Projects / AI Innovation Challenge

Leverage AI to Crowdsource the Mapping of the Datasphere

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In this 8-weeks challenge, you will join 50 AI changemakers to identify possible maps of the Datasphere and crowdsource datasets that can be used to model different visualization tools from the perspective of individuals and/or organizations. 

The problem

Data increasingly underpins and reflects most human activities. The volume of data, be it personal or non-personal, collected and produced, stored, utilized, and in transit, grows at an accelerated pace. The Datasphere is the notional space where all of this digital data exists.

How we collectively govern the Datasphere will strongly determine the future of human society in the 21st century and our capacity to deal with major global challenges such as health, energy, climate change, and food security. Unfortunately, persistent misuse of data and general mistrust among governments, companies, and civil society hamper the search for cooperative solutions for data governance. The rising tensions call for a bold paradigm shift on data governance models and for innovative mechanisms for perceiving and visualizing the Datasphere.

The Datasphere Initiative seeks to bring a new, holistic and positive approach to the governance of the Datasphere. It aims to be a platform to improve coordination and accelerate the adoption of concrete proposals to overcome the current tensions and polarization around data. It will do so by raising awareness, producing evidence-based analysis, and catalyzing human-centric technical, policy, and institutional innovations. The vision of the Datasphere Initiative is a Datasphere that fosters trust, prosperity, sustainability, and well-being for all.

The project goals

This project seeks to develop an AI system to enable the visualization of the Datasphere as a whole and its different dimensions, building on datasets of personal and/or non-personal data. The ultimate objective is to make the Datasphere tangible for users and decision-makers and generate an emotional reaction to catalyze a rethinking of how the Datasphere could be reclaimed and governed. 

This project can be broken down into 4 steps:

  • Step 1: Identify relevant datasets stemming from individuals or organizations, public or private
  • Step 2: Automate such data collection for mapping and cartography the Datasphere, enabling predictive modeling on how the Datasphere will continue to expand.
  • Step 3: Develop an interactive dashboard where the Datasphere can be visualized in its different layers and segments of different Dataspheres (by user profile or geography; company type or sector; theme)
  • Step 4: Explore the idea of building an AI system to personalize the individual’s current position or “journey through the Datasphere” (e.g. web-based, browser ad-on, etc), while maintaining the notion of connectedness to a broader Datasphere. 

Why join? The uniqueness of Omdena AI Challenges

A collaborative experience you never had in your working life! For the next eight weeks, you will not only build AI solutions to make a real-world impact but also go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join a global and collaborative team of changemakers. Omdena AI Challenges are not a competition or hackathon but a real-world project that will take your experience of what is possible through collaboration to a new level.

Find more information on how an Omdena project works
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Your benefits

Address a significant real-world problem with your skills

Get hired at top companies by building your Omdena project portfolio (via certificates, references, etc.)

Access paid projects, speaking gigs, and writing opportunities


Good English

A very good grasp in computer science and/or mathematics

Student, (aspiring) data scientist, (senior) ML engineer, data engineer, or domain expert (no need for AI expertise)

Programming experience with Python

Understanding of Natural Language Processing, Machine Learning and Predictive Modeling.

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