TrilemmaFoundation · mattfaltyn · Aug 18, 2026 · Aug 18, 2026
@@ -147,7 +147,7 @@ No application code changes required.
 
 ## Catalog
 
-The catalog currently contains 100 operational datasets spanning natural
+The catalog currently contains 141 operational datasets spanning natural
 hazards, weather, water, climate, flood risk, drought, space weather, global
 disaster alerts, public health, clinical research, cybersecurity, package
 graphs, legislation, sanctions screening, government spending and procurement,
@@ -158,8 +158,10 @@ broadband, bridges, EV charging, consumer finance, education, K-12 directories,
 housing prices, rents, food and product recalls, elections, European statistics,
 live transit feeds, aviation, provider directories, drinking water, preprints,
 pageviews, agriculture, tropical cyclones, crime, companies, occupations,
-global forecasts, live OSM, food products, nursing homes, LEI, and euro-area
-statistics. The YAML files in
+global forecasts, live OSM, food products, nursing homes, LEI, euro-area
+statistics, patents, vital statistics, mortgages, OECD and national statistics,
+Mauna Loa CO2, sea ice, caselaw, lobbying, Certificate Transparency, and
+public GitHub activity. The YAML files in
 [`data/datasets`](data/datasets) are the source of truth for the current list.
 
 ## Deploy to Vercel

@@ -0,0 +1,79 @@
+id: cdc-uscs-cancer-statistics
+name: U.S. Cancer Statistics
+description: >
+  Official federal cancer incidence and mortality statistics for building
+  state comparison and cancer-burden dashboards.
+theme: Health, Food & Safety
+url: https://www.cdc.gov/united-states-cancer-statistics/index.html
+access_type:
+  - download
+  - api
+api_key_required: false
+free_to_access: true
+size_gb_min: 0
+size_gb_max: 0.2
+formats:
+  - CSV
+  - JSON
+  - Excel
+license: U.S. Public Domain with CDC attribution and use requirements
+license_url: https://www.cdc.gov/other/agencymaterials.html
+url_checks:
+  source_marker: United States Cancer Statistics
+  license_marker: public domain
+domains:
+  - Public Health
+  - Epidemiology
+data_types:
+  - Tabular
+  - Aggregated Data
+tasks:
+  - Incidence Comparison
+  - Trend Analysis
+  - Public Health Planning
+difficulty: intermediate
+geography:
+  - United States
+temporal_coverage: USCS incidence and mortality years published by CDC and NCI
+update_frequency: annual
+provider: Centers for Disease Control and Prevention
+source_type: government
+last_verified: 2026-08-18
+getting_started:
+  overview: >
+    USCS is the official federal combination of NPCR and SEER incidence with
+    NVSS mortality. Start with a public state-level incidence table, not the
+    SEER*Stat research file. Rates are age-adjusted to a standard population
+    and are not individual case listings.
+  prerequisites:
+    - Python 3.10 or newer
+    - A notebook environment such as Jupyter or Google Colab
+    - An internet connection
+  access_steps:
+    - Open the USCS page and locate Data Visualizations or public-use tables.
+    - Download or query one recent state incidence file.
+    - Keep state, cancer site, year, and age-adjusted rate.
+  python:
+    packages:
+      - pandas
+      - requests
+    code: |
+      import pandas as pd
+      import requests
+
+      response = requests.get(
+          "https://data.cdc.gov/resource/5kmb-vjq2.json",
+          params={"$limit": 50, "$order": "year DESC"},
+          timeout=30,
+      )
+      response.raise_for_status()
+      stats = pd.DataFrame(response.json())
+      stats["retrieved_at_utc"] = pd.Timestamp.now(tz="UTC")
+      print(stats.head())
+  first_project:
+    title: Compare state cancer incidence rows
+    goal: Test whether a USCS public table can power a bounded state-burden sketch.
+    steps:
+      - Keep geography, site, year, and age-adjusted rate from the extract.
+      - Rank a single site-year by rate and flag missing geographies.
