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12,309 results for “America”
Fig. 5 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 5. Disparalona (Mixopleuroxus) hamata (Birge, 1879), parthenogenetic female. A. First limb. B. Idem, stiff setae. C. Idem, ODL and IDL. D. Second limb. E. Third limb. F. Fourth limb. G. Idem, distal endite. H. Fifth limb. Scale bar = 50 μm.
Fig. 1 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 1. Disparalona (Mixopleuroxus) leptorhyncha (Daday, 1905), parthenogenetic female. A–B. Habitus. C. Ventral view of carapace. D. Anterior portion of ventral margin of carapace. E. Posterior portion and posteroventral corner of carapace. F. Ornamentation of carapace. G. Rostrum. H. Idem, frontal view. I. Head shield. J–K. Main head pores. L. Maxilla. M. Labrum. N. Idem, frontal view. O. Antennule. P. Antenna. Scale bars: C–E, G–I, L–O = 50 μm; A = 80 μm. Figs B, D, F, K were not scaled.
Fig. 9 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 9. Disparalona (Mixopleuroxus) lucianae sp. nov., parthenogenetic female. A–E. Postabdomen. A. Lateral view. B–D. Marginal denticles. E. Dorsal view of postabdominal claw (separation between claws not represented). Scale bar = 50 μm. Figs C–D were not scaled.
Fig. 8 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 8. Disparalona (Mixopleuroxus) lucianae sp. nov., parthenogenetic female. A. First limb. B. Idem, stiff setae. C. Second limb. D. Third limb. E. Idem, epipodite. F–G. Fourth limb. H. Idem, distal endite. I. Elements. J. Fifth limb. Scale bar = 50 μm.
Fig. 7 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 7. Disparalona (Mixopleuroxus) lucianae sp. nov., parthenogenetic female. A–B. Habitus. C. Ventral view of carapace. D–E. Ornamentation of carapace. F. Anterior portion of ventral margin of carapace. G. Middle portion of ventral margin of carapace. H. Posterior portion and posteroventral corner of carapace. I. Rostrum. J. Head shield. K. Main head pores. L. Labrum. M. Antennule. N. Antenna. Scale bars = 50 μm. Figs B, D–E were not scaled.
Fig. 3 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 3. Disparalona (Mixopleuroxus) leptorhyncha (Daday, 1905), parthenogenetic female. A–D. Postabdomen. A. Lateral view. B–C. Marginal denticles. D. Dorsal view of postabdominal claw (separation between claws not represented). Scale bars = 50 μm. Figs B–C, were not scaled.
Fig. 4 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 4. Disparalona (Mixopleuroxus) hamata (Birge, 1879), parthenogenetic female. A. Habitus. B. Ventral view of carapace. C. Anterior portion of ventral margin of carapace. D. Middle portion of ventral margin of carapace. E. Posterior portion and posteroventral corner of carapace. F. Rostrum. G. Idem, frontal view. H. Main head pores. I. Labrum. J. Idem, frontal view. K. Maxilla. L. Antennule. M. Antenna. Scale bars: C–L = 50 μm; A–B = 100 μm.
Fig. 12 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 12. Disparalona (Mixopleuroxus) tenuispina sp. nov., parthenogenetic female. A–B. Postabdomen. A. Lateral view. B. Marginal denticles. Scale bar = 50 μm. Fig. B was not scaled.
Fig. 13 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 13. Pictorial key to separation of species of Disparalona Fryer, 1968 occurring in South America.
Fig. 2 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 2. Disparalona (Mixopleuroxus) leptorhyncha (Daday, 1905), parthenogenetic female. A. First limb. B. Second limb. C. Idem, exopodite. D. Third limb. E. Fourth limb. F. Idem, distal endite. G. Fifth limb. Scale bars = 50 μm.
Fig. 11 in A revision of the genus Disparalona (Cladocera, Chydorinae) in South America
Fig. 11. Disparalona (Mixopleuroxus) tenuispina sp. nov., parthenogenetic female. A. First limb. B. Idem, stiff setae. C. Second limb. D. Third limb. E. Fourth limb. F. Fifth limb; Scale bar = 50 μm.
Climatic dissimilarity data for North America at 1 km resolution.
