The global dataset covers the time period 1800-2016, with yearly observations for all countries that have been independent at any point in time since WWII. Within the category of democracies, we first make a distinction between republics and monarchies. Republics are then classified into presidential, semi-presidential, and parliamentary systems. Within the category of monarchies, most systems are parliamentary but a few countries are conferred to the category semi-monarchies. Autocratic countries are classified into the following main categories: absolute monarchy, military rule, party-based rule, personalist rule, and oligarchy. Within the categories party-based rule and oligarchy a number of subcategories are also identified.

This dataset contains parametric data (epicentre, magnitude, depth, etc) for over one million earthquakes worldwide. The dataset has been compiled gradually over a period of thirty years from original third-party catalogues, and parameters have not been revised by BGS, although erroneous entries have been flagged where found. The dataset is kept in two versions: the complete "master" version, in which all entries for any single earthquake from contributing catalogue are preserved, and the "pruned" version, in which each earthquake is represented by a single entry, selected from the contributing sources according to a hierarchy of preferences. The pruned version, which is intended to be free from duplicate entries for the same event, provides a starting point for studies of seismicity and seismic hazard anywhere in the world.

Platts World Refinery Database provides an in-depth, historical and forward-looking view of the entire downstream value chain, from crude inputs to detailed product outputs.

The popularity of cloud database management systems (DBMSs) are on the rise, growing from 1.6 percent in 2016 to 3.7 percent in 2019, based on the ranking scores of DBSMs. Amazon DynamoDB is was the most popular cloud DBMS at the end of 2019, ranking 16th among all DBMSs.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in Afghanistan was 1,000 individuals. Gender-matched sampling was used during the final stage of selection.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

The World Terrestrial Ecosystems map classifies the world into areas of similar climate, landform, and land cover, which form the basic components of any terrestrial ecosystem structure. This map is important because it uses objectively derived and globally consistent data to characterize the ecosystems at a much finer spatial resolution (250-m) than existing ecoregionalizations, and a much finer thematic resolution (431 classes) than existing global land cover products. This item was updated on Apr 14, 2023 to distinguish between Boreal and Polar climate regions in the terrestrial ecosystems. Cell Size: 250-meter Source Type: ThematicPixel Type: 16 Bit UnsignedData Projection: GCS WGS84Extent: GlobalSource: USGS, The Nature Conservancy, EsriUpdate Cycle: NoneWhat can you do with this layer?This map allows you to query the land surface pixels and returns the values of all the input parameters (landform type, landcover/vegetation type, climate region) and the name of the terrestrial ecosystem at that location.This layer can be used in analysis at global and local regions. However, for large scale spatial analysis, we have also provided an ArcGIS Pro Package that contains the original raster data with multiple table attributes. For simple mapping applications, there is also a raster tile layer. This layer can be combined with the World Protected Areas Database to assess the types of ecosystems that are protected, and progress towards meeting conservation goals. The WDPA layer updates monthly from the United Nations Environment Programme.Developing the World Terrestrial EcosystemsWorld Terrestrial Ecosystems map was produced by adopting and modifying the Intergovernmental Panel on Climate Change (IPCC) approach on the definition of Terrestrial Ecosystems and development of standardized global climate regions using the values of environmental moisture regime and temperature regime. We then combined the values of Global Climate Regions, Landforms and matrix-forming vegetation assemblage or land use, using the ArcGIS Combine tool (Spatial Analyst) to produce World Ecosystems Dataset. This combination resulted of 431 World Ecosystems classes.Each combination was assigned a color using an algorithm that blended traditional color schemes for each of the three components. Every pixel in this map is symbolized by a combination of values for each of these fields.The work from this collaboration is documented in the publication:Sayre et al. 2020. An assessment of the representation of ecosystems in global protected areas using new maps of World Climate Regions and World Ecosystems - Global Ecology and Conservation More information about World Terrestrial Ecosystems can be found in this Story Map.

The Gracillariidae is one of the largest families of primitive moths (Lepidoptera). Gracillariid moths are generally distributed throughout the world except Antarctica, and they are more numerous in tropical areas. Many species of Gracillariidae are serious pests of agricultural and ornamental plants. The Global Taxonomic Database of Gracillariidae currently holds information on 150 genus-group names and in total 2.427 species-group names, belonging to 111 genera.

