An Environmental Quality Index (EQI) for all counties in the United States for the time period 2000-2005 was developed which incorporated data from five environmental domains: air, water, land, built, and socio-demographic. The EQI was developed in four parts: domain identification; data source identification and review; variable construction; and data reduction using principal components analysis (PCA). The methods applied provide a reproducible approach that capitalizes almost exclusively on publically-available data sources. The primary goal in creating the EQI is to use it as a composite environmental indicator for research on human health. A series of peer reviewed manuscripts utilized the EQI in examining health outcomes. This dataset is not publicly accessible because: This series of papers are considered Human health research - not to be loaded onto ScienceHub. It can be accessed through the following means: The EQI data can be accessed at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: EQI data, metadata, formats, and data dictionary all available at website. This dataset is associated with the following publications: Gray, C., L. Messer, K. Rappazzo, J. Jagai, S. Grabich, and D. Lobdell. The association between physical inactivity and obesity is modified by five domains of environmental quality in U.S. adults: A cross-sectional study. PLoS ONE. Public Library of Science, San Francisco, CA, USA, 13(8): e0203301, (2018). Patel, A., J. Jagai, L. Messer, C. Gray, K. Rappazzo, S. DeflorioBarker, and D. Lobdell. Associations between environmental quality and infant mortality in the United States, 2000-2005. Archives of Public Health. BioMed Central Ltd, London, UK, 76(60): 1, (2018). Gray, C., D. Lobdell, K. Rappazzo, Y. Jian, J. Jagai, L. Messer, A. Patel, S. Deflorio-Barker, C. Lyttle, J. Solway, and A. Rzhetsky. Associations between environmental quality and adult asthma prevalence in medical claims data. ENVIRONMENTAL RESEARCH. Elsevier B.V., Amsterdam, NETHERLANDS, 166: 529-536, (2018).

Leaf Area Index (LAI) is a fundamental vegetation structural variable that drives energy and mass exchanges between the plant and the atmosphere. Moderate-resolution (300m – 7km) global LAI data products have been widely applied to track global vegetation changes, drive Earth system models, monitor crop growth and productivity, etc. Yet, cutting-edge applications in climate adaptation, hydrology, and sustainable agriculture require LAI information at higher spatial resolution (< 100m) to model and understand heterogeneous landscapes. This dataset was built to assist a machine-learning-based approach for mapping LAI from 30m-resolution Landsat images across the contiguous US (CONUS). The data was derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) Version 6 LAI/FPAR, Landsat Collection 1 surface reflectance, and NLCD Land Cover datasets over 2006 – 2018 using Google Earth Engine. Each record/sample/row includes a MODIS LAI value, corresponding Landsat surface reflectance in green, red, NIR, SWIR1 bands, a land cover (biome) type, geographic location, and other auxiliary information. Each sample represents a MODIS LAI pixel (500m) within which a single biome type dominates 90% of the area. The spatial homogeneity of the samples was further controlled by a screening process based on the coefficient of variation of the Landsat surface reflectance. In total, there are approximately 1.6 million samples, stratified by biome, Landsat sensor, and saturation status from the MODIS LAI algorithm. This dataset can be used to train machine learning models and generate LAI maps for Landsat 5, 7, 8 surface reflectance images within CONUS. Detailed information on the sample generation and quality control can be found in the related journal article. Resources in this dataset:Resource Title: README. File Name: LAI_train_samples_CONUS_README.txtResource Description: Description and metadata of the main datasetResource Software Recommended: Notepad,url: https://www.microsoft.com/en-us/p/windows-notepad/9msmlrh6lzf3?activetab=pivot:overviewtab Resource Title: LAI_training_samples_CONUS. File Name: LAI_train_samples_CONUS_v0.1.1.csvResource Description: This CSV file consists of the training samples for estimating Leaf Area Index based on Landsat surface reflectance images (Collection 1 Tire 1). Each sample has a MODIS LAI value and corresponding surface reflectance derived from Landsat pixels within the MODIS pixel. Contact: Yanghui Kang (kangyanghui@gmail.com)
Column description

