Graph and download economic data for Consumer Price Index: All Items: Total for India (CPALTT01INM657N) from Feb 1957 to Jan 2024 about India, all items, CPI, price index, indexes, and price.

Graph and download economic data for Dow Jones Industrial Average (DJIA) from 2015-08-03 to 2025-08-01 about stock market, average, industry, and USA.

Environment and Climate Change Canada’s (ECCC) Climate Research Division (CRD) and the Pacific Climate Impacts Consortium (PCIC) previously produced statistically downscaled climate scenarios based on simulations from climate models that participated in the Coupled Model Intercomparison Project phase 5 (CMIP5) in 2015. ECCC and PCIC have now updated the CMIP5-based downscaled scenarios with two new sets of downscaled scenarios based on the next generation of climate projections from the Coupled Model Intercomparison Project phase 6 (CMIP6). The scenarios are named Canadian Downscaled Climate Scenarios–Univariate method from CMIP6 (CanDCS-U6) and Canadian Downscaled Climate Scenarios–Multivariate method from CMIP6 (CanDCS-M6). CMIP6 climate projections are based on both updated global climate models and new emissions scenarios called “Shared Socioeconomic Pathways” (SSPs). Statistically downscaled datasets have been produced from 26 CMIP6 global climate models (GCMs) under three different emission scenarios (i.e., SSP1-2.6, SSP2-4.5, and SSP5-8.5), with PCIC later adding SSP3-7.0 to the CanDCS-M6 dataset. The CanDCS-U6 was downscaled using the Bias Correction/Constructed Analogues with Quantile mapping version 2 (BCCAQv2) procedure, and the CanDCS-M6 was downscaled using the N-dimensional Multivariate Bias Correction (MBCn) method. The CanDCS-U6 dataset was produced using the same downscaling target data (NRCANmet) as the CMIP5-based downscaled scenarios, while the CanDCS-M6 dataset implements a new target dataset (ANUSPLIN and PNWNAmet blended dataset). Statistically downscaled individual model output and ensembles are available for download. Downscaled climate indices are available across Canada at 10km grid spatial resolution for the 1950-2014 historical period and for the 2015-2100 period following each of the three emission scenarios. A total of 31 climate indices have been calculated using the CanDCS-U6 and CanDCS-M6 datasets. The climate indices include 27 Climdex indices established by the Expert Team on Climate Change Detection and Indices (ETCCDI) and 4 additional indices that are slightly modified from the Climdex indices. These indices are calculated from daily precipitation and temperature values from the downscaled simulations and are available at annual or monthly temporal resolution, depending on the index. Monthly indices are also available in seasonal and annual versions. Note: projected future changes by statistically downscaled products are not necessarily more credible than those by the underlying climate model outputs. In many cases, especially for absolute threshold-based indices, projections based on downscaled data have a smaller spread because of the removal of model biases. However, this is not the case for all indices. Downscaling from GCM resolution to the fine resolution needed for impacts assessment increases the level of spatial detail and temporal variability to better match observations. Since these adjustments are GCM dependent, the resulting indices could have a wider spread when computed from downscaled data as compared to those directly computed from GCM output. In the latter case, it is not the downscaling procedure that makes future projection more uncertain; rather, it is indicative of higher variability associated with finer spatial scale. Individual model datasets and all related derived products are subject to the terms of use (https://pcmdi.llnl.gov/CMIP6/TermsOfUse/TermsOfUse6-1.html) of the source organization.

Explore the AI Global Index Dataset, which benchmarks 62 countries based on AI investment, innovation, and implementation.

Graph and download economic data for NASDAQ Composite Index (NASDAQCOM) from 1971-02-05 to 2025-07-30 about NASDAQ, composite, stock market, indexes, and USA.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

The Consumer Price Index (CPI) is a measure of the average change over time in the prices paid by urban consumers for a market basket of consumer goods and services. Indexes are available for the U.S. and various geographic areas. Average price data for select utility, automotive fuel, and food items are also available. Prices for the goods and services used to calculate the CPI are collected in 75 urban areas throughout the country and from about 23,000 retail and service establishments. Data on rents are collected from about 43,000 landlords or tenants. More information and details about the data provided can be found at http://www.bls.gov/cpi

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Graph and download economic data for Global Price Index of All Commodities (PALLFNFINDEXQ) from Q1 2003 to Q2 2025 about World, commodities, price index, indexes, and price.

Graph and download economic data for Import Price Index (End Use): All Commodities (IR) from Sep 1982 to Jun 2025 about end use, imports, headline figure, commodities, price index, indexes, price, and USA.

This dataset comes from INSEE’s Macro-Economic Data Bank (BDM). The BDM is the main database of series and indices on all economic and social fields. The data required for the short-term diagnosis are available for download, for the most part over several decades, at fine or aggregated sectoral levels. Demographic series (which can go back to the 19th century), figures on wages, transport, or exchange rates complete this supply.

Prices and consumer price indices

Consumer price indices of households Average retail prices Currency processing coefficient

Price or cost of production indices and import price indices

Agriculture Industry Construction Services

Price and lease revision indices in the real estate sector

House price indices

Lease revision indices (indexation of contracts)

Prices and prices of raw materials

Graph and download economic data for Consumer Price Index: All Items: Total for Denmark (DNKCPALTT01IXNBM) from Jan 1967 to Dec 2023 about Denmark, all items, CPI, price index, indexes, and price.

