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14,239 results for “STRUCTURE”
HEroBM: a deep equivariant graph neural network for high-fidelity backmapping from coarse-grained to all-atom structures
<p><span>Molecular simulations play a pivotal role in chemistry, biology, and material sciences, enabling the</span><br><span>study of complex dynamic properties within systems. Coarse-grained (CG) techniques have emerged</span><br><span>as indispensable tools in this domain, facilitating the sampling of large-scale systems and extending</span><br><span>simulation timescales by simplifying system representation. However, CG approaches involve a trade-</span><br><span>off: they sacrifice atomistic details that may be crucial for understanding the underlying processes.</span><br><span>To address this challenge, a recommended strategy is to identify key CG conformations and employ</span><br><span>backmapping methods to retrieve atomistic coordinates. Currently, rule-based methods often yield</span><br><span>suboptimal geometries and rely on energy relaxation, resulting in less-than-optimal outcomes. In</span><br><span>contrast, machine learning techniques offer higher accuracy but may lack transferability between</span><br><span>systems or be tied to specific CG mappings. In this study, we present HEroBM, a dynamic and scalable</span><br><span>method that utilizes deep equivariant graph neural networks and a hierarchical approach to achieve</span><br><span>high-resolution backmapping. HEroBM is capable of handling any type of CG mapping, providing a</span><br><span>versatile and efficient protocol for reconstructing atomistic structures with high accuracy. Grounded</span><br><span>in local principles, HEroBM spans the entire chemical space and can be applied across systems of</span><br><span>varying composition and sizes. We demonstrate the versatility of our framework through a range of</span><br><span>biological systems, including a complex real-case scenario. Here, our end-to-end backmapping approach</span><br><span>accurately generates atomistic coordinates for a G protein-coupled receptor bound to an organic small</span><br><span>molecule within a cholesterol/phospholipid bilayer. The high-fidelity HEroBM backmapping enables</span><br><span>researchers to effortlessly transition between CG and all-atom simulations, opening unprecedented</span><br><span>avenues for molecular investigations.</span></p>
Catalyst Supraparticles: Tuning the Structure of Spray‐Dried Pt/SiO2 Supraparticles via Salt‐Based Colloidal Manipulation to Control their Catalytic Performance
<p>This data publication is based on the metadata and raw datasets underlying the manuscript: P. Groppe, J. Reichstein, S. Carl, C. Cuadrado Collados, B.-J. Niebuur, K. Zhang, B. Apeleo Zubiri, J. Libuda, T. Kraus, T. Retzer, M. Thommes, E. Spiecker, S. Wintzheimer, K. Mandel, Catalyst Supraparticles: Tuning the Structure of Spray-Dried Pt/SiO2 Supraparticles via Salt-Based Colloidal Manipulation to Control their Catalytic Performance. Small 2024, 2310813. https://doi.org/10.1002/smll.202310813</p> <p>A detailed description of the dataset is given in the attached "Raw data assignment.xlsx"</p>
Synthesis, Structure and Redox Properties of Single-atom Bridged Diuranium Complexes Supported by Aryloxides
<p>This upload contains raw data (NMR, X-Ray Diffraction, Electrochemistry, SQUID and Elemental Analysis) files for the article</p>
Data from Neutral genetic structuring of pathogen populations during rapid adaptation
<p><strong>Datasets and temporary dataframes relating to the article "Neutral genetic structuring of pathogen populations during rapid adaptation".</strong></p> <p>These datasets and temporary dataframes are necessary to run the scripts from the public GitLab repository: <a href="https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation">https://gitlab.com/saubin.meline/neutral-genetic-structuring-adaptation</a>. Please refer to this public GitLab repository for the latest version of the codes and to perform all analyses presented in the article.</p> <p>Original datasets from the demogenetic model:</p> <ul> <li>Output_RandomDesign.txt</li> <li>Output_RegularDesign_With_host_alternation.txt</li> <li>Output_RegularDesign_Without_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_With_host_alternation.txt</li> <li>Output_RandomDesign_Mnull_Medoid_Without_host_alternation.txt</li> </ul> <p>All remaining files correspond to temporary dataframes generated by the scripts in the GitLab repository, provided here for reproducibility of the results and to save time at certain time-consuming scripts.</p>
