Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
189
datasets available to search
ShareScore release 0.9.0
Dataset results
189 results for “thermodynamics”
Research data supporting "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection"
<p>Experimental research raw data supporting the publication by Lin, Y. et al, 2019, "Residue-Specific Solvation Directed Thermodynamic and Kinetic Control over Peptide Self-Assembly with 1D/2D Structure Selection", ACS Nano. DOI: 10.1021/acsnano.8b08117.</p> <p>Molecular simulation data is available upon reasonable request from irene.yarovsky@rmit.edu.au.</p>
Dataset - Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models
<p><strong>Simulation data needed to reproduce the results from the manuscript “Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models”</strong><br> by V. Pandey, N. Hadadi and V. Hatzimanikatis</p> <p>"REMI manuscript - simData" folder contains all simulation data which can be used to generate results of the paper: <br> • Expression_data: This folder contains Transcriptomics data from both studies: Ishii et al (see test_expr.mat) and Holm et al.<br> • Fluxdata: Fluxomics data can be found form the studies Ishii et al and Holm et al.<br> • Metabolomics: This contains metabolomics data of aforementioned both studies.<br> • ModelsSolutions: We generated different models using with thermodynamics (TGex, TGexM, TM) and without thermodynamics models (Gex, GexM, M). Gex indicates integration with only gene expression, GexM indicates gene expression and metabolite, and M indicates only metabolites. ‘T’ is used for thermodynamic models. Models for different mutants and conditions (e.g. pgm, pgi) can be found in the corresponding folders (TGex, TGexM, TM, Gex, GexM, and M). Variables with the ‘store’ tag comprises flux solutions, correlation values and percentage error between simulation and experiment fluxes.<br> • AlternativeMCS: We generated alternative states for MCS and saved results.<br> • FVAMM: This is the result flux variability analysis can be found in this folder.<br> • Scatter_plot: Scatter plots indicates correlation between measured and model predicted fluxes.</p> <p> </p>
Thermodynamics-driven, high-throughput analysis of protein stability with MoltenProt
<p>Data for thermal unfolding curves for various proteins.</p> <p>Measured parameters: intrinsic fluorescence (330nm, 350nm), scattering</p> <p>Files in XLSX format represent annotated output data from NanoTemper Prometheus NT.48.</p> <p> </p>
Supplementary data for the manuscript "Thermodynamic properties of isoprene and monoterpene derived organosulfates estimated with COSMOtherm"
<p>.cosmo and .energy files of various isoprene and monoterpene derived organosulfates and methyl bisulfate (neutral and deprotonated), IEPOX (neutral) and hydrated sodium (cation).</p>
Linkage between projected warm season precipitation systems and thermodynamic and microphysical changes over eastern China
<p>Data used in the manuscript "Linkage between projected warm season precipitation systems and thermodynamic and microphysical changes over eastern China" which was submitted to Journal of Geophysical Research: Atmospheres. </p>
Data for: Many-body thermodynamics on quantum computers via partition function zeros
<p>Partition functions are ubiquitous in physics: they are important in determining the thermodynamic properties of many-body systems, and in understanding their phase transitions. As shown by Lee and Yang, analytically continuing the partition function to the complex plane allows us to obtain its zeros and thus the entire function. Moreover, the scaling and nature of these zeros can elucidate phase transitions. Here we show how to find partition function zeros on noisy intermediate-scale trapped ion quantum computers in a scalable manner, using the XXZ spin chain model as a prototype, and observe their transition from XY-like behavior to Ising-like behavior as a function of the anisotropy. While quantum computers cannot yet scale to the thermodynamic limit, our work provides a pathway to do so as hardware improves, allowing the future calculation of critical phenomena for systems beyond classical computing limits.</p>
Data for: Thermodynamics of a dilute Bose gas: a path-integral Monte-Carlo study
