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opencc-by-4.0May 2023View details →

Comparing thermal regime stages along a small Yakutian fluvial valley with point scale measurements, thermal modeling and near surface geophysics

<ul> <li>Dataset associated with the paper :</li> </ul> "Comparing thermal regime stages along a small Yakutian fluvial valley with point scale measurements, thermal modeling and near surface geophysics" Emmanuel Léger 1 , Albane Saintenoy 1 ,Christophe Grenier 2,&dagger; , Antoine Séjourné1 , Eric Pohl2,3 , Frédéric Bouchard4, Marc Pessel1 , Kirill Bazhin5 , Kencheeri Danilov5, François Costard1 , Claude Mugler2 , Alexander Fedorov5 , and Ivan Khristoforov5 and Pavel Konstantinov5 1 Laboratoire Geosciences Paris-Saclay, Université Paris-Saclay, CNRS, GEOPS, 91405, Orsay, France. 2 Laboratoire des Sciences du Climat et de L’Environnement (LSCE), CEA CNRS UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France 3 Department of Geosciences, University of Fribourg, Fribourg, Switzerland 4 Universite de Sherbrooke Département de géomatique appliquée, Sherbrooke, QC, CA 5 Melnikov Permafrost Institute, Yakutsk, Yakutia.

openMay 2023View details →

Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)

THE PUBLICATION The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice. THE DATA This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below. *** PLEASE NOTE *** This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com.  <table> <tbody> <tr> <td>Citation</td> <td>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</td> <td>E.g. 1</td> </tr> <tr> <td>First Author</td> <td>Surname of the main author, for reference purposes only.</td> <td>E.g. Al-Atrash</td> </tr> <tr> <td>Publication</td> <td>Publication year</td> <td>E.g. 2020</td> </tr> <tr> <td>Dependent A</td> <td>List of variables used to measure thermal perception</td> <td>E.g. Neutral  temperature/ Thermal sensation</td> </tr> <tr> <td>Dependent B</td> <td>Scale used to measure each dependent variable</td> <td> </td> </tr> <tr> <td>Interaction A</td> <td>List of interaction effect(s) included in the explanatory/predictive model(s) </td> <td>E.g. Thermal and age/ Thermal and acoustical and personality</td> </tr> <tr> <td>Interaction B</td> <td>Is/are the effect(s) statistically significant?</td> <td>E.g. yes/ no/ (unknown)</td> </tr> <tr> <td>Crossed A</td> <td>List of crossed effect(s) included in the explanatory/predictive model(s) </td> <td>E.g. Acoustical/ Personality/ Age</td> </tr> <tr> <td>Crossed B</td> <td> </td> <td>*Note: Temperature is a main effect and is not included in the list</td> </tr> <tr> <td>Explanatory A</td> <td>Type of explanatory model</td> <td>E.g. Observation/ Statistical/ N/A</td> </tr> <tr> <td>Explanatory B</td> <td>Description of the explanatory model</td> <td>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</td> </tr> <tr> <td>Predictive A</td> <td>Does the article include a predictive model?</td> <td>E.g. yes/ no</td> </tr> <tr> <td>Predictive B</td> <td>Type of predictive algorithm</td> <td>E.g. Logistic regression/ N/A</td> </tr> <tr> <td>Predictive C</td> <td>Description or formulation of the predictive model</td> <td>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</td> </tr> <tr> <td>Performance</td> <td>Reported predictive performance</td> <td>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</td> </tr> <tr> <td>Location</td> <td>City in which the measurements take place</td> <td>E.g. Paris</td> </tr> <tr> <td>Period</td> <td>Period over which the measurements take place</td> <td>E.g. Jan-Feb 2020</td> </tr> <tr> <td>Start time</td> <td>Time of day at which the measurements begin</td> <td>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</td> </tr> <tr> <td>Study type</td> <td>Type of building and whether the experimental conditions are controlled by the experiment leader</td> <td>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</td> </tr> <tr> <td>Building layout</td> <td>Building layout</td> <td>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</td> </tr> <tr> <td>Exposure</td> <td>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</td> <td>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</td> </tr> <tr> <td>Number of buildings/chambers</td> <td>Number of different locations used for conducting measurements</td> <td>E.g. 1</td> </tr> <tr> <td>Number of participants</td> <td>Number of individuals who take part in each experiment</td> <td>*Note: Outliers who are subsequently excluded from the modelling phase are not  included.</td> </tr> <tr> <td>Survey type</td> <td>Description of the type of survey used for subjective measurements</td> <td>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</td> </tr> <tr> <td>Survey content</td> <td>Are the contents of the survey provided in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey source</td> <td>Is/are the source(s) of the survey items mentioned in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey reliability</td> <td>Is the reliability of the survey items reported in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey duration</td> <td>Is the survey duration reported in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Context A</td> <td>Overview of the contextual information provided by the authors</td> <td>E.g. Room layout/ Room dimennsions</td> </tr> <tr> <td>Context B</td> <td>Qualitative/quantitative contextual information</td> <td>E.g. Figure containing room layout/ 3m x 3m x 5m</td> </tr> <tr> <td>Contextual variables A</td> <td>List of contextual variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Contextual variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. figure</td> </tr> <tr> <td>Social variables A</td> <td>List of social variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Social variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. [1,2,3,4,5]</td> </tr> <tr> <td>Personal variables A</td> <td>List of contextual variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Personal variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. [red, blue]</td> </tr> <tr> <td>Physical variables A</td> <td>List of physical variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Physical variables B</td> <td>Range of values included in the experiment and their respective units</td> <td>E.g. dB(A)</td> </tr> <tr> <td>Full-factorial</td> <td>Is/are the experiment(s) full-factorial?</td> <td>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</td> </tr> <tr> <td>(Participant) Control</td> <td>Do participants have control over one or more experimental conditions?</td> <td>E.g. Yes/ No</td> </tr> <tr> <td>With/between subjects</td> <td>Are the experimental conditions shared between or within the participants?</td> <td>E.g. w/ b</td> </tr> <tr> <td>Fixed variables A</td> <td>List of variables reported as constant during the measurements</td> <td>E.g. Relative humidity/ Metabolic rate</td> </tr> <tr> <td>Fixed variables A</td> <td>(Range of) values and their respective units.</td> <td>E.g. 30-40%/ 1.2 met</td> </tr> <tr> <td>Summary</td> <td>Description of the research outome (outcome of the explanatory and/or predictive modelling)</td> <td>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</td> </tr> <tr> <td>Evaluation</td> <td>Are the participants invited to evaluate their experience once the experiment has been completed? </td> <td>E.g. Yes/ no</td> </tr> </tbody> </table> Note: The data in v1.1.0 has not yet been optimised for analytics.

