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380 results for “Pressure Data”
Data from: Grazing pressure affects offspring sex ratio in a socially monogamous passerine on the Tibet Plateau
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Data from: The role of gravity in the evolution of mammalian blood pressure
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Data from: Adaptive evolution in locomotor performance: how selective pressures and functional relationships produce diversity
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Data from: Selective pressures on MHC class II genes in the guppy (Poecilia reticulata) as inferred by hierarchical analysis of population structure.
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Data from: Corrosion resistant systems of formate packer fluid for G3/N80/TP110SS pipes at high temperature, high pressure and high H2S/CO2 ratios
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Data from: The evolution of bat vestibular systems in the face of potential antagonistic selection pressures for flight and echolocation
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Data from: Timing is everything: fishing‐season placement may represent the most important angling‐induced evolutionary pressure on Atlantic salmon populations
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Data from: Pore pressure evolution and mass loss of broken gangue during the seepage
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Data from: Shifts in selective pressures on snake phototransduction genes associated with photoreceptor transmutation and dim-light ancestry
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Data from: Evolution of cross-resistance to medical triazoles in Aspergillus fumigatus through selection pressure of environmental fungicides
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Data from: High hunting pressure selects for earlier birth date: wild boar as a case study
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Data from: Pressure-induced enhancement in the thermoelectric properties of monolayer and bilayer SnSe2
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Data from: Effects of time pressure and time passage on face-matching accuracy
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Pressure and turbidity data collected in Hamelin Pool, Western Australia
Time series of wave height and water turbidity were recorded at a ~2.5 m deep subtidal location in Hamelin Pool in Western Australia from July 2017 to March 2018.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from SNPP CrIMSS and Aqua AIRS, V2 (SNDR13IML3SSDFCVPD)
The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Spatial Statistical Data Fusion (SSDF) Level 3: CONUS Near-Surface Vapor Pressure Deficit from Aqua AIRS, V2 (SNDRAQIL3SSDFCVPD)
This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS).The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.
Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar (EOLE1) at GES DISC
The Eole 1 Raw Temperature, Pressure and Location Data Near 200 mbar product was obtained from the experimenter and originally consisted of a BCD tape generated on a CDC 6600 computer, subsequently converted to ASCII characters. The data are arranged sequentially by orbit. Data from each orbit are contained in a single record and consist of a heading giving the orbit number, the number of balloons contacted, and a control number. Following the heading, the balloon number, date of observation, location, and ambient temperature and pressure are listed. A maximum of 25 balloon contacts may appear in a single record. Empty records with no balloon contacts have been omitted. These data were obtained from balloons near 200 mbar and are for the region between 30 deg S and 60 deg S. The upper level wind speed and direction can be generated from these data by comparing individual balloon locations obtained from successive orbits. Eole, also known as the Cooperative Application Satellite (CAS-A), was the the second French experimental relay and meteorological satellite and the first launched by NASA under a cooperative agreement with the Centre National d'Etudes Spatiales (CNES).
BOREAS AFM-05 Level-2 Upper Air Network Standard Pressure Level Data
The BOREAS AFM-05 team collected and processed data from the numerous radiosonde flights during the project. The goals of the AFM-05 team were to provide large scale definition of the atmosphere by supplementing the existing AES aerological network, both temporally and spatially. This data set includes basic upper-air parameters interpolated at 0.5 kiloPascal increments of atmospheric pressure from data collected from the network of upper-air stations during the 1993, 1994, and 1996 field campaigns over the entire study region.
