First launched in 2010, PlayStation Plus is Sony’s subscription service which allows PlayStation users to play online multiplayer. As of March 2023, the service had over 47.4 million subscribers, a decrease from its peak of 48 million subscriptions in December 2021. In June 2022, Sony combined its PlayStation Plus and PlayStation Now cloud gaming services and the revamped PlayStation Plus service now features a three-tier subscription model. The existing service became PlayStation Plus Essential, with higher priced tiers PlayStation Plus Extra and PlayStation Plus Premium adding access to downloadable and streamable gaming titles which were formerly available under PlayStation Now.

PlayStation Plus perks As well as enabling gamers to play with their friends online, PlayStation Plus comes with several other perks. PS Plus members are given early access to upcoming games and are given discounts on PlayStation Store items. One of the biggest perks is the gift of two PlayStation 4 and one PS5 game every month. These so-called Instant Game Collection games have ranged from blockbuster games such as Call of Duty: Black Ops 4 (PS4) and A Plague Tale: Innocence (PS5), to smaller games such as a limousine driving game, Roundabout, and Knowledge is Power, a local multiplayer quiz game. In total, PlayStation Plus Essential subscribers were offered 1,304 U.S. dollars’ worth of free video games in 2022.

 PlayStation still going strong Announced in 2019, Sony's PlayStation 5 was released in November 2020. The PS5, along with Microsoft's Xbox Series S and Series X consoles, which were launched in the same month, are part of the ninth generation of video game consoles. Industry estimates that the PS5 will generate over 67 million unit sales by 2024. However, the rollout of the flagship console was marred by limited supply and scalpers taking advantage of the situation, driving up the price of the console for thousands of dollars in the secondary market.

Context

Video games have greatly contributed, and continue to contribute to the expansion of the entertainment industry. When the first video game, Pong, was launched in an arcade machine in 1972, it ignited a video game craze that quickly swept over the youth. With this, businesses such as Atari Games and Nintendo saw the golden opportunity of investing in a developing entertainment sector and began churning out gaming software and hardware. This caused the rise of the video game industry, which has generated over $109 billion in revenue and 2.2 billion gamers since its conception 50 years ago.

In this industry with over 47 million daily active users, Steam has been operating for almost 16 years. Its constant improvement to better accommodate users has made its development notable in the video game industry.

Steam is a digital distribution platform tailored to gamers and game developers. While it initially catered to PC games, the platform soon expanded its availability to home video game consoles such as the Xbox and Sony PlayStation. In Steam, gamers can log in to the website to conveniently purchase and play games online, a better alternative to buying physical copies of the games and manually downloading it on the computer.

#

https://images.vice.com/vice/images/articles/meta/2015/04/11/vendor-trash-imagining-the-future-of-video-game-retail-410-1428758025.jpg" alt="game">

#

Content

A lot of gamers write reviews at the game page and have an option of choosing whether they would recommend this game to others or not. However, determining this sentiment automatically from text can help Steam to automatically tag such reviews extracted from other forums across the internet and can help them better judge the popularity of games.

Game overview information for both train and test are available in single file game_overview.csv inside train.zip

Acknowledgements

Steam digital distribution.

Inspiration

• Predict whether the reviewer recommended the game titles available in the test set on the basis of review text and other information.