opencc-by-4.0May 2023View details →

Data & scripts - The effect of temperature-dependent material properties on simple thermal models of subduction zones

Data and scripts used in Van Zelst et al. (2023, Solid Earth): 'The effect of temperature-dependent material properties on simple thermal models of subduction zones'. Includes the data and figures for the benchmark of Van Keken et al. (2008) plus the results from our code xFieldstone; processing and visualisation scripts; raw and final figures; and all results for all model runs used in the publication (as listed in Table 1 in the paper).

opencc-by-4.0May 2023View details →

Products and Models for "A broadband thermal emission spectrum of the ultra-hot Jupiter WASP-18b"

Close-in giant exoplanets with temperatures greater than 2,000 K (“ultra-hot Jupiters”) have been the subject of extensive efforts to determine their atmospheric properties using thermal emission measurements from the Hubble and Spitzer Space Telescopes1–3. However, previous studies have yielded inconsistent results because the small sizes of the spectral features and the limited information content of the data resulted in high sensitivity to the varying assumptions made in the treatment of instrument systematics and the atmospheric retrieval analysis3–12. Here we present a dayside thermal emission spectrum of the ultra-hot Jupiter WASP-18b obtained with the NIRISS13 instrument on JWST. The data span 0.85 to 2.85 μm in wavelength at an average resolving power of 400 and exhibit minimal systematics. The spectrum shows three water emission features (at <6σ confidence) and evidence for optical opacity, possibly due to H-, TiO, and VO (combined significance of 3.8σ). Models that fit the data require a thermal inversion, molecular dissociation as predicted by chemical equilibrium, a solar heavy-element abundance (“metallicity”, M/H = 1.03-0.51+1.11 x solar), and a carbon-to-oxygen (C/O) ratio less than unity. The data also yield a dayside brightness temperature map, which shows a peak in temperature near the sub-stellar point that decreases steeply and symmetrically with longitude toward the terminators.

publicJul 2017View details →

Data from: Modelling and simulation of a thermally induced optical transparency in a dual micro-ring resonator

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publicMay 2020View details →

Data from: Adaptive thermal plasticity enhances sperm and egg performance in a model insect

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publicMay 2024View details →

Dataset and scripts from: Predicting organismal response to marine heatwaves using dynamic thermal tolerance landscape models

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publicSep 2024View details →

Experimental evolution reveals that males evolving within warmer thermal regimes improve reproductive performance under heatwave conditions in a model insect

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publicJun 2024View details →

Data from: Thermal plasticity in protective wing pigmentation is modulated by genotype and food availability in an insect model of seasonal polyphenism

