Find research datasets worth reusing

opencc-by-4.0Jan 2021View details →

qdgc South Sudan

QDGC tables delivered in geopackage file - - - - - - - - - - - - - - - - - - - - - - QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth. Within each geopackage file you will find a number of tables with these names: -tbl_qdgc_01 -tbl_qdgc_02 -tbl_qdgc_03 -tbl_qdgc_04 -tbl_qdgc_05 -etc The attributes for each table are: qdgc Unique Quarter Degree Grid Cell reference string area_reference Country level_qdgc QDGC level cellsize degrees decimal degree for the longitudal and latitudal length of the cell lon_center Longitude center of the cell lat_center Latitudal center of the cell area_km2 Calculated area for the cell geom Geometry Metadata -------- Geodata GCS_WGS_1984 Datum: D_WGS_1984 Prime Meridian: 0 Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this: - st_area(st_transform(geom, 102022))/1000000) Licensing --------- Creative Commons Attribution 4.0 International Conditions ---------- Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc Thankyou -------- The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them: - Tanzania Wildlife Research Institute - Dept of Biology, NTNU, Norway - Norwegian Environment Agency - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey References ---------- * http://en.wikipedia.org/wiki/QDGC * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/ * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection * http://www.safe.com Ragnvald Larsen Trondheim 23rd of January, 2021 ragnvald@mindland.com www.mindland.com

opencc-by-4.0Dec 2023View details →

Transport Starter Data Kit: Historical socio-transport data for South Sudan

This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger and freight activity, segregated by mode and fuel. Additionally, historical data on energy intensities, load factors, vehicle stock, population (total, urban, rural, growth), and GDP (total, agriculture, construction, mining, manufacturing, service, energy, growth) are included in the kit, within the 'Data' tab. The historical data can be used as a foundation for transport-energy modelling and/or to identify areas of improvement. This data was verified through consultation with relevant stakeholders before publishing. The definition used for each vehicle mode is found in the 'Definitions' tab, and the description of each data observation status is found in the 'Notes' tab. All data sources are linked where possible.