UID: Unique identifier. Format: LATITUDE_LONGITUDE_SENSOR_PATHROW_DATE
Landsat_ID: Landsat image ID Date: Landsat image date in "YYYYMMDD" Latitude: Latitude (WGS84) of the MODIS LAI pixel center Longitude: Longitude (WGS84) of the MODIS LAI pixel center MODIS_LAI: MODIS LAI value in "m2/m2" MODIS_LAI_std: MODIS LAI standard deviation in "m2/m2" MODIS_LAI_sat: 0 - MODIS Main (RT) method used no saturation; 1 - MODIS Main (RT) method with saturation NLCD_class: Majority class code from the National Land Cover Dataset (NLCD) NLCD_frequency: Percentage of the area cover by the majority class from NLCD Biome: Biome type code mapped from NLCD (see below for more information) Blue: Landsat surface reflectance in the blue band Green: Landsat surface reflectance in the green band Red: Landsat surface reflectance in the red band Nir: Landsat surface reflectance in the near infrared band Swir1: Landsat surface reflectance in the shortwave infrared 1 band Swir2: Landsat surface reflectance in the shortwave infrared 2 band Sun_zenith: Solar zenith angle from the Landsat image metadata. This is a scene-level value. Sun_azimuth: Solar azimuth angle from the Landsat image metadata. This is a scene-level value. NDVI: Normalized Difference Vegetation Index computed from Landsat surface reflectance EVI: Enhanced Vegetation Index computed from Landsat surface reflectance NDWI: Normalized Difference Water Index computed from Landsat surface reflectance GCI: Green Chlorophyll Index = Nir/Green - 1

Biome code

1 - Deciduous Forest
2 - Evergreen Forest
3 - Mixed Forest
4 - Shrubland
5 - Grassland/Pasture
6 - Cropland
7 - Woody Wetland
8 - Herbaceous Wetland

Reference Dataset: All data was accessed through Google Earth Engine Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., & Moore, R. (2017). Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. MODIS Version 6 Leaf Area Index/FPAR 4-day L5 Global 500m Myneni, R., Y. Knyazikhin, T. Park. MOD15A2H MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500m SIN Grid V006. 2015, distributed by NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD15A2H.006 Landsat 5/7/8 Collection 1 Surface Reflectance Landsat Level-2 Surface Reflectance Science Product courtesy of the U.S. Geological Survey. Masek, J.G., Vermote, E.F., Saleous N.E., Wolfe, R., Hall, F.G., Huemmrich, K.F., Gao, F., Kutler, J., and Lim, T-K. (2006). A Landsat surface reflectance dataset for North America, 1990–2000. IEEE Geoscience and Remote Sensing Letters 3(1):68-72. http://dx.doi.org/10.1109/LGRS.2005.857030. Vermote, E., Justice, C., Claverie, M., & Franch, B. (2016). Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. Remote Sensing of Environment. http://dx.doi.org/10.1016/j.rse.2016.04.008. National Land Cover Dataset (NLCD) Yang, Limin, Jin, Suming, Danielson, Patrick, Homer, Collin G., Gass, L., Bender, S.M., Case, Adam, Costello, C., Dewitz, Jon A., Fry, Joyce A., Funk, M., Granneman, Brian J., Liknes, G.C., Rigge, Matthew B., Xian, George, A new generation of the United States National Land Cover Database—Requirements, research priorities, design, and implementation strategies: ISPRS Journal of Photogrammetry and Remote Sensing, v. 146, p. 108–123, at https://doi.org/10.1016/j.isprsjprs.2018.09.006 Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excel

This is the data used for the development of the Index Index model.

The Case Mix Index (CMI) is the average relative DRG weight of a hospital’s inpatient discharges, calculated by summing the Medicare Severity-Diagnosis Related Group (MS-DRG) weight for each discharge and dividing the total by the number of discharges. The CMI reflects the diversity, clinical complexity, and resource needs of all the patients in the hospital. A higher CMI indicates a more complex and resource-intensive case load. Although the MS-DRG weights, provided by the Centers for Medicare & Medicaid Services (CMS), were designed for the Medicare population, they are applied here to all discharges regardless of payer. Note: It is not meaningful to add the CMI values together.

Discover published data which is local in nature. A local search will return results which include the statewide dataset, which can then be searched and/or filtered to view a specific locality. For numerous statewide datasets, it provides quick access to local information across a broad range of categories from health to transportation, from recreation to economic development; Find local farmer’s markets, child care regulated facilities, solar installations, food service establishment inspections, and much more. Datasets may be searched on one or more local attributes (e.g., county, city), depending upon the granularity of the data. See the overview document http://on.ny.gov/1SB66oL in the “About” section of the source dataset for ways to search specific localities within Statewide datasets.

