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.
101
datasets available to search
ShareScore release 0.9.0
Dataset results
101 results for “catalysis”
Dataset for "Magnetic catalysis in the (2+1)-dimensional Gross-Neveu model"
<p>We study the Gross-Neveu model in 2 + 1 dimensions in an external magnetic field B. We<br> first summarize known mean-field results, obtained in the limit of large flavor number N f , before<br> presenting lattice results using the overlap discretization to study one reducible fermion flavor,<br> N f = 1. Our findings indicate that the magnetic catalysis phenomenon, i.e., an increase of the chiral<br> condensate with the magnetic field, persists beyond the mean-field limit for temperatures below the<br> chiral phase transition and that the critical temperature grows with increasing magnetic field. This<br> is in contrast to the situation in QCD, where the broken phase shrinks with increasing B while the<br> condensate exhibits a non-monotonic B-dependence close to the chiral crossover, and we comment on<br> this discrepancy. We do not find any trace of inhomogeneous phases induced by the magnetic field.</p> <p> </p> <p>If you use this data, please cite the corresponding paper:<br> https://doi.org/10.48550/arXiv.2302.05279 (or better the not-yet-existing published version)</p>
Catalysis of Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by D. andersoni (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the chromogenic substrate Tos-Gly-Pro-Arg-p-nitroanilide by α-thrombin in the presence of an anticoagulant produced by <em>Dermacentor andersoni</em>, for fixed concentration of enzyme (and modulator) and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and <em>PNAS 116 (28) 13873-13878</em> (<a href="https://doi.org/10.1073/pnas.1905177116">https://doi.org/10.1073/pnas.1905177116</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Tos-Gly-Pro-Arg-p-nitroanilide in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (0.15 nM)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Catalysis of Ac-DEVD-AMC by procaspase-3 (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the fluorogenic substrate Ac-DEVD-AMC to the fluorescent substrate AMC by recombinant procaspase-3 obtained in yeast cell extracts, for fixed concentration of enzyme and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Ac-DEVD-AMC in micromolar (µM) concentration</p> <p>Line 2: Concentration of protein in yeast extract (0.123 mg/mL)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Diffraction images used to solve the structures published in the article "An Epoxide Intermediate in Glycosidase Catalysis"
<p>Raw diffraction images used for generating the structures published in the article "An Epoxide Intermediate in Glycosidase Catalysis" (available <a href="https://doi.org/10.1021/acscentsci.0c00111">here</a>). Full single-crystal datasets, including images that were not used in the final analyses, are published. The software used for the processing of each dataset is listed in their respective PDB entries.</p> <p> </p> <p>If you find this useful, please contact me at <a href="mailto:lukasz.sobala@hirszfeld.pl">lukasz.sobala@hirszfeld.pl</a>, I am just interested in how these data are used!</p>
17O hyperfine spectroscopy in surface chemistry and catalysis
<ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, Computer Simulation and Analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DTA</strong>, <strong>m</strong>, <strong>opj</strong>, <strong>out</strong>, and <strong>f34</strong>.</li> <li>Information on <strong>origin of the data</strong>: <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong> and <strong>DTA</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension<strong> m</strong></li> <li>cwEPR spectroscopic spectra with simulations with filename extension <strong>opj</strong></li> <li>Periodic DFT computations with(out) filename extensions <strong>out </strong>and <strong>f34 </strong>in ASCII format</li> <li>Molecular cluster DFT computations with filename extensions <strong>in</strong> and <strong>out</strong> in ASCII format</li> </ul> </li> <li>Are the data <strong>generated</strong> (e.g. by a machine) or <strong>collected</strong> (e.g. by means of a survey)? <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band Pulsed-EPR spectroscopic measurements were generated by ELEXYS 580 EPR spectrophotometer equipped with SHQ cavity and ER035 M NMR gaussmeter produced by Bruker.</li> <li>Periodic DFT computations were generated using distributed parallel version of CRYSTAL17 code.</li> <li>Molecular cluster DFT computations were generated using the ORCA (v5.0.2) code.</li> </ul> </li> <li><strong>If the dataset includes multiple files that relate to each other:</strong> <ul> <li>Files in <strong>PARACAT_WP4_20230309_01_ORCA</strong> folder includes molecular cluster DFT computation inputs and outputs in ASCII format.</li> <li>Files in <strong>PARACAT_WP4_20230309_02_CRYSTAL</strong> folder includes periodic DFT computation inputs and outputs in ASCII format.