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380 results for “Pressure Data”
Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries (Supporting Data)
<p>This data accompanies the paper "Fully-coupled pressure-based finite-volume framework for the simulation of fluid flows at all speeds in complex geometries", published in Journal of Computational Physics (2017), http://dx.doi.org/10.1016/j.jcp.2017.06.009.</p>
Multitrophic metacommunity dataset, with metapopulation level butterfly data, in addition to predation pressure and floral abundance
<p>This dataset includes metapopulation level butterfly data within a butterfly metacommunity sampled across 15 sites at Burnt Lands Provincial Park in Ontario, Canada, during the summer of 2019. It includes floral abundance at each site for each sampling round. It also includes information on the number of predator attacks on experimental clay butterfly models at each site. It contains data on site size and connectivity for the 15 sites, as well as the length of butterfly survey transects sampled in each site.</p>
Data from: Homeostatic control of deep sleep and molecular correlates of sleep pressure in Drosophila
<p class="MsoNormal">Homeostatic control of sleep is typically addressed through mechanical stimulation-induced forced wakefulness and the measurement of subsequent increases in sleep. A major confound attends this approach: biological responses to deprivation may reflect a direct response to the mechanical insult rather than to the loss of sleep. Similar confounds accompany all forms of sleep deprivation and represent a major challenge to the field. Here we describe a new paradigm for sleep deprivation in <em>Drosophila</em> that fully accounts for sleep-independent effects. Our results reveal that deep sleep states are the primary target of homeostatic control and establish the presence of multi-cycle sleep rebound following deprivation. Furthermore, we establish that specific deprivation of deep sleep state results in state-specific homeostatic rebound. Finally, by accounting for the molecular effects of mechanical stimulation during deprivation experiments, we show that serotonin levels track sleep pressure in the fly's central brain. Our results illustrate the critical need to control for sleep-independent effects of deprivation when examining the molecular correlates of sleep pressure and call for a critical reassessment of work that has not accounted for such non-specific effects.</p>
Data and code for replication of: "Global warming and heat extremes to exacerbate inflationary pressures. M. Kotz, F. Kuik, E. Liz, C. Nickel. Nature Communications Earth & Environment (2023)."
<p>This repository contains secondary data and code necessary to reproduce the results of the manuscript:</p><p>Global warming and heat extremes to exacerbate inflationary pressures.</p><p>M. Kotz, F. Kuik, E. Liz, C. Nickel. Nature Communications Earth & Environment (2023).</p><p> </p><p>For further information please contact: maxkotz@pik-potsdam.de</p><p> </p><p>This document contains:</p><p>1. An outline of the data included in the repository.</p><p>2. An outline of the code included in the repository.</p><p> </p><p>See the README for further details.</p><p> </p><p>Credit and thanks go to Miles Parker, Chiara Osbat and Emanuele Franceschi for compiling the inflation data which is used in this study. Inflation data provided here has been anonymised (countries shuffled and names replaced by random letter combinations) to enable reproduction of our results, while limiting further use. Moreover, inflation in terms of the change in the logarithm of prices is included, whereas the level of price indices are excluded. For full inflation data please see the forthcoming publication by Miles Parker, Chiara Osbat,and Emanuele Franceschi (contact Miles.Parker@ecb.europa.eu for further enquiries into the raw inflation data).</p>
Data from: Blood pressure pulsations modulate central neuronal activity via mechanosensitive ion channels
<p><span>The transmission of heartbeat through the cerebral vascular system is known to cause intracranial pressure pulsations. Here we report that arterial pressure pulsations can directly modulate central neuronal activity. </span><span>In a semi-intact rat brain preparation, vascular pressure pulsations elicit correlated local field oscillations in the olfactory bulb (OB) mitral cell layer. These oscillations do not require synaptic transmission, but reflect baroreceptive transduction in mitral cells. This transduction is mediated by an excitatory mechanosensitive ion channel and modulates neuronal spiking activity. Indeed, in awake animals, the heartbeat entrains the activity of a subset of OB neurons within ~20 ms. Thus, we propose that this fast intrinsic interoceptive mechanism can modulate perception, e.g. during arousal, within the OB and also possibly across various brain areas.</span></p>
Thermodynamic parameters and elasticity data of phase D at high pressures and temperatures
<p>This dataset contains all the original data for figures in the article "Thermoelasticity of phase D and implications for low-velocity anomalies and local discontinuities at the uppermost lower mantle" submitted to American Mineralogist.</p>
