This data asset brings together information collected since 2001 on Public Private Partnerships (PPPs) that have been supported by USAID. For the purposes of this dataset a PPP is defined as a USAID-supported development project or initiative which engages the private sector (including corporations, foundations, and other non-governmental actors) as a core resource partner. Due to changes in Agency data collection systems, standards, and internal organization, the data has been collected according to different mechanisms, definitions, and timeframes from 2001-2015, and therefore cannot be considered to be comprehensive of all PPPs during this period.

This spreadhseet contains historical information regarding public-private partnerships from FY 2001 to FY 2014. It is part of the data asset that brings together information collected since 2001 on Public Private Partnerships (PPPs) that have been supported by USAID. For the purposes of this dataset a PPP is defined as a USAID-supported development project or initiative which engages the private sector (including corporations, foundations, and other non-governmental actors) as a core resource partner. Due to changes in Agency data collection systems, standards, and internal organization, the data has been collected according to different mechanisms, definitions, and timeframes from 2001-2016, and therefore cannot be considered to be comprehensive of all PPPs during this period.

These data were collected to evaluate the Partnership for Long-Term Care (PLTC), a project in which the Robert Wood Johnson Foundation awarded grants to four states -- California, Connecticut, Indiana, and New York -- to work with private insurers to create long-term care insurance policies that were more affordable and provided better protection against impoverishment than those generally available. PLTC policies combine private long-term care insurance with special Medicaid eligibility standards that protect assets of the insured once private insurance benefits are exhausted. This collection was extracted from a database compiled from data submitted by three of the PLTC states: California, Connecticut, and Indiana (New York refused participation). It comprises seven parts, which can be linked together using common identifying variables. Part 1, Insured, describes the characteristics of each issued policy and includes variables covering the effective policy date, policy type, elimination periods, maximum benefits, inflation protection mode, and annualized premium, as well as the year of birth, sex, marital status, and state of residence of the insured. Each insured person is represented by one or more records: one record for the initial PLTC policy, plus a separate record for each change to the policy, if any. Part 2, Changes, consists of policy change records used to update the policies in Part 1. Assessments for benefits are recorded in Part 3. This file includes variables on the assessment date, whether the insured met policy criteria at the time of the assessment, disability date, deficiencies in activities of daily living, and MSQ and Folstein test scores. Parts 4-6 describe service payments and utilization: reporting period (quarter), type of service received by the insured, service amount billed, days of service rendered, and amount of remaining benefits (dollars and days). Part 7 contains information on persons denied application to PLTC policies, including date of denial, type and amount of coverage sought, reason for denial, and the sex, year of birth, and marital status of the applicant.

KNOMAD, the Global Knowledge Partnership on Migration and Development, is a brain trust for the global migration community. It is an open, inclusive, multidisciplinary knowledge partnership that draws on experts to create and synthesize knowledge for use by policy makers in sending, receiving and transit countries. KNOMAD aims to generate a menu of policy choices, based on analytical evidence, evaluation of policies, data collection, and quality control through peer-review. It provides technical assistance and undertakes pilot projects. KNOMAD works in close coordination with the Global Forum on Migration and Development (GFMD) and the U.N. agencies working on migration.

For further details, please refer to https://www.knomad.org/data/faqs

This collection includes only a subset of indicators from the source dataset.

Data for the Collaborative Governance Case Database case. Contains qualitative and quantitative information on the conditions, processes, and outcomes of a specific instance of collaborative governance involving public, private, and/or community actors. This is a case entry for the Collaborative Governance Case Database. This database provides a collective repository for collaborative governance case studies from around the world.

Summary of authors, disciplines, journals, and research hotspots of PPP articles from 2000 to 2023.

This dataset provides the indicators to measure OECD countries' efforts for Sustainable Development Goal 17 - Strengthen the means of implementation and revitalize the global partnership for sustainable development.

The OECD dataset on the SDGs has been built using global UNSDG data and complementary OECD data, which allows the analysis to be tailored to the policy challenges faced by OECD countries. The methodology builds on data from the UN Global SDG Database and relevant OECD Databases such as Green Growth Indicators, OECD Environment Statistics, OECD Main Science and Technology Indicators, OECD Science, Technology and R&D Statistics, OECD Compendium of Productivity Indicators, amongst others. The OECD acts as the (co-)custodian of a number of indicators and directly supplies data to the UN Global Database in areas including ODA and other international flows, gender-based legal discrimination, access to civil justice and others.

Details on the construction of the database can be found in the methodology note.

The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change.

