This dataset provides values for REDBOOK INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

India's main stock market index, the SENSEX, rose to 82080 points on June 24, 2025, gaining 0.22% from the previous session. Over the past month, the index has declined 0.12%, though it remains 5.16% higher than a year ago, according to trading on a contract for difference (CFD) that tracks this benchmark index from India. BSE SENSEX Stock Market Index - values, historical data, forecasts and news - updated on June of 2025.

Updated investor sentiment index dataset up to December 2014 (including both Baker and Wurgler's sentiment index, and Huang, Jiang, Tu and Zhou (2015 RFS)'s investor sentiment index)

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_4e5473934b66f14ea04accae91487a26/view

This data set contains vector polygons representing the boundaries of all hardcopy cartographic products produced as part of the Environmental Sensitivity Index (ESI) for Alabama. This data set comprises a portion of the ESI data for Alabama. ESI data characterize the marine and coastal environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources.

ERA5–Drought is a global reconstruction of drought indices from 1940 to present. The dataset comprises two standardised drought indices: - the Standardised Precipitation Index (SPI) - the Standardised Precipitation-Evapotranspiration Index (SPEI). The SPI measures the precipitation deficit that accumulated over the preceding months and evaluates the deficit with respect to a reference period. The SPEI is an extension of the SPI and incorporates potential evapotranspiration to capture the impact of temperature on drought. SPI and SPEI values are in units of standard deviation from the standardised mean, i.e., negative values indicate drier-than-usual periods while positive values correspond to wetter-than-usual periods. Both indices can be used to identify the onset and the end of drought events as well as their severity. In ERA5–Drought, SPI and SPEI are calculated using precipitation and potential evapotranspiration from the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses (ERA5). ERA5 combines model data with observations from across the world to provide a globally complete and consistent description of the atmosphere. Drought indices are calculated for a range of accumulation windows (1/3/6/12/24/36/48 months) using the reference period from 1991–2020. All data is regridded to a regular grid of 0.25 degrees, making it suitable for many common applications. SPI and SPEI are calculated using both the ERA5 reanalysis (single realisation from the monthly means of daily means(moda) stream) and the ensemble of the reanalysis (10 realisations from the monthly means of daily means for ensemble members (edmo) stream), enabling uncertainty assessment of drought occurrence and intensity. The quality of the derived indices is evaluated using significance testing. The dataset currently covers 1940 to near-real time and is updated monthly. The consolidated data set is updated 2-3 months behind real time, while the intermediate data set is updated with 1 month of delay. New versions of the dataset are published as settings, such as the reference period, are updated or bug fixes are applied. Bug Fixes will be released using a minor revision (i.e. v1.1), while changes to the reference period will be released as major revisions (i.e. v2.0). Bug Fixes will be published to the Known Issues area on the Documentation tab. A more detailed description of the ERA5–Drought dataset and comparisons to other drought indices can be found in the associated dataset paper (see Documentation Tab). Information on access and usage examples, e.g. to calculate the area in drought, are provided in these guidelines. The dataset is produced by ECMWF.

This dataset shows the concentration of cyanobacteria cells/ml in fresh water bodies and estuaries of the Ohio and Florida derived from 300x300 meter MEdium Resolution Imaging Spectrometer (MERIS) satellite imagery. This dataset was produced through partnership with the National Oceanic and Atmospheric Administration (NOAA), the National Aeronautics and Space Administration (NASA), the United States Geological Survey (USGS), and the United States Environmental Protection Agency (USEPA). This cyanobacteria dataset was derived using the European Space Agency (ESA) Envisat satellite and MERIS instrument. MERIS is a 68.5 degree field-of-view nadir-pointing imaging spectrometer which measures the solar radiation reflected by the Earth in 15 spectral bands (visible and near-infrared). MERIS imagery was used to identify long-wavelength spectral bands (from red through near-infrared portion of the spectrum) to locate algal blooms within freshwaters and estuaries of the continental United States. This dataset is associated with the following publication: Urquhart, E., B. Schaeffer, R. Stumpf, K. Loftin, and J. Wedell. .A method for examining temporal changes in cyanobacterial harmful algal bloom spatial extent using satellite remote sensing. Harmful Algae. Elsevier B.V., Amsterdam, NETHERLANDS, 67: 144-152, (2017).

This dataset provides values for MBA PURCHASE INDEX reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_378818f5f6dd1c83a42b5c63b943b0bc/view

What does the data show?