<p>Path-integral Monte-Carlo results of the thermodynamics of a homogeneous dilute Bose gas. Data used to plot the figures for internal energy, pressure, isothermal compressibility and contact parameter.</p>
Thermodynamic model input files
<p>PerpleX (6.7.2) input files for P-X modelling of Mars mantle, olivine and orthopyroxene systems. Contains refined olivine solution model parameters O(fei), Wad(fei) and Ring(fei) to fit experiments of Katsura and Ito (1989) and Fei et al. (1991); other contents either were pre-existing or irrelevant parameterizations. </p>
WaterKit: thermodynamic profiling of protein hydration sites (3/3)
<p><strong>Overview</strong></p> <p>This archive (3/3) contains the results (kith, nram and streptavidin) for the following study:</p> <ul> <li> <p>WaterKit: thermodynamic profiling of protein hydration sites (https://doi.org/10.26434/chemrxiv-2022-grlsr)</p> </li> </ul> <p>Each zip archive contains the following files:</p> <ul> <li>charmm-gui: output from CHARMM-gui</li> <li>xx_amber: GIST output from MD simulations</li> <li>figures: convergence, predictions per waterkit model plots, etc...</li> <li>pdbbind_ligands: contains all the ligands used to define the ligand binding site</li> <li>waterkit: contains all the results from the WaterKit calculations <ul> <li>All the WaterKit parameters tested: <ul> <li>ad : acceptor/donor anchor points</li> <li>all : used all hydrogen atoms as anchor points</li> <li>vina : used O_DA vina probe for the spherical model (otherwise TIP3P model)</li> <li>300 : temperature</li> <li>3 : number of hydration layers placed during the calculations</li> <li>min_100_2.5 : 100 steps of minimization with 2.5 kcal/mol/A**2 constraints</li> </ul> </li> <li>Each directory contains: <ul> <li>output from the GIST analysis (gist-*.dx)</li> <li>cluster.pdb: All the hydration sites identified</li> <li>protein.nc : WaterKit "trajectory"</li> <li>protein_system.prmtop : Amber top file</li> <li>pymol_*.pse : PyMol session with all the results (WK, MD)</li> <li>results_*.png : Comparison plots between WaterKit predictions and MD simulations per hydration sites</li> </ul> </li> </ul> </li> <li>analysis_xxx.ipynb: Jupyter notebook containing all the analysis (needs utils.py file)</li> <li>protein_prepared.pdbqt: structure used for the WK calculations</li> <li>results_md.csv : contains all the raw results from the MD simulations</li> <li>results_wk.csv : contains all the raw results from the WK calculations</li> <li>results_md_md_comp_energy_*.csv : comparison results between MD simulations (energy)</li> <li>results_md_md_comp_placement_*.csv : comparison results between MD simulations (placement)</li> <li>results_md_wk_comp_energy_*.csv : comparison results between MD simulations and WK (energy)</li> <li>results_md_wk_comp_placement_*.csv : comparison results between MD simulations and WK (placement)</li> <li>results_wk_wk_comp_energy_*.csv : comparison results between WK calculations (energy)</li> <li>results_wk_wk_comp_placement_*.csv : comparison results between WK calculations (placement)</li> </ul> <p> </p>
WaterKit: thermodynamic profiling of protein hydration sites (2/3)
<p><strong>Overview</strong></p> <p>This archive (2/3) contains the results (fa10 and pgh2) for the following study:</p> <ul> <li> <p>WaterKit: thermodynamic profiling of protein hydration sites (https://doi.org/10.26434/chemrxiv-2022-grlsr)</p> </li> </ul> <p>Each zip archive contains the following files:</p> <ul> <li>charmm-gui: output from CHARMM-gui</li> <li>xx_amber: GIST output from MD simulations</li> <li>figures: convergence, predictions per waterkit model plots, etc...</li> <li>pdbbind_ligands: contains all the ligands used to define the ligand binding site</li> <li>waterkit: contains all the results from the WaterKit calculations <ul> <li>All the WaterKit parameters tested: <ul> <li>ad : acceptor/donor anchor points</li> <li>all : used all hydrogen atoms as anchor points</li> <li>vina : used O_DA vina probe for the spherical model (otherwise TIP3P model)</li> <li>300 : temperature</li> <li>3 : number of hydration layers placed during the calculations</li> <li>min_100_2.5 : 100 steps of minimization with 2.5 kcal/mol/A**2 constraints</li> </ul> </li> <li>Each directory contains: <ul> <li>output from the GIST analysis (gist-*.dx)</li> <li>cluster.pdb: All the hydration sites identified</li> <li>protein.nc : WaterKit "trajectory"</li> <li>protein_system.prmtop : Amber top file</li> <li>pymol_*.pse : PyMol session with all the results (WK, MD)</li> <li>results_*.png : Comparison plots between WaterKit predictions and MD simulations per hydration sites</li> </ul> </li> </ul> </li> <li>analysis_xxx.ipynb: Jupyter notebook containing all the analysis (needs utils.py file)</li> <li>protein_prepared.pdbqt: structure used for the WK calculations</li> <li>results_md.csv : contains all the raw results from the MD simulations</li> <li>results_wk.csv : contains all the raw results from the WK calculations</li> <li>results_md_md_comp_energy_*.csv : comparison results between MD simulations (energy)</li> <li>results_md_md_comp_placement_*.csv : comparison results between MD simulations (placement)</li> <li>results_md_wk_comp_energy_*.csv : comparison results between MD simulations and WK (energy)</li> <li>results_md_wk_comp_placement_*.csv : comparison results between MD simulations and WK (placement)</li> <li>results_wk_wk_comp_energy_*.csv : comparison results between WK calculations (energy)</li> <li>results_wk_wk_comp_placement_*.csv : comparison results between WK calculations (placement)</li> </ul> <p> </p>
WaterKit: thermodynamic profiling of protein hydration sites (1/3)
<p><strong>Overview</strong></p> <p>This archive (1/3) contains the results (fabp4, fkb1a, hivpr, hs90a, ital) for the following study:</p> <ul> <li> <p>WaterKit: thermodynamic profiling of protein hydration sites (https://doi.org/10.26434/chemrxiv-2022-grlsr)</p> </li> </ul> <p>Each zip archive contains the following files:</p> <ul> <li>charmm-gui: output from CHARMM-gui</li> <li>xx_amber: GIST output from MD simulations</li> <li>figures: convergence, predictions per waterkit model plots, etc...</li> <li>pdbbind_ligands: contains all the ligands used to define the ligand binding site</li> <li>waterkit: contains all the results from the WaterKit calculations <ul> <li>All the WaterKit parameters tested: <ul> <li>ad : acceptor/donor anchor points</li> <li>all : used all hydrogen atoms as anchor points</li> <li>vina : used O_DA vina probe for the spherical model (otherwise TIP3P model)</li> <li>300 : temperature</li> <li>3 : number of hydration layers placed during the calculations</li> <li>min_100_2.5 : 100 steps of minimization with 2.5 kcal/mol/A**2 constraints</li> </ul> </li> <li>Each directory contains: <ul> <li>output from the GIST analysis (gist-*.dx)</li> <li>cluster.pdb: All the hydration sites identified</li> <li>protein.nc : WaterKit "trajectory"</li> <li>protein_system.prmtop : Amber top file</li> <li>pymol_*.pse : PyMol session with all the results (WK, MD)</li> <li>results_*.png : Comparison plots between WaterKit predictions and MD simulations per hydration sites</li> </ul> </li> </ul> </li> <li>analysis_xxx.ipynb: Jupyter notebook containing all the analysis (needs utils.py file)</li> <li>protein_prepared.pdbqt: structure used for the WK calculations</li> <li>results_md.csv : contains all the raw results from the MD simulations</li> <li>results_wk.csv : contains all the raw results from the WK calculations</li> <li>results_md_md_comp_energy_*.csv : comparison results between MD simulations (energy)</li> <li>results_md_md_comp_placement_*.csv : comparison results between MD simulations (placement)</li> <li>results_md_wk_comp_energy_*.csv : comparison results between MD simulations and WK (energy)</li> <li>results_md_wk_comp_placement_*.csv : comparison results between MD simulations and WK (placement)</li> <li>results_wk_wk_comp_energy_*.csv : comparison results between WK calculations (energy)</li> <li>results_wk_wk_comp_placement_*.csv : comparison results between WK calculations (placement)</li> </ul> <p> </p>
Dataset related to the publication "Thermodynamic effects in a gas modulatedInvar-based dual Fabry–Pérot cavityrefractometer"
<p>The data set consists of; The published paper, all figures that present measurement or simulation data in .png and .fig format and the underlying data plotted in the figures in text format. The published plots were generated from the fig files. The text files were generated by reading the plotted data from the fig files. The files are named Fig_XX were XX corresponds to the figure number in the publication. The format of the text file is as follows. Before every data set there is a header consisting of; The number of the subplot where the data is plotted (Plot: XX), the number of the data set in the sub plot (DataSet: XX), and the color of the line or marker in the plot (Color: XX). The description of what each color represents can be found in the publication.</p>