The Harmonized World Soil Database (HWSD) was established in 2008 by the International Institute for Applied Systems Analysis (IIASA) and FAO, and in partnership with International Soil Reference and Information Centre (ISRIC), the European Soil Bureau Network (ESBN) and the Institute for Soil Sciences Chinese Academy of Sciences (CAS). The data entry and harmonization within a Geographic Information System (GIS) was carried out at IIASA, with verification of the database undertaken by all partners. HWSD was then updated in 2013 (HWSD v1.2) and in 2023 (HWSD v2.0).

This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database.

The GIS raster image file is linked to the soil attribute database. The HWSD v2.0 soil attribute database provides information on the soil unit composition for each of the near 30 000 soil association mapping units. The HWSD v2.0 Viewer, provided with the database, creates this link automatically and provides direct access to the soil attribute data and the soil association information.

Note: A tutorial for accessing HWSD ver. 2.0 using R (prepared by David Rossiter, June 2023) has been added as an 'associated resource' (NOTE: Needs the SQLite version of HWSD v2 as provided below).

The global GPU Database market size was valued at USD 552.1 million in 2022 and is projected to reach USD 5,152.6 million by 2029, exhibiting a CAGR of 21.7% during the forecast period. The growth of the market is attributed to the rising demand for real-time data analytics, the increasing adoption of artificial intelligence and machine learning, and the growing need for high-performance computing solutions. North America is expected to dominate the GPU Database market throughout the forecast period. The region is home to a large number of technology companies and is an early adopter of new technologies. The Asia Pacific region is expected to experience the fastest growth during the forecast period. The region is witnessing a rapid increase in the adoption of artificial intelligence and machine learning technologies. Major players in the market include NVIDIA Corporation, AMD, Intel, and IBM. These companies are investing heavily in research and development to develop new and innovative GPU Database solutions. Recent developments include: In March 2024, NVIDIA partnered with Google Cloud to offer GPU-accelerated databases and analytics on the Google Cloud Platform. By combining NVIDIA's GPU technology with Google Cloud's infrastructure, the partnership aims to enhance data processing and analysis capabilities for developers and enterprises. This partnership is expected to accelerate AI development, drive innovation, and position both companies as leaders in the market. , In March 2023, NVIDIA partnered with Microsoft Azure to bring GPU-accelerated databases and analytics to Azure. This partnership harnesses NVIDIA's GPU technology to enhance Azure's capabilities, enabling efficient handling of complex data workloads and boosting AI development. By integrating NVIDIA's AI Enterprise software with Azure Machine Learning, the partnership provides a robust platform for enterprises to build and manage AI applications. This strategic move solidifies NVIDIA and Microsoft's positions as leaders in the market. .

The global database market, currently valued at $131.67 billion (2025), is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 14.21% from 2025 to 2033. This surge is driven by several key factors. The increasing adoption of cloud-based solutions offers scalability and cost-effectiveness, fueling market expansion. Furthermore, the burgeoning demand for real-time data analytics across diverse sectors, including BFSI (Banking, Financial Services, and Insurance), retail & e-commerce, and healthcare, is significantly boosting database market growth. The rise of big data and the need for robust data management solutions to handle massive datasets are other significant contributors. While on-premises deployments still hold a significant market share, particularly among large enterprises with stringent security requirements, the cloud segment is projected to witness the highest growth rate over the forecast period. The market is segmented by deployment (cloud, on-premises), enterprise size (SMEs, large enterprises), and end-user vertical (BFSI, retail & e-commerce, logistics & transportation, media & entertainment, healthcare, IT & telecom, others). Competition is intense, with established players like MongoDB, MarkLogic, Redis Labs, and Teradata alongside tech giants such as Microsoft, Amazon, and Google vying for market share through innovation and strategic partnerships. The competitive landscape is characterized by both established vendors and new entrants, leading to continuous innovation in database technologies. The market is witnessing a shift towards NoSQL databases, driven by the need to handle unstructured data and the increasing popularity of cloud-native applications. However, challenges such as data security concerns, the complexity of managing distributed database systems, and the need for skilled professionals to manage and maintain these systems pose potential restraints. The market's growth trajectory is largely positive, with continued expansion anticipated across all key segments and regions. North America and Europe are currently the dominant markets, but rapid growth is expected in Asia-Pacific, driven by increased digitalization and technological advancements in developing economies such as India and China. This comprehensive report provides an in-depth analysis of the global database market, encompassing historical data (2019-2024), current estimates (2025), and future forecasts (2025-2033). It examines key market segments, growth drivers, challenges, and emerging trends, offering valuable insights for businesses, investors, and stakeholders seeking to navigate this dynamic landscape. The study period covers the significant evolution of database technologies, from traditional relational databases to the rise of NoSQL and cloud-based solutions. The report utilizes a robust methodology and extensive primary and secondary research to provide accurate and actionable market intelligence. Keywords include: database market size, database market share, cloud database, NoSQL database, relational database, database management system (DBMS), database market trends, database market growth, database technology. Recent developments include: January 2024: Microsoft and Oracle recently announced the general availability of Oracle Database@Azure, allowing Azure customers to procure, deploy, and use Oracle Database@Azure with the Azure portal and APIs.November 2023: VMware, Inc. and Google Cloud announced an expanded partnership to deliver Google Cloud’s AlloyDB Omni database on VMware Cloud Foundation, starting with on-premises private clouds.. Key drivers for this market are: Increasing Penetration Of Trends Like Big Data And IoT, Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Potential restraints include: Increasing Penetration Of Trends Like Big Data And IoT, Increase In The Volume Of Data Generated And Shift Of Enterprise Operations. Notable trends are: Retail and E-commerce to Hold Significant Share.