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

National Coverage.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above. The sample is nationally representative.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in the majority of economies was 1,000 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

The World Ocean Database 1998 (WOD98) is comprised of five CD-ROMs containing profile and plankton/biomass data in compressed format. WOD98-01 through WOD98-04 contain observed level data, WOD98-05 contains all the standard level data. World Ocean Database 1998 (WOD98) expands on World Ocean Atlas 1994 (WOA94) by including the additional variables nitrite, pH, alkalinity, chlorophyll, and plankton, as well as all available metadata and meteorology. WOD98 is an International Year of the Ocean product. WOD98-01 Observed Level Data; North Atlantic 30° N-90° N; WOD98-02 Observed Level Data; North Atlantic 0°-30° N, South Atlantic; WOD98-03 Observed Level Data; North Pacific 20° N-90° N; WOD98-01 Observed Level Data; North Pacific 0°-20° N; South Pacific, Indian; WOD98-01 Standard Level Data for all Ocean Basins. Discs may be created by burning the appropriate .iso file(s) in the data/0-data/disc_image/ directory to blank CD-ROM media using standard CD-ROM authoring software. Software that was developed or provided with this NODC Standard Product may be included in the disc_image/ directory as part of a disc image, but executable software that was developed or provided with this NODC Standard Product has been excluded from the disc_contents/ directory.

The International Development Association (IDA) credits are public and publicly guaranteed debt extended by the World Bank Group. IDA provides development credits, grants and guarantees to its recipient member countries to help meet their development needs. Credits from IDA are at concessional rates. Data are in U.S. dollars calculated using historical rates. This dataset contains the latest available snapshot of the IDA Statement of Credits and Grants.

World partner is available for all reporters except for "Euro Area (20)". "Extra Euro Area (20) Trade" partner is only available for "Euro Area (20)". "European Union" and "Extra EU Trade" partners are only available for the EU.

INFORMATION ABOUT THE CONTENT OF THIS DATABASE

This database comprises wave, beach and runup parameters measured on different beaches around the world. It is a compilation of data published in previous works, with the aim of making all data available in one single repository. More information about methods of data acquisition and data processing can be found in the original papers that describe each experiment. To know how to cite each of the dataset provided here, please check section 3. Please make sure to cite the appropriate publication when using the data. Collecting the data is hard work and needs to be acknowledged. 1. Files content: All data files contain the same structure: Column 1 – R2%: 2-percent exceedance value for runup [m]; Column 2 – Set: setup [m]; Column 3 – Stt: total swash excursion [m]; Column 4 – Sinc: incident swash [m]; Column 5 – Sig: infragravity swash [m]; Column 6 – Hs*: significant deep-water wave height [m]; Column 7 – Tp: peak wave period [s]; Column 8 – tanβ: foreshore beach slope; Column 9 – D50**: Median sediment size [mm] NaN values may be found when the data were not available in the original dataset. *Hs values from field measurements were deshoaled from the depth of measurement to a depth equals to 80m, assuming normal approach and linear theory (we followed the approach presented in Stockdon et al., where great care is paid to make the data comparable). **D50 values were obtained from reports and papers describing the beaches. 2. List of datasets Stockdon et al. 2006: Data recompiled from 10 experiments carried out in 6 beaches (US and NL coasts). Files’ names correspond to the beach and year of the experiments: Original data: available using the link https://pubs.usgs.gov/ds/602/ Senechal et al. 2011: This dataset comprises the measurements carried out in Truc Vert beach, France. The file’s name includes the name of the beach and the year of the experiment. Original data: a table with the full content of the parameters measured during the experiment can be found in Senechal et al. (2011). Guedes et al. 2011: This dataset comprehends data measured at Tairua beach (New Zeland coast). The file’s name indicates the name of the beach and the year of the experiment. Original data: this web. Guedes et al. 2013: This dataset comprehends data measured at Ngarunui beach (Raglan - New Zeland coast). The file’s name represents the name of the beach and the year of the experiment. Original data: this web. Gomes da Silva et al. 2018: Dataset measured during two field campaigns in Somo beach, Spain, in 2016 and 2017. The files names represent that name of the beach and the year of the experiment. Original data: https://data.mendeley.com/datasets/6yh2b327gd/4

Power et al. 2019: Dataset compiled from previous works, comprising field and laboratory measurements: Poate et al. (2016): field; Nicolae-Lerma et al. (2016): field; Atkinson et al. (2017): field; Mase (1989): Laboratory; Baldock and Huntley (2002): Laboratory; Howe (2016): Laboratory; Original data:www.sciencedirect.com/science/article/pii/S0378383918302552

Due to the character limit of this description, please refer to the https://coastalhub.science/wave-runup-read-me for the references list.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world’s most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of almost 145,000 people in 139 economies, representing 97 percent of the world’s population. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19–related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Additionally, phone surveys were not a viable option in 16 economies in 2021, which were then surveyed in 2022.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Lesotho is 1010.

Mode of data collection

Face-to-face [f2f]

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

World Database on Protected Areas (WDPA) adalah sumber informasi terbaru dan paling lengkap tentang kawasan lindung, yang diperbarui setiap bulan dengan kiriman dari pemerintah, organisasi non-pemerintah, pemilik lahan, dan komunitas. Situs ini dikelola oleh World Conservation Monitoring Centre (UNEP-WCMC) dari United Nations Environment Programme dengan dukungan dari IUCN dan …

Privatization Database provides information on privatization transactions of at least US$1 million in developing countries from 2000 to 2008. Prior to this effort the most comprehensive information could be found in the World Bank’s Privatization Transactions database, which covered the years 1988 through 1999.

