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11,982 results for “africa”
Copernicus Global Land Service: Land Cover 100m: epoch 2018: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518037">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2018, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the near-real time (nrt) epoch 2018, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2017) and three months pastor (Jan-March 2019) data. The nrt map can then be supplied in the fourth month after the most recent completed calendar year, and is updated (consolidated) afterwards by using a full year of paster data (when epoch 2019-nrt is produced, epoch 2018 is consolidated).</p> <p>The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for this near-real time epoch</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2018">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p>
Copernicus Global Land Service: Land Cover 100m: epoch 2017: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3518035">this global dataset</a> instead.</em></strong></p> <p>emonstration land cover maps over Africa at 100m resolution for epoch year 2017, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>For the consolidated epoch 2017, the classifier and regression models of base year 2015 are used, and the time window of the classified metrics covers one full year prior (2016) and pastor (2018) data. The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for the base year (epoch 2015). The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) year (epoch 2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2017">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
Copernicus Global Land Service: Land Cover 100m: epoch 2015: Africa demo (deprecated)
<p><strong><em>This demo dataset over Africa is deprecated. Please see <a href="https://doi.org/10.5281/zenodo.3243508">this global dataset</a> instead.</em></strong></p> <p>Demonstration land cover maps over Africa at 100m resolution for epoch year 2015, from the global component of the Copernicus Land Service and derived from PROBA-V satellite observations.</p> <p>The maps include the main discrete classification (23 classes aligned with UN-FAO's LCCS), a set of cover fractions (%) for the 10 main classes and additional quality layers (e.g. density of input data).</p> <p>As a base year, the classification and regression models for 2015 are saved for re-use in subsequent consolidated (with full year prior and pastor observations) and near-real time years (with full year prior and 3 months pastor data). The layers with the probability of the discrete classification and the standard deviation of the cover fractions are only provided for this base epoch. The Change Consistency Layer that checks consistency between classifier and break detection, is only available for the most recent (near-real time) epoch (2018).</p> <p> </p> <p><a href="https://africa.lcviewer.vito.be/2015">View the maps and area statistics</a></p> <p><a href="https://land.copernicus.eu/global/documents/lcc100/all/pum">Product User Manual</a></p> <p><a href="https://land.copernicus.eu/global/products/lc">More land cover change product information and documentation</a></p> <p> </p>
Coronavirus COVID-19 (2019-nCoV) Data Repository for Africa
<p>The purpose of this repository is to collate data on the ongoing coronavirus pandemic in Africa. Our goal is to record detailed information on each reported case in every African country. We want to build a line list – a table summarizing information about people who are infected, dead, or recovered. The table for each African country would include demographic, location, and symptom (where available) information for each reported case. The data will be obtained from official sources (e.g., WHO, departments of health, CDC etc.) and unofficial sources (e.g., news). Such a dataset has many uses, including studying the spread of COVID-19 across Africa and assessing similarities and differences to what’s being observed in other regions of the world.</p> <p>See the repo here <a href="https://github.com/dsfsi/covid19africa">https://github.com/dsfsi/covid19africa</a></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>
Whole genome sequencing of Turkish genomes reveals functional private alleles and impact of genetic interactions with Europe, Asia and Africa.
<p>BACKGROUND:</p> <p>Turkey is a crossroads of major population movements throughout history and has been a hotspot of cultural interactions. Several studies have investigated the complex population history of Turkey through a limited set of genetic markers. However, to date, there have been no studies to assess the genetic variation at the whole genome level using whole genome sequencing. Here, we present whole genome sequences of 16 Turkish individuals resequenced at high coverage (32×-48×).