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163 results for “Somalia”
COMPREHENSIVE LIVESTOCK HEALTH PROGRAM: TARGETED TREATMENT AND HOLISTIC INTERVENTIONS FOR MAJOR PREVALENT DISEASES IN THE LIVESTOCK FARMING COMMUNITY OF DAYNILE DISTRICT, MOGADISHU, SOMALIA.
<p>The general objective of this project was to intervene with the most common livestock diseases in Dayniile district by carrying out a comprehensive campaign for treatment and control. The specific objectives consisted of a treatment campaign, improving infrastructure for establishing disinfectant foot dips and hand washing points, providing disinfectant tools, and finalising community engagement and education by doing training at the farm level.<br>The team visited different donors and added their contribution. After collecting sufficient funds from various sources, the team began the procurement of the necessary materials. This included purchasing veterinary drugs and supplies from local pharmacies and other essentials like stationery. The first activity was treatment campaigns, which were a central aspect of the project. Over 290 animals were treated for various diseases and conditions. The farm manager was informed of the diagnoses, and upon receiving their permission, the appropriate treatments were administered. The second intervention action was a vaccination campaign. The team vaccinated a total of 70 animals against clostridial bacteria, which is one of the most common camel diseases encountered in the area. The third intervention was the establishment of biosecurity facilities at select livestock farms. Among all the farms involved in the project, five were chosen for the provision of enhanced biosecurity measures. These measures included the installation of foot dips and teat dips. The fourth activity was educating livestock farmers on strategies for controlling and preventing livestock diseases. The training was held at Beder Camel Dairy Farm and attended by approximately 10 individuals, comprising 3 females and 7 males. The content of the training was three modules: the first was general farm biosecurity, the second was operational biosecurity, and the third was concern for vaccination. Recommendation: We recommend that each farm hire livestock health specialists to easily implement disease prevention steps and promptly solve each new case.<br> We recommend the livestock association, veterinary clinics, and other institutions working on livestock do routine campaigns that facilitate the determination of prevalent diseases and the treatment of those cases</p>
National Checklists 2017: Somalia 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 Somalia collected using effechecka and geonames polygons
National Checklists 2019: Somalia 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 Somalia collected using effechecka and geonames polygons
qdgc Somalia
<p>QDGC tables delivered in geopackage file<br> - - - - - - - - - - - - - - - - - - - - - -<br> 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.<br> <br> <br> Within each geopackage file you will find a number of tables with these names:<br> -tbl_qdgc_01<br> -tbl_qdgc_02<br> -tbl_qdgc_03<br> -tbl_qdgc_04<br> -tbl_qdgc_05<br> -etc<br> <br> <br> The attributes for each table are:<br> qdgc Unique Quarter Degree Grid Cell reference string<br> area_reference Country<br> level_qdgc QDGC level<br> cellsize degrees decimal degree for the longitudal and latitudal length of the cell<br> lon_center Longitude center of the cell<br> lat_center Latitudal center of the cell<br> area_km2 Calculated area for the cell<br> geom Geometry<br> <br> <br> Metadata<br> --------<br> Geodata GCS_WGS_1984<br> Datum: D_WGS_1984<br> Prime Meridian: 0<br> <br> <br> 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:<br> - st_area(st_transform(geom, 102022))/1000000)<br> <br> <br> Conditions<br> ----------<br> Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc<br> <br> <br> Thankyou<br> --------<br> The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:<br> - Tanzania Wildlife Research Institute<br> - Dept of Biology, NTNU, Norway<br> - Norwegian Environment Agency<br> - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey<br> <br> <br> References<br> ----------<br> * http://en.wikipedia.org/wiki/QDGC<br> * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/<br> * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection<br> * http://www.safe.com</p>
Infrastructure Climate Resilience Assessment Data Starter Kit for Somalia
<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, 2025)</li> <li>railways (OpenStreetMap, 2025)</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 (2025) 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>
Transport Starter Data Kit: Historical socio-transport data for Somalia
<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>
National Checklists: Somalia 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>
Fig. 3 in A new species of Pseudoblepharispermum (Asteraceae, Plucheeae) from NE Somalia
Fig. 3. – Pollen grain of Pseudoblepharispermum tuddense Baldesi & Pignotti, detail of spinae and colporus.
Fig. 1 in A new species of Pseudoblepharispermum (Asteraceae, Plucheeae) from NE Somalia
Fig. 1. – Pseudoblepharispermum tuddense Baldesi & Pignotti. A. Habit; B. Leaf surface hair, with comma-shaped distal cell; C. Anthers, with visible short tails at their base; D. Hermaphrodite, functionally male floret. [Merla, Azzaroli & Fois s.n., FT006212] [Drawing: L. Vivona]
Fig. 4 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 4 Larger benthic foraminifera from the Maastrichtian of Iran (Tarbur Fm.: a-b, d, f), Turkey (Garzan Fm.: c), Qatar (Simsima Formation: e). a-c Canalispina iapygia Robles-Salcedo et al. (a-b, Fasa section; c from Çoruh et al., 1997, pl. 76, fig. 3 as Siderolites calcitrapoides). d-f Dictyoconella complanata Henson (d, f Naghan section, e from Henson, 1948, pl. 10, fig. 14). T = Tarburina zagrosiana Schlagintweit & Rashidi in f. m.t. = marginal trough in e and d.
