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772 results for “fronts”
Figs. 417–419. Wadotes daxiensis Chamberlin, female. 417. Carapace, front view. 418. Chelicera, ventral view. 419 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 417–419. Wadotes daxiensis Chamberlin, female. 417. Carapace, front view. 418. Chelicera, ventral view. 419. Trachea, dashed line refers to position of epigastric furrow.
Figs. 388–390. Tonsilla truculenta Wang and Yin. 388. Male palp, prolateral view. 389. Carapace, front view. 390 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 388–390. Tonsilla truculenta Wang and Yin. 388. Male palp, prolateral view. 389. Carapace, front view. 390. Chelicera, ventral view.
Figs. 314–316. Longicoelotes karschi, new species. 314. Male palp, retrolateral view. 315. Carapace, front view. 316 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 314–316. Longicoelotes karschi, new species. 314. Male palp, retrolateral view. 315. Carapace, front view. 316. Chelicera, ventral view.
Figs. 269–271. Himalcoelotes martensi, new species. 269. Male palp, prolateral view. 270. Carapace, front view. 271 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 269–271. Himalcoelotes martensi, new species. 269. Male palp, prolateral view. 270. Carapace, front view. 271. Chelicera, ventral view.
Figs. 231–233. Femoracoelotes platnicki Wang and Ono. 231. Male palp, prolateral view. 232. Carapace, front view. 233 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 231–233. Femoracoelotes platnicki Wang and Ono. 231. Male palp, prolateral view. 232. Carapace, front view. 233. Chelicera, ventral view.
Figs. 169–171. Coronilla gemata Wang, female. 169. Carapace, front view. 170. Chelicera, ventral view. 171 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 169–171. Coronilla gemata Wang, female. 169. Carapace, front view. 170. Chelicera, ventral view. 171. Trachea, dashed line refers to position of epigastric furrow.
Figs. 131–133. Coelotes pseudoterrestris Schenkel. 131. Male palp, retrolateral view. 132. Carapace, front view. 133 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 131–133. Coelotes pseudoterrestris Schenkel. 131. Male palp, retrolateral view. 132. Carapace, front view. 133. Chelicerae, ventral view.
Figs. 103–107. Coelotes exitialis Koch. 103. Female, epigynum. 104. Female, vulva. 105. Carapace, front view. 106. Chelicera, ventral view. 107 in A Generic-Level Revision Of The Spider Subfamily Coelotinae (Araneae, Amaurobiidae)
Figs. 103–107. Coelotes exitialis Koch. 103. Female, epigynum. 104. Female, vulva. 105. Carapace, front view. 106. Chelicera, ventral view. 107. Male palp, retrolateral view.
Text-fig. 1. Stutzeliastrobus bohemicus (BAYER) J.KVAČEK, No. NM-F 2746, Harcov, lectotype. a – surface view of ovuliferous cone photograph, scale bar 10 mm, b – microCT isosurface of ovuliferous cone, scale bar 10 mm, c – microCT longitudinal section of ovuliferous cone with segmented seeds, scale bar 10 mm, d – microCT longitudinal section of ovuliferous cone in yellow, seeds in red, e – 3D visualised bract-scale complex bearing three seeds, adaxial view, scale bar 2.5mm, f – 3D visualised bract-scale complex bearing two seeds, lateral view (incomplete reconstruction of the scale visualises the front seed), scale bar 2.5mm. in Stutzeliastrobus Bohemicus Comb. Nov. - Basal Cupressaceae Conifer From The Cenomanian Of The Bohemian Cretaceous Basin, Central Europe
Text-fig. 1. Stutzeliastrobus bohemicus (BAYER) J.KVAČEK, No. NM-F 2746, Harcov, lectotype. a – surface view of ovuliferous cone photograph, scale bar 10 mm, b – microCT isosurface of ovuliferous cone, scale bar 10 mm, c – microCT longitudinal section of ovuliferous cone with segmented seeds, scale bar 10 mm, d – microCT longitudinal section of ovuliferous cone in yellow, seeds in red, e – 3D visualised bract-scale complex bearing three seeds, adaxial view, scale bar 2.5mm, f – 3D visualised bract-scale complex bearing two seeds, lateral view (incomplete reconstruction of the scale visualises the front seed), scale bar 2.5mm.
Text-fig. 3. Front views of hemi-maxillaries from Prémontré (Paris Basin, MP 10), showing the large infraorbitary foramen of a) SLP29PR-1312, stored in the Montpellier University collections (ISE-M), Hartenbergeromys hautefeuillei; b) SLP29PR-960, Pantrogna marandati. Scale bar 1 mm. in A Reevaluation Of The Taxonomic Status Of The Rodent Masillamys Tobien, 1954 From Messel (Germany, Late Early To Early Middle Eocene, 48-47 M.Y.)
Text-fig. 3. Front views of hemi-maxillaries from Prémontré (Paris Basin, MP 10), showing the large infraorbitary foramen of a) SLP29PR-1312, stored in the Montpellier University collections (ISE-M), Hartenbergeromys hautefeuillei; b) SLP29PR-960, Pantrogna marandati. Scale bar 1 mm.
Figure 27. Anelosimus vittatus, female. A–C, prosoma. A, front. B, sternum. C, labium. D in Morphological phylogeny of cobweb spiders and their relatives (Araneae, Araneoidea, Theridiidae)
Figure 27. Anelosimus vittatus, female. A–C, prosoma. A, front. B, sternum. C, labium. D, epigynum; note scape (1-1), here unique to A. vittatus and A. pulchellus, but also present in A. ethicus (pers. observ.). E, spinnerets. F, cheliceral promarginal teeth. G, tarsal organ on palpus, a tarsal organ much larger than setal bases and with broad opening (198-1) is a synapomorphy of theridiids minus Hadrotarsinae and Latrodectinae (enlarged tarsal organ clade). Scale bars: A–D, 100 mm; E–G, 20 mm.