Measuring the usage of informatics resources such as software tools and databases is essential to quantifying their impact, value and return on investment. We have developed a publicly available dataset of informatics resource publications and their citation network, along with an associated metric (u-Index) to measure informatics resources’ impact over time. Our dataset differentiates the context in which citations occur to distinguish between ‘awareness’ and ‘usage’, and uses a citing universe of open access publications to derive citation counts for quantifying impact. Resources with a high ratio of usage citations to awareness citations are likely to be widely used by others and have a high u-Index score. We have pre-calculated the u-Index for nearly 100,000 informatics resources. We demonstrate how the u-Index can be used to track informatics resource impact over time. The method of calculating the u-Index metric, the pre-computed u-Index values, and the dataset we compiled to calculate the u-Index are publicly available.

1) Data Introduction • The AI Global Index Dataset is a comprehensive index that benchmarks 62 countries based on the level of AI investment, innovation, and implementation, including seven key indicators (human resources, infrastructure, operational environment, research, development, government strategy, commercialization) and general information by country (region, cluster, income group, political system).

2) Data Utilization (1) AI Global Index Dataset has characteristics that: • This dataset consists of a total of 13 columns with 5 categorical variables (regions, clusters, etc.) and 8 numerical variables (scores for each indicator), covering 62 countries. • The seven key indicators are classified into three pillars: △ implementation (human resources/infrastructure/operational environment) △ innovation (R&D) △ investment (government strategy/commercialization), and assess each country's overall AI ecosystem capabilities in multiple dimensions. (2) AI Global Index Dataset can be used to: • Global AI leadership pattern analysis: Correlation analysis between seven indicators can identify AI strengths and weaknesses by country and perform group comparisons by region and income level. • Machine learning-based predictive model: It can be used for data science education and application, such as country-specific index prediction through regression analysis or classification of AI development types through clustering.

The real-time index database market is experiencing robust growth, driven by the increasing demand for immediate insights from large volumes of data across diverse sectors. The market's expansion is fueled by the proliferation of IoT devices generating massive real-time data streams, the need for faster decision-making in competitive environments, and the rise of sophisticated analytics applications requiring rapid data access. Cloud-based solutions dominate the market due to their scalability, cost-effectiveness, and ease of deployment, attracting both individual users and large enterprises. However, concerns around data security and latency in cloud-based systems present some restraints. The on-premises segment, while smaller, continues to cater to businesses with stringent data sovereignty requirements or those managing exceptionally sensitive information. Key players like Elastic, Amazon Web Services, Apache Solr, Splunk, and Microsoft are shaping the market landscape through continuous innovation and competitive offerings. Geographic distribution reflects the concentration of technological infrastructure and data generation, with North America and Europe currently leading the market, followed by the Asia-Pacific region showing significant potential for future growth. The market's Compound Annual Growth Rate (CAGR) suggests a consistent upward trajectory, indicating continued investment and market expansion throughout the forecast period. The competitive dynamics are marked by a mix of established players and emerging entrants. Established players leverage their existing infrastructure and customer bases, while new entrants focus on niche areas and innovative solutions. The market is also witnessing increased adoption of hybrid models combining cloud and on-premises solutions to balance cost-efficiency, security, and performance. Future growth will depend on technological advancements, particularly in areas like distributed ledger technology and edge computing, which will enhance the real-time capabilities and scalability of index databases. Furthermore, the increasing focus on data governance and regulatory compliance will also influence market adoption and shape the development of future solutions. The market is anticipated to witness a sustained period of growth, fueled by the ever-growing demand for real-time data analytics and insights across various sectors and regions.

This dataset was developed to model habitat suitability for two ungulate species on the island of Lanai. This includes raster data derived from WorldView-2 data to create a bare ground index. This index, in addition to other datasets, was used to create the habitat suitability models. Datasets and indices derived for use in modeling efforts, as well as suitability models, are included within this data release.

