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1,049 results for “height”
Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel
<p>Raw data associated with a paper submission.<br> " Variation of texture anisotropy and hardness with build parameters and wall height in directed-energy-deposited 316L steel" submitted to Additive Manufacturing.</p> <p>Contained are all the raw images used in figures, as well as csv's of any data pltoted in graphs.</p> <p>Raw images captured during printing of various processing parameters<br> EBSD scans (.ctf) of all disucssed samples </p> <p>Wall definitions (EBSD compared to paper)<br> Wall 1 - Wall A1 300 W 2750 mm/s<br> Wall 2 - Wall D 500 W 2250 mm/s<br> Wall 3 - Wall B 300 W 2250 mm/s<br> Wall 4 - Wall C 500 W 2750 mm/s<br> Wall 5 - Wall A2 300 W 2750 mm/s</p>
Relative width and height of handwritten letters. R code.
<p>Heigths and widths of handwritten letters of 21 writers. 500 letters of each writer.</p> <p>R code for data analysis. The codes are in beta phase, the codes have no help or explanations. Units mm/100.</p>
Canopy Height Map of Los Angeles County Native Habitat Areas
<p>Using LARIAC4 (2016) LIDAR imagery this Canopy Height Model was derived using the lidR, terra and sf packages in R at a resolution of 1 meter. Native habitat areas were selected based on occurence of native flora taxa from iNaturalist in each LARIAC tile. Tiles with no or very few native plants were not included. Additionally, LA County north of the Santa Clara river and San Gabriel mountains was not included, please contact me if you need additional areas. Non-organic features such as power lines, houses, and some high altitude LIDAR noise are still present in the data, future efforts are needed to remove these. Processing took place on the Occidental College computing cluster using a machine with 192GB RAM and 32 cores and took approximately 3 days. The code used to generate this CHM is available here: https://gist.github.com/max-mapper/d52ad9df2f9ed4d191e67955f950e044. The CHM is available as Cloud Optimized Geotiff, which can be viewed in QGIS dynamically over HTTPS without requiring a full download.</p>
Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France
<p>Maps of forest height, aboveground biomass (AGB)* and volume (VOL)* at 10 m spatial resolution for the year 2020 on France. </p> <p>* AGB and Volume maps are available on request.</p> <p>The methodology and validation of the maps are presented here: https://hal.science/hal-04249151</p> <p>Please cite :</p> <p>David Morin, Milena Planells, Stéphane Mermoz, Florian Mouret. Estimation of forest height and biomass from open-access multi-sensor satellite imagery and GEDI Lidar data: high-resolution maps of metropolitan France. 2023. hal-04249151</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Evaluation of Upper Tropospheric Geopotential Height Anomalies over the Tropical and Subtropical Oceans in CMIP6 Models Using GNSS Radio Occultation Observations
<p>The set-up of CESM2-CAM6 sensitivity experiments for winter season (Dec-Jan-Feb: DJF), with prognostic falling ice radiative effects on (SON) and off (NOS), is an updated two-moment stratiform cloud scheme (MG2, Gettelman & Morrison, 2015) in the CESM2 atmospheric component of CAM6. CESM2-CAM6 participated in CMIP6. Both the NOS and SON simulations were configured following the same approach as the CMIP6 "historical" run spanning from 1980 to 2014.</p> <p> </p> <p>The data are:</p> <p> </p> <p>TS: skin temperature (K)</p> <p>TAUX: zonal surface wind stress</p> <p>TAUY: meridinal surface wind stress</p> <p>DTCOND: moist condensation heating rate</p> <p>QRL: long wave heating rate</p> <p>OMEGA: vertical motion</p> <p>Z3: geopotential height</p> <p> </p>
Fig. 2. Cytaeis tetrastyla, bell height 2 in Hydromedusae observed during night dives in the Gulf Stream
Fig. 2. Cytaeis tetrastyla, bell height 2 mm. (A) Lateral view, note medusa buds on upper part of manubrium, BFLA4066. (B) Lateral view with focus on the frontal exumbrella showing the typical flared tentacle bases of Cytaeis medusae, BFLA4073. (C) BFLA4069, note green tentacle tips, a colour likely due to interference effects and not pigments.
Fig. 3. Amphinema turrida, bell height approximately 6 in Hydromedusae observed during night dives in the Gulf Stream
Fig. 3. Amphinema turrida, bell height approximately 6 mm. (A) Lateral view. (B-D) Oblique views from oral side, note the presence of thin cirri.
Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains
<p>Dec 15, 2021</p> <p> </p> <p><strong>Precipitation, low-level jet, and geopotential height data for analyzing sources of predictability in the US northern Great Plains</strong></p> <p> </p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola</p> <p> </p> <p><strong>Motivation</strong></p> <p>The data presented here was used to investigate the uskills of precipitation in the US northern Great Plains, and it can be cited as described below. The original data for producing this data is from the Climate Forecast System (CFS) retrospective reanalysis and reforecast as well as precipitation data from the Climate Prediction Center (CPC) from the National Oceanic and Atmospheric Administration (NOAA). Also, gridded data is from the North American Regional Reanalysis (NARR) from the National Centers for Environmental Prediction (NCEP).</p> <p> </p> <p><strong>License </strong></p> <p>Creative Commons CC-BY</p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p> eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p> </p> <p><strong>Disclaimer</strong></p> <p>The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.</p> <p><strong>Description of the dataset</strong></p> <p>Files are provided with the following features:</p> <p><strong>List of cases: </strong></p> <p> files.0.00.dy.txt</p> <p><strong>Low-level jet (or the GP-LLJ index)</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> LLJ_pc_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> from CFS models,</p> <p><strong> </strong>eof1.v850.cfs.1982-2009.dy.tar</p> <p> pc1.v850.cfs.1982-2009.dy.tar</p> <p> from NARR model,</p> <p> pc1.vwnd.narr.1982-2009.tar</p> <p><strong>The geopotential height (or CGT index): </strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/eof/cfs/0.35.cases/</p> <p>Master file:<strong> Z200_mode_corr_1D_pdf_full.m</strong></p> <p>With input data</p> <p> xt-reco-z200.Full.123.z200.cfs.1982-2009.12-60.tar</p> <p> xt-reco-z200.Full.z200.narr.1982-2009.bin.tar</p> <p><strong>Precipitation at the US Great Plains:</strong></p> <p>Originally located at /home/cmc542/2019/sum-pred/clim/yrcases</p> <p>Master file: <strong>prec_corr_cfs_1D_pdf_full.m</strong></p> <p>With input data:</p> <p> prec.cfs.MW.1982-2009.tar</p> <p> prec.cpc.MW.1982-2009.tar</p> <p><strong>Correlation patterns:</strong></p> <p> Precipitation: PREC.NGP.corr.txt</p> <p> LLJ: LLJ.pcs.corr.narr.pdf.txt</p> <p> Z200: Z200.pcs.corr.narr.pdf.txt</p> <p><strong>Credit</strong></p> <p>Carlos M. Carrillo and Francisco Muñoz-Arriola, 2021: “Sources of Subseasonal Predictability of Rainfall in the Northern Great Plains”, <em>Journal of Applied Meteorology and Climatology</em>. In review.</p> <p><strong>Grant funding</strong></p> <p>This research was funded by the U.S. Geological Survey (USGS), the U.S. Department of Agriculture (USDA), the Daugherty Water for Food Global Institute (DWFI) at the University of Nebraska-Lincoln (UNL), and the UNL’s Layman Award.</p>
Fig. 12. Seguenziidae. A–B. Seguenzia elegans Jeffreys, 1885, BANGAL 0711, V10, 1720 m, height 3.4 in The Mollusca of Galicia Bank (NE Atlantic Ocean)
Fig. 12. Seguenziidae. A–B. Seguenzia elegans Jeffreys, 1885, BANGAL 0711, V10, 1720 m, height 3.4 mm. C–D. Seguenzia formosa Jeffreys, 1876, BANGAL 0711, V10, height 3.1 mm. E–G. Carenzia carinata (Jeffreys, 1877), BANGAL 0711, V10, height 4.2 mm. H–J. Ancistrobasis reticulata (Philippi, 1844), BANGAL 0711, V6, 909 m, height 6.5 mm. Scale bars = 1 mm.
Fatiando a Terra Data: Earth - Geoid height grid at 10 arc-minute resolution
<p>Global 10 arc-minute resolution grids of geoid height with respect to the WGS84 ellipsoid.</p> <p><strong>Note:</strong> This is a processed and formatted version of the source dataset below. It's meant for use in documentation and tutorials of the <a href="https://www.fatiando.org">Fatiando a Terra</a> project. Please <strong>cite the original authors</strong> when using this dataset.</p> <p><strong>Changes made: </strong>Convert the grid from the ASCII format of ICGEM to CF-compliant netCDF. Add relevant metadata, including names, units, datum, etc. Fix grid coordinates to be generated by <code>numpy.linspace</code> instead <code>numpy.arange</code> (or the equivalent used by ICGEM internally) to guarantee equal spacing to a higher accuracy. Export to compressed netCDF.</p> <p><strong>Source: </strong><a href="https://doi.org/10.5880/icgem.2015.1">EIGEN-6C4</a> spherical harmonic model (generated by the <a href="http://icgem.gfz-potsdam.de/home">ICGEM calculation service</a>)</p> <p><strong>Source license: </strong><a href="https://doi.org/10.5880/icgem.2015.1">CC-BY</a></p> <p><strong>Repository: </strong><a href="https://github.com/fatiando-data/earth-geoid-10arcmin">https://github.com/fatiando-data/earth-geoid-10arcmin</a></p>
The height-dependent delayed ionospheric response to solar EUV - artificial run
