The Gross Domestic Product (GDP) in the United States expanded 3.80 percent in the second quarter of 2025 over the previous quarter. This dataset provides the latest reported value for - United States GDP Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The Gross Domestic Product (GDP) in the United States expanded 2.10 percent in the second quarter of 2025 over the same quarter of the previous year. This dataset provides the latest reported value for - United States GDP Annual Growth Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Urban Growth Areas (UGAs) are legally defined entities in Oregon and Washington that the Census Bureau includes in the MTDB in agreement with each State. UGAs, which are defined around incorporated places, are used to regulate urban growth. UGA boundaries, which need not follow visible features, are delineated cooperatively by State and local officials in Oregon and Washington. Each UGA is identified by a 5-digit numeric census code, usually associated with the incorporated place for which the UGA is named. The UGAs for the 2020 Census were those in effect as of January 1, 2020.

View economic output, reported as the nominal value of all new goods and services produced by labor and property located in the U.S.

Inflation Rate in the United States increased to 3 percent in September from 2.90 percent in August of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The number of social media users in the United States was forecast to continuously increase between 2024 and 2029 by in total 26 million users (+8.55 percent). After the ninth consecutive increasing year, the social media user base is estimated to reach 330.07 million users and therefore a new peak in 2029. Notably, the number of social media users of was continuously increasing over the past years.The shown figures regarding social media users have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

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How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

The increase in wildfires, particularly in the western U.S., represents one of the greatest threats to multiple native ecosystems. Despite this threat, there is currently no central repository to store both past and current wildfire perimeter data. Currently, wildfire boundaries can only be found in disparate local or national datasets. These datasets are generally restricted to specific locations, fire sizes, or time periods. Our objective was to create a comprehensive national wildfire perimeter dataset by combining all freely available wildfire datasets that we could download. We combined and dissolved individual wildfire polygons from multiple datasets if they were in the same year and overlapped each other or were within 1km of the fire boundary. This combined dataset includes spatial summary statistics such as number of times burned, earliest fire of record, and most recent fire of record.

Dataset Versioning for Analytics Market Outlook

According to our latest research, the Global Dataset Versioning for Analytics market size was valued at $1.3 billion in 2024 and is projected to reach $6.7 billion by 2033, expanding at a robust CAGR of 20.1% during the forecast period of 2025–2033. The primary driver fueling this growth is the exponential rise in data-driven decision-making across industries, necessitating advanced solutions for managing, tracking, and auditing datasets throughout their lifecycle. As organizations increasingly rely on analytics for business intelligence, the need for robust dataset versioning tools to ensure data integrity, compliance, and reproducibility has become paramount, propelling the market’s rapid expansion globally.

Regional Outlook

North America currently commands the largest share of the global Dataset Versioning for Analytics market, accounting for nearly 40% of the total market value in 2024. This dominance is underpinned by the region’s mature technology ecosystem, high adoption rates of advanced analytics platforms, and a strong presence of leading software vendors and cloud service providers. The United States, in particular, has been at the forefront due to its robust regulatory frameworks around data governance and the proliferation of data-centric enterprises in sectors such as BFSI, healthcare, and IT. Additionally, ongoing investments in digital transformation and the early embrace of machine learning and AI-driven analytics further cement North America’s leadership position in this market.

The Asia Pacific region is poised to be the fastest-growing market, with an anticipated CAGR of 23.4% between 2025 and 2033. This rapid acceleration is driven by the digitalization wave sweeping across emerging economies such as China, India, and Southeast Asian nations. Massive investments in cloud infrastructure, government-backed data localization policies, and the burgeoning need for scalable analytics solutions among SMEs are key growth catalysts. Moreover, the region’s expanding e-commerce, fintech, and healthcare sectors are generating unprecedented volumes of data, prompting organizations to adopt sophisticated dataset versioning tools to maintain data quality, compliance, and auditability. Strategic partnerships between global technology leaders and local enterprises are also fostering innovation and adoption.