Supporting data for: Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics
<p>This repository contains supporting data and code for the paper titled "Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics" by Bernadette Mohr, Diego van der Mast, and Tristan Bereau.</p>
Original data for publication "Intracluster ligand rearrangement: an NMR-based thermodynamic study"
<p>Original data for publication "Intracluster ligand rearrangement: an NMR-based thermodynamic study" published in Nanoscale, 2023.</p> <p>Original data used for the Figures are provided.</p>
ENSO influence on water vapor transport and thermodynamics over Northwestern South America
<p>Datasets used, generated and analysed during the study "ENSO influence on water vapor transport and thermodynamics over Northwestern South America"</p>
Sharing data and code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols"
<p>The public data repository contains the data and plotting code supporting the article entitled "Thermodynamically enhanced precipitation extremes due to counterbalancing influences of anthropogenic greenhouse gases and aerosols". The dataset includes annual maximum one-day precipitation (Rx1day), its proxy computed by a physical scaling diagnostic (scaling), and the decomposed components of the scaling (i.e., thermodynamic response, dynamic response, and their interaction). Several reanalyses (including ERA5 and JRA55) and CMIP6 simulations under several scenarios (including ALL, GHG, AER, and piControl) are applied to compute the historical Rx1day, scaling, and the associated components following a GitHub Python repository (<a href="https://github.com/oliverangelil/precip_extremes_scaling">https://github.com/oliverangelil/precip_extremes_scaling</a>). Note that the decomposed components are calculated as anomalies. </p> <p>The data results of the extreme precipitation decomposition in the NetCDF format are available in zip files “<em><strong>ERA5</strong></em>”, "<em><strong>JRA55</strong></em>", "<em><strong>ALL</strong></em>", "<em><strong>GHG</strong></em>", "<em><strong>AER</strong></em>", "<em><strong>NAT</strong></em>", and "<em><strong>piControl</strong></em>". Code for visualizations is available in the "<em><strong>Jupyter Notebooks</strong></em>" zip file.</p>
A High-Level Quantum Chemical Study of the Thermodynamics Associated with Chlorine Transfer between N-Chlorinated Nucleobases
<p>Geometries of the isomers of the N-Chlorinated nucleobases (adenine, guanine and thymine) as well as the lowest energy structures of the DNA bases (adenine, cytosine, guanine and thymine) obtained at the B3LYP/6-31G(2df,p) level of theory (in Cartesian Coordinates).</p> <p> </p> <p><strong>ABSTRACT: </strong>The relative free energies of the isomers formed upon <em>N</em>-chlorination of each nitrogen atom within the DNA nucleobases (adenine, guanine, and thymine) have been obtained using the high-level G4(MP2) composite ab initio method (the free energies of the <em>N</em>-chlorinated isomers of cytosine have been reported at the same level of theory previously). Having identified the lowest energy <em>N</em>-chlorinated derivatives for each nucleobase, we have computed the free energies associated with chlorine transfer from <em>N</em>-chlorinated nucleobases to other unsubstituted bases. Our results provide quantitative support pertaining to the results of previous experimental studies, which demonstrated that rapid chlorine transfer occurs from an <em>N</em>-chlorothymidine to cytidine or adenosine. The results of our calculations in the gas-phase reveal that chlorine transfer from <em>N</em>-chlorothymine to either cytosine, adenine, or guanine proceed via exergonic processes with D<em>G</em><sup>o</sup> values of ­–50.3 (cytosine), –28.0 (guanine), and –6.7 (adenine) kJ mol<sup>–1</sup>. Additionally, we consider the effect of aqueous solvation by augmenting our gas-phase G4(MP2) energies with solvation corrections obtained using the conductor-like polarizable continuum