Wind-driven rain refers to falling rain blown by a horizontal wind so that it falls diagonally towards the ground and can strike a wall. The annual index of wind-driven rain is the sum of all wind-driven rain spells for a given wall orientation and time period. It’s measured as the volume of rain blown from a given direction in the absence of any obstructions, with the unit litres per square metre per year.

Wind-driven rain is calculated from hourly weather and climate data using an industry-standard formula from ISO 15927–3:2009, which is based on the product of wind speed and rainfall totals. Wind-driven rain is only calculated if the wind would strike a given wall orientation. A wind-driven rain spell is defined as a wet period separated by at least 96 hours with little or no rain (below a threshold of 0.001 litres per m2 per hour).

The annual index of wind-driven rain is calculated for a baseline (historical) period of 1981-2000 (corresponding to 0.61°C warming) and for global warming levels of 2.0°C and 4.0°C above the pre-industrial period (defined as 1850-1900). The warming between the pre-industrial period and baseline is the average value from six datasets of global mean temperatures available on the Met Office Climate Dashboard: https://climate.metoffice.cloud/dashboard.html. Users can compare the magnitudes of future wind-driven rain with the baseline values.

What is a warming level and why are they used?

The annual index of wind-driven rain is calculated from the UKCP18 local climate projections which used a high emissions scenario (RCP 8.5) where greenhouse gas emissions continue to grow. Instead of considering future climate change during specific time periods (e.g., decades) for this scenario, the dataset is calculated at various levels of global warming relative to the pre-industrial (1850-1900) period. The world has already warmed by around 1.1°C (between 1850–1900 and 2011–2020), so this dataset allows for the exploration of greater levels of warming.

The global warming levels available in this dataset are 2°C and 4°C in line with recommendations in the third UK Climate Risk Assessment. The data at each warming level were calculated using 20 year periods over which the average warming was equal to 2°C and 4°C. The exact time period will be different for different model ensemble members. To calculate the value for the annual wind-driven rain index, an average is taken across the 20 year period. Therefore, the annual wind-driven rain index provides an estimate of the total wind-driven rain that could occur in each year, for a given level of warming.

We cannot provide a precise likelihood for particular emission scenarios being followed in the real world in the future. However, we do note that RCP8.5 corresponds to emissions considerably above those expected under current international policy agreements. The results are also expressed for several global warming levels because we do not yet know which level will be reached in the real climate; the warming level reached will depend on future greenhouse emission choices and the sensitivity of the climate system, which is uncertain. Estimates based on the assumption of current international agreements on greenhouse gas emissions suggest a median warming level in the region of 2.4-2.8°C, but it could either be higher or lower than this level.

What are the naming conventions and how do I explore the data?

Each row in the data corresponds to one of eight wall orientations – 0, 45, 90, 135, 180, 225, 270, 315 compass degrees. This can be viewed and filtered by the field ‘Wall orientation’.

The columns (fields) correspond to each global warming level and two baselines. They are named 'WDR' (Wind-Driven Rain), the warming level or baseline, and ‘upper’ ‘median’ or ‘lower’ as per the description below. For example, ‘WDR 2.0 median’ is the median value for the 2°C projection. Decimal points are included in field aliases but not field names; e.g., ‘WDR 2.0 median’ is ‘WDR_20_median’.

Please note that this data MUST be filtered with the ‘Wall orientation’ field before styling it by warming level. Otherwise it will not show the data you expect to see on the map. This is because there are several overlapping polygons at each location, for each different wall orientation.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

What do the ‘median’, ‘upper’, and ‘lower’ values mean?

Climate models are numerical representations of the climate system. To capture uncertainty in projections for the future, an ensemble, or group, of climate models are run. Each ensemble member has slightly different starting conditions or model set-ups. Considering all of the model outcomes gives users a range of plausible conditions which could occur in the future.

For this dataset, the model projections consist of 12 separate ensemble members. To select which ensemble members to use, annual wind-driven rain indices were calculated for each ensemble member and they were then ranked in order from lowest to highest for each location.

The ‘lower’ fields are the second lowest ranked ensemble member. The ‘upper’ fields are the second highest ranked ensemble member. The ‘median’ field is the central value of the ensemble.

This gives a median value, and a spread of the ensemble members indicating the range of possible outcomes in the projections. This spread of outputs can be used to infer the uncertainty in the projections. The larger the difference between the lower and upper fields, the greater the uncertainty.