The actual boundaries were most recently adjusted in: January 2025.
Boundary information source: State Scientific Production Enterprise “Kartographia”.
Contributor: OCHA Field Information Services Section (FISS).
COD-AB quality level: cod-enhanced (geometry and attributes verified and standardized) and live services available.
https://codgis.itos.uga.edu/arcgis/rest/services/COD_External/UKR_pcode/FeatureServerfeature server.
Most recent COD-AB review conclusion: The COD-AB does not require any update.
OCHA country status: Operational (country office).
An edge-matched (COD-EM) dataset is available
Most recent COD-AB review date: January 2025.
Deepest administrative level: ADM4.
COD-PS HDX URL: https://data.humdata.org/dataset/cod-ps-ukr.
Deepest administrative level with complete coverage: ADM3.
COD-EM HDX link: https://data.humdata.org/dataset/cod-em-ukr.
Administrative level required by humanitarian community: ADM2.
Note: The Ukrainian government has not fully implemented its new administrative structure reforms in the Autonomous Republic of Crimea [UA01] and Sevastopol [UA85].
Consequently, 31 ADM4 features within the Sevastopol ADM3 polygon retain P-codes that do not correctly conform to [UA85]. This will be rectified in a COD-AB update once the government has regained control of the Crimean Peninsula.
Note: Some administrative feature names were adjusted in a September/October 2024 administratrive feature renaming exercise. See reference table at https://data.humdata.org/dataset/cod-ab-ukr.
Note: The Ukraine COD-AB was most recently adjusted in early 2024 - affecting administrative level 3 (hromadas) only.
Note: The population statistics common operational dataset (COD-PS-UKR) is only available via special request. See Ukraine - Subnational Population Statistics. Users should use the 'Request Data' button.
Note: Limitations of the Ukrainian-to-Russian transliteration method mean that more than one Ukrainian name may be represented by a single Russian feature name.
Note: Feature name attribute fields in this dataset incorrectly use 'UA' to represent the Ukrainian language. The correct language code is 'UK'. This will be resolved in 2026.

These data were produced by WorldPop at the University of Southampton. These data include gridded estimates of population at approximately 100m for 2021, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Turkey in 2021 provided in the Common Operational Dataset on Population Statistics (COD-PS) and built-up surfaces/volumes/height covariates extracted from GHSL datasets. The constrained and unconstrained top-down disaggregation method was used to produce the datasets. The modelling work and geospatial data processing was led by Bondarenko M., Priyatikanto R., Sorichetta A. Oversight was provided by Tatem A.J.

For further details, please, read the Release Statement.

Recommended citations

Bondarenko M., Priyatikanto R., Sorichetta A., and Tatem A.J.. 2023 Gridded population estimates for Turkey using UN COD-PS estimates 2021, version 1.0. WorldPop University of Southampton. doi:10.5258/SOTON/WP00758

License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release@worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release@worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

These data were produced by WorldPop at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km for 2022, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Sudan in 2022 provided in the Common Operational Dataset on Population Statistics (COD-PS) and built-up surfaces/volumes covariates extracted from GHSL datasets; GHS-BUILT-Surface epoch 2020 layer , combined with Digitize Africa building footprints, were used to delineate settled areas. The constrained and unconstrained top-down disaggregation method was used to produce the datasets, i.e. population was only estimated within areas classified as containing built settlement. The modelling work and geospatial data processing was led by Bondarenko M. and Leasure D.R.. Oversight was provided by Tatem A.J.

For further details, please, read the Release Statement.

Recommended citations

Bondarenko M., Leasure D.R., and Tatem A.J. 2023 Gridded population estimates for Sudan using UN COD-PS estimates 2022, version 2.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00761

License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release@worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release@worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

These data were produced by WorldPop at the University of Southampton and the ‘Smart Cities and Spatial Development’ team at the German Remote Sensing Data Center (DFD) of the German Aerospace Center (DLR). These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Ukraine in 2020 provided in the Common Operational Dataset on Population Statistics (COD-PS) and building height/area/fraction/volume covariates extracted from the World Settlement Footprint (WSF) imperviousness and WSF-3D by DLR. The constrained top-down disaggregation method was used to produce the datasets. The modelling work and geospatial data processing was led by Bondarenko M., Palacios-Lopez D., Sorichetta A., Leasure D.R., ,Zeidler J., Marconcini M., and Esch T.. Oversight was provided by Tatem A.J. Internal WorldPop peer reviews that helped to improve the results and documentation was provided by Lazar A.N..

Main data sources

• The German Aerospace Centre’s (DLR) WSF imperviousness and WSF 3D products (WSF-3D).
• Subnational population estimates for Ukraine in 2020 provided in the Common Operational Dataset on Population Statistics (COD-PS). The subnational population estimates were produced using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since then.
• Subnational Administrative Boundaries for Ukraine provided by OCHA .
• Geospatial covariate layers available at WorldPop.

For further details, please, read the Release Statement.

