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Données ouvertes

Bibliothèque

Jeux de données ouverts, entièrement documentés — interrogeables ici, et lisibles par n’importe quel LLM.

Les titres et les descriptions proviennent des sources de données, en anglais.

154 jeux de données

signals

  • US Monetary Policy Signals (derived)

    US monetary policy signals (money-supply growth, policy-rate momentum, QE/QT tracker)

    Daily-to-monthly monetary signals derived from FRED's US money and policy series: 30-period annualized change volatility, 3-month momentum, year-over-year change (the money-supply growth gauge), 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-date cross-series volatility rank, and the QE/QT tracker (3-month percent change of total Federal Reserve balance-sheet assets; negative = quantitative tightening). Covers the effective federal funds rate (daily and monthly), M1, M2, the monetary base and total Fed balance-sheet assets. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED; US-government series from the Board of Governors of the Federal Reserve System).

    • monetary-policy
    • federal-reserve
    • money-supply
    • fed-funds
    lignes
    30 924
    Qualité
    98
    Mis à jour
    24 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • Money-Velocity Signals (derived)

    US money-velocity signals (M1V, M2V circulation gauges)

    Quarterly US money-velocity signals from FRED (1959 ->): the velocity of M1 and M2 money stocks (nominal GDP / money), with quarter-on-quarter and year-on-year changes, 30-quarter change volatility, 3-sigma anomaly flags, drift forecasts, 10-year velocity z-scores, a slow-circulation flag and the M1-minus-M2 circulation gap. The monetarist-transmission lens: how fast money circulates through the economy. The velocity companion to the money-stock levels in us-monetary-signals. All rows normalized to country_code USA. Raw series: Federal Reserve Bank of St. Louis via FRED.

    • money
    • velocity
    • monetarism
    • m1
    lignes
    540
    Qualité
    99
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • Norway Category-Inflation Heat Signals (derived)

    Norway category-inflation heat signals (SSB CPI by COICOP group, monthly heat-map)

    Category-level Norwegian inflation signals derived from Statistics Norway's CPI by goods/services group: 12-month inflation rates for twelve COICOP categories plus the all-items total since 2000, with month-on-month rate changes, acceleration gauges, 12-month annualized change volatility, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, per-month cross-category heat ranks, each category's gap versus the all-items total, high-heat (>4%) flags and the share of categories running above 2%. The monetizable signals layer on top of raw Statistics Norway price data. Raw series: SSB StatBank CPI by derived series and goods/services group.

    • inflation
    • cpi
    • norway
    • ssb
    lignes
    4 228
    Qualité
    100
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • OECD Business-Cycle Signals (derived)

    OECD business-cycle signals (CLI turning points, trend gaps, expansion/contraction phases)

    Signals derived from the OECD Composite Leading Indicator (amplitude adjusted, monthly): CLI distance from long-term trend (cli_gap vs 100), 3/6-month momentum, turning-point detection (peaks/troughs of the smoothed CLI), expansion/contraction phase flags, below-trend streaks, 30-month annualized volatility of CLI changes, 3-sigma anomaly flags, naive-drift 1-month forecasts and a per-month cross-country volatility rank. Covers 18 countries (AUS, BRA, CAN, CHN, DEU, ESP, FRA, GBR, IDN, IND, ITA, JPN, KOR, MEX, TUR, USA, ZAF plus aggregates G20, G7, NAFTA, A5M, G4E), monthly 1955 -> present. All rows carry canonical country_code so they join cleanly with country-keyed macro data. Raw series: OECD Data Explorer (Main Economic Indicators).

    • business-cycle
    • leading-indicator
    • cli
    • oecd
    lignes
    14 263
    Qualité
    97
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial interdit

  • OpenAlex AI research institutions (agent-curated)

    Top AI research institutions (weekly)

