Context

The dataset provided include players data for the ultimate team mode in fifa 21 .

Columns Description

here is a description of some columns in the dataset : - Ratings : Player Rating - Position : Player Position - Version : Card Version - PS : Price on Playstation , if 0 then it is not available in market - SKI : Skills rating of player ( from 0 to 5 ) - WF : Weak Foot Skills ( from 0 to 5 ) - WR : Work rate of player on the field , and given in the formula ( Attack Work rate / Defence Work rate ) , each value can be ( low , medium , high ) - PAC : Player Pace (Speed) - SHO : Player Shooting power
- PAS : Player Pass - DRI : Player Dribble - DEF : Player Defence - PHY : Player Physicality - Body : player height given in cm and feet followed by type of body of player ( for some players the game have custom body for them ) - Popularity : popularity of using the player - BS : Base stats - IGS : In game stats

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 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.

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.

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.