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136 results for “temperature-dependence”
Temperature-dependent fold-switching mechanism of the circadian clock protein KaiB
<p>Derived data accompanying publication of <em>Temperature-dependent fold-switching mechanism of the circadian clock protein KaiB</em> (Zhang et al., PNAS 2024).</p> <p> </p> <p>This dataset contains data for fold-switching of KaiB from simulations performed using the Upside coarse-grained model (Jumper et al. PLoS Comput. Bio 2017). Files contained include collective variables, kinetic quantities (committors), and initial structures used to seed unbiased simulations. These data should be sufficient recreate the analysis shown in the associated publicaion. Raw trajectory files have not been deposited due to their size; contact the author (Spencer Guo) to request.</p>
Temperature-dependent Lamb wave signals in highly anisotropic CFRP
<p>The dataset contains signals of propagating Lamb waves in highly anisotropic carbon fibre reinforced polymer (CFRP). The reinforcement is unidirectional along 0 deg. A detailed description of the material and its parameters is given in [1]. The arrangement of piezoelectric actuator A and sensors S1-S7 is shown in figure "plate_angular_pzt_arrangement_50x50.png". Sensors are placed at propagation angles from 0 deg to 90 deg with a step of 15 deg. It should be noted that two piezoelectric transducers bonded to both sides of the plate were used as the actuator. It allowed for exciting Lamb waves with dominant A0 and S0 modes, respectively. Hence, there are two respective zip files with data.</p> <p>The following parameters were used during measurements:</p> <ul> <li>Temperatures: T=[50,40,30,20,10,0,-10,-20,-30,-40,-50];</li> <li>Number of cycles in Hann windowed signals: no_of_cycles=[2,2.5,3];</li> <li>Carrier frequencies of excitation signals [kHz]: frequencies=[20:10:250];</li> <li>Number of averages: 50;</li> <li>Sampling frequency: 10 MHz.</li> </ul> <p>The following equipment was used in the experiment:</p> <ul> <li>Environmental chamber by Angelantoni Test Technologies, model MyDiscovery 600 C;</li> <li>National Instruments waveform generator PXIe-5413;</li> <li>Krohn-Hite voltage amplifier model 7500;</li> <li>Cedrat Technologies LWDS amplifier (used as a charge amplifier);</li> <li>National Instruments oscilloscope PXIe-5105.</li> </ul> <p>Files in CSV format contain environmental chamber data (temperature programme, actual temperature and humidity over time, etc.). This can be read and plotted in Matlab by running the script “Read_plot_environmental_chamber.m”. There is another file “Read_plot_environmental_chamber_plus_DS18B20_RH20_KROHN_A0.m” in which temperature was registered also by DS18B20 digital sensor. It loops over all measurements so that it can be used also for reading signals from “niscope_avg_waveform.mat” in respective subfolders. In particular, sensor signals are stored in the “niscope_avg_waveform” variable, a matrix of dimensions 8192x7.</p>
Temperature-Dependent THz Properties and Emission of Organic Crystal BNA
<p>This dataset is accompanying the paper "Temperature-Dependent THz Properties and Emission of Organic Crystal BNA"</p> <p><strong>General data acquisition:</strong></p> <p>The data was acquired with a modified Menlo Tera K-15 THz-TDS, consisting of a photoconductive emitter/receiver and four off-axis-parabolic mirrors (OAP). The second and third OAP, focusing and collecting the THz, are with a longer focus length to have enough space for the cryostat (Janis ST-100), which is equipped with 3 mm z-cut quartz windows for entry and exit of the THz beam. The delay line offers delays up to 1600 ps but the range was restricted to cut out the reflections from the z-cut quartz windows. Instead of averaging with Menlo’s own software ScanControl, each single trace is read out. 10 000 traces are saved for each unique measurement condition (crystal orientation, temperature) and saved in a single HDF-5 file. HDF-5 is an efficient (binary), cross-platform data format and can be read easily by i.e. Python or Matlab.</p> <p> </p> <p><strong>The structure is as follows:</strong></p> <p><strong>raw_data </strong></p> <p>The folder raw_data contains four folders. The folder “dark” contains a single file since this is independent of crystal orientation and temperature of the cryostat. For this measurement, the THz beam was blocked but all electronics, selected delay range etc. kept the same, to measure the noise-floor of the system.</p> <p>The folder reference was captured with the cryostat incl. windows, vacuum and crystal holder in place. Even though there should be no change in the transfer function by changing the temperature (due to the large aperture of the crystal holder), we still recorded reference traces for each temperature.</p> <p>The folder “BNA_orientation_001” contains the data with the organic crystal BNA in vertical orientation (<001>).</p> <p>The folder “BNA_orientation_100” contains the data with the organic crystal BNA in horizontal orientation (<100>).