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411 results for “Democratic Republic of the Congo”
Figs 36–38 in New species of Platypalpus (Diptera: Hybotidae) from the Democratic Republic of the Congo
Figs 36–38. Platypalpus yangambensis sp. nov., paratype ♂. 36. Right epandrial lamella. 37. Epandrium with cerci, dorsal view. 38. Left epandrial lamella. Scale bars = 0.1 mm.
Figs 14–17 in New species of Platypalpus (Diptera: Hybotidae) from the Democratic Republic of the Congo
Figs 14–17. Platypalpus lokonda sp. nov., paratype ♂. 14. Habitus, lateral view. 15. Head and thorax, dorsal view. 16. Wing. 17. Mid legs, posterior view.
qdgc Democratic Republic of the Congo
<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> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> <br> <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> <br> <br> The attributes for each table are:<br> <br> <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> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <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> <br> <br> Licensing<br> ---------<br> Creative Commons Attribution 4.0 International<br> <br> <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. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received 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> <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 23rd of January, 2021<br> <br> <br> <br> ragnvald@mindland.com<br> www.mindland.com</p>
Fig. 6 in A new species of the genus Cyrtonion (Coleoptera: Hydrophilidae: Megasternini) from the Democratic Republic of the Congo
Fig. 6. Known distribution of the species of the genus Cyrtonion Hansen, 1989. Green area covers distribution of evergreen rain forest.
Figs. 1-4 in A new species of the genus Cyrtonion (Coleoptera: Hydrophilidae: Megasternini) from the Democratic Republic of the Congo
Figs. 1-4. Aedeagophores of Cyrtonion Hansen, 1989 species. 1-2 – C. moto sp. nov., holotype; 3 – C. ghanense Hansen, 1989, paratype; 4 – C. sculpticolle (Régimbart, 1907), paratype. 1 – aedeagus; 2-4 – apical portion of the median lobe.
Infrastructure Climate Resilience Assessment Data Starter Kit for Democratic Republic of the Congo
<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, 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> <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 (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> 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 Democratic Republic of the Congo
<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: Democratic Republic of the Congo 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>
Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda
<p>Datasets underlying the publication "Community access to rectal artesunate for malaria (CARAMAL): a large-scale observational implementation study in the Democratic Republic of the Congo, Nigeria and Uganda":</p> <p><strong>Figure 6: </strong>Number of children enrolled in the Patient Surveillance System (grey bars), and percentage of these children being administered rectal artesunate (RAS), by country.</p> <p><strong>Figure 8:</strong> Overall case fatality ratio (CFR) in patients with danger signs and a positive malaria test at enrolment across the entire study period, by enrolment location and country. Data for Uganda excludes enrolments at PHCs (N=34).</p>
Supplement to: Enhanced surveillance of monkeypox in Bas-Uélé, Democratic Republic of Congo: the limitations of symptom-based case definitions
<p>This repository contains data and R code which are supplements to:</p> <p><em>Enhanced surveillance of monkeypox in Bas-Uélé, Democratic Republic of Congo: the limitations of symptom-based case definitions</em><br> Gaspard Mande, Innocent Akonda, Anja De Weggheleire, Isabel Brosius, Laurens Liesenborghs, Emmanuel Bottieau, Noam Ross, Guy Crispin Gembue, Robert Colebunders, Erik Verheyen Ngonda Daulya, Herwig Leirsh, and Anne Laudisoit (2022).</p> <p>A data dictionary and instructions for reproducing the analysis are provided. Details can be found in the README.md file.</p>
FIGURE 7 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 7. Species of the Clarioides subgenus from the Congo River basin: A, AMNH 250869 Clarias angolensis; B, AMNH 227571 Clarias buthupogon, marbled variant; C, AMNH 274802 Clarias buthupogon, dark variant; and D, AMNH 268382 Clarias gabonensis. Scale bars = 1 cm.
FIGURE 8 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 8. Maxillary barbel length plotted against standard length for Clarias monsembulai (open circles), Clarias angolensis (shaded circles), and two type specimens of Clarias angolensis macronema (diamonds).
FIGURE 6 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 6. Collection localities of Clarias monsembulai within the Congo River Basin (upper inset). Coded regions of the map (lower inset) correspond to the freshwater ecoregions of the world obtained from www. feow.org.
