Links and Resources

• Storm Events Database Explorer — Interactive map and search interface
• Storm Events Database on Source Cooperative — Cloud-optimized Parquet files
• Earth Genome blog post on the project — Technical process and discovery work
• The Commons case study — Project background and case study
• NOAA Storm Events Database — Original NOAA dataset and beta interface
• GeoParquet.io — Chris Holmes’s project for working with Parquet files
  
  More show notes and transcript at https://greatdataproducts.com/episodes/2026/02/keuter-andrick-storm-events/

The conversation explores why open data initiatives have evolved over the years and how administrative priorities shape public data tool availability. Denice emphasizes that federal data underpins economic growth, public health decisions, and governance at every level. She describes how data users can engage with data stewards to create feedback loops that improve data quality, and why nonprofits and civil society organizations play an essential role in both data collection and advocacy.

Throughout the discussion, Denice and Jed examine the balance between official government data products and innovative tools built by external organizations. They discuss creative solutions for filling data gaps, the importance of identifying tools as "powered by federal data" to preserve datasets, and strategies for protecting federal data accessibility for the long term.

LINKS AND RESOURCES

- Denice Ross at the Federation of American Scientists: https://fas.org/expert/denice-ross/

- The federal data and tools that died this year (Marketplace): https://www.marketplace.org/episode/2025/11/25/the-federal-data-and-tools-that-died-this-year

TAKEAWAYS

1. Federal data underpins daily life — From public health decisions to economic planning, federal datasets inform choices that affect Americans whether they realize it or not.

2. Data tools require active protection — When administrative priorities shift, public data tools can disappear. Building awareness of data dependencies helps preserve access.

3. Feedback loops improve data quality — Data users should engage directly with data stewards. Public participation in the data lifecycle leads to better, more relevant datasets.

4. Civil society fills critical gaps — Nonprofits and external organizations can collect data and advocate for data resources in ways government cannot.

5. Disaggregated data drives equity — Breaking down aggregate statistics reveals disparities and enables targeted interventions that benefit underserved communities.

6. External innovation complements government stability – A healthy ecosystem keeps federal data stable while enabling community-driven tools to evolve and serve specific needs.

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Great Data Products is brought to you by Source Cooperative. Learn more at https://greatdataproducts.com

• STAC Specification: https://stacspec.org/
• STAC: A Retrospective, Part 2: https://element84.com/software-engineering/stac-a-retrospective-part-2-why-stac-was-successful/
• Emergent Standards white paper: https://tial.org/publications/white-paper-003-emergent-standards-enabling-collaborations-across-institutions/
• STAC Auth Proxy: https://github.com/developmentseed/stac-auth-proxy
• FilmDrop UI: https://console.demo.filmdrop.element84.com/
• Planet Planetary Variables: https://www.planet.com/products/planetary-variables/
• CommonSpace: https://www.commonspace.world/
• "You Just Haven't Earned It Yet Baby": https://www.youtube.com/watch?v=jc9F0bh5OXc

Great Data Products is brought to you by Source Cooperative: https://source.coop

Links and Resources

- Protomaps – a free, customizable base map you can self-host

- PMTiles Viewer – drag-and-drop viewer for .pmtiles files

- Browse 2.7 billion building footprints in PMTiles in the Google-Microsoft-OSM Open Buildings - combined by VIDA product on Source

- Emergent standards white paper from the Institutional Architecture Lab

Key takeaways:

1. Ship a killer app if you want a new format to gain traction — The Protomaps base map is the product that makes the PMTiles format matter.

2. Single-file, object storage first — PMTiles runs from a bucket or an SD card, with a browser-based viewer for offline use.

3. Design simple, future‑proof specifications — Keep formats small and reimplementable with minimal dependencies; simplicity preserves longevity and portability.

4. Prioritize the developer experience — Single-binary installs, easy local preview, and eliminating incidental complexity drive adoption more than raw capability.

5. Build the right pipeline for the job — Separate visualization-optimized packaging from analysis-ready data; don’t force one format to do everything.

The conversation traces three datasets that shaped AI: MNIST (1994), the handwritten digits dataset that became machine learning's "Hello World"; ImageNet (2008), Fei-Fei Li's image dataset that launched deep learning through AlexNet's 2012 breakthrough; and Common Crawl (2007), Gil Elbaz's web crawling project that fueled 60% of GPT-3's training data. Drew argues that great data products create ecosystems around themselves, using the Enron email dataset as an example of how a single data release can generate thousands of research papers and enable countless startups. The episode concludes with a discussion of benchmarks as modern data products and the challenge of creating sustainable data infrastructure for the next generation of AI systems.

Links and Resources:

- Common Crawl Foundation Event - October 22nd event at Stanford!

- Cloud-Native Geospatial Forum Conference 2026 - 6-9 October 2026 at Snowbird in Utah!

- Why LLM Advancements Have Slowed: The Low-Hanging Fruit Has Been Eaten - Drew's blog post that inspired this conversation

- Unicorns, Show Ponies, and Gazelles - Jed's vision for sustainable data organizations

- ARC AGI Benchmark - François Chollet's reasoning benchmark

- Thinking Machines Lab - Mira Murati's reproducibility research lab

- Terminal Bench - Stanford's coding agent evaluation benchmark

- Data Science at the Singularity - David Donoho's masterful paper examining the power of frictionless reproducibility

- Rethinking Dataset Discovery with DataScout - New paper examining dataset discovery

- MNIST Dataset - The foundational machine learning dataset on Hugging Face

Key Takeaways

1. Great data products create ecosystems - They don't just provide data, they enable entire communities and industries to flourish

2. Benchmarks are data products with intent - They encode values and shape the direction of AI development

3. We've consumed the easy wins - The internet and graphics innovations that powered early AI breakthroughs are largely exhausted

4. The future is specialized - Progress will come from domain-specific datasets, benchmarks, and applications rather than general models

5. Data markets need new models - Traditional approaches to data sharing may not work in the AI era