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60 results for “Africa Assessment”
Data from: Genome wide assessment of genetic variation and population distinctiveness of the pig family in South Africa
<p>Genetic diversity is of great importance and a prerequisite for genetic improvement and conservation programs in pigs and other livestock populations. The present study provides a genome wide analysis of the genetic variability and population structure of pig populations from different production systems in South Africa relative to global populations. A total of 234 pigs sampled in South Africa and consisting of village (n = 91), commercial (n = 60), indigenous (n = 40), Asian (n = 5) and wild (n = 38) populations were genotyped using Porcine SNP60K BeadChip. In addition, 389 genotypes representing village and commercial pigs from America, Europe and Asia were accessed from a previous study and used to compare population clustering and relationships of South African pigs with global populations. Moderate heterozygosity levels, ranging from 0.204 for Warthogs to 0.371 for village pigs sampled from Capricorn municipality in Eastern Cape province of South Africa were observed. Principal Component Analysis of the South African pigs resulted in four distinct clusters of (i) Duroc; (ii) Vietnamese; (iii) Bush pig and Warthog and (iv) a cluster with the rest of the commercial (SA Large White and Landrace), village, Wild Boar and indigenous breeds of Koelbroek and Windsnyer. The clustering demonstrated alignment with genetic similarities, geographic location and production systems. The PCA with the global populations also resulted in four clusters that where populated with (i) all the village populations, wild boars, SA indigenous and the large white and landraces; (ii) Durocs (iii) Chinese and Vietnamese pigs and (iv) Warthog and Bush pig. <i>K</i>= 10 (The number of population units) was the most probable ADMIXTURE based clustering, which grouped animals according to their populations with the exception of the village pigs that showed presence of admixture. AMOVA reported 19.92% – 98.62% of the genetic variation to be within populations. Sub structuring was observed between South African commercial populations as well as between Indigenous and commercial breeds. Population pairwise <i>F<sub>ST</sub></i>analysis showed genetic differentiation <i>(P < 0.05)</i>between the village, commercial and wild populations. A per marker per population pairwise <i>F<sub>ST</sub></i>analysis revealed SNPs associated with QTLs for traits such as meat quality, cytoskeletal and muscle development, glucose metabolism processes and growth factors between both domestic populations as well as between wild and domestic breeds. Overall, the study provided a baseline understanding of porcine diversity and an important foundation for porcine genomics of South African populations.</p>
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>
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).
CropSuite – Crop suitability assessment for 48 crops under rainfed and irrigated conditions for Africa
<p>Here, we provide all data and maps for the paper submitted to Geoscientific Model Development (GMD).</p> <p>The maps include the crop suitability, climate suitability, most limiting factor, potential for multiple cropping, the optimal sowing date and the suitable sowing days for 48 crops for Africa with and without the consideration of climate variability.</p> <div> <ol> <li>Alfalfa (Medicago sativa)</li> <li>Arabica Coffee (Coffea arabica)</li> <li>Avocado (Persea americana)</li> <li>Banana (Musea spp.)</li> <li>Barley (Hordeum vulgare)</li> <li>Beans (Phaseolus vulgaris)</li> <li>Cabbage (Brassica oleracca)</li> <li>Carrot (Daucus carota)</li> <li>Cashew (Anacardium occidentale)</li> <li>Cassava (Manihot esculenta)</li> <li>Castor Bean (Ricinus commuis)</li> <li>Chickpea (Cicer arietinum)</li> <li>Citrus (Citrus spp.)