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Fig. 5 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 5. Neoclita pringlei gen. et sp. nov., ♀, specimen in its natural habitat (photo: Lynette Clennell, Matatiele, 6 Dec. 2018).
Fig. 3 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 3. Neoclita pringlei gen. et sp. nov., paratype, ♀, total length = 16.8 mm. A. Habitus, dorsal view. B. Habitus, ventral view. (South Africa, Eastern Cape Province, Matatiele Nature Reserve, 6 Dec. 2008, R. Perissinotto and L. Clennell leg.)
Fig. 2 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 2. Neoclita pringlei gen. et sp. nov., holotype, ♂. A. Aedeagus, dorsal view. B. Aedeagus, lateral view. (South Africa, Eastern Cape Province, Matatiele Nature Reserve, 6 Dec. 2008, R. Perissinotto and L. Clennell leg.)
Fig. 1 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 1. Neoclita pringlei gen. et sp. nov., holotype, ♂, total length = 16.3 mm. A. Habitus, dorsal view. B. Habitus, ventral view. (South Africa, Eastern Cape Province, Matatiele Nature Reserve, 6 Dec. 2008, R. Perissinotto and L. Clennell leg.)
Fig. 6 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 6. Matatiele Nature Reserve, showing the mountain summit with the typical habitat of Neoclita pringlei gen. et sp. nov. (photo: Lynette Clennell, Matatiele, 6 Dec. 2008).
Infrastructure Climate Resilience Assessment Data Starter Kit for South Africa
<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>
- Tergite 2 smooth to superficially punctate laterally (a), if ambiguous (some E. tombeaodiba) then tergite 1 stouter, less than 1.5x longer than apically wide; general coloration bright yellow (b); tropical Africa ………………………………………………………………………………………10 in A review of the Afrotropical Rhyssinae (Hymenoptera: Ichneumonidae) with the descriptions of five new species
- Tergite 2 smooth to superficially punctate laterally (a), if ambiguous (some E. tombeaodiba) then tergite 1 stouter, less than 1.5x longer than apically wide; general coloration bright yellow (b); tropical Africa ………………………………………………………………………………………10
The name of the game: Palaeoproteomics and radiocarbon dates further refine the presence and dispersal of caprines in Eastern and Southern Africa
<p><span>We report the first large-scale palaeoproteomics research on eastern and southern African zooarchaeological samples, thereby refining our understanding of early caprine (sheep and goat) pastoralism in Africa. Assessing caprine introductions is a complicated task because of their skeletal similarity to endemic wild bovid species and the sparse and fragmentary state of relevant archaeological remains. Palaeoproteomics has previously proved effective in clarifying species attributions in African zooarchaeological materials, but few comparative protein sequences of wild bovid species have been available. Using newly generated collagen type I sequences for wild species, as well as previously published sequences, we assess species attributions for elements originally identified as caprine or "unidentifiable bovid" from seventeen eastern and southern African sites that span seven millennia. We identified over 70% of the archaeological remains and the direct radiocarbon dating of domesticate specimens allows refinement of the chronology of caprine presence in both African regions. These results thus confirm earlier occurrences in eastern Africa and the systematic association of domesticated caprines with wild bovids at all archaeological sites. The combined biomolecular approach highlights repeatability and accuracy of the methods for conclusive contribution in species attribution of archaeological remains in dry African environments.</span></p>
FIGURE 4. A in A new small barb (Cyprininae: Smiliogastrini) from the Louesse, Lekoumou (upper Niari basin), and Djoulou (upper Ogowe basin) rivers in the Republic of Congo, west-central Africa
FIGURE 4. A, Known distributional range of Enteromius walshae; yellow stars indicate collection sites and red star indicates collection locality of holotype (AMNH 266856). B, Type locality of holotype, in a small tributary of the Mandoro River (upper Louesse River basin).
FIGURE 1 in A new small barb (Cyprininae: Smiliogastrini) from the Louesse, Lekoumou (upper Niari basin), and Djoulou (upper Ogowe basin) rivers in the Republic of Congo, west-central Africa
FIGURE 1. Enteromius species sampled (table 1), photographed immediately postmortem or in preservation: A, kuiluensis; B, martorelli; C, prionacanthus; D, rouxi, E, rubrostigma; F, trispilomimus; G, walshae new species; H, nigroluteus; I, brichardi; J, camptacanthus; K, chiumbeensis; L, diamounanganai; M, guirali; N, holotaenia; O, jae; P, catenarius; Q, castrasibutum. No tissues are currently available for E. catenarius. Photograph of E. castrasibutum courtesy of Jon Armbruster.
