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Open datasets, fully documented — searchable here, and readable by any LLM.

3 datasets

rankings
  • Defense Spending Signals (derived)

    Global defense-spending signals

    Annual military-expenditure burden signals from the World Bank World Development Indicators (SIPRI via WDI, 1960 ->, % of GDP, keyless): per-economy 5-year changes, 10-year OLS trend slopes, 3-sigma anomaly flags, linear-extrapolation forecasts, 10-year z-scores, per-year cross-country ranks, and high/low spender and rearmament-surge flags. The defense-burden lens — rearmament waves and drawdowns normalized across economies — not covered elsewhere in the catalog. Country labels normalized to ISO alpha-3. Raw data: World Bank WDI indicator MS.MIL.XPND.GD.ZS.

    • defense
    • military-expenditure
    • sipri
    • world-bank
    rows
    8,608
    Quality
    96
    Updated
    Sep 22, 2026
    Fresh
    License
    No commercial use
  • Education Spending Signals (derived)

    Global education-spending signals

    Annual government education-expenditure signals from the World Bank World Development Indicators (UNESCO via WDI, 1970 ->, % of GDP, keyless): per-economy 5-year changes, 10-year OLS trend slopes, 3-sigma anomaly flags, linear-extrapolation forecasts, 10-year z-scores, per-year cross-country ranks, and high/low spender and investment-push flags. The education-investment lens — which governments are building human capital and which are disinvesting — not covered elsewhere in the catalog. Country labels normalized to ISO alpha-3. Raw data: World Bank WDI indicator SE.XPD.TOTL.GD.ZS.

    • education
    • human-capital
    • unesco
    • world-bank
    rows
    5,432
    Quality
    95
    Updated
    Sep 22, 2026
    Fresh
    License
    No commercial use
  • GDELT global news attention & tone intelligence

    GDELT global news attention & tone (weekly)

    Weekly country-level intelligence from the GDELT 2.1 global event database: every country generating >= 5 root events over the trailing 7 complete days, scored for news attention and instability. Deterministic pipeline: keyless GDELT 2.1 fifteen-minute update files (https://data.gdeltproject.org/gdeltv2/, terms allow unlimited academic/commercial/governmental use with citation + link); root-event dedupe (IsRootEvent=1 + GLOBALEVENTID dedupe); FIPS 10-4 country codes resolved to ISO alpha-3 via an embedded crosswalk (unknown codes dropped loudly) with canonical names from hub.normalize; per-country aggregation of event volume, article coverage, mean tone (AvgTone -100..100), Goldstein scale (-10..+10), conflict/cooperation shares from QuadClass, and the dominant CAMEO root event category. attention_score = 100 * min-max(log1p(event_count)) ranked as attention_rank (score desc, event_count desc, country_code asc). instability_score = 100 * (0.50 * conflict_share + 0.30 * negative_tone_share + 0.20 * clip(-mean_goldstein/10, 0, 1)). Week-granular as-of stamping (fetched_at = window-end midnight UTC): a second ingest of the same window is a true no-op. Primary key: (week, country_code). Nullability: no nulls are emitted. Caveats: GDELT coverage expands over time and varies by language, so absolute volume is media attention as much as event count; tone is a dictionary measure of sentiment, not sentiment itself; the >= 5 event gate drops quiet countries. Only event metadata and aggregates are stored — no article bodies, no per-row source URLs. GDELT terms require citation of the GDELT Project and a link to https://www.gdeltproject.org/. Sample use: order by attention_rank for the week's most-covered countries, or filter instability_score >= 50 for elevated-conflict coverage.

    • news
    • signals
    • geopolitics
    • rankings
    rows
    220
    Quality
    100
    Updated
    Sep 25, 2026
    Fresh
    License
    Commercial use OK

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