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1,118 results for “Angola”
National Checklists 2017: Angola 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 Angola collected using effechecka and geonames polygons
National Checklists 2019: Angola 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 Angola collected using effechecka and geonames polygons
Fig. 5 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 5. Abyssomysis cornuta gen. et sp. nov., holotype, adult male with body length 6.6 mm (A: ZMH 58250), paratypes female 7.9 mm (B–C, E–F, H: ZMH 58256) and male 6.7 mm (D, G: ZMH 58254). A–B. Cephalic region of male (A) and female (B), dorsal; left antennula, right antenna and setae of antennal scale omitted. C. Detail of panel (B) showing lobe from terminal segment of antennula. D. Head in lateral view, setae of antennal scale omitted. E. Carapace expanded on slide. F–G. Eyeplate expanded on slide, for female (F) and male (G). H. Antenna, dorsal.
Fig. 2 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 2. Petalophthalmus cristatus sp. nov., holotype, adult female with body length 28.3 mm (ZMH 58247). A. Right mandible with palpus. B. Detail of A, showing outer lobe ending in a spine. C. Masticatory parts of both mandibles. D. Labium. E. Maxilla. F. Thoracopod 1. G. Endopod of thoracopod 2.
Fig. 3 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 3. Petalophthalmus cristatus sp. nov., holotype, adult female with body length 28.3 mm (ZMH 58247). A. Endopod and exopod of thoracopod 3. B. Thoracopod 4. C. Endopod and exopod of thoracopod 5. D. Detail of C (non-modified setae omitted), showing dactylus and distal portion of propodus. E. Endopod and exopod of thoracopod 6. F. Detail of E, showing dactylus. G. Second order detail of E, showing distal portion of dactylus with nail. H. Endopod and exopod of thoracopod 8. I. Detail of H, showing distal portion of dactylus with nail. J–N. Series of pleopods 1–5, rostral (J–K, M–N) or caudal (L) aspect. O. Tip of pleopod 1. P. Tip of pleopod 3.
Fig. 7 in Lophogastrida and Mysida (Crustacea) of the "DIVA-1" deep-sea expedition to the Angola Basin (SE-Atlantic)
Fig. 7. Abyssomysis cornuta gen. et sp. nov., holotype, adult male with body length 6.6 mm (D: ZMH 58250), paratypes female 7.9 mm (A, C, G: ZMH 58256) and males 6.7 mm (B, F: ZMH 58254), 6.8 mm (E, H–I: SMF 55190). A. Sixth thoracic sympod with oostegite. B. Eighth thoracic sympod with exopod and penis. C–D. Pleon in female (C) and male (D), lateral. E. First male pleopod. F. Fourth male pleopod. G. Fifth female pleopod. H. Uropods, ventral. I. Telson.
Figure 35-40. Ectinorus hirsutus n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figure 35-40. Ectinorus hirsutus n. sp. 35. Thorax, holotype ♁. 36. Ninth tergite and ninth sternite, paratype ♁ (B-71635). 37. Spermatheca and bursa copulatrix, allotype ♀. 38. Hind legs, holotype ♁. 39. Eighth sternite, holotype ♁. 40. Seventh sternite, allotype ♀. Scale 35-36 and 39-40 = 200 µm, 37 = 100 µm
Figure 32-34. Ectinorus hirsutus n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figure 32-34. Ectinorus hirsutus n. sp. 32. Head and pronotum, holotype ♁. 33. Head and pronotum, allotype ♀. 34. Aedeagus, holotype ♁. Scale = 200 µm
Figure 26-27. Thaumapsylla wilsoni n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figure 26-27. Thaumapsylla wilsoni n. sp., holotype ♁. 26. Whole body overview. 27. Pronotal ctenidia and thorax. Scale 200 µm
Figure 14-18. 14-16. Aphropsylla truncata n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figure 14-18. 14-16. Aphropsylla truncata n. sp. 14. Aedeagus, paratype ♁ (B-74177). 15. Hind femur and tibia, allotype ♀. 16. Hind tarsi, holotype ♁. 17. Aphropsylla conversa, hind tarsi, holotype ♁. 18. Aphropsylla wollastoni, hind tarsi, lectotype ♁. Scale 14 = 100 µm, 15-18 = 200 µm