Figs 15–28 in New taxa of the subfamily Phaneropterinae (Orthoptera: Tettigoniidae) from Africa: the tribes Otiaphysini and Preussiini
Figs 15–28. Species of the genus Tetraconcha, males, details of structure. 15–21 – T. bicolor sp. n.; 22–28 – T. unicolor sp. n. 15, 25 – stridulatory apparatus of left tegmen; 16, 26 – stridulatory apparatus of right tegmen; 17, 22 – left tegmen; 18, 23 – costal part of left hind wing; 19, 24 – cercal apex from above; 20, 27 – abdominal apex from above and slightly behind; 21, 28 – stridulatory vein of left tegmen ventrally. Рис. 15–28. ВиΑы роΑа Tetraconcha, самцы, ΑетаΛи строения. 15–21 – T. bicolor sp. n.; 22–28 – T. unicolor sp. n. 15, 25 – стриΑуΛяционный аппарат Λевого наΑкрыΛья; 16, 26 – стриΑуΛяционный аппарат правого наΑкрыΛья; 17, 22 – Λевое наΑкрыΛье; 18, 23 – костаΛьная часть Λевого заΑнего крыΛа; 19, 24 – вершина церка сверху; 20, 27 – вершина брюшка сверху и сΛегка сзаΑи; 21, 28 – стриΑуΛяционная жиΛка Λевого наΑкрыΛья вентраΛьно.
Figs 1–14 in New taxa of the subfamily Phaneropterinae (Orthoptera: Tettigoniidae) from Africa: the tribes Otiaphysini and Preussiini
Figs 1–14. Species of the genus Drepanophyllum, general view and details of structure. 1–8 – D. irisovi sp. n.; 9–11 – D. corrosifolium ugandense subsp. n.; 12–13 – D. marmoratum; 14 – D. c. corrosifolium, syntype. 1 – male, dorsal view; 2, 12 – female, lateral view; 3–4 – stridulatory apparatus: 3 – of left male tegmen, 4 – of right male tegmen; 5, 10 – male abdominal apex from above and slightly behind; 6, 11 – stridulatory vein of male left tegmen ventrally; 7 – female abdominal apex from side; 8 – female genital plate from below; 9 – male left tegmen; 13–14 – male right cercus from more or less above. 12 – after Brunner-Wattenwyl [1891]; 13–14 – after Cigliano et al. [2022]. Рис. 1–14. ВиΑы роΑа Drepanophyllum, общий виΑ и ΑетаΛи строения. 1–8 – D. irisovi sp. n.; 9–11 – D. corrosifolium ugandense subsp. n.; 12–13 – D. marmoratum; 14 – D. c. corrosifolium, синтип. 1 – самец, виΑ сверху; 2, 12 – самка, виΑ сбоку; 3–4 – стриΑуΛяционный аппарат: 3 – Λевого наΑкрыΛья самца, 4 – правого наΑкрыΛья самца; 5, 10 – вершина брюшка самца сверху и сΛегка сзаΑи; 6, 11 – стриΑуΛяционная жиΛка Λевого наΑкрыΛья самца вентраΛьно; 7 – вершина брюшка самки сбоку; 8 – генитаΛьная пΛастинка самки снизу; 9 – Λевое наΑкрыΛье самца; 13–14 – правый церк самца боΛее иΛи менее сверху. 12 – по [Brunner-Wattenwyl, 1891]; 13–14 – по [Cigliano et al., 2022].
Figs 29–38 in New taxa of the subfamily Phaneropterinae (Orthoptera: Tettigoniidae) from Africa: the tribes Otiaphysini and Preussiini
Figs 29–38. Species of the genera Enochletica and Weissenbornia, males, general view and details of structure. 29–33 – E. simulata sp. n.; 34–35 – E. ostentatrix; 36–38 – W. praestantissima aurea subsp. n. 29, 36 – male, dorsal view; 30–31 – stridulatory apparatus: 30 – of left tegmen, 31 – of right tegmen; 32, 34, 38 – abdominal apex from above and slightly behind; 33, 35, 37 – stridulatory vein of left tegmen ventrally. Рис. 29–38. ВиΑы роΑов Enochletica и Weissenbornia, самцы, общий виΑ и ΑетаΛи строения. 29–33 – E. simulata sp. n.; 34–35 – E. ostentatrix; 36–38 – W. praestantissima aurea subsp. n. 29, 36 – самец, виΑ сверху; 30–31 – стриΑуΛяционный аппарат: 30 – Λевого наΑкрыΛья, 31 – правого наΑкрыΛья; 32, 34, 38 – вершина брюшка сверху и сΛегка сзаΑи; 33, 35, 37 – стриΑуΛяционная жиΛка Λевого наΑкрыΛья вентраΛьно.
