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21,281
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21,281 results for “Molecular”
CO excitation, molecular gas density and interstellar radiation field in local and high-redshift galaxies
<p>This dataset includes the SED fitting figures and full sample table produced in the study of Liu et al. (2020, ApJ). Two example figures are shown in the Figure 1 of the paper. And selected columns of the full sample table is shown in the Table 1 of the paper. </p> <p> </p>
Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers
<p>Supplementary files for Molecular Differences Between Squamous Cell Carcinoma and Adenocarcinoma Cervical Cancer Subtypes: Potential Prognostic Biomarkers</p>
Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site
<p>Metabolomics dataset used in the publication "Modeling the metabolic profile of Mytilus edulis reveals molecular signatures linked to gonadal development, sex and environmental site"</p> <p>Jaanika Kronberg, Jonathan J. Byrne, Jeroen Jansen, Philipp Antczak, Adam Hines, John Bignell, Ioanna Katsiadaki, Mark R. Viant and Francesco Falciani </p> <p>Metabolomics dataset for metabolic bins 1 to 1045 for 376 mussels as used in the publication.</p> <p>Mussel metadata are described in a separate file (spectrum number, sample label, sex, site, species, month, temperature of water, salinity of water, ADG rate, gonadal stage, parasite load)</p> <p>Species 1: Mytilus edulis, species 2: hybrid, species 3: Mytilus galloprovincialis</p>
A dissymmetric [Gd2] coordination molecular dimer hosting six addressable spin qubits. Open data sets
<p>Includes data relevant for publication with DOI <a href="https://doi.org/10.1038/s42004-020-00422-w">10.1038/s42004-020-00422-w</a> plus a table with information about how the data were obtained and processed.</p>
An intense, cold, velocity-controlled molecular beam by frequency-chirped laser slowing - supporting data
<p>These are the data presented in figures 3, 4, 5, 6 and 7 of our paper "An intense, cold, velocity-controlled molecular beam by frequency-chirped laser slowing". The first line of each data file explains the content. The second line labels the columns. The remaining rows give the data.</p>
Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome
<p>The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 inter-protein and 26 intra-protein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6 and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino-acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/26S-PIPs or the README file.</p>
MelanoDB: Dataset files of clinical and molecular features of advanced melanoma patients treated with MAPK inhibitors
<p>MAPK inhibitors have significantly improved overall survival in patients with metastatic melanoma disease but their efficacy is still limited by primary or acquired resistance. Several studies have attempted to predict response to MAPK inhibitor therapy, however the lack of a consistent cohort prevents better definition of associations between treatment efficacy and clinical and/or molecular features. Here, we present MelanoDB, a collection of patients with metastatic melanoma treated with MAPK inhibitors. We formatted data from 8 different studies for a total of 417 cases to gather common clinical and molecular features. Whole or partial exome sequencing is available for 191 cases and gene expression for 132 cases. We provide a web application to explore the integrated data and its distribution among the collected studies, and we share this dataset to the scientific community according to FAIR principles</p> <p>These data are available under the licence CC-BY-SA.</p> <p>Here we provide a web application viewer of the database content: http://melanodb-ircm.montp.inserm.fr/</p> <p>You are requested to cite this repository in the case of using these data in a publication.</p>
Strongly Enhanced Cooperative Surface Propensity of Atmospherically Relevant Organic Molecular Ions in Aqueous Solution - data
