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12,309 results for “America”
Figure 18 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 18. Geographical distribution of Geophis hoffmanni. This species is also known also from 'Colombia' without further locality data. Symbols may indicate more than one locality.
Figure 15 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 15. Geographical distribution of three species of Geophis in western Panama. Geophis tectus sp. nov. is virtually sympatric with G. brachycephalus in the upper Río Chirripó drainage and with G. talamancae near Boquete.
Figure 13 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 13. Multivariate analysis for the five species of the Geophis brachycephalus complex; upper, males; lower, females. GML, Gorgas Memorial Laboratory specimen referred to Geophis tectus sp. nov.
Figure 16 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 16. Geographical distribution of four species of Geophis in Panama and western Colombia. G. betanensis and G. nigroalbus are sympatric in Colombia.
Figure 11 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 11. Multivariate nMDS plot of four populations of the Geophis brachycephalus complex; upper for males, lower for females. 'Stress' is a measure of structure in a nMDS plot, with values <0.2 indicating a meaningful ordination. 'Inquirenda' indicates the position of specimens we refer to Geophis, species inquirenda with respect to individuals from the other four populations (CR, Costa Rica; Boq, Boquete; Ler, Lérida; Col, Colombia). Characters included in analysis were tail length/total length ratios, scale counts (ventral and subcaudal) and dorsal colour pattern.
Figure 9 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 9. Comparison of variation in total segmental counts in four population samples of the Geophis brachycephalus complex.
Figure 7 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 7. Sexual dimorphism in extent of keeling on dorsal scales in three population samples of the Geophis brachycephalus complex.
Figure 17 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 17. Geographical distribution of two species of Geophis in Nicaragua and Costa Rica. Symbols may indicate more than one locality.
Figure 4 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 4. Dorsal patterns in Geophis: A, dorsal pattern of G. betaniensis; B, dark transverse bands typical of Geophis dunni.
Figure 3 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 3. Dorsal patterns in Geophis: A, light spots, typical of snakes from western Panama; B, longitudinal lateral light stripes found in many Costa Rican snakes; C, light blotches and transverse bands, typical of many snakes from Costa Rica.
Figure 2 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 2. Head plates and coloration in Geophis: A, dorsal view showing light head band; B, ventral view; note two preventrals (postgenials) between midgular and first ventral.
Figure 1 in Not so rare snakes: a revision of the Geophis sieboldi group (Colubridae: Dipsadinae) in lower Central America and Colombia
Figure 1. Lateral head scalation in Geophis: A, pattern typical of G. zeledoni; B, pattern typical of G. brachycephalus complex; C, variant pattern; D, pattern typical of G. hoffmanni. PT, posterior temporal; U, upper postsupralabial; L, lower postsupralabial; PI, postinfralabial; number of supralabials and infralabials indicated by Arabic numerals; number of jubals indicated by Roman numerals.
Figure 4. A–E in Postcranial morphology of the extinct caviine rodent Microcavia criolloensis (late Pleistocene, South America)
Figure 4. A–E, left manus: A, Microcavia niata (JCT-1515); B, Microcavia australis (MLP-DZV-26.VIII.01.21); C, Galea spixii (MN-34417); D, E, Microcavia criolloensis (BRA-3-355 and BRA-3, respectively). F–K, left pes: F, Microcavia niata (JCT-1515); G, Microcavia australis (MLP-DZV-26.VIII.01.21) (inverted image from right); H, Galea musteloides (MLP- DZV-5.VI.00.9); I, Galea spixii (MN-34417); J, K, Microcavia criolloensis (FC-DPV-1686 and 847, respectively).
Figure 3. A–D in Postcranial morphology of the extinct caviine rodent Microcavia criolloensis (late Pleistocene, South America)
Figure 3. A–D, selected articulated skeletal remains of Microcavia criolloensis: A, skull, mandible and part of postcranial skeleton articulated with a close up of left manus (BRA-3-355); B, lateral view of articulated distal end of left humerus, ulna, radius, carpus and metacarpus (BRA-3); C, lateral view of distal end of left tibia and radius, tarsus, metatarsus and phalanges articulated (FC-DPV-847); D, left leg (FC-DPV-1686); E, 'burrow' structures in Sopas Formation (northern Uruguay, Sopas Creek); F, lateral view of scapula of M. niata (JCT-1515) – arrow indicates the teres major process.
