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Fig. 7. Dordrecht Mountain, where a in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 7. Dordrecht Mountain, where a second population of Neoclita pringlei gen. et sp. nov. was Frst recorded in Dec. 2013 (photo: Lynette Clennell, Dordrecht, 31 Dec. 2015).
Fig. 4 in Neoclita pringlei (Scarabaeidae, Cetoniinae), a new relict genus and species from the Drakensberg Range of South Africa
Fig. 4. Neoclita pringlei gen. et sp. nov., ♂, specimen in its natural habitat (photo: Lynette Clennell, Matatiele, 6 Dec. 2018).
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>
More on the green lynx spider Peucetia viridis (Blackwall, 1858) in South Africa (Araneae: Oxyopidae)
<p>The green lynx spider <i>Peucetia viridis</i> (Blackwall, 1858) has a wide distribution in Spain, Greece, Africa, and the Middle East and was introduced to the Caribbean Island. In South Africa, it has a wide distribution and is known from eight provinces. The general morphology of the species is discussed, with photographs of live specimens and notes on their behaviour, biology, distribution, and conservation status in South Africa. The species is also reported from agroecosystems in South Africa.</p>
Transport Starter Data Kit: Historical socio-transport data for selected countries in Africa, Asia, and South America
<p>This Transport Starter Data Kit contains historical annual data (1990–2021) on passenger activity, freight activity, vehicle stock, energy intensity, and load factor segregated by mode and fuel, where available. Additionally, historical data on 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><p>Countries included: Angola, Burundi, Benin, Burkina Faso, Brazil, Botswana, Central African Republic, Côte d'Ivoire, Cameroon, Democratic Republic of the Congo, Congo, Colombia, Djibouti, Algeria, Egypt, Eritrea, Ethiopia, Gabon, Ghana, Guinea, Gambia, Guinea-Bissau, Equatorial Guinea, Indonesia, Kenya, Cambodia, Republic of Korea, Lao People's Democratic Republic, Liberia, Libya, Lesotho, Morocco, Mali, Myanmar, Mozambique, Mauritania, Malawi, Malaysia, Namibia, Niger, Nigeria, Philippines, Rwanda, Sudan, Senegal, Sierra Leone, Somalia, South Sudan, Eswatini, Chad, Togo, Thailand, Tunisia, Taiwan Province of China, United Republic of Tanzania, Uganda, Viet Nam, South Africa, Zambia, Zimbabwe.</p>
Transport Starter Data Kit: Historical socio-transport data for South Africa
<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>
Figs 2, 3 in Seasonal variations in ixodid tick populations on a commercial game farm in the Limpopo Province, South Africa
Figs 2, 3. Numbers of Rhipicephalus (Boophilus) decoloratus collected in wetter and drier months (2), and in warmer and cooler months (3).
Fig. 2. Haplotype network calculated from the E in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa
Fig. 2. Haplotype network calculated from the E. andrei COI haplotypes found in the South African earthworm groups investigated. The size of the circles is proportional to the number of earthworms sharing the same haplotype. The numbers on the branches indicate the positions of mutations on the COI sequences, mv1 represents a median vector (intermediate haplotypes, not found in this study).
Fig. 1 in Molecular assessment of commercial and laboratory stocks of Eisenia spp. (Oligochaeta: Lumbricidae) from South Africa
Fig. 1. Neighbour-joining tree based on the K2P method. Bootstrap support obtained for specific nodes are reported. Genbank accession numbers or BOLD process IDs are provided in brackets for the sequences downloaded from either Genbank or BOLD. Allolobophoridella eiseni and Microscolex phosphoreus were included as outgroups. Asterisk indicates dubious E. andrei sequences from BOLD.
rwpfunctionality: Water point functionality assessment in nine sub-Saharan Africa and South Asia countries
Water point monitoring data associated with the paper "[Rural water point functionality estimates and associations: evidence from nine countries in sub-Saharan Africa and South Asia](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023WR034679)" (Murray, Anna L et al., 2024).
