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131 results for “Dissipation”
Data from: Testing the heat dissipation limit theory in a breeding passerine
The maximum work rate of animals has recently been suggested to be determined by the rate at which excess metabolic heat generated during work can be dissipated (heat dissipation limitation theory; HDL). As a first step towards testing this theory in wild animals, we experimentally manipulated brood size in breeding marsh tits (Poecile palustris) to change their work rate. Parents feeding nestlings generally operated at above-normal body temperatures. Body temperature in both males and females increased with maximum ambient temperature and with manipulated work rate, sometimes even exceeding 45 °C, which is close to suggested lethal levels for birds. Such high body temperatures have previously only been described for birds living in hot and arid regions. Thus, reproductive effort in marsh tits may potentially be limited by the rate of heat dissipation. Females had lower body temperatures, a possible consequence of their brood patch serving as a thermal window facilitating heat dissipation. Because increasing body temperatures are connected to somatic costs, we suggest that the HDL theory may constitute a possible mediator of the trade-off between current and future reproduction. It follows that globally increasing, more stochastic, ambient temperatures may restrict the capacity for sustained work of animals in the future.
Simulation data for "Tuning Adhesion and Energy Dissipation in Polymer Films between Solid Surfaces via Grafting and Cross-Linking"
<p>LAMMPS input and data files, Jupyter notebooks used for the analysis of the MD simulations.</p>
Data and code for "Centimeter-scale nanomechanical resonators with low dissipation"
<p>This dataset contains the data and the code supporting the publication "Centimeter-scale nanomechanical resonators with low dissipation".</p>
Mechanical data of rotary shear experiments, temperature measurements, and temperature numerical models for the manuscript: "Mechanical energy dissipation during seismic dynamic weakening in calcite-bearing faults"
<p>All data included in this data repository is ancillary to the manuscript "Energy dissipation during dynamic weakening in calcite-bearing fault rocks", submitted to Journal of Geophysical Research: Solid Earth. </p><p>The data consists in time series of high velocity friction experiments run with SHIVA (INGV, Rome), time series acquired from a two-color pyrometer (UC3M), the synchronization of the two, and numerical models. The data format is .mat, proprietary to Matlab, but they can be easily accessed with Python (see <a href="https://docs.scipy.org/doc/scipy/reference/generated/scipy.io.loadmat.html">link</a>). Each .mat contains vector of the measured variables when opened from Matlab, or dictionaries when opened using the scipy.loadmat() function. </p><p>SHIVA and PYRO red data (calibrated data), fin data (synchronized data), and shivaRED vect data (numerical model data) are included in this data repository in separate folders. Numerical models are grouped in subfolder by type of model (the relation fin data to model is 1:n). We included the scripts to convert SHIVA raw data into SHIVA red data (<a href="https://github.com/aretu/shivaUNIX">link to shivaUNIX</a>), SHIVA and PYRO red data into fin data (/scripts/syncing2021.m), to obtain numerical models from fin data (<a href="https://github.com/aretu/shivaRED">link to shivaRED</a>), and to plot data (/scripts/making plots.ipynb).</p>
Dataset for "Output control of dissipative nonlinear multimode amplifiers via spacetime symmetry mapping"
<p>Note: Simulation codes and instructions are available at <a href="https://github.com/joe851642001/MWAT" target="_new" rel="noopener">GitHub - MWAT</a>.</p>
Dissipation of selected pesticide residues in grapes by using ozone enriched atmosphere
<p>Research article</p>
Modified Frequency Distribution Function in Wave Breaking Dissipation
<p>Code and data supporting manuscript "Modified Frequency Distribution Function in Wave Breaking Dissipation" submitted to JGR Oceans</p>
Dataset for paper "Coherent and Dissipative Coupling in a Magnetomechanical System" by P. Carrara et al.
