The World Development Indicators is a compilation of relevant, high-quality, and internationally-comparable statistics about global development and the fight against poverty. The database contains 1,600 time-series indicators for 217 economies and more than 40 country groups, with data for many indicators going back more than 50 years.

This dataset contains Development Indicators for the period 1960-2020. Data from world bank group.

Follow datasource.kapsarc.org for timely data to advance energy economics research.

This dataset contains data from the World Development Indicators on World View with data on global trends with indicators on population, population density, urbanization, Gross National Income (GNI), and Gross_Domestic_Product (GDP).

The global chemical & biological indicators market will be about USD 549.8 million by 2025 and will reach USD 904.2 million by 2035, at a Compound Annual Growth Rate (CAGR) of 5.1% during 2023-2035.

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New Zealand key economic data for months August 2001 to date.

The BILBI indicators portal is an interface where users can explore CSIRO's biodiversity indicators for any signatory countries of the United Nations Convention on Biological Diversity (CBD), plus USA. CSIRO’s global biodiversity indicators (BHI, BERI and PARC) enable governments and organisations to plan and track progress towards biodiversity goals. These indicators are all recognised as component indicators in the CBD Kunming-Montreal Global Biodiversity Framework and provide high-resolution coverage of a large proportion of Earth’s biodiversity across the entire land surface of the planet.

The Biodiversity Habitat Index (BHI) represents the proportion of biodiversity retained within a given area (such as a country or an ecoregion) in relation to the degree of habitat loss, degradation and fragmentation experienced.

The Bioclimatic Ecosystem Resilience Index (BERI) measures the capacity of landscapes to retain species diversity in the face of climate change, as a function of the area, connectivity and integrity of natural ecosystems across those landscapes.

The Protected Area Representativeness and Connectedness Indices (PARC) represent the diversity of biological communities within a protected area system, as well as how connected protected areas are within the broader landscape.

This portal provides a country-level overview of CSIRO's biodiversity indices with a link to download indicator values for countries as well as biomes within countries. More information can be found at https://research.csiro.au/macroecologicalmodelling/bilbi . Lineage: There are 3 indicators available via this interface: BHI, BERI, and PARC. All of these indicators are derived using CSIRO's BILBI biodiversity modeling infrastructure (Biogeographic modelling Infrastructure for Large-scale Biodiversity Indicators). BILBI operates on a global 30-second grid and integrates terrain-adjusted climate data from WorldClim (www.WorldClim.org, v1) and soil data from SoilGrids (www.SoilGrids.org, v1), alongside biodiversity records for more than 400,000 plant, vertebrate and invertebrate species from the Global Biodiversity Information Facility (GBIF). Spatial biodiversity models are generated using generalized dissimilarity modelling for each WWF biome/realm combination.

For BHI and BERI, habitat condition is assessed using downscaled Land Use Harmonisation surfaces, which combine land cover data from the ESA Land Cover Climate Change Initiative (www.esa-landcover-cci.org/) and coefficients from the PREDICTS database (www.nhm.ac.uk/our-science/our-work/biodiversity/predicts.html).

The PARC-representativeness Indicator additionally incorporates protected area data from the World Database on Protected Areas (www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas).

Graph and download economic data for Composite Leading Indicators: Composite Leading Indicator (CLI) Normalized for Germany (DEULOLITONOSTSAM) from Jan 1961 to Jan 2024 about leading indicator and Germany.

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.

The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.

For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

Contains data from the World Bank's data portal covering the following topics which also exist as individual datasets on HDX: Agriculture and Rural Development, Aid Effectiveness, Economy and Growth, Education, Energy and Mining, Environment, Financial Sector, Health, Infrastructure, Social Protection and Labor, Poverty, Private Sector, Public Sector, Science and Technology, Social Development, Urban Development, Gender, Millenium development goals, Climate Change, External Debt, Trade.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

An economy's financial markets are critical to its overall development. Banking systems and stock markets enhance growth, the main factor in poverty reduction. Strong financial systems provide reliable and accessible information that lowers transaction costs, which in turn bolsters resource allocation and economic growth. Indicators here include the size and liquidity of stock markets; the accessibility, stability, and efficiency of financial systems; and international migration and workers\ remittances, which affect growth and social welfare in both sending and receiving countries.

