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5,117 results for “argentina”
Figure 3 in The first Hapalotremus Simon, 1903 (Araneae: Theraphosidae) from Argentina: description and natural history of Hapalotremus martinorum sp. nov.
Figure 3. Hapalotremus martinorum sp. nov., female (A–B). Hapalotremus albipes Simon, 1903, female (SMF37093) (C). (A) Sternum, ventral view; (B) spermathecae, dorsal view; (C) spermathecae, ventral view. Scale = 1.0 mm.
Figure 2 in Life cycle of Huarpea fallax (Hymenoptera: Sapygidae) in a xeric forest in Argentina
Figure 2. Emergence pattern of individuals of Huarpea fallax obtained from nests of wild bees collected in trap nests in a xeric forest of Argentina (n = 11).
Survey data, models and dated samples of the Pliocene shorelines of Camarones, Argentina (Ver 1.1).
<p>The dataset cosists of a spreadsheet containing data on GPS surveys, dynamic topography extracted from published models (gplates.org), Shell preservation scoring, Strontium Isotopic Stratigraphy ages, and Global mean Sea Level calculations.</p> <p>Version 1.1 contains fixes to small errors and formulas.</p>
Dataset on: Land slugs in plant nurseries, a potential cause of dispersal in Argentina
<p>Commercial plant nurseries may serve as causes of dispersal of land snails and slugs (native and non-native) through the trade of plants and the related transport of eggs and small individuals that may pass unnoticed. Studies on the possible role of plant nurseries as a potential cause of dispersal of slugs in South America are lacking. To explore the role of garden centers, we collected and identified slugs in 12 commercial nurseries in two cities in the province of Buenos Aires, Argentina. Eight species of slugs were found. Based on our findings we validate the existence of <em>Deroceras laeve</em> and <em>Belocaulus angustipes</em> for Argentina and confirm the existence of <em>Ambigolimax valentianus</em>, which was recently cited for Argentina. We recommend that plant nurseries be regularly monitored given that snail and slug species are accidentally spread through trade in plants.</p>
qdgc Argentina
<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>
Figure 1 in A redescription of the chigger Hannemania achalai Alzuet and Mauri, 1987 (Acariformes: Prostigmata: Leeuwenhoekiidae) in frogs from Sierra Grande, Cordoba, Argentina
Figure 1 Hannemania achalai Alzuet and Mauri, 1987 (larva): A – dorsal aspect of idiosoma, B – ventral aspect of idiosoma, C – ventral aspect of gnathosoma, D – dorsal aspect of gnathosoma, E – prodorsum showing scutum and eyes, F – palpal tarsus, G – palpal claw, H – lateral aspect of tarsus I.
#EsLey. Lessons on the Legalisation of Abortion in Argentina
<p>This video was presented at the LSE Conference 'Knowledge Beyond Boundaries' on June 17, 2021.</p>
FIGURES 28 – 35 in Description of Ptychocroca, a new genus from Chile and Argentina, with comments on the Bonagota Razowski group of genera (Lepidoptera: Tortricidae: Euliini)
FIGURES 28 – 35. Adult males of Ptychocroca. 28, P. apenicillia; 29, P. nigropenicillia; 30, P. keelioides, 31, P. lineabasalis; 32, P. crocoptycha; 33, P. crocoptycha; 34, P. galenia; 35, P. s i m p l e x.
FIGURES 36 – 41 in Description of Ptychocroca, a new genus from Chile and Argentina, with comments on the Bonagota Razowski group of genera (Lepidoptera: Tortricidae: Euliini)
FIGURES 36 – 41. Adults of Haemateulia, Apotomops, and Bonagota. 36, H. haematitis; 37, H. barrigana; 38, H. barrigana; 39. A. boliviana; 40. A. spomotopa; 41. B. salubricola.
FIGURES 1 – 2. Pterygosoma patagonica, n in Description of a new pterygosomatid mite (Acari, Actinedida: Pterygosomatidae) parasitic on Liolaemus spp. (Iguania: Liolaemini) from Argentina
FIGURES 1 – 2. Pterygosoma patagonica, n. sp., female. 1, Dorsal aspect; 2, Apical fold with retrieved gnathosoma.
FIGURE 3 in Description of a new pterygosomatid mite (Acari, Actinedida: Pterygosomatidae) parasitic on Liolaemus spp. (Iguania: Liolaemini) from Argentina
FIGURE 3. Chaetotaxy of tibia, genu, femur and trochanter of Pterygosoma patagonica n. sp .. Boxed loci correspond to variation (scheme after Jack, 1964).