<p>Dataset pertaining to the manuscript "Boosting aerosol surface effects: strongly enhanced cooperative surface propensity of atmospherically relevant organic molecular ions in aqueous solution", published in <a href="https://doi.org/10.5194/acp-25-3503-2025">Atmos. Chem. Phys., 25, 3503–3518, 2025</a>. Using liquid-jet photoelectron spectroscopy, we investigate the surface propensity of various carbonaceous species in aqueous solution. We cover a range of substances relevant to atmospheric climate models. Here we give the data of Fig.s 1-3 of our manuscript in numeric form, and document the underlying photoemission spectra including all relevant metadata.</p> <p>Experimental data are documented in the NeXus format (extension .nxs). For a description see:<br>The NeXus Data Format definition (v2024.02), https://manual.nexusformat.org/index.html<br>NXmpes expansion for FAIRmat data (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/classes/contributed_definitions/NXmpes.html<br>NXmpes_liquid expansion to NXmpes (v.2024.07), https://fairmat-nfdi.github.io/nexus_definitions/mpes-liquid/classes/contributed_definitions/NXmpes_liquid.html</p> <p>The following files are provided:<br>'Data Collection_Core.nxs' - Photoemission data, core level spectra<br>'Data Collection_Valence.nxs'<strong> </strong> - Photoemission data, valence spectra</p> <p>Ascii data of figures 1a, 2 and 3:<br>'Figure 1 data.txt'<br>'Figure 2 data.txt'<br>'Figure 3 data.txt'</p> <p>Contact person for questions regarding this data set: Uwe Hergenhahn, uhe@fhi.mpg.de . If you use these data for your scientific work we kindly ask you to send us a copy of your published results.</p> <p>Acknowledgements: We acknowledge DESY (Hamburg, Germany), a member of the Helmholtz Association HGF, for the provision of experimental facilities. Parts of this research were carried out at PETRA III, and we would like to thank Moritz Hoesch and his team for assistance in using beamline P04. Beamtime was allocated for proposal I-20220937 EC. Harmanjot Kaur and Bernd Winter acknowledge the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 883759, AQUACHIRAL). Stephan Thürmer acknowledges support from JSPS KAKENHI (grant no. JP20K15229) and ISHIZUE 2024 of Kyoto University. Florian Trinter acknowledges funding by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – project 509471550, Emmy Noether Programme. Florian Trinter and Bernd Winter acknowledge support by the MaxWater initiative of the Max-Planck-Gesellschaft. Olle Björneholm acknowledges support from the Swedish Research Council (VR) through project 2023-04346 and the Swedish Foundation for International Cooperation in Research and Higher Education (STINT) through project 202100-2932. Ricardo Marinho, Joel Pinheiro, and Arnaldo Naves de Brito acknowledge support from the Swedish–Brazilian collaboration STINT-CAPES (process no. 88881.465527/2019-01). Arnaldo Naves de Brito acknowledges support from FAPESP (the São Paulo Research Foundation, process no. 2017/11986-5), Shell and ANP (Brazil’s National Oil, Natural Gas and Biofuels Agency), and CNPq-Brazil (process no. 401581/2016-0). Harmanjot Kaur and Shirin Gholami acknowledge support by the IMPRS for Elementary Processes in Physical Chemistry.</p> <p>Financial support: This research has been supported by the European Research Council, Horizon Europe (grant no. 883759); the Japan Society for the Promotion of Science (grant no. JP20K15229); the Deutsche Forschungsgemeinschaft (grant no. 509471550); the Vetenskapsrådet (grant no. 2023-04346), the Swedish Foundation for International Cooperation in Research and Higher Education (grant no. 202100-2932); the Fundação de Amparo à Pesquisa do Estado de São Paulo (grant no. 2017/11986- 5); and the Conselho Nacional de Desenvolvimento Científico e Tecnológico (grant no. 401581/2016-0).</p> <p>Version history:<br>1 - initial release<br>2 - numbering of figures adapted to published version, photoemission data added.</p>
Supporting Data - Taxonomic reassessment of Tetrapygus niger (Arbacioida, Echinoidea): molecular and morphological evidence support its placement in Arbacia