In general, persons of 15 years or older took part in this study, the well-being figures only apply to the Dutch population of 18 years or older. Therefore, this table consists of well-being figures of the Dutch population of 18 years or older in terms of happiness, life satisfaction, satisfaction with education opportunities, work, travel time, daily activities, physical health, mental health, weight, the financial situation, the house, the neighborhood, social life and the amount of free time. In addition, concerns about the financial future, feelings of unsafety and trust in others are included. Satisfaction with work and the travel time to and from work are measured only for people who have a paid job for 12 hours per week or more. For people who do not work or work less than 12 hours per week satisfaction with daily activities is reported. These subjects are reported for gender, age, education level and migration background.

Data available from: 2013

Status of the figures: The figures in this table are definite.

Changes as of March 20th, 2025. Not applicable, the table has been updated with data from 2024.

When will new figures be published? New figures on 2025 will be published around mid-2026.

Graph and download economic data for Composite Leading Indicators: Composite Consumer Confidence Amplitude Adjusted for Brazil (CSCICP03BRM665S) from Jun 1994 to Jan 2024 about consumer sentiment, composite, Brazil, and consumer.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic or targeted sampling design, structured implementation, standard core methods and indicators, electronic data capture and management, and integration with remote sensing. Each record represents a sample visit during which a suite of the BLM Riparian and Wetland AIM methods were applied, with the geometry marking the center of the plot as taken in the Plot Characterization form. Attributes are the BLM Riparian and Wetland AIM core indicators, which include plot-level measures of vegetation and soil condition such as plant species cover and composition, plant height, and woody structure. In addition, some plots may have some contingent and annual use indicators, including measures of hummock cover and characteristics, water quality, stubble height, soil alteration, and riparian woody use. Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (BLM AIM Wetland Database) at the BLM National Operations Center. Annual Use data (i.e., annual use indicators) are omitted from the public version of these data but can be made available upon request. General Definitions The species list used for data collection was originally developed from a full download of all species in USDA PLANTS shown as occurring in BLM-administered states. The state-level occurrence of species in this list have been adjusted over time as individual species were found to be missing from individual state lists. Traits used in indicator calculations for all species observed at a given monitoring plot can be found in the I_SpeciesIndicator feature service, where the traits are listed by plant. A full species list can also be provided by request by the National Riparian and Wetland AIM Team. Once finalized, it will be added to the WetlandAIM database, likely in spring of 2024. In general, traits are assigned at the species-level. Genera and family-level records were only given trait values if all species within that taxonomic group were considered to have one trait (e.g., all species of Tamarix are nonnative, so the genus level code is also considered nonnative). To assign Growth Habit and Duration to unknown plants, information recorded in the Unknown Plants form was used to fill in traits. For example, if a plant was identified as a Carex species (unknown code CAREX_01), the growth habit (graminoid) would be taken from the full species list since all Carex species are graminoids, and the duration would be taken from the plot-specific matching entry in Unknown Plants. Nativity Status: The nativity status of all species was taken from the USDA Plants Database and was ranked at a national scale. All plants identified to species are ‘Native’, ‘Nonnative’, or ‘Cryptogenic’. The term cryptogenic refers to species with both native and nonnative genotypes. Genera and family-level plants were only given a nativity status if all species within that taxonomic group were considered either native or nonnative (e.g. all species of Tamarix are nonnative, so the genus level code is also considered nonnative). Noxious: Noxious status are designated for each political state (i.e. StateCode) developed using the most recent state noxious list available online. Wetland Indicator Status: Wetland Indicator Status was taken from the U.S. Army Corps of Engineers’ National Wetland Plant List (NWPL 2020, version 3.5; https://wetland-plants.usace.army.mil/). Plants are ranked by ecoregion into one of the following rating categories based on an estimated frequency with which it is thought to occur in wetlands: obligate (OBL), facultative wetland (FACW), facultative (FAC), facultative upland (FACU), or upland (UPL), The five rating categories were first developed through an exhaustive review of the botanical literature and best professional judgement of national and regional experts, and has since undergone multiple rounds of revision by a national panel. C-Values: Coefficients of Conservatism (C-values) are assigned to species by a panel of experts, typically at a state level. C-values range from 0 to 10 and represent an estimated probability that a plant is likely to occur in a landscape relatively unaltered from pre-European settlement conditions (see table of C-Value Interpretation below). The Mean C-value is calculated at a plot level by averaging the C-values of all species in a given plot. Mean C-value is a stand-alone indicator of floristic quality, one of several indicators under the Floristic Quality Assessment (FQA) approach to assesses the degree of "naturalness" of an area. C-Value Interpretation 0 = Non-native species. Very prevalent in new ground or non-natural areas 1-3 = Commonly found in non-natural areas 4-6 = Equally found in natural and non-natural areas 7-9 = Obligate to natural areas but can sustain some habitat degradation 10 = Obligate to high-quality natural areas (relatively unaltered from pre-European settlement) C-values were compiled from several sources, listed below. CO = Smith, P., G. Doyle, and J. Lemly. 2020. Revision of Colorado’s Floristic Quality Assessment Indices. Colorado Natural Heritage Program, Colorado State University, Fort Collins, Colorado. MT = Pipp, Andrea. 2017. Coefficient of Conservatism Rankings for the Flora of Montana: Part III. Report to the Montana Department of Environmental Quality, Helena, Montana. Prepared by the Montana Natural Heritage Program, Helena, Montana. 107 pp. WA = Rocchio, F.J, and R. Crawford. 2013. Floristic Quality Assessment for Washington Vegetation. Washington Natural Heritage Program, Washington Department of Natural Resources, Olympia, WA. (Values for Eastern Washington used). WY = Washkoviak L, B. Heidel, and G. Jones. 2017. Floristic Quality Assessment for Wyoming Flora: Developing Coefficients of Conservatism. Prepared for the U.S. Army Corps of Engineers. The Wyoming Natural Diversity Database, Laramie, Wyoming. 13 pp. plus appendices. AZ, CA, ID, NM, NV, OR, UT = Great Lakes Environmental Center (GLEC), Inc. and M.S. Fennessy. 2019. Project to Assign C-Values to Western State for use in the USEPA National Wetland Condition Assessment. Great Lakes Environmental Center, Traverse City, MI. Live: The Core Methods measure Live vs. Standing Dead plant cover, i.e., if a pin drop hits a dead plant part (even if it’s on a living plant), that hit is considered a dead hit. If a pin hits both a live and a dead plant part in the same pin drop, that hit is considered live. Growth Habit: The form of a plant. In this dataset, plants are either Forb, Graminoid, Shrub, Tree, and, in Alaska only, Liverwort, Moss, Hornwort, and Lichen. Growth habitat was derived from USDA PLANTS. If more than one growth habit was designated in USDA PLANTS, the most common growth habit was determined by consulting the USDA plants database and other literature and was applied across all states where it occurs. Graminoids include all grasses, rushes, sedges, arrow grasses, and quillworts (Poaceae, Cyperaceae, Juncaceae, Juncaginaceae, and Isoetes). Forbs include vascular, non-woody plants, but exclude graminoids. Shrubs are defined as perennial multi-stemmed woody plants usually less than 4-5 m in height. Trees are generally perennial woody plants with a single stem, normally greater than 4 to 5 m in height. Duration: The life length of a plant. In this dataset, plants are either Perennial or Annual. Biennial plants are classified as Annuals. Duration was derived from USDA PLANTS. If more than one duration was designated in USDA PLANTS, the most common duration for each state was determined by consulting the USDA plants database and applied across all administrative states where it occurs. Nonvasculars: Nonvascular species were not included in LPI data collection in the lower-48 except as generic “non-plant” codes. In Alaska, a full list of nonvascular species from the Alaska Vegetation Plots Database (https://akveg.uaa.alaska.edu/) including mosses, hornworts, liverworts, and lichens was used during data collection. In terms of indicator calculations, nonvasculars were not included in plot-level plant counts and cover (i.e. cover of various plant trait categories like nativity, duration, or growth habit), but were instead transferred into the simplified non-plant codes to be calculated into moss and lichen cover indicators. Cover by species of these nonvasculars can be found in the SpeciesIndicators table. Preferred Forbs: A set of specific forb species that are preferred by Sage Grouse birds. State preferred forb lists were developed by state botanists in collaboration with wildlife and sage-grouse experts and were based on a combination of peer reviewed literature and local knowledge. These lists were then combined to create one national list.

