SSI Eligibility & Payment information.

The State Review Framework is a primary means by which EPA conducts oversight of three core federal statutes: Clean Air Act, Clean Water Act, and Resource Conservation and Recovery Act. The routine, nationwide review provides a consistent process for evaluating the performance of state, local and EPA compliance and enforcement programs. The overarching goal of the reviews is to ensure fair and consistent enforcement necessary to protect human health and the environment.

This study uses historical records from 36 archives in the United States to analyze 8,437 enslaved people's sale and/or appraisal prices from 1797 to 1865.

This dataset includes over 49,000 well records from the state well drillers databases in Minnesota, Wisconsin, and Michigan. Each state well has at a minimum the well depth and a static water level. Static water levels were mostly determined when the well was constructed. Data included in this shapefile include the well construction date, well depth, well elevation (if determined), type of well, the methods used for determining the well location and elevation (if determined), casing and screen depths (where reported), the static water level and a date, and the year the well was constructed. The field names from each of the state databases were harmonized to merge the data, and a table of the original field names is included. Tabular files are included with codes describing the original field names mapped to the combined field names, and descriptions of codes used to describe the well location method, well depth method, and well type for each state. The USGS wells in this dat ...

US Industry Data by State, by Industry

Number of Establishments, Sales, Payroll, and Employees

By Gary Hoover [source]

About this dataset

This data set provides a detailed look into the US economy. It includes information on establishments and nonemployer businesses, as well as sales revenue, payrolls, and the number of employees. Gleaned from the Economic Census done every five years, this data is a valuable resource to anyone curious about where the nation was economically at the time. With columns including geographic area name, North American Industry Classification System (NAICS) codes for industries, descriptions of those codes meaning of operation or tax status, and annual payroll, this information-rich dataset contains all you need to track economic trends over time. Whether you’re a researcher studying industry patterns or an entrepreneur looking for market insight — this dataset has what you’re looking for!

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides detailed US industry data by state, including the number of establishments, value of sales, payroll, and number of employees. All the data is based on the North American Industry Classification System (NAICS) code for each specific industry. This will allow you to easily analyze and compare industries across different states or regions.

Research Ideas

• Analyzing the economic impact of a new business or industry trends in different states: Comparing the change in the number of establishments, payroll, and employees over time can give insight into how a state is affected by a new industry trend or introduction of a new service or product.
• Estimating customer sales potential for businesses: This dataset can be used to estimate the potential customer base for businesses in different geographic areas. By analyzing total business done by non-employers in an area along with its estimated population can help estimate how much overall sales potential exists for a given region.
• Tracking competitor performance: By looking at shipments, receipts, and value of business done across industries in different regions or even cities, companies can track their competitors’ performance and compare it to their own to better assess their strategies going forward

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: 2012 Industry Data by Industry and State.csv | Column name | Description | |:----------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------| | Geographic area name | The name of the geographic area the data is for. (String) | | NAICS code | The North American Industry Classification System (NAICS) code for the industry. (String) | | Meaning of NAICS code | The description of the NAICS code. (String) | | Meaning of Type of operation or tax status code | The description of the type of operation or tax status code. (String) ...

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .

United States State Leading Index: Massachusetts data was reported at 0.270 % in Jul 2018. This records a decrease from the previous number of 1.314 % for Jun 2018. United States State Leading Index: Massachusetts data is updated monthly, averaging 1.687 % from Jan 1982 (Median) to Jul 2018, with 439 observations. The data reached an all-time high of 5.452 % in Jun 1983 and a record low of -4.495 % in Jan 2009. United States State Leading Index: Massachusetts data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.S008: State Leading Index.

United States State Leading Index: Montana data was reported at 3.783 % in Jul 2018. This records a decrease from the previous number of 3.837 % for Jun 2018. United States State Leading Index: Montana data is updated monthly, averaging 1.360 % from Jan 1982 (Median) to Jul 2018, with 439 observations. The data reached an all-time high of 5.435 % in Sep 1988 and a record low of -4.711 % in Dec 2008. United States State Leading Index: Montana data remains active status in CEIC and is reported by Federal Reserve Bank of Philadelphia. The data is categorized under Global Database’s USA – Table US.S008: State Leading Index.