Emerging economies in Latin America and the Middle East & Africa are experiencing steady but comparatively slower adoption of dataset versioning solutions. Key challenges include limited digital infrastructure, budget constraints, and a shortage of skilled data professionals. However, localized demand is gradually rising as governments and enterprises recognize the importance of robust data management for regulatory compliance and digital competitiveness. In these regions, international vendors are collaborating with local IT firms to tailor solutions that address unique market needs, while policy reforms aimed at data privacy and security are beginning to create a more conducive environment for adoption. Despite current hurdles, these markets represent significant untapped potential over the long term.

Report Scope

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

US Electricity Prices and Sales by State, Sector, and Year

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F8734253%2Fdba0dac3571f37e79f2891a6ffd80d5c%2Fus%20electric%20flag.png?generation=1712518711362350&alt=media" alt=""> This comprehensive dataset offers a detailed look at the United States electricity market, providing valuable insights into prices, sales, and revenue across various states, sectors, and years. With data spanning from 2001 onwards to 2024, this dataset is a powerful tool for analyzing the complex dynamics of the US electricity market and understanding how it has evolved over time.

The dataset includes eight key variables: | Column Name | Description | |-------|-------| | year | The year of the observation | | month | The month of the observation | | stateDescription | The name of the state | | sectorName | The sector of the electricity market (residential, commercial, industrial, other, or all sectors) | | customers | The number of customers (missing for some observations) | | price | The average price of electricity per kilowatt-hour (kWh) in cents | | revenue | The total revenue generated from electricity sales in millions of dollars | | sales | The total electricity sales in millions of kilowatt-hours (kWh) |

By providing such granular data, this dataset enables users to conduct in-depth analyses of electricity market trends, comparing prices and consumption patterns across different states and sectors, and examining the impact of seasonality on demand and prices.

One of the primary applications of this dataset is in forecasting future electricity prices and sales based on historical trends. By leveraging the extensive time series data available, researchers and analysts can develop sophisticated models to predict how prices and demand may change in the coming years, taking into account factors such as economic growth, population shifts, and policy changes. This predictive power is invaluable for policymakers, energy companies, and investors looking to make informed decisions in the rapidly evolving electricity market.

Another key use case for this dataset is in investigating the complex relationships between electricity prices, sales volumes, and revenue. By combining the price, sales, and revenue data, users can explore how changes in prices impact consumer behavior and utility company bottom lines. This analysis can shed light on important questions such as the price elasticity of electricity demand, the effectiveness of energy efficiency programs, and the potential impact of new technologies like renewable energy and energy storage on the market.

Beyond its immediate applications in the energy sector, this dataset also has broader implications for understanding the US economy and society as a whole. Electricity is a critical input for businesses and households across the country, and changes in electricity prices and consumption can have far-reaching effects on economic growth, competitiveness, and quality of life. By providing such a rich and detailed portrait of the US electricity market, this dataset opens up new avenues for research and insights that can inform public policy, business strategy, and academic inquiry.

I hope you all enjoy using this dataset and find it useful! 🤗

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Approximately 90% of pine rockland habitat in South Florida and the Florida Keys, USA, has been lost, fragmented, and degraded due to urbanization and other anthropogenic disturbances. Low-lying islands and coastal areas are also becoming increasingly vulnerable to sea-level rise and high tide flooding, which is rapidly increasing in frequency, depth, and extent, putting these areas and the pine rockland habitat they contain at particular risk to these threats. We evaluated changes in habitat under future sea level rise conditions and human development for two species of snakes that are endemic to the pine rocklands, Rim Rock Crowned snake (Tantilla oolitica) and Key Ringneck snake (Diadophis punctatus acrinus), both of which are state-listed endangered species and are under consideration for federal listing. We used recent and historical species records to determine the current extent of available habitat in South Florida. We then predicted: 1) how much (area and percentage) of habitat currently available to these species will be lost due to sea level rise/development, and (2) how does the quality of remaining rockland habitat change in future due to SLR and habitat degradation? We also asked whether threats differ between species and regions. Our results predict that salt water intrusion will negatively affect upland habitat by 2050 with 80% of the existing pine rockland habitat degraded with 42 cm of sea level rise. Moreover, short-term stochastic events, such as storm surge and king tides, will increasingly inundate the root zone of pine and other terrestrial vegetation before complete inundation. Our results further predict that most of the terrestrial habitat used by these species will be underwater by 2080, indicating that sea level rise will likely change current pine rockland habitat into more halophytic habitat (mangrove or salt marsh wetland) in about 50 to 60 years.