Figures 10-13. 10-11. Eighth tergite and anal stylet. 10. Aphropsylla truncata n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figures 10-13. 10-11. Eighth tergite and anal stylet. 10. Aphropsylla truncata n. sp., allotype ♀. 11. Aphropsylla conversa, "neallotype" = allotype? ♀. 12-13. Spermatheca. 12. Aphropsylla truncata n. sp., paratype ♀. 13. Aphropsylla conversa, "neallotype" = allotype? ♀. Scale 10, 12-13 = 100 µm, 11 = 200 µm
Figures 5-9. 5-7. Aprhopsylla truncata n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figures 5-9. 5-7. Aprhopsylla truncata n. sp. 5. Th orax and abdomen, paratype ♁ (B-74177). 6. Sternum eight, paratype ♁ (B-74129). 7. Process of ninth tergite (P1), paratype ♁ (B-74177), lower arrow = P2, upper arrow = P3. 8. Aphropsylla conversa, process of ninth tergite (P1), holotype ♁. 9. Aphropsylla wollastoni, process of ninth tergite (P1), lectotype ♁. Scale 5-6 = 200 µm, 7-9 = 100 µm
Figure 19-25. Rhinolophopsylla traubi n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figure 19-25. Rhinolophopsylla traubi n. sp. 19. Head and pronotum, paratype ♀ (B-46466). 20. Th orax, holotype ♁. 21. Abdomen and terminal segments, holotype ♁. 22. Eight and ninth tergites, aedeagus, and ninth sternite, holotype ♁. 23. Distotarsome 3, paratype ♁ (B-46466). 24. Hind tibia and tarsi, paratype ♁ (B-46466). 25. Spermatheca, bursa copulatrix, and terminal segments, paratype ♀ (B-46466). Scale 19-22 and 24 = 200 µm, 23 and 25 = 100 µm
Figure 28-31. Thaumapsylla wilsoni n in A description of four new species of fleas (Insecta, Siphonaptera) from Angola, Ethiopia, Papua New Guinea, and Peru
Figure 28-31. Thaumapsylla wilsoni n. sp. 28. Head and pronotum, holotype ♁. 29. Ninth tergite, paratype ♁ (B-84148). 30. Aedeagus, distal arm of ninth sternite, and eighth sternite, paratype male (B84148). 31. Hind legs, paratype ♁ (B-87002). Scale = 200 µm
qdgc Angola
<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>
FIGURE 1, A female lateral view, Paratype ZMH K 40074, B in Paranarthrura Hansen, 1913 (Crustacea: Tanaidacea) from the Angola Basin, description of Paranarthrura angolensis n. sp.
FIGURE 1, A female lateral view, Paratype ZMH K 40074, B female dorsal view with detail of cuticula, C Male dorsal view, Paratype ZMH K 40081, D pleon male lateral view (Scale bar = 1 mm), E cephalothorax in ventral view, F Antennule, G Detail of antennule tip, H Antenna, I Detail of antenna tip (Scale bar = 0.5 mm), J uropods (Scale bar = 0.05 mm), K Pleopod. (Scale bar = 0.05 mm).
Infrastructure Climate Resilience Assessment Data Starter Kit for Angola
<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 Angola
<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: Angola 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>
Fig. 1 in Joaquim José da Silva (c. 1755-1810): his life, natural history collecting activities, and involvement in the so-called first scientific expedition in the interior of Angola
Fig. 1. – "Aspecto da embocadura do Rio Dande" [Aspect of the mouth of the River Dande] with Joaquim José da Silva (left) and José António (right). [a. "Forno da cal" [lime oven]; b. "Armazem de a-guardar" [storage]; c. "Sanzallas" [dwellings]; d. "Armazem da madeira" [timber storage]; e. "Igreja que foi dos Jesuitas" [church that was of the Jesuits]; f. "Ponta do Mussule(?)" [Mussule(?) Tip] [SILVA, J.J. (post. 1785: fig. 84); painting executed by José António] [© Arquivo Histórico dos Museus da Universidade de Lisboa]
ScienceDex guides
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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)
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.