This data is a compilation of information from Virginia State Police investigators and local law enforcement used for soliciting tips on unsolved cases throughout the Commonwealth on the Virginia Cold Case Database (https://coldcase.vsp.virginia.gov/). The site displays unsolved homicides, missing persons cases, and unidentified remains.

The Cognitive State Discrimination Dataset was collected using a 64-channel electroencephalography (EEG) system at a sampling rate of 512 Hz. The EEG electrodes were placed according to the international 10-20 system, ensuring standard placement for optimal brain wave capture. The dataset comprises EEG recordings from multiple participants as they performed a series of cognitive tasks designed to elicit different mental states.

1. Participant and Task Information: Participants: Human subjects (with individual identifiers provided in the Participant_ID column) engaged in various cognitive tasks. Tasks: The tasks include memory recall, arithmetic calculations, and visual pattern recognition, chosen to invoke different cognitive states, such as attention, working memory, and problem-solving.
2. Data Collection Protocol: The EEG data was recorded in a controlled laboratory environment designed to minimize external distractions. Each participant underwent a session lasting approximately 30 minutes, with breaks between tasks to avoid fatigue. Baseline recordings were taken before task onset to capture resting-state EEG activity.
3. EEG Features: The dataset includes 64 EEG channels capturing brain activity during the tasks. Each channel's data is recorded as power spectral density (PSD), a key measure of the brain's oscillatory activity across different frequency bands. This includes both low-frequency and high-frequency brain waves that are indicative of different cognitive processes. In addition to the PSD, Event-Related Potentials (ERP) are also included for specific tasks. ERPs reflect the brain's response to stimuli during cognitive tasks, providing crucial insight into task-related mental states. Coherence metrics between different EEG channels can also be derived to measure the synchrony of brain regions, which is important for understanding how different brain areas work together during cognitive tasks.
4. Data Structure: The dataset consists of multiple columns: Participant_ID: Unique identifier for each participant. Task: The cognitive task being performed (e.g., memory recall, arithmetic, etc.). Channel_1_PSD to Channel_64_PSD: Power Spectral Density values for each of the 64 EEG channels. ERP_Memory_Recall, ERP_Arithmetic, ERP_Visual_Pattern: Event-Related Potentials for the specific tasks. Time_Stamp: The timestamp of the EEG recording, which allows for time-series analysis. Target_Label: The label indicating the cognitive state for classification (e.g., active, resting, focused).

Listing of state tax credit and subsidies awarded by NYS Homes & Community Renewal’s Office of Finance and Development. Details include award amount, developer name, project location, and accomplishments for completed projects based on project types.

Dataset includes occupational licenses by state. Data is provided by the state. All licenses are coded to an O*NET-SOC code and by state. CareerOneStop.org web service available upon request.

Drug utilization data are reported by states for covered outpatient drugs that are paid for by state Medicaid agencies since the start of the Medicaid Drug Rebate Program. The data includes state, drug name, National Drug Code, number of prescriptions and dollars reimbursed. Data descriptions are available on Medicaid.gov: https://www.medicaid.gov/medicaid/prescription-drugs/state-drug-utilization-data/state-drug-utilization-data-faq/index.html

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 4.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS4_0_VectorAnalysis_Script_Python3.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). Vector Analysis ("PADUS4_0VectorAnalysis_GAP_PADUS_Only_ClipCENSUS.zip") data was created by clipping the PAD-US 4.0 Spatial Analysis and Statistics results to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS4_0_VectorAnalysisFile_OtherExtents_ClipCENSUS2022.zip"). Comma-separated Value (CSV) tables ("PADUS4_0_SummaryStatistics_TabularData_CSV.zip") provided as an alternative format and enable users to explore and download summary statistics of interest from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 4.0 combined file without other extent boundaries ("PADUS4_0VectorAnalysis_GAP_PADUS_Only_ClipCENSUS.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS4_0VectorAnalysis_State_Clip_CENSUS2022" feature class ("PADUS4_0_VectorAnalysisFile_OtherExtents_ClipCENSUS2022.gdb") is the source of the PAD-US 4.0 Raster Analysis child item. Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://ngda-portfolio-community-geoplatform.hub.arcgis.com/pages/portfolio ), agencies are the best source of their lands data.