<p>These artificial runs use the TIE-GCM v2.0 model in its 2.5 × 2.5 configuration as the real condition model run. In order to converge to stable initial condition the TIE GCM runs for 30 days prior to the final simulation starting at 21 September 2010. This initial run was configured with default parameters. Both model runs start at 21 October 2010 and are calculated with default parameters except for the switched off auroral parameterization as well as the switched off high-latitude potential model. The influence of noise in solar activity is reduced by applying an artificial noise free sinusoidal time series for the F10.7 input.</p>
Dates and 500 hPa geopotential height of P90, P95 and P99 blocking days
<p>These .mat files contain the dates of blocking days over two domains around the Antarctic Peninsula: a domain located to the west (150-90ºW, 50-70ºW) and a domain located over and to the east of the Peninsula (90-30ºW, 50-70ºW). Extreme blocking on each domain occurs when the mean 500 hPa geopotential height, averaged over the domain, is larger than the 90% percentile. Very extreme blocking occurs when the mean 500 hPa geopotential height, averaged over the domain, is larger than the 95% and 99% percentiles.</p> <p>Variables included within each file:</p> <p>- date_500mb_p90</p> <p>- date_500mb_p95</p> <p>- date_500mb_p99</p> <p>They represent the dates of extreme and very extreme blocking on each domain</p> <p>- geo_h500mb_p90</p> <p>- geo_h500mb_p95</p> <p>- geo_h500mb_p99</p> <p>They represent the corresponding 500 hPa mean geopotential height averaged over each domain during extreme (values larger than 90% percentile) and very extreme (values larger than the 95% and 99% percentiles) blocking days.</p> <p>Also included on each file are variables:</p> <p>- date_500mb_p90_sor</p> <p>- date_500mb_p95_sor</p> <p>- date_500mb_p99_sor</p> <p>which are just the dates of extreme and very extreme blocking days sorted in ascending order.</p> <p>For more details on this dataset, see </p> <p><strong>J. C. Marín</strong>, D. Bozkurt, Barrett, B., 2022: Atmospheric blocking trends and seasonality around the Antarctic Peninsula. Accepted in Journal of Climate.</p>
Рис. 5. Passage height preferences (M, ± SD) of birds migrating over Polonyna Borzhava mountain ridge in autumn 2018. in Autumn Migration Of Birds Over Polonyna Borzhava (Ukrainian Carpathians)
Рис. 5. Passage height preferences (M, ± SD) of birds migrating over Polonyna Borzhava mountain ridge in autumn 2018.
IODP Expedition 372A Laser height profile (section half)
<p>Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.</p>
IODP Expedition 374 Laser height profile (section half)
<p>Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.</p>
FIG. 2 in Relations between metatarsal proximal extremity parameters and weight and height at the withers of the dromedary (Camelus dromedarius Linnaeus, 1758) in the Sahraoui and Targui "breeds".
FIG. 2. — Principal component analysis (PCA), graphs for seven parameters per bone, 43 right metatarsal bones.A, graph of variables;B, scatterplot of individuals; C, 95% confidence ellipsis for breed; D, 95% confidence ellipsis for sex; E, 95% confidence ellipsis for breed and sex. Abbreviations: BpT, proximal width with T at the extremity for the metatarsal; BW, body weight; Dim, Factor of the PCA (Dim 1 = Factor 1, Dim 2 = Factor 2); DpT, proximal depth with T at the extremity for the metatarsal; F, female; GC, surface for the great cuneiform bone; HW, height at the withers; M, male; NC1, great cranial articular surface for the cuboid bone; NC2, little caudal articular surface for the cuboid bone; S, Sahraoui breed; SA, total proximal articular surface; SF, female Sahraoui; SM, male Sahraoui; T, Targui breed; TF, female Targui; TM, male Targui.
IODP Expedition 352 Laser height profile (section half)
<p>Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.</p>
IODP Expedition 351 Laser height profile (section half)
<p>Height profile data were measured on the Section Half Multisensor Logger (SHMSL) by a rangefinding laser and recorded in uncorrected height units in millimeters in CSV files.</p>
Text-fig. 2. The methods of measurements. H – horizontal plane, HB – body height, SL – skull length, TL – total body length, 1 – the angle which the dorsal lobe of the caudal fin forms with the horizontal plane, 2 – the angle which the ventral lobe of the caudal fin forms with the horizontal plane, 3 – the angle which the scale row in front of the anal fin forms with the horizontal plane. in Actinopterygians Of The Broumov Formation (Permian) In The Czech Part Of The Intra-Sudetic Basin (The Czech Republic)
Text-fig. 2. The methods of measurements. H – horizontal plane, HB – body height, SL – skull length, TL – total body length, 1 – the angle which the dorsal lobe of the caudal fin forms with the horizontal plane, 2 – the angle which the ventral lobe of the caudal fin forms with the horizontal plane, 3 – the angle which the scale row in front of the anal fin forms with the horizontal plane.
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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.