Abdominal colour patterns of the sand diving spider Ammoxenus amphalodes (Araneae: Gnaphosidae) from South Africa
<p>The two types of abdominal patterns found in <em>Ammoxenus</em> species are discussed, with emphasis on <em>A. amphalodes</em> Dippenaar & Meyer, 1980. With images of live specimens, the two patterns are shown. Within the genus, there is large interspecific similarity, but intraspecific variability regarding the abdominal colour pattern. Due to these variations found species are sometimes wrongly identified.</p>
Fig. 6 in An overview of the Dactylosomatidae (Apicomplexa: Adeleorina: Dactylosomatidae), with the description of Dactylosoma kermiti n. sp. parasitising Ptychadena anchietae and Sclerophrys gutturalis from South Africa
Fig. 6. (A–K). Possible development of Dactylosoma kermiti n. sp. in the gut or haemocoel from the mosquitoes Uranotaenia (Pseudoficalbia) mashonaensis and U. (Pfc.) montana, from infected Sclerophrys gutturalis. (A) Intracellular meront. (B) Intra- and extracellular meront. (C–D) Merging of gametes. (E) Ookinete. (F) Immature oocyst. (G–I) Free sporozoites. (J) Probable meront producing immature merozoites. (K) Probable meront, producing long and slender mature merozoites. Vacuoles – arrow (A–B); Nucleus – arrow (D–K); Condensed chromatin – arrowhead (B, D–K). Scale bars 10 μm.
Fig. 5 in An overview of the Dactylosomatidae (Apicomplexa: Adeleorina: Dactylosomatidae), with the description of Dactylosoma kermiti n. sp. parasitising Ptychadena anchietae and Sclerophrys gutturalis from South Africa
Fig. 5. (A-D). dipterans observed feeding on Ptychadena anchietae and Sclerophrys gutturalis in situ.(A–B). African phlebotomine sand flies (arrows) Sergentomyia sp. feeding on Ptychadena anchietae in situ. (C–D) Mosquitoes (arrows), Uranotaenia (Pseudoficalbia) mashonaensis and U. (Pfc.) montana feeding on Sclerophrys gutturalis in situ.
Fig. 4 in An overview of the Dactylosomatidae (Apicomplexa: Adeleorina: Dactylosomatidae), with the description of Dactylosoma kermiti n. sp. parasitising Ptychadena anchietae and Sclerophrys gutturalis from South Africa
Fig. 4. Consensus phylogram of haemogregarines based on 18S rDNA sequences. Tree topologies for Bayesian inference (BI) and Maximum likelihood (ML) analyses were similar (represented on the ML tree), showing the phylogenetic relationships for D. kermiti n. sp. and Dactylosoma sp. ex Pel. lessonae (represented in bold), compared to other species of Haemogregarina, Hepatozoon, Karyolysus, Hemolivia, and Adelina and Klossia as outgroup. Clades that neither produced 0.80 posterior probability (BI) or 70 bootstrap (ML) nodal support values were omitted. The scale bar represents 0.02 nucleotide substitutions per site. The host, geographical distribution (according to the zoogeographical realms), and if known the vector and life history cycle are also provided for the different sequences using symbols and pictograms. Asterisks (*) indicate the proposed life history strategy of D. kermiti n. sp. based on data from the current study.
Fig. 1 in An overview of the Dactylosomatidae (Apicomplexa: Adeleorina: Dactylosomatidae), with the description of Dactylosoma kermiti n. sp. parasitising Ptychadena anchietae and Sclerophrys gutturalis from South Africa
Fig. 1. (A–L). Dactylosoma kermiti n. sp. from the grass frog Ptychadena anchietae. (A–H) Primary merogony. (A) Young trophozoite. (B–D) Trophozoites. (E) Young meront. (F–G) Primary meronts. (H) Merozoites. (I–L) Secondary merogony. (I) Secondary meront. (J) Immature gamont. (K) Gamont. (L) Extracellular gamont. Arrowheads show condensed chromatin (A–I); arrows show vacuoles (B–E). All images captured from the deposited slides [NMB P 534 – 535]. Scale bar 10 μm.
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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.