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25 results for “Eswatini”
National Checklists 2017: Eswatini 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 Eswatini (formerly Swaziland) collected using effechecka and geonames polygons
Transport Starter Data Kit: Historical socio-transport data for Eswatini
<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>
Infrastructure Climate Resilience Assessment Data Starter Kit for eSwatini
<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)</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> 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=11539">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> 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> 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> 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>
National Checklists: Eswatini Species List
<p>Data from: GBIF.org (23 January 2025) GBIF Occurrence Download <a href="https://doi.org/10.15468/dl.vd2ajk" target="_blank" rel="noopener">https://doi.org/10.15468/dl.vd2ajk</a></p>
CCG Starter Data Kit: Eswatini
<p>A starter data kit for Eswatini</p>
Eswatini
The Kingdom of Eswatini is a landlocked country in Southern Africa. It is bordered by Mozambique to its northeast and South Africa to its north, west, south and southeast. At no more than 200 kilometres (120 mi) north to south and 130 kilometres (81 mi) east to west, Eswatini is one of the smallest countries in Africa; despite this, its climate and topography are diverse, ranging from a cool and mountainous highveld to a hot and dry lowveld. Source: Objaverse 1.0 / Sketchfab
The Eswatini Study on Neurocognitive Performance in Adolescents Living With HIV
ClinicalTrials.gov study NCT07165639. IPD Sharing: YES. Countries: 1. Publications: 6.
Database Bd modelling for South Africa, Lesotho and eSwatini
<p>This dataset was used to create a predictive distribution map of <em>Batrachochytrium dendrobatidis</em> in South Africa, based on lineage. It contains records obtained from published resources, as well as records from fieldwork done. Lineage was identified using published lineage-specific primers, in conjunction with confirmed lineage typing by whole genome sequencing from O'Hanlon <em>et al</em>. 2018.</p>
FIGURE 4 in Cyphia deliae (Campanulaceae: Cyphioideae), a new species from South Africa and Eswatini
FIGURE 4. Habitat of Cyphia deliae, with the town of Barberton in the distance. Photograph by D. Oosthuizen.
FIGURE 5. Cyphia bolusii. A. Habit. B. Leaves. C & D in Cyphia deliae (Campanulaceae: Cyphioideae), a new species from South Africa and Eswatini
FIGURE 5. Cyphia bolusii. A. Habit. B. Leaves. C & D. Flowers, showing buds with rounded tips and calyx lobes with entire margins. Photographs by K. Braun.
FIGURE 3. A in Cyphia deliae (Campanulaceae: Cyphioideae), a new species from South Africa and Eswatini
FIGURE 3. A comparison of the flower morphology in Cyphia deliae (A, C, & E) and C. bolusii (B, D & F), as seen in dried herbarium material. A. Flower bud, showing acute tip. B. Flower bud, showing rounded tip. C. Flower in side view; note villose petals. D. Flower in side view; note pubescent petals. E. Flower with petals removed; note sparsely hairy back of anthers. F. Flower; note densely hairy back of anthers. Photographs by H. Steyn.
FIGURE 2. Cyphia deliae. A. Habit. B. Leaves. C & D in Cyphia deliae (Campanulaceae: Cyphioideae), a new species from South Africa and Eswatini
FIGURE 2. Cyphia deliae. A. Habit. B. Leaves. C & D. Flowers, showing flower buds with acute tips and calyx lobes with teeth along the margin. E. Flower with calyx and petals removed (note hairs on back of anthers). F. Young fruit. Photographs by W. McCleland (A), D. Oosthuizen (B–E), H. Steyn (F).
A burned area database from Sentinel-2 imagery (2016-2022) for Madagascar, southern Mozambique, Eswatini and eastern South Africa
<p>This database includes georeferenced burned area at 20 m and fire dates covering the period 2016-2022 for Madagascar, southern Mozambique (Maputo, Maputo City, Gaza, Inhambane), Eswatini, and eastern South Africa (Limpopo, Mpumalanga, KwaZulu-Natal, Eastern Cape). The classification of burned areas has been done based on 165,833 Sentinel-2 scenes (2A and 2B), by applying a two-phased algorithm on the probability output of a random forest model. The product has been validated in Madagascar with long temporal reference burned area units distributed into two fire activity strata. The accuracy analysis performed for the years 2019 and 2021 revealed a Dice coefficient of ≥79%, commission errors ≤18% and omission error ≤24% with a relative bias of about -8%. Intercomparisons with other available burned area products (FireCCISFD11, FireCCISFD20, GABAM, FireCCI51, C3SBA11, MCD64) indicated a consistent performance throughout the entire period. The product is provided in shapefiles, divided into four-month periods. Each shapefile contains a field named “BurnDate” indicating the date when the burned area was detected in format YYYYMMDD. Missing values indicate areas that were not burned, while zero values represent areas that were not considered in the mapping process due to persistent pixel low quality conditions.</p>
The Eswatini PRISM Study on Adolescents Living With HIV
ClinicalTrials.gov study NCT07101458. IPD Sharing: YES. Countries: 1. Publications: 4.
HPV Vaccination in HIV Infected and HIV Uninfected Adolescents in Eswatini
ClinicalTrials.gov study NCT04982614. IPD Sharing: NO. Countries: 1. Publications: 0.
Effectiveness of Covid-19 Vaccination in Eswatini Against SARS-CoV-2 Associated Hospitalization and Death
ClinicalTrials.gov study NCT04914832. IPD Sharing: Not stated. Countries: 1. Publications: 4.
Strengthening Primary Healthcare Delivery for Diabetes and Hypertension in Eswatini
ClinicalTrials.gov study NCT04183413. IPD Sharing: YES. Countries: 1. Publications: 2.
Database Bd modelling for South Africa, Lesotho and eSwatini
Open the record for dataset details and reuse information.
Risk Factors Associated with COVID-19 Infections among Healthcare Workers in Eswatini: A Cross-Sectional Study
<p>Samson Malwa Haumba</p>
Eswatini Ring Study: Increasing PrEP Options for Women
ClinicalTrials.gov study NCT05889533. IPD Sharing: YES. Countries: 1. Publications: 0.
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