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4,763 results for “South Africa”
Anyskop Blowout Prehistoric Dataset, Western Cape, South Africa
<p>These Stone Age archaeological datasets were collected in 2001 and 2002 by a team from the Department of Early Prehistory and Quaternary Ecology of the University of Tübingen (Germany) headed by Nicholas J. Conard. Many South African researchers collaborated on this project, with Pippa Haarhoff, John Compton, Dave Roberts, and Stephan Woodborne deserving special mention.</p> <p>The field work took place at the Anyskop Blowout (ANY1) located within the West Coast Fossil Park near Langebaanweg, Western Cape, South Africa. The field work was conducted with the help of students from the universities of Tübingen and Cape Town. The datasets are predominantly in English (with some German as well) and include field data in the MAIN table. Further analytical data for many classes of artifacts include: LITHICS, FAUNA, POTTERY, MODERN, BUCKETS, REFITS.</p> <p>All collected materials are curated by the Iziko South African Museums in Cape Town under accession numbers SAM-AA-8903 (finds collected by other teams before 2001) and SAM-AA-9007 (finds from this study, 2001-2002). Some of the finds are exhibited in the museum at the West Coast Fossil Park.</p> <p>Funding for this research project came mainly from the German Research Foundation (DFG - CO 226/5-1, 5-2, 5-5 and 5-6) and the University of Tübingen. Significant support was provided by the Iziko South African Museums, the West Coast Fossil Park, and the University of Cape Town.</p>
Woody vegetation composition and structure at long-term monitoring plots on the Stevenson-Hamilton Research Supersite, Kruger National Park, South Africa (2012)
This dataset contains measurements of woody vegetation composition and structural attributes collected in 2012 from long-term ecological monitoring plots located on the Stevenson-Hamilton Research Supersite in the Kruger National Park, South Africa. The study region is characterized by granitic soils, broad-leaved savanna vegetation, and a long history of fire, herbivory, and climate-driven ecological dynamics. Vegetation surveys were conducted in sixteen 0.25-ha sampling plots to quantify woody species composition, stem density, and size structure. Additional measurements of vegetation structure were collected, including grass biomass, canopy cover, canopy height, and canopy diversity, providing a broader assessment of both woody and herbaceous layers. These data establish an important baseline for monitoring ecological change, evaluating woody vegetation dynamics under variable fire and herbivore regimes, and supporting ongoing research on savanna ecosystem functioning within the Kruger National Park.
Hoedjiespunt Middle Stone Age Dataset, Western Cape, South Africa
<p>This Middle Stone Age archaeological dataset from Hoedjiespunt 1 was collected in 2011 by a team from the Department of Early Prehistory and Quaternary Ecology of the University of Tübingen (Germany) headed by Nicholas J. Conard. South African and European researchers collaborated on this project, with John E. Parkington, Katherine Kyriacou, Deano Stynder, Graham Avery, and Chantal Tribolo making substantial contributions. The site is located within the property of Transnet National Ports Authority in the municipality of Saldanha, Western Cape, South Africa.</p> <p>The locality of Hoedjiespunt 1 was well known as a paleontological site since at least the 1990s, when the site yielded several important Middle Pleistocene hominin remains dated between 200,000 and 350,000 years. The paleontological site also yielded a well preserved assemblage of fauna, including terrestrial and marine mammals, shellfish and ostrich eggshell. The excavators interpreted the accumulation of these finds as the remains of a hyena den. Cultural remains such as lithic artifacts were absent from the paleontological site, which is situated immediately below the archaeological site.</p> <p>The 2011 field work at the archaeological site of Hoedjiespunt 1 took place with the help of students from the universities of Tübingen and Cape Town. The datasets are predominantly in English (with some parts in German) and include field data in the MAIN table. Further analytical data for several classes of artifacts include: LITHICS, FAUNA, OCHRE, and BUCKETS.