Release content

1. ukr_pop_2020_100m_constrained_v2.zip
2. ukr_pop_2020_1km_constrained_v2.zip
3. ukr_agesex_2020_100m_constrained_v2.zip
4. ukr_agesex_2020_1km_constrained_v2.zip
5. ukr_agesex_0_18_2020_100m_constrained_v2.zip
6. ukr_agesex_0_18_2020_1km_constrained_v2.zip

Recommended citations

Bondarenko M., Palacios-Lopez D., Sorichetta A., Leasure D.R., Zeidler J., Marconcini, M., Esch T., and Tatem A.J. 2022 Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 2.0. WorldPop and DLR, University of Southampton. doi:10.5258/SOTON/WP00735

License

These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

These data were produced by WorldPop at the University of Southampton and World Bank Group . These data include gridded estimates of population at approximately 100m for 2019, 2023 and 2024 along with estimates of the number of people belonging to individual age-sex groups. These results were produced using subnational population estimates for Yemen provided in the Common Operational Dataset on Population Statistics (2019, 2023 COD-PS and 2024 COD-PS ) and Subnational Administrative Boundaries for Yemen provided by OCHA.

For further details, please, read the Release Statement.

Recommended citations

WorldPop and World Bank Group. 2024 Gridded population estimates for Yemen using UN COD-PS estimates 2019, 2023 and 2024, version 1.0. https://data.worldpop.org/repo/prj/WP_WB/YEM/v1/

License These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release@worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release@worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

Democratic People's Republic of Korea administrative level 0-2 boundaries (COD-AB) dataset.

These administrative boundaries were established in: 2008

NOTE: See COD-PS caveats about two COD-AB features lacking COD-PS matches.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from World Food Programme

Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Democratic People's Republic of Korea COD-PS.

No edge-matched (COD-EM) version of this COD-AB has yet been prepared.

Please see the COD Portal.

Administrative level 1 contains 11 feature(s). The normal administrative level 1 feature type is ""Province, Special City"".

Administrative level 2 contains 179 feature(s). The normal administrative level 2 feature type is ""County, City, Special City"".

Recommended cartographic projection: Asia South Albers Equal Area Conic

This metadata was last updated on January 13, 2025.

DPR Korea administrative level 0 (country), 1 (province, special city), and 2 (county, city, special city) 2008 population statistics

REFERENCE YEAR: 2008

These population statistics tables are suitable for database or GIS linkage to the DPR Korea administrative level 0-2 boundaries. Note, however the caveat below that two administrative level 2 features in the COD-AB do not have corresponding records in the COD-PS table.