opencc-by-sa-4.0Dec 2023View details →

Infrastructure Climate Resilience Assessment Data Starter Kit for South Sudan

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. These extracts are derived from global datasets which have been clipped to the national scale (or subnational, in cases where national boundaries have been split, generally to separate outlying islands or non-contiguous regions), using Natural Earth (2023) boundaries, and is not meant to express an opinion about borders, territory or sovereignty. Human-induced climate change is increasing the frequency and severity of climate and weather extremes. This is causing widespread, adverse impacts to societies, economies and infrastructures. Climate risk analysis is essential to inform policy decisions aimed at reducing risk. Yet, access to data is often a barrier, particularly in low and middle-income countries. Data are often scattered, hard to find, in formats that are difficult to use or requiring considerable technical expertise. Nevertheless, there are global, open datasets which provide some information about climate hazards, society, infrastructure and the economy. This "data starter kit" aims to kickstart the process and act as a starting point for further model development and scenario analysis. Hazards: <ul> <li>coastal and river flooding (Ward et al, 2020)</li> <li>extreme heat and drought (Russell et al 2023, derived from Lange et al, 2020)</li> <li>tropical cyclone wind speeds (Russell 2022, derived from Bloemendaal et al 2020 and Bloemendaal et al 2022)</li> </ul> Exposure: <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2023)</li> <li>railways (OpenStreetMap, 2023)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. To learn more about related concepts, there is a free short course available through the Open University on <a href="https://www.open.edu/openlearncreate/course/view.php?id=11539">Infrastructure and Climate Resilience</a>. This <a href="https://opsis.eci.ox.ac.uk/courses/2-infra-for-resil/">overview of the course</a> has more details. These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: <ul> <li> <a href="https://github.com/tomalrussell/snkit"><code>snkit</code></a> helps clean network data </li> <li> <a href="https://github.com/nismod/snail"><code>nismod-snail</code></a> is designed to help implement infrastructure exposure, damage and risk calculations </li> </ul> The <a href="https://github.com/nismod/open-gira"><code>open-gira</code></a> repository contains a larger workflow for global-scale open-data infrastructure risk and resilience analysis. For a more developed example, some of these datasets were key inputs to a regional climate risk assessment of current and future flooding risks to transport networks in East Africa, which has a related online visualisation tool at <a href="https://east-africa.infrastructureresilience.org/">https://east-africa.infrastructureresilience.org/</a> and is described in detail in Hickford et al (2023). References <ul> <li> Arderne, Christopher, Nicolas, Claire, Zorn, Conrad, & Koks, Elco E. (2020). Data from: Predictive mapping of the global power system using open data [Dataset]. In Nature Scientific Data (1.1.1, Vol. 7, Number Article 19). Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.3628142">10.5281/zenodo.3628142</a> </li> <li> Bloemendaal, Nadia; de Moel, H. (Hans); Muis, S; Haigh, I.D. (Ivan); Aerts, J.C.J.H. (Jeroen) (2020): STORM tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/12705164.v3">10.4121/12705164.v3</a> </li> <li> Bloemendaal, Nadia; de Moel, Hans; Dullaart, Job; Haarsma, R.J. (Reindert); Haigh, I.D. (Ivan); Martinez, Andrew B.; et al. (2022): STORM climate change tropical cyclone wind speed return periods. 4TU.ResearchData. [Dataset]. DOI: <a href="https://doi.org/10.4121/14510817.v3">10.4121/14510817.v3</a> </li> <li> Global Energy Observatory, Google, KTH Royal Institute of Technology in Stockholm, Enipedia, World Resources Institute. (2018) Global Power Plant Database. Published on Resource Watch and Google Earth Engine; <a href="http://resourcewatch.org/">resourcewatch.org/</a> </li> <li> Hickford et al (2023) Decision support systems for resilient strategic transport networks in low-income countries – Final Report. Available online: <a href="https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries">https://transport-links.com/hvt-publications/final-report-decision-support-systems-for-resilient-strategic-transport-networks-in-low-income-countries</a> </li> <li> Lange, S., Volkholz, J., Geiger, T., Zhao, F., Vega, I., Veldkamp, T., et al. (2020). Projecting exposure to extreme climate impact events across six event categories and three spatial scales. Earth's Future, 8, e2020EF001616. DOI: <a href="https://doi.org/10.1029/2020EF001616">10.1029/2020EF001616</a> </li> <li> Natural Earth (2023) Admin 0 Map Units, v5.1.1. [Dataset] Available online: <a href="https://www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details/">www.naturalearthdata.com/downloads/10m-cultural-vectors/10m-admin-0-details</a> </li> <li> OpenStreetMap contributors, Russell T., Thomas F., nismod/datapkg contributors (2023) Road and Rail networks derived from OpenStreetMap. [Dataset] Available at <a href="https://global.infrastructureresilience.org">global.infrastructureresilience.org</a> </li> <li> Pesaresi M., Politis P. (2023): GHS-BUILT-S R2023A - GHS built-up surface grid, derived from Sentinel2 composite and Landsat, multitemporal (1975-2030) European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea">data.europa.eu/89h/9f06f36f-4b11-47ec-abb0-4f8b7b1d72ea</a>, doi:10.2905/9F06F36F-4B11-47EC-ABB0-4F8B7B1D72EA </li> <li> Russell, T., Nicholas, C., & Bernhofen, M. (2023). Annual probability of extreme heat and drought events, derived from Lange et al 2020 (Version 2) [Dataset]. Zenodo. DOI: <a href="https://doi.org/10.5281/zenodo.8147088">10.5281/zenodo.8147088</a> </li> <li> Schiavina M., Freire S., Carioli A., MacManus K. (2023): GHS-POP R2023A - GHS population grid multitemporal (1975-2030). European Commission, Joint Research Centre (JRC) PID: <a href="http://data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe">data.europa.eu/89h/2ff68a52-5b5b-4a22-8f40-c41da8332cfe</a>, doi:10.2905/2FF68A52-5B5B-4A22-8F40-C41DA8332CFE </li> <li> Ward, P.J., H.C. Winsemius, S. Kuzma, M.F.P. Bierkens, A. Bouwman, H. de Moel, A. Díaz Loaiza, et al. (2020) Aqueduct Floods Methodology. Technical Note. Washington, D.C.: World Resources Institute. Available online at: <a href="https://www.wri.org/publication/aqueduct-floods-methodology">www.wri.org/publication/aqueduct-floods-methodology</a>. </li> </ul>

opencc-zeroAug 2024View details →

National Checklists: South Sudan Species List

Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a>

opencc-by-4.0Apr 2021View details →

CCG Starter Data Kit: South Sudan

A starter data kit for South Sudan

opencc-by-4.0May 2023View details →

South Sudan OAE Data

The age-specific OAE data from South Sudan have been used to estimate parameters of new OnNCHOSIM-OAE model, published in PLoS Neglected Tropical Disease.