+      - Explain that USCS public tables are aggregated rates, not SEER research microdata, and cannot identify individuals.
@@ -0,0 +1,73 @@
+id: census-lehd-lodes
+name: Census LEHD Origin-Destination Employment Statistics
+description: >
+  Block-level origin-destination jobs data for building commute, workforce, and
+  local employment-map prototypes.
+theme: Geospatial & Infrastructure
+url: https://lehd.ces.census.gov/data/
+access_type:
+  - download
+api_key_required: false
+free_to_access: true
+size_gb_min: 0.01
+size_gb_max: 50
+formats:
+  - CSV
+  - GZIP
+license: U.S. Census Bureau data-use terms
+license_url: https://www.census.gov/data/developers/about/terms-of-service.html
+url_checks:
+  source_marker: LEHD Origin-Destination Employment Statistics
+  license_marker: Terms of Service
+domains:
+  - Labor Economics
+  - Local Economics
+  - Transportation
+data_types:
+  - Tabular
+  - Geospatial
+  - Origin-Destination
+tasks:
+  - Commute Analysis
+  - Workforce Mapping
+  - Site Selection
+difficulty: intermediate
+geography:
+  - United States
+temporal_coverage: annual LODES vintages with state-specific start years
+update_frequency: annual
+provider: U.S. Census Bureau
+source_type: government
+last_verified: 2026-08-18
+getting_started:
+  overview: >
+    LODES Origin-Destination files count jobs connecting home and work census
+    blocks. Start with one small state's OD main file. Counts are synthesised
+    from administrative records, some states are missing in some years, and
+    block-to-block files are large.
+  prerequisites:
+    - Python 3.10 or newer
+    - A notebook environment such as Jupyter or Google Colab
+    - An internet connection
+  access_steps:
+    - Open the LEHD data page and choose LODES OD for one small state.
+    - Download the JT00 OD main gzip for the latest vintage listed.
+    - Keep workplace block, residence block, and total jobs.
+  python:
+    packages:
+      - pandas
+    code: |
+      import pandas as pd
+
+      jobs = pd.read_csv(
+          "https://lehd.ces.census.gov/data/lodes/LODES8/vt/od/vt_od_main_JT00_2022.csv.gz",
+          nrows=200,
+      )
+      print(jobs.head())
+  first_project:
+    title: Inspect Vermont LODES job flows
+    goal: Test whether a one-state OD extract can power a bounded commute sketch.
+    steps:
+      - Keep workplace geocode, residence geocode, and total jobs from the first rows.
+      - Sum jobs by workplace census tract prefix and list empty geocodes.
+      - Explain that LODES is synthesised administrative data, some states are omitted, and a sample of rows is not a statewide commute census.
@@ -0,0 +1,77 @@
+id: certificate-transparency-crtsh
+name: Certificate Transparency Search
+description: >
+  Issued TLS certificates indexed from public CT logs for building domain
+  certificate monitors and issuance alerts.
+theme: Technology & Cybersecurity
+url: https://certificate.transparency.dev/
+access_type:
+  - api
+api_key_required: false
+free_to_access: true
+size_gb_min: 0
+size_gb_max: 0.05
+formats:
+  - JSON
+license: Certificate Transparency log public data / crt.sh use
+license_url: https://certificate.transparency.dev/
+url_checks:
+  source_marker: Certificate Transparency
+  license_marker: Certificate Transparency
+domains:
+  - Cybersecurity
+  - Internet Infrastructure
+data_types:
+  - Event Data
+  - Registry Data
+  - Tabular
+tasks:
+  - Certificate Monitoring
+  - Alerting
+  - Asset Discovery
+difficulty: beginner
+geography:
+  - Global
+temporal_coverage: certificates submitted to participating CT logs
+update_frequency: continuous
+provider: Sectigo crt.sh
+source_type: company
+last_verified: 2026-08-18
+getting_started:
+  overview: >
+    crt.sh queries public Certificate Transparency logs. Start with one
+    registered domain and a JSON search. CT logs are the origin; crt.sh is the
+    query surface. Logs can lag, expired certificates remain, and this is not a
+    complete inventory of every private PKI.