</p> <p>RESULTS:</p> <p>We show that the genetic variation of the contemporary Turkish population clusters with South European populations, as expected, but also shows signatures of relatively recent contribution from ancestral East Asian populations. In addition, we document a significant enrichment of non-synonymous private alleles, consistent with recent observations in European populations. A number of variants associated with skin color and total cholesterol levels show frequency differentiation between the Turkish populations and European populations. Furthermore, we have analyzed the 17q21.31 inversion polymorphism region (MAPT locus) and found increased allele frequency of 31.25% for H1/H2 inversion polymorphism when compared to European populations that show about 25% of allele frequency.</p> <p>CONCLUSION:</p> <p>This study provides the first map of common genetic variation from 16 western Asian individuals and thus helps fill an important geographical gap in analyzing natural human variation and human migration. Our data will help develop population-specific experimental designs for studies investigating disease associations and demographic history in Turkey.</p>
Islam West Africa Collection (IWAC)
<p>Directed by <a href="https://www.frederickmadore.com/" target="_blank" rel="noopener">Frédérick Madore</a>, the <a href="https://islam.zmo.de/s/westafrica/" target="_blank" rel="noopener"><em>Islam West Africa Collection</em> (IWAC)</a> is a collaborative, open-access digital database that currently contains over 5,000 archival documents, newspaper articles, Islamic publications of various kinds, audio and video recordings, and photographs on Islam and Muslims in Burkina Faso, Benin, Niger, Nigeria, Togo and Côte d'Ivoire. Most of the documents are in French, but some are also available in Hausa, Arabic, Dendi, and English. The site also indexes over 800 references to relevant books, book chapters, book reviews, journal articles, dissertations, theses, reports and blog posts. This project, hosted by the <a href="https://www.zmo.de/en" target="_blank" rel="noopener">Leibniz-Zentrum Moderner Orient (ZMO)</a> and funded by the Berlin Senate Department for Science, Health and Care, is a continuation of the award-winning <a href="https://web.archive.org/web/20231207083222/https://islam.domains.uflib.ufl.edu/s/bf/page/home" target="_blank" rel="noopener"><em>Islam Burkina Faso Collection</em></a> created in 2021 in collaboration with <a href="https://librarypress.domains.uflib.ufl.edu/" target="_blank" rel="noopener">LibraryPress@UF</a>.</p> <p>This dataset contains all the metadata of the items in the Collection, the Jupyter notebooks that were used to create the visualisations that showcase the possibilities of <a href="https://islam.zmo.de/s/westafrica/page/digital-humanities" target="_blank" rel="noopener">digital humanities</a> with the IWAC, and a copy of the spreadsheets that were used to create the <a href="https://islam.zmo.de/s/westafrica/page/exhibits" target="_blank" rel="noopener">digital exhibits</a> using <a href="https://timeline.knightlab.com/" target="_blank" rel="noopener">Timeline JS</a>.</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>
U-Pb on zircon data from 'Evidence for large disturbances of the Ediacaran geomagnetic field from West Africa'
<p>This dataset provides the tabular U-Pb data on zircon presented in Robert et al. 2023 'Evidence for large disturbances of the Ediacaran geomagnetic field from West Africa', Precambrian Research, 394, 107095.</p> <p>The data are provided both as an xls formatted as in the original publication (in Table 1), and in a condensed .csv. Please the README file for a description of the columns of the csv.</p>
Centres of Excellence in Africa
<p>List of Centres of Excellence in Africa by name, host institution(s), country, date of establishment, initiative</p>
HornMT – Machine Translation Benchmark Dataset for Languages in the Horn of Africa
<p>The <strong>HornMT</strong> repository contains data and the associated metadata for the project <a href="https://lesan.ai/benchmark">Machine Translation Benchmark Dataset for Languages in the Horn of Africa</a>. It is a multi-way parallel corpus that will serve as a benchmark to accelerate progress in machine translation research and production systems for languages in the Horn of Africa.</p> <p>Supported Languages</p> <table> <tbody> <tr> <td> <p>Language</p> </td> <td> <p>ISO 639-3 code</p> </td> </tr> </tbody> <tbody> <tr> <td> <p>Afar</p> </td> <td> <p>aaf</p> </td> </tr> <tr> <td> <p>Amharic</p> </td> <td> <p>amh</p> </td> </tr> <tr> <td> <p>English</p> </td> <td> <p>eng</p> </td> </tr> <tr> <td> <p>Oromo</p> </td> <td> <p>orm</p> </td> </tr> <tr> <td> <p>Somali</p> </td> <td> <p>som</p> </td> </tr> <tr> <td> <p>Tigrinya</p> </td> <td> <p>tir</p> </td> </tr> </tbody> </table> <p><strong> </strong></p> <p>data/ contains one text file per language and each file contains news snippets in the same order for each language.</p> <p>data<br> ├── aar.txt<br> ├── amh.txt<br> ├── eng.txt<br> ├── orm.txt<br> ├── som.txt<br> └── tir.txt</p> <p>metadata.tsv contains tab separated data describing each news snippet. The metadata contains the following fields.</p> <ul> <li> <p><strong>Scope</strong> - describes whether the news is global or local. It takes two values: Global news and Local news.