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1,080 results for “Cameroon”
Climate records for Bulu, Ndian Division, SW Cameroon
<p>The file ‘Bulu-Ndian_Climate_1984-2017.csv' (format: comma-separated values) contains year, month, and day number in the first columns, followed by maximum (‘<em>maxT</em>’, <sup>o</sup>C) and minimum (‘<em>minT</em>’, <sup>o</sup>C) temperatures, volume of evaporated radiometer water (‘<em>radi</em>’, ml/day) and rainfall (‘<em>rain</em>’, mm/day) at Bulu, Ndian Department, SW Cameroon (4<sup>o</sup>55’53.47” N, 8<sup>o</sup>51’32.70” E; 51 m elevation [Google Earth]). This location is close to eastern border of Korup National Park, and 7.5 km SW of the town of Mundemba. The station is operated by PAMOL Plantations Plc, Cameroon. Readings were taken manually at 07:00 h each day, a record of the previous 24 hours. The date entered into the file was accordingly that of the previous day. Rainfall was measured in a standard copper collecting gauge, temperature read on thermometers inside a Stevenson screen, and radiation measured using Gunn-Bellani radiometers (Baird and Tatlock, London), one for each of two successive periods of time.</p> <p>To convert ‘<em>radi</em>’ [V] to radiation in W/m<sup>2</sup> [R] the following calibration equations can be applied for the radiometers over their corresponding periods of operation:</p> <ol> <li>R = 24.1 + 12.3∙V (01.01.1984 - 02.04.2009)</li> <li>R = 21.1 + 14.7∙V (30.05.2011 – 31.05.2017)</li> </ol> <p>(Between 03.04.2009 and 29.05.2011 no data were recorded (values ‘NA’). This was due to accidental breakage of the original instrument and a delay whilst a suitable replacement was found and installed.)</p> <p>The details of the calibrations of the two radiometers are explained in an appendix to the following related paper in preparation: “Mast fruiting in <em>Microberlinia bisulcata</em> and further evidence for the nutrient resource limitation hypothesis”, by Newbery, D. M., Schwan, S. Chuyong, G. B., Neba, G. A., Etta, C., Norghauer, J. M. and Worbes, M. [Bibliographic details subject to updating.]</p> <p>We acknowledge the help and support of their Technical Officers Cyprain Lantang and Daniel Tabi at Bulu, and the assistance of Marlise Zimmermann (IPS, Bern) with entering the data into computer files from photographed pages of the climate record books. The data were checked and curated by D. M. Newbery.</p> <p>These climate data have been of invaluable use to our ecosystem and vegetation research of the forests of Southern Korup National Park, the main field site for which lies c. 12 km NW of Bulu. They are also of interest for regional climate mapping because other stations in this part of western Central Africa are very sparse and their records incomplete.</p> <p>Colbertson E. Etta, PAMOL Plantations Plc, Lobe Oil Palm Estate, PMB 03, Ekondo Titi, SW Region, Cameroon. George B. Chuyong, Department of Plant Science, University of Buea, P. O. Box 63, Buea, SW Region, Cameroon. David M. Newbery, Institute of Plant Sciences, University of Bern, Altenbergrain 21, CH-3013, Bern, Switzerland.</p>
National Checklists 2017: Cameroon 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 Cameroon collected using effechecka and geonames polygons
National Checklists 2019: Cameroon 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 Cameroon collected using effechecka and geonames polygons
Epidemiology of onchocerciasis-associated epilepsy in the Mbam and Sanaga river valleys of Cameroon: impact of more than 13 years of ivermectin
<p>Dataset contains information collected during door-to-door epilepsy surveys in onchocerciasis-endemic villages of Cameroon.</p>
Fig. 4 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 4. Phylogenetic analysis of the subgenus Sophophora and Lissocephala aff. diola Tsacas & Lachaise, 1979. Conventions as for Fig. 3.
Fig. 2 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 2. Percent divergence of the morphospecies DNA barcode from the closest neighbor found in the barcode database.