</p> <p>All of the archaeological materials collected in 2011 are curated by the Department of Archaeology of the University of Cape Town in Rondebosch, South Africa. Funding for this research came mainly from the Heidelberg Academy of Sciences and Humanities and the University of Tübingen. Significant support was provided by the Department of Archaeology of the University of Cape Town and the Iziko South African Museums.</p> <p> </p> <p>Importnat references for the paleontological excavations are listed here, while the main publications associated with the 2011 excavations are presented below in the reference section: </p> <p>Berger, L.R. & Parkington, J.E. (1995). A new Pleistocene hominid-bearing locality at Hoedjiespunt, South Africa. American Journal of Physical Anthropology 98: 601-609. <a href="https://doi.org/10.1002/ajpa.1330980415">https://doi.org/10.1002/ajpa.1330980415</a></p> <p>Churchill, S.E., Berger, L.E. & Parkington, J.E. (2000). A Middle Pleistocene human tibia from Hoedjiespunt, Western Cape, South Africa. South African Journal of Science 96: 367-368. <a href="https://hdl.handle.net/10520/AJA00382353_8943">https://hdl.handle.net/10520/AJA00382353_8943</a> </p> <p>Stynder, D.D., Moggi-Cecchi, J. Berger, R.L. & Parkington, J.E. (2001). Human mandibular incisors from the late Middle Pleistocene locality of Hoedjiespunt 1, South Africa. Journal of Human Evolution 41: 369-383. <a href="https://doi.org/10.1006/jhev.2001.0488">https://doi.org/10.1006/jhev.2001.0488</a></p>
Data for SARS-CoV-2 Reinfection Trends in South Africa: Monthly Report (2022-12-07)
<p>This version contains a single file, with time series data for the most recent <a href="https://www.nicd.ac.za/diseases-a-z-index/disease-index-covid-19/surveillance-reports/sarscov2-reinfection-trends-in-south-africa-monthly-report/">monthly report on SARS­-CoV-­2 Reinfection Trends in South Africa</a>:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> </ul> <p>Note: There may be some inconsistencies with the numbers of infections through time in earlier versions of this data set due to back-filling of late-arriving data.</p> <p> </p> <p>Note: Earlier versions of this data set included data files for Pulliam, JRC, C van Schalkwyk, B Lombard, N Govender, A von Gottberg, C Cohen, MJ Groome, J Dushoff, K Mlisana, and H Moultrie. <a href="https://www.science.org/doi/10.1126/science.abn4947">Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa</a>. DOI: 0.1126/science.abn4947</p> <p>For code and more details see: <a href="https://github.com/jrcpulliam/reinfections/releases/tag/v3.0">https://github.com/jrcpulliam/reinfections/releases/tag/v3.0</a> or <a href="https://zenodo.org/record/6108448">10.5281/zenodo.6108448</a></p> <p>The version of this data set associated with the publication (available via the links above) included the following files:</p> <ul> <li><code>ts_data.csv</code> - national daily time series of newly detected putative primary infections (<code>cnt</code>), suspected second infections (<code>reinf</code>), suspected third infections (<code>third</code>), and suspected fourth infections (<code>fourth</code>) by specimen receipt date (<code>date</code>)</li> <li><code>demog_data.csv</code> - counts of individuals eligible for reinfection (<code>total</code>), who have 0 suspected reinfections (<code>no_reinf</code>) or >0 suspected reinfections (<code>reinf</code>) by province (<code>province</code>), age group (5-year bands, <code>agegrp5</code>), and sex (M = Male, F = Female, U = Unknown, <code>sex</code>)</li> <li><code>posterior_90_null.RData</code> - posterior samples from the MCMC fitting procedure (as used in the manuscript)</li> <li><code>sim_90_null.RDS</code> - simulation results (as used in the manuscript)</li> <li><code>emp_haz_sens_an.RDS</code> - output of sensitivity analysis of relative empirical hazard estimation to assumed observation probabilities (as used in the manuscript)</li> </ul>
Supplementary Table S27.1: Animal species native to South Africa that have invasive populations elsewhere.
