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123 results for “Thermal Model”
Naturally segregating variants contributing to thermal tolerance in a D. melanogaster model system.
<p>Main_Incapacitation.zip and Incapacitation_founders.zip contain raw thermal tolerance scores for individuals measured within the heat box. Each folder is labeled with the RIL or founder ID and replicates within each file are labeled with group numbers. </p> <p>RNAi_files_to_tar.txt contains the metadata for the Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip.</p> <p>Combined_tracks_RNAi_1.Rds.zip and Combined_tracks_RNAi_2.Rds.zip. contains raw data for RNAi lines measured on the heat plate. </p> <p>plate_finder-kinglab-2021-05-02.zip contains the DeepLabCut model used for finding the corners of aluminum mounting plate used to hold the fly vials for thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use.</p> <p>fly_tracker_2-king-2021-09-27.zip contains the DeepLabCut model used for tracking individual flies during thermal sensitivity testing. This directory contains the training data as well as the trained and evaluated model. No retraining should be necessary for use.</p> <p>fly_tracker_batch.py is a python (>= 3.0) script that processes the raw movie files collected via the Raspberry Pi. This script uses the plate finder DeepLabCut model to find the corners of the plate, rotate and crop the images, and output movie files for individual flies. It then uses the fly tracker DeepLabCut model to track the flies and output the data for subsequent processing in R.</p>
Mars Thermal + Non-Thermal Modeling Data from figures
<p>This folder contains all the values from all the figures present in the paper titled "Evidence of Non-thermal Hydrogen in the Exosphere of Mars Resulting in Enhanced Water Loss", by Bhattacharyya et al.</p>
Data from: Alternative forms of brook trout nest site selection alter modeled offspring thermal experience and emergence phenology in groundwater-influenced streambeds
Open the record for dataset details and reuse information.
Data from: Determining critical periods for thermal acclimatisation using a Distributed Lag Non-linear Modelling approach
Open the record for dataset details and reuse information.
Slab transport of fluids to deep focus earthquake depths - thermal modeling constraints and evidence from diamonds
<p>This data set contains earthquake data and thermal models of subduction zones used in Shirey, S. B., Wagner, L. S., Walter, M. J., Pearson, D. G., & van Keken, P. E., "Slab transport of fluids to deep focus earthquake depths - thermal modeling constraints and evidence from diamonds", submitted to AGU Advances.</p> <p>There are four zip files:<br> 1) Events.zip contains the earthquake location data;<br> 2) PTeq.zip contains the estimated pressure and temperature in the EQ locations as projected onto slab top and Moho<br> 3) ThermalModels.zip contains the temperature along paths parallel to the slab top for each subduction zone<br> 4) ThermalModels_vtu.zip contains the temperature on the full computational grid<br> <br> See the README files for information on the data formats for 1-3.</p>
Data from: Modelling and simulation of a thermally induced optical transparency in a dual micro-ring resonator
This paper introduces the simulation and modelling of a novel dual micro-ring resonator. The geometric configuration of the resonators, and the implementation of a simulated broadband excitation source, results in the realization of optical transparencies in the combined through port output spectrum. The 130 nm silicon on insulator rib fabrication process is adopted for the simulation of the dual-ring configuration. Two titanium nitride heaters are positioned over the coupling regions of the resonators, which can be operated independently, to control the spectral position of the optical transparency. A third heater, centrally located above the dual resonator rings, can be used to red shift the entire spectrum to a required reference resonant wavelength. The free spectral range with no heater currents applied is 4.29 nm. For a simulated heater current of 7 mA (55.7 mW heater power) applied to one of the through coupling heaters, the optical transparency exhibits a red shift of 1.79 nm from the reference resonant wavelength. The ring-to-ring separation of approximately 900 nm means that it can be assumed that there is a zero ring-to-ring coupling field in this model. This novel arrangement has potential applications as a gas mass airflow sensor or a gas species identification sensor.