<p>This dataset contains the accession numbers and links of the sequences of the specimens analyzed by this work, other sequences used for the analyses can be found in the original article. The species from which the sequences were extracted are: Tetrapygus niger Molina, 1782; Arbacia dufresnii Blainville, 1825; Arbacia spatuligera Valenciennes, 1846 and Coelopleurus floridanus A. Agassiz, 1872. The accession numbers for the Cytochrome Oxidase subunit I (COI) and 28S of the nuclear genome are presented separately.</p><p>In addition, the morphological data of Tetrapygus niger (Test diameter, test height and peristome diameter) presented in this study are shown, as well as their collectors, corresponding collection, country and locality.</p>
Molecular Thermometer Fig.2 - Dataset
<p>This is the data set used to generate Fig.2 in the paper https://doi.org/10.1103/PRXQuantum.4.040314</p>
Data for "A Quantum Definition of Molecular Structure"
<p>Supplemental data for our article "A Quantum Definition of Molecular Structure".</p><p>Version 1.1.0 contains data for additional k-medoids runs performed on different subsets of the complete sample.</p>
Data for the publication "Sodium Triflate Water-in-Salt Electrolyte in Advanced Battery Applications: A First-principles Based Molecular Dynamics Study"
<p>The datasets 'CONTCAR_aiMLMD' and 'CONTCAR_AIMD' represent the final structures obtained from the aiMLMD and AIMD simulations, respectively. These simulations were conducted using VASP at T=333K and c=9.25 m.</p> <p>The datasets 'NP.rdf' and 'MSD_NP.xlsx' represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. The associated MD simulation was performed using a nonpolarizable force field in the LAMMPS package at T=333K and c=9.25 m. The file 'dataNP.lmp' includes the initial configuration for this simulation. The GROMOS parameters were employed for LJ interactions of sodium and all other force field parameters were set according to Table 1 in the manuscript.</p> <p>The datasets 'P.rdf' and 'MSD_P.xlsx,' respectively, represent the radial pair distribution functions at different time steps and the time-dependent variations of mean squared displacement for sodium in 10 segments of the classical MD trajectory. These data were obtained employing the Drude oscillator model in the LAMMPS package at T=333K and c=10 m. The file 'dataP.lmp' includes the initial configuration for this simulation. The simulation was conducted using the optimal force field parameters 'Sys. 1,' as described in table 3 of the manuscript.</p> <p>The second column in the files 'NP.rdf' and 'NP.rdf' represents the distance from sodium. The subsequent odd columns display the radial distribution functions for the Na-C, Na-F, Na-S, Na-O, Na-Na, Na-Hw, and Na-Ow pairs, while the even columns present the coordination numbers for the same atom pairs.</p>
Two 100 ns NVT molecular dynamics simulations of dsDNA and dsRNA "GGGG" 18-mers (GCGGGGGGGGGGGGGGGC)
<p>Supporting information for "Molecular origin of distinct hydration dynamics in double helical DNA and RNA sequences" by E. Frezza, D. Laage and E. Duboué-Dijon, <span><em>J. Phys. Chem. Lett.</em></span> <span>2024</span><span>, 15</span><span>, </span><span>4351–4358</span><br>Two 100 ns-long NVT molecular dynamics simulation: one of dsDNA "GGGG" 18-mer (GCGGGGGGGGGGGGGGGC) and one of the analogous dsRNA. The nucleic acid is explicitly solvated in water and neutralized with 0.15M KCl. Simulations were performed using the Gromacs 5 software. DNA is described with the Amber 99SB-ILDN force field with the BSC0 modifications, RNA is described with the Amber 99SB-ILDN force field with the BSC0 and χOL3 modifications, the SPC/E force field is used for water, and the Joung Cheatham paraeters for ions. The shared coordinates are saved every 500fs, twice less frequently than the original trajectories used for the publication, to reduce the size of the shared dataset below the allowed size limit.</p>
Data related to the article "Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes"