</p> <p><strong>averaged_corrected_data</strong></p> <p>The folder “averaged_corrected_data” reduces the large amount of raw data due to averaging. The program “Correct@TDS” (developed in the group of Dr. Romain Peretti, Terahertz Photonics Group @ IEMN - CNRS (UMR 8520), publication in preparation), is used to fit specific correction parameters for the delay, dilatation, amplitude noise and periodic sampling. The mean data is saved for each temperature in a text file called “mean.txt”. The other output of “Correct@TDS” is diagnostic information about the correction parameters and about the standard deviation in frequency- and time-domain.</p> <p><strong>extracted_n_alpha</strong></p> <p>The folder “extracted_n_alpha” contains the refractive index, absorption coefficient and more in a single HDF-5 file, extracted by the program phoeniks (<a href="https://github.com/TimVog/phoeniks">https://github.com/TimVog/phoeniks</a>), which is developed in our group. All results for the paper are saved in the internal folder structure of the HDF-5 file (for crystal orientation and temperature).</p> <p>The folder “nelly” shows the extraction of n and alpha done with Nelly [1] (<a href="https://github.com/YaleTHz/nelly">https://github.com/YaleTHz/nelly</a>) for the vertical orientation, which was used for the supplementary document.</p> <p> </p> <p>[1] Nelly: A User-Friendly and Open-Source Implementation of Tree-Based Complex Refractive Index Analysis for Terahertz Spectroscopy</p> <p>Uriel Tayvah, Jacob A. Spies, Jens Neu, and Charles A. Schmuttenmaer</p> <p>Analytical Chemistry 2021 93 (32), 11243-11250</p> <p>DOI: 10.1021/acs.analchem.1c02132</p> <p> </p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ni-based alloy Inconel IN718
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of Ni-based alloy Inconel IN718 between room temperature and 800 °C in an additively manufactured variant (laser powder bed fusion, PBF‑LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of Ti-6Al-4V
<p>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of titanium alloy Ti-6Al-4V between room temperature and 400 °C in an additively manufactured variant (laser-based directed energy deposition with powder as feedstock, DED-LB/M) and from a conventional process route (hot rolled bar). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</p>
BAM reference data: Temperature-dependent Young's and shear modulus data for additively and conventionally manufactured variants of austenitic stainless steel AISI 316L
<p><span>This BAM reference dataset reports the elastic properties (Young's modulus, shear modulus) of austenitic stainless steel AISI 316L between room temperature and 900 °C in an additively manufactured variant (laser powder bed fusion, PBF</span><span>‑</span><span>LB/M) and from a conventional process route (hot rolled sheet). It was generated in an accredited test laboratory using calibrated measuring equipment. The calibrations meet the requirements of the test procedure and are metrologically traceable. The dataset was audited as BAM reference data.</span></p>
Temperature-dependent evolutionary speed shapes the evolution of biodiversity patterns across tetrapod radiations
<p>Biodiversity varies predictably with environmental energy around the globe, but the underlying mechanisms remain incompletely understood. The evolutionary speed hypothesis predicts that environmental energy shapes variation in speciation rates through temperature- or life history-dependent rates of evolution. To test whether variation in evolutionary speed can explain the relationship between energy and biodiversity in birds, mammals, amphibians, and reptiles, we simulated diversification over 65 million years of geological and climatic change with a spatially explicit eco-evolutionary simulation model. We modeled four distinct evolutionary scenarios in which speciation-completion rates were dependent on temperature (M1), life history (M2), temperature and life history (M3), or were independent of temperature and life-history (M0). To assess the agreement between simulated and empirical data, we performed model selection by fitting supervised machine learning models to multidimensional biodiversity patterns. We show that a model with temperature-dependent rates of speciation (M1) consistently had the strongest support. In contrast to statistical inferences, which showed no general relationships between temperature and speciation rates in tetrapods, we demonstrate how process-based modeling can disentangle the causes behind empirical biodiversity patterns. Our study highlights how environmental energy has played a fundamental role in the evolution of biodiversity over deep time.</p>
Fig. 4 in Temperature-dependent development of Xyleborus glabratus (Coleoptera: Curculionidae: Scolytinae)
Fig. 4. Mean ± SE number of teneral adults per 5 galleries per log encountered every other day in the avocado logs at 4 constant temperatures.