LI-COR (LI-850) sensor data obtained by the Antarctic Modeling Observation System (ATMOS) project during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of CO2 (pCO2)
<p>LI-COR (LI 850) sensor data obtained by the "Antarctic Modeling Observation System" (ATMOS) project. These data were collected in the southern summer of 2021/2022 during the 40th Brazilian Antarctic Operation (OPERANTAR XL) and were used to calculate the partial pressure of seawater CO2 (pCO2sea)</p> <p>The LI-850 carbon dioxide analyzer was installed in the laboratory aft of H41 together with a balancer to measure the CO2 concentration of the water. The collection system occurs as follows: the ship's saltwater piping system collects seawater, when this water enters the balancer it generates turbulence. The turbulence generated causes the CO2 present in the water to come into balance with the air. The air that comes out of the balancer is pumped into the LI-850, by its internal pump, and thus, the equipment measures the concentration of CO2 present in the water. To ensure that the air inside the balancer is actually balanced with the seawater, the air leaving the LI-850 is pumped back into the balancer, closing the circuit. From these data it is possible to calculate pCO2sea.</p>
Raw data for journal article: "Intracochlear pressure in cadaver heads under bone conduction and intracranial fluid stimulation"
<p>This is a data set containing the raw data for figures 7-14 from the journal article:</p> <p>"Intracochlear pressure in cadaver heads under bone conduction and intracranial fluid stimulation"</p> <p>Original article DOI: 10.1016/j.heares.2022.108506</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/35459531/</p>
Raw data for journal article: "Intracochlear pressure and temporal bone motion interaction under bone conduction stimulation""
<p>This is a data set containing the raw data for figures 3-6 from the journal article:</p> <p>"Intracochlear pressure and temporal bone motion interaction under bone conduction stimulation"</p> <p>Original article DOI: 10.1016/j.heares.2023.108818</p> <p>Original article link: https://pubmed.ncbi.nlm.nih.gov/37267833/</p> <p> </p> <p>The Fig 4-8 data are contained within MATLAB figure (.fig) files, all saved with MATLAB version R2020a.</p> <p>Fig 9-10 data are 3D velocity data for 3 cadaver heads (CH1-3), each recorded at the left (L) and right (R) side, all within the folder "Velocity data".</p> <p>This data are stored within a folder structure indicating the stimulation condition (defined in the journal article). For example "Velocity data\CH1-L\Stim @ BAHA" contains data for the left side of cadaver head 1 (CH1) with stimulation "Stim @ BAHA", as defined in the journal article above. </p> <p>For each combination of cadaver head and stimulation condition there is a TXT file (comma delimited) for the real and imaginary data at each stimulation frequency, and orthogonal velocity axis (X,Y,Z based on the anatomical coordinate system defined in the journal article) as well as the combined (maximum) velocity vector. The data set also includes a TXT file with the position (in same coordinate system the velocity data) of each measurement point and a list of stimulation frequencies.</p> <p>The data set also includes the geometry of the skull bone surface of each cadaver head (CH) in the form of STL file, all within the folder "Skull surface data".</p>
The lag and cumulative response of vegetation WUE to water vapor pressure deficit on the Shiyang River Basin, northwest of China_data description
<p>Data description</p> <p>This document provides details about the data description in the manuscript. The variables are described below along with their collection methods and calculation processes:</p> <p>the lag effect: The zip file of“The lag effect” contains three files: "rET_lag_result_z", "rGPP_lag_result"_, and" rWUE_lag_result_z".</p> <p>rET_lag_result_z: "rET_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_lag_result_z: "rGPP_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_lag_result_z: "rWUE_lag_result_z" database is a dataset used to analyze the lag effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Cumulative effect: The zip file of“The Cumulative effect” contains three files: "rET_acc_result_z", "rGPP_acc_result"_, and" rWUE_acc_result_z".</p> <p>rET_acc_result_z: "rET_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Evapotranspiration (ET)</p> <p>rGPP_acc_result_z: "rGPP_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Gross Primary Productivity (GPP) </p> <p>rWUE_acc_result_z: "rWUE_acc_result_z" database is a dataset used to analyze the Cumulative effect of water vapor pressure deficit (VPD) on Water Use Efficiency (WUE)</p> <p> </p> <p><br>the Slop_Partition: The zip file of“The Slop_Partition”contains three files: "Slop_ET_Partition", "Slop_GPP_Partition"_, and" Slop_WUE_Partition".</p> <p>Slop_ET_Partition: "Slop_ET_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Evapotranspiration (ET)</p> <p>Slop_GPP_Partition: "Slop_GPP_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Gross Primary Productivity (GPP)</p> <p>Slop_WUE_Partition: " Slop_WUE_Partition" database is a dataset used to calculate the temporal and spatial dynamic change·trend of Water Use Efficiency (WUE)</p> <p> </p> <p> </p>
Data sets for "Pressure destabilizes oxygen vacancies in bridgmanite" by H.Fei et al.