Automated temporal front tracking toolbox in Matlab
<p>This toolbox provides the Matlab scripts that achieve temporal tracking of coherently evolving density fronts in numerical modes. It consists of three components: (1) scripts to detect density fronts based on the Canny edge detection algorithm at each time step in the model outputs; (2) scripts to automatically track coherently evolving front in time; and (3) scripts to do front pruning and remove the incoherent frontal segment that shows inconsistent frontal propagation direction. A dataset ('G_time_rho.mat') containing modeled density at successive time steps is provided for demonstration. More details of this method can be found in our work that is expected to be published soon (Wu, X., F. Feddersen and S. N. Giddings, 2021, Automated temporal tracking of coherently evolving density fronts in numerical models, Journal of Atmospheric and Oceanic Technology, in revision). Contact Xiaodong Wu (x1wu@ucsd.edu) for any questions. </p>
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>
Fracture maps and calving fronts for Thwaites Glacier western terminus 2015-2021
<p>These data comprise observations of severe crevassing and calving front position over the Thwaites Glacier Ice Tongue (TGIT) between 2015 and 2021 in geotiff form, along with bitmap versions of Sentinel-1 backscatter images from which the observations were derived. A version of UNet was used to create the data from the backscatter images.<br> These data were collected in 2021 for the study of structural change on the TGIT.</p> <p>File information: tgit_cfs.tar.gz is a gz-compressed directory of binary calving front segmentations of the Thwaites Glacier Ice Tongue in geotiff format.<br> tgit_fms.tar.gz is a gz-compressed directory of binary fracture segmentations of the Thwaites Glacier Ice Tongue in geotiff format.</p>
Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"
<p>These are data sets and tables used in the paper "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado" to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.</p>
Pelagic Fish at the Barents Sea Polar Front in May 2022
<p>## Methods</p> <p>### Study area</p> <p>This dataset is the result from sampling at 5 stations at the Polar Front in the western part of the Barents Sea.</p> <p>### Time coverage</p> <p>The samples were collected between 20 May 2022 and 25 May 2022.</p> <p>### Sampling</p> <p>5 pelagic trawl samples were collected with a Harstad pelagic trawl, which has an effective height of 9-11 m and width of 10-12 m when towed at ca. 3 knots. The mesh size of the inner liner of the cod end was 10 mm. The pelagic trawl was towed at ca. 3 knots for 20-30 min and abundances were standardized by converting to catch per unit effort (expressed in kilograms per cubic meter).</p> <p>### Sample analysis</p> <p>All organisms were identified to the nearest species or genus onboard. Throughout all stations, capelin had a large size distribution, so individuals similar in length were sorted into approximate size classes (small, medium, and large). The total number and weight of each species was recorded. For large catches, subsamples of 20-30 individuals were taken with representing length distributions of the catch. The standard length, height at the anus (up to the nearest 1 mm), and weight (up to the nearest 0.1 g) were measured for all specimens in the (sub)sample.</p> <p>### Fish stomach content analysis</p> <p>The stomachs were isolated and immediately preserved in 70% ethanol. For each individual stomach, the level of fullness (from 0: empty, to 4: full), prey composition (the count and % volume each prey item takes up in the stomach), and the level of digestion for each prey item (from 1: newly eaten, to 5: digested or non-identifiable) were estimated and recorded.</p>
Macrozooplankton at the Barents Sea Polar Front in May 2022
<p># Macrozooplankton across the Barents Sea Polar Front in May 2022</p> <p>## Methods</p> <p>### Study area</p> <p>This dataset is the result from sampling at 6 stations across the Polar Front in the western part of the Barents Sea.</p> <p>### Time coverage</p> <p>The samples were collected between 19 May 2022 and 25 May 2022.</p> <p>### Sampling</p> <p>11 macrozooplankton samples were collected with a Tucker trawl (1 m<sup>2</sup> opening and 1500 μm mesh size) and towed for 10 minutes at ca. 2 knots. The targeted depth at each station was determined from the sound scattering layer identified in the echogram from the vessel's echosounder.</p> <p>### Sample analysis</p> <p>Relative abundance from the Tucker trawl samples were analyzed per station, and taxa were counted and dried at 60 C° in pre-weighted recipients for dry weight measurements. The obtained abundances were standardized and converted to catch per unit effort, expressed in milligrams per cubic meter.</p>
model results from NHWAVE simulations of lobe-and-cleft instabilities at a river plume front
<p>The dataset contains the results from NHWAVE simulations of the lobe-and-cleft instabilities at a river plume front. The data format is .mat. The postprocessing scripts are included. </p>
Experimental and Simulation Data for "Hierarchical structure formation by crystal growth-front instabilities during ice templating" (2023) PNAS
<pre>Experimental and Simulation Data for: "Hierarchical structure formation by crystal growth-front instabilities during ice templating" by Kaiyang Yin, Kaihua Ji, Louise Strutzenberg Littles, Rohit Trivedi, Alain Karma, Ulrike G.K. Wegst (2023) PNAS, DOI: 10.1073/pnas.2210242120. </pre>
Boldness and physiological variation in round goby populations along their Baltic Sea invasion front
<p><strong>Data/code for the paper:</strong></p> <p>Galli, A., Behrens, J. W., Gesto, M., & Moran, N. P. (2023). Boldness and physiological variation in round goby populations along their Baltic Sea invasion front. <em>Physiology & Behavior</em>, 114261. <a href="https://doi.org/10.1016/j.physbeh.2023.114261">https://doi.org/10.1016/j.physbeh.2023.114261</a></p>
ScienceDex guides
Understand access before you commit
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.