DatazimutsDatazimuts
← Tous les jeux de données

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).

Source: Global R&D Expenditure Signals (derived)2,690 lignesMis à jour: 22/09/2026Licence: CC-BY-4.0
rdinnovationresearchtechnologycompetitivenessproductivityunescoworld-bankmomentumanomaly-detectionforecastingranksignals

Qualité

94.7

Attribution

World Bank, World Development Indicators (derived signals by Frontier Data Hub)

Schéma

ColonneTypeDescription
datestringReference year (World Bank WDI, annual).
countrystringEconomy name as published by the World Bank.
country_codestringISO 3166-1 alpha-3 code for the economy.
series_idstringWorld Bank WDI indicator code: GB.XPD.RSDV.GD.ZS (research and development expenditure as a percent of GDP).
series_labelstringIndicator title as published in the World Bank World Development Indicators.
valuefloatExpenditures for research and development are current and capital expenditures (both public and private) on creative work undertaken systematically to increase knowledge, including knowledge of humanity, culture, and society, and the use of knowledge for new applications. R&D covers basic research, applied research, and experimental development (UNESCO Institute for Statistics, via World Bank WDI). (unit: percent of GDP)
change_5y_ppfloat
trend_slope_10yfloat
anomaly_flaginteger
forecast_5yfloat
intensity_z_10yfloat
rankinteger
high_rd_flaginteger
laggard_flaginteger
catchup_flaginteger

Exemple de lignes

datecountrycountry_codeseries_idseries_labelvaluechange_5y_pptrend_slope_10yanomaly_flagforecast_5yintensity_z_10yrankhigh_rd_flaglaggard_flagcatchup_flag
2016-01-01AngolaAGOGB.XPD.RSDV.GD.ZSResearch and development expenditure (% of GDP, UNESCO via World Bank WDI)0.03229——0——96010
2007-01-01AlbaniaALBGB.XPD.RSDV.GD.ZSResearch and development expenditure (% of GDP, UNESCO via World Bank WDI)0.08411——0——97010
2008-01-01AlbaniaALBGB.XPD.RSDV.GD.ZSResearch and development expenditure (% of GDP, UNESCO via World Bank WDI)0.14973——0——88010
2021-01-01AlbaniaALBGB.XPD.RSDV.GD.ZSResearch and development expenditure (% of GDP, UNESCO via World Bank WDI)0.18998——0——81010
2022-01-01AlbaniaALBGB.XPD.RSDV.GD.ZSResearch and development expenditure (% of GDP, UNESCO via World Bank WDI)0.19997——0——73010

Télécharger un échantillon

Téléchargez l'échantillon complet de ce jeu de données (lignes d'exemple, pas le jeu complet).

Télécharger l'exemple CSVTélécharger l'exemple JSON

Utiliser avec un LLM

Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).

cURL

curl "https://datazimuts.com/v1/datasets/rd_signals/global_rd_expenditure_signals" | jq '{title, rows, columns_count, license}'

Python

import requests

ds = requests.get("https://datazimuts.com/v1/datasets/rd_signals/global_rd_expenditure_signals").json()
print(ds["title"], ds["rows"], "rows")

# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
    print(row)

Point d’accès API: https://datazimuts.com/v1/datasets/rd_signals/global_rd_expenditure_signals

Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.

Global R&D expenditure signals (innovation intensity)