The Gross Domestic Product (GDP) in the United States was worth 29184.89 billion US dollars in 2024, according to official data from the World Bank. The GDP value of the United States represents 27.49 percent of the world economy. This dataset provides - United States GDP - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This dataset contains counts of live births for California counties based on information entered on birth certificates. Final counts are derived from static data and include out of state births to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all births that occurred during the time period.

The final data tables include both births that occurred in California regardless of the place of residence (by occurrence) and births to California residents (by residence), whereas the provisional data table only includes births that occurred in California regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by parent giving birth's age, parent giving birth's race-ethnicity, and birth place type. See temporal coverage for more information on which strata are available for which years.

Dataset Lineage Tracking Market Outlook

According to our latest research, the Global Dataset Lineage Tracking market size was valued at $1.2 billion in 2024 and is projected to reach $6.8 billion by 2033, expanding at a CAGR of 21.2% during 2024–2033. The primary growth driver for this market is the accelerating adoption of advanced data governance frameworks across highly regulated sectors such as BFSI, healthcare, and government. As organizations increasingly rely on complex data ecosystems to inform decision-making and ensure regulatory compliance, the need for robust dataset lineage tracking solutions has become paramount. These solutions enable enterprises to visualize, audit, and manage the flow of data throughout its lifecycle, thereby mitigating risks associated with data breaches, compliance failures, and operational inefficiencies. The market is further propelled by the proliferation of big data analytics, cloud computing, and automation technologies, which are amplifying the complexity and volume of enterprise data, necessitating sophisticated lineage tracking capabilities.

Regional Outlook

North America currently commands the largest share of the global dataset lineage tracking market, accounting for more than 38% of the total market value in 2024. This dominance is attributed to the region’s mature IT infrastructure, early adoption of advanced analytics tools, and stringent regulatory mandates around data privacy and governance, such as the CCPA and HIPAA. The presence of leading technology vendors and a highly skilled workforce further cements North America’s leadership position. Enterprises in the United States and Canada are increasingly investing in data lineage solutions to streamline compliance, improve operational transparency, and support digital transformation initiatives. The robust venture capital environment and frequent strategic partnerships among industry leaders are also fostering innovation and driving market growth within the region.

The Asia Pacific region is emerging as the fastest-growing market, projected to expand at a remarkable CAGR of 25.5% during the forecast period. Rapid digitalization, a surge in cloud adoption, and the proliferation of e-commerce and fintech startups are key drivers fueling demand for dataset lineage tracking solutions. Countries such as China, India, and Singapore are witnessing a significant uptick in investments aimed at modernizing data infrastructure and enhancing regulatory compliance. Government-led digital initiatives, coupled with the rising awareness of data quality and risk management, are encouraging enterprises across sectors to adopt advanced lineage tracking tools. The influx of foreign direct investment and the establishment of regional data centers by global tech giants are further accelerating market growth in Asia Pacific.

In emerging economies across Latin America, the Middle East, and Africa, the adoption of dataset lineage tracking solutions remains in its nascent stages but is gaining momentum. These regions face unique challenges, including limited IT budgets, a shortage of skilled data professionals, and evolving regulatory landscapes. However, as governments introduce data protection laws and local enterprises recognize the business value of robust data governance, the demand for lineage tracking is expected to rise. The increasing penetration of cloud services and the gradual shift towards digital transformation are creating new opportunities for vendors to expand their footprint. Localization of solutions and strategic collaborations with regional IT service providers are proving essential for overcoming adoption barriers and fostering sustainable growth in these markets.

