48 Import Shipments Found of Union Gap with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

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 released PAD-US 3.0 Statistics and Reports in the summer of 2022. 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 3.0 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 51,000 protected areas from PAD-US 3.0, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the 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 (https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/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 51,000 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.

Indonesia Poverty Gap Index: Central Sulawesi data was reported at 2.640 % in 2018. This records an increase from the previous number of 2.550 % for 2017. Indonesia Poverty Gap Index: Central Sulawesi data is updated yearly, averaging 2.845 % from Dec 2005 (Median) to 2018, with 14 observations. The data reached an all-time high of 4.870 % in 2008 and a record low of 2.110 % in 2014. Indonesia Poverty Gap Index: Central Sulawesi data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE006: Poverty Gap Index: by Province.

In 2010, IFC conducted a study to estimate the number of micro, small, and medium enterprises (MSMEs) in the world, and to determine the degree of access to credit and use of deposit accounts for formal and informal MSMEs. The study used primarily data from the World Bank Enterprise Surveys (ES). In 2011 the data was revisited as new enterprise surveys became available. The resulting database, IFC Enterprise Finance Gap Database, covers 177 countries.

This NOAA Climate Data Record (CDR) of Zonal Mean Ozone Binary Database of Profiles (BDBP) dataset is a vertically resolved, global, gap-free and zonal mean dataset that was created with a multiple-linear regression model. The dataset has a monthly resolution and spans the period 1979 to 2007. It provides global product in 5 degree zonal bands, and 70 vertical levels of the atmosphere. The regression is based on monthly mean ozone concentrations that were calculated from several different satellite instruments and global ozone soundings. Due to the regression model that was used to create the product, various basis function contributions are provided as unique levels or tiers. To understand the different contributions of basis functions, the data product is provided in five different "Tiers". - Tier 0: raw monthly mean data that was used in the regression model - Tier 1.1: Anthropogenic influences (as determined by the regression model) - Tier 1.2: Natural influences (as determined by the regression model) - Tier 1.3: Natural and volcanic influences (as determined by the regression model) - Tier 1.4: All influences (as determined by the regression model, CDR variable)

41063 Global import shipment records of Spark,gap with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

description: The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by U. S. Geological Survey Gap Analysis Program, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. Please note that PAD-US version 1.4 is now the most current version available. Please access PAD-US 1.4 here: http://gapanalysis.usgs.gov/padus/data/. The geodatabase contains four feature classes such as, ‘Marine Protected Areas (MPA)’ and ‘Easements’ that each contains uniquely associated attributes. These two feature classes are combined with the PAD-US ‘Fee’ feature class to provide a full inventory of protected areas in a common schema (i.e. ‘Combined’ file). Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee and MPAs under both. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. The geodatabase contains a Marine Protected Area (MPA) feature class and Easements feature class, each with uniquely associated attribute. These two feature classes are combined with the PAD-US fee feature class with standard PAD-US attributes to provide a full inventory of protected areas in a common schema. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new “Date of Establishment” field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas. Additional dates will be provided in future updates. 4) A national wilderness area update from wilderness.net 5) The ”Access” field that describes public access to protected areas as defined by data stewards or categorical assignment by Primary Designation Type. . The new “Access Source” field documents local vs. categorical assignments. See the PAD-US Standard Manual for more information: gapanalysis.usgs.gov/padus 6) The transfer of conservation measures (i.e. GAP Status Codes, IUCN Categories) and documentation (i.e. GAP Code Source, GAP Code Date) from PAD-US version 1.2 or categorical assignments (see PAD-US Standard) when not provided by data stewards 7) Integration of non-sensitive National Conservation Easement Database (NCED) easements from August 2011, July 2012 with PAD-US version 1.2 easements. Duplicates were removed, unless 'Stacked' = Y and multiple easements exist. 8) Unique ID's transferred from NCED or requested for new easements. NCED and PAD-US are linked via Source UID in the PAD-US version 1.3 Easement feature class. 9) Official (member and eligible) MPAs from the NOAA MPA Inventory (March 2011, www.mpa.gov) translated into the PAD-US schema with conservation measures transferred from PAD-US version 1.2 or categorically assigned to new protected areas. Contact the PAD-US Coordinator for documentation of categorical GAP Status Code assignments for MPAs. 10) Identified MPA records that overlap existing protected areas in the PAD-US Fee feature class (i.e. PADUS Overlap field in MPA feature class). For example, many National Wildlife Refuges and National Parks are also MPAs and are represented in the PAD-US MPA and Fee feature classes.(ei; abstract: The Protected Areas Database of the United States (PAD-US) is a geodatabase, managed by U. S. Geological Survey Gap Analysis Program, that illustrates and describes public land ownership, management and other conservation lands, including voluntarily provided privately protected areas. Please note that PAD-US version 1.4 is now the most current version available. Please access PAD-US 1.4 here: http://gapanalysis.usgs.gov/padus/data/. The geodatabase contains four feature classes such as, ‘Marine Protected Areas (MPA)’ and ‘Easements’ that each contains uniquely associated attributes. These two feature classes are combined with the PAD-US ‘Fee’ feature class to provide a full inventory of protected areas in a common schema (i.e. ‘Combined’ file). Legitimate and other protected area overlaps exist in the full inventory, with Easements loaded on top of Fee and MPAs under both. Parcel data within a protected area are dissolved in this file that powers the PAD-US Viewer. As overlaps exist, GAP creates separate analytical layers to summarize area statistics for "GAP Status Code" and "Owner Name". Contact the PAD-US Coordinator for more information. The lands included in PAD-US are assigned conservation measures that qualify their intent to manage lands for the preservation of biological diversity and to other natural, recreational and cultural uses; managed for these purposes through legal or other effective means. The geodatabase includes: 1) Geographic boundaries of public land ownership and voluntarily provided private conservation lands (e.g., Nature Conservancy Preserves); 2) The combination land owner, land manager, management designation or type, parcel name, GIS Acres and source of geographic information of each mapped land unit 3) GAP Status Code conservation measure of each parcel based on USGS National Gap Analysis Program (GAP) protection level categories which provide a measurement of management intent for long-term biodiversity conservation 4) IUCN category for a protected area's inclusion into UNEP-World Conservation Monitoring Centre's World Database for Protected Areas. IUCN protected areas are defined as, "A clearly defined geographical space, recognized, dedicated and managed, through legal or other effective means, to achieve the long-term conservation of nature with associated ecosystem services and cultural values" and are categorized following a classification scheme available through USGS GAP; 5) World Database of Protected Areas (WDPA) Site Codes linking the multiple parcels of a single protected area in PAD-US and connecting them to the Global Community. The geodatabase contains a Marine Protected Area (MPA) feature class and Easements feature class, each with uniquely associated attribute. These two feature classes are combined with the PAD-US fee feature class with standard PAD-US attributes to provide a full inventory of protected areas in a common schema. As legitimate and other overlaps exist in the combined inventory GAP creates separate analytical layers to obtain area statistics for "GAP Status Code" and "Owner Name". PAD-US version 1.3 Combined updates include: 1) State, local government and private protected area updates delivered September 2011 from PAD-US State Data Stewards: CO (Colorado State University), FL (Florida Natural Areas Inventory), ID (Idaho Fish and Game), MA (The Commonwealth's Office of Geographic Information Systems, MassGIS), MO (University of Missouri, MoRAP), MT (Montana Natural Heritage Program), NM (Natural Heritage New Mexico), OR (Oregon Natural Heritage Program), VA (Department of Conservation and Recreation, Virginia Natural Heritage Program). 2) Select local government (i.e. county, city) protected areas (3,632) across the country (to complement the current PAD-US inventory) aggregated by the Trust for Public Land (TPL) for their Conservation Almanac that tracks the conservation finance movement across the country. 3) A new “Date of Establishment” field that identifies the year an area was designated or otherwise protected, attributed for 86% of GAP Status Code 1 and 2 protected areas.