This dataset provides a series of climate indices derived from reanalysis and model simulations data hosted on the Copernicus Climate Data Store (CDS). These indicators describe how climate variability and change of essential climate variables can impact sectors such as health, agriculture, forestry, energy, tourism, or water and coastal management. Those indices are relevant for adaptation planning at the European and national level and their development was driven by the European Environment Agency (EEA) to address informational needs of climate change adaptation national initiatives across the EU and partner countries as expressed by user requirements and stakeholder consultation. The indices cover the hazard categories introduced by the IPCC and the European Topic Centre on Climate Change Impacts, Vulnerability and Adaptation (ETC-CCA). They are also made available interactively through CDS Toolbox public visualisation apps on the European Climate Data Explorer hosted on EEA’s Climate-adapt site. The indices are either downloaded from the CDS where available, or calculated through a specific CDS Toolbox workflow. In this way both the calculations and the resulting data are fully traceable. As they come from different datasets the underlying climate data differ in their technical specification (type and number of climate and impact models involved, bias-corrected or not, periods covered etc.). An effort was made in the dataset selection to limit the heterogeneity of the underlying dataset as ideally the indices should come from the same dataset with identical specifications. The indices related to temperature, precipitation and wind (20 out of 30) were calculated from atmospheric variables in the same datasets: 'Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections', and 'ERA5 hourly data on single levels from 1940 to present'. The other indices are directly available from CDS datasets generated by specific theme projects. More information about this dataset can be found in the documentation. The underlying datasets hosted on the CDS are:

ERA5 hourly data on single levels from 1940 to present - used to calculate most of the temperature, precipitation and wind speed indicators as it provides the historical and observation based baseline used to monitor the indicators. Climate and energy indicators for Europe from 2005 to 2100 derived from climate projections - used to calculate most of the temperature, precipitation and wind speed indicators as it provides bias-corrected sub-daily data. It is used for all the indicators except those specified in the following datasets below. Fire danger indicators for Europe from 1970 to 2098 derived from climate projections - provides the high fire danger days and fire weather indicators. Hydrology-related climate impact indicators from 1970 to 2100 derived from bias adjusted European climate projections - provides the river flood, river discharge, aridity actual, and mean soil moisture indicators. Mountain tourism meteorological and snow indicators for Europe from 1950 to 2100 derived from reanalysis and climate projections - provides the snowfall amount index. Water level change indicators for the European coast from 1977 to 2100 derived from climate projections - provides the relative sea level rise and extreme sea level indicators.

This dataset was produced on behalf of the Copernicus Climate Change Service.

description: Health reporting area (HRA) and zip code-level indicators for monitoring the impact of the Affordable Care Act in King County, WA. Topic areas range from access to care to population health. Imported to Socrata to allow data to be pulled as JSON from SODA to feed into Leaflet.js-based maps on an external site.; abstract: Health reporting area (HRA) and zip code-level indicators for monitoring the impact of the Affordable Care Act in King County, WA. Topic areas range from access to care to population health. Imported to Socrata to allow data to be pulled as JSON from SODA to feed into Leaflet.js-based maps on an external site.

Selected macro-economic data. Historical data available in excel format. This data is avalable in the tables coded M1 to M14.