cldf-datasets/szetosinitic: Chinese Structure Dataset from Szeto et al.'s (2018) paper in CLDF-Format
<p>This is a structural dataset originally published along with a paper by Szeto et al. (2018) on Chinese dialect classification:</p> <blockquote> <p>Szeto, P. Y.; Ansaldo, U. & Matthews, S.Typological variation across Mandarin dialects: An areal perspective with a quantitative approach Linguistic Typology, 2018, 22, 233-275.</p> </blockquote>
Rapid structure determination of microcrystalline molecular compounds using electron diffraction (nanoArgovia Project A3EDPI)
<p>The are the data linked to the publication "Rapid structure determination of microcrystalline molecular compounds using electron diffraction", <a href="https://doi.org/10.1002/anie.201811318">10.1002/anie.201811318</a>. Electron Diffraction data collected with an EIGER X 1M detector (DECTRIS Ltd.).</p> <p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration. Images of the respective crystals have '_img_' in their file names. The log files for recording the stage alpha angle are included with the same name and suffix .txt. See publication for details.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p> <p>The crystallographic data (CIF-files) have been uploaded to the ICSD (High--throughput Structural Chemistry with Electron Diffraction) and CSD (https://www.ccdc.cam.ac.uk/) respectively:</p> <p>Paracetamol from Grippostad CCDC 1856579<br> electron structure of MBBF4 CCDC 1856580</p> <p>ZSM-5 x227 CSD 1856581</p> <p>ZSM-5 x331 CSD 1856582</p> <p>ZSM-5 x79 CSD 1856583<br> ZSM-5 x811 CSD 1856584</p> <p> </p>
Homologous membrane protein structures (HOMEP) dataset version v2
<p><strong>Protein structures from the dataset of Homologous MEmbrane Protein structures (HOMEP)</strong> version v2 created in 2010, published in 2013. A more automated version of HOMEP v1: <a href="https://doi.org/10.5281/zenodo.2646534">10.5281/zenodo.2646534</a><br> </p> <p><strong>Table 1</strong> = List of protein databank structure entries<br> From Stamm et al, PLOS One 2013, <a href="https://www.ncbi.nlm.nih.gov/pubmed/23469223">https://www.ncbi.nlm.nih.gov/pubmed/23469223</a>, Supplementary Table 1, with the following entries:<br> Family grouping, Protein databank identifier, Name, Source organism, Resolution (Å)</p> <p> </p> <p><strong>Table 2</strong> = List of pairs of structures<br> From Stamm et al, PLOS One 2013, <a href="https://www.ncbi.nlm.nih.gov/pubmed/23469223">https://www.ncbi.nlm.nih.gov/pubmed/23469223</a>, Supplementary Table 2, with the following entries:<br> Family grouping, PDB code for first structure, Chain ID from PDB1, PDB for second structure, Chain ID from PDB2, protein structural difference (PSD), % sequence identity</p> <p> </p> <p><strong>File S2 HOMEP2 Dataset.tar.gz</strong> = Protein databank format files (PDB) are attached in the Dataset tar zipped file, organized by family. From Stamm et al, PLOS One 2013, <a href="https://www.ncbi.nlm.nih.gov/pubmed/23469223">https://www.ncbi.nlm.nih.gov/pubmed/23469223</a>, Supplementary dataset.</p>
Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"
<p>Research data supporting "Block copolymer-directed single diamond hybrid structures derived from X-ray nanotomography"</p>
Structure and dynamics of growing networks of Reddit threads
<p>Data used in the paper "<a href="https://doi.org/10.1007/s41109-024-00654-y" target="_blank" rel="noopener">Structure and dynamics of growing networks of Reddit threads</a>".</p> <p>This dataset is made of 6366 threads collected from the r/AmITheAsshole community on Reddit. The dataset contains a total of 6,372,251 comments. The collected threads constitute the “top” submissions — those having the highest score, measured as the difference between upvotes and downvotes of a post. We downloaded them using PRAW, running 10 different queries across various temporal scopes, and then cleaning the obtained dataset by removing duplicated threads. Please refer to the paper, specifically to <a href="https://appliednetsci.springeropen.com/articles/10.1007/s41109-024-00654-y/tables/3" target="_blank" rel="noopener">Table 3</a>, for more details about the dataset.</p> <p><strong>If you use this data, please cite the following source:</strong> Goglia, D., Vega, D. Structure and dynamics of growing networks of Reddit threads. Appl Netw Sci 9, 48 (2024). https://doi.org/10.1007/s41109-024-00654-y</p>
Datasets and Jupyter notebook for the structural analysis of protein-RNA interface evolution
