The O*NET Database contains hundreds of standardized and occupation-specific descriptors on almost 1,000 occupations covering the entire U.S. economy. The database, which is available to the public at no cost, is continually updated by a multi-method data collection program. Sources of data include: job incumbents, occupational experts, occupational analysts, employer job postings, and customer/professional association input.

Data content areas include:

• Worker Characteristics (e.g., Abilities, Interests, Work Styles)
• Worker Requirements (e.g., Education, Knowledge, Skills)
• Experience Requirements (e.g., On-the-Job Training, Work Experience)
• Occupational Requirements (e.g., Detailed Work Activities, Work Context)
• Occupation-Specific Information (e.g., Job Titles, Tasks, Technology Skills)

This is the sample database from sqlservertutorial.net. This is a great dataset for learning SQL and practicing querying relational databases.

Database Diagram:

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F4146319%2Fc5838eb006bab3938ad94de02f58c6c1%2FSQL-Server-Sample-Database.png?generation=1692609884383007&alt=media" alt="">

Terms of Use

The sample database is copyrighted and cannot be used for commercial purposes. For example, it cannot be used for the following but is not limited to the purposes: - Selling - Including in paid courses

Overview

The FG-NET Aging Database is a widely used image collection for age estimation and age progression research. It contains 1,002 images of 82 individuals, spanning ages from 0 to 69 years. Each individual in the dataset has multiple images taken at different ages, providing a comprehensive resource for studying the ageing process.

Content

• Total Images: 1,002
• Number of Subjects: 82
• Age Range: 0 to 69 years
• Image Format: JPEG

Each image is annotated with the age of the individual at the time the photograph was taken, allowing for precise age-related studies. The dataset is ideal for tasks such as age estimation, age progression/regression, and facial recognition across different age groups.

Applications

This dataset has numerous applications in various fields, including but not limited to: - Computer Vision: Developing and testing algorithms for age estimation and age progression. - Machine Learning: Training models to predict age from facial images. - Healthcare: Studying ageing patterns for medical research and diagnostics. - Security: Enhancing facial recognition systems to account for ageing.

Citation

If you use this dataset in your research, please cite the original creators of the FG-NET Aging Database:

[FG-NET Aging Database. Available at: http://yanweifu.github.io/FG_NET_data/FGNET.zip]

Acknowledgements

We thank the FG-NET Aging Database team for making this dataset available to the public and for their contributions to the research community.

Abstract

TIER.net is an electronic patient management system that is used for monitoring and evaluation of HIV care and treatment programmes in government health facilities throughout South Africa. The system was designed as part of a 3-tier approach to implementing a full electronic medical records (EMR) system. This approach provides a flexible solution that allows facilities to transition towards EMR in stages, as their infrastructure improves, and resources become available. TIER.net forms the second tier, whereby patient's paper clinical records are entered into a non-networked computer at the health facility and transferred periodically to a central database.

The TIER.net database contains information on clinic visit attendance, laboratory results and ART dispensing records for all patients on ART. The system was implemented in uMkhanyakude district in 2013; patient records from all visits before 2013 were back-captured into the system. AHRI has a memorandum of agreement with the Department of Health to receive the TIER.net data for the 17 clinics in the Hlabisa health sub-district and Hlabisa hospital. A dedicated data entry clerk based in each clinic enters information from patients' paper clinical records into the TIER.net system after each patient visit. Laboratory results are manually entered into TIER.net after they have been received by the clinic (i.e. are not imported electronically from the National Health Laboratory Service (NHLS) system). Currently, pre-ART visits are not recorded in TIER.net, although modules to capture HIV testing and pre-ART care may be implemented in the future.

