ADR Awareness Centre

The European AI, Data and Robotics stakeholders operate from diverse historic backgrounds. To tap into the potentials of the diverse knowledge and resources generated in the AI, Data, and Robotics ecosystem, an all-inclusive collaboration approach between these stakeholders is necessary to make the ADR resources available and accessible to all.

For this, the ADR Awareness Centre is developed as an open repository of ADR educational resources and materials to enable collaboration and alignment among all projects within the Partnership, relevant external projects, and the public. The Awareness Centre will collect, validate, and publish educational resources and materials related to AI, Data, and Robotics.

- Who is this for? Everyone. Every project and organization in Europe including the general member of the public
- What does this Centre provide? AI, Data, and Robotics educational resources and materials produced in the EU
- When a new resource becomes available? The list of the resources will be updated when a new resource is submitted and validated
- Why should I publish a resource? Firstly, publishing your materials will promote and make them accessible to all. Secondly, it increases the transparency of your work and improves your and your organizations reputation. Last but not the least, your material will help address ADR challenges and contribute to shaping the future of ADR in Europe.
- How can I contribute and publish my resources? The Centre designed a very simple process for you to submit and publish your resources. You only need to submit your resource here and we will review and get it published and publicly accessible to all.
- What kinds of resources can I publish? The Centre only collects and publishes ADR related educational resources that can be publicly available and accessible to all. Copyrighted and licensed contents will not be published in the Centre.

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400+

Available resource

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Resource types

Displaying 2 resources

Software resources

Software resources

100-Driver: A Large-scale, Diverse Dataset for Distracted Driver Classification

A large-scale, diverse posture-based distracted diver dataset, with more than 470K images taken by 4 cameras observing 100 drivers over 79 hours from 5 vehicles.

Category

Data sharing and Data spaces

Target audience

Individual Citizens/Members of the Society, Policy Makers, Private Sector, Public Sector, Researchers and Academic

Source

Adra-e

Online Course

AI in Middle School Science Topics

Any instance where there is a large amount of data to be examined or processed that would take a lot of time for a person to examine, AI can do it and not get distracted, fatigued, or bored. This makes it a helpful tool for scientists to use.

Category

Sensing and processing methods, Architecture of sensing and perception systems, Meta-data contexts

Target audience

Individual Citizens/Members of the Society, Private Sector, Public Sector, Researchers and Academic

Source

Adra-e

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