A revised version of this dataset has been published: https://doi.org/10.17026/dans-zyu-xkhc. The files of this dataset are therefore no longer accessible.This dataset contains the underlying data for the study: Kenya public weather processed by the Global Yield Gap Atlas project. Open Journal for Agricultural Research : ODjAR.The Global Yield Gap Atlas project (GYGA - http://yieldgap.org ) has undertaken a yield gap assessment following the protocol recommended by van Ittersum et. al. (van Ittersum et. al., 2013). One part of the activities consists of collecting and processing weather data as an input for crop simulation models in sub-Saharan African countries including Kenya. This publication covers weather data for 10 locations in Kenya. The project looked for good quality weather data in areas where crops are pre-dominantly grown. As locations with good public weather data are sparse in Africa, the project developed a method to generate weather data from a combination of observed and other external weather data. One locations holds actually measured weather data, the other 9 locations show propagated weather data. The propagated weather data consist on TRMM rain data (or NASA POWER if TRMM is not available) and NASA POWER Tmax, Tmin, and Tdew data corrected based on calibrations with short-term (<10 years) observed weather data. sources (Van Wart et.al. 2015). Date Submitted: 2016-01-05 DANS converted the original doc file into pdf/a format. Both the doc and pdf/a format are available.

Effective management of aquatic resources, wild and farmed, has implications for the livelihoods of dependent communities, food security, and ecosystem health. Good management requires information on the status of harvested species, yet many gaps remain in our understanding of these species and systems, in particular the lack of taxonomic resolution of harvested species. To assess these gaps we compared the occurrence of landed species (freshwater and marine) from the United Nations Food and Agriculture Organization (FAO) global fisheries production database to those in the International Union for Conservation of Nature (IUCN) Red List and the RAM Legacy Stock Assessment Database, some of the largest and most comprehensive global datasets of consumed aquatic species. We also quantified the level of resolution and trends in taxonomic reporting for all landed taxa in the FAO database. Of the 1,695 consumed aquatic species or groups in the FAO database considered in this analysis, a large portion (35%) are missing from both of the other two global datasets, either IUCN or RAM, used to monitor, manage, and protect aquatic resources. Only a small number of all fished taxa reported in FAO data (150 out of 1,695; 9%) have both a stock assessment in RAM and a conservation assessment in IUCN. Furthermore, 40% of wild caught landings are not reported to the species level, limiting our ability to effectively account for the environmental impacts of wild harvest. Landings of invertebrates (44%) and landings in Asia (>75%) accounted for the majority of harvest without species specific information in 2018. Assessing the overlap of species which are both farmed and fished to broadly map possible interactions – which can help or hinder wild populations - we found 296 species, accounting for 12% of total wild landings globally, and 103 countries and territories that have overlap in the species caught in the wild and produced through aquaculture. In all, our work highlights that while fisheries management is improving in many areas there remain key gaps in data resolution that are critical for fisheries assessments and conservation of aquatic systems into the future.