Figs 29–46 in Taxonomy of Podoscirtinae (Orthoptera: Gryllidae). Part 13: new taxa of the subtribe Podoscirtina from Africa
Figs 29–46. Parametrypa, female (29–34, 37, 40–46) and male (35, 36, 38, 39): 29–31, P. fortipes fortipes; 32–34, P. fortipes spiculata, stat. nov.; 35–40, P. pubescens sp. nov.; 41–43, P. longispinosa sp. nov.; 44–46, P. dentata sp. nov. Head with pronotum, dorsolateral and slightly anterior view (29, 32, 35, 41, 44); proximal half of hind tibia (30, 33, 37, 42, 45), dorsal and slightly inner view (left) as well as dorsal and slightly outer view (right); genital plate from below (31, 34, 40, 43, 46); hind femur, outer view (36); abdominal apex from above (38) and from below (39).
Figs 1–9 in Taxonomy of Podoscirtinae (Orthoptera: Gryllidae). Part 13: new taxa of the subtribe Podoscirtina from Africa
Figs 1–9. Malawitrella gen. nov. and Kilimagryllus, male: 1–3, M. sotshivkoi sp. nov.; 4–6, K. bilobulatus sp. nov. (holotype); 7–9, K. bilobulatus limpopo subsp. nov. Head in front (1, 4, 7); body from above with tegmina directed upwards and aside (2) and with tegmina in rest position (5, 8); right tegmen (3); abdominal apex from above and slightly from behind (6, 9).
Figs 10–17 in Taxonomy of Podoscirtinae (Orthoptera: Gryllidae). Part 13: new taxa of the subtribe Podoscirtina from Africa
Figs 10–17. Malawitrella sotshivkoi sp. nov., male: 10, body from side with tegmina directed upwards and aside; 11, metanotal gland; 12, 13, abdominal apex from above and slightly from behind (12), and from below (13); 14–16, genitalia from side (14), from above (15) and from below (16); 17, spermatophore from side.
BIO4AFRICA_Report on Inclusive and sustainable bio-based business models for rural Africa_D5.2_30/09/2022_v1.0
<p>This dataset constitutes Deliverable D5.2 Report on the 'Inclusive and sustainable bio-based business models for rural Africa', which has been the result of Task 5.2 of the BIO4AFRICA H2020 project. The deliverable aims at co-designing the inclusive and sustainable business models for the bio-based technologies transferred and adapted to each pilot case of the project bio-based products in Uganda, Ghana, Cote D'Ivoire, and Senegal.</p>
Fig. 5 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 5.—(A) Dorsal; (B) lateral; (C) ventral; (D) apical (above), and basal (bellow) views of the baculum of the paratype Pseudoromicia principis sp. nov. (EBD 17358M). Photographs by Laura Torrent.
Fig. 6 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 6.—(A) Detail of the upper incisors (top left) and drawings of the (a) lower incisors and (b) upper incisors (bottom) of the holotype of Pseudoromicia principis sp. nov. (EBD 17475M). Photograph by Joxerra Aihartza and drawings by Joaquín López-Rojas. (B) Details of the rhinarium of (c) the holotype of P. principis sp. nov. (EBD 17475M) and of (d) Nycticeinops happoldorum (ZFMK-MAM-2009.0029), from Hutterer et al. (2019). Photograph by Laura Torrent. (C) Drawing of the tragi of: (e) P. principis sp. nov. (EBD 17358M, paratype); (f) P. brunnea from Van Cakenberghe and Happold (2013) and (g) P. brunnea (DM13229) from Monadjem et al. (2013) and (D) dorsal and ventral views (from left to right) of bacula of: (h) P. principis sp. nov. (EBD 17358M, paratype); (i) P. rendalli from Hill and Harrison (1987); and (j) P. brunnea (DM13229) from Monadjem et al. (2013). Drawings of P. principis sp. nov. baculum by Joaquín López-Rojas.
Fig. 4 in A new Pipistrelle bat from the oceanic Island of Príncipe (Western Central Africa)
Fig. 4.—Skull and mandible of the holotype of Pseudoromicia principis sp. nov. (EBD 17475M) in dorsal, ventral, lateral, front and back views as well as mandible top view. Photographs by Joxerra Aihartza.
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.