<p>This dataset complements the publication "Coherent and Dissipative Coupling in a Magnetomechanical System" by Carrara P. et al.</p> <p>The data hierarchy is explained in the files "**_README.txt", which also report relevant metadata.</p>
Figure data for article "Fluctuation-induced Bistability of Fermionic Atoms Coupled to a Dissipative Cavity"
<p>The files contain the data depicted in the figures of the article "Fluctuation-induced Bistability of Fermionic Atoms Coupled to a Dissipative Cavity", arXiv:2409.16035 (2024)</p> <p>The format of the data and to which figure it corresponds is described in the file "read_me_metadata.txt".</p>
Data for "Air pollution deterioration prior to dissipation induced by complex topography: a case study in the Sichuan Basin, southwestern China"
Open the record for dataset details and reuse information.
The dynamics of tropical storm Mulan-induced destruction in 3D structure and dissipation in kinetic energy of an anticyclonic eddy in the northern South China Sea
<p>dataset for The dynamics of tropical storm Mulan-induced destruction in 3D structure and dissipation in kinetic energy of an anticyclonic eddy in the northern South China Sea</p>
Data from: The travel speeds of large animals are limited by their heat-dissipation capacities
<div> <h2>Purpose</h2> <p>This is an extension of the travel speed dataset published by Dyer et al. 2023 in PLOS Biology that includes additional metadata. Data follow the same aggregation to species level by grouping records across unique combinations of study and species while preserving variation across important categorical covariates (listed below). </p> </div> <div> <h2>Scripts</h2> <ul> <li><code>scripts/modelling/fit_model.R</code>: Fits mechanistic locomotion models to aggregated data using RStan. Plots predictions and outputs parameter estimates of competing models.</li> </ul> </div> <div> <h2>Data Columns</h2> <p>Each heading below describes a column of the travel speed dataset.</p> <p><strong>move_speed_ref</strong>: Unique reference number for each study reporting animal travel speed. The corresponding references are contained within the supplementary reference list file.</p> <p><strong>move_mass_ref</strong>: Unique reference number for each study reporting animal body mass. The corresponding references are contained within the supplementary reference list file.</p> <p><strong>scientific_name</strong>: Scientific name of the species according to the taxonomy of the Global Biodiversity Information Facility (accessed via GBIF.org during 2022). A small number of studies that report only the genus name or common name of the species are reported in our dataset as <em>Genus sp.</em> (e.g. <em>Gazella sp.</em>). One study, which estimated the travel speed and body mass of an unknown species of mouse via camera traps, is reported in our dataset as <em>Amazon mouse sp.</em></p> <p><strong>move_movement_mode</strong>: Categorical value indicating whether the reported travel speed corresponds to an animal engaged in flying, running, or swimming. This allows species to be accommodated that are capable of multi-modal locomotion (e.g. northern elephant seal, <em>Mirounga angustirostris</em>).</p> <p><strong>taxon_group</strong>: Categorical value indicating membership to one of eight animal groups (amphibian, arthropod, cnidarian, bird, fish, mammal, mollusc, reptile).</p> <p><strong>thermo_reg</strong>: Categorical value indicating thermoregulatory strategy, i.e. the contribution of metabolic heat production to the maintenance of core body temperature during rest:<br>- <strong>ectotherm</strong>: negligible contribution, body temperature matches ambient temperature<br>- <strong>mesotherm</strong>: weak contribution with partial thermal stability, as in tunas, lamnid sharks, and leatherback sea turtles<br>- <strong>endotherm</strong>: strong contribution with metabolic stability even when ambient temperatures are significantly below body temperature</p> <p><strong>move_medium</strong>: Categorical value indicating whether the animal’s travel speed was measured during locomotion within the terrestrial realm (air) or aquatic realm (water).</p> <p><strong>move_speed_method</strong>: Categorical value indicating whether travel speed was estimated directly (i.e. instantaneously via direct observation in real-time, animal-attached speedometer, or video recording) or indirectly from higher-resolution telemetry data (i.e. from changes in an animal’s spatial coordinates at intervals < 30 minutes apart).</p> <p><strong>move_study_condition</strong>: Categorical value indicating whether travel speed was estimated under natural field conditions or under a controlled laboratory setting such as within an aquarium or mesocosm. Travel speeds of the smallest animals (e.g. arthropods) can only feasibly be estimated within a controlled setting.</p> <p><strong>move_avgspeed_value</strong>: Categorical value indicating whether average travel speed was reported as a mean or median within the original study.