Fig. 3 in Temperature-dependent development of Xyleborus glabratus (Coleoptera: Curculionidae: Scolytinae)
Fig. 3. Mean ± SE number of pupae per 5 galleries per log encountered every other day in the avocado logs at 4 constant temperatures.
Fig. 1 in Temperature-dependent development of Xyleborus glabratus (Coleoptera: Curculionidae: Scolytinae)
Fig. 1. Mean ± SE number of eggs per 5 galleries per log observed every other day in the avocado logs at 4 constant temperatures.
A quantitative model of temperature-dependent diapause progression
<p>R-code and data for reproducing the results of von Schmalensee, Süess et al. 2024 <em>PNAS</em></p> <p>Scripts will run/load the models and save the raw figures in the figure folder.</p> <p>Remember to install the required packages (see 'functions_packages.R' in the functions folder).</p> <p>R version 4.3.3 and brms version 2.21.0 was used.</p>
Data from: Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing
<p>This dataset contains the data for the publication " Temperature-dependent mechanical behavior of aluminum AM structures generated via multi-layer friction surfacing "</p>
Tailoring magnetic hysteresis of Fe-Ni additive manufactured permalloy via multiphysics-multiscale simulations: Temperature-dependent parameters, thermodynamic database, results, and utilities
<p>This dataset contains temperature-dependent parameters and thermodynamic database, supplementary data and utilities of the publication "Tailoring magnetic hysteresis of additive manufactured Fe-Ni permalloy via multiphysics-multiscale simulations of process-property relationships" (<a href="http://doi.org/10.1038/s41524-023-01058-9">Yang et al., 2023</a>).</p> <p>We performed non-isothermal phase-field simulations of SLS process of the Fe<sub>21.5</sub>Ni<sub>78.5</sub> permalloy and subsequential mesoscopic thermo-elasto-plastic calculations and nanoscopic chemical order-disorder (<span>\(\gamma/\gamma'\)</span>) transition simulations as well as micromagnetic hysteresis calculations on nanostructures. Temperature-dependent parameters are employed. We then investigate the dependence of the fusion zone size, the residual stress and plastic strain, and the magnetic hysteresis of AM-produced Fe<sub>21.5</sub>Ni<sub>78.5 </sub>on beam power and scan speed.</p> <p>This dataset contains:</p> <ul> <li><em>feni_cac.tdb</em>: Thermodynamic database of the Fe-Ni binary system based on <a href="https://doi.org/10.1016/j.intermet.2010.02.026">Cacciamani et al., 2010</a></li> <li><em>average_values.csv</em>: Average quantities for creating the contours in Fig. 6a, 6b, 7a, 7b, 8a, and Supp. Fig. 10a, 10b.</li> <li><em>mesostructures.zip</em>: Containing resampled mesostructures from SLS single scan simulations (final timestep) with associated temperature, stress, and strain evolution. Nodal values are explained in Table 1. Naming pattern is <ul> <li>SLS-TEP__<power>-<scan_speed>__.e</li> </ul> </li> <li><em>parameters.zip</em>: Containing temperature-dependent parameters for performing SLS simulations and thermo-elasto-plastic calculations with fine (1K) temperature increments. The same temperature-dependent parameters with coarse temperature increments are already listed as Supp. Table 1, 2.</li> <li><em>sampled_point_data.zip</em>: Containing mechanical quantities on sampled points and corresponding results of nanoscopic <span>\(\gamma'\)</span> phase fraction (<span>\(\Psi_{\gamma'}\)</span>) and magnetic coercivity <span>\(H_\mathrm{c}\)</span>. Naming pattern is <ul> <li>mech__<power>-<scan_speed>__.csv</li> <li>Psi__<power>-<scan_speed>__.csv</li> <li>Hc__<power>-<scan_speed>__.csv</li> </ul> </li> <li><em>utilities.zip</em>: Containing Python utilities to perform calculations of free energy density and related thermodynamic quantities, extracting parameters from <em>feni_cac.tdb. </em><br><strong>Notice: </strong><a href="https://pycalphad.org/docs/latest/">pyCALPHAD</a> (ver 0.8.4) is requested for performing the Python utilities.