<p>This is EPMA, XRD, and Mossbauer datasets for the article "Pressure destabilizes oxygen vacancies in bridgmanite" by H. Fei et al.</p>
Seafloor pressure data from 2019 deployment at the Hikurangi subduction zone, New Zealand
<p>We include here hourly seafloor pressure time series (and locations) from a 2019 deployment at the Hikurangi subduction zone, used in "Using seafloor geodesy to detect vertical deformation at the Hikurangi subduction zone: insights from self-calibrating pressure sensors and ocean general circulation models", a paper submitted to JGR: Solid Earth in January 2022.</p> <p><strong>APG_hikurangi_2019.json/.mat: </strong>Seafloor pressure time series data (JSON and MATLAB format) from a deployment at the Hikurangi subduction zone in 2019. The files contain the hourly time series in datetime (UTC) and pressure in hectopascals, with the convention that a decrease in pressure is equivalent to a reduction in the height of the water column (seafloor uplift).</p> <p>The only processing that has been applied to the data is filtering using a 2-day corner lowpass filter for all sites, and the A-0-A correction for the POBS sensors (which are therefore drift corrected). All APGs not equipped with A-0-A still contain sensor drift. Each time series has been adjusted using the mean of the absolute data, which is why the time series for the sites plot about zero - amplitude has been preserved.</p> <p><strong>locations_APG_hikurangi_2019.csv: </strong>Locations of the seafloor pressure sites from a deployment at the Hikurangi subduction zone in 2019. Indicated for each site are the sensor’s institute (UTIG - University of Texas Institute for Geophysics, Austin, USA; GNS Science - GNS Science, New Zealand; LDEO - Lamont-Doherty Earth Observatory, Columbia University, USA; KU - Kyoto University and Tohoku University, Japan), A-0-A drift correction capability, deployment longitude, latitude, and depth, and whether there are usable data. The sensors without usable data either contained data logger issues or were not recovered, and are not included in APG_hikurangi_2019.json/.mat.</p>
Pathogen reduction data when applying high pressure processing to milk/colostrum and ready-to-eat foods
<p><strong>Introduction</strong></p> <p>This dataset was built by members of the EFSA Working Group on high pressure processing (HPP) of food during the preparatory work on the BIOHAZ Scientific Opinion on the efficacy and safety of high pressure processing of food (EFSA-Q-2020-00380) (<a href="https://doi.org/10.2903/j.efsa.2022.7128">https://doi.org/10.2903/j.efsa.2022.7128</a>).</p> <p>It was used to answer to the following Terms of Reference (ToR) of the mandate:</p> <p>ToR2. To assess the efficacy of HPP when applied to raw milk and raw colostrum from ruminants, and in particular:</p> <p>a. To recommend minimum requirements as regards time and pressure of the HPP, and other factors if relevant, for the control of <em>Mycobacterium</em> spp., <em>Brucella</em> spp., <em>Listeria monocytogenes</em>, <em>Salmonella</em> spp. and Shiga toxin-producing <em>Escherichia coli</em> (STEC), to achieve an equivalent efficacy to that of thermal pasteurisation.</p> <p> </p> <p>ToR3. To assess the efficacy of HPP when applied to foods known to cause human listeriosis and in particular:</p> <p>a. To recommend minimum requirements as regards time and pressure of the HPP, and other factors if relevant, to reduce significantly <em>L. monocytogenes</em> levels (e.g. by a certain log reduction), and assuming that the parameters influencing the growth of <em>L. monocytogenes</em> remain unchanged (e.g. shelf-life and storage conditions);</p> <p>b. To assess the efficacy on other relevant pathogens when applying the minimum requirements identified in a.</p> <p>A literature search was conducted to retrieve studies reporting on pathogen-specific parameters when treating milk/colostrum and ready-to-eat foods with HPP. A modelling approach was used to fit the derived log<sub>10</sub> reductions (logR) at specific pressure-holding time combinations. More information can be found in the Scientific Opinion.