About the Resource Hub
An open access database collecting information about how AI, data centers, and the environment interact.
Hi, I’m Nate.
I’m the author of the AI & Environment Resource Hub. I help data centers develop responsibly by connecting environmental research, policy, and practical tools.
Visit my portfolio ↗A curated collection, made navigable
The Resource Hub brings together public information about AI, data centers, and environmental systems. Its five pathways connect resources through environmental topics, computing lifecycle stages, and perspectives.
The collection includes research, policy, tools, organizations, public professional profiles, courses, podcasts, and media with a substantive environmental or sustainable computing connection.
How the data is prepared
- Import only the approved public spreadsheet tabs; submission responses stay outside the public website.
- Normalize links while preserving original IDs and spreadsheet rows.
- Flag duplicate candidates, missing titles, and invalid links for review.
- Apply searchable labels from titles, summaries, and existing metadata, then keep them open to editorial correction.
A Data Commons–inspired structure, without pretending it is the same thing
The Hub publishes one shared schema that links every resource to topics, pathways, lifecycle stages, provenance, and source checks. That is the useful idea behind a data commons: different collections become searchable through consistent entities and relationships.
The Hub does not currently run on Google Data Commons or its MCP server. Google Data Commons is an open-source knowledge-graph platform built primarily around places, statistical variables, observations, and provenance. This editorial resource library has different core objects, so its static JSON and taxonomy are currently simpler, faster, and easier to audit.
A future integration could map stable Hub IDs and geographic entities into Data Commons while keeping this site’s source-level metadata authoritative. It should be added only after testing whether graph queries materially improve discovery.
Artwork from Better Images of AI
The environmental-system and pathway cards use work from the Better Images of AI library. Each work is licensed under CC BY 4.0. Images were resized, reformatted, and cropped for card presentation.
- Glitched Landscape by Cristóbal Ascencio & Archival Images of AI + AIxDESIGN / CC BY 4.0
- Server Pool by ulysse.gks & FARI / CC BY 4.0
- Distorted Sand Mine by Lone Thomasky & Bits&Bäume / CC BY 4.0
- Distorted Fish School by Lone Thomasky & Bits&Bäume / CC BY 4.0
- The Environmental Impact of Data Centers in Vulnerable Ecosystems by Gloria Mendoza / CC BY 4.0
- The AI Race by Gloria Mendoza / CC BY 4.0
- AI Lands by XK Studio & Google DeepMind / CC BY 4.0
- The AI-Deal by Daniela Zampieri / CC BY 4.0
- Fine Art Tuning by ulysse.gks & FARI / CC BY 4.0
- The Horizon I by Wesley Goatley & BRAID / CC BY 4.0
What the collection can tell you
Counts show what is currently included in the Resource Hub. They do not measure environmental impact, scientific consensus, source quality, or effectiveness. The collection is not comprehensive, and a resource can belong to more than one topic.
Some dates, locations, languages, and classifications may be missing or uncertain. Tool access details are provided when available. Use each record’s source link to review the original material.
Corrections & contributions
Found something that should change? Use the correction link on any resource page so the record ID and title are included automatically.