<p>The present repository contains data and code related to our manuscript "Structural comparison of protein-RNA homologous interfaces reveals widespread overall conservation contrasted with versatility in polar contacts". In the manuscript, we analyze the evolution of protein-RNA interfaces by building a dataset of protein-RNA interologs (homologous interfaces) and exploring how interface contacts are conserved between homologous interfaces, as well as possible explanations for non-conserved contacts.</p> <p>This repository contains the following files:</p> <ul> <li>DataAnalysisNotebook.ipynb is a Jupyter notebook to reproduce contact conservation analysis and all figures from our manuscript, and to explore data</li> <li>env.yaml is an environment file in order to build a Conda/Mamba environment to run the Jupyter notebook </li> <li>2022-02-21-PDB.csv contains data from the PDB about 3D structures of complexes containing interacting protein and RNA chains (PDB structure identifier, chain identifiers, experimental technique and resolution)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.tsv contains more detailed information about interacting protein and RNA chains from these complexes (PDB and chain identifiers, protein and RNA size, interface size and number of contacts)</li> <li>2022-02-21-PDB_proteinchainscontactingRNAchains.groupbp.txt.selectXE_2.50_p30_r10_pi5_ri5_rep_bc-100.out_RNAcl_0.99.tsv contains the same detailed information, restricted to the filtered dataset used as a starting point in our interolog search pipeline</li> <li>PDBinterfaceAlign.csv contains information about the structural alignment of pairs of protein-RNA interactions (structural alignment TM-scores, sequence identity and coverage)</li> <li>DataInterologsParam.tsv contains information about a pre-filtered set of 2587 potential interologs (including interface RMSD, sequence identity and coverage and interface size)</li> <li>DataInterologsContactsFixedSASA.tsv contains detailed information about conserved and non-conserved contacts in the final set of 2022 interologs (atomic contacts, apolar contacts, hydrogen bonds, salt bridges and stacking information for aminoacid-nucleotide pairs, as well as information about whether each belongs to the interface, secondary structures, and the aminoacid surface accessibility and evolutionary conservation metrics) - compared to version 1, the calculation of solvent accessibility was fixed for a number of interolog pairs</li> <li>DataCons.csv contains precomputed contact conservation metrics for each of the 2022 interolog pairs, for fast reproduction of manuscript figures</li> <li>DataInterologsContactsResampledMaintainStructSeqId.tsv, DataInterologsContactsShuffled.tsv and DataInterologsShuffled.tsv relate to baselines computed for contact conservation assessment</li> <li>clan.txt, clan_membership.txt, ecod.latest.domains.uniq.txt, rfam_interfaces_977.txt, DataGroupsECOD.tsv, DataGroupesRFAM.tsv, DataGroupsRFAMClan.tsv, DataInterfaceGroupsECOD.tsv and DataInterfaceGroupsRFAM.tsv relate to the ECOD (respectively Rfam) classification of protein domains (respectively RNA) in protein-RNA interfaces from our dataset</li> <li>ListeIntraHbonds.pkl and ListeIntraSaltBridges.pkl are pickle-format data files containing intra-molecular hydrogen bonds and salt bridges (respectively) that are used to analyse scenarii of compensation for non-conserved polar contacts.</li> </ul>
Docked structures from "Optimizing active learning for free energy calculations"
<p>This archive contains the docked TYK2 structures used in the paper "Optimizing active learning for free energy calculations" (<a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.ailsci.2022.100050" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.ailsci.2022.100050</span></span></a>). AM1-BCC charges are stored in the field "AM1Cache" in the SD file. The charges can be extracted using the code sample below. </p> <p> </p> <pre><code>from rdkit import Chem import base64 import pickle suppl = Chem.SDMolSupplier("10k_most_similar_tyk2_charged.sdf", removeHs=False) for mol in suppl: am1 = mol.GetProp("AM1Cache") am1_charges = pickle.loads(base64.b64decode(mol.GetProp("AM1Cache"))) assert len(am1_charges) == mol.GetNumAtoms(), "Charge cache has different number of charges than mol atoms"</code></pre>
SFEM Dataset for Structural Health Monitoring
<p>The data is generated through Spectral Finite Element Methods (SFEM) solver for Ultrasonic Guided Wave based Structural Health Monitoring. The data is used in repository "https://github.com/mahindrautela/DINS-SHM" and the paper "Ultrasonic guided wave based structural damage detection and localization using model assisted convolutional and recurrent neural networks".</p>
Two decades of body length measurements in size-structured larval and juvenile fish populations in English rivers.