Fig. 6 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 6 Pseudedomia hamaouii Rahaghi from the Campanian (Lopha Limestone Member: b), and upper Maastrichtian of Iran (Tarbur Formation: a, d), and Somalia (Auradu Formation: c). a Bioclastic packstone with P. hamaouii Rahaghi, Siderolites calcitrapoides Lamarck (S), and Omphalocyclus macroporus Lamarck (O); Fasa section. b from Rahaghi (1976, pl. 1, fig. 11). c from Luger (2018, pl. 16, fig. 10 as Pseudedomia sp.). d Fasa section.
Fig. 7 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 7 Pseudorbitolina schroederi Luger from the Maastrichtian of Somalia (Auradu Formation, a-b), and Iran (Tarbur Formation, c-d). a, b from Luger (2018, pl.7, figs. 7-8; holotype in 7), c-d from Naghan section (d from Schlagintweit et al. (2016b, fig. 11c as Pseudorbitolina marthae).
Fig. 3 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 3 Larger benthic foraminifera from the Maastrichtian of Iran (Tarbur Fm.: a, d, f-h, j-k, m-n, p-t), Somalia (Auradu Formation: b-c, e, l, o), and Turkey (Garzan Fm.: i). a-b Accordiella? tarburensis Schlagintweit & Rashidi (a from Schlagintweit and Rashidi, 2016, fig. 6a, holotype, Mandegan section; b from Luger, 2018, pl. 13, fig. 6 as Dukhania? cherchii, holotype). c, g Dictyoconus bakhtiari Schlagintweit, Rashidi & Babadipour (c from Schlagintweit et al., 2016b, fig. 10b, Naghan section; g from Luger (2018, pl. 6, fig. 4 as Dictyoconus sp. 1). d, e-f, h Gyroconulina columellifera Schroeder & Darmoian (e from Luger, 2018, pl. 7, fig. 3; d from Schlagintweit et al., 2016a, fig. 4k, Mandegan section; f, h Naghan section). i–n Gen. et sp. indet. (i from Çoruh et al., 1997, pl. 76, fig. 5 as Dictyoconella complanata; l from Luger, 2018, pl. F-2, fig. 9 as Antalyna korayi; j-k, m-n Naghan section). o–t Antalyna korayi Farinacci & Köylüoğlu (o from Luger, 2018, pl. F-2, fig. 10; p-t Naghan section).
Fig. 2 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 2 Above: Subdivison of the Maastrichtian stage: comparison of different used substages and biostratigraphic use of selected larger benthic foraminifera. Examples: Loftusia minor (acc. to Meriç and Görmüş, 2001), Siderolitidae (acc. to Robles-Salcedo et al., 2018, 2019), and relationship to biozonation of Wynd (1965) (modified herein). Below: Example of the Palaeoelphidium multiscissuratum subzone (new name) of the Omphalocyclus-Loftusia assemblage zone sensu Wynd (1965), upper Maastrichtian Tarbur Formation, SW Iran. Loftusia sp. in the middle with agglutinated test of Palaeoelphidium multiscissuratum (Smout) (detail from Luger, 2018, pl. 26, fig. 10, illustrated as Laffiteina aff. jaskii Rahaghi), and Omphalocyclus (O).
Fig. 1 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 1 Distribution of Maastrichtian shallow-water carbonates along the margins of the northern Arabic and northeastern African plates (modified from Scotese, 2001). For lithostratigraphy and distribution see Barrier and Vrielynck, 2008).
Fig. 5 in Maastrichtian Larger Benthic Foraminifera From The Arabian Plate Sensu Lato: New Data From Somalia, Turkey, And Iran
Fig. 5 Cyclopsinella steinmanni (Munier-Chalmas) from the upper Maastrichtian of Somalia (Auradu Formation: a), Iran (Tarbur Formation: b-c, e-f), and C. steinmanni from the upper Santonian of France (d). a from Luger (2018, pl. 4, fig. 10 as Saudia sp.). b-c, e-f Naghan section. Note the aligned pillars (partly fusing laterally) in b and f, and sporadic rudimentary short rafter (r) in e. d from Gendrot (1964, pl. 1, fig. 10). Abbreviations: pi = pillar, r = rafter, s = septum.
Text-fig. 1. Chara braunii from Lake Magadi, Tanzania. a – top of plants with gametangia, b – oospore with 6 ridges, 400 µm long. in Some Finds Of Charophytes From East-Africa (Zambia, Tanzania, Kenya And Somalia)
Text-fig. 1. Chara braunii from Lake Magadi, Tanzania. a – top of plants with gametangia, b – oospore with 6 ridges, 400 µm long.
Linked collectors and determiners for: A striking new species of Blepharis (Acanthaceae) from north-eastern Somalia.
Natural history specimen data linked to collectors and determiners held within, "A striking new species of Blepharis (Acanthaceae) from north-eastern Somalia". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/7589124a-14e5-4c0f-a89b-0168a8318252">https://bionomia.net/dataset/7589124a-14e5-4c0f-a89b-0168a8318252</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/7589124a-14e5-4c0f-a89b-0168a8318252">https://gbif.org/dataset/7589124a-14e5-4c0f-a89b-0168a8318252</a>. Formatted as a Frictionless Data package.
CCG Starter Data Kit: Somalia
<p>A starter data kit for Somalia</p>
Fig. 2 in A new species of Pseudoblepharispermum (Asteraceae, Plucheeae) from NE Somalia
Fig. 2. – Distribution map of Pseudoblepharispermum tuddense Baldesi & Pignotti.
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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)
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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.