</li> <li>Cocoa (Theobroma cacao)</li> <li>Coconut (Cocos nucifera)</li> <li>Cotton (Gossypium hirsutum)</li> <li>Cowpea (Vigna unguiculata)</li> <li>Green Pepper (Capsium annuum)</li> <li>Groundut (Arachis hypogaea)</li> <li>Guava (Psidium guijava)</li> <li>Maize (Zea mais)</li> <li>Mango (Mangifera indica)</li> <li>Millets (Pennisetum americanum)</li> <li>Oil Palm (Elaeis guineensis)</li> <li>Olive (Olea europacae)</li> <li>Onion (Allium cepa)</li> <li>Papaya (Carica papaya)</li> <li>Pea (Pisum sativum)</li> <li>Pineapple (Ananas comosus)</li> <li>Potato (Solanum tuberosum)</li> <li>Rapeseed (Brassica napus)</li> <li>Rice (Oryza sativa)</li> <li>Robusta Coffee (Coffea canephora)</li> <li>Rubber tree (Hevea brasiliensis)</li> <li>Rye (Secale cereale)</li> <li>Safflower (Carthamus tinctorius)</li> <li>Sesame (Sesamum indicum)</li> <li>Sorghum (Sorghum bicolor)</li> <li>Soy (Glycine maximum)</li> <li>Sugar Cane (Saccharum officinarum)</li> <li>Sunflower (Helianthus annus)</li> <li>Sweet Potato (Ipomoea batatas)</li> <li>Tea (Camellia senesis)</li> <li>Tobacco (Nicotiana tabacum)</li> <li>Tomato (Solanum lycopersicum esculentum)</li> <li>Watermelon (Colocynthis citrullus)</li> <li>Wheat (Triticum aesticum)</li> <li>Yam (Dioscorea)</li> </ol> </div> <p>The maps are provided for each crop seperately for rainfed and irrigated conditions and combined using data on currently irrigated areas based on Maier et al. (2018).</p> <p>In addition, maps for the comparison between crop suitability and MapSPAM2020 are provided.</p> <p>Zabel, Knüttel, Poschlod (2024): CropSuite – A comprehensive open-source crop suitability model considering climate variability for climate impact assessment. Preprint. DOI: <a href="https://doi.org/10.5194/egusphere-2024-2526">https://doi.org/10.5194/egusphere-2024-2526</a>.</p>
Figure 2 in A floristic assessment of grassland diversity loss in South Africa
Figure 2: Non-Metric Multidimensional Scaling (NMDS) ordination for: A, transformed and untransformed grasslands; B, within land-use type transformation. Squares, transformed grasslands; circles, untransformed grasslands; Empty symbols, urban sites; filled symbols, agricultural sites.
Fig. 2 in An ecological and conservation assessment of the fauna of Bombyliidae (Diptera) occurring in the Mkhuze, Phinda and False Bay reserves, KwaZulu-Natal, South Africa
Fig. 2. Phenology of bombyliid activity. Each bar represents the number of unique species sampled in the months identified: November (N), January (J) and March (M). A unique species is a species only sampled in that month or combination of months. No sampling was undertaken in December or February.
Fig. 3 in An ecological and conservation assessment of the fauna of Bombyliidae (Diptera) occurring in the Mkhuze, Phinda and False Bay reserves, KwaZulu-Natal, South Africa
Fig. 3 Individual based species accumulation curve for the Mkhuze and Phinda game reserves and False Bay Park based on all Bombyliidae species sampled during the survey. The Michaelis-Menten equation predicts an asymptote at 31.7 species (R2=0.99).
Fig. 4 in An ecological and conservation assessment of the fauna of Bombyliidae (Diptera) occurring in the Mkhuze, Phinda and False Bay reserves, KwaZulu-Natal, South Africa
Fig. 4. Number of sites required to sample the bombyliid fauna. Sampling based species accumulation curves for (1) minimum sampling effort at each site based on 2 transects (O), and (2) maximum sampling effort based on 4 transects (). The Michaelis-Menten equation was fitted to extrapolate and to estimate the optimal number of sampling sites required (Table 2). R2=0.99 for each curve.
Fig. 1 in An ecological and conservation assessment of the fauna of Bombyliidae (Diptera) occurring in the Mkhuze, Phinda and False Bay reserves, KwaZulu-Natal, South Africa
Fig. 1. Study region located in north-eastern KwaZulu-Natal, South Africa, showing the vegetation types of the reserves identified by Muchina and Rutherford (2005). Forty-three 1 ha invertebrate sites were sampled (indicated by white dots) for one day, using a variety of methods; 20 of these sites were re-sampled in different months and years.