This dataset provides download statistics for all package downloads from the Python Package Index (PyPI). It also includes a dataset containing all the metadata for every distribution released on PyPI. The data is streamed in near-real-time from PyPI CDN, after which it is periodically loaded into the BigQuery dataset. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

The Social Vulnerability Index (SoVI®) 2006-10 measures the social vulnerability of U.S. Census tracts to environmental hazards. The index is a comparative metric that facilitates the examination of the differences in social vulnerability among counties. SoVI® is a valuable tool for policy makers and practitioners. It graphically illustrates the geographic variation in social vulnerability. It shows where there is uneven capacity for preparedness and response and where resources might be used most effectively to reduce the pre-existing vulnerability. SoVI® also is useful as an indicator in determining the differential recovery from disasters.

The index synthesizes 30 socioeconomic variables, which the research literature suggests contribute to reduction in a community’s ability to prepare for, respond to, and recover from hazards. SoVI® data sources include primarily those from the United States Census Bureau.

The data are compiled and processed by the Hazards and Vulnerability Research Institute at the University of South Carolina with funding via the NOAA Office for Coastal Management. The data are standardized and placed into a principal components analysis to reduce the initial set of variables into a smaller set of statistically optimized components. Adjustments are made to the components’ cardinality (positive (+) or negative (-)) to insure that positive component loadings are associated with increased vulnerability, and negative component loadings are associated with decreased vulnerability. Once the cardinalities of the components are determined, the components are added together to determine the numerical social vulnerability score for each census tract.

SoVI® 2006-10 marks a change in the formulation of the SoVI® metric from earlier versions. New directions in the theory and practice of vulnerability science emphasize the constraints of family structure, language barriers, vehicle availability, medical disabilities, and healthcare access in the preparation for and response to disasters, thus necessitating the inclusion of such factors in SoVI®. Extensive testing of earlier conceptualizations of SoVI®, in addition to the introduction of the U.S. Census Bureau’s five-year American Community Survey (ACS) estimates, warrants changes to the SoVI® recipe, resulting in a more robust metric. These changes, pioneered with the ACS-based SoVI® 2005-09 carry over to SoVI® 2006-10, which combines the best data available from both the 2010 U.S. Decennial Census and five-year estimates from the 2006-2010 ACS at the census tract level.

These data are available for download from: http://www.coast.noaa.gov/digitalcoast/data/sovi

The dataset provides climate extreme indices related to temperature and precipitation as defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), as well as selected heat stress indicators (HSI). The indices are provided for historical and future climate projections (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) included in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and used in the 6th Assessment Report of the Intergovernmental Panel on Climate Change (IPCC). This dataset provides a comprehensive source of pre-calculated and consistent ETCCDI and heat stress indicators commonly used by the climate science and impact communities. The majority of models used in this catalogue entry are now available in the Climate Data Store though the indices offered in this entry additionally include ensemble members obtained from the Earth System Grid Federation. The indices are calculated from CMIP6 models that have the necessary daily resolved data for both historical and at least two of the future projections. In addition, four of the chosen models contained a large number of ensemble members in order to enable the estimation of the associated uncertainty in the spread of model outcomes when calculating the ETCCDI indices (CanESM5, EC-Earth3, MIROC6 and MPI-ESM1-2-LR). All the ETCCDI indices in this dataset are calculated using the climdex.pcic R package, which was developed, evaluated and approved by the ETCCDI. To facilitate the usage of heat stress indicators in combination with thresholds on absolute values, this dataset additionally provides bias-adjusted heat stress indicators. Bias adjustment is carried out using the ISIMIP3b bias-adjustment method and employs the WATCH Forcing Data methodology applied to ERA5 (WFDE5) dataset as reference. Providing both pre-calculated bias-adjusted data and data without bias adjustment is of great value for climate and impact studies since the calculation of these datasets also are computationally expensive. The WFDE5 dataset is also available in the Climate Data Store. The heat stress indicators combine near-surface air temperature, near-surface specific humidity, and surface air pressure to give indications of adverse effects of heat on human health. Other variables like wind or solar radiation are not considered, and the selected heat stress indicators thus represent indoor conditions or calm conditions in the shade. This dataset was produced on behalf of the Copernicus Climate Change Service.

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This collection of data are the outputs from the O‘ahu Community Heat Assessment lead by the City & County of Honolulu Office of Climate Change, Sustainability & Resiliency. On August 31, 2019, City staff and community volunteers collected heat and humidity data across O‘ahu’s neighborhoods. Those data were analyzed to produce heat index maps.To learn more, see the Community Heat Assessment Report at bit.ly/oahucommunityheatassessment

The Centers for Disease Control Social Vulnerability Index shows which communities are especially at risk during public health emergencies because of factors like socioeconomic status, household composition, racial composition of neighborhoods, or housing type and transportation. The CDC SVI uses 15 U.S. census variables to identify communities that may need support before, during, or after disasters. Learn more here. The condition is the overall ranking of four social theme rankings where lower values indicate high vulnerability and high values indicate low vulnerability. Quintiles for this condition were determined for all the Census tracts in King County. Quintile 1 is the most vulnerable residents, Quintile 5 is the least vulnerable residents. Data is released every 2 years following the American Community Survey release in December of the year following the Survey. The most recent data for 2018 was downloaded from the ATSDR website.

Graph and download economic data for Home Price Index (Low Tier) for New York, New York (NYXRLTNSA) from Jan 1987 to May 2025 about low tier, New York, HPI, housing, price index, indexes, price, and USA.