Last week, we closed applications for our New Commons Challenge—an open innovation challenge seeking to foster the use of data commons for the development of AI for local decision-making and humanitarian response.

The response to the New Commons Challenge has been a powerful signal of the growing interest in data commons as a catalyst for public interest AI and locally grounded impact. Between April and June, we received 173 submissions from around the world. 

Although the Challenge focused on two priority areas—humanitarian response and local decision-making—the proposals spanned a wide range of data types and use cases.

As we move into Phase Two, we are leveraging the current review process as a listening exercise—helping us gauge the appetite for and potential of data commons around the world. What kinds of initiatives are resonating most with AI, data and other practitioners? Where is this momentum coming from—and what can it tell us about the emerging future of collaborative data governance?

Geography

Of the 173 submissions, 154 are eligible for the current Phase One review. These entries span 51 countries from across the world and reflect the widespread global interest in data commons initiatives. 

A summary of which regions applicants submitted proposals from

A mapping of the cities that applicants indicated they were submitting proposals from

Award Type

Submissions fall into two categories: 103 entries (67%) for Award 1, focusing on creating a new data commons, and 51 entries (33%) for Award 2, focusing on enhancing an existing data commons. 

All applicants have to be affiliated with an established organization, and submissions primarily come from non-profits, start-ups, and research organizations (universities or research institutions).

Applicants 

A breakdown of the types of entities that submitted proposals

Data and Submission Type

Nearly all submissions involve multiple types of data, but the most common dataset types included:

• Text or unstructured data (language and linguistics, legal data, research and education, news and media, biomedical data)

• Structured data (e.g. national statistics, sensor networks, administrative data)

• Imagery (e.g. knowledge graphs, spatial images, artwork, data visualizations, geospatial datasets)

Many submissions also incorporated audio and video data, signaling a clear broadening of the types of data being considered for use in data commons. This trend reflects a growing recognition that multimodal data can offer valuable insights, especially when designing AI systems responsive to local needs and contexts.

The New Commons Challenge is specifically interested in data commons that support disaster response or local decision-making. Around 75 submissions focused exclusively on supporting local decision making. Of the 69 total submissions focused on humanitarian response, the vast majority also discussed how their data commons would support local decision making in crisis response or prevention. 

Next Steps: Phase Two

After we conclude our review of Phase One concept notes, selected applicants will be invited to submit a full proposal outlining their use case and implementation. An interdisciplinary group will assess proposals for feasibility, innovation, and impact potential. 

Of the shortlisted applications, two teams will each receive $100,000 in funding, along with mentorship, networking opportunities, and global exposure to support the execution of their data commons.

Decisions will be finalized in the coming months. To learn more about The New Commons Challenge, you can visit newcommons.ai. 

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