AHRI's new clinical database - ACCDB (Africa Centre Clinical Database) TIER.net has been integrated with AHRI's previous HIV care clinical database (ARTemis), which contains records for HIV positive patients in care at 7 clinics in the sub-district, to create a new combined clinical database, ACCDB. The ACCDB contains clinical records for all HIV positive patients on ART since 2004, when the Hlabisa HIV treatment and Care programme was implemented. Linkage between the two databases is based on personal identifiers, using algorithms developed at AHRI.
The patient records in ARTemis were captured prospectively by AHRI clinical staff from 2004 until 2012, whereas records from this period were back-captured into TIER.net. Therefore, the information in ARTemis pre-2013 was used to fill in missing data in TIER.net, with the following decision rules:
Patients who had records in TIER.net and not in ARTemis: All information retained Patients who had records in ARTemis but not in TIER.net: No pre-ART records retained (TIER.net only contains records after ART initiation, whereas ARTemis also captured pre-ART information). Retained in new database if the patient started ART and died, transferred out, or was lost to follow-up before 2013.
Patients on ART with records in ARTemis from 2013 onwards, but no record in TIER, were assumed to be a result of a linkage mismatch and were not retained.

Patients who had records in both TIER.net and ARTemis: Visit records from TIER.net only (i.e. visits records from ARTemis are not combined with those in TIER). Laboratory results from ARTemis up to 2013 are combined with results in TIER The new clinical database has been linked with the AHRI population surveillance database (ACDIS) and the Hlabisa hospital admissions database (HIS). A longitudinal analysis of the HIV care cascade (Haber et al. From HIV infection to therapeutic response: a population-based longitudinal HIV cascade-of-care study in KwaZulu-Natal, South Africa. Lancet HIV 2017; 4(5):e223-e230) has been rerun on the new combined TIER.net and ARTemis data, and results are consistent with those obtained from the ARTemis data alone (although the pre-ART stages must be excluded).
TIER.net data are transferred to AHRI from the central Department of Health on a monthly basis and integrated into ACCDB bi-annually, and the linkages with ACDIS and HIS are updated. (The linkage with the ARTemis database is not re-run since this database is no longer active).
In April 2017, there were >47,000 individuals on ART in the TIER database, of whom approximately 12,000 were linked to ACDIS and 6500 were linked to HIS.
Although TIER.Net data has been included up to July 2017, the current dataset should be analysed only up to end 2016, because it has been linked to the Dec 2016 analytical database. The updated ACCDB data for 2017 will be available towards the end of the first quarter of 2018, linked to the end of 2017 analytical database relase.

Analysis unit

Individual

This is a database of NISE Net NanoDays Kits, with links to information about the kits and instructional videos. This database was created in 2014 and there may be some additional content added since then. This database contains all the...