<p>Contains input files and data used to generate the figures of the article:</p> <p>Impedance of nanocapacitors from molecular simulations to understand the dynamics of confined electrolytes<br>(Giovanni Pireddu, Connie J. Fairchild, Samuel P. Niblett, Stephen J. Cox and Benjamin Rotenberg)</p> <p>ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-2ccrw</p> <p>Published version: to be inserted upon publication</p> <p>The folder EXAMPLE_INPUT_FILES contains typical [MetalWalls](https://doi.org/10.21105/joss.02373) ([repository](https://gitlab.com/ampere2/metalwalls)) and [LAMMPS]([repository](https://github.com/lammps/lammps)) input files used to perform the molecular simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper (see below).</p> <p><br>Notes: <br>1) In the file names, the notation 'M01', 'M05', 'M10' and 'M15' refers to the salt concentration in each system (0.1, 0.5, 1.0 and 1.5, respectively). 'W' refers to pure water (0 M) systems.<br>2) In the file names, the notation 'd1', 'd2', 'd3', 'd4', refers to different interelectrode distances (d1= 2.56 nm; d2= 5.07 nm; d3= 9.80 nm; d4= 19.84 nm) <br>3) The files containing the polarization cross-correlation are marked with 'AxB' indicating the cross-correlation between the contributions A and B. Specifically A and B can be: <br> - T = total<br> - I = ion<br> - W = water</p> <p><br>Figure 1:<br>- Panel B<br> - 'Fig1_CapConcentration': Differential capacitance scaled by electrode area as a function of NaCl concentration<br>- Panel C<br> - 'Fig1_QACF_*': Electrode charge autocorrelation function<br>- Panel D<br> - 'Fig1_Norm_QACF_*': Normalized electrode charge autocorrelation function<br> - 'Fig1_NormChar_*': Normalized non-equilibrium charge response</p> <p>Figure 2:<br>- Panel A: <br> - 'Fig2_ReZ_*': Real part of impedance<br>- Panel B:<br> - 'Fig2_nImZ_*': Negative imaginary part of impedance<br>- Panel C:<br> - 'Fig2_ReZint_*': Real part of interfacial impedance<br> - 'Fig2_Resistivities.dat': Resistivity as a function of NaCl concentration (bulk, confined, Nernst-Einstein)<br>- Panel D:<br> - 'Fig2_nImZint_*': Negative imaginary part of interfacial impedance<br> - 'Fig2_ECM*': Capacitor contributions to the imaginary part of interfacial impedance (finite concentrations)<br> - 'Fig2_ECW1.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Full cell capacitance taken into account<br> - 'Fig2_ECW2.dat': Capacitor contributions to the imaginary part of interfacial impedance (pure water). Interfacial capacitance taken into account </p> <p>Figure 3:<br>- Panel A:<br> - 'Fig3_ReCond_Peyman_M10.dat': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br> - 'Fig3_ReCond_Querry_M10.dat': Real part of conductivity (data from: MR Querry, RC Waring, WE Holland, GM Hale, W Nijm, Optical Constants in the Infrared for Aqueous Solutions of NaClt. J. Opt. Soc. Am. 62 (1972)) <br> - 'Fig3_ReCond_Vinh_M10.dat': Real part of conductivity (data from: NQ Vinh, et al., High-precision gigahertz-to-terahertz spectroscopy of aqueous salt solutions as a probe of the femtosecond-to-picosecond dynamics of liquid water. The J.<br>Chem. Phys. 142, 164502 (2015).)<br> - 'Fig3_ReCond_M10.dat': Real part of conductivity from MD simulations<br>- Panel B:<br> - 'Fig3_ReCond_M*/W.dat': Real part of conductivity from MD simulations<br> - 'Fig3_ReCond_Peyman_M*': Real part of conductivity (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))<br>- Panel C:<br> - 'Fig3_Cond0.dat': Static conductivity as a function of concentration (MD data)<br> - 'Fig3_Cond0_Buchner.dat': Static conductivity as a function of concentration (data from: R Buchner, GT Hefter, PM May, Dielectric relaxation of aqueous nacl solutions. The J. Phys. Chem. A 103, 1–9 (1999))<br> - 'Fig3_Cond0_Peyman.dat': Static conductivity as a function of concentration (data from: A Peyman, C Gabriel, E Grant, Complex permittivity of sodium chloride solutions at microwave frequencies. Bioelectromagnetics 28, 264–274 (2007))</p> <p>Figure 4:<br>- Panel A: <br> - 'Fig4_ReZ_d*': Real part of impedance (MD simulations)<br> - 'Fig4_ReZEC_d*': Real part of impedance (equivalent circuit model)<br>- Panel B:<br> - 'Fig4_nImZ_d*': Negative