+  prerequisites:
+    - Python 3.10 or newer
+    - A notebook environment such as Jupyter or Google Colab
+    - A domain you are authorized to inspect
+  access_steps:
+    - Open crt.sh and try one Identity search in the browser.
+    - Request JSON for that domain with output=json.
+    - Keep issuer name, common name, and not-after date.
+  python:
+    packages:
+      - pandas
+      - requests
+    code: |
+      import pandas as pd
+      import requests
+
+      response = requests.get(
+          "https://crt.sh/",
+          params={"q": "example.com", "output": "json"},
+          timeout=60,
+      )
+      response.raise_for_status()
+      certificates = pd.DataFrame(response.json())
+      certificates["retrieved_at_utc"] = pd.Timestamp.now(tz="UTC")
+      print(certificates[["issuer_name", "common_name", "not_after"]].head())
+  first_project:
+    title: List certificates for one domain
+    goal: Test whether a CT search can power a bounded issuance monitor.
+    steps:
+      - Deduplicate certificate ids and keep issuer, common name, and expiry.
+      - Flag certificates whose not-after date is in the past.
+      - Explain that CT coverage depends on participating logs and is not a private-PKI inventory.
@@ -0,0 +1,76 @@
+id: chrome-ux-report
+name: Chrome UX Report API
+description: >
+  Real-user Chrome experience metrics for building Core Web Vitals monitors
+  from origin-level CrUX records.
+theme: Technology & Cybersecurity
+url: https://developer.chrome.com/docs/crux
+access_type:
+  - api
+api_key_required: true
+free_to_access: true
+size_gb_min: 0
+size_gb_max: 0.01
+formats:
+  - JSON
+license: Google APIs Terms of Service
+license_url: https://developers.google.com/terms
+url_checks:
+  source_marker: Chrome UX Report
+  license_marker: Google APIs Terms of Service
+domains:
+  - Web Performance
+  - Software
+data_types:
+  - Aggregated Data
+  - Time Series
+tasks:
+  - Performance Monitoring
+  - Origin Comparison
+  - Alerting
+difficulty: beginner
+geography:
+  - Global
+temporal_coverage: rolling 28-day CrUX collection windows
+update_frequency: daily
+provider: Google
+source_type: company
+last_verified: 2026-08-18
+getting_started:
+  overview: >
+    The CrUX API returns origin-level p75 metrics such as LCP and INP. Start
+    with one origin you are authorized to monitor. CrUX covers opted-in Chrome
+    users, not synthetic tests, and small origins can be missing.
+  prerequisites:
+    - Python 3.10 or newer
+    - A notebook environment such as Jupyter or Google Colab
+    - A Google API key saved in the CRUX_API_KEY environment variable
+  access_steps:
+    - Enable the Chrome UX Report API in Google Cloud and save CRUX_API_KEY.
+    - Read the query endpoint documentation.
+    - Request metrics for one HTTPS origin.
+  python:
+    packages:
+      - pandas
+      - requests
+    code: |
+      import os
+      import pandas as pd
+      import requests
+
+      response = requests.post(
+          "https://chromeuxreport.googleapis.com/v1/records:queryRecord",
+          params={"key": os.environ["CRUX_API_KEY"]},
+          json={"origin": "https://www.example.com"},
+          timeout=30,
+      )
+      response.raise_for_status()
+      record = pd.json_normalize(response.json())
+      print(record)
+  first_project:
+    title: Read one origin's Core Web Vitals
+    goal: Test whether CrUX can power a bounded real-user performance card.
+    steps:
+      - Confirm p75 LCP, INP, and CLS fields returned for the origin.
+      - Compare those p75 values to the published "good" thresholds.
+      - Explain that CrUX is opted-in Chrome field data and that small origins can be absent.