‘Lower’, ‘median’ and ‘upper’ are also given for the baseline periods as these values also come from the model that was used to produce the projections. This allows a fair comparison between the model projections and recent past.

Data source

The annual wind-driven rain index was calculated from hourly values of rainfall, wind speed and wind direction generated from the UKCP Local climate projections. These projections were created with a 2.2km convection-permitting climate model. To aid comparison with other models and UK-based datasets, the UKCP Local model data were aggregated to a 5km grid on the British National Grid; the 5 km data were processed to generate the wind-driven rain data.

Useful links

Further information on the UK Climate Projections (UKCP). Further information on understanding climate data within the Met Office Climate Data Portal.

SNAP Participation Rates. National PAI dataset - https://www.fns.usda.gov/snap/calculating-snap-program-access-index-step-step-guide

The MODIS level-3 Vegetation Indices Daily Rolling-8-Day Near Real Time (NRT), MOD13A4N data are provided everyday at 500-meter spatial resolution as a gridded level-3 product in the Sinusoidal projection. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes and global and regional climate. These data also may be used for characterizing land surface biophysical properties and processes including primary production and land cover conversion.Note: This is a near real-time product only. Standard historical data and imagery for MOD13Q4N (250m) and MOD13A4N (500m) are not available. Users can either use the NDVI standard products from LAADS web (https://ladsweb.modaps.eosdis.nasa.gov/search/) or access the science quality MxD09[A1/Q1] data and create the NDVI product of their own.

We propose a general double tree structured AR-GARCH model for the analysis of global equity index returns. The model extends previous approaches by incorporating (i) several multivariate thresholds in conditional means and volatilities of index returns and (ii) a richer specification for the impact of lagged foreign (US) index returns in each threshold. We evaluate the out-of-sample forecasting power of our model for eight major equity indices in comparison to some existing volatility models in the literature. We find strong evidence for more than one multivariate threshold (more than two regimes) in conditional means and variances of global equity index returns. Such multivariate thresholds are affected by foreign (US) lagged index returns and yield a higher out-of-sample predictive power for our tree structured model setting.

Bookologia Indices Dataset

Bookologia is a specialized search engine of books, to find any book within seconds.This dataset contains three Elasticsearch indices extracted from Bookologia database: authors_data, book_links, and books_data_bis.Each index stores structured metadata relevant to authors, books, and associated resources.This dump is suitable for tasks involving metadata analysis, author-book linking, recommendation systems, or search enhancement.More about the project… See the full description on the dataset page: https://huggingface.co/datasets/blankresearch/Bookologia.

About this dataset

Context

The dataset tabulates the population of Index by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Index. The dataset can be utilized to understand the population distribution of Index by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Index. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Index.

Key observations

Largest age group (population): Male # 45-49 years (16) | Female # 40-44 years (15). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Index population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Index is shown in the following column.
• Population (Female): The female population in the Index is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Index for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Index Population by Gender. You can refer the same here

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

Dataset

This dataset was created by ABDUL-HAMID SHERRIFF

Released under Other (specified in description)

Contents

Analysis of ‘Home Price Index’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/PythonforSASUsers/hpindex on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

The Federal Housing Finance Agency House Price Index (HPI) is a broad measure of the movement of single-family house prices. The HPI is a weighted, repeat-sales index, meaning that it measures average price changes in repeat sales or refinancings on the same properties. The technical methodology for devising the index, collection, and publishing the data is at: http://www.fhfa.gov/PolicyProgramsResearch/Research/PaperDocuments/1996-03_HPI_TechDescription_N508.pdf

Content

Contains monthly and quarterly time series from January 1991 to August 2016 for the U.S., state, and MSA categories. Analysis variables are the aggregate non-seasonally adjusted value and seasonally adjusted index values. The index value is 100 beginning January 1991.

Acknowledgements

This data is found on Data.gov

Inspiration

Can this data be combined with the corresponding census growth projections either at the state or MSA level to forecast 24 months out the highest and lowest home index values?

--- Original source retains full ownership of the source dataset ---

Aerial photographs are retrievable on a frame by frame basis. The aerial photo inventory contains imagery from various sources that are now archived at the Earth Data Analysis Center.

The International Price Program (IPP) produces Import/Export Price Indexes (MXP) containing data on changes in the prices of nonmilitary goods and services traded between the U.S. and the rest of the world. For more information and data visit: https://www.bls.gov/mxp/