Can Citizen Science and Decentralized Storage Revolutionize AI?

In the rapidly evolving landscape of artificial intelligence, open data is emerging as a pivotal resource. The value of open data in building AI models cannot be overstated. Depending on the licensing, open data can be immediately integrated into model training and development, accelerating advancements across various fields.

Sources of open data are diverse, including governments, NGOs, and crowdsourced initiatives. Governments and NGOs often have the means to provide and finance the necessary storage infrastructure for this data. However, the realm of crowdsourcing, particularly through citizen science, presents unique challenges.

Citizen science, where individuals voluntarily contribute to scientific research, is flourishing, particularly in fields like environmental monitoring and astronomy. For example, platforms like iNaturalist allow people to document biodiversity, while projects like Galaxy Zoo enable amateurs to classify galaxies. Another flourishing area is weather data collection. Initiatives like Weather Underground leverage data from personal weather stations worldwide, providing granular weather data that enhances forecasting models. As this movement grows, so does the need for efficient and sustainable storage solutions for the data generated. It’s crucial that this data remains accessible for as long as it serves a purpose, ideally through a unified endpoint, ensuring ease of access and use.

Web3 technologies offer a promising alternative with their decentralized storage solutions, providing a serverless architecture that aligns well with the needs of distributed data collection efforts. While onboarding costs and user experience have been hurdles, ongoing improvements are making these technologies more viable.

To truly harness the power of citizen science data, we need a seamless system where data from experiments or monitoring can be stored and accessed effortlessly. This system should adhere to the FAIR principles (Findable, Accessible, Interoperable, Reusable) of research, without placing undue burdens on the citizen scientists themselves.

What is needed for such an ecosystem to emerge? Is there enough interest within the citizen science community to drive this forward? The answers to these questions will shape the future of open data and its integration with AI.