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"MEOP (Marine Mammals Exploring the Oceans Pole to Pole) is a consortium of international researchers dedicated to sharing animal-derived data and knowledge about the polar oceans. Since 2004, several hundred thousands profiles of temperature and salinity have been collected by instrumented animals. The use of elephant seals has been particularly effective to sample the Southern Ocean and the North Pacific. Other seal species have been successfully used in the North Atlantic, such as hooded seals. These hydrographic data have been assembled in a quality-controlled database, the MEOP-CTD database. These data are profiles of temperature (°C). Each profile is located in space and time. It must be emphasised that the dataset of each individual CTD-SRDL has been edited and corrected separately, as a given CTD-SRDL has its own specificities in terms of data accuracy and quality of the estimated correction. A post-processing procedure is applied on hydrographic data in order to ensure the best possible data quality. For more details, please visit https://www.meop.net/database/data-processing-and-validation.html." cdm_altitude_proxy=PRES cdm_data_type=TimeSeriesProfile cdm_profile_variables=time,latitude,longitude cdm_timeseries_variables=PLATFORM_NUMBER citation="We recommend the following citation for these data: Marine Mammals Exploring the Oceans Pole to Pole (MEOP). [YEAR]. Animal-borne Temperature Profiles (TEMP). Available at https://www.meop.net/database/meop-databases/meop-ctd-database.html. Accessed via [project] on YYYY-MM-DD. Please also refer to https://www.meop.net/database/how-to-cite.html on how to acknowledge these data appropriately." Conventions=CF-1.6 Sea-mammals-1.1, COARDS, ACDD-1.3 data_DOI=https://www.seanoe.org/data/00343/45461/ data_format_original=netCDF data_mode=D data_update=2024-03-08 data_update_frequence=≥ yearly data_url=https://www.seanoe.org/data/00343/45461/ distribution_statement=Follow MEOP data policy standards, cf. http://www.meop.net/the-dataset/data-access.html. Data available free of charge. User assumes all risk for use of data. User must display citation in any publication or product using data. User must contact PI prior to any commercial use of data Easternmost_Easting=179.9984 featureType=TimeSeriesProfile format_version=1.1 geospatial_lat_max=87.7764 geospatial_lat_min=-78.66 geospatial_lat_units=degrees_north geospatial_lon_max=179.9984 geospatial_lon_min=-179.9998 geospatial_lon_units=degrees_east history=Marine mammal observation infoUrl=https://www.meop.net/database/meop-databases/meop-ctd-database.html institution=MEOP (Marine Mammals Exploring the Oceans Pole to Pole) keywords_vocabulary=GCMD Science Keywords naming_authority=EMODnet Physics Northernmost_Northing=87.7764 number_light_profiles=0.0 owner=MEOP consortium (Marine Mammals Exploring the Oceans Pole to Pole) owner_url=http://www.meop.net platform_type=organism references=https://www.seanoe.org/data/00343/45461/ source=In situ observations sourceUrl=(local files) Southernmost_Northing=-78.66 standard_name_vocabulary=CF Standard Name Table v85 subsetVariables=PLATFORM_NUMBER,nation,location,species time_coverage_end=2024-02-22T12:45:00Z time_coverage_start=2004-01-27T11:49:00Z variables=DC_REFERENCE, DATA_STATE_INDICATOR, DATA_MODE, INST_REFERENCE, WMO_INST_TYPE, JULD, JULD_QC, JULD_LOCATION, LATITUDE, LONGITUDE, POSITION_QC, POSITIONING_SYSTEM, PROFILE_PRES_QC, PROFILE_PSAL_QC, PROFILE_TEMP_QC, PRES, PRES_QC, PRES_ADJUSTED, PRES_ADJUSTED_QC, PRES_ADJUSTED_ERROR, TEMP, TEMP_QC, TEMP_ADJUSTED, TEMP_ADJUSTED_QC, TEMP_ADJUSTED_ERROR, PSAL, PSAL_QC, PSAL_ADJUSTED, PSAL_ADJUSTED_QC, PSAL_ADJUSTED_ERROR, PARAMETER, SCIENTIFIC_CALIB_EQUATION, SCIENTIFIC_CALIB_COEFFICIENT, PRES_INTERP, TEMP_INTERP, PSAL_INTERP Westernmost_Easting=-179.9998

Resource Title: NDVI_raw . File Name: NDVI_raw.xlsxResource Description: Raw bimonthly NDVI values for Grass-Cast sites. Resource Title: ANPP. File Name: ANPP.xlsxResource Description: Dataset for annual aboveground net primary productivity (ANPP). Excel sheet is broken into two tabs, 1) 'readme' describing the data, 2) 'ANPP' with the actual data. Resource Title: Grass-Cast_sitelist . File Name: Grass-Cast_sitelist.xlsxResource Description: This provides a list of sites-studies that are currently incorporated into the Database as well as meta-data and contact info associated with the data sets. Includes a 'readme' tab and 'sitelist' tab. Resource Title: Grass-Cast_AgDataCommons_overview. File Name: Grass-Cast_AgDataCommons_download.htmlResource Description: Html document that shows database overview information. This document provides a glimpse of the data tables available within the data resource as well as respective meta-data tables. The R script (R markdown, .Rmd format) that generates the html file, and can be used to upload the Grass-Cast associated Ag Data Commons data files can be downloaded at the 'Grass-Cast R script' zip folder. The Grass-Cast files still need to be locally downloaded before use, but we are looking to make a download automated.

The Africa Soil Profiles Database, Version 1.2, is compiled by ISRIC - World Soil Information (World Data Center for Soils) as a project activity for the Globally integrated- Africa Soil Information Service (AfSIS) project (www.africasoils.net/data/legacyprofile). It replaces version 1.1.