imaginary part of impedance (MD simulations)<br> - 'Fig4_nImZEC_d*': Negative imaginary part of impedance (equivalent circuit model)</p> <p>Figure 5:<br>- 'Fig5_TauQ.dat': timescales from the total charge autocorrelation functions<br>- 'Fig5_iontot.dat': timescales from the TxI autocorrelation function<br>- 'Fig5_RC.dat': timescales from the RC estimates<br>- 'Fig5_RbulkC.dat': timescales from the RbulkC estimates<br>- 'Fig5_Taudiff.dat': timescales from the difference between electrolyte and pure water QACFs<br>- 'Fig5_taud.dat': tau_d analytical timescales<br>- 'Fig5_tauDebye.dat': tau_Debye analytical timescales<br>- 'Fig5_taumix.dat': tau_mix analytical timescales</p> <p>Figure 6:<br>- Panel A:<br> - 'Fig6_Static_*: Static correlation between polarization contributions as a function of salt concentration<br>- Panel B:<br> - 'Fig6_Dynamic_EQ_*_M01' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M01' Dynamical correlations between polarization contributions (non-equilibrium MD results)<br>- Panel C:<br> - 'Fig6_Dynamic_EQ_*_M10' Dynamical correlations between polarization contributions (equilibrium MD results)<br> - 'Fig6_Dynamic_NEQ_*_M10' Dynamical correlations between polarization contributions (non-equilibrium MD results)</p> <p> </p> <p> </p>
Data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry"
<p>Raw data for "Dynamic of binary molecular systems – advantages and limitations of NMR relaxometry". DOI of article: https://doi.org/10.1063/5.0188257</p>
Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons - Dataset
<p>Dataset complement to "Strong coupling electron-photon dynamics: a real-time investigation of energy redistribution in molecular polaritons" - includes output and video files obtained using the <a href="https://etprogram.org/">eT program</a>, an open-source electronic (and molecular-polaritonic) structure program.</p> <p>See the paper at <a href="https://doi.org/10.1103/PhysRevResearch.6.033283">https://doi.org/10.1103/PhysRevResearch.6.033283</a></p>
Molecular Dynamic Simulation on the Role of CL5D in Accelerate the Product Dissociation of SIRT6
<p>The source data used to generate figures in the main text is stored in the ‘Source Data.xlsx’ file, and 'Source Data Description.docx' is a brief description of the source data table.<br>'SIRT6.prmtop' and 'SIRT6.inpcrd' are initial structure of SIRT6 system,'SIRT6-CL5D.prmtop' and 'SIRT6-CL5D.inpcrd' are initial structure of SIRT6-CL5D system.<br>'SIRT6_equ.pdb', 'SIRT6-CL5D'_equ.pdb are snapshots of the equilibrium structure of the SIRT6 system and the SIRT6-CL5D system, respectively.<br>'ramd.conf' is an example configuration file that uses RAMD simulations to obtain the AR6 dissociation path in the SIRT6 system, with the acceleration of 0.0625 kcal/Å/g and a cutoff distance of 0.005 Å.<br>'win1.conf' and 'win1.in' are example configuration files for the first window of the umbrella sampling, which calculates the dissociation energy barrier of AR6 in the SIRT6 system,'win1.in' is the parameter file for umbrella sampling, with A 2.5 kcal/mol/Ų spring constant, and window center is 9 Å.</p> <p> </p>
Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens
<p>This repo contains Seurat objects, differential expression analysis results, and pathway gene lists for the manuscript "Systematic reconstruction of molecular pathway signatures using scalable single-cell perturbation screens"<br>List of files:</p> <p>1. Seurat_object_IFNB_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-beta pathway<br>2. Seurat_object_IFNG_Perturb_seq.rds: Seurat object of the Perturb-seq data for Interferon-gamma pathway<br>3. Seurat_object_TNFA_Perturb_seq.rds: Seurat object of the Perturb-seq data for TNF-alpha pathway<br>4. Seurat_object_TGFB1_Perturb_seq.rds: Seurat object of the Perturb-seq data for TGF-beta1 pathway<br>5. Seurat_object_INS_Perturb_seq.rds: Seurat object of the Perturb-seq data for insulin pathway<br>6. Pathway_genelist.rds: The pathway gene lists from MultiCCA analysis<br>7. Pathway_Exclusive_genelist.rds: The pathway exclusive gene lists generated from Pathway_genelist.rds<br>8. HClust_Pathway_celltype_specific_genelist.rds: The cell-line specific pathway gene lists from hierarchical clustering analysis independently done on each cell line<br>9. DE_results_all_pathway.zip: The DE test results for all the regulators, cell lines, and pathways (from Mixscale weighted DE test.)<br>10. Bulk_RNAseq_Seurat_object_IFNG_and_TGFB_stim.rds: Seurat object for the bulk RNA-seq data for interferon-gamma and TGF-beta stimulation experiments<br>11. Parse_Guide_Capture_Protocol.pdf: The guide RNA capture protocol developed for Parse Evercode Whole Transcriptome kit</p>