FIGURE 5 in Description of a new pterygosomatid mite (Acari, Actinedida: Pterygosomatidae) parasitic on Liolaemus spp. (Iguania: Liolaemini) from Argentina
FIGURE 5. Distribution of Pterygosoma patagonica n. sp. (black) within the range of Liolaemus spp. (grey).
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.
FIGURE 11. Trechisibus longipenis n in Nine new Trechisibus species from Peru and Argentina (Coleoptera: Carabidae: Trechinae)
FIGURE 11. Trechisibus longipenis n. sp. HT: habitus (a); median lobe of aedeagus in lateral view (b); median lobe of aedeagus in dorsal view (c).
FIGURE 5 in Nine new Trechisibus species from Peru and Argentina (Coleoptera: Carabidae: Trechinae)
FIGURE 5. Median lobe of aedeagus of the ‘ Trechisibus lamasi group’ species: T. lamasi Etonti & Mateu, 1992 (a); T. recuayi n. sp. HT (b); T. decensii Allegro, Giachino & Sciaky, 2008 HT (c).
Infrastructure Climate Resilience Assessment Data Starter Kit for Argentina
<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>
Fig. 3 in New Eocene South American native ungulates from the Quebrada de los Colorados Formation at Los Cardones National Park, Argentina
Fig. 3. New fossil specimens of Typotheria and Toxodontia from the Quebrada de Los Colorados Formation exposed at Quebrada Grande locality, Los Cardones National Park (Salta Province), Casamayoran SALMA. A, B. Colbertiasp. A. IBIGEO-P 58a, left maxillary fragment with M2–M3 in occlusal view. B. IBIGEO-P 58b, left maxillary fragment with P4–M3 in occlusal view. C–E. Pampahippus secundus Deraco and García-López, 2016. C. IBIGEO-P 62, right trigonid and incomplete talonid of a lower right molar in occlusal view. D. IBIGEO-P 63, fragmented left p3 or p4 in occlusal view. E. IBIGEO-P 64, left m1? in occlusal (E1), labial (E2), and lingual (E3) views. Photographs (A1–E1, E3, E4), explanatory drawings (A2–E2). Dashed area indicates broken or missing dental areas.
Fig. 5 in Systematic revision of a Miocene sperm whale from Patagonia, Argentina, and the phylogenetic signal of tympano-periotic bones in Physeteroidea
Fig. 5. Schematic comparisons of the periotic of MLP 76-IX-5-1, "Preaulophyseter gualichensis" Caviglia and Jorge, 1980 (A) with "Aulophyseter" rionegrensis (B), Acrophyseter deinodon (C, modified from Lambert et al. 2016), Zygophyseter varolai (D, modified from Bianucci and Landini 2006), Aulophyseter morricei (E, modified from Kellogg 1927), Orycterocetus crocodilinus (F, modified from Kellogg 1965), and Physeter macrocephalus (G, modified from Kasuya 1973). In dorsal (A1–G1), ventral (A2–G2), medial (A3–G3), and lateral (A4–C4, E4–G4) views. Black areas indicate anatomical foramina. Not to scale.
Fig. 1 in New Eocene South American native ungulates from the Quebrada de los Colorados Formation at Los Cardones National Park, Argentina
Fig. 1. New fossil materials of Litopterna, Astrapotheria, and Notostylopidae from the Quebrada de Los Colorados Formation exposed at Quebrada Grande locality, Los Cardones National Park (Salta Province), Casamayoran SALMA, Eocene. A. cf. Ernestokokenia sp. (IBIGEO-P 65), fragmented left upper cheek tooth in occlusal view. B. Astrapotheria indet. (IBIGEO-P 66), left maxillary fragment with three broken molariforms in lingual (B1) and occlusal (B3) views, and detail showing Hunter-Schreger bands (B4); dashed area in B2 indicates broken or missing dental areas. C. Homalostylops sp. (IBIGEO-P 57b), right m2? in occlusal (C1), lingual (C3), and labial (C4) views. D.?Homalostylops sp. (IBIGEO-P 57a), left m1? in occlusal view. Photographs (A1, B1, B3, B4, C1, C3, C4, D1), explanatory drawings (A2, B2, C2, D2).
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.