This dataset collection is a structured compilation of tables sourced from Lantmäteriet (The Land Survey), a website based in Sweden. These tables are meticulously organized into rows and columns, providing a clear presentation of related data. The dataset's relevance and value stem from its comprehensive nature, as it encapsulates several aspects within one or multiple tables. The dataset collection is designed for ease of navigation, thereby enabling efficient data analysis and extraction.

CFNAI Employment Index in the United States decreased to -0.05 points in June from -0.01 points in May of 2025. This dataset includes a chart with historical data for the United States CFNAI Employment Index.

The county election administration index for 2016, 2018, and 2020 was used for the Michael J. Ritter and Caroline J. Tolbert (2024) "Measuring County Election Administration in the United States" article published in the Election Law Journal. The data as well as a codebook are made available here.

About this dataset

Context

The dataset tabulates the Index population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Index. The dataset can be utilized to understand the population distribution of Index by age. For example, using this dataset, we can identify the largest age group in Index.

Key observations

The largest age group in Index, WA was for the group of age 10 to 14 years years with a population of 24 (14.63%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Index, WA was the Under 5 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 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

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Index is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Index total population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Age. You can refer the same here

The Standardized Precipitation Index (SPI) was generated for certain Environment Canada long-term climate stations in Ontario.

The SPI quantifies the precipitation deficit and surplus for multiple time scales, including:

one month three months six months nine months 12 months 24 months

You can use the SPI to study the impact of dry and wet weather conditions to create comprehensive water management approaches.

The SPI data package is distributed as a Microsoft Access Geodatabase.

This is a legacy dataset that we no longer maintain or support.

The documents referenced in this record may contain URLs (links) that were valid when published, but now link to sites or pages that no longer exist.

Additional Documentation

Standardized Precipitation Index - User Guide (PDF)

Status Completed: production of the data has been completed

Maintenance and Update Frequency
Not planned: there are no plans to update the data

Contact
Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

An alphabetical index including proper names (persons, places, projects, institutions) and literary works as mentioned in the Proceedings of the Workshop Linking Manuscripts from the Coptic, Ethiopian, and Syriac Domain: Present and Future Synergy Strategies Hamburg, 23 and 24 February 2018.

As of June 28, 2010, the Master Veteran Index (MVI) database based on the enhanced Master Patient Index (MPI) is the authoritative identity service within the VA, establishing, maintaining and synchronizing identities for VA clients, Veterans and beneficiaries. The MVI includes authoritative sources for health identity data and contains over 17 million patient entries populated from all VHA facilities nationwide. The MVI provides the access point mechanism for linking patient's information to enable an enterprise-wide view of patient information, uniquely identifies all active patients who have been admitted, treated, or registered in any VHA facility, and assigns a unique identifier to the patient. The MVI correlates a patient's identity across the enterprise, including all VistA systems and external systems, such as Department of Defense (DoD) and the Nationwide Health Information Network (NwHIN). The MVI facilitates the sharing of health information, resulting in coordinated and integrated health care for Veterans. New Information Technology systems must be interoperable with the MVI and legacy systems will establish integration by October 1, 2012. The Healthcare Identity Management (HC IdM) Team within VHA's Data Quality Program is the steward of patient identity data, performing maintenance and support activities.

This data has been superseded by a newer version of the dataset. Please refer to NOAA's Climate Divisional Database for more information. The U.S. Climate Divisional Dataset provides data access to current U.S. temperature, precipitation and drought indeces. Divisional indices included are: Precipitation Index, Palmer Drought Severity Index, Palmer Hydrological Drought Index, Modified Palmer Drought Severity Index, Temperature, Palmer Z Index, Cooling Degree Days, Heating Degree Days, 1-Month Standardized Precipitation Index (SPI), 2-Month (SPI), 3-Month (SPI), 6-Month (SPI),12-Month (SPI) and the 24-Month (SPI). All of these Indices, except for the SPI, are available for Regional, State and National views as well. There are 344 climate divisions in the CONUS. For each climate division, monthly station temperature and precipitation values are computed from the daily observations. The divisional values are weighted by area to compute statewide values and the statewide values are weighted by area to compute regional values. The indices were computed using daily station data from 1895 to present.

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.

A monthly measure of the volume of services performed by the for-hire transportation sector. The index covers the activities of local mass transit, intercity passenger rail, and passenger air transportation.

Licensed under: Creative Commons Attribution 4.0