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189 results for “thermodynamics”
Dataset of "Marcus cross relation in the space of H-atom abstraction reactions boosted through off-diagonal thermodynamics"
<p>Proton-coupled electron transfer (PCET) and hydrogen-atom transfer (HAT) reactions play critical roles in biological processes and modern organic synthesis. The kinetics of these processes can align with the principles described in the renowned Marcus cross relation (MCR), a framework initially formulated to describe electron transfer mechanisms. The MCR provides an outstanding link between the kinetics of PCET/HAT reaction involving two distinct reactants and two related auxiliary self-exchange reactions – each between a molecule of one of the reactants and its coupled radical. In this study, we investigate the applicability and limitations of the canonical MCR across over 300 PCET and HAT reactions, providing a comprehensive theoretical analysis. Our findings reveal the need for an enhanced framework that incorporates ‘off-diagonal’ thermodynamic factors—asynchronicity and frustration. Of these factors, asynchronicity, which quantifies the imbalance between the proton vs. electron transfer components of the reaction, is identified as the dominant contributor to the improved predictive accuracy of the MCR. Notably, the incorporation of off-diagonal thermodynamics yields a more pronounced enhancement for HAT reactions than for PCET reactions. This advancement offers a refined theoretical basis for understanding H-atom abstraction mechanisms and underscores the importance of off-diagonal effects in PCET/HAT chemistry.</p>
Thermodynamics data of Alkali Feldspars from FPMD simulations
<p>We computed the thermodynamic properties (pressure, temperature, internal energy, heat capacity) and thermoelastic coefficients (isobaric expansivity, isothermal compressibility, thermal pressure coefficient) on the two alkali feldspars end-members using <em>ab initio</em> molecular dynamics simulation in the 2000-20000 K temperature range and in the 0.5-6 g.cm<sup>-3</sup> density range.</p> <p>Simulations are performed using the Vienna Ab Initio Simulation Package (VASP) (Kresse and Furthmuller, 1996) in the canonical (NVT) ensemble with a timestep of 0.5-2 fs for 5-20 ps depending on the temperature and density. We model the feldspar end-members in a cubic cell containing 208 atoms (16 formula units) and 1024 or 1152 electrons for the Na- and K-feldspars respectively. For simulations at low density we used pseudopotentials which require a lower plane wave energy cutoff, set to 370 eV. For Na-end-member, we also used hard pseudopotentials at high density in order to reduce the overlap of electronic spheres, in particular for Na-Na pairs. The energy cutoff for this set of pseudopotentials is 950 eV. Additional details can be found in the manuscript.</p> <p> </p> <p>There is one dataset for each different composition and set of pseudopotentials used:</p> <ol> <li>kobsch-ds01.txt --> NaAlSi<sub>3</sub>O<sub>8</sub></li> <li>kobsch-ds02.txt --> NaAlSi<sub>3</sub>O<sub>8 </sub>and set of pseudopotentials with a lower plane wave energy cutoff than in 01</li> <li>kobsch-ds03.txt --> NaAlSi<sub>3</sub>O<sub>8</sub> and harder pseudopotentials than in 01</li> <li>kobsch-ds04.txt --> KAlSi<sub>3</sub>O<sub>8</sub></li> <li>kobsch-ds05.txt --> KAlSi<sub>3</sub>O<sub>8</sub> and set of pseudopotentials with a lower plane wave energy cutoff than in 04</li> </ol> <p> </p> <p>Each file present the arithmetic time averages of the pressure (P), temperature (T) and internal energy (E). The standard deviation of the data to the mean is indicated by stdev_X, where X is P, T or E. The statistical error to the mean (err_X) is computed using the blocking method as described by Flyvbjerg and Petersen (1989). The sign '>' is indicated before the value of the statistical error when no convergence was reached during the estimation of this error. The heat capacity Cv is computed using fluctuations on both potential and kinetic energies (Allen and Tildesley, 1989) and its statistical error stdev_Cv is computed using the bootstrap