</p> <p><strong>Description</strong></p> <p>Two MS-Excel files contain the extracted data.</p> <p>One file consists of the data used for answering ToR2 and contains data on HPP inactivation of <em>Mycobacterium bovis</em>, <em>L. monocytogenes</em>, <em>Salmonella</em> spp., STEC, <em>Campylobacter</em> spp., and <em>Staphylococcus aureus</em> in milk and colostrum from ruminants.</p> <p>The other file was used for answering ToR3 and contains data on HPP inactivation of <em>L. monocytogenes</em>, <em>Salmonella</em> spp. and <em>E. coli</em> in three types of RTE food categories: category “cooked meat products”, category “smoked and gravad fish” and category “soft or semi-soft and fresh cheese”.</p> <p>Both files contain metadata related to product characteristics (e.g. the food matrix and composition), contamination characteristics (e.g. pathogen/strain used for inoculation, inoculation level, medium used for enumeration), HPP treatment characteristics (e.g. target pressure, time), and outcome (e.g. log<sub>10</sub> reduction).</p>
Data: Human pressure effect on biodiversity-multifunctionality relationship in large Neotropical wetlands
<p>Data from manuscript Human pressure drives biodiversity–multifunctionality relationships in neotropical wetlands.<br> Moi et al.</p> <p>This is a dataset compiled from 72 lakes distributed across four neotropical wetlands of Brazil (Amazon, Araguaia, Pantanal, and Paraná). Dataset included single ecosystem functions: nutrient concentrations (in situ measurements of N and P water concentrations), metabolism (daily changes in water O2 concentration), biomass at multiple trophic levels (algae, herbivores, carnivores, detritivores, and omnivores), microorganism abundance (bacterial cell densities), availability of photosynthetically active radiation (light availability underwater), and variation in habitat complexity under water (variation in plant above-bottom cover). Dataset also included measures of aquatic biodiversity, including species richness and functional diversity of seven organismal groups (fish, aquatic macrophytes, microcrustaceans, rotifers, phytoplankton, ciliates, and testate amoebae). Finally, the dataset includes measures of ecosystem multifunctionality, human pressure (Human Footprint), and local environmental covariates (depth, conductivity, pH, precipitation, temperature). All data came from standardized samples. </p>
Dataset of "Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes"
<p>Dataset of the article <em>Pressure from data-driven estimation of velocity fields using snapshot PIV and fast probes </em>(<a href="https://doi.org/10.1016/j.expthermflusci.2022.110647">https://doi.org/10.1016/j.expthermflusci.2022.110647</a>). A data-driven method is applied to combine non-time-resolved velocity field and fast probe data, and achieve time-resolved velocity and pressure field.</p> <p>The codes processing data here are on https://github.com/erc-nextflow/Data-driven-pressure-estimation-with-EPOD.</p> <p>This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement No 949085, NEXTFLOW).</p>
Load, pressure, rubble pile geometry and video data from model-scale tests on shallow water ice-structure interaction
<p>The data is obtained from model-scale experiments on shallow water ice-structure interaction. During the conducted experiments, a ten-meter wide initially intact ice sheet was pushed against a sloping structure of the same width. As the ice failed against the structure, a grounded rubble pile accumulated in front of it. The structure consisted of ten identical one-meter-wide segments and the horizontal load on each of these segments was measured independently with load cells. These measurements are presented as load-time datasets. The horizontal load acting on the false bottom was measured with load cells and are also presented as load-time datasets. Furthermore, the ice pressure on two of the segments was measured with tactile sensors. These pressure measurements are presented as array-based pressure-time datasets. Video