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739 results for “Uruguay”
Water temperature in the hidden, subglacial lake at Uruguay Island, Antarctic Peninsula region, 2020-2021, and additional data sets.
The dataset contains temperature measurements in a small subglicer (hidden) lake of Antarctic Peninsula region at several levels of depth. The measurements cover almost a full year and provide an understanding of the temperature and hydrological regime of the water body. Weather measurement data and statistics is provided additionally.
National Checklists 2017: Uruguay 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 Uruguay collected using effechecka and geonames polygons
National Checklists 2019: Uruguay 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 Uruguay collected using effechecka and geonames polygons
Fig. 5 in Seasonal and longitudinal variation in fish assemblage structure along an unregulated stretch of the Middle Uruguay River
Fig. 5. Detrended Correspondence Analysis (DCA) applied to ordinate samples according to variations in fish composition and abundance along the Uruguay River. Rectangles depict groups confirmed by a Multiple Response Permutation Procedure (Tab. 2). Sites: S1 = upstream; S6 = downstream. Seasons: Au= Autumn; Sp= Spring; Su= Summer and Wi= Winter.
Fig. 2 in Seasonal and longitudinal variation in fish assemblage structure along an unregulated stretch of the Middle Uruguay River
Fig. 2. Variation (mean ±standard deviation) in species richness and biomass (CPUEb/100m2) along the river channel (A and C) and among seasons (B and D), in the Middle Uruguay River. Sites: S1 = upstream; S6 = downstream. Different letters indicate statistical difference (p <0.05).
Fig. 6 in A new species of Gymnogeophagus Miranda Ribeiro from Uruguay (Teleostei: Cichliformes)
Fig. 6. Distribution of Gymnogeophagus terrapurpura. Yellow dot indicates type locality. Map modified from Shuttle Radar Topography Mission (SRTM), Courtesy NASA/JPL-Caltech.
Fig. 5 in A new species of Gymnogeophagus Miranda Ribeiro from Uruguay (Teleostei: Cichliformes)
Fig. 5. Pharyngeal jaws of a 54.8 mm SL Gymnogeophagus terrapurpura paratype (ZVC-P 7060); (A) Ventral view of left upper pharyngeal jaws; (B) Dorsal view of lower pharyngeal jaws; p2-4 = pharyngobranchials 2-4. Scale bar = 1 mm.
Fig. 3 in A new species of Gymnogeophagus Miranda Ribeiro from Uruguay (Teleostei: Cichliformes)
Fig. 3. Soft dorsal fin pigmentation patterns: (A-F) Gymnogeophagus terrapurpura, (G-I) G. rhabdotus, (J-K) G. meridionalis.
Fig. 4 in A new species of Gymnogeophagus Miranda Ribeiro from Uruguay (Teleostei: Cichliformes)
Fig. 4.Anal fin pigmentation patterns: (A-C) Gymnogeophagus terrapurpura, (D-E) G. rhabdotus, (F) G. meridionalis.
Fig. 1 in A new species of Gymnogeophagus Miranda Ribeiro from Uruguay (Teleostei: Cichliformes)
Fig. 1. Gymnogeophagus terrapurpura, new species, ZVC-P 12490, 60.0 mm SL, holotype, cañada de La Lana, rio Santa Lucía basin, Canelones Department, Uruguay.
Fig. 2 in A new species of Gymnogeophagus Miranda Ribeiro from Uruguay (Teleostei: Cichliformes)
Fig. 2. Non-type live specimens of (A) Gymnogeophagus terrapurpura from rio Santa Lucía basin, Canelones Department, Uruguay, (B) G. rhabdotus and (C) G. meridionalis. (B) and (C) both from rio Negro basin, Tacuarembó Department, Uruguay.
FIGURES 6 7 in Redescription, shell variability and geographic distribution of Plagiodontes dentatus (Wood, 1828) (Gastropoda: Orthalicidae: Odontostominae) from Uruguay and Argentina
FIGURES 6 7. SEM photographs of the teleoconch sculpture near the aperture lip. 6, Plagiodontes dentatus; 7, P. multiplicatus.
FIGURES 14 16 in Redescription, shell variability and geographic distribution of Plagiodontes dentatus (Wood, 1828) (Gastropoda: Orthalicidae: Odontostominae) from Uruguay and Argentina
FIGURES 14 16. SEM photographs of the apertural teeth in Plagiodontes spp. 14, P. dentatus; 15, P. multiplicatus (arrow indicates the presence of a denticle on the columellar tooth); 16, P. patagonicus.
Infrastructure Climate Resilience Assessment Data Starter Kit for Uruguay
<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>
Figure 3 in Trichocline maxima (Compositae, Mutisieae) a rare Pampean daisy rediscovered after 70 years in Uruguay
Figure 3. Distribution map of Trichocline maxima. The orange dot represents the latest record of the species that is reported in this work. The black dot in Uruguay is from collections cited in the additional examined material section. The black dot in Brazil represents the possible location of Malme's (1931) citation of a Sellow's collection: Inter Rio Pardo et Bagé.
Figure 1. A-G. Trichocline maxima. A in Trichocline maxima (Compositae, Mutisieae) a rare Pampean daisy rediscovered after 70 years in Uruguay
Figure 1. A-G. Trichocline maxima. A. General view of the species habit; B. Capitulum; C. Involucre; D. Detail of achene and pappus; E. Detail of rosette leaves, scapes and xylopodium; F. Detail of the rosette and lobate leaves; G. Habitat. Photos credits: A-C, F and G by José M. Bonifacino; D by Fábio P. Torchelsen.
Fig. 31. Parafluda banksi Chickering, 1946 in Two new species of Sarinda Peckham & Peckham, 1892, with an update on Sarindini in Uruguay (Araneae: Salticidae)
Fig. 31. Parafluda banksi Chickering, 1946, face and chelicerae. A, C. Male (FCE-Ar 11094). B, D. Female (FCE-Ar 9542). A–B. Face. C–D. Chelicera.
Fig. 28. Sarinda marcosi Piza, 1937 in Two new species of Sarinda Peckham & Peckham, 1892, with an update on Sarindini in Uruguay (Araneae: Salticidae)
Fig. 28. Sarinda marcosi Piza, 1937, photographs of genitalia. A–D. Male pedipalp (FCE-Ar 14321). A. Ventral view. B. Retrolateral view. C. Dorsal view. D. Prolateral view. E–F. Epigynum (FCE-Ar 14321). E. Ventral view. F. Dorsal view.
Fig. 25. Sarinda marcosi Piza, 1937 in Two new species of Sarinda Peckham & Peckham, 1892, with an update on Sarindini in Uruguay (Araneae: Salticidae)
Fig. 25. Sarinda marcosi Piza, 1937, photographs in vivo. A. Site where the species was collected. B, D, F. Male. C, E, G. Female.
Fig. 24 in Two new species of Sarinda Peckham & Peckham, 1892, with an update on Sarindini in Uruguay (Araneae: Salticidae)
Fig. 24. Sarinda contraluz Hagopián & Bustamante sp. nov., paratype, ♀ (FCE-Ar 13968), SEM of spinnerets. A. General view. B. Anterior lateral spinneret. C. Posterior median spinneret. D. Posterior lateral spinneret. Abbreviations: ac = aciniform gland; alS = anterior lateral spinneret; mAP = minor ampullate spigot; MAP = major ampullate spigot; pi = piriform gland; plS= posterior lateral spinneret; pmS = posterior median spinneret.
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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.