method. </p> <p>We computed the thermoelastic coefficients only for densities (<span class="math-tex">\(\rho\)</span>) above 1.5 g.cm<sup>-3</sup>. The thermal pressure coefficient (TPC = <span class="math-tex">\(\frac{\partial P}{\partial T}\big|_V\)</span>) is the slope of linear fit of P vs. T isochores. The isothermal compressibility (<span class="math-tex">\(\beta = -\frac{1}{\rho} \frac{\partial \rho}{\partial P}\big|_T\)</span>) is computed using central finite differences on our P vs. <span class="math-tex">\(\rho\)</span> isotherms. The isobaric expansivity (<span class="math-tex">\(\alpha = \frac{1}{\rho} \frac{\partial \rho}{\partial T}\big|_P\)</span>) is computed using the previously computed <span class="math-tex">\(\beta\)</span> and TPC.</p>
Additional steady-state simulations of Miocene Antarctic ice-sheet variability using 3D thermodynamical ice-sheet model IMAU-ICE
<div> </div> <div> <div> <div>We supplement our previous dataset (<a href="https://doi.pangaea.de/10.1594/PANGAEA.939114">doi:10.1594/PANGAEA.939114</a>), with six additional steady-state simulations of the Miocene Antarctic ice sheet using the reference Miocene settings.</div> <div> </div> <div>IMAU-ICE was run using a 40x40km grid covering the Antarctic continent. Initial conditions were obtained from reconstructions of the Antarctic bathymetry and bedrock topography pertaining to 23 to 24 million years (Myr) ago (dataset <a href="https://doi.pangaea.de/10.1594/PANGAEA.923109" target="_self">doi:10.1594/PANGAEA.923109</a>). The simulations were forced by climate input data obtained from GENESIS simulations with varying CO2 levels (280 to 840 ppm) and Antarctic ice sheet cover (no ice to a large East-Antarctic ice sheet), and with present-day insolation. We utilized a matrix interpolation method to construct the time-varying climate forcing, based on the prescribed CO2 levels and ice cover simulated by IMAU-ICE.</div> <div> </div> <div>For each simulation, we provide the run script, 1D output variables including CO2 level and the sea level contribution of the Antarctic ice sheet, and 3D output variables including ice thickness, bedrock and surface height, surface mass balance, basal mass balance, ice velocities, and ice temperatures. For more information, please contact L.B. Stap at l.b.stap@uu.nl.</div> </div> </div>
Data for "An autonomous quantum machine to measure the thermodynamic arrow of time"
<p>Numerical simulation data from the article "An autonomous quantum machine to measure the thermodynamic arrow of time"</p> <p>J. Monsel, C. Elouard, A. Auffèves <em>npj Quantum Inf</em> <strong>4</strong>, 59 (2018). <a href="https://doi.org/10.1038/s41534-018-0109-8" target="_blank" rel="noopener">https://doi.org/10.1038/s41534-018-0109-8</a></p> <p>See the jupyter notebook for the data analysis and figures.</p> <p>The code to perform the numerical simulations is given in the repository <a href="https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system" target="_blank" rel="noopener">https://gitlab.com/juliette.monsel/jarzynski-equality-in-optomechanical-system</a>.</p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Ideal gas thermodynamic functions for NO from the total partition sum and its moments
<p><span><span>to be published in the Journal of Physical and Chemical Reference Data</span></span></p> <p><span><span> </span>The total internal partition sum, <em>Q</em><sub>int</sub>(<em>T</em>), and the translational partition sum, <em>Q</em><sub>trans</sub>(<em>T</em>), were computed for six isotopologues of NO: <sup>14</sup>N<sup>16</sup>O,<sup> 15</sup>N<sup>16</sup>O, <sup>14</sup>N<sup>18</sup>O, <sup>14</sup>N<sup>17</sup>O,<sup> 15</sup>N<sup>18</sup>O, <sup>15</sup>N<sup>17</sup>O.<span> </span>These were used to determine the total partition sum, <em>Q</em> (<em>T</em>), and its first and second moments, Q'(T)</span><span></span><span>, and Q"(T) </span><span></span><span>.<span> </span>The total internal partition sum was computed using term values determined using the term values of Qu <em>et al.</em> [MNRAS, 504, 5768-5777, (2021)] for <sup>14</sup>N<sup>16</sup>O and Wong <em>et al</em>. [MNRAS, 470, 882-897, (2017)] for the other isotopologues.