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openbsd-2-clause-netbsdFeb 2020View details →

A zero to four parameter instationary thermal-block-type benchmark model for parametric model order reduction

We specify a new benchmark for parametric model order reduction that is scalable both in degrees of freedom as well as parameter dimension.

opencc-by-4.0Sep 2020View details →

Scott-E surface roughness and thermal model results

Supporting Information for "Geomorphic evidence for the presence of ice deposits in lunar permanently shadowed regions" by Moon et al.  Dataset S1 – S4. Dataset S1. ds01_ElevRough.asc is an ASCII raster file for elevation-derived roughness [unitless]. Dataset S2. ds02_BriRough.asc is an ASCII raster file for brightness-derived roughness [DN/m]. Dataset S3. ds03_Tmax.asc is an ASCII raster file for modeled annual maximum temperature [K]. Dataset S4. ds04_Dice.asc is an ASCII raster file for modeled depth to thermally stable water ice [m]. All maps are in south polar stereographic projection.

opencc-by-4.0Jan 2024View details →

Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"

Databases generated for manuscript "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures" There are two zip files containing generated databases: The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location.  The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin.

opencc-by-4.0Aug 2020View details →

Thermal modeling of a high-energy prismatic lithium-ion battery cell and module based on a new thermal characterization methodology

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opencc-by-4.0Oct 2024View details →

Model Catalytic Studies on the Thermal Dehydrogenation of the Benzaldehyde/Cyclohexylmethanol LOHC System on Pt(111)

Primary data, meta data, and corresponding lists of figures & tables are included.

opencc-by-4.0Nov 2021View details →

Landau collision operator in the CUDA programming model applied to thermal quench plasmas

Reproducability data for IPDPS paper rebuttal.

opencc-by-4.0Jun 2022View details →

Modeling Thermal Emission Under Lunar Surface Environmental Conditions

Codes and data required to reproduce results from Prem et al. (2022), Modeling Thermal Emission Under Lunar Surface Environmental Conditions, Planetary Science Journal (https://doi.org/10.3847/PSJ/ac7ced). The file flowchart.pdf contains an overview of the workflow to model ambient and anisothermal thermal emission spectra using the codes contained in codes.zip. The file lab_spectra.xlsx contains the laboratory spectra used in the publication, together with citation information. Please feel free to contact lead author Dr. Parvathy Prem (parvathy.prem@jhuapl.edu) with any questions.

opencc-by-4.0Oct 2022View details →

Data for: A general framework for modelling thermal and hydric constraints on eggs developing in soil

Data files to reproduce all the figures and appendices of "A general framework for modelling thermal and hydric constraints on eggs developing in soil".

opencc-by-4.0Oct 2022View details →

Dataset for modelling the apparent von Kármán parameter in thermally and sedimentologically stratified air flows

The dataset is used to support the article titled as "Modelling the apparent von Kármán parameter in thermally and sedimentologically stratified air flows" by the same authors. There are two spreadsheets in the datasheet.xlsx file. The first one is the 5-min block averaged data derived from the measurements by a meterologoical tower. The second one is the sand transport and saltation sensor data derived from a number of 5-10 min measurements by sand trap stacks and a Sensit. 1. Description for Spreadsheet "Tower Data":  Date: Data collection date Run_number: the run number counting from the start of the experiment. Measurements were taken for 5 min for each run. UA1: resultant wind speed (m/s) measured by a 2D ultrasonic anemometer at 0.19 m above the bed UA2: resultant wind speed (m/s) measured by a 2D ultrasonic anemometer at 0.49 m above the bed UA3: resultant wind speed (m/s) measured by a 2D ultrasonic anemometer at 0.76 m above the bed UA: resultant wind speed (m/s) measured by a 3D ultrasonic anemometer at 1.49 m above the bed R_square: the R2 value for log-linear curve fitting between wind speed measured by ultrasonic anemometers and elevation R_f: flux Richardson number Rf u_star: shear velocity u* (m/s) zeta: stability parameter ζ phi_m: dimensionless wind shear  Φm kappa_a: apparent von Kármán parameter κa KE: KE values measured by Sensit Q: Calculated sediment transport rate Q ( g m-1 s-1) using KE-Q relationship described in spreadsheet "Trap data"   2. Description for Spreadsheet "Trap Data": KE: KE values measured by Sensit Q:  sediment transport rate Q ( g m-1 s-1) measured by sand traps The figure with regression equation describes the relationship between KE and Q.

opencc-by-4.0Sep 2019View details →

Evaluation of soil thermal conductivity schemes for use in land surface modeling

The Common Land Model outputs for the paper "Evaluation of soil thermal conductivity schemes for use in land surface modelling".