footage filmed from two different video angles is included in the data. In addition, the coordinates of the rubble pile geometries at the end of each experiment are published. The data includes the top and side rubble pile geometries. In total, seven experiments were conducted. The data can be used by researchers, engineers and designers who work with ice structure interaction related issues in order to, for instance, optimize the design of offshore structures, improve ice load predictions or develop future experiments and simulations. A full description of the experimental set-up and the published data is submitted to the journal Data in Brief. </p>
Data, scripts and supplementary information for analysis of the functioning of a three-species microcosm under cadmium pressure
<p>Data files of different experiments conducted with a three-species microcosm + Rscript for infering paremeters from a mechanistic EDO based model describing the functioning of the microcosm and the effect of cadmium + JAGS script of the model + supplementary information</p>
Data for: Deep Learning of Model- and Reanalysis- Based Precipitation and Pressure Mismatches over Europe
<p>This study focuses on using UNet Convolutional Neural Networks to predict the spatiotemporal mismatches (errors) between TSMP-G2A model-based and COSMO-REA6 reanalysis-based precipitation and surface pressure over Europe.</p> <p>The following data are provided in this dataset:</p> <p>1) The remapped and NetCDF-merged TSMP-G2A and COSMO-REA6 precipitation and surface pressure over the study area (EU-11 EUROCORDEX, ~0.11 degrees) for the years 1995-2017. Files: COSMO-REA6_PREPROCESSED.zip and TSMP_PREPROCESSED.zip</p> <p>2) The actual and predicted spatiotemporal mismatch data for training, validation, and testing periods (1995-2017). Files: MISMATCH_ACTUAL.zip and MISMATCH_PREDICTED.zip<br> </p> <p>References for original TSMP-G2A and COSMO-REA6 data:<br> TSMP-G2A: http://doi.org/10.17616/R31NJMGR<br> COSMO-REA6: doi:10.1002/qj.2486, 2015</p>
Original data for publications: Study of the Temperature- and Pressure-Dependent Structural Properties of Alkali Hydrido-closo-borate Compounds, and: Pressure-induced phase transitions in Na2B12H12, structural investigation on a candidate for solidstate electrolyte
<p>Original data for publications (part of University of Geneva only):</p> <p>Study of the Temperature- and Pressure-Dependent Structural<br> Properties of Alkali Hydrido-closo-borate Compounds<br> Inorg. Chemistry 2022 61, 5224-5233, <br> https://doi.org/10.1021/acs.inorgchem.1c03681</p> <p>Pressure-induced phase transitions in Na2B12H12,<br> structural investigation on a candidate for solidstate electrolyte<br> Acta Cryst. B 2019 B75 406-413<br> https://doi.org/10.1107/S2052520619004670</p> <p> </p>
Replication data for Optical properties of SiV and GeV color centers in nanodiamonds under hydrostatic pressures up to 180 GPa
<p>We investigate the optical properties of silicon-vacancy (SiV) and germanium-vacancy (GeV) color centers in nanodiamonds under hydrostatic pressure up to 180 GPa. The nanodiamonds were synthetized by Si or Ge-doped plasma assisted chemical vapor deposition and, for our experiment, pressurized in a diamond anvil cell.</p> <p>Data_SiV : replication data for the center wavelength of the photoluminescence of the SiV center as a function of pressure. The file is composed of 4 columns: first is pressure (in GPa), second is error in pressure (in GPa), third is center wavelength (in nm) and fourth is error in center wavelength (in nm).</p> <p>Data_GeV : replication data for the center wavelength of the photoluminescence of the GeV center as a function of pressure. The file is composed of 4 columns: first is pressure (in GPa), second is error in pressure (in GPa), third is center wavelength (in nm) and fourth is error in center wavelength (in nm).</p>
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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)
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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.