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229 results for “Togo”
Figure 12a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 12a. - PellaeaduraFigure 12a.General aspect of the fern (upper face)Figure 12b.Lower face of a fertile frondFigure 12c.Insertion of a fertile pinnae of the rachis: overview of the false indusiumFigure 12d.Sori <br> General aspect of the fern (upper face)
Figure 15a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 15a. - PterisburtoniiFigure 15a.General aspect of the frond (upper face)Figure 15b.Lower face of the lamina, showing sori <br> General aspect of the frond (upper face)
Figure 11d. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 11d. - DoryopteriskirkiiFigure 11a.General aspect of the fernFigure 11b.Shape of the laminaFigure 11c.Underside of a fertile frondFigure 11d.Details of sori <br> Details of sori
Figure 3. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 3. - Distribution of Pteridaceae in Togo. Zones 1 to 5 indicated on the map correspond to the ecological zones of Togo as defined by Ern (1979).
Figure 11a. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 11a. - DoryopteriskirkiiFigure 11a.General aspect of the fernFigure 11b.Shape of the laminaFigure 11c.Underside of a fertile frondFigure 11d.Details of sori <br> General aspect of the fern
Figure 11b. from: The Pteridaceae family diversity in Togo - Biodiversity Data Journal 3: e5078 (15 July 2015) https://doi.org/10.3897/BDJ.3.e5078
Figure 11b. - DoryopteriskirkiiFigure 11a.General aspect of the fernFigure 11b.Shape of the laminaFigure 11c.Underside of a fertile frondFigure 11d.Details of sori <br> Shape of the lamina
Infrastructure Climate Resilience Assessment Data Starter Kit for Togo
<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 Togo
<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: Togo 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>
Urban Agriculture and Health: Insights from Anonymized Expert Interviews on Spatial Planning in Greater Lomé, Togo
<p>Transcripts of anonymized interviews with 11 urban planning experts in Greater Lomé on the subject of urban agriculture, health and spatial planning.</p>
Figure 13 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 13. – Indicator species for each cluster of the dendrogram resulting from the self-organizing map procedure (n = 10). IndVal values (in %) are shown in brackets. Shown indicator values (p <0.05) are only those greater than 25%.
Figure 2 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 2. – Variation (average and standard deviation) in species richness by sampling sites. Site order follows the upstream-downstream gradient.
Figure 12 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 12. – Correlation circle of the environmental variable, which discriminate clusters (n = 3) defined by the self-organizing map for stations in the F1 x F2 design for the factorial discriminant analysis. Dis, distance from source; FoBa, forest area; CaHi, canopy height; Vol, flow velocity; Cond, conductivity; Trans, water transparency; Alt, altitude.
Figure 7 in Spatial and temporal variations of fish communities in the longitudinal gradient of the Mono River (Benin and Togo: West Africa)
Figure 7. – Hierarchical classification of the nodes of the Kohonen map based on the species richness of the sites (n = 10). A: Self-organizing map (SOM) (20 nodes); B: Hierarchical clustering of the SOM nodes with a Ward linkage method and a Euclidean distance: the numbers (i.e. ranging from 1 to 20) correspond to those assigned on each node of the SOM.
Рис. 2. Черношапочные сурки и их местообитания на хребте КоΑар: A — виΑ на ЦентраΛьный КоΑар и ΑоΛину р. СреΑний Сакукан; B — местообитание сурков поΑ переваΛом; C — местообитание сурков по берегам р. Того; D — местообитание сурков на вершине гребня, каΑр с фотоΛовушки; E — сурки; F — черношапочный сурок обΛизывает пΛасты каменного угΛя, каΑр из виΑеосъемки Fig. 2. Black-capped marmots and their habitats on the Kodar Ridge: A — view of the Central Kodar and the valley of the Middle Sakukan River; B — habitat of marmots under the mountain pass; C — habitat of marmots along the banks of the Togo River; D — marmot habitat at the top of the mountain ridge, camera trap frame; E — marmots; F — the black-capped marmot licks coal, freeze frame from video in On the ecology of the Doppelmayer`s Black-capped marmot (Marmota camtschatica doppelmayeri Birula, 1922): Kodar Mountain Ridge, Transbaikalia, Russia
Рис. 2. Черношапочные сурки и их местообитания на хребте КоΑар: A — виΑ на ЦентраΛьный КоΑар и ΑоΛину р. СреΑний Сакукан; B — местообитание сурков поΑ переваΛом; C — местообитание сурков по берегам р. Того; D — местообитание сурков на вершине гребня, каΑр с фотоΛовушки; E — сурки; F — черношапочный сурок обΛизывает пΛасты каменного угΛя, каΑр из виΑеосъемки Fig. 2. Black-capped marmots and their habitats on the Kodar Ridge: A — view of the Central Kodar and the valley of the Middle Sakukan River; B — habitat of marmots under the mountain pass; C — habitat of marmots along the banks of the Togo River; D — marmot habitat at the top of the mountain ridge, camera trap frame; E — marmots; F — the black-capped marmot licks coal, freeze frame from video
Figures 4−6 in Two new species of Strigocossus Houlbert, 1916 (Lepidoptera, Cossidae, Zeuzerinae) from Togo and Zambia
Figures 4−6. Genitalia of Strigocossus (coll. ANHRT): 4. S. takanoi sp. n., holotype; 5. S. takanoi sp. n., paratype, female; 6. S. sanbenai sp. n., holotype.
Figures 7−8 in Two new species of Strigocossus Houlbert, 1916 (Lepidoptera, Cossidae, Zeuzerinae) from Togo and Zambia
Figures 7−8. Habitats of the new Strigocossus species: 7. Hillwood Farm, Ikelenge, NW Zambia, habitat of S. takanoi (photo by Lydia Mulvaney); 8. Fazao-Malfakassa National Park, Togo, habitat of S. sanbenai (photo by Marios Aristophanous).
Figures 1−3 in Two new species of Strigocossus Houlbert, 1916 (Lepidoptera, Cossidae, Zeuzerinae) from Togo and Zambia
Figures 1−3. Adult specimens of Strigocossus (coll. ANHRT): 1. S. takanoi sp. n., holotype, male; 2. S. takanoi sp. n., paratype, female; 3. S. sanbenai sp. n., holotype, male.
FIG. 1 in Amphibians of Togo: taxonomy, distribution and conservation status
FIG. 1. — Collecting sites; for definitions of the five ecological zones, see text. For the locality data, see Table 1. Abbreviation: EZ, ecological zone.
FIG. 14 in Amphibians of Togo: taxonomy, distribution and conservation status
FIG. 14. — Dendrogram of the distribution using Ward method of amphibian species of Togo present in the different ecosystems.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.