The Systems Integrator Databases market, valued at $485.41 million in 2025, is projected to experience robust growth, driven by the increasing adoption of cloud-based solutions, the rising demand for data integration across diverse industries, and the expanding need for sophisticated data management capabilities. The market's Compound Annual Growth Rate (CAGR) of 9.92% from 2025 to 2033 indicates significant expansion opportunities. Key growth drivers include the digital transformation initiatives undertaken by businesses across various sectors, leading to a surge in data volume and complexity requiring efficient integration and management. The strong demand for real-time analytics and data-driven decision-making further fuels market expansion. Segment-wise, Infrastructure Integration services are expected to dominate, followed by Software/Application Integration and Consulting services. The Automotive, Aerospace & Defense, and IT & Telecom sectors are anticipated to be the largest end-user industries due to their high reliance on data-intensive operations and the need for seamless data flow across their systems. While growth is projected across all regions, North America and Europe are anticipated to maintain a significant market share, followed by Asia-Pacific, driven by the presence of established players and increasing technological advancements in these regions. Market restraints include the high initial investment costs associated with implementing and maintaining complex systems integration projects, the need for specialized expertise in database management and integration, and security concerns related to data breaches and unauthorized access. However, these challenges are being addressed by the emergence of more affordable cloud-based solutions, the availability of skilled professionals through training programs, and the development of robust cybersecurity measures. The competitive landscape is characterized by the presence of several large multinational technology corporations and specialized systems integrators, leading to intense competition and continuous innovation in solutions and services. The market is expected to witness strategic partnerships, mergers, and acquisitions to expand market share and enhance service offerings. Over the forecast period, the focus will shift towards AI-driven database solutions and enhanced data security protocols, further shaping the market landscape. Recent developments include: March 2024 - SymphonyAI, a prominent provider of enterprise AI SaaS solutions for key industries, has unveiled a strategic partnership with Tata Consultancy Services (TCS). This collaboration aims to bolster and expedite market penetration in various global business sectors. While the primary emphasis lies on enhancing financial crime detection capabilities, the partnership also seeks to introduce the advantages of targeted predictive and generative AI to a broad spectrum of industries., November 2023 - NEC Corporation has announced a system integration (SI) partnership pact with Gurobi Optimization, LLC (Gurobi). This collaboration aims to seamlessly incorporate Gurobi's renowned mathematical optimization solver, the Gurobi Optimizer, into NEC's suite of solutions.. Key drivers for this market are: Advancements and adoption of cloud-technologies, Benefits of increasing productivity while reducing IT Management cost. Potential restraints include: Advancements and adoption of cloud-technologies, Benefits of increasing productivity while reducing IT Management cost. Notable trends are: Software/Application Integration to have a Significant Growth.

Series Name: Amount of United States dollars committed to public-private partnerships for infrastructure million USD realSeries Code: GF_COM_PPPI_KDRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 17.17.1: Amount in United States dollars committed to public-private partnerships for infrastructureTarget 17.17: Encourage and promote effective public, public-private and civil society partnerships, building on the experience and resourcing strategies of partnershipsGoal 17: Strengthen the means of implementation and revitalize the Global Partnership for Sustainable DevelopmentFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

The Integrated Strategic Tracking and Recruiting Database (iSTAR) Data Inventory contains measured and modeled partnership and contact data. It is comprised of basic organization information, their relationship with CPPD, and relative contact information.The statutory authority leading to the collection of this information comes from Title I Section XIV of the Clean Air Act.Sustance classes include CAPs, and data is collected daily.