Important Note: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. Soil is a key natural resource that provides the foundation of basic ecosystem services. Soil determines the types of farms and forests that can grow on a landscape. Soil filters water. Soil helps regulate the Earth's climate by storing large amounts of carbon. Activities that degrade soils reduce the value of the ecosystem services that soil provides. For example, since 1850 35% of human caused green house gas emissions are linked to land use change. The Soil Science Society of America is a good source of of additional information.Bulk density is an important property of soil. Soil is a mixture of mineral particles, organic material and open spaces known as pores. The size and distribution of the pores affect how water is stored and how nutrients move through the soil. When a soil is compacted it looses pore space and bulk density increases resulting in lower water storage and higher runoff.Dataset SummaryThis layer provides access to a 30 arc-second (roughly 1 km) cell-sized raster with attributes related to the density of soil derived from the Harmonized World Soil Database v 1.2. The values in this layer are for the dominant soil in each mapping unit (sequence field = 1).Attribute values for topsoil (0-30 cm) and subsoil (0-100 cm) are provided for bulk density (derived from available analyzed data) and reference bulk density (statistical estimate based on soil texture). The data are in units of kg/dm3. Topsoil Reference Bulk DensityTopsoil Bulk Density Subsoil Reference Bulk DensitySubsoil Bulk DensityThe layer is symbolized with the Topsoil Bulk Density field.The document Harmonized World Soil Database Version 1.2 provides more detail on the difference between bulk density and reference bulk density.Other attributes contained in this layer include:Soil Mapping Unit Name - the name of the spatially dominant major soil groupSoil Mapping Unit Symbol - a two letter code for labeling the spatially dominant major soil group in thematic mapsData Source - the HWSD is an aggregation of datasets. The data sources are the European Soil Database (ESDB), the 1:1 million soil map of China (CHINA), the Soil and Terrain Database Program (SOTWIS), and the Digital Soil Map of the World (DSMW).Percentage of Mapping Unit covered by dominant componentObstacles to Roots - Depth to obstacles to roots in 6 classes. Only in the European Soil Database, not available for other regions.More information on the Harmonized World Soil Database is available here.Other layers created from the Harmonized World Soil Database are available on ArcGIS Online:World Soils Harmonized World Soil Database – ChemistryWorld Soils Harmonized World Soil Database - Exchange CapacityWorld Soils Harmonized World Soil Database – GeneralWorld Soils Harmonized World Soil Database – HydricWorld Soils Harmonized World Soil Database – TextureThe authors of this data set request that projects using these data include the following citation:FAO/IIASA/ISRIC/ISSCAS/JRC, 2012. Harmonized World Soil Database (version 1.2). FAO, Rome, Italy and IIASA, Laxenburg, Austria.What can you do with this layer?This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. The source data for this layer are available here.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started follow these links:Living Atlas Discussion GroupSoil Data Discussion GroupThe Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

This dataset was originally created in 2015 as a part of a stocktaking exercise initiated by the Integrated Digital Solutions (IDS) Group to present an inventory of all WBG investments including large ICT/e-Gov components for various sector reforms since 1995. The dataset includes the details of ICT investments in seven categories, and their mapping to four GovTech focus areas, together with the cost, duration, and outcome ratings of completed activities, in addition to key project data extracted from the WBG operations portal. The first version of the “DG Projects Database” including 1,100+ projects funded in 130+ countries was released publicly within the WBG Data Catalog in June 2015. There were several updates on the dataset since then (Aug 2017, Dec 2019, Jan 2020, and Jul 2020). The latest version (October 2022) presents the details of 1,449 projects funded in 147 countries. This dataset can be used by all practitoners involved in the design of digital government/GovTech activities to learn from relevant investments, search the contents of project documents (PAD, ICR, IEG review), and expand/customize the resulting data sets for various needs (operational support, project design, research, monitoring and quality assurance, training, etc.).

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of Japan, Eastern Asia. The time period coverage is from 450 to -54 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.

World partner is available for all reporters. "European Union" and "Extra EU Trade" partners are only available for the EU. Indices by Products are only available for Reporter "World".

This archived Paleoclimatology Study is available from the NOAA National Centers for Environmental Information (NCEI), under the World Data Service (WDS) for Paleoclimatology. The associated NCEI study type is Borehole. The data include parameters of borehole with a geographic location of United States Of America. The time period coverage is from 450 to -16 in calendar years before present (BP). See metadata information for parameter and study location details. Please cite this study when using the data.