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Applications for the Trinity Challenge on Antimicrobial Resistance are now closed Read submitted solutions here

 

Exclusive Tools

 

Proprietary Resources Exclusively Available To Challenge Participants.

Below you can find a selection of resources our Members have agreed to grant access to any Solution Team that could utilise them in a meaningful way. Apart from these, Members will also provide other datasets Solution Teams might request.

Risk Resilience Center

Swiss Re & Palantir

A platform that pairs publicly available COVID-19 data with analytical tooling, helping organizations make data-driven decisions. It pulls data from over 100 global sources and includes a case map which provides a holistic view of the situation in over 200 countries and their constituent states, cities, and provinces.

Watch the overview video

Dr. Evidence platforms

Dr. Evidence

Doctor Evidence software platforms offer the capability to search and analyse the vast and growing universe of publicly available medical information, including COVID-19 related items, in real time. DOC Search is an artificial intelligence specialty biomedical search engine that comprehensively encompasses the landscape of global literature. DOC Analytics is a rapid analytical tool that generates direct, network and cohort analysis from artificial intelligence search results.

Learn more about Dr. Evidence software

 

Open Access Tools

 

 

Cuebiq Dashboards

Cuebiq

Cuebiq's Mobility Dashboard is a publicly accessible resource using Cuebiq's first party data to understand how population behaviours are evolving by day and by week during the COVID-19 epidemic. Cuebiq’s Mobility Insights include: Cuebiq Mobility Index, Contact Index, Traveler Analysis, Home Switcher Trend, Economic Reopening, Shelter-in-Place, Social Inequality and Mobility Flows. As a Member of The Trinity Challenge or Challenge participant, you get exclusive onboarding and training sessions.

Watch the overview video

cubiq

Kedro

Kedro

Kedro is an open-source Python framework for creating reproducible, maintainable and modular data science code. It borrows concepts from software engineering and applies them to machine-learning code; applied concepts include modularity, separation of concerns and versioning.

Watch the overview video

kedro

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