Telemetry data from: Realized thermal niche approach eliminates temperature bias in 3 bioenergetic model estimates
<h4>Raw data for Ivanova et al paper in Ecology and Evolution</h4><p>Datafile is an .rds file.</p>
Thermal modeling of subduction zones with prescribed and evolving 2D and 3D slab geometries data
<p>Deforming subduction zone finite element model temperature, velocity, surface and flux field data as reported in the work:</p> <p>N. Sime, C. R. Wilson and P. E. van Keken<br> Thermal modeling of subduction zones with prescribed and evolving 2D and 3D slab geometries.</p>
Data and Models of Thermal Density Currents Research in Daheiting Reservoir
<p><span>This database includes the field measurement results from cruise surveys, profile observations, and benthic observations in the Daheiting Reservoir, along with the retrospective model and the average-year model. These data and models are used to study the oxygenation benefits of thermal density currents and their regulation measures.</span></p>
The model data of Potential Impact of Spring Thermal Forcing over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation
<p>This is the model data of "Potential Impact of Spring Thermal Forcing over the Tibetan Plateau on the Following Winter El Niño–Southern Oscillation". The data includes the last 20 years data of control run (CTRL), the 20 years data of TP–T experiment, and the wave activity flux difference between ensemble means of TP–T and CTRL. 2D is two dimensions. 3D is three dimensions.</p>
Datasets and code of the manuscript 'Insights into the Aerodynamic versus Radiometric Surface Temperature Debate in Thermal-based Evaporation Modeling'
<p>This contains the datasets and codes that were used to generate the results and discussions in the manuscript</p>
Output files of the 3-D thermal modeling in the Alaska subduction zonne
<p>Data products (μ'=0.01275) for ‘Temperature distribution for interplate seismic events in the Alaska subduction zone based on 3-D thermal modeling’ by Kaya Iwamoto, Nobuaki Suenaga and Shoichi Yoshioka.</p>
Mapping Phyllosilicates on the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Model Applications Datasets
<p>We provide laboratory spectra of pure minerals and mineral mixtures and the corresponding metadata. Derived from the laboratory data, we include the PLS model through coefficients. Through the application of the model, we provide the prediction values in volume% for Mg-rich serpentine, cronstedtite, and saponite for the BBD1, EQ3, and TAG datasets.</p>
Mapping Phyllosilicates on the Asteroid Bennu Using Thermal Emission Spectra and Machine Learning Model Applications
<p>We provide the laboratory spectra and metadata that was used to construct the PLS model coefficients. Through the application of the model, we provide the prediction values in volume% for Mg-rich serpentine, cronstedtite, and saponite for the BBD1, EQ3, and TAG datasets.</p>
Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models
<p>Marine heatwaves (MHWs) can cause thermal stress in marine organisms, experienced as extreme 'pulses' against the gradual trend of anthropogenic warming. When thermal stress exceeds organismal capacity to maintain homeostasis, organism survival becomes time-limited and can result in mass mortality events. Current methods of detecting and categorizing MHWs rely on statistical analysis of historic climatology, and do not consider biological effects as a basis of MHW severity. The reemergence of ectotherm thermal tolerance landscape models provides a physiological framework for assessing the lethal effects of MHWs by accounting for both the magnitude and duration of extreme heat events. Here, we used a simulation approach to understand the effects of a suite of MHW profiles on organism survival probability across 1) three thermal tolerance adaptive strategies, 2) interannual temperature variation, and 3) seasonal timing of MHWs. We identified survival isoclines across MHW magnitude and duration where acute (short duration-high magnitude) and chronic (long duration-low magnitude) events had equivalent lethal effects on marine organisms. While most research attention has focused on chronic MHW events, we show similar lethal effects can be experienced by more common but neglected acute marine heat spikes. Critically, a statistical definition of MHWs does not accurately categorize biological mortality. By letting organism responses define the extremeness of a MHW event, we can build a mechanistic understanding of MHW effects from a physiological basis. Organism responses can then be transferred across scales of ecological organization and better predict marine ecosystem shifts to MHWs. </p>
Data set for study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"
<p>Input data for the study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"</p>
Data from: Thermal plasticity in protective wing pigmentation is modulated by genotype and food availability in an insect model of seasonal polyphenism