C.G.S. 4-67p requires executive branch agencies to designate an Agency Data Officer to serve as the primary contact for inquiries, requests, and concerns about access to data at their agency, and to be responsible for implementing the provisions of C.G.S. 4-67p, including the development of an inventory of the agency's high value data and the creation of an open data access plan for the agency.

Definitions can be found here: https://www.cga.ct.gov/current/pub/chap_050.htm#sec_4-67p

This dataset includes a list of the name and contact information for the Agency Data Officers.

The COVID Tracking Project collects information from 50 US states, the District of Columbia, and 5 other US territories to provide the most comprehensive testing data we can collect for the novel coronavirus, SARS-CoV-2. We attempt to include positive and negative results, pending tests, and total people tested for each state or district currently reporting that data.

Testing is a crucial part of any public health response, and sharing test data is essential to understanding this outbreak. The CDC is currently not publishing complete testing data, so we’re doing our best to collect it from each state and provide it to the public. The information is patchy and inconsistent, so we’re being transparent about what we find and how we handle it—the spreadsheet includes our live comments about changing data and how we’re working with incomplete information.

From here, you can also learn about our methodology, see who makes this, and find out what information states provide and how we handle it.

This layer shows living arrangements of children and adults by age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.This layer is symbolized to show the percentage of adults living with a spouse. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B09005 (formerly B09008), B09021Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The National Conference of State Legislatures provides you with up-to-date, real-time information on law enforcement legislation that has been introduced in the 50 states and the District of Columbia. The database contains policing bills and executive orders introduced as of May 25, 2020, that are in response to recent events. Policing topics include oversight and data, training, standards and certification, use of force, technology, policing alternatives and collaboration, executive orders and other timely issues.

The database represents delineations of aspen stands associated with stand assessment data (SIERRA_SP_PTS) collected in aspen stands on lands administered by the Sierra District, California Department of Parks and Recreation, Placer, Eldorado, Sierra and Inyo Counties, California. This data was gathered during the summer of 2005. The stands were evaluated according to their priority with regard to stand loss. The dataset has 15 aspen stands, of which six have been assessed. Field surveys were conducted by trained USFS field staff. Surveys were not complete for the watersheds, and so this is not considered a comprehensive database of aspen in the region. Aspen stands were assessed and delineated using a protocol developed by the lead investigators. Associated with this polygon layer is a point layer (SIERRA_SP_PTS) containing aspen stand assessments conducted in conjunction with the aspen stand delineations. Data Compilation: The Aspen Delineation Project (ADP) is a collaborative effort of the U.S. Forest Services Pacific Southwest Region, the California Department of Fish and Games Resource Assessment Program, and the California Office of Bureau of Land Management. Principal Investigator for ADP is David Burton; visit: www.aspensite.org for more information regarding the ADP. The Department of Fish and Games, Resource Assessment Program compiled this information from the collaborating agencies and other researchers, and formatted the data into a common database for the purpose of facilitating access to data related to the conservation of Quaking Aspen in California. This information portal falls within the ADP goals to help agencies and land managers identify, map, treat, and monitor aspen habitats. This dataset is a portion of a master database compiled during a year long effort in 2005 to pull together current GIS layers and maps depicting Aspen communities in California.

This map layer portrays the State boundaries of the United States, Puerto Rico, and the U.S. Virgin Islands. The map layer was created by extracting county polygon features from the CENSUS 2006 TIGER/Line files produced by the U.S. Census Bureau. These files were then merged into a single file and county boundaries within States were removed. This is a revised version of the July 2012 map layer.The data and related materials are made available through Esri (http://www.esri.com) and are intended for educational purposes only (see Access and Use Constraints section).