FIGURE 5 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 5. Exposed cleithrum (demarcated by dashed line) of A, Clarias buthupogon, AMNH 227571, 162 mm SL and B, Clarias monsembulai, AMNH 244162, 183 mm SL. Scale bar = 1 cm.
FIGURE 4 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 4. Suprabranchial organ of Clarias monsembulai, AMNH 244162, 165 mm SL (suprapreopercle removed). Scale bar = 1 cm.
FIGURE 3 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 3. Computed tomography scan reconstruction of the skull and pectoral girdle of Clarias monsembulai (at left and above), AMNH 252267, 179 mm SL in A, dorsal; B, ventral; and C, lateral views. Abbreviations: ang, anguloarticular; apal, autopalatine; br, branchiostegal rays; ch-a, anterior ceratohyal; ch-p, posterior ceratohyal; cl, cleithrum; cor, coracoid; den, dentary; fr, frontal; hhv, ventral hypohyal; hm, hyomandibula; io-ii, infraorbital ii; io-iii, infraorbital iii; io-iv, infraorbital iv; iop, interopercle; lac, lacrimal; leth, lateral ethmoid; meth, mesethmoid; mx, maxilla; na, nasal; ns, neural spines; op, opercle; o-susp, os suspensorium; pmx, premaxilla; pop, preopercle; pp-v4, parapophysis of 4th vertebra; pp-v5, parapophysis of 5th vertebra; ps, pectoral spine; pt, pterotic; pt-scl, posttemporo-supracleithrum; q, quadrate; rad, radials; soc, supraoccipital; sph, sphenotic; spop, suprapreopercle; trsc, transcapular process; uh, urohyal; vo, vomer. Scale bars = 1 cm.
FIGURE 2 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 2. Lateral view of Clarias monsembulai new species, holotype AMNH 244176, 226 mm SL. Luilaka River at Ilenge, Salonga National Park. Scale bar = 1 cm.
FIGURE 1 in A New Species of Air-breathing Catfish (Clariidae: Clarias) from Salonga National Park, Democratic Republic of the Congo
FIGURE 1. Computed tomography scans showing dorsal view of the skull of A, AMNH 59064 Clarias laeviceps and B, AMNH 274797 Clariallabes melas with the suprapreopercle (spop) and sphenotic (sph) highlighted in red.
Fig. 1. – Keetia namoyae O. Lachenaud & Q. Luke. A in Keetia namoyae (Rubiaceae, Vanguerieae), a new species from eastern Democratic Republic of Congo
Fig. 1. – Keetia namoyae O. Lachenaud & Q. Luke. A. Flowering twig; B. Fruiting twig; C. Node with stipules; D. Detail of a domatia; E. Flower bud; F. Open flower; G. Longitudinal section of flower; H. fruit. [Bytebier & Luke 2796, BR] [Drawing: Antonio Fernandez]
Quantitative ethnobotany of multiple-use species and management of the Yangambi Biosphere Reserve in the Democratic Republic of the Congo
<p>The Yangambi Biosphere Reserve (YBR) is confronted to huge challenge for the livelihood of local communities and biodiversity or natural resources conservation. The lack of scientific information on the spatial distribution of useful woody species is a constraint to develop the sustainable management of forest resources. Hence the relevance of this study, carried out in the villages of Yaselia, Lilanda and Bagbanye, located on the outskirts of this protected area of YBR. It aims to identify the most useful woody species and analyse their socio-cultural use value, number of uses based on local community's commitments as well as to determine their abundance beyond village forests, to contribute to reforestation and conservation policies in the Yangambi landscape. To do this, we combined an ethnobotanical survey with a forest inventory. The results obtained showed that species such as Entandrophragma cylindricum, Petersianthus macrocarpus, Ricinodendron heudelotti, Scorodophloeus zenkeri, Pentaclethra macrophylla, Uapaca guineensis, Blighia welwitschii, Chrysophyllum lacourtianum, Dacryodes edulis, and Gilbertiondendron dewevrei, have high use and cultural value for local communities. Unfortunately, these species of high use and socio-cultural value are in low density in the village forests around the YBR. These results emphasize the necessity to implement as promptly as possible a management strategy including these useful species. This could be done through sustainable traditional agroforestry projects which will valorise existing resources and generate income for the local population to meet their livelihoods and avoid their incursion into the YBR which is the biodiversity sanctuary.</p>
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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)
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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.