Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
269
datasets available to search
ShareScore release 0.9.0
Dataset results
269 results for “Botswana”
National Checklists 2017: Botswana Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details<p></p>A list of species from Botswana collected using effechecka and geonames polygons
National Checklists 2019: Botswana Species List
Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See <p></p>https://github.com/diatomsRcool/checklists for details.<p></p>A list of species from Botswana collected using effechecka and geonames polygons
Supplementary file 1 from: Moliner Cachazo L, Makati K, Chadwick MA, Catford JA, Price BW, Mackay AW, Guiry MD, Murray-Hudson M, Murray-Hudson F (2023) A review of the freshwater diversity in the Okavango Delta and Lake Ngami (Botswana): taxonomic composition, ecology, comparison with similar systems and conservation status. Aquatic Sciences
<p>Dataset with 2,204 freshwater species from the Okavango Delta and Lake Ngami (Botswana), with additional 355 species found in other areas of Botswana that are likely to be present in the study region. The dataset covers the following groups: amphibians, birds, fishes, macroinvertebrates, macrophytes, mammals, reptiles, phytoplankton, and zooplankton. The following information is given for each species: status in the Okavango Delta and Lake Ngami (present/potentially present); conservation status globally, Phylum, Class, Order, Family, Genus, species name, cited synonyms, common name, habitat, presence in high water, presence in low water, ecology, distribution in continental Africa, confirmed locations in the Okavango Delta, site coordinates, references, notes.</p>
Figures 42-44. Sibianor kenyaensis Logunov, 2001, male from Botswana. 42 general appearance, dorsal view 43 ditto, lateral view 44 in Further notes on the Harmochireae of Africa (Araneae, Salticidae, Pelleninae)
Figures 42-44. Sibianor kenyaensis Logunov, 2001, male from Botswana. 42 general appearance, dorsal view 43 ditto, lateral view 44 clypeus and chelicerae, front view. (scale bars: 1 mm).
Fig. 42. Distribution maps. A–B. Laephotis angolensis Monard, 1935. C–D. Laephotis botswanae Setzer, 1971. E–F in The bats of the Congo and of Rwanda and Burundi revisited (Mammalia: Chiroptera)
Fig. 42. Distribution maps. A–B. Laephotis angolensis Monard, 1935. C–D. Laephotis botswanae Setzer, 1971. E–F. Mimetillus moloneyi (Thomas, 1891). A, C, E. Distribution in the CRB area. B, D, F. Pan-African distribution.
qdgc Botswana
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> 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.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> <br> <br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> 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:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com<br> <br> <br> <br> <br> Ragnvald Larsen<br> Trondheim 16th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Botswana
<p> This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. </p> <p> 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. </p> <p> 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. </p> <p>Hazards:</p> <ul> <li>coastal and river flooding (Ward et al, 2020; Baugh et al, 2024)</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> <p>Exposure:</p> <ul> <li>population (Schiavina et al, 2023)</li> <li>built-up area (Pesaresi et al, 2023)</li> <li>roads (OpenStreetMap, 2025)</li> <li>railways (OpenStreetMap, 2025)</li> <li>power plants (Global Energy Observatory et al, 2018)</li> <li>power transmission lines (Arderne et al, 2020)</li> </ul> <p>Contextual information:</p> <ul> <li>elevation (European Union and ESA, 2021)</li> <li>land-use and land cover (Copernicus Climate Change Service and Climate Data Store, 2019)</li> <li>administrative boundaries from geoBoundaries (Runfola et al., 2020)</li> </ul> <p> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. </p> <p> 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=12278">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. </p> <p> These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: </p> <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> <p> 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. </p> <p> 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). </p> <p><strong>References</strong></p> <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> Baugh, Calum; Colonese, Juan; D'Angelo, Claudia; Dottori, Francesco; Neal, Jeffrey; Prudhomme, Christel; Salamon, Peter (2024): Global river flood hazard maps. European Commission, Joint Research Centre (JRC) [Dataset] PID: <a href="http://data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif">data.europa.eu/89h/jrc-floods-floodmapgl_rp50y-tif</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> Copernicus Climate Change Service, Climate Data Store, (2019): Land cover classification gridded maps from 1992 to present derived from satellite observation. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). DOI: <a href="https://doi.org/10.24381/cds.006f2c9a">10.24381/cds.006f2c9a</a> (Accessed on 09-AUG-2024) </li> <li> Copernicus DEM - Global Digital Elevation Model (2021) DOI: <a href="https://doi.org/10.5270/ESA-c5d3d65">10.5270/ESA-c5d3d65</a> (produced using Copernicus WorldDEM™-90 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved) </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 (2025) 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> Runfola D, Anderson A, Baier H, Crittenden M, Dowker E, Fuhrig S, et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. DOI: <a href="https://doi.org/10.1371/journal.pone.0231866">10.1371/journal.pone.0231866</a>. </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>
Transport Starter Data Kit: Historical socio-transport data for Botswana
<p>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.</p>
National Checklists: Botswana 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>
Radiocarbon and soil properties along the Kalahari moisture gradient in Botswana
<p>This dataset presents radiocarbon data from four sites located in Botswana. The dataset first presents general information about the sites (on the tab "site"), followed by more detailed information (on the tab "profile") about all sampling locations. On the 'layer' tab, information about selected soil properties and the radiocarbon values are shown.</p> <p>The dataset is part of the International Soil Radiocarbon Database (ISRaD) and associated with the following publication: Dintwe et al. (2015) Soil organic C and total N pools in the Kalahari: potential impacts of climate change on C sequestration in savannas, Plant Soil, 396_27-44, doi: 0.1007/s11104-014-2292-5.</p> <p> </p>
Figs 4 A and B in Trichodina diaptomi (Ciliophora: Peritrichia) from Two Calanoid Copepods from Botswana and South Africa, with Notes on its Life History
Figs 4 A and B. Diagrammatic drawings of the denticles of two different specimens of Trichodina diaptomi Šrámek-Hušek, 1953 from the carapace of Metadiaptomus transvaalensis from the Nata River, Botswana to illustrate variation in denticle shape.