The Africa Soil Profiles Database is a compilation of georeferenced and standardised legacy soil profile data for Sub-Saharan Africa. Version 1.2 (November 2014) identifies 18,532 unique soil profiles inventoried from a wide variety of data sources and includes profile site and layer attribute data. Soil analytical data are available for 15,564 profiles of which 14,197 are georeferenced, including the attributes as specified by GlobalSoilMap.net. Soil attribute values are standardized according to SOTER conventions and are validated according to routine rules. Odd values are flagged. The degree of validation, and associated reliability of the data, varies because reference soil profile data, that are previously and thoroughly validated, are compiled together with non-reference soil profile data of lesser inherent representativeness.

IntroductionThis study aimed to evaluate the validity of algorithms based on electronic health data in identifying cases of acute heart failure and acute exacerbation of chronic heart failure at multiple institutions using the Medical Information Database Network (MID-NET®) in Japan.MethodsData were collected from March 8, 2021 to March 31, 2021, from the data source of three hospitals among the MID-NET® cooperating medical institutions. All Possible Cases were defined by combining ICD-10 codes related to acute heart failure and abnormal values of serum B-type natriuretic peptide (BNP) or N-terminal pro-brain natriuretic peptide (NT-proBNP). Eighteen algorithms were created using various data sources in MID-NET®, including electronic medical records, diagnostic procedure combination (DPC) data, and health insurance claims data. True cases were determined by reviewing medical records obtained independently by two experienced physicians.ResultsThe kappa coefficient among the three medical institutions was 0.94 (95% confidence interval: 0.90–0.98). Among the 18 algorithms, the highest positive predictive value (PPV) of the three medical institutions was 77.78% for Algorithm 8 which was constructed using ICD-10 codes in DPC disease data, moderate or high range of abnormal BNP (≥100 pg/mL) or NT-proBNP (≥400 pg/mL), and medications for acute heart failure. The highest sensitivity at 89.53% was observed for Algorithm 9. This algorithm was constructed using a combination of disease codes entered in electronic medical records, DPC, or health insurance claims data, abnormal BNP values in the moderate or high range (≥100 pg/mL), and medications for acute heart failure. However, its PPV was the lowest among 18 algorithms, generally reflecting the inverse relationship between PPV and sensitivity. The same tendency was seen in the sensitivity study. Cases with stable chronic heart failure, renal insufficiency, assessment for cardiac function, or severe circulatory failure inflated false-positive cases in this study.ConclusionValidated algorithms for identifying acute heart failure and acute exacerbation of chronic heart failure were successfully established. Using these algorithms should facilitate more appropriate pharmacoepidemiological studies related to acute heart failure and contribute to better drug safety assessments based on real-world data in Japan.

Turkmenistan TM: Net Migration data was reported at -50,002.000 Person in 2012. This records an increase from the previous number of -62,717.000 Person for 2007. Turkmenistan TM: Net Migration data is updated yearly, averaging -35,795.000 Person from Dec 1962 (Median) to 2012, with 11 observations. The data reached an all-time high of 43,633.000 Person in 1992 and a record low of -125,883.000 Person in 2002. Turkmenistan TM: Net Migration data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkmenistan – Table TM.World Bank: Population and Urbanization Statistics. Net migration is the net total of migrants during the period, that is, the total number of immigrants less the annual number of emigrants, including both citizens and noncitizens. Data are five-year estimates.; ; United Nations Population Division. World Population Prospects: 2017 Revision.; Sum;

This controlled data release focuses on CP-NET's initial Clinical Database which solely focused on children and youth, aged 2-18, with a confirmed diagnosis of hemiplegic cerebral palsy (CP). The Hemi-NET Clinical Database has data on 320 children and youth from across Ontario. The released data is organized around the following platforms: (1) Clinical Risk Factor Platform: clinically relevant neonatal and obstetric risk factors from obstetrical and neonatal health charts, (2) Genomics Platform: saliva samples acquired from the index child and both biological parent(s), (3) Neuroimaging Platform: standardized coding of clinically acquired neuroimaging, (4) Neurodevelopmental Platform: standardized assessments of gross motor, fine motor, language, cognitive, behavioural function, and self-reported quality of life.

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.

History of Work

Here you find the History of Work resources as Linked Open Data. It enables you to look ups for HISCO and HISCAM scores for an incredible amount of occupational titles in numerous languages.