<span> </span>These term values are the best available and hence provide the most accurate total internal partition sums and its first and second moments.<span> </span>The uncertainties in <em>Q</em><sub>int</sub>(T), its moments, and the resulting thermodynamic functions were determined in terms of the uncertainty in the term values and the uncertainty due to the convergence of the partition sum and its moments.<span> </span>From these quantities the isobaric heat capacity, the Helmholtz energy, the entropy, the enthalpy, the Gibbs function, and the JANAF [Chase <em>et al</em>., J. Phys. Chem. Ref. Data, 14, 1-856, 1985] functions: <em>hef</em>, and <em>gef</em> and their uncertainties were computed on a 1 K grid from 1 to 9000 K.<span> </span>The data are compared with the literature values.<span> </span>The resulting thermodynamic quantities are the most accurate determined from direct summation of <em>Q</em>(<em>T</em>), </span><span>Q'(T)</span><span></span><span>, and Q"(T)</span>.</p>
Thermodynamic data for astrochemistry
<p>Collection of thermodynamic properties of 78 chemical species. The properties include partition functions, rotational and vibrational temperatures, electronic energy levels, the electronic potential energies at 0K., and several minimum energy geometries. The main purpose of this data is to compute the Gibbs free energies of the species, to be used in chemical reaction rates. Therefore, these are provided in the output directory (up to 3000K). A easily exportable JSON file with all thermodynamic properties is also available in the output directory. The data has partially been collected from several databases and individual papers. Missing data has been computed by ourselves. All input data is homogenised to a single format, which is not the case when collected from different sources. The reference folder contains extensive information on the sources where we got the data from. More detailed explanations can be found in the several README files. All processing has been performed using my repository of python scripts (https://bitbucket.org/JelsB/thermochemistry). We encourage people to extend the scripts with more features and extend the data with more chemical species. This work strives towards consistency, since we (and others) found inconsistencies between the thermodynamic databases <em>NIST/JANAF tables</em> and NASA's <em>Third Millennium Ideal Gas and Condensed Phase Thermochemical Database for Combustion with Updates from Active Thermochemical Tables. </em></p>
Maintenance of Convectively Coupled Kelvin waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing (Code and Data)
<p>This is the dataset and code for generating all figures for the journal article named "Maintenance of Convectively Coupled Kelvin Waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing," The article was written by Mu-Ting Chien and Daehyun Kim and submitted to Geophysical Research Letters in 2024.</p>
Modeled dynamic and thermodynamic sea ice growth in the Arctic 1980-2019 from NAOSIM
<p>This data set is related to the paper "Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth" by Ricker et al. (2021). Please refer to this study for further details.</p> <p>Ricker, R., Kauker, F., Schweiger, A., Hendricks, S., Zhang, J., & Paul, S. (2021). Evidence for an Increasing Role of Ocean Heat in Arctic Winter Sea Ice Growth, Journal of Climate, 34(13), 5215-5227. Retrieved Nov 24, 2022, from https://journals.ametsoc.org/view/journals/clim/34/13/JCLI-D-20-0848.1.xml</p>
NOAA PSL thermodynamic profiles retrieved from ASSIST infrared radiances with the optimal estimation physical retrieval TROPoe during SPLASH