ChemTastesDB: A Curated Database of Molecular Tastants
<p><em><strong>ChemTastesDB</strong></em> is a database that includes curated information of 4075 molecular tastants. <strong><em>ChemTastesDB</em></strong> is distributed to the scientific community to expand the information of molecular tastants, which could assist the analysis of the relationships between molecular structure and taste, as well as <em>in</em> <em>silico</em> (QSAR/QSPR) studies for taste prediction. Examples of QSPR approaches for the prediction of molecular taste are given in the following publication: <em>Rojas, C., Abril-González, M., Ballabio, D. & García, F. (2025). ChemTastesPredictor: An ensemble of machine learning classifiers to predict the taste of molecular tastants. Chemometrics and Intelligent Laboratory Systems. 261, 105380. <a href="https://doi.org/10.1016/j.chemolab.2025.105380">https://doi.org/10.1016/j.chemolab.2025.105380</a>.</em></p> <p>The 4075 molecular tastants are categorized into one of the five basic tastes (sweet, bitter, umami sour and salty), as well as to other classes related to non-basic tastes (tasteless, non-sweet, non-bitter, multitaste and miscellaneous). The molecules are categorized into following ten classes: sweet (1313), bitter (1615), umami (220), sour (49), salty (16), multitaste (179), tasteless (232), non-sweet (304), non-bitter (28), and miscellaneous (119).</p> <p><strong><em>ChemTastesDB</em></strong> provides the following information for each molecule: name, PubChem CID, CAS registry number, canonical SMILES string, class taste and the reference to the scientific sources from where data were retrieved. In addition, the molecular structure in the HyperChem (<em>.hin</em>) format of each compound is provided.</p> <p>This is version 2.1 of the <em><strong>ChemTastesDB</strong></em>. In this new version, 1131 newly curated compounds were added. These new molecules were retrieved from 52 new bibliographic references.</p>
Data related to the article "A molecular perspective on induced charges on a metallic surface"
<p>Contains input files and data used to generate the figures of the article:</p> <p>A molecular perspective on induced charges on a metallic surface<br> (Giovanni Pireddu, Laura Scalfi, and Benjamin Rotenberg)</p> <p>arXiv: <a href="https://arxiv.org/abs/2110.11103">https://arxiv.org/abs/2110.11103</a></p> <p>The folder EXAMPLE_INPUT_FILES contains typical <a href="https://doi.org/10.21105/joss.02373">MetalWalls</a> (<a href="https://gitlab.com/ampere2/metalwalls">repository</a>) input files used to perform the simulations.</p> <p>The folder DATA_FIGURES contains the processed data used to plot all the figures of the paper.</p> <p>The data files are named according to the ion distance considered:<br> - 'd1' corresponds to 1.50 Å;<br> - 'd2' corresponds to 3.14 Å;<br> - 'd3' corresponds to 5.40 Å;<br> - 'd4' corresponds to 7.03 Å;<br> - 'd5' corresponds to 15.00 Å.</p> <p>The data files are reported in three formats:<br> - induced charge density maps are in matrix format (arranged in several rows with each value corresponding to the values on the map).</p> <p> The coordinates of each point on the map are stored in the first row and first column.<br> - Solvent charge density maps are arranged in columns: (i) x coordinate, (ii) y coordinate, (iii) solvent charge density.<br> - Radial profiles are arranged in columns: (i) r, (ii) charge density, (iii) radial integral.</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.