The United States Geological Survey (USGS) - Science Analytics and Synthesis (SAS) - Gap Analysis Project (GAP) manages the Protected Areas Database of the United States (PAD-US), an Arc10x geodatabase, that includes a full inventory of areas dedicated to the preservation of biological diversity and to other natural, recreation, historic, and cultural uses, managed for these purposes through legal or other effective means (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/protected-areas). The PAD-US is developed in partnership with many organizations, including coordination groups at the [U.S.] Federal level, lead organizations for each State, and a number of national and other non-governmental organizations whose work is closely related to the PAD-US. Learn more about the USGS PAD-US partners program here: www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards. The United Nations Environmental Program - World Conservation Monitoring Centre (UNEP-WCMC) tracks global progress toward biodiversity protection targets enacted by the Convention on Biological Diversity (CBD) through the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) available at: www.protectedplanet.net. See the Aichi Target 11 dashboard (www.protectedplanet.net/en/thematic-areas/global-partnership-on-aichi-target-11) for official protection statistics recognized globally and developed for the CBD, or here for more information and statistics on the United States of America's protected areas: www.protectedplanet.net/country/USA. It is important to note statistics published by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Center (www.marineprotectedareas.noaa.gov/dataanalysis/mpainventory/) and the USGS-GAP (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-statistics-and-reports) differ from statistics published by the UNEP-WCMC as methods to remove overlapping designations differ slightly and U.S. Territories are reported separately by the UNEP-WCMC (e.g. The largest MPA, "Pacific Remote Islands Marine Monument" is attributed to the United States Minor Outlying Islands statistics). At the time of PAD-US 2.1 publication (USGS-GAP, 2020), NOAA reported 26% of U.S. marine waters (including the Great Lakes) as protected in an MPA that meets the International Union for Conservation of Nature (IUCN) definition of biodiversity protection (www.iucn.org/theme/protected-areas/about). USGS-GAP plans to publish PAD-US 2.1 Statistics and Reports in the spring of 2021. The relationship between the USGS, the NOAA, and the UNEP-WCMC is as follows: - USGS manages and publishes the full inventory of U.S. marine and terrestrial protected areas data in the PAD-US representing many values, developed in collaboration with a partnership network in the U.S. and; - USGS is the primary source of U.S. marine and terrestrial protected areas data for the WDPA, developed from a subset of the PAD-US in collaboration with the NOAA, other agencies and non-governmental organizations in the U.S., and the UNEP-WCMC and; - UNEP-WCMC is the authoritative source of global protected area statistics from the WDPA and WD-OECM and; - NOAA is the authoritative source of MPA data in the PAD-US and MPA statistics in the U.S. and; - USGS is the authoritative source of PAD-US statistics (including areas primarily managed for biodiversity, multiple uses including natural resource extraction, and public access). The PAD-US 2.1 Combined Marine, Fee, Designation, Easement feature class (GAP Status Code 1 and 2 only) is the source of protected areas data in this WDPA update. Tribal areas and military lands represented in the PAD-US Proclamation feature class as GAP Status Code 4 (no known mandate for biodiversity protection) are not included as spatial data to represent internal protected areas are not available at this time. The USGS submitted more than 42,900 protected areas from PAD-US 2.1, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the May 2021 WDPA, available at www.protectedplanet.net. The NOAA is the sole source of MPAs in PAD-US and the National Conservation Easement Database (NCED, www.conservationeasement.us/) is the source of conservation easements. The USGS aggregates authoritative federal lands data directly from managing agencies for PAD-US (www.communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/), while a network of State data-stewards provide state, local government lands, and some land trust preserves. National nongovernmental organizations contribute spatial data directly (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards). The USGS translates the biodiversity focused subset of PAD-US into the WDPA schema (UNEP-WCMC, 2019) for efficient aggregation by the UNEP-WCMC. The USGS maintains WDPA Site Identifiers (WDPAID, WDPA_PID), a persistent identifier for each protected area, provided by UNEP-WCMC. Agency partners are encouraged to track WDPA Site Identifier values in source datasets to improve the efficiency and accuracy of PAD-US and WDPA updates. The IUCN protected areas in the U.S. are managed by thousands of agencies and organizations across the country and include over 42,900 designated sites such as National Parks, National Wildlife Refuges, National Monuments, Wilderness Areas, some State Parks, State Wildlife Management Areas, Local Nature Preserves, City Natural Areas, The Nature Conservancy and other Land Trust Preserves, and Conservation Easements. The boundaries of these protected places (some overlap) are represented as polygons in the PAD-US, along with informative descriptions such as Unit Name, Manager Name, and Designation Type. As the WDPA is a global dataset, their data standards (UNEP-WCMC 2019) require simplification to reduce the number of records included, focusing on the protected area site name and management authority as described in the Supplemental Information section in this metadata record. Given the numerous organizations involved, sites may be added or removed from the WDPA between PAD-US updates. These differences may reflect actual change in protected area status; however, they also reflect the dynamic nature of spatial data or Geographic Information Systems (GIS). Many agencies and non-governmental organizations are working to improve the accuracy of protected area boundaries, the consistency of attributes, and inventory completeness between PAD-US updates. In addition, USGS continually seeks partners to review and refine the assignment of conservation measures in the PAD-US.