<p>This dataset contains daily files with thermodynamic profiles retrieved with the optimal estimation physical retrieval TROPoe (TROPoe, Turner and Löhnert 2014; Turner and Blumberg 2019; Turner and Löhnert 2021). The profiles are retrieved every 10 min from instantaneous radiances observed with an Atmospheric Sounder Spectrometer by Infrared Spectral Technology (ASSIST, Rochette et al. 2009).</p> <p>The ASSIST was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The spectral bands used in the retrieval are in the wavenumber range from 612 - 905.4 cm<sup>-1</sup> and are specified in Turner and Löhnert (2021). Additional input data in TROPoe are cloud base height from a collocated ceilometer, temperature, water vapor mixing ratio, and pressure from colocated near-surface measurements and from hourly analysis profiles from the operational Rapid Refresh (RAP, Benjamin et al. 2021) weather prediction model at the closest grid point. The latter are used only outside the atmospheric boundary layer (ABL) above 4 km above ground level (AGL) and provide information in the middle and upper troposphere where little to no information content is available from the infrared radiances.</p> <p>In addition to these temporally resolved input data, TROPoe requires an a priori dataset (prior) which provides mean climatological estimates of thermodynamic profiles and specifies how temperature and humidity covary with height as an input (for details see e.g. Djalalova et al. 2022). The prior is a key component of the retrieval and provides a constraint on the ill-posed inversion problem. For this study, we computed the prior from operational radiosondes launched near Denver, CO, and re-centered the mean profiles of water vapor and temperature to account for the elevation difference between the East River Valley and the launch site near Denver to get a more representative prior.</p> <p>The file format is netcdf and the file naming conventions are</p> <p>NOAA_PSL_ASSIST_RoaringJudy_yyyymmdd.cdf</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p> </p> <p>The time stamp of all data is in UTC.</p> <p>Selected basic variables are (many more provided):</p> <p> </p> <table> <tbody> <tr> <td> <p>Name</p> </td> <td> <p>Dimension</p> </td> <td> <p>Unit</p> </td> </tr> <tr> <td> <p>base_time</p> </td> <td> <p>Single value</p> </td> <td> <p>Seconds (since 00 UTC 1 Jan 1970)</p> </td> </tr> <tr> <td> <p>time_offset</p> </td> <td> <p>Time</p> </td> <td> <p>Second (since base_time)</p> </td> </tr> <tr> <td> <p>hour</p> </td> <td> <p>Time</p> </td> <td> <p>Hours since 00UTC this day</p> </td> </tr> <tr> <td> <p>height</p> </td> <td> <p>Height</p> </td> <td> <p>km AGL</p> </td> </tr> <tr> <td> <p><strong>temperature </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, temperature</p> </td> </tr> <tr> <td> <p><strong>waterVapor </strong></p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, water vapor mixing ratio</p> </td> </tr> <tr> <td> <p>theta</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, potential temperature</p> </td> </tr> <tr> <td> <p>pressure</p> </td> <td> <p>Time, Height</p> </td> <td> <p>hPa, pressure</p> </td> </tr> <tr> <td> <p>rh</p> </td> <td> <p>Time, Height</p> </td> <td> <p>%, relative humidity</p> </td> </tr> <tr> <td> <p>dewpt</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, dew point temperature</p> </td> </tr> <tr> <td> <p>thetae</p> </td> <td> <p>Time, Height</p> </td> <td> <p>K, equivalent potential temperature</p> </td> </tr> <tr> <td> <p>sigma_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>C, 1-sigma uncertainty temperature</p> </td> </tr> <tr> <td> <p>sigma_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>g/kg, 1-sigma uncertainty water vapor</p> </td> </tr> <tr> <td> <p>cdfs_temperature</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for temperature</p> </td> </tr> <tr> <td> <p>cdfs_waterVapor</p> </td> <td> <p>Time, Height</p> </td> <td> <p>cumulative degrees of freedom for water vapor</p> </td> </tr> </tbody> </table> <p>Bold variables are the main retrieved profiles, from which the other variables are derived.</p> <p>Note that the vertical resolution of the retrieved profiles decreases with height, because of the broadening of the weighting function as a function of height. Thus, there are relatively few independent pieces of information in the profiles, this is reflected in the cumulative degree of freedom variables. The majority of the information from the ASSIST is in the lowest 2-3 km, above that most information comes from the RAP model.