<p>Phenotypic variation in natural populations results from complex interactions between organisms and their changing environments. The environment shapes both phenotypic frequencies (during adaptation) and organismal phenotypes (through phenotypic plasticity). Developmental plasticity, in particular, refers to the phenomenon whereby an organism's phenotype depends on the environmental conditions during development. It can match phenotype to ecological conditions and help organisms to cope with environmental heterogeneity, including differences between alternating seasons. Experimental studies of developmental plasticity often focus on the impact of individual environmental cues and do not take explicit account of genetic variation. In contrast, natural environments are complex, comprising multiple variables with combined effects that are poorly understood and may vary among genotypes. We investigated the effects of multifactorial environments on the development of the seasonally plastic eyespots of <em>Bicyclus anynana</em> butterflies. Eyespot size depends on developmental temperature and is involved in alternative seasonal strategies for predator avoidance. In nature, both temperature and food availability undergo seasonal fluctuations. However, our understanding of how thermal plasticity in eyespot size varies in response to food availability and across genotypes remains limited. To address this, we investigated the combined effects of temperature (T; two levels: 20°C and 27°C) and food availability (N; two levels: control and limited) during development. We examined their impact on wing and eyespot size in adult males and females from multiple genotypes (G; 28 families). We found evidence of thermal and nutritional plasticity and temperature-by-nutrition interactions (significant TxN) on the size of eyespots in both sexes. Food limitation resulted in relatively smaller eyespots and tempered the effects of temperature. Additionally, we found differences among families for thermal plasticity (significant GxT effects), but not for nutritional plasticity (non-significant GxN effects) nor for the combined effects of temperature and food limitation (non-significant GxTxN effects). Our results reveal the context dependence of thermal plasticity, with the slope of thermal reaction norms varying across genotypes and across nutritional environments. We discuss these results in light of the ecological significance of pigmentation and the value of considering thermal plasticity in studies of the biological impact of climate change.</p>
Data used in: Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city
<p>This dataset contains the processed data and analysis code used in the article:</p> <div>Hall, T.W., Blunn, L., Grimmond, S., McCarroll, N., Merchant, C.J., Morrison, W., et al. (2024) Utility of thermal remote sensing for evaluation of a high-resolution weather model in a city. <em>Quarterly Journal of the Royal Meteorological Society</em>, 150(760), 1771–1790. Available from: <div><a href="https://doi.org/10.1002/qj.4669">https://doi.org/10.1002/qj.4669</a></div> <div> </div> <div>The data consists of LST data, UM100 model output and ancillary files (all netCDF format).</div> <div> </div> <div><em>LST_data</em> contains:</div> </div> <ol> <li>Landsat LST data retrieved in this study (CALC) on four study days, LST data from FORTH and NASA JPL on two days</li> <li>MODIS LST data for 2018-07-15</li> </ol> <p><em>UM100_output</em> contains model output from initial and final runs for the four study days</p> <p>The python script <em>plot.py </em>can be used to generate the figures shown in this article. </p>
Supporting model output for 'Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming'.
<p>Model output used to generate figures in the manuscript 'Empirical stream thermal sensitivities may underestimate stream temperature response to climate warming'.</p>
Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds
<p>The tables in this repository represent the data used in the figures and analyses of the paper "Experimental Validation of Cryobot Thermal Models for the Exploration of Ocean Worlds", published in the Planetary Science Journal. The provided data was collected between 2020 and 2022.</p> <ul> <li>AllResults.xlsx: compilation of tables 4, 5, 6, and 7 on the paper.</li> <li>WarmA1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>WarmA2.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Total Power [W], H6 Power [W], H5 Power [W], H4 Power [W], H3 Power [W], H2 Power [W], H1 Power [W]".</li> <li>CryoA1.xlsx: data presented in tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth Estimation [m], Power [W]".</li> <li>CryoB1.xlsx: data presented in figures 9 and 10, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoB3.xlsx: data presented in figures 6, 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC1.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC2.xlsx: data presented in figures 9, 10, 11, and 12, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Depth [m], Power [W]".</li> <li>CryoC3.xlsx: data presented in figures 9, 10, and 11, and tables 4, 5, 6, and 7 on the paper. The spreadsheet columns are "Time [hrs], Truncated Coarse Depth [m], Power [W]".</li> </ul> <p> </p>
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.