Japan Capital Input Gap data was reported at 1.020 % in Jun 2018. This records an increase from the previous number of 0.908 % for Mar 2018. Japan Capital Input Gap data is updated quarterly, averaging -0.087 % from Mar 1983 (Median) to Jun 2018, with 142 observations. The data reached an all-time high of 4.570 % in Dec 1990 and a record low of -4.667 % in Mar 2009. Japan Capital Input Gap data remains active status in CEIC and is reported by Bank of Japan. The data is categorized under Global Database’s Japan – Table JP.A053: SNA 2008: Potential Growth Rate and Output Gap.

Indonesia Poverty Gap Index: West Kalimantan data was reported at 1.180 % in 2018. This records a decrease from the previous number of 1.230 % for 2017. Indonesia Poverty Gap Index: West Kalimantan data is updated yearly, averaging 1.300 % from Dec 2005 (Median) to 2018, with 14 observations. The data reached an all-time high of 2.470 % in 2006 and a record low of 1.180 % in 2018. Indonesia Poverty Gap Index: West Kalimantan data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE006: Poverty Gap Index: by Province.

Gender pay gap data, with year on year change and extended information (such as part-time mean and median, bonus & BIK info, etc. for Global Payments. Data is available for 2022-2024 for most companies.

23533 Global export shipment records of Spark,gap with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

GapMaps Foot Traffic Data by Azira provides actionable insights on consumer travel patterns at a global scale empowering Marketing and Operational Leaders to confidently reach, understand, and market to highly targeted audiences and optimize their business results.

2511 Global import shipment records of Spark Gap with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

Indonesia Poverty Gap Index: Urban data was reported at 1.080 % in Sep 2018. This records a decrease from the previous number of 1.170 % for Mar 2018. Indonesia Poverty Gap Index: Urban data is updated semiannually, averaging 1.210 % from Sep 2015 (Median) to Sep 2018, with 7 observations. The data reached an all-time high of 1.290 % in Sep 2015 and a record low of 1.080 % in Sep 2018. Indonesia Poverty Gap Index: Urban data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GAE005: Poverty Gap Index.

Indonesia Poverty Gap Index: Annual: Rural data was reported at 2.370 % in 2018. This records a decrease from the previous number of 2.490 % for 2017. Indonesia Poverty Gap Index: Annual: Rural data is updated yearly, averaging 2.490 % from Mar 2016 (Median) to 2018, with 3 observations. The data reached an all-time high of 2.740 % in 2016 and a record low of 2.370 % in 2018. Indonesia Poverty Gap Index: Annual: Rural data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Global Database’s Indonesia – Table ID.GAE005: Poverty Gap Index.

Indonesia Poverty Gap Index: South Sulawesi data was reported at 1.550 % in 2018. This records a decrease from the previous number of 1.720 % for 2017. Indonesia Poverty Gap Index: South Sulawesi data is updated yearly, averaging 1.775 % from Dec 2005 (Median) to 2018, with 14 observations. The data reached an all-time high of 3.460 % in 2008 and a record low of 1.410 % in 2014. Indonesia Poverty Gap Index: South Sulawesi data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE006: Poverty Gap Index: by Province.

Indonesia Poverty Gap Index: North Sumatera: South Tapanuli Regency data was reported at 1.290 % in 2018. This records a decrease from the previous number of 1.410 % for 2017. Indonesia Poverty Gap Index: North Sumatera: South Tapanuli Regency data is updated yearly, averaging 1.435 % from Dec 2005 (Median) to 2018, with 14 observations. The data reached an all-time high of 2.970 % in 2006 and a record low of 1.060 % in 2016. Indonesia Poverty Gap Index: North Sumatera: South Tapanuli Regency data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE007: Poverty Gap Index: by Regency.

Indonesia Poverty Gap Index: Central Java: Grobogan Regency data was reported at 1.670 % in 2018. This records a decrease from the previous number of 2.030 % for 2017. Indonesia Poverty Gap Index: Central Java: Grobogan Regency data is updated yearly, averaging 2.525 % from Dec 2005 (Median) to 2018, with 14 observations. The data reached an all-time high of 4.820 % in 2005 and a record low of 1.670 % in 2018. Indonesia Poverty Gap Index: Central Java: Grobogan Regency data remains active status in CEIC and is reported by Central Bureau of Statistics. The data is categorized under Indonesia Premium Database’s Socio and Demographic – Table ID.GAE007: Poverty Gap Index: by Regency.