</li> <li>Files in <strong>PARACAT_WP4_20230309_03_CW </strong>folder includes CW-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; simulations in m format; and results plotted in opj format.</li> <li>Files in <strong>PARACAT_WP4_20230309_04_Pulse</strong> folder includes subfolders of VO/ZSM-5 and Zn/ZSM-5 that contain Pulsed-EPR spectroscopic measurements and computer simulations/analyses, original data are in DTA/DSC formats; files in m format were used to process the data.</li> </ul> </li> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>HYSCORE </strong>– HYperfine Sublevel CORrelation spectroscopy, <strong>ENDOR</strong> – Electron Nuclear DOuble Resonance, <strong>DFT</strong> – Density Functional Theory</li> <li>definitions of variables: <strong>Magnetic field, Temperature</strong></li> <li>units of measurement: <strong>Gauss (G), K</strong></li> </ul> </li> </ul>
Data Set for the Journal Article "Autonomous Reaction Network Exploration in Homogeneous and Heterogeneous Catalysis"
<p>This dataset includes the XYZ structures of the centroids of all compounds found. Charge and multiplicity are given in the comment line of each XYZ file.</p>
Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Electrochemical data shown in A. Fasano, A. Jacq-Bailly, J. Wozniak, V. Fourmond, and C. Léger, « Catalytic Bias and Redox-Driven Inactivation of the Group B FeFe Hydrogenase CpIII », ACS Catalysis (2024). doi: 10.1021/acscatal.4c01352
<p>Text file of all the electrochemical data shown in the following paper: A. Fasano, A. Jacq-Bailly, J. Wozniak, V. Fourmond, and C. Léger, « Catalytic Bias and Redox-Driven Inactivation of the Group B FeFe Hydrogenase CpIII », ACS Catalysis (2024). <a href="dx.doi.org/10.1021/acscatal.4c01352" target="_blank" rel="noopener">doi: 10.1021/acscatal.4c01352</a></p>
Which bridge to cross, which mountain to climb – supramolecular photocatalysis outpacing conventional catalysis
<p>The file contains all raw data for the manuscript entitled "Which bridge to cross, which mountain to climb – supramolecular photocatalysis outpacing conventional catalysis" (i.e. Figs. 3-10).</p>
The central role of oxo clusters in zirconium-based esterification catalysis
<p>Data of the figures in the publication "<strong>The central role of oxo clusters in zirconium-based esterification catalysis</strong>".<br>DOI: <a href="https://doi.org/10.1002/smsc.202400369">https://doi.org/10.1002/smsc.202400369</a></p> <p>The <em>.pxp</em> documents contain the experimental data of the figures in the manuscript and SI and they can be opened/edited with the software IGOR Pro 6.3 or higher.</p> <p> </p> <p><strong>Figure 1:</strong> Structural representation of catalysts used in this article. A) Nanocrystal (ZrO2 ), B) Metal-organic framework (UiO-66), (C) Zr6 oxo cluster (Zr6-acetate) and D) Zr12 oxo cluster (Zr12-acetate)</p> <p><strong>Figure 2:</strong> Catalytic esterification of oleic acid with ethanol in ortho-dichlorobenzene (o-DCB). The catalyst is either Zr12-oleate, ZrO2 nanocrystals or the MOF UiO-66. The reactions were performed in triplicate.</p> <p><strong>Figure 3:</strong> Catalytic esterification, comparing Zr12 oxo clusters and UiO-66, for different carboxylic acid substrates. The conditions are identical to Figure 1: 120 °C, 12 mol% zirconium, 0.2 M carboxylic acid, molecular sieves, and four equivalents of ethanol.</p> <p><strong>Figure 4:</strong> Catalytic esterification of oleic acid with hexanol. The reaction is either done in mesitylene (using four equivalents hexanol), without mesitylene (using four equivalents hexanol), or without mesitylene and a reduced 1.2 equivalents of hexanol. In the latter case, we recovered the catalyst and used this for a second catalytic reaction. The dotted line corresponds to the maximum yield that can be obtained when excluding the oleate ligands on the catalyst surface.</p> <p><strong>Figure 5: </strong>PDF refinement for A) Zr12-oleate cluster before catalysis, and after the first and second round of catalysis, B) and for catalyst recovered after 30 min with and without molecular sieves using Zr(OR)4 as the catalyst. The values in square brackets correspond to the ratio of monomer to dimer-cluster in the fit. The refinement is performed using both Zr6- and Zr12-propionate structures obtained from the single crystal structure (CCDC 604529).</p> <p><strong>Figure S1 - S24:</strong> Figures from Supporting Information.</p>