</p> <p><strong>move_bodymass_kg</strong>: Continuous value indicating the average adult body mass (units: kilograms) of the species. In cases where the study’s reference numbers <code>move_speed_ref</code> and <code>move_mass_ref</code> differ, we referred to secondary literature sources to assign the average adult body mass of the species. In cases where only body length was given, we used published allometric equations to estimate the wet body mass.</p> <p><strong>move_avgspeed_ms</strong>: Continuous value indicating the average (geometric mean) travel speed (units: metres per second) of the species reported within the study.</p> <p><strong>move_speed_source</strong>: Categorical value indicating the sampling level at which travel speed measurements were reported within the study.<br>- <code>indv</code>: travel speed reported from an individual animal. - <code>avgs</code>: travel speed reported as an average (mean or median) across multiple individuals from the same species.</p> <p><strong>move_individuals_n</strong>: Integer value indicating the number of individual animals from which travel speed measurements were obtained within the study.</p> <p><strong>move_measurements_n</strong>: Integer value indicating the number of travel speed measurements taken across individuals within the study. This value can exceed <code>move_individuals_n</code> when repeated measurements were made from the same individuals.</p> <p><strong>move_ygeo_min</strong>: Continuous value indicating the minimum reported latitude (decimal degrees) of the study location(s).</p> <p><strong>move_ygeo_max</strong>: Continuous value indicating the maximum reported latitude (decimal degrees) of the study location(s).</p> <p><strong>move_xgeo_min</strong>: Continuous value indicating the minimum reported longitude (decimal degrees) of the study location(s).</p> <p><strong>move_xgeo_max</strong>: Continuous value indicating the maximum reported longitude (decimal degrees) of the study location(s).</p> <p><strong>move_date_min</strong>: Earliest reported date of data collection within the study (format: YYYY-MM-DD).</p> <p><strong>move_date_max</strong>: Latest reported date of data collection within the study (format: YYYY-MM-DD).</p> <p><strong>move_study_country</strong>: Country in which the study was conducted.</p> <p><strong>move_study_location</strong>: A description of the study location.</p> </div> <div> <h2>Version Notes</h2> <p>This updated version of the travel speed dataset extends the aggregated dataset published in <br>Dyer et al. 2023, PLOS Biology<strong> </strong>through the inclusion of additional metadata fields.<br>Metadata follow the same aggregation to species level by grouping records across unique combinations of study and species while preserving variation across important categorical covariates (listed above).</p> <div> <h3>Key Updates</h3> <ul> <li><strong>Added spatial metadata</strong>: minimum and maximum latitude/longitude, study country, and study location.</li> <li><strong>Added temporal metadata</strong>: earliest and latest reported dates of data collection (YYYY-MM-DD).</li> <li><strong>Refined sampling information</strong>: number of individuals (<code>move_individuals_n</code>) and number of speed measurements (<code>move_measurements_n</code>) are now reported, where available.</li> </ul> </div> </div>
Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics
<p>This is training dataset for our publication with title "Physics-Informed Neural Networks and Beyond: Enforcing Physical Constraints in Quantum Dissipative Dynamics" at arXiv https://doi.org/10.48550/arXiv.2404.14021</p>
Body Heat Content and Dissipation in Obese and Normal Weight Adults
ClinicalTrials.gov study NCT00266500. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Data from: Testing the heat dissipation limit theory in a breeding passerine
Open the record for dataset details and reuse information.
Pressure Correction in the Calibration of Heat Dissipation Sensors
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Heat dissipation behaviour of birds in seasonally hot, arid-zones: are there global patterns?
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Adrenergic-induced ERK3 pathway drives lipolysis and suppresses energy dissipation
GEO Series GSE142424. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Protein kinase D1 deletion in adipocytes enhances energy dissipation and protects against adiposity
GEO Series GSE104797. Mus musculus. 8 samples. Type: Expression profiling by high throughput sequencing.
Region-specific pathogenesis and dissipation of intraregional heterogeneity in cerebellar neurodegeneration
GEO Series GSE255633. Mus musculus. 49 samples. Type: Expression profiling by high throughput sequencing.
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International Brain Laboratory public data
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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.