Figure 1 in Postcranial morphology of the extinct caviine rodent Microcavia criolloensis (late Pleistocene, South America)
Figure 1. Geographical distribution of living species of Microcavia (1: M. niata, 2: M. shiptoni, 3: M. australis), extinct species in the pampean region (4: M. robusta, M. chapalmalensis and M. reigi), in south-eastern Brazil (5: Microcavia sp.) and geographical locations of the late Pleistocene Microcavia criolloensis in Uruguay indicated by asterisks. 1–4 following Anderson (1997), Quintana (1996), Tognelli et al. (2001), Marquet et al. (1993); 5 following Ubilla et al. (2008).
FIG. 2. — Souzalopesmyia polleti n in Souzalopesmyia Albuquerque, 1951 (Diptera: Muscidae): new species from South America with an updated phylogeny based on morphological evidence, in Touroult J. (ed.), "Our Planet Reviewed" 2015 large-scale biotic survey in Mitaraka, French Guiana.
FIG. 2. — Souzalopesmyia polleti n. sp.: A-D, ♂: sternite 5, dorsal view (A); epandrium, cercal plate and surstyli, dorsal view (B); epandrium, cercal plate and surstyli, lateral view (C); hypandrium and associated structures, lateral view (D); E-H, ♀: ovipositor, dorsal view (E); ovipositor, ventral view (F); spermatheca (G). Scale bars: 0.5 mm.
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (JSON format)
<p>DL-FRONT is a Deep Learning Neural Network (DLNN) that was trained to detect weather fronts using spatial grids of near-surface atmospheric variables. The dataset is composed of hourly JSON files containing geospatial vector polylines describing the locations of four types of weather fronts—cold front, warm front, stationary front, and occluded front, over the time span 1980-2018.</p> <p>This dataset is the product of processing data from the National Aeronautics and Space Administration (NASA) <a href="https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/">Modern-Era Retrospective analysis for Research and Applications, Version 2</a> (MERRA-2). DL-FRONT processed MERRA-2 hourly data grids of instantaneous measures of air pressure reduced to mean sea level, air temperature at 2 meters, specific humidity at 2 meters, and wind velocity at 10 meters over the time span 1980 - 2018 to produce this dataset. The original MERRA-2 data were resampled at 1 degree resolution over the spatial range 31W - 171W x 10N - 77N using bicubic interpolation.</p> <p>At each hourly time step the network produced a set of spatial grids with the same resolution and spatial range as the input, one for each of the five categories mentioned above. Each cell in a spatial grid for a given category records the network-assigned probability (from 0.0 to 1.0) that the cell is in a weather front boundary region of that category (or, for the "no front" category, the probability that the cell is not in any weather front boundary region).</p> <p>Each probability map was then processed to obtain polyline skeletons of the weather front boundary regions found by DL-FRONT. These vector representations of the fronts were then written to JSON files—one file for each hour. Each JSON file contains one top-level object composed of name/value pairs with the names issuanceDate, validDate, ColdFronts, WarmFronts, OccludedFronts, and StationaryFronts. The name/value pairs for createDate and validDate are always present. The other name/value pairs are only present if there is corresponding data. The values for issuanceDate and validDate are UTC timestamp strings.</p> <p>The ColdFronts, WarmFronts, StationaryFronts, and OccludedFronts names in the top-level object, when present, have values that are arrays. In each case, the array is composed of one or more objects. Each object represents a front of the given type. Each object is composed of five name/value pairs with the names lats, lons, cols, rows, and confidence. The value for the name confidence is a number that is the average of the values of the probability map cells intersected by the front polyline. The values associated with the names lats, lons, cols, and rows are arrays. These arrays represent the vertices of a polyline describing the location of a frontal boundary in both geospatial and grid cell coordinates.</p>
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
Figures 93–100. Cybocephalus kathrynae T. R. Smith. 93 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 93–100. Cybocephalus kathrynae T. R. Smith. 93) Antenna. 94) Proleg. 95) Mesoleg. 96) Metaleg. 97) Median lobe. 98) Median lobe, lateral view. 99) Basal plate. 100) Metanotum.
Figures 85–92. Cybocephalus iviei T. R. Smith. 85 in Review of the Cybocephalidae (Coleoptera) of North America and the West Indies with descriptions of two new species of Cybocephalus Erichson
Figures 85–92. Cybocephalus iviei T. R. Smith. 85) Antenna. 86) Proleg. 87) Mesoleg. 88) Metaleg. 89) Median lobe. 90) Median lobe, lateral view. 91) Basal plate. 92) Metanotum.
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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.