</p> <p>Because of strong emission in the infrared from clouds, clouds strongly impact the ability to retrieve profiles from the ASSIST and care should be taken when analyzing the retrievals in the presence of clouds. </p> <p><strong>References: </strong></p> <p>Rochette, L., W. L. Smith, M. Howard, and T. Bratcher, 2009: ASSIST, atmospheric sounder spectrometer for infrared spectral technology: Latest development and improvement in the atmospheric sounding technology. Imaging spectrometry XIV, Vol. 7457 of, SPIE, 9–17.</p> <p>Turner, D. D., and U. Löhnert, 2014: Information content and uncertainties in thermodynamic profiles and liquid cloud properties retrieved from the ground-based atmospheric emitted radiance interferometer (AERI). J. Appl. Meteor. Climatol., 53, 752–771, https://doi.org/10.1175/JAMC-D-13-0126.1.</p> <p>Turner, D. D., and W. G. Blumberg, 2019: Improvements to the AERIoe thermodynamic profile retrieval algorithm. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12, 1339–1354, https://doi.org/10.1109/JSTARS.2018.2874968.</p> <p>Turner, D. D., and U. Löhnert, 2021: Ground-based temperature and humidity profiling: Combining active and passive remote sensors. Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021.</p>
Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain: open data and code
<p>Quality controlled temperature data at daily resolution at CTS, HRR, HYS, NVC, RSI and SGV and the most relevant codes for data processing used in:</p> <p>García-Pereira, F., González-Rouco, J. F., Schmid, T., Melo-Aguilar, C, Vegas-Cañas, C., Steinert, N. J., Roldán-Gómez, P. J., Cuesta-Valero, F. J., García-García, A., Beltrami, H., and de Vrese, H.: "Thermodynamic and hydrological drivers of the subsurface thermal regime in Central Spain". Earth Surf. Dynam., submitted, 2023.</p> <p>All data can be also freely obtained for research from the original data sources, GuMNet (https://www.ucm.es/gumnet/) and AEMET (https://www.aemet.es/en/datos_abiertos). Further details of the code are available upon request to the corresponding author (Félix García-Pereira, felgar03@ucm.es).</p>
Thermodynamic properties of ammonia-water (NH3H2O mixture). In Esperanto
<p>Thermodynamic data for the ammonia-water mixture are adapted from: Ibrahim, O. M. (1993). Thermodynamic properties of ammonia-water mixtures. In ASHRAE Transactions: Symposia (Vol. 93, p. 1495). <br> <br> </p>
Concentration-, Temperature- and Solvent-Dependent Self-Assembly: Merocyanine Dimerization as a Showcase Example for Obtaining Reliable Thermodynamic Data
<p><strong>Abstract:</strong> Mathematical models for the concentration-, temperature- and solvent-dependent analysis of self-assembly equilibria are derived for the most simple case of dimer formation, to highlight the assumptions these models and the thus determined thermodynamic parameters are based on. The three models were applied to UV/Vis absorption data for the dimerization of a highly dipolar merocyanine dye in 1,4-dioxane. Isothermal titration calorimetry (ITC) dilution experiments were performed as an independent reference technique. While the concentration-dependent analysis is according to our studies the most reliable method, also the less time-consuming temperature-dependent evaluation can give accurate results in the present example, despite small thermochromic effects. In contrast, the strong negative solvatochromism of the merocyanine tampers with the results from the solvent-dependent evaluation. Even though the studies presented in this work are limited to the monomer-dimer equilibrium of a dipolar dye, the basic principles can be transferred to other chromophores and different self-assembly models, including those for supramolecular polymerization.</p>
Supplementary Material for "Thermodynamic Reaction Control of Nucleoside Phosphorolysis"