Data can be queried (obtained) via the SPARQL endpoint or via the example queries. If the Linked Open Data format is new to you, you might enjoy these data stories on History of Work as Linked Open Data and this user question on Is there a list of female occupations?.

NEW version - CHANGE notes

This version is dated Apr 2025 and is not backwards compatible with the previous version (Feb 2021). The major changes are: - incredible simplification of graph representation (from 81 to 12); - use of sdo (https://schema.org/) rather than schema (http://schema.org); - replacement of prov:wasDerivedFrom with sdo:isPartOf to link occupational titles to originating datasets; - etl files (used for conversion to Linked Data) now publicly available via https://github.com/rlzijdeman/rdf-hisco; - update of issues with language tags; - specfication of language tags for english (eg. @en-gb, instead of @en); - new preferred API: https://api.druid.datalegend.net/datasets/HistoryOfWork/historyOfWork-all-latest/sparql (old API will be deprecated at some point: https://api.druid.datalegend.net/datasets/HistoryOfWork/historyOfWork-all-latest/services/historyOfWork-all-latest/sparql ) .

There are bound to be some issues. Please leave report them here.

Figure 1. Part of model illustrating the basic relation between occupations, schema.org and HISCO. https://druid.datalegend.net/HistoryOfWork/historyOfWork-all-latest/assets/601beed0f7d371035bca5521" alt="hisco-basic">

Figure 2. Part of model illustrating the relation between occupation, provenance and HISCO auxiliary variables. https://druid.datalegend.net/HistoryOfWork/historyOfWork-all-latest/assets/601beed0f7d371035bca551e" alt="hisco-aux">

Net primary productivity (NPP) over global grasslands is crucial for understanding the terrestrial carbon cycling and for the assessments of wild herbivores food security. During the past few decades, numerous field investigations have been conducted to estimate grassland NPP since the measuring criterion released by the International Biological Program. However, a comprehensive NPP database, particularly for belowground NPP (BNPP), in global grasslands is rare to date. Here, field NPP measurements from 438 publications (1957–2018) in global grasslands were collected, critically filtered, and incorporated in a comprehensive global database with observations for aboveground NPP (ANPP), BNPP, total NPP (TNPP), and BNPP fraction (fBNPP). Associated information on geographical locations, climatic records, grassland types, land use patterns, manipulations subjected to manipulative experiments, sampling year of study sites as well as NPP measurement methods are also documented. This database ...

The HANPP Collection: Human Appropriation of Net Primary Productivity as a Percentage of Net Primary Productivity represents a map identifying regions in which human consumption of NPP is greatly in excess of production by local ecosystems. Humans appropriate net primary productivity through the consumption of food, paper, wood and fiber, which alters the composition of the atmosphere, levels of biodiversity, energy flows within food webs and the provision of important ecosystem services. Net primary productivity (NPP), the net amount of solar energy converted to plant organic matter through photosynthesis, can be measured in Units of elemental carbon and represents the primary food energy source for the world's ecosystems.

Net-Chinese Co., Ltd. Whois Database, discover comprehensive ownership details, registration dates, and more for Net-Chinese Co., Ltd. with Whois Data Center.

The Global Estimated Net Migration by Decade: 1970-2000 data set provides estimates of net migration over the three decades from 1970 to 2000. Because of the lack of globally consistent data on migration, indirect estimation methods were used. The authors relied on a combination of data on spatial population distribution for four time slices (1970, 1980, 1990, and 2000) and subnational rates of natural increase in order to derive estimates of net migration on a 30 arc-second (~1km) grid cell basis. Net migration was estimated by subtracting the population in time period 2 from the population in time period 1, and then subtracting the natural increase (births minus deaths). The residual was considered to be net migration (in-migrants minus out-migrants). The authors ran 13 geospatial net migration estimation models based on outputs from the same number of imputation runs for urban and rural rates of natural increase.This data set represents the average of those runs. These data are reliable at broad scales of analysis (e.g. ecosystems or regions), but are generally not reliable for local level analyses. The data were produced for the United Kingdom Foresight project on Migration and Global Environmental Change.