Assessment of acetochlor use areas in the Sahel region of Western Africa using geospatial methods
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Data from: Genome wide assessment of genetic variation and population distinctiveness of the pig family in South Africa
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Assessing Grid Affordability for Universal Electricity Access in Sub-Saharan Africa
<p>This repository contains all the necessary original data for the publication <em>"Assessing Grid Affordability for Universal Electricity Access in Sub-Saharan Africa."</em> It includes the original documents detailing the tariff structures of 48 SSA countries, as well as population data, the Gini index, and gross national income per capita, which are used to simulate income distribution. Additionally, it provides calculator tools for estimating electricity bills based on the Multi-Tier Framework and assessing grid electricity affordability by comparing simulated income with electricity costs.</p> <p>The related code and result datasets supporting this publication are available through the GitHub public repository: <a href="https://rb.gy/f68tx5" target="_new" rel="noopener">https://rb.gy/f68tx5</a>.</p>
Assessment of the Effectiveness of Coarse Resolution Fire Products in Monitoring Long-term Changes in Fire Regime within Protected Areas in South Africa
<p>The dataset includes fire histrory (shapefiles with data perimeters for 2003-2020) within 3 protected areas in South Africa, Blyde, Manyeleti, and Songimvelo.</p>
Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa
<p><strong>Dataset Name:</strong><br><em>Literature Data and Archetype Parameter Sheets for the publication, named Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa.</em></p> <p><strong>Description:</strong><br>This dataset includes Excel sheets containing literature sources and archetypal data on Western Asian and North African countries' residential dwelling typologies. As well as Vacancy rates used and simulation results.</p> <p><strong>Files:</strong><br>The following files are included in the dataset:</p> <ul> <li> <em>[CountryName]_LiteratureSources.xlsx:</em> Excel sheet containing literature sources and references,</li> <li> <em>[CountryName]_ArchetypeParameters.xlsx</em>: Archetype models' semantic, geometric, and technical data used in the generation of energy models,</li> <li><em> VacantHouses.xlsx</em>: Vacant house rates for the countries, the found articles on the web, literature sources, etc.,</li> <li> <em>Resource Use Results:</em> BuildME Simulation Results</li> </ul> <p><strong>Usage:</strong><br>The dataset is intended for researching and analyzing the Western Asian and North African countries' residential buildings. The literature sources included in the [CountryName]_LiteratureSources.xlsx and [CountryName]_ArchetypeParameters.xlsx files can be used to verify, support, or reproduce the research findings.</p> <p><strong>License:</strong><br>The dataset is licensed under Creative Commons Attribution 4.0 International.</p> <p><strong>Citation:</strong><br>If you use this dataset in your research, please cite it as follows and contact the corresponding author:</p> <p>Akin, Sahin, Aida Eghbali, Chibuikem Chrysogonus Nwagwu, and Edgar Hertwich. 2024. “Archetype-based Life-Cycle Assessment of National Residential Building Stocks: Resource Use and Greenhouse Gas Emissions in Western Asia and Northern Africa” https://doi.org/10.5281/zenodo.13380340.</p> <p><strong>Contact:</strong><br>The archetypes' energy models (DesignBuilder or IDF files) can be provided on request. If you have any questions or comments about the dataset, please contact <strong>sahin.akin@ntnu.no, the corresponding author.</strong></p>
Public health emergency operations centres in Africa: A cross-sectional study assessing the implementation status of core components and areas for improvement, December 2021
<div> <p><span><strong>Objective:</strong> </span><span>To assess implementation status of Public Health Emergency Operations Centres (PHEOCs) in all countries in Africa.</span></p> <p><span><strong>Design:</strong> </span><span>Cross-sectional.</span></p> <p><span><strong>Setting:</strong> </span><span>Fifty-four national PHEOC focal points in Africa responded to an online survey between May and November 2021. Included variables aimed to assess capacities for each of the four PHEOC core components. To assess the PHEOCs' functionality, criteria were defined from among the collected variables by expert consensus based on PHEOC operations' prioritisation. We report results of the descriptive analysis, including frequencies of proportions.</span></p> <p><span><strong>Results:</strong> </span><span>A total of 51 (93%) African countries responded to the survey. Among these, 41 (80%) have established a PHEOC. Twelve (29%) of these met 80% or more of the minimum requirements and were classified as fully functional. Twelve (29%) and 17 (41%) PHEOCs that met 60%–79% and below 60% of the minimum requirements were classified as functional and partially functional, respectively. </span></p> <p><span><strong>Conclusions:</strong> </span><span>Countries in Africa made considerable progress in setting up and improving functioning of PHEOCs. One-third of the responding countries with a PHEOC have one fulfilling at least 80% of the minimum requirements to operate the critical emergency functions. There are still several African countries that either do not have a PHEOC or whose PHEOCs only partially meet these minimal requirements. This calls for significant collaboration across all stakeholders to establish functional PHEOCs in Africa.</span></p> </div>