    Weekly ranking of the institutions driving AI research, from the official keyless OpenAlex API (https://api.openalex.org, CC0 data). Trailing 30 complete days: works tagged with the Artificial intelligence concept (C154945302) are grouped by institution (top 200), and a second all-works group_by over the same window (batched, with a single-count fallback) supplies each institution's total output so an AI share of its own production can be computed. Institution identity is resolved on OpenAlex's canonical ids (ROR-linked), so one university never appears twice; countries are normalized to ISO alpha-3 via hub.normalize; the OpenAlex institution type is carried as a coarse classification. The impact_score is a documented 0-100 composite = 50% min-max-normalized AI works per day + 50% min-max-normalized AI share of trailing-30-day output, and institution_rank orders by impact_score descending (ties: AI works desc, then OpenAlex id). as-of stamping is day-granular (window end), so re-running inside the same window is a no-op. Columns: ISO week of the window end, as-of date, window start/end, OpenAlex id / institution URL / name / ROR, ISO alpha-3 country code and name, institution type, AI works and total works in the window, AI share (%), AI works per day, impact score (0-100), institution rank. Primary key: (week, openalex_id). Cadence: weekly. Nullability: country_code, ror, homepage_url may be empty when OpenAlex has none; ai_works, total_works, ai_share_pct, impact_score and institution_rank are never null. Caveats: OpenAlex concept tagging is automated — bulk mis-tagging can inflate a few rows (see the facility-type rows near the top); counts are full (not fractional) attributions, so co-authored works credit every institution; ai_share is clipped at 100% if the API's two sweeps ever disagree. Sample use: order by institution_rank for the week's leading AI research producers, or filter country_code = 'CHN'.

    • ai
    • ai-research
    • technology
    • signals
    lignes
    200
    Qualité
    100
    Mis à jour
    25 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • Output & Business-Cycle Signals (derived)

    US output & business-cycle signals (industrial production momentum, capacity utilization, recession streaks, anomalies, forecasts)

    Signals derived from FRED's US output and business-cycle series: 30-period annualized volatility of monthly changes, 3-month momentum, year-over-year percent change, 3-sigma anomaly flags, naive-drift 1-month forecasts, a per-month cross-series volatility rank, and the NBER recession-month streak counter. Covers INDPRO (industrial production index), TCU (capacity utilization), DGORDER (manufacturers' durable goods orders) and USREC (NBER recession indicator). All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED).

    • industrial-production
    • capacity-utilization
    • durable-goods
    • business-cycle
    lignes
    4 483
    Qualité
    98
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • Poverty & Inequality Signals (derived)

    Global poverty & inequality signals (Gini and poverty headcount trajectories, anomaly flags)

    Country-level signals derived from World Bank poverty and inequality indicators (Gini index, poverty headcount at $3.00 and $4.20 a day, 2021 PPP): 10-year point changes, OLS trend slopes over trailing survey years, 3-sigma anomaly flags, 5-year linear-extrapolation forecasts, per-year cross-country ranks, a poverty-improvement flag and a high-inequality flag. All rows are normalized to ISO alpha-3 country_code so they join cleanly with country macro data. Raw data: World Bank Poverty and Inequality Platform (keyless API, non-commercial terms).

    • poverty
    • inequality
    • gini
    • development
    lignes
    7 977
    Qualité
    92
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial interdit

  • Productivity-Pay Gap Signals (derived)

    US productivity-pay gap signals (decoupling gauge, compensation vs output per hour)

    Quarterly signals derived from BLS productivity and costs data (redistributed by FRED, 1947 ->): 30-quarter annualized change volatility, 1-quarter momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-series volatility rank, the real pay-minus-productivity gap (both rebased to 1947 = 100 — the decoupling gauge) and a 10-year gap z-score (the decoupling-regime gauge). All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: U.S. Bureau of Labor Statistics.

    • productivity
    • wages
    • compensation
    • labor-market
    lignes
    636
    Qualité
    99
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • Corporate Profit Signals (derived)

    US corporate profit signals (profit momentum, economy-wide margin, profitability regime, anomalies)

    Quarterly signals derived from BEA corporate-profits data (redistributed by FRED): 30-quarter annualized change volatility, 1-quarter momentum, year-over-year change, 3-sigma anomaly flags vs a trailing 12-quarter baseline, naive-drift 1-quarter forecasts, a per-quarter cross-series volatility rank, the economy-wide profit margin (profits as % of GDP) and a 20-quarter margin z-score (the profitability-regime gauge). Covers corporate profits after tax from 1947. All rows are normalized to country_code USA so they join cleanly with US macro data. Raw series: Federal Reserve Bank of St. Louis (FRED); underlying data: U.S. Bureau of Economic Analysis.