<p>Animal species native to South Africa that have invasive populations elsewhere. Sorted by expected chronological appearance in the first place they were recorded as alien species. Notes are made on whether the introduction is known to be (Y) or not (N) from South Africa (or unknown U). Pathways are according to the CBD pathway classification scheme (Harrower et al. 2017), along with an indication of whether the introduction was intentional or accidental. Species that have multi-continental distributions, and which may in addition have some introduced populations are shown at the end of the table.</p>
Soil organic carbon stocks and trends (1984-2019) predicted at 30m spatial resolution for topsoil in natural areas of South Africa
<p>Link to scientific publication: <a href="https://doi.org/10.1016/j.scitotenv.2021.145384">https://doi.org/10.1016/j.scitotenv.2021.145384</a></p> <p>Soil organic carbon (SOC) stocks (kg C m-2) are predicted over natural areas (excluding water, urban, and cultivated) of South Africa using a machine learning workflow driven by optical satellite data and other ancillary climatic, morphometric and biological covariates. The temporal scope covers 1984-2019. The spatial scope covers 0-30cm topsoil in South Africa natural land area (84% of the country). See methodology in linked publication for details. Data are provided here at 30m spatial resolution in GeoTIFF files. There is a dataset for the long-term average SOC and trend in SOC. Each dataset is split into four files (suffix *_1, *_2 etc.) covering separate regions of South Africa for ease of download. The raster files are:</p> <ul> <li>"SOC_mean_30m..." - average of annual SOC predictions between 1984 and 2019. Values are expressed in kg C m-2</li> <li>"SOC_trend_30m..." - long-term trend in SOC derived from the Sens slope (M) across annual SOC values between 1984 and 2019. Pixel values (Y) are expressed as a percentage change over the 35 years relative to the long-term mean (X). Y = M / X * 100 * 35 years</li> </ul> <p>NB: All files are scaled by *100 and converted to floating data point to save space. To back-convert to original values, simply divide the raster values by 100.</p>
Quick keys to the Bominae genera of South Africa (Araneae: Thomisidae)
<p>In this paper, keys are provided to identify the genera <em>Avelis</em> Simon, 1895, <em>Holopelus</em> Simon, 1886, <em>Parabomis,</em><br>1901 and <em>Thomisops</em> Karsch, 1879 and their species in the field and from photographs. With their small and round bodies they resemble seeds and may easily be overlooked in the field. The latest information on their distribution and conservation status in South Africa is provided.</p>
Records of Artema atlanta Walckenaer, 1837 from South Africa (Araneae: Pholcidae)
<p>Records of the spider <em>Artema atlanta </em>Walckenaer, 1837 from South Africa are presented. The general morphol-ogy of live specimens is discussed, and photographs are provided, with notes on their behaviour and distribution.</p>
InSAR stack of Western Cape, South Africa from Sentinel-1 ascending track 29 processed with SNAP
<p>A stack of unwrapped interferograms on Western Cape, South Africa.</p> <p>Sensor: Sentinel-1ascending track 29</p> <p>Time: 2019.03.03 - 2019.05.14, 7 acquisitions, 15 interferograms</p> <p>Processor: SNAP (accessed on 14 July 2019)</p> <p>Tropospheric delay estimated from ERA-5 using PyAPS is attached.</p> <p>This is an input dataset for the time series analysis with <a href="https://github.com/insarlab/MintPy/">MintPy</a>.</p>
Dataset for the identification of hypertension in school-aged children from Gqeberha, South Africa
<p>Dataset used to evaluate and compare different international references to identify hypertension among South African school-aged children from disadvantaged communities.</p> <p>It encompasses anonymized, unique, identification numbers, anthropometric and blood pressure measures, as well as blood pressure percentiles and the assigned categories derived from four different reference populations (American, German, global and the study population).</p>
Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022.
