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opencc-zeroAug 2024View details →

National Checklists 2017: Lesotho Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for detailsA list of species from Lesotho collected using effechecka and geonames polygons

opencc-zeroAug 2024View details →

National Checklists 2019: Lesotho Species List

Lists of taxa for each country and a few other administrative zones harvested from effechecka using simplified versions of geonames polygons. See https://github.com/diatomsRcool/checklists for details.A list of species from Lesotho collected using effechecka and geonames polygons

opencc-by-4.0Dec 2022View details →

Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho

These are pseudo-anonymised data from the MISTRAL study: "Head-to-head comparison of nasal and nasopharyngeal sampling using SARS-CoV-2 rapid antigen testing in Lesotho". The data dictionary explains the data available in the dataset. Between December 2020 and September 2021, 2131 individuals with either COVID symptoms or contact with a COVID-positive case were included from two hospitals in Lesotho and had a valid PCR results.

opencc-by-4.0Oct 2023View details →

Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data

Dataset with Zambia data belonging to the publication: Evaluation of COVID-19 antigen rapid diagnostic tests for self-testing in Lesotho and Zambia - Zambia data

opencc-by-4.0Jan 2021View details →

qdgc Lesotho

QDGC tables delivered in geopackage file - - - - - - - - - - - - - - - - - - - - - - QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth. Within each geopackage file you will find a number of tables with these names: -tbl_qdgc_01 -tbl_qdgc_02 -tbl_qdgc_03 -tbl_qdgc_04 -tbl_qdgc_05 -etc The attributes for each table are: qdgc Unique Quarter Degree Grid Cell reference string area_reference Country level_qdgc QDGC level cellsize degrees decimal degree for the longitudal and latitudal length of the cell lon_center Longitude center of the cell lat_center Latitudal center of the cell area_km2 Calculated area for the cell geom Geometry Metadata -------- Geodata GCS_WGS_1984 Datum: D_WGS_1984 Prime Meridian: 0 Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this: - st_area(st_transform(geom, 102022))/1000000) Licensing --------- Creative Commons Attribution 4.0 International Conditions ---------- Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Thankyou -------- The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them: - Tanzania Wildlife Research Institute - Dept of Biology, NTNU, Norway - Norwegian Environment Agency - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey References ---------- * http://en.wikipedia.org/wiki/QDGC * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/ * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection * http://www.safe.com Ragnvald Larsen Trondheim 20th of January, 2021 ragnvald@mindland.com www.mindland.com

opencc-by-sa-4.0Dec 2023View details →

Infrastructure Climate Resilience Assessment Data Starter Kit for Lesotho

This starter data kit collects extracts from global, open datasets relating to climate hazards and infrastructure systems. 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. 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. Hazards: <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> Exposure: <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> The spatial intersection of hazard and exposure datasets is a first step to analyse vulnerability and risk to infrastructure and people. 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. These Python libraries may be a useful place to start analysis of the data in the packages produced by this workflow: <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> 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. 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). References <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>

opencc-by-4.0Dec 2023View details →

Transport Starter Data Kit: Historical socio-transport data for Lesotho

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.

opencc-zeroAug 2024View details →

National Checklists: Lesotho Species List

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>

opencc-by-4.0Mar 2019View details →

Fig. 2 in Population Densities And Community Structure Of Birds Breeding In A Suburban Wooded Grassland In The Highveld Of Lesotho

Fig. 2. Numbers (right axis; dot or square signs) of species (A), all breeding pairs (B), and breeding pairs of selected bird species (C: Ss — Streptopelia senegalensis, Sc — Streptopelia capicola; D: RwS — Onychognathus morio, ES — Sturnus vulgaris; E — Passer griseus; F — Ploceus velatus; G — Columba guinea, H — Lanius collaris) in relation to the rainfall (columns; in mm per annum; left axis).

Fig. 1 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 1. – Species richness (A) and geographic distribution (B-F) of Grimmia Hedw. species in South Africa and Lesotho. A. Distribution of species richness in the genus Grimmia Hedw. at the Quarter-Degree Square (QDS) scale; B. Grimmia consobrina Müll. Hal.; C. Grimmia donniana Sm. (stars) and G. elongata Kaulf. (circles); D. Grimmia fuscolutea Hook.; E. Grimmia kidderi James; F. Grimmia laevigata (Brid.) Brid.

Fig. 2 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 2. – Geographic distribution of Grimmia Hedw. species in South Africa and Lesotho. A. Grimmia longirostris Hook.; B. Grimmia montana Bruch & Schimp.; C. Grimmia orbicularis Wilson; D. Grimmia pulvinata (Hedw.) Sm.; E. Grimmia pygmaea Müll. Hal.; F. Grimmia sessitana De Not. (circles) and G. tortuosa Hook. f. & Wilson (star).

Fig. 15. – Grimmia tortuosa Hook. f in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 15. – Grimmia tortuosa Hook. f. & Wilson: A. Plant; B. Transverse section of stem, with central strand; C. Transverse section of stem, without central strand; D. Leaf; E. Outlines of transverse section of leaf; F. Cells in leaf base; G. Cells at leaf margin in the broadest part of leaf; H. Transverse section of leaf[A-H: Hedderson 14459, BOL]

Fig. 7. – Grimmia kidderi James. A in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 7. – Grimmia kidderi James. A. Plant; B. Young shoot; C. Leaflet of young shoot; D. Transverse section of stem; E. Leaves; F. Outlines of transverse section of leaf; G. Cells in leaf base; H. Cells in transitional part; I. Transverse section of leaf. [A-C: Hedderson 13724, BOL; D-G, I: Hedderson 13081, BOL; H: Hedderson 13744, BOL]

Fig. 5. – Grimmia elongata Kaulf. A in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 5. – Grimmia elongata Kaulf. A. Transverse section of stem, with central strand; B. Transverse section of stem, without central strand; C, D. Leaves; E. Outlines of transverse section of leaf; F. Cells in leaf base and transitional part; G. Dorsal cells of costa in apical part; H. Transverse section of leaf. [A, B, E: Hedderson 15449, BOL; C, F: Hedderson 15448, BOL; D, H: Hedderson 15455, BOL; G: Matcham 1094a, herb. Matcham]

Fig. 4 in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 4. – Grimmia donniana Sm. A. Transverse section of stem; B. Leaf; C. Outlines of transverse section of leaf; D. Cells in leaf base and transitional part; E. Transverse section of leaf.

Fig. 11. – Grimmia orbicularis Wilson. A in A revision of Grimmia (Grimmiaceae) from South Africa and Lesotho

Fig. 11. – Grimmia orbicularis Wilson. A. Transverse section of stem; B, C. Leaves; D. Outlines of transverse section of leaf; E, G. Cells in leaf base; F. Cells in transitional part; H. Transverse section of leaf. [A: Hedderson 15285, BOL; B, E: MacOwen s.n., BM; C, F-H: Hedderson 15264, BOL; D, Hedderson 13047, BOL]