    • corporate-profits
    • profit-margin
    • earnings
    • business-cycle
    lignes
    318
    Qualité
    99
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • PyPI Download Statistics (hugovk top-pypi-packages)

    PyPI data-ecosystem package download velocity (monthly)

    Month-over-month download velocity for 158 curated data/ML-ecosystem PyPI packages (August 2026 window vs July 2026 window, from hugovk/top-pypi-packages trailing-30-day snapshots). Each row is one package with its curated category (16 categories: ml-frameworks, nlp-llm, dataframes, visualization, etl-orchestration, mlops, geospatial, statistics, data-infra, notebooks, timeseries, explainability, vision, scraping, audio, bio), 30-day downloads for both windows, absolute and percentage change, velocity rank (by percentage change among packages with >=1M previous-window downloads — a documented floor against tiny-base percentages), popularity rank within the curated set, share of all top-15,000 PyPI downloads, new-entrant flag, and a per-row PyPI project link. Package names are entity-resolved per PEP 503 (scikit_learn == scikit-learn). Columns: as-of date, month, package, category, downloads and previous-window downloads, absolute/percentage change, velocity and popularity ranks, share of top-15000, new-entrant flag, previous month, source URL, row hash. Primary key: package. Cadence: monthly; window labels are fixed per run so identical input produces an identical content hash. Caveats: counts include mirrors/CI reinstalls (distribution volume, not unique users); the top-15,000 cutoff hides the long tail. Agent-curated by intel-1 (2026-09-25T07:05Z): collection = two keyless JSON snapshots (GitHub Pages + pinned commit), transformation = curated taxonomy + PEP 503 resolution + velocity scoring. Download counts are non-copyrightable facts; aggregation published publicly with no reuse restriction — commercial_use = yes. Sample use: order by velocity_rank for the fastest-accelerating data packages, or filter category = 'nlp-llm' for the LLM stack leaderboard.

    • python
    • data-science
    • machine-learning
    • open-source
    lignes
    158
    Qualité
    100
    Mis à jour
    25 sept. 2026
    À jour
    Licence
    Usage commercial OK

  • Global R&D Expenditure Signals (derived)

    Global R&D expenditure signals (innovation intensity)

    Annual global R&D-intensity signals from the World Bank World Development Indicators (GB.XPD.RSDV.GD.ZS, ~193 economies, 1996 -> 2024): 5-year changes, OLS trend slopes, 3-sigma anomaly flags, 5-year extrapolation forecasts, 10-year intensity z-scores, per-year intensity ranks, and innovation-leader (>=3%), laggard (<0.5%), and catch-up gauges. The innovation-input lens — R&D spending as % of GDP is the standard proxy for an economy's investment in future productivity. Country codes validated via the shared normalization layer. Raw indicator: UNESCO Institute for Statistics via the World Bank (keyless API).

    • rd
    • innovation
    • research
    • technology
    lignes
    2 690
    Qualité
    95
    Mis à jour
    22 sept. 2026
    À jour
    Licence
    Usage commercial interdit

  • Real Yield & Breakeven Inflation Signals (derived)

    US real yield & breakeven inflation signals (real curve slope, breakeven term spread, anomalies)

    Daily/monthly signals derived from FRED's US real-yield and inflation-expectations series: 30-period annualized change volatility, ~3-month momentum, 3-sigma anomaly flags vs a trailing 12-month baseline, naive-drift 1-month forecasts, a per-date cross-series volatility rank, the 30Y-5Y real curve slope (the real-rate term-premium gauge) and the 30Y-vs-5Y breakeven term spread (long-run vs medium-term inflation expectations). Covers 5Y/7Y/20Y/30Y TIPS real yields, 5Y and 30Y breakeven inflation rates and the 10-year real interest rate. No overlap with the nominal-yield bond-market signals dataset. All rows are normalized to country_code USA. Raw series: Federal Reserve Bank of St. Louis (FRED).

    • real-yields
    • tips
    • breakeven-inflation
    • inflation-expectations
    lignes
    28 236
    Qualité
    99
    Mis à jour
    24 sept. 2026
    À jour
    Licence
    Usage commercial OK

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