zenodo36/100

South Sudan

[South Sudan](https://en.wikipedia.org/wiki/South_Sudan) is a landlocked country in east/central Africa. It is landlocked by Ethiopia, Sudan, Central African Republic, Democratic Republic of the Congo, Uganda and Kenya. It has a population of 11.06 million, of which 525,953 live in the capital and largest city Juba. Source: Objaverse 1.0 / Sketchfab

opencc-byFeb 2022View details →

Subspecies and Distribution. . p. perforatus E. Geoffroy Saint-Hilaire, 1818 — Nile Valley in Egypt and N Sudan. . p. haedinusThomas, 1915 - Middle East (Israel, Arabian Peninsula, and S Iran) E to India, and E Africa from Djibouti to Kenya (including Lamu I) and Tanzania; it may also occur in Eritrea. . p. senegalensis Desmarest, 1820 - scattered localities in W Africa, from S Mauritania, Senegal, and Guinea-Bissau E to NW Nigeria and W Cameroon; it may occur in Guinea and Ivory Coast. . p. sudani Thomas, 1915 — C & S Sudan, South Sudan, Uganda, and S through the Congo Basin to N & E Botswana, S Zimbabwe, and NE South Africa in Emballonuridae

Subspecies and Distribution. . p. perforatus E. Geoffroy Saint-Hilaire, 1818 — Nile Valley in Egypt and N Sudan. . p. haedinusThomas, 1915 - Middle East (Israel, Arabian Peninsula, and S Iran) E to India, and E Africa from Djibouti to Kenya (including Lamu I) and Tanzania; it may also occur in Eritrea. . p. senegalensis Desmarest, 1820 - scattered localities in W Africa, from S Mauritania, Senegal, and Guinea-Bissau E to NW Nigeria and W Cameroon; it may occur in Guinea and Ivory Coast. . p. sudani Thomas, 1915 — C & S Sudan, South Sudan, Uganda, and S through the Congo Basin to N & E Botswana, S Zimbabwe, and NE South Africa

Distribution. Extent of this species' dis tribution is not yet known; recorded with certainty in Morocco, Senegal, Saudi Ara bia, and Yemen. It is thought to be con tinuously distributed from Mauritania and Senegal E to South Sudan, Ethiopia, and Eritrea. However, boundary between this species and the morphologically identical H. coffer is not known. in Family Hipposideridae (Old World Leaf-nosed Bats)

Distribution. Extent of this species' dis tribution is not yet known; recorded with certainty in Morocco, Senegal, Saudi Ara bia, and Yemen. It is thought to be con tinuously distributed from Mauritania and Senegal E to South Sudan, Ethiopia, and Eritrea. However, boundary between this species and the morphologically identical H. coffer is not known.

Distribution. Patchily in Africa N of the equator from Guinea Bissau and Guinea E to South Sudan and extreme NW Uganda. in Hipposideridae

Distribution. Patchily in Africa N of the equator from Guinea Bissau and Guinea E to South Sudan and extreme NW Uganda.

Distribution. Only known with certainty from Mozambique (based on recent morphometric studies), but populations from S South Sudan, Kenya, Tanzania, Congo Basin to N Angola and S to Mozambique and NE South Africa, including Unguja I (Zanzibar Archipelago) are tentatively included here. in Rhinolophidae

Distribution. Only known with certainty from Mozambique (based on recent morphometric studies), but populations from S South Sudan, Kenya, Tanzania, Congo Basin to N Angola and S to Mozambique and NE South Africa, including Unguja I (Zanzibar Archipelago) are tentatively included here.

opencc-by-4.0May 2017View details →

South Sudan Synthetic Ecosystem

South Sudan synthetic ecosystem dataset consisting of tables for persons and households. This dataset was created by the SPEW R Package using the following input data sources: GeoHive counts and IPUMS-I shapefiles and microdata.

Distribution. Sub-Saharan Africa; virtually eradicated from W Africa, and greatly reduced in C and NE Africa. The largest populations exist in Botswana, Tanzania, and Zimbabwe, which account for approximately half of the estimated number of African Wild Dogs remaining in the wild. Other populations occur in Central African Republic, Ethiopia, Kenya, Mozambique, Namibia, South Africa, Sudan, and Zambia. Potential small populations (less than 100 individuals) may exist in Cameroon, Chad, Senegal, and Somalia. in Canidae

Distribution. Sub-Saharan Africa; virtually eradicated from W Africa, and greatly reduced in C and NE Africa. The largest populations exist in Botswana, Tanzania, and Zimbabwe, which account for approximately half of the estimated number of African Wild Dogs remaining in the wild. Other populations occur in Central African Republic, Ethiopia, Kenya, Mozambique, Namibia, South Africa, Sudan, and Zambia. Potential small populations (less than 100 individuals) may exist in Cameroon, Chad, Senegal, and Somalia.