Data for: Tissue evolution: Mechanical interplay of adhesion, pressure, and heterogeneity
<p>Tissue evolution: mechanical interplay of adhesion, pressure, and heterogeneity</p> <p>Full Data for corresponding publication.</p> <p> </p> <p>All folders are named by the actual simulation values used, not by the rescaled values shown in the paper<br> All folders contain data files, trajectory files are converted and zipped, as well as the executable and the starting configuration file.<br> Start simulations with ./cell_dpd starting_configuration.sconf</p> <p>The simulation for Fig.1 can be found in folder /free_evolution/<br> folder structure: /free_evolution/LXxLYxLZ/GB_F1/DeltaG_alpha/ with LX, LY, LZ simulation box lengths in x,y,z direction, GB and F1 the simulation parameters of the host tissue, DeltaG difference in G of neighbouring species and alpha the relative change of f1 to G of neighbouring species.</p> <p>The simulation for Fig.2 and Fig.S3 can be found in folder /heterogeneity/sharp_treshold/<br> folder structure: /heterogeneity/sharp_treshold/pm/tradeoff/ with mutation probability pm and tradeoff parameter between changes in G and F1.</p> <p>The simulation for Fig.S1 can be found in folder /heterogeneity/division_rate/tradeoff/ with tradeoff parameter between changes in G and F1.</p> <p>The simulation for the division rate simulations of Fig.S2 can be found in folder /pair_competition/12x12x12/40_6.0/division_rate/</p> <p>The simulation for Figs. 4, 5 and S4 can be found in folder /pair_competition/12x12x12/40_6.0/sharp_treshold/<br> folder structure: /pair_competition/LXxLYxLZ/GW_F1W/sharp_treshold/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M).</p> <p>The simulations for Fig.6 can be found in folder /mutationrate/<br> folder structure: /mutationrate/LXxLYxLZ/GW_F1W/pm/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M) and mutation probability pm.</p> <p>The simulations for Fig.7 can be found in folder /survival/<br> folder structure: /survival/LXxLYxLZ/GW_F1W/GM/F1M/ with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M).</p> <p>The simulation for Fig.S5 can be found in folder /unstable/<br> folder structure: /unstable/LXxLYxLZ/GW_F1W/GM/F1M/phi0/sim_number with LX, LY, LZ simulation box lengths in x,y,z direction, GW and F1W the simulation parameters of host tissue and vice versa for mutant (M), initial number fraction phi0 and simulation number sim_number</p> <p>Folder /scripts/ contains all scripts to analyze the simulation results, described in the following:</p> <p>get_phi.py : Outputs file with cell number fractions from input file containing absolute cell numbers.<br> Called by : python get_phi.py input output<br> input: Name input file (in all simulations numcells.dat)<br> output: Name output file</p> <p>minimize_uid: Outputs minimized trajectory file as input.min<br> Called by: ./minimize_uid input<br> input: name trajectory file (in all simulations traj.dat)</p> <p>convert2xyz_spec: Outputs trajectory in xyz format as input.xyz for pair competitions with n=2<br> Called by : ./convert2xyz_spec input<br> input: name trajectory file (minimized file from minimize_uid)</p> <p>convert2xyz_new: Outputs trajectory in xyz format as input.xyz for heterogeneity simulations for n=21 species<br> Called by : ./convert2xyz_new input<br> input: name trajectory file (minimized file from minimize_uid)</p> <p>block_average.py : Outputs file with block averaged value from input file.<br> Called by : python block_average.py input output column n_or_t t_n_start t_n_end<br> input: Name input file<br> output: Output file name<br> column: column to be averaged<br> n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> t_n_start: time/line number to start average<br> t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end</p> <p>cluster_analysis.py : Number of clusters of each species, cluster analysis by DB-SCAN algorithm with minimal points=1 and potential cut-off distance as size treshold<br> Called by: python cluster_analysis.py input output<br> input: trajectory file in xyz format<br> output: name of output file</p> <p>neighbour_analysis.py : Analysis of the cell species average cell species of the cells in interaction range at each frame of the trajectory,<br> second column gives average of total neighbours per cell, following two columns for species 0 average number of identical <br> and different cell species, vice versa for species 1 in the next two last columns<br> Called by: python neighbour_analysis.py input output<br> input: trajectory file in xyz format<br> output: name of output file</p> <p>get_average_cluster.py : averages number of clusters over given time/frame number<br> Called by : python get_average_cluster.py input output n_or_t t_n_start t_n_end<br> input: Name input file<br> output: Output file name<br> n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> t_n_start: time/line number to start average<br> t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end<br> get_average_neighbour.py : averages number of clusters over given time/frame number<br> Called by : python get_average_neighbours.py input output n_or_t t_n_start t_n_end<br> input: Name input file<br> output: Output file name<br> n_or_t: If 0, interprete following arguments as time, else as line numbers to start/end<br> t_n_start: time/line number to start average<br> t_n_end: time/line number to end average. If not given, t_n_start is interpreted, how much time/many line numbers to go back from the end</p> <p><br> Folder /notebooks/ contains all jupyter notebooks to create the plots, starting from that directory as root directory</p> <p>Folder /src/ contains the source code for the individual simulation setups</p> <p>Folder /plots/ contains all plots created with the jupyter notebooks</p>
Data and figures used in "Pressure torque of torsional Alfvén modes acting on an ellipsoidal mantle"
<p>Data, plotting routines and analysis routines to reproduce all results from the article <a href="https://doi.org/10.1093/gji/ggaa166">"Pressure torque of torsional Alfvén modes acting on an ellipsoidal mantle"</a>. The package uses the freely available code <a href="https://github.com/fgerick/Mire.jl">Mire.jl</a>.</p> <p><strong>Prerequisites</strong></p> <p>Installed texlive, python/python3 with matplotlib >v2.1 for support of latest colormaps. A working Julia >v1.3.</p> <p><br> <strong>Install</strong></p> <p>Open the repository directory and run</p> <pre><code class="language-bash">julia install_local.jl</code></pre> <p>to install the package.</p> <p><strong>Run</strong></p> <p>To run the calculations and the plots you simply run</p> <pre><code>using Elltorque Elltorque.run(true)</code></pre> <p><br> from within the Julia REPL (takes around 2-3h). To run without calculating the data use</p> <pre><code>Elltorque.run(false)</code></pre> <p> </p>
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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.