Figs 3 A–E in Trichodina diaptomi (Ciliophora: Peritrichia) from Two Calanoid Copepods from Botswana and South Africa, with Notes on its Life History
Figs 3 A–E. Micrographs of Metadiaptomus transvaalensis with Trichodina diaptomi Šrámek-Hušek, 1953 on the carapace collected from the Nata River, Botswana in August 2012. Whole specimen of M. transvaalenis with several trichodinids visible on dorsal surface of body (A), M. transvaalensis with several trichodinids visible on dorsal surface opposite locomotory appendages of copepod (C) and close-up views of dorsal carapace of M. transvaalensis with trichodinids clearly visible on surface of copepod (B, D, E) with two detached trichodinids visible in D.
Figs 2 A–F in Trichodina diaptomi (Ciliophora: Peritrichia) from Two Calanoid Copepods from Botswana and South Africa, with Notes on its Life History
Figs 2 A–F. Micrographs of silver impregnated adhesive discs (A, B, E and F) and haematoxylin stained specimens (C and D), showing the nuclear apparatus (C) and adoral spiral (D) of Trichodina diaptomi Šrámek-Hušek, 1953 from the Nata River (A–D) and Rustfontein Dam (E and F).
Fig. 13 in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Fig. 13. Unrooted Maximum-likelihood (ML) consensus tree inferred from 18S SSU rDNA sequences illustrating the phylogenetic position of Trichodina koloti sp. nov. (at the top of the tree in bold), derived from 2551 nucleotide positions. Support values at the nodes are for bootstrap values/Bayesian posterior probabilities (ML/BI). Synonyms for both T. koloti and T. hypsilepis Wellborn, 1967 are indicated in brackets.
Figs 12a–f in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Figs 12a–f. Denticle dimensions, as proposed by van As and Basson (1989; 1992), of Trichodina hypsilepis Wellborn, 1967 (syn. T. heterodentata) as recorded by and redrawn from: a – Population A of Duncan (1977), b – Population B of Duncan (1977), c – from van As and Basson (1989), d – from Tang and Zhao (2007), e – from Pádua et al. (2012), f – from Valladão et al. (2013).
Figs 10a–f in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Figs 10a–f. Denticle dimensions, as by van As and Basson (1989, 1992), of Trichodina koloti sp. nov. representatives from six different populations collected at the Nxamasere Floodplain, where a–e from Sclerophrys gutturalis (Power, 1927) and f – S. poweri (Hewitt, 1935) tadpoles during the 2016 winter (July to August) expedition (scale = 10 µm).
Figs 2–5 in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Figs 2–5. Micrographs of representative Trichodina koloti sp. nov. specimens from each of the six populations measured from the Nxamasere Floodplain; 2 – Haematoxylin stained nuclear material; 3–5 – collected from the skin and gills of Sclerophrys gutturalis (Power, 1927) (scale = 10 µm).
Fig. 1 in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Fig. 1. Map of the Okavango River System in southern Africa, including the Nxamasere Floodplain where Sclerophrys gutturalis (Power, 1923) and S. poweri (Hewitt, 1953) were collected (redrawn and adapted from West et al. 2015) (scale = 200 km).
Figs 11a–f in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Figs 11a–f. Denticle dimensions, as proposed by van As and Basson (1989, 1992), of Trichodina koloti sp. nov. as recorded by and redrawn from: a and b – Arthur and Lom (1984), c – Kruger et al. (1993a), d – Dias et al. (2009), e and f – Pala et al. (2018).
Figs 6–9 in A New Trichodina Species (Peritrichia: Mobilida) from Anuran Tadpole Hosts, Sclerophrys spp. in the Okavango Panhandle, Botswana, with Comments on this Taxon
Figs 6–9. Micrographs of representative Trichodina koloti sp. nov. specimens from each of the six populations measured from the Nxamasere Floodplain; 6–8 – collected from the skin and gills of Sclerophrys gutturalis (Power, 1927); 9 – collected from the skin and gills of S. poweri (Hewitt, 1935) tadpoles during the 2016 winter (July to August) expedition (scale = 10 µm).
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.