</li> </ul> <p>Table 1. Nodal values in an exodus file Nodal value name Symbol Meaning Unit T <span>\(T\)</span> Normalized Temperature by <span>\(T_\mathrm{M}\)</span> - c <span>\(\rho\)</span> Substance order parameter - pb <span>\(\xi\)</span> Fusion zone indicator - eps (eps_11, eps_12, eps_13, eps_22, eps_23, eps_33) <span>\({\varepsilon}\)</span> Strain - epsp (epsp_11, epsp_12, epsp_13, epsp_22, epsp_23, epsp_33) <span>\({\varepsilon}_\mathrm{pl}\)</span> Plastic Strain - peeq <span>\(p_\mathrm{e}\)</span> Accumulated plastic strain - sigma (sigma_11, sigma_12, sigma_13, sigma_22, sigma_23, sigma_33) <span>\({\sigma}\)</span> Stress MPa vonmises <span>\(\sigma_\mathrm{e}\)</span> von Mises stress MPa u (u_X, u_Y, u_Z) <span>\(\mathbf{u}\)</span> Displacement μm</p> <p> </p> <p><strong>Notice</strong>: The raw transient outputs are not cured in this dataset due to the vast file size. Please contact the authors to acquire related files/utilities.</p>
Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations"
<p>Model output from "The chance of freezing – a conceptional study to parameterize temperature-dependent freezing by including randomness of ice-nucleating particle concentrations", accepted for publication in Atmospheric Chemistry and Physics, 2023, same authors.<br> The simulations were done using MIMICA version4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
Data from: The thermal sensitivity of growth and survival in a wild reptile with temperature-dependent sex determination
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Temperature-dependent evolutionary speed shapes the evolution of biodiversity patterns across tetrapod radiations
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Adult sex-ratio bias does not lead to detectable adaptive offspring sex allocation via nest-site choice in a turtle with temperature-dependent sex determination
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Temperature-dependent Raman, FTIR data and breakdown temperature of phlogopite
<p>This dataset contains all new data corresponding to figures in the manuscript and the supporting information, including temperature-dependent FTIR, Raman data, and breakdown temperature from previous studies and this study.</p>
Spectral data presented in Hinrichs J L, Lucey P G. Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil.
<p>In this dataset, we present the spectral data in paper: Hinrichs, J. L., & Lucey, P. G. (2002). Temperature-dependent near-infrared spectral properties of minerals, meteorites, and lunar soil. <em>Icarus</em>, <em>155</em>(1), 169-180.</p>
Supplementary material: Modeling and Compensating Temperature-dependent Non-uniformity Noise in IR Microbolometer Cameras
<p><strong>Abstract</strong>: Images rendered by uncooled microbolometer-based infrared (IR) cameras are severely degraded by the spatial non-uniformity (NU) noise. The NU noise imposes a fixed-pattern over the true images, and the intensity of the pattern changes with time due to the temperature instability of such cameras. In this paper, we present a novel model and a compensation algorithm for the spatial NU noise and its temperature-dependent variations. The model separates the NU noise into two components: a constant term, which corresponds to a set of NU parameters determining the spatial structure of the noise, and a dynamic term, which scales linearly with the fluctuations of the temperature surrounding the array of microbolometers. We use a black-body radiator and samples of the temperature surrounding the IR array to offline characterize both the constant and the temperature-dependent NU noise parameters. Next, the temperature-dependent variations are estimated online using both a spatially uniform Hammerstein-Wiener estimator and a pixelwise least mean squares (LMS) estimator. We compensate for the NU noise in IR images from two long-wave IR cameras. Results show an excellent non-uniformity correction performance and a root mean square error of less than 0.25◦C, when array’s temperature varies approximately 15◦C.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.