R&D in wireless networking typically depends on experimentation to make realistic evaluations, since simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility. Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce: either the original community testbed is unavailable – offline or running other experiments – or the custom testbed used is inaccessible. Fed4FIRE+ wireless testbeds such as w-iLab.t and NITOS, although deployed in controlled environments, do not fully address the problem. The CONCRETE tool used in such testbeds assures the repeatability and reproducibility of experiments, but ignores executions whose results are also representative of the system operation and often reveal unpredicted behaviour that must be understood. What if we could make any wireless experiment repeatable and reproducible under the same exact conditions? What if we could share the same Fed4FIRE+ testbed execution conditions among an "infinite" number of users? What if we could run wireless experiments faster than in real time? INESC TEC has been developing the Offline Experimentation (OE) approach that combines the best of simulation and experimentation to achieve the above-mentioned goals. By relying on Network Simulator 3 (ns-3) and its good simulation capabilities from the MAC to the application layer, we have been exploring how ns-3 can be used to replicate real-world wireless experiments using real traces containing 1) position of nodes and 2) the quality of each radio link. The SIMBED project aimed at running a set of wireless experiments on top of the controlled environments of w-ilab.t and NITOS Fed4FIRE+ testbeds to further validate the OE approach. For that purpose, we configured different fixed and mobile experimental scenarios, representative of Wi-Fi range of operation, and measured the attained network performance using metrics such as throughput and Round-Trip Time (RTT). Then, we repeated each experiment using, both, Pure Simulation (PS) and OE approaches based on ns-3, also measuring the network performance for the same set of executions of experiments for all the different scenarios. By comparing the performance metrics of each real experiment with its PS and OE counterparts, we were able to measure the relative error of each simulation approach relatively to the real experiments, as well as the accuracy gains introduced by the OE approach when compared to the PS traditional alternative. The main results show that it is possible to repeat and reproduce real experiments in ns-3, using the OE approach, achieving closer to real performance than using the PS approach. For all the experiments performed in SIMBED, using the OE approach resulted in an average accuracy gain of 59% when comparing to the PS approach. These results were important for validating a PhD thesis contribution related to the OE approach, as well as for producing two conference papers and one journal paper. The SIMBED results increased our confidence on the accuracy of the OE approach and are envisioned to foster the adoption of the OE approach by the networking community, in complement to the use of real experimentation. The following dataset presents the results of the SIMBED project, organized in different folders, for each subset of experiments carried on: SubExp#1: Static point-to-point Wi-Fi communications using auto-rate (Minstrel) SubExp#1.1: Using w-iLab.2 (medium to high SNR scenarios) SubExp#1.2: Using w-iLab.2 (low SNR scenarios) SubExp#1.3: Using NITOS SubExp#1.4: Using w-iLab.1 (datacenter room) SubExp#2: Static point-to-point Wi-Fi communications using fixed rate SubExp#3: Mobile point-to-point Wi-Fi communications using auto-rate (Minstrel) SubExp#4: Static multiple access Wi-Fi communications using auto-rate (Minstrel) SubExp#4.1: Using w-iLab.2 (bidirectional) (medium to high SNR scenarios) SubExp#4.2: Using w-iLab.2 (bidirectional) (low SNR scenarios) SubExp#4.3: Using NITOS (bidirectional) SubExp#4.4: Using w-iLab.1 (bidirectional) SubExp#4.5: Using NITOS (2 STAs) SubExp#4.6: Using w-iLab.2 (2 STAs) SubExpExample: contains raw experimental logs, parsed data and simulation results, to show how data extracted from the nodes is processed to be compatible with the OE approach and comparable with OE and PS simulation results. Each experiment has an individual folder, named according to the date and time of the experiment and the nodes used. Inside, there’s a folder for the parsed experimental results, which contains This folder contains the details and parsed logs of the experiment, as follows: date_time.cfg – configuration details of the experiment da...

The actual boundaries were most recently adjusted in: January 2025.
Boundary information source: State Scientific Production Enterprise “Kartographia”.
Contributor: OCHA Field Information Services Section (FISS).
COD-AB quality level: cod-enhanced (geometry and attributes verified and standardized) and live services available.
Most recent COD-AB review conclusion: The COD-AB does not require any update.
An edge-matched (COD-EM) dataset is available
COD-EM HDX link: https://data.humdata.org/dataset/cod-em-ukr.
OCHA country status: Operational (country office).
Most recent COD-AB review date: January 2025.
Deepest administrative level: ADM4.
COD-PS HDX URL: https://data.humdata.org/dataset/cod-ps-ukr.
Live featureserver: https://codgis.itos.uga.edu/arcgis/rest/services/COD_External/UKR_pcode/FeatureServer.
Deepest administrative level with complete coverage: ADM3.
Administrative level required by humanitarian community: ADM2.
Note: The Ukrainian government has not fully implemented its new administrative structure reforms in the Autonomous Republic of Crimea [UA01] and Sevastopol [UA85].
Consequently, 31 ADM4 features within the Sevastopol ADM3 polygon retain P-codes that do not correctly conform to [UA85]. This will be rectified in a COD-AB update once the government has regained control of the Crimean Peninsula.
Note: Some administrative feature names were adjusted in a September/October 2024 administratrive feature renaming exercise. See reference table at https://data.humdata.org/dataset/cod-ab-ukr.
Note: The Ukraine COD-AB was most recently adjusted in early 2024 - affecting administrative level 3 (hromadas) only.
Note: The population statistics common operational dataset (COD-PS-UKR) is only available via special request. See Ukraine - Subnational Population Statistics. Users should use the 'Request Data' button.
Note: Limitations of the Ukrainian-to-Russian transliteration method mean that more than one Ukrainian name may be represented by a single Russian feature name.
Note: Feature name attribute fields in this dataset incorrectly use 'UA' to represent the Ukrainian language. The correct language code is 'UK'. This will be resolved in 2026.
Note: ADM0 is not affected by the September/October 2024 administrative feature renaming exercise.
Typical feature type: oblast (region) with three exceptions.
P-code format: UAnn.
COD-PS compatibility: ADM0, ADM1, ADM2.
OCHA region: Europe.
Feature count: 27.
The ADM1 layer can be linked by P-CODE to the ADM1 Population statistics.
Note: Kyiv (city) [UA80] and Sevastopol [UA85] are each a 'city with special status' rather than an 'oblast'.
Note: Crimea [UA01] is an 'autonomous republic' rather than an 'oblast'.
Note: However, Ukraine Population Statistics are only available on special request.