This dataset contains coded and themed data from the round 1 survey of the TBPI JLA PSP. The survey was launched and invited respondents to submit their unanswered research questions about the assessment, diagnosis, treatment and long term follow up of brachial plexus injuries. Data was inputted into a online Google form between January 2023 and March 2023. Data was coded and summary questions developed ready for evidence checking and entering into an interim survey (launched in Autumn 2023)

ArtScan, a project of the Arts Education Partnership, is a searchable clearinghouse of the latest state policies supporting education in and through the arts from all 50 states and the District of Columbia. Since 1999, the Arts Education Partnership has tracked state policies for arts education in the 50 states and the District of Columbia. In 2013, AEP, with the cooperation of Education Commission of the States, merged its State Policy Database with the Education Commission of the States' database, ArtScan. To update the information for the 2014 edition of ArtScan, AEP staff conducted a comprehensive search of state education statutes and codes on each state's relevant websites. The new structure for the 2014 ArtScan allows users to explore the data in multiple ways, including a state-level profile for all policy areas, a comparison of selected states and policy areas, and several types of 50-state reports. There are at least five ways to engage with the data housed in ArtScan. Capture a snapshot of all the data ArtScan has to offer about your state including state policies in 14 policy areas. Create custom side-by-side comparison reports using a search engine that allows you to choose individual states and policy areas/data points of interest to you. Compare the policies of all 50 states and the District of Columbia within specific policy areas (e.g. requirements for high school art education). Explore a summary of state policies for arts education identified in statute or code for all 50 states and the District of Columbia. Learn more about ArtScan and explore an analysis of the findings in A Snapshot of State Policies for Arts Education (March 2014).

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 FB: AS: IBF Only: DB: Individuals, Partnerships, & Corps (IP) data was reported at 2.919 USD bn in Dec 2019. This records an increase from the previous number of 2.463 USD bn for Sep 2019. United States FB: AS: IBF Only: DB: Individuals, Partnerships, & Corps (IP) data is updated quarterly, averaging 5.128 USD bn from Sep 2009 (Median) to Dec 2019, with 42 observations. The data reached an all-time high of 10.906 USD bn in Sep 2009 and a record low of 2.463 USD bn in Sep 2019. United States FB: AS: IBF Only: DB: Individuals, Partnerships, & Corps (IP) data remains active status in CEIC and is reported by Federal Reserve Board. The data is categorized under Global Database’s United States – Table US.KB043: Balance Sheet: Foreign Banks: All States.

The Healthcare Cost and Utilization Project (HCUP) Nationwide Emergency Department Sample (NEDS) is the largest all-payer emergency department (ED) database in the United States. yielding national estimates of hospital-owned ED visits. Unweighted, it contains data from over 30 million ED visits each year. Weighted, it estimates roughly 145 million ED visits nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. Sampled from the HCUP State Inpatient Databases (SID) and State Emergency Department Databases (SEDD), the HCUP NEDS can be used to create national and regional estimates of ED care. The SID contain information on patients initially seen in the ED and subsequently admitted to the same hospital. The SEDD capture information on ED visits that do not result in an admission (i.e., treat-and-release visits and transfers to another hospital). Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality, HCUP data inform decision making at the national, State, and community levels. The NEDS contain information about geographic characteristics, hospital characteristics, patient characteristics, and the nature of visits (e.g., common reasons for ED visits, including injuries). The NEDS contains clinical and resource use information included in a typical discharge abstract, with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). It includes ED charge information for over 85% of patients, regardless of expected payer, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NEDS excludes data elements that could directly or indirectly identify individuals, hospitals, or states.Restricted access data files are available with a data use agreement and brief online security training.

This dataset is a raster format GeoTIFF representing the percentage of density in each pixel of the mangroves coverage. It is part of the Global Land Cover-SHARE (GLC-SHARE) database at the global level created by FAO, Land and Water Division in partnership and with contribution from various partners and institutions.