Catalysis of L-cysteine by IscS in the presence of IscU (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of L-cysteine to sulfur by the desulfurase IscS in the presence of the scaffold protein IscU. This kinetic system was described in the PhD thesis "New tools for enzymatic characterization and inhibitor screening: post-Michaelis-Menten kinetic analysis" by Maria Filipa Pinto (<a href="https://hdl.handle.net/10216/126202">https://hdl.handle.net/10216/126202</a>), and this dataset was then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate L-cysteine in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (1 µM)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Catalysis of Ac-DEVD-AMC by caspase-3 (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the fluorogenic substrate Ac-DEVD-AMC to the fluorescent substrate AMC by recombinant purified caspase-3, for fixed concentration of enzyme and variation of concentration of initial substrate. It was originally documented in <em>Biophysical Chemistry 252 (2019) 106193</em> (<a href="https://doi.org/10.1016/j.bpc.2019.106193">https://doi.org/10.1016/j.bpc.2019.106193</a>), and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate Ac-DEVD-AMC in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (1 U)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Catalysis of MUF-triNAG by hen egg-white lysozyme (dataset formatted for analysis by interferENZY)
<p><strong>Main description</strong></p> <p>This dataset depicts the catalysis of the fluorogenic substrate MUF-triNAG to the fluorescent substrate MUF by hen egg-white lysozyme, for fixed concentration of enzyme and variation of concentration of initial substrate. It was originally documented in <em>Phys. Chem. Chem. Phys., 2020, 22, 16143-16149</em> (<a href="https://doi.org/10.1039/D0CP02469D">https://doi.org/10.1039/D0CP02469D</a>) and then used as a study case for the webserver interferENZY (a web-based tool for enzymatic assay validation and standardized kinetic analysis; visit <a href="https://interferenzy.i3s.up.pt">https://interferenzy.i3s.up.pt</a> for more information). To this end, it was converted to the format here presented: tab-separated *.txt input required for interferENZY analysis.</p> <p> </p> <p><strong>Dataset organization</strong></p> <p>Line 1: Tab-separated initial concentrations of substrate MUF-triNAG in micromolar (µM) concentration</p> <p>Line 2: Concentration of enzyme (0.25 µM)</p> <p>Line 3: Units of time</p> <p>Line 4: Units of concentration for substrate values and measurements</p> <p>Line 5: Dataset name</p> <p>Line 6 and downwards: Tab-separated column-pairs of the progress curves (time,Product) corresponding to the indicated values of initial concentrations of substrate in line 1</p> <p> </p> <p><strong>Contact information:</strong></p> <p>Maria Filipa Pinto (mfpinto@i3s.up.pt)<br> Pedro M. Martins (pmartins@ibmc.up.pt)</p> <p>i3S – Instituto de Investigação e Inovação em Saúde, Universidade do Porto, Rua Alfredo Allen, 208, 4200-135 Porto, Portugal. Telephone number: +351 226 074 900</p>
Data for The role of halogens in Au-S bond cleavage for energy-differentiated catalysis at the single-bond limit
<p>The source data for figures in <strong>The role of halogens in Au-S bond cleavage for energy-differentiated catalysis at the single-bond limit </strong></p>
The role of dynamics in heterogeneous catalysis: surface diffusivity and N2 decomposition on Fe(111)
<p>Data related to the molecular dynamics simulations reported in the manuscript "The role of dynamics in heterogeneous catalysis: surface diffusivity and N2 decomposition on Fe(111)"</p><p>- Inputs of the MD simulations of surface morphology and dynamics (LAMMPS)</p><p>- Inputs of the OPES simulations for N2 adsorption and dissociation (LAMMPS+PLUMED)</p><p>- MD outputs: trajectory files</p><p>- Code for analysis and post-processed data</p><p>- Jupyter notebook to reproduce the pictures reported in the manuscript</p>
Impact of Catalysis-Relevant Oxidation and Annealing Treatments on Nanostructured GaRh Alloys
<p>Dataset of "Impact of Catalysis-Relevant Oxidation and Annealing Treatments on Nanostructured GaRh Alloys"</p>
Formation of Methanol via Fischer-Tropsch Catalysis by Cosmic Iron Sulphide
<p>This supporting material contains:</p> <ul> <li>Cartesian coordinates of the PBE optimized minima and transition states for the reactions under study, in XYZ format.</li> <li>Inputs for the <a href="https://www.cp2k.org/">CP2K</a> package.</li> <li>Vibrational calculations with all the frequencies.</li> <li>Kinetic data (RRKM).</li> </ul>
Conformational Ensembles Reveal the Origins of Serine Protease Catalysis - auxiliary data and code
<p>EnsemblePDB.zip - package version used to create pseudo-ensembles in the paper "Conformational Ensembles Reveal the Origins of Serine Protease Catalysis"</p> <p>serine_protease_ensembles.zip - data and code used to generate and analyze the data presented in the paper "Conformational Ensembles Reveal the Origins of Serine Protease Catalysis"</p>
Automating Computational Chemistry in Multiscale Catalysis Electronic Data Compendium
<p>Electronic data compendium containing supplemental datasets and figures, primarily detailing lateral interactions and adlayer properties at catalytic surfaces.</p> <p>Part of the physical print of the thesis "Automating Computational Chemistry in Multiscale Catalysis" by B. Klumpers.</p>
Photoactive Nickel Complexes in Cross-Coupling Catalysis
<p>ChemDraw figures to the minireview published in <em>Chem. Eur. J.</em> <strong>2021</strong>, <em>27</em>, 2770-2278; doi: <a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/chem.202003974"> 10.1002/chem.202003974</a>.</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.