ADMINISTRATIVE LEVEL 4 metadata: Typical feature type: ADM4 features may be of 'city', 'settlement', or 'village' type. However, all 'village' features are currently categorized in the 'ADM4_TYPE' as a 'settlement'. This is expected to be resolved in 2026.
P-code format: UAnnnnnnnnnn.
Feature count: 29707.
COD-PS compatibility: no ADM4 population statistics are available.
COD-PS HDX URL: https://data.humdata.org/dataset/cod-ps-ukr.
Note: 329 ADM4 features were affected by the September/October 2024 administrative feature renaming excercise.
Specific details are provided in the 'ADM4' tab of the 'UKR_Admin234_Renaming_ReferenceTable_20250123.xlsx' reference table available on the HDX dataset Ukraine - Subnational Administrative Boundaries.
Note: The COD-AB ADM4 boundary attributes for this COD-AB have not been adjusted.
The Ukrainian government has not fully implemented its new administrative structure reforms in the Autonomous Republic of Crimea [UA01] and Sevastopol [UA85].
Consequently, 31 ADM4 features within the Sevastopol ADM3 polygon retain P-codes that do not correctly conform to [UA85]. This will be rectified in a COD-AB update once the government has regained control of the Crimean Peninsula.

These data were produced by the WorldPop Research Group at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using Subnational Population Statistics 2020 for Ukraine provided in the Common Operational Dataset on Population Statistics (COD-PS) and ORNL LandScan HD for Ukraine 2022 settlement layer.

The datasets are produced using the "top-down" method, with both the unconstrained and constrained top-down disaggregation methods used to produce two different datasets. The differences between constrained and un-constrained methods are described here .

Main data sources

• Subnational Population Statistics for Ukraine provided by Common Operational Dataset on Population Statistics (COD-PS). The subnational population statistics were estimated using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since the last census.
• Settlement layer ORNL LandScan HD for Ukraine.
• Subnational Administrative Boundaries for Ukraine provided by OCHA.
• Geospatial covariate layers available at WorldPop.

For further details, please, read the Release Statement.

Release content

1. ukr_pop_2020_100m_unconstrained_v1_0.zip
2. ukr_pop_2020_100m_constrained_v1_0.zip
3. ukr_pop_2020_1km_unconstrained_v1_0.zip
4. ukr_pop_2020_1km_constrained_v1_0.zip
5. ukr_agesex_2020_100m_unconstrained_v1_0.zip
6. ukr_agesex_2020_100m_constrained_v1_0.zip
7. ukr_agesex_2020_1km_unconstrained_v1_0.zip
8. ukr_agesex_2020_1km_constrained_v1_0.zip
9. ukr_agesex_0_18_2020_1km_unconstrained_v1_0.zip
10. ukr_agesex_0_18_2020_1km_constrained_v1_0.zip

Recommended citations

Bondarenko M., Sorichetta A., Leasure DR. and Tatem AJ. 2022 Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 1.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00734

License

These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

Marshall Islands administrative level 0-2 boundaries (COD-AB) dataset.

NOTE: WARNING: The geoservices are not scaled correctly. Users are advised to rely on the shapefiles or geodatabase. The COD-AB omits MH0609 feature contained in COD-PS. The disbursed nature of this archipelago country does not make it suitable for EMF mapping.

This COD-AB was most recently reviewed for accuracy and necessary changes in October 2024. The COD-AB does not require any update.

