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567 results for “Gabon”
Fig. 3 in Unexpected discovery of six new species of Aphyosemion (Cyprinodontiformes, Aplocheilidae) in the Wonga-Wongué Presidential Reserve in Gabon
Fig. 3. Genetic distances (Kimura 2) between all pairs of species or populations. Only one population was used for group A to G, respectively population 4, 8, 9, 13, 14, 16 and Aphyosemion alpha Huber, 1998.
qdgc Gabon
<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 20th of January, 2021<br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
FIGURE 9 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 9. Waveforms and power spectra of EODs from Stomatorhinus walkeri (A – D), S. ater (E, F) and S. patrizii (G, H) on the same time scale as those shown in Fig. 5. EOD waveforms are centered about the largest headnegative peak, normalized to the same peaktopeak height and plotted with head positivity upward for each trace. Power spectra are normalized so that the peak energy is adjusted to 0 dB. Individual specimen numbers are indicated beneath waveforms; F = female, M = male.
FIGURE 8 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 8. Scores from types of S. ivindoensis n. sp. and five other Stomatorhinus species on the second and third factors of a principal components analysis of the covariance matrix calculated from 24 logtransformed morphometric measures. Twelve nontype specimens of S. walkeri and one nontype of S. polli included. Variables loading most heavily on the second principal component are caudal peduncle depth, interorbital distance and internostril distance. Variables loading most heavily on the third principal component are eye diameter, postorbital head length and caudal
FIGURE 5 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 5. EOD waveforms (left) and power spectra (right) for Stomatorhinus ivindoensis n. sp. The holotype of S. ivindoensis, a male, is shown in A and B. Peaks are numbered in order from P 0 to P 3. EOD waveforms are centered about the largest headnegative peak and plotted with head positivity upward. Power spectra are normalized so that the peak energy is adjusted to 0 dB. EODs of reproductive males (E, F) are longer in duration and have a lower peak power frequency than those of females (C, D). Individual specimen numbers are indicated beneath waveforms; F = female, M = male, H = holotype. Time base = 0.1 millisecond.
FIGURE 7 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 7. Selected morphometric ratios of S. ivindoensis n. sp. compared to those in S. fuliginosus (A) and S. polli (B). IO = interorbital distance, HW = head width, CPD = caudal peduncle depth measured at terminus of anal fin, CPL = caudal peduncle length, E = eye diameter, HL = head length.
FIGURE 4 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 4. Typical forest creek habitat of Stomatorhinus ivindoensis n. sp.: upper “ Balé Creek ” within the Ipassa Plateau Reserve near Makokou, Gabon.
FIGURE 3 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 3. Collection sites of Stomatorhinus ivindoensis n. sp. (circles) and S. walkeri (triangles) in the Ivindo and Ogooué River basins of Gabon and the KouilouNiari basin of the Republic of Congo. Shaded areas delimit the boundary of the Lower Guinea ichthyofaunal province of West Central Africa.
FIGURE 2 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 2. Photograph (A) and radiograph (B) of preserved holotype of Stomatorhinus ivindoensis n. sp., CU 85157, 43.8 mm SL, male; (C) live paratype specimen no. 1004, CU 75437, 40.8 mm SL, male; (D) preserved paratype, CU 85465, 52.2 mm SL, female.
FIGURE 1 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 1. Drawing of Stomatorhinus ivindoensis n. sp. holotype, 43.8 mm SL. Drawn by Vera Ming Wong.
FIGURE 6 in A new Stomatorhinus (Osteoglossomorpha: Mormyridae) from the Ivindo River, Gabon, West Central Africa
FIGURE 6. Species of Stomatorhinus to which Stomatorhinus ivindoensis n. sp. is closely compared in study: (A) S. walkeri syntype BMNH 1867.5. 3.16, 85.6 mm SL; (B) S. walkeri live specimen no. 2880, CU 80237, 95 mm SL; (C) S. fuliginosus syntype MRAC 6652, 37.7 mm SL; (D) S. fuliginosus syntype MRAC 6648, 33.5 mm SL; (E) S. polli paratype MRAC 138993, 61.3 mm SL; (F) S. polli paratype MRAC 138977, 58.7 mm SL.
FIGURE 9 in Revision of the genus Werneria Poche, 1903, including the descriptions of two new species from Cameroon and Gabon (Amphibia: Anura: Bufonidae)
FIGURE 9: Buëa waterfall on Mount Cameroon (1974), the type locality of Werneria preussi. Compare W. Böhme for scale.
FIGURE 1 in Revision of the genus Werneria Poche, 1903, including the descriptions of two new species from Cameroon and Gabon (Amphibia: Anura: Bufonidae)
FIGURE 1: Dorsal view of Werneria species; a: W. iboundji nov. sp. female, holotype, IRSNB 1929; b: W. bambutensis male, MHNG 1453.17; c: W. tandyi male, MHNG 1453.21; d: W. submontana nov. sp. male, holotype, ZFMK 69699; e: W. mertensiana male, ZFMK 69137; f: W. preussi male, SMF 24180.
FIGURE 8 in Revision of the genus Werneria Poche, 1903, including the descriptions of two new species from Cameroon and Gabon (Amphibia: Anura: Bufonidae)
FIGURE 8: a: W. preussi female from Buëa, Mt. Cameroon; b: W. submontana nov. sp. male, Bakossi Mts., Lake Edib; c: W. mertensiana male, Mt. Kupe (photo: K. H. Jungfer); d: ventral view of Werneria sp., Monte Alén National Park, Equatorial Guinea (photo: I. de la Riva).
FIGURE 12 in Revision of the genus Werneria Poche, 1903, including the descriptions of two new species from Cameroon and Gabon (Amphibia: Anura: Bufonidae)
FIGURE 12: Type locality of Werneria iboundji nov. sp. at the base of the large waterfall at Mount Iboundji, Gabon. The specimens have been collected among the rocks at the water edge, compare person below arrow for scale (photo: T. Stévart).
FIGURE 11 in Revision of the genus Werneria Poche, 1903, including the descriptions of two new species from Cameroon and Gabon (Amphibia: Anura: Bufonidae)
FIGURE 11: Habitats of Werneria submontana nov. sp. on the Bakossi Mts.; a: Edib Hills, surroundings of Lake Edib (type locality); b: small forest creek on Mwendelengo Mts., Kodmin, several of the paratypes have been collected beneath the stones on the creek’s bank; c: watercatchments at Mt. Kupe; the toads were living in the spray zone of the small, artificial waterfall.
FIGURE 2 in Revision of the genus Werneria Poche, 1903, including the descriptions of two new species from Cameroon and Gabon (Amphibia: Anura: Bufonidae)
FIGURE 2: Ventral view of Werneria species; a: W. iboundji nov. sp. female, holotype, IRSNB 1929; b: W. bambutensis male, MHNG 1453.17; c: W. tandyi male, MHNG 1453.21; d: W. submontana nov. sp. male, holotype, ZFMK 69699; e: W. mertensiana male, ZFMK 69137; f: W. preussi male, SMF 24180.
Infrastructure Climate Resilience Assessment Data Starter Kit for Gabon
<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 Gabon
<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: Gabon 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>
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.