<p>The raster data layers contained in this archive represent climatic dissimilarity as the distance in multivariate climate space (represented by the 1st and 2nd axes of a principal components analysis based on 11 bioclimatic variables) between current (1981-2010) and future (either 2041-2070 ("2055") or 2071-2100 ("2085")) time periods. Ensemble data are based on mean projections from 15 CMIP5 models (CanESM2, ACCESS1.0, IPSL-CM5A-MR, MIROC5, MPI-ESM-LR, CCSM4, HadGEM2-ES, CNRM-CM5, CSIRO Mk 3.6, GFDL-CM3, INM-CM4, MRI-CGCM3, MIROC-ESM, CESM1-CAM5, GISS-E2R) that were chosen to represent all major clusters of similar AOGCMs. Projections from 8 of these 15 GCMs were also used to generate dissimilarity values based on individual GCM projections. Input bioclimatic data was developed by statistical downscaling using the ClimateNA software developed by T. Wang. Additional description of the data can be found at https://adaptwest.databasin.org/pages/climatic-dissimilarity.</p>
Spatial patterns of uncertainty in climate exposure metrics for North America at 1km resolution
<p>The data provided below represents the degree of uncertainty or variation between 8 individual general circulation models (GCM) for three metrics commonly used to assess the intensity of exposure to climate change. The three exposure metrics (forward and backward <a href="https://adaptwest.databasin.org/pages/adaptwest-velocitywna">climatic velocity</a> and <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">local climatic dissimilarity</a>) were calculated based on the first two principal components (PC) scores derived from <a href="https://adaptwest.databasin.org/pages/climatic-dissimilarity">11 different climate variables</a>. Frameworks and heuristics supporting climate adaptation for conservation often rely on projections of climate change or climate exposure. However, projections of climate change vary among alternative GCM outputs, different emissions scenarios, and different future time periods. The potential for these model predictions to vary geographically presents a source of uncertainty in assigning climate-informed conservation strategies to landscapes. Regions with high agreement among predictions could be more confidently assigned a climate-informed strategy, whereas regions with less agreement among predictions may require a more cautious approach. More information on the data can be found at https://adaptwest.databasin.org/pages/uncertainty-climate-metrics.</p>
WRF-Chem output. Resolution: 24 km. Domain: Eastern North America
<p>Derived products from WRF-Chem simulations at 24 km resolution.</p> <p>Details given in: Crippa P. et al. (2019): Sensitivity of simulated aerosol properties over eastern North America to WRF-Chem parameterizations. Manuscript published in the Journal of Geophysical Research.</p> <p>14 netcdf files:</p> <p>Crippaetal_JGR2019_AOD.nc contains monthly mean AOD cloud screened for each ensemble member.</p> <p>Crippaetal_JGR2019_net_radiation.nc contains monthly mean surface net radiation for each ensemble member.</p> <p>Crippaetal_JGR2019_NO3_fine.nc contains monthly mean NO3 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_SO4_fine.nc contains monthly mean SO4 in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PM25_daily.nc contains daily PM<sub>2.5 </sub>concentrations for each ensemble member.</p> <p>Crippaetal_JGR2019_O3_daily.nc contains daily mean surface ozone concentrations in the fine aerosol mode for each ensemble member.</p> <p>Crippaetal_JGR2019_PBLH.nc contains monthly mean planetary boundary layer height for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_T2.nc contains monthly mean surface temperature at 2m for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_QPBL.nc contains monthly mean specific humidity in the PBL for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_PPT.nc contains monthly total precipitation for each ensemble member regridded on the MERRA-2 resolution</p> <p>Crippaetal_JGR2019_lat_WRF-Chem.nc contains the latitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lon_WRF-Chem.nc contains the longitude coordinates of the WRF-Chem grid</p> <p>Crippaetal_JGR2019_lat_MERRA2.nc contains the latitude coordinates of the MERRA2 grid</p> <p>Crippaetal_JGR2019_on_MERRA2.nc contains the longitude coordinates of the MERRA2 grid</p>
Historical PM2.5 dataset across North America
<p>This dataset is the estimated long-term (1981-2016) concentrations of ambient fine particulate matter across North America, which combines information from chemical transport modeling, satellite remote sensing, and ground-based monitoring. The estimates included information from updated historical emissions inventories and meteorological data, fine resolution satellite-based estimates of PM<sub>2.5</sub>, and ground-based measurements of PM<sub>2.5</sub>, PM<sub>10</sub> and total suspended particles (TSP) measurements.</p>
Fig. 1. Camposella insignata n in A new genus in the dipterous family Cyrtidae from South America
Fig. 1. Camposella insignata n. gen. et sp. n. Last tarsal joint and claws, showing absence of pulvilli. b. Drawing showing excavated face and rudimentary proboscis. Most of the antennae are cut away in this view of the head.