Sourced from Secretariat of the Pacific Community, Statistics for Development Division

Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Marshall Islands COD-PS.

As this is an island country, no edge-matched (COD-EM) version of this COD-AB is required.

Please see the COD Portal.

Administrative level 1 contains 25 feature(s).

Administrative level 2 contains 153 feature(s).

These administrative boundaries were established in: 2021

Recommended cartographic projection: Oceania

Dominican Republic administrative level 0-4 boundaries (COD-AB) dataset.

The date that these administrative boundaries were established is unknown.

NOTE: This COD-AB was updated by OCHA FIS in March 2025 without the most complete quality control processes. The live geoservices have not been similarly updated. The accompanying COD-EM has not been similarly updated. The COD-PS extends only to ADM2

This COD-AB was most recently reviewed for accuracy and necessary changes in February 2025. The COD-AB does not require any update.

Sourced from OCHA Field Information Services Section (FISS)

Live geoservices (provided by Information Technology Outreach Services (ITOS) with funding from USAID) are available for this COD-AB. USERS SHOULD BE AWARE that changes to COD-AB datasets on HDX since February 2025 will NOT be reflected in the live geoservices. Please see COD_External. (For any earlier versions please see here, here, and here.) Vetting, configuration, and geoservices provision by Information Technology Outreach Services (ITOS) with funding from USAID.

This COD-AB is suitable for database or GIS linkage to the Dominican Republic COD-PS.

An edge-matched (COD-EM) version of this COD-AB is available on HDX here.

Please see the COD Portal.

Administrative level 1 contains 10 feature(s). The normal administrative level 1 feature type is 'region'.

Administrative level 2 contains 32 feature(s). The normal administrative level 2 feature type is 'province or national district (distrito nacional)'.

Administrative level 3 contains 158 feature(s). The normal administrative level 3 feature type is 'municipio'.

Administrative level 4 contains 383 feature(s). The normal administrative level 4 feature type is 'dm(?)'.

Recommended cartographic projection: North America Albers Equal Area Conic

This metadata was last updated on March 04, 2025.

This Common Operational Dataset on Population Statistics (COD-PS) is estimated using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since the last census.

REFERENCE YEAR: 2022

The COD-PS is age- and sex-disaggregated at ADM-1 level (i.e. Oblast) and has a reference date of 1 January, 2022.

The ukr_admpop_2022.xlsx spreadsheet includes a table of sex and age disaggregated 2022 projected population statistics of the 30 administrative level 4 features that correspond to cities of more than 100,000 people (excluding Kyiv city).

These tables are suitable for database or GIS linkage to the Ukraine - Subnational Administrative Boundaries and Ukraine - Subnational Edge-matched Administrative Boundaries layers using the ADM0, and ADM2_PCODE fields.

The Clothing Keypoints Dataset aims to enhance the precision of fashion-related AI applications by providing a large-scale collection of images for keypoint detection tasks. This dataset includes internet-collected images that span a wide array of scenarios, including e-commerce platforms, fashion shows, social media, and offline user-generated content. It is meticulously annotated to identify keypoints on clothing items, facilitating the development of algorithms for pose estimation, size fitting, style matching, and interactive shopping experiences. The dataset includes classified labels, bounding boxes, and keypoints for 80 different clothing types, making it a comprehensive resource for improving the accuracy and reliability of fashion AI systems.

The People and Safety Belt Semantic Segmentation Dataset is specifically curated for industrial applications, consisting of CCTV images captured within a factory environment at a resolution of 1920 x 1080 pixels. This dataset focuses on both instance and semantic segmentation, providing annotations for people and the seat belts they are wearing, aimed at enhancing safety compliance monitoring.

Discover our Mandarin Language Dataset Wake Word, meticulously curated and designed for training wake word detection models. This comprehensive dataset offers a diverse range of Mandarin wake words in various contexts, enabling robust and accurate model development for speech recognition applications tailored to Mandarin-speaking users.

Discover a comprehensive Wake Word Italian Dataset meticulously curated for training and testing voice recognition systems. This dataset offers a diverse collection of audio samples in Italian, enabling researchers and developers to enhance wake word detection algorithms tailored for Italian-speaking users.