model. In an aqueous solution, we obtain the following G4(MP2) free energies associated with chlorine transfer from <em>N</em>-chlorothymine to the three other nucleobases: –58.4 (cytosine), –26.4 (adenine), and –18.7 (guanine) kJ mol<sup>–1</sup>. Therefore, our calculations, whether in the gas phase or in an aqueous solution, clearly indicate that chlorine transfer from any of the <em>N</em>-chlorinated nucleobases to cytosine provides a thermodynamic sink for the active chlorine. This thermodynamic preference for chlorine transfer to cytidine may be particularly deleterious since previous experimental studies have shown that nitrogen-centered radical formation (via N–Cl bond homolysis) is more easily achieved in <em>N</em>-chlorinated cytidine than in other <em>N</em>-chlorinated nucleosides.</p>
Sample programs of an eco-redox model for the article: Microbial redox cycling enhances ecosystem thermodynamic efficiency and productivity
<p><span>Microbial life in low-energy ecosystems relies on individual energy conservation, optimizing </span><span>energy use in response to interspecific competition, and mutualistic interspecific syntrophy. Our study proposes a novel community-level strategy for increasing energy use efficiency. By</span> <span>utilizing a</span><span>n</span> <span>oxidation-reduction (redox) reaction network model that represents microbial redox metabolic interactions, we </span><span>investigated multiple species-level competition and cooperation within the network</span><span>. Our results suggest that microbial functional diversity allows for metabolic handoffs</span><span>, which in turn lead to increased energy use efficiency. Furthermore, the mutualistic division of labor and the resulting </span><span>complexity of redox pathways actively </span><span>drive material cycling, further promoting energy exploitation. Our findings reveal the potential of self-organized ecological interactions to develop efficient energy utilization strategies, with important implications for microbial ecosystem functioning and </span><span>co-</span><span>evolution of life and Earth.</span></p>
A fresh thermodynamic outlook of hydrogen production by water splitting from an exergy-based perspective
<p>The upload files is the origin data and calculation procedure for the article titled "A fresh thermodynamic outlook of hydrogen production by water splitting from an exergy-based perspective".</p>
Logical and thermodynamical reversibility: optimized experimental implementation of the NOT operation
<p><strong>Dataset for the article:</strong></p> <p>Logical and thermodynamical reversibility: optimized experimental implementation of the NOT operation</p> <p>to be published in PRE</p> <p><strong>Files description: </strong></p> <p><strong>.fig:</strong> Matlab Figures used to create the figures of the article</p> <p><strong>Analysis.mat:</strong> mat file storing the analysis of 4538 bitflip protocols.</p> <ul> <li>dQall: average infinitesimal heat evolution along a protocol</li> <li>dt: sampling rate</li> <li>dW: average infinitesimal work evolution along a protocol</li> <li>f0: resonance frequency of the cantilever</li> <li>Klist: average kinetic energy evolution along a protocol</li> <li>N: number of protocols</li> <li>Qhisto: array of the average heat released for the N protocols</li> <li>sigma_m: position variance implemented in the FPGA at the beginning of the N protocols</li> <li>sigmalist: position variance recalibrated for each protocol </li> <li>tau1: duration of the bitflip</li> <li>Teq: duration of the equlivration step</li> <li>trelax: time between protocols</li> <li>Ulist: average potential energy evolution along a protocol</li> <li>Vplot: protocol driving for the plot</li> <li>Whisto: array of the average work released for the N protocols</li> <li>x0plot: protocol driving for the plot</li> <li>xplot: position signal for plotting </li> </ul> <p><strong>bitflip.m: </strong></p> <p>Analysis code to obtain the Analysis.mat type of files from the dataset of N protocols.</p> <p><strong>bitflip_01.mat to bitflip_1000.mat</strong></p> <p>Experimental data of bitflip protocols. The driving and the system position are stored, and some acquisition parameters. These files are designed to be analysed by bitflip.m</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.