DL-FRONT MERRA-2 vectorized weather fronts over North America, 1980-2018 (netCDF 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 <a href="http://www.unidata.ucar.edu/software/netcdf/docs/">netCDF-4</a> files. Each file contains one year of hourly geospatial data grids 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 weather front 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. These front polylines were then rasterized into geospatial data grids and stored by year into netCDF-4 files that conform to the <a href="http://cfconventions.org/">Climate and Forecast Metadata Conventions</a>. The front data in each file is stored in a netCDF variable with dimensions (time, front type, y, x), where x and y are geospatial dimensions. There is a 2D geospatial data grid for each time step for each of the 4 front types—cold, warm, stationary, and occluded.</p> <p>There are two large groupings of the netCDF files. One group uses a data grid based on the <a href="https://www.ncdc.noaa.gov/data-access/model-data/model-datasets/north-american-regional-reanalysis-narr">North American Regional Reanalysis</a> (NARR) <a href="https://www.nco.ncep.noaa.gov/pmb/docs/on388/tableb.html#GRID221">grid</a>, which is a Lambert Conformal Conic projection coordinate reference system (CRS) centered over North America. The NARR grid is quite close the the spatial range of data displayed on the WPC workstations used to perform surface analysis and identify front locations. The native NARR grid has grid cells which are 32 km on each side. Our grid covers the same extents with cells that are 96 km on each side.</p> <p>The other group uses a 1° latitude/longitude data grid centered over North America with extents 171W – 31W / 10N – 77 N. The files in this group are identified by the name MERRA2, because they were used with data from the NASA MERRA-2 dataset, which uses a latitude/longitude data grid.</p> <p>There are a number of files within each group. The files all follow the naming convention merra2_[masked]_<grid>_<n>wide_<year>.nc, where [masked] indicates that the presence of the word <em>masked</em> is optional and <grid> is either <em>merra2-1deg</em> or <em>narr-96km</em>. The the sequence <n>wide indicates the width with which the fronts were drawn, and <year> is the year for the data stored in the file.</p> <p>The files marked as masked had a mask applied to the data grids that corresponded to the envelope of the geospatial region where there are, on average, 40 or more front crossing of any type per year, as determined using the <a href="https://dx.doi.org/10.5281/zenodo.2651361">Coded Surface Bulletin </a>dataset.</p> <p>The <n>wide portion of the file names takes two forms—<em>1wide</em> and <em>3wide</em>. The fronts in the <em>1wide</em> files were rasterized by drawing the front polylines with a width of one grid cell. The fronts in the <em>3wide</em> files were rasterized by drawing the front polylines with a width of 3 grid cells.</p> <p>Within each grid group, there are four subsets of files:</p> <ul> <li>merra2_masked_<grid>_1wide_<year>.nc</li> <li>merra2_masked_<grid>_3wide_<year>.nc</li> <li>merra2_<grid>_1wide_<year>.nc</li> <li>merra2_<grid>_3wide_<year>.nc</li> </ul>
Fig. 29 in A review of the cleptoparasitic bee genus Epeolus Latreille, 1802 (Hymenoptera: Apidae) in the Caribbean, Central America and Mexico
Fig. 29. Antenna (basal portion) of female. A. E. minimus (Robertson, 1902), in which F2 is noticeably longer than wide. B. E. asperatus Cockerell, 1910, in which F2 is not noticeably longer than wide. Scale bars = 0.5 mm.
Fig. 27 in A review of the cleptoparasitic bee genus Epeolus Latreille, 1802 (Hymenoptera: Apidae) in the Caribbean, Central America and Mexico
Fig. 27. Head of female, lateral view. A. E. luteipennis Friese, 1916, in which the frontal keel lacks a tooth-like process. B. E. odyneroides sp. nov., holotype (KUNHM SM0253729), in which the frontal keel has a small tooth-like process (blue arrow). C. E. australis Mitchell, 1962, in which the frontal keel (blue arrow) is strongly raised. D. E. brumleyi Onuferko, 2018, paratype, in which the frontal keel (blue arrow) is weakly protuberant by comparison. Scale bars = 1 mm.
Fig. 26 in A review of the cleptoparasitic bee genus Epeolus Latreille, 1802 (Hymenoptera: Apidae) in the Caribbean, Central America and Mexico
Fig. 26. Epeolus spp., habitus of female, ventral view. A. E. basili Onuferko, 2018, paratype showing antennae, legs, and metasomal sterna with similar reddish-orange coloration. B. E. pusillus Cresson, 1864 showing color contrast between reddish-orange legs and dark brown antennae and metasomal sterna. Scale bars = 3 mm.
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.