<p>This is the supplementary material for our publication "Thermodynamic Reaction Control of Nucleoside Phosphorolysis".</p> <p>The .pdf file contains the supplementary information: Author Contributions, Figure S1 and Tables S1-S3.</p> <p>The .zip file contains the raw data, metadata and results from Figure 1, 2, 4 and 5.</p> <p>The .xlsx file contains</p> <ol> <li>the apparent transformed values of the reaction enthalpy and entropy of nucleosides <strong>1</strong>-<strong>24</strong></li> <li>the calculated apparent Gibbs free energies of nucleosides <strong>1</strong>-<strong>24</strong> and</li> <li>the implementation of these values for the calculation of equilibrium conversions of nucleosides <strong>1</strong>-<strong>24</strong> with variable (adjustable) reaction conditions (concentrations of the nucleoside, phosphate and reaction temperature).</li> </ol> <p>For the software employed for spectra unmixing, please see doi: 10.5281/zenodo.3243376 and our previous work (doi: 10.3390/mps2030060) as well as its supporting material (doi: 10.5281/zenodo.3333469) for clarification.</p>
Vertical profiles of stable water isotopes and thermodynamic properties from research flights during the L-WAIVE field campaign in June 2019
<p>This datasets contains the measurements of stable water isotopes conducted during the Lacustrine-Water vApor Isotope inVentory Experiment (L-WAIVE) field campaign taking place in June 2019 in the Annecy valley in the French Alps (Chazette et al. 2021). The measurements were conducted using a Picarro laser spectrometer L2130-i that was installed on an ultralight aircraft. The Picarro measurements of atmospheric humidity are merged measurements of thermodynamic properties by a fast-response temperature and humidity probe (iMet XQ-2; see also Chazette et al. 2021) interpolated on 10s temporal resolution.</p> <p>The data is provided on a one file per flight. All variables are described in README.</p> <p>This dataset has been used in Thurnherr et al. (submitted) for a comparison study of stable water isotopes measurements from various platforms and COSMOiso model simulations.</p>
Numerical Calculation of the Thermodynamic Properties of Silver Erbium Alloys for Use in Metallic Magnetic Calorimeters - Data
<p>Data from simulations of the specific heat and magnetization of Ag:Er alloys. The parameter range we consider are temperatures between 1mK and 1K, external magnetic fields of up to 20mT, and erbium concentrations of up to 2000ppm.</p>
A Data Resource for Prediction of Thermodynamic Properties of Small Molecules
<p>We developed a database of 2869 experimental values of enthalpy of formation and 1403 values for entropy for substances composed of stable small molecules, derived from the literature. We developed a model for predicting enthalpy of formation and entropy from semiempirical quantum mechanical calculations of energy and atom counts, and applied the model to a comprehensive database of 16,417 small molecules. The database of small-molecule thermodynamic properties will be useful for predicting the outcome of any process that might involve the generation or destruction of volatile products, such as atmospheric chemistry, volcanism, or waste pyrolysis. Additionally, the collected experimental thermodynamic values will be of value to others developing models to predict enthalpy and entropy.</p>
Dataset to "Bulk thermodynamics determines surface hydrogen concentrations in membranes"
<p>Dataset to "Bulk thermodynamics determines surface hydrogen concentrations in membranes" as published in Advanced Materials Interfaces</p>
Raw data for "Interplay of Kinetic and Thermodynamic Reaction Control Explains Incorporation of Dimethylammonium Iodide into CsPbI3"
<p>Raw solid-state NMR and XRD data, and input files for DFT and MD calculations shown in https://doi.org/10.1021/acsenergylett.2c00877</p>
A simple model for daily basin-wide thermodynamic sea ice thickness growth retrieval: Data
<p>Data for:</p> <p>Anheuser, J., Liu, Y., and Key, J.: A daily basin-wide sea ice thickness retrieval methodology: Stefan's Law Integrated Conducted Energy (SLICE), The Cryosphere Discuss. [preprint], <a href="https://doi.org/10.5194/tc-2021-333">https://doi.org/10.5194/tc-2021-333</a>, in review, 2021.</p> <p> </p> <p>Scripts for producing data and figures can be found at:</p> <p>https://doi.org/10.5281/zenodo.6561431</p> <p> </p> <p> </p>
ScienceDex guides
Understand access before you commit
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.