Public health emergency operations centres in Africa: A cross-sectional study assessing the implementation status of core components and areas for improvement, December 2021
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Data from: Using a trait-based approach for assessing the vulnerability and resilience of hillslope seep wetland vegetation cover to disturbances in the Tsitsa River catchment, Eastern Cape, South Africa
<p>Hill slope seep wetlands are ecologically and economically important ecosystems as they supply a variety of ecosystem services to society. In South Africa, livestock grazing is recognised as one of the most important disturbance factors changing the structure and function of hill slope seep wetlands. This study sought to investigate the potential effect of livestock grazing on the resilience and vulnerability of hillslope seep wetland vegetation cover using a trait based approach (TBA). Changes in vegetation cover were used as a surrogate for indicating grazing intensity. The degree of human disturbances was assessed using the Anthropogenic Activity Index (AAI). A TBA was developed using seven plant traits, resolved into 27 trait attributes. Based on the developed approach, plant species were grouped into vulnerable and resilient groups in relation to grazing pressure. It was then predicted that species belonging to the vulnerable group would be less dominant at the highly disturbed sites, as well as in the winter season when grazing pressure is at its peak. The approach developed enabled accurate predictions of the responses of hillslope plant species to grazing pressure seasonally, but spatially, only for the summer season. The predicted responses during the winter season across sites did not match the observed results, which could be attributed to the difficulty in species identification and accurate estimation of vegetation cover during winter. Overall, the approach developed here provides a general framework for applying the TBA and can thus be tested and applied elsewhere.</p>
Data from: Assessing the vulnerability of Africa's freshwater fishes to climate change: a continent-wide trait-based analysis
Climate change is a key driver of biodiversity loss across the globe, and freshwater fishes are predicted to be among the most vulnerable taxa. African freshwater ecosystems are home to one of the most unique and diverse ichthyo-faunas on the planet, and freshwater fish species provide essential livelihoods for millions of people living in riparian communities across the continent. Although nearly one sixth of African freshwater fishes have been designated as endangered or vulnerable to extinction by the IUCN Red List assessment, the effects of climate change on these species have not been explored on a continent-wide scale. In this study, we present the first trait-based climate change vulnerability assessment (CCVA) comprising the majority (85%) of Africa's currently described freshwater fishes. We assembled data relating to three dimensions of vulnerability including sensitivity, adaptive capacity, and exposure. In addition, we developed an index of 'conservation value' based on traditional conservation metrics including extinction risk, endemism, and provision of ecosystem services. We found that almost 40% of African freshwater fishes are vulnerable to climate change, mostly owing to the many species with highly specialized habitat and life-history requirements, and because of the numerous anthropogenic stressors they face. High proportions of species within the Nothobranchiidae and Cichlidae families were found to be vulnerable. Regions with high frequencies of vulnerable species included the African rift valley lakes, the Congo River drainage, and the coastal rivers of West Africa. Several important data deficiencies were identified relating to species' population sizes, genetic variability, and life history traits, and constitute priority research areas for the future. In addition, we highlighted some cases where traditional conservation approaches overlook species and regions that are predicted to be threatened by climate change.
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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.