Report Scope

Time series data for the statistic Manufacturing, value added (current US$) and country United States. Indicator Definition:Manufacturing refers to industries belonging to ISIC divisions 15-37. Value added is the net output of a sector after adding up all outputs and subtracting intermediate inputs. It is calculated without making deductions for depreciation of fabricated assets or depletion and degradation of natural resources. The origin of value added is determined by the International Standard Industrial Classification (ISIC), revision 3. Data are in current U.S. dollars.The indicator "Manufacturing, value added (current US$)" stands at 2.91 Trillion usd as of 12/31/2024, the highest value at least since 12/31/1998, the period currently displayed. Regarding the One-Year-Change of the series, the current value constitutes an increase of 2.56 percent compared to the value the year prior.The 1 year change in percent is 2.56.The 3 year change in percent is 20.94.The 5 year change in percent is 28.40.The 10 year change in percent is 44.95.The Serie's long term average value is 1.93 Trillion usd. It's latest available value, on 12/31/2024, is 50.91 percent higher, compared to it's long term average value.The Serie's change in percent from it's minimum value, on 12/31/1997, to it's latest available value, on 12/31/2024, is +110.65%.The Serie's change in percent from it's maximum value, on 12/31/2024, to it's latest available value, on 12/31/2024, is 0.0%.

Vision Dataset Versioning Platform Market Outlook

According to our latest research, the Global Vision Dataset Versioning Platform market size was valued at $514 million in 2024 and is projected to reach $2.13 billion by 2033, expanding at a robust CAGR of 16.7% during the forecast period of 2025–2033. The primary growth driver for this market is the surging adoption of artificial intelligence and machine learning technologies across industries that heavily rely on computer vision, such as autonomous vehicles, healthcare, and retail. As organizations increasingly deploy AI-driven solutions, the need for efficient management, tracking, and versioning of large and complex vision datasets has become critical, fueling the demand for advanced vision dataset versioning platforms globally.

Regional Outlook

North America currently commands the largest share of the Vision Dataset Versioning Platform market, accounting for approximately 38% of the global revenue in 2024. This dominance is attributed to the region’s mature technology ecosystem, the presence of leading AI and computer vision solution providers, and a high concentration of research institutions and innovation hubs. The United States, in particular, has witnessed significant investments from both public and private sectors in AI research and development, which has accelerated the adoption of dataset versioning platforms for managing the data lifecycle of vision-based projects. Furthermore, favorable data governance policies and a robust regulatory framework have encouraged enterprises to invest in scalable and secure dataset management solutions, consolidating North America’s leadership in this market.

The Asia Pacific region is poised to be the fastest-growing market for Vision Dataset Versioning Platforms, projected to register a remarkable CAGR of 20.3% from 2025 to 2033. This accelerated growth is driven by the rapid digital transformation initiatives undertaken by emerging economies such as China, India, and South Korea, where government-backed AI programs and increased funding for smart city projects are fueling demand for advanced computer vision applications. The proliferation of cloud computing infrastructure, coupled with a burgeoning startup ecosystem focused on AI and robotics, has created fertile ground for the adoption of vision dataset versioning solutions. Additionally, multinational technology companies are expanding their presence in the region, establishing R&D centers and strategic partnerships, further catalyzing market growth in Asia Pacific.

Emerging economies in Latin America and the Middle East & Africa are gradually embracing Vision Dataset Versioning Platforms, albeit at a slower pace compared to developed regions. These markets face unique challenges, including limited access to advanced AI infrastructure, a shortage of skilled professionals, and regulatory uncertainties surrounding data privacy and cross-border data flows. However, localized demand is rising in sectors such as security and surveillance, agriculture, and healthcare, where vision-based solutions are increasingly being piloted to address region-specific challenges. Policy reforms aimed at digital innovation and international collaborations with technology providers are expected to gradually improve adoption rates, although the overall market share from these regions remains modest relative to global figures.

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