors that impact the transmission of COVID-19 (particularly those that map to the NASEM/WHO open scientific questions). You've already shown great results in producing meaningful insights to help address the pandemic! - Kaggle Team
- COVID-19 Open Research Dataset Challenge (CORD-19) in response to the COVID-19 pandemic
- COVID-19 Global Forecasting Challenge week 2 of Kaggle's COVID19 forecasting series: The primary goal is not only to forecast accurately, but to find factors that impact transmission rate of COVID-19. You are encouraged to pull in, curate, and share data sources that might be helpful. If you find variables that look like they impact the transmission rate, please share your findings in a notebook.
- COVID-19 Dataset Challenge: Kagglers will need to find, curate, share -- and join -- useful public datasets. You can review the relevant threads for sharing datasets and discussing dataset ideas to get an idea of the types of things that Kagglers find most useful. For this challenge we are only considering public datasets on Kaggle.
Acknowledgments
- John Hopkins University The Center for Systems Science and Engineering (CSSE) for making the data available to the public ...COVID-19 Map
- White House Office of Science and Technology Policy (OSTP) for pulling together the key open questions.
Research Tools
- ELEMENT AI Leveraging a semantic search model, users can search with keywords, phrases and even copy entire paragraphs of text into the search bar, yielding articles that contain content that is semantically similar -- helping to uncover potential patterns across research papers that may not otherwise have been perceptible.