Subspecies and Distribution. P. ¢. enistata Sparrman, 1783 — E African coast (S Egypt, Sudan, Eritrea, Djibouti, Ethiopia, Somalia, Kenya, NE Uganda to C Tanzania). P. c. septentrionalis Rothschild, 1902 — most of S Africa (S Angola, S Zambia, SW Mozambique, Namibia, Botswana, Zimbabwe, Swaziland, Lesotho, and South Africa). in Hyaenidae

Subspecies and Distribution. P. ¢. enistata Sparrman, 1783 — E African coast (S Egypt, Sudan, Eritrea, Djibouti, Ethiopia, Somalia, Kenya, NE Uganda to C Tanzania). P. c. septentrionalis Rothschild, 1902 — most of S Africa (S Angola, S Zambia, SW Mozambique, Namibia, Botswana, Zimbabwe, Swaziland, Lesotho, and South Africa).

Subspecies and Distribution. H. p. parvula Sundevall, 1847 — NE South Africa, Mozambique, and Zimbabwe. H. p. wore Thomas, 1919 — NE Mozambique and Tanzania. H. p. mimetra Thomas, 1926 — NW Botswana and N Namibia. H. p. nero Thomas, 1928 — C Namibia. H. p. ruficeps Kershaw, 1922 — Zambia (Southern Province & Kafue area). H. p. undulata Peters, 1852 — N & E Africa from Ethiopia and Sudan to Malawi. H. p. varia Thomas, 1902 — C Africa from Angola to Uganda. in Herpestidae

Subspecies and Distribution. H. p. parvula Sundevall, 1847 — NE South Africa, Mozambique, and Zimbabwe. H. p. wore Thomas, 1919 — NE Mozambique and Tanzania. H. p. mimetra Thomas, 1926 — NW Botswana and N Namibia. H. p. nero Thomas, 1928 — C Namibia. H. p. ruficeps Kershaw, 1922 — Zambia (Southern Province & Kafue area). H. p. undulata Peters, 1852 — N & E Africa from Ethiopia and Sudan to Malawi. H. p. varia Thomas, 1902 — C Africa from Angola to Uganda.

Distribution. Widely distributed, ranges from the sub-Saharan belt, from Senegal to the Red Sea Coast in Sudan, and S to South Africa, also occurs on Zanzibar I. in Herpestidae

Distribution. Widely distributed, ranges from the sub-Saharan belt, from Senegal to the Red Sea Coast in Sudan, and S to South Africa, also occurs on Zanzibar I.

Subspecies and Distribution. I. a. albicauda Cuvier, 1829 — Senegal to E Sudan, Eritrea, and N Somalia; also Arabian Peninsula. I. a. dialeucos Hollister, 1916 — N Kenya, S Somalia, and S Ethiopia. I. a. grandis Thomas, 1890 — S Angola, Zambia, S Tanzania to South Africa. I. a. ibeana Thomas, 1904 — DR Congo to C Kenya. I. a. loandae Thomas, 1904 — N Angola and S DR Congo. I. a. loempo Temminck, 1853 — W Africa (Guinea). in Herpestidae

Subspecies and Distribution. I. a. albicauda Cuvier, 1829 — Senegal to E Sudan, Eritrea, and N Somalia; also Arabian Peninsula. I. a. dialeucos Hollister, 1916 — N Kenya, S Somalia, and S Ethiopia. I. a. grandis Thomas, 1890 — S Angola, Zambia, S Tanzania to South Africa. I. a. ibeana Thomas, 1904 — DR Congo to C Kenya. I. a. loandae Thomas, 1904 — N Angola and S DR Congo. I. a. loempo Temminck, 1853 — W Africa (Guinea).

Distribution. Iberian Peninsula, N Africa, and the Middle East in S Turkey, Syria, Lebanon, Jordan, and Israel; in Sub-Saharan Africa from Senegal and Gambia to E Africa in Sudan, Ethiopia, Somalia, and Kenya and then S to Gabon, Angola, N Namibia, N Botswana, N Zimbabwe, Mozambique, and South Africa. Occurrence in Europe (Portugal and Spain) likely due to introduction from North Africa. in Herpestidae

Distribution. Iberian Peninsula, N Africa, and the Middle East in S Turkey, Syria, Lebanon, Jordan, and Israel; in Sub-Saharan Africa from Senegal and Gambia to E Africa in Sudan, Ethiopia, Somalia, and Kenya and then S to Gabon, Angola, N Namibia, N Botswana, N Zimbabwe, Mozambique, and South Africa. Occurrence in Europe (Portugal and Spain) likely due to introduction from North Africa.