Fig. 3 in Connecting systematic and ecological studies using DNA barcoding in a population survey of Drosophilidae (Diptera) from Mt Oku (Cameroon)
Fig. 3. Phylogenetic analysis of the genus Zaprionus and Microdrosophila aff. mamaru (Burla, 1954). This tree is the neighbor-joining tree. The maximum likelihood tree gives the same topology. Nodes with a bootstrap value lower than 50% were merged. Bootstrap values were calculated over 1000 repeats. Above nodes: bootstrap values for maximum likelihood using a GTR + G + I model. Below nodes: bootstrap values for neighbor-joining using the Kimura-2p distance.
FIG. 2 in Annotated checklist of bats (Mammalia: Chiroptera) of Mount Cameroon, southwestern Cameroon
FIG. 2. — Habitat sampled for bats on Mount Cameroon: A, slow flowing streams; B, cultivated farmland; C, fallow farmland; D, beside fruiting trees; E, cleared farmland; F, understory of primary forest; G, ecotone forest/ alpine grassland; H, cave; I, waterhole. Photos: © Aaron Manga Mongombe
FIG. 1 in Annotated checklist of bats (Mammalia: Chiroptera) of Mount Cameroon, southwestern Cameroon
FIG. 1. — Map of Cameroon, showing localities listed in the text (See Appendix 1 for names of localities).
Dataset for: African manatee (Trichechus senegalensis) habitat suitability at Lake Ossa, Cameroon using trophic state models and predictions of submerged aquatic vegetation
<p>See research article here: https://onlinelibrary.wiley.com/doi/epdf/10.1002/ece3.8202</p> <p>Aim: The present study aims at investigating the past and current trophic status of Lake Ossa and evaluating its potential impact on African manatee health.</p> <p>Location: Lake Ossa is known as a refuge for the threatened African manatees in Cameroon. Little information exists on the water quality and health of the ecosystem as reflected by its chemical and biological characteristics.</p> <p>Methods: Aquatic biotic and abiotic parameters including water clarity, nitrogen, phosphorous and chlorophyll concentrations were measured monthly during four months at each of 18 water sampling stations evenly distributed across the lake. These parameters were then compared with historical values obtained from the literature to examine the dynamic trophic state of Lake Ossa.</p> <p>Results: Results indicate that Lake Ossa’s trophic state parameters doubled in only three decades (from 1985 to 2016), moving from a mesotrophic to a eutrophic state. The decreasing nutrient gradient moving from the mouth of the lake (in the south) to the north indicates that the flow of the adjacent Sanaga River is the primary source of nutrient input. Further analysis suggests that the poor transparency of the lake is not associated with chlorophyll concentrations but rather with the suspended sediments brought-in by the Sanaga River. Consequently, our model demonstrated that despite nutrient enrichment, less than 5% of the lake bottom surface sustained submerged aquatic vegetation. Thus, shoreline emergent vegetation is the primary food available for the local manatee population. During the dry season, water recedes drastically and disconnects from the dominant shoreline emergent vegetation, decreasing accessibility for manatees.</p> <p>Main conclusions: The current study revealed major environmental concerns (eutrophication and sedimentation) that may negatively impact habitat quality for manatees. Efficient land use and water management across the entire watershed may be necessary to mitigate such issues.</p>
qdgc Cameroon
<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 47 in A new Afrotropical Ogovea (Opiliones, Cyphophthalmi) from Cameroon, with a discussion on the taxonomic characters in the family Ogoveidae
FIGURE 47. Distribution map of the three known species of the genus Ogovea in the Gulf of Guinea. The position of O. grossa is inaccurate since only the name of a river was provided.
FIGURES 33 – 38. 33 in A new Afrotropical Ogovea (Opiliones, Cyphophthalmi) from Cameroon, with a discussion on the taxonomic characters in the family Ogoveidae
FIGURES 33 – 38. 33. Left chelicera of male Huitaca ventralis, ectal view; 34. Left chelicera of female Neogovea sp., mesal view; 35. Tarsal claw II of Huitaca ventralis, mesal view; 36. Tarsal claw II of Metagovea philippi, ectal view; 37. detail of the basal position of the adenostyle in Huitaca ventralis, dorsal view; 38. Tarsus IV of male of Metagovea philippi, ectal view.
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 Cameroon
<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>
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.