<p>Long term ecological datasets are valuable in providing context and understanding to complex ecological processes that occur over broad temporal scales, and provide a baseline for analysing change. Monitoring of fish populations in UK waterbodies and elsewhere is typically through measuring the length of individual fish caught in surveys. Through this method, the age structure of fish populations can be determined, as well as over winer survival rates and future recruitment success and cohort sizes can be predicted. The larval and juvenile period are when fish are considered most vulnerable to predation, competition, disease and environmental perturbations. </p> <p><br>This study presents the first long-term larval and juvenile fish lengths dataset for 67 survey sites over two decades (1999-2018) from the rivers Ancholme, Warwickshire Avon, Don, Trent, and Yorkshire Ouse (including the Swale, Ure, Nidd and Wharfe) in the United Kingdom. These rivers represent a range of topographical and biotopical characteristics. For the majority of this study, surveys were conducted on a monthly or fortnightly basis making both annual and seasonal analyses of size structure, growth and body length possible. Although there is some variation in the sampling frequency and some locations varied throughout the study according to requirements. In total, more than 380,000 larval or juvenile fish of 30 species were measured, likely representing one of the most comprehensive datasets of its type.</p> <p>Surveys were conducted in river margins, where the velocity was slowest and larval and juvenile fish tend to aggregate. Fish were captured using a 25 x 3 m micromesh (3 mm mesh size) seine net that was set in a rectangle parallel to the bank. This net capture fish as small as 5 mm and is the most appropriate method of catching larvae and juvenile fish, although occasionally some larger adult fish may have also been captured and measured as part of this dataset for completeness. All fish were identified to species and measured to standard length (mm) and released at the point of capture. The exception was the smallest larvae, which were euthanised with an overdose of methanesulphonate (MS-222) and preserved in 4% formalin solution for microscopic examination.</p> <p><br>The dataset contains 384,090 rows and 13 columns. Each row corresponds to a single fish that was measured at each site and date. Associated site information (site name, location, area fished (m<sup>2</sup>) and survey date) is reported for each row. When only a fraction of the catch was processed, the sub-sample size was reflected in the Count column (e.g. when half the sample was processed, the numbers of fish measured or only counted were multiplied by two). This enables accurate densities to be calculated as the total number of both measured and unmeasured fish is recorded.</p> <p>Description of columns found in the dataset:</p> <p> </p> <table> <tbody> <tr> <td> <p><strong>Column heading</strong></p> </td> <td> <p><strong>Column description</strong></p> </td> <td> <p><strong>Data type</strong></p> </td> <td> <p><strong>Units</strong></p> </td> </tr> <tr> <td> <p>Fish _Catchment</p> </td> <td> <p>The river catchment/basin location of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_River</p> </td> <td> <p>The river/watercourse location of each fish site.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_SiteName</p> </td> <td> <p>The name of each fish site</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Fish_Latitude</p> </td> <td> <p>The latitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Longitude</p> </td> <td> <p>The longitude of each fish site (WGS 1984)</p> </td> <td> <p>Integer</p> </td> <td> <p>Decimal degrees</p> </td> </tr> <tr> <td> <p>Fish_Area</p> </td> <td> <p>Area of fish site surveyed</p> </td> <td> <p>Integer</p> </td> <td> <p>m<sup>-2</sup></p> </td> </tr> <tr> <td> <p>Fish_SurveyDate</p> </td> <td> <p>Date fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>dd/mm/yyyy</p> </td> </tr> <tr> <td> <p>Fish_Year</p> </td> <td> <p>Year fish survey was carried out</p> </td> <td> <p>Integer</p> </td> <td> <p>yyyy</p> </td> </tr> <tr> <td> <p>Common_Name</p> </td> <td> <p>The common/vernacular name of each fish taxon recorded in the dataset.</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Latin_Name</p> </td> <td> <p>The scientific name of each fish taxon recorded in the dataset</p> </td> <td> <p>Text</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Net_Number</p> </td> <td> <p>The net number the fish in a given survey were caught on</p> </td> <td> <p>Integer</p> </td> <td> <p>n/a</p> </td> </tr> <tr> <td> <p>Length_mm</p> </td> <td> <p>Length of individual fish caught</p> </td> <td> <p>Integer</p> </td> <td> <p>mm</p> </td> </tr> <tr> <td> <p>Count</p> </td> <td> <p>Count of fish caught accounting for sub- sampling</p> </td> <td> <p>Integer</p> </td> <td> <p>Number of fish</p> </td> </tr> </tbody> </table> <p> </p>
Direct imaging of carbohydrate stereochemistry structural dataset
<p>Dataset includes. </p> <ol> <li>Training data for the NequIP model (all_4NPxG_mod_E.extxyz) including structures, energies and force components.</li> <li>Zip file (4NPxG_training_run.zip) containing training parameters (config.yaml) and metrics (.csv files) and the deployed NequIP model (deployed_model.pth) used for minima hopping in the corresponding study.</li> <li>Bayesian Optimization Structure Search results for conformers (alpha/beta-4-Nitrophenyl-D-Galacturonide_opt.extxyz) and isolated adsorbates (alpha/beta_isolated_adsorbates_opt.extxyz) on Au(111). DFT relaxed structures.</li> <li>CREST NADG conformers (crest_conformers_alpha.extxyz). </li