This dataset contains World Population Indicators. Data from The World Bank . Follow datasource.kapsarc.org for timely data to advance energy economics research.

This table gives an overview of expenditure on regular education within the Netherlands.

The government finances schools, colleges and universities. It pays for research which is done by universities on its behalf. Furthermore it provides student grants and loans, allowances for school costs, provisions for students with a disability and child care allowances as well as subsidies to companies and non-profit organisations. The government reclaims unjustified payments for student grants and loans and allowances for school costs. It also receives interest and repayments on student loans as well as EU subsidies for education.

Parents and/or students have to pay tuition fees for schools, colleges and universities, parent contributions and contributions for school activities. They also have to purchase books and materials, pay for transport from home to school and back for students who are not eligible for subsidised transport, pay for private tutoring, pay interest and repayments on student loans, and repay wrongfully received student grants, loans and allowances for school costs. Parents and/or students receive child care allowances, provisions for students with a disability and an allowance for school costs as well as student grants and loans and scholarships of companies.

Companies and non-profit organisations incur costs for supervising trainees and apprentices who combine learning with work experience. They also contribute to the cost of work related education of their employees and spend money on research that is outsourced to colleges for higher professional education and universities. Furthermore they contribute to the childcare allowances given to households and provide scholarships to students. Companies receive subsidies and tax benefits for the creation of apprenticeship places and trainee placements and for providing transport for pupils.

Organisations abroad contract universities in the Netherlands to undertake research for them. The European Union provides funds and subsidies for education to schools, colleges and universities as well as to the Dutch government. Foreign governments contribute to international schools in the Netherlands that operate under their nationality.

The table also contains various indicators used nationally and internationally to compare expenditure on education and place it in a broader context. The indicators are compounded on the basis of definitions of Statistics Netherlands and/or the OECD (Organisation for Economic Cooperation and Development). All figures presented have been calculated according to the standardised definitions of the OECD.

In this table tertiary education includes research and development, except for the indicator Expenditure on education institutions per student, excluding R&D.

The statistic on Education spending is compiled on a cash basis. This means that the education expenditure and revenues are allocated to the year in which they are paid out or received. However, the activity or transaction associated with the payment or receipt can take place in a different year.

Statistics Netherlands published the revised National Accounts in June 2024. Among other things, GDP and total government expenditures have been adjusted upwards as a result of the revision.

Data available from: 1995

Status of the figures: The figures from 1995 to 2022 are final. The 2023 figures are provisional.

Changes as of 31 December 2024: The final figures of 2021 and 2022 and the provisional figures of 2023 have been added. As a result of the revision of the National Accounts, among other things, GDP and total government expenditures have been adjusted upwards. The indicators in this table that are expressed as a percentage of GDP and total government expenditure have been updated for the entire time series from 1995 on the basis of the revised figures.

When will new figures be published? The final figures for 2023 and the provisional figures for 2024 will be published in December 2025. More information on the revision policy of National Accounts can be found under 'relevant articles' under paragraph 3.

This feature class includes monitoring data collected nationally to understand the status, condition, and trend of resources on BLM lands. Data are collected in accordance with the BLM Assessment, Inventory, and Monitoring (AIM) Strategy. The AIM Strategy specifies a probabilistic sampling design, standard core indicators and methods, electronic data capture and management, and integration with remote sensing. Attributes include the BLM aquatic core indicators: pH, conductivity, temperature, pool depth, length, frequency, streambed particles sizes, bank stability and cover, floodplain connectivity, large woody debris, macroinvertebrate biological integrity, ocular estimates of vegetative type, cover, and structure and canopy cover. In addition, the contingent indicators of total nitrogen and phosphorous, turbidity, bank angle, thalweg depth profile and quantitative vegetation estimates (see the Data Structure and Attribute Information section for exact details on attributes). Data were collected and managed by BLM Field Offices, BLM Districts, and/or affiliated field crews with support from the BLM National Operations Center. Data are stored in a centralized database (BLM AIM Lotic Database) at the BLM National Operations Center.