</p> </li> <li> <p><strong>Category</strong> - News category covering the following 12 topics</p> <ul> <li> <p>Art and Culture</p> </li> <li> <p>Business and Economy</p> </li> <li> <p>Conflicts and Attacks</p> </li> <li> <p>Disaster and Accidents</p> </li> <li> <p>Entertainment</p> </li> <li> <p>Environment</p> </li> <li> <p>Health</p> </li> <li> <p>International Relations</p> </li> <li> <p>Law and Crime</p> </li> <li> <p>Politics</p> </li> <li> <p>Science and Technology</p> </li> <li> <p>Sport</p> </li> </ul> </li> <li> <p><strong>Source</strong> - List of one or more URLs from which the news content is extracted or based on.</p> </li> <li> <p><strong>Domain</strong> - TLD corresponding to the URL(s) in Source.</p> </li> <li> <p><strong>Date</strong> - The publication date of the source article. The format is yyyy-mm-dd.</p> </li> </ul> <p>Other formats</p> <p>All the data and associated metadata together in one file is also available in other file formats.</p> <p><strong>HornMT.xlsx</strong> - data and associated metadata in xlsx format.</p> <p><strong>HornMT.json</strong> - data and associated metadata in json format.</p> <p>Below is an example row.</p> <pre><code class="language-javascript">{ "data":{ "eng":"The World Meteorological Organisation reports that the ozone layer is damaged to its worst extent ever in the Arctic.", "aaf":"Baad Metrolojih Eglali Areketekeh Addal Ozonih qelu faxe waktik lafetle calat biyakisem xayose.", "amh":"የአለም የአየር ንብረት ድርጅት በአርክቲክ አካባቢ ያለው የኦዞን ምንጣፍ ከፍተኛ ጉዳት እንደደረሰበት አስታወቀ፡፡", "orm":"Dhaabbanni Meetiroolojii Addunyaa baqqaanni oozonii Arkiitik keessatti gara sadarkaa isa hamaa haga ammaatti akka miidhame gabaase.", "som":"Ururka Saadaasha Hawada Adduunka ayaa ku warramaya in lakabka ozoneka ee Ka koreeya dhulka baraflayda uu waxyeelladii abid ugu darnaa soo gaadhay.", "tir":"ውድብ ሜትሮሎጂ ዓለም ኣብ ኣርክቲክ ዝርከብ ናሕሲ ኦዞን ኣዝዩ ብዝኸፍአ ደረጃ ከምዝተጎድአ ሓቢሩ፡፡" }, "metadata":{ "scope":"Global", "category":"Science and Technology", "source":"https://www.independent.co.uk/environment/climate-change/ozone-layer-damaged-by-unusually-harsh-winter-2263653.html", "domain":"www.independent.co.uk", "date":"2011-04-05" } }</code></pre> <p><strong>Team</strong></p> <p>Afar</p> <ul> <li> <p>Mohammed Deresa</p> </li> <li> <p>Yasin Nur</p> </li> </ul> <p>Amharic</p> <ul> <li> <p>Tigist Taye</p> </li> <li> <p>Selamawit Hailemariam</p> </li> <li> <p>Wako Tilahun</p> </li> </ul> <p>Oromo</p> <ul> <li> <p>Gemechis Melkamu</p> </li> <li> <p>Galata Girmaye</p> </li> </ul> <p>Somali</p> <ul> <li> <p>Abdiselam Mohamed</p> </li> <li> <p>Beshir Abdi</p> </li> </ul> <p>Tigrinya</p> <ul> <li> <p>Berhanu Abadi Weldegiorgis</p> </li> <li> <p>Michael Minassie</p> </li> <li> <p>Nureddin Mohammedshiek</p> </li> </ul> <p><strong>Project Leaders</strong></p> <ul> <li> <p>Asmelash Teka Hadgu <a href="mailto:asme@lesan.ai">asme@lesan.ai</a></p> </li> <li> <p>Gebrekirstos G. Gebremeskel <a href="mailto:gebrekirstos.gebremeskel@ru.nl">gebrekirstos.gebremeskel@ru.nl</a></p> </li> <li> <p>Abel Aregawi <a href="mailto:abel@lesan.ai">abel@lesan.ai</a></p> </li> </ul> <p><strong>License</strong></p> <p>Shield: <a href="http://creativecommons.org/licenses/by/4.0/">CC BY 4.0</a></p> <p>This work is licensed under a<br> <a href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.</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>
Plot Observations of Wetland Vegetation in Sub-Saharan Africa
<p>An R-Image containing plot-observations, Cocktail definitions and syntaxonomy using the packages <a href="https://docs.ropensci.org/taxlist/">taxlist</a> and <a href="https://github.com/kamapu/vegtable">vegtable</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>
European Investment Bank Projects in ACP, OCT, Africa, Asia, and Latin America (1957-2024)
<p>This dataset offers a comprehensive analysis of European Investment Bank (EIB) projects in Africa, the Caribbean, and the Pacific (ACP) regions, Overseas Countries and Territories (OCT), Asia, and Latin America, spanning from 1975 to 2023. The dataset includes information on 2,558 projects; each entry in the dataset includes key project details such as the project’s sector, date of signature, and financial commitments. All numbers are in 2015 euros.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2010
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2010.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2013
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2013.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
ERA5-Land selected indicators daily aggregates for Africa, 2015
<p>This deposit contains NetCDF files with daily aggregates from Copernicus Era5-Land for eight selected indicators, covering Africa for 2015.</p> <p>Each file represents one indicator aggregation for one month of the year. Inside each NetCDF file, the layers contain the daily aggregates.</p> <p>For 2m dewpoint pressure, 10m u-component of wind, 10m v-component of wind, surface pressure, the mean function was used for aggregation. For total precipitation, the sum function was used for aggregation. For 2m temperature, the functions maximum, mean, and minimum were used for aggregation.</p> <p>Those files were created using the <a href="https://github.com/ErikKusch/KrigR">KrigR</a> package.</p>
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.