<p>Time series data of COVID-19 cases (rT-PCR-confirmed), hospitalisations (laboratory-confirmed), and hospital-associated deaths (laboratory confirmed) in South Africa, by imputed dates of symptom onset, from the start of the pandemic in March 2020 through April 2022. These data were used to estimate the time-varying reproduction number (R) in South Africa, as described in https://www.medrxiv.org/content/10.1101/2022.07.22.22277932v1.full.</p>
Regional model results (combined) for the six transition potentials (one for Africa, Australia, Asia, Europe, North America, and South America)
<p>Results for the six regional models showing areas of high potential to transition from tree cover to tree cover loss to areas of low potential to transition.</p>
National Checklists 2017: South Africa 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 South Africa collected using effechecka and geonames polygons
National Checklists 2019: South Africa 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 South Africa collected using effechecka and geonames polygons
Open and Lite Techno-economic Dataset for Long-term Energy Systems Modelling in the Republic of South Africa
<p>An open-source lite techno-economic dataset for long term energy systems modelling in the Republic of South Africa. Includes data on electricity generation and demand, electricity imports and exports, power transmission and distribution, residual capacity, capacity factor, operational lifetime, and fixed, variable and capital costs of electricity generation technologies. It also contains estimates for renewable potential and fossil fuel reserves in South Africa.</p>
The immature Homo naledi ilium from the Lesedi Chamber, Rising Star Cave, South Africa
<p>To use any of these data, please cite: Cofran Z, VanSickle C, Valenzuela R, García-Martínez D, Walker CS, Hawks J, Zipfel B, Williams SA, & Berger LR. 2022. The immature <em>Homo naledi</em> ilium from the Lesedi Chamber, Rising Star Cave, South Africa. American Journal of Biological Anthropology 179:3–17. (https://onlinelibrary.wiley.com/doi/full/10.1002/ajpa.24522)</p> <p>Lesedi Ilium Landmark Dataset_R1.csv = A comma separated values (.csv) format file containing 148 3D landmarks describing shape of the right ilium, for 23 immature humans, <em>Australopithecus</em> fossils MLD 7 and MLD 25, and two reconstructions of the <em>Homo naledi</em> fossil U.W. 102a-138. The first naledi reconstruction utilizes a reference template based on MLD 7 and MLD 25, and the second reconstruction is based on the average of the human ilia. The .csv file contains columns for individual ID, landmark name, and the x-, y-, and z-coordinates of the landmark, and each row is a unique landmark coordinate.</p> <p>Lesedi_Ilium_Height_Data.csv = A comma separated values (.csv) format file containing developmental stage and iliac height (in mm) for 43 humans, U.W. 102a-138, and three <em>Australopithecus</em> fossils (MLD 7, MLD 25, and the left and right sides of Sts 14).</p> <p>A 3D mesh of the U.W. 102a-138 ilium is available on Morphosource: https://www.morphosource.org/concern/media/000383216?locale=en</p>
Coronavirus disease (COVID-19) case data - South Africa
<p>COVID 19 Data for South Africa created, maintained and hosted by <a href="https://dsfsi.github.io/">DSFSI research group</a> at the University of Pretoria</p> <p><strong>Disclaimer:</strong> We have worked to keep the data as accurate as possible. We collate the COVID 19 reporting data from NICD and South Africa DoH. We only update that data once there is an official report or statement. For the other data, we work to keep the data as accurate as possible. If you find errors let us know. </p> <p>See original GitHub repo for detailed information <a href="https://github.com/dsfsi/covid19za">https://github.com/dsfsi/covid19za</a></p>
SI2: How circular is an extractive economy? South Africa's export orientation results in low circularity and insufficient societal stocks for service-provisioning
<p>Supporting information SI2 for the manuscript under review:</p> <p>How circular is an extractive economy? South Africa’s export orientation results in low circularity and insufficient societal stocks for service-provisioning </p> <p> </p> <p>It provides the data used and the basic mass balanced calculation for a circularity assessment.</p>
Mechanisms influencing physically sequestered soil carbon in temperate restored grasslands in South Africa and North America
This dataset contains a measurement of physically protected carbon (microaggregate-within-macroaggregate C) and potential drivers of physically protected C accumulation during grassland restoration. The data were collected from three independent grassland restorations from agriculture in North America and South Africa. Northeast Kansas, USA data were collected in May 2013. Northeast Free State, RSA data were collected in September–November 2005. Southeast Nebraska, USA data were collected in October 2008–May 2008. Aggregate fractionations were performed by hierarchical wet sieving (Six et al 2000). Carbon and N quantification were done by flash combustion-gas chromatography. Microbial biomass C quantification was performed with chloroform fumigation-incubation (chloroform fumigation-extraction in the case of northeast Kansas). Phospholipid fatty acid biomass analysis was conducted using the methods of Bligh and Dyer (1959).
Figure 2 in New species of the plesiomorphic genus Nixonia Masner (Hymenoptera, Platygastroidea, Platygastridae) from South Africa
Figure 2. Nixonia mcgregori van Noort & Johnson, sp. n., female, holotype. A habitus, lateral view B habitus, dorsal view C head, mesosoma, lateral view D mesosoma, dorsal view E head, anterior view F metasoma dorsal view. Scale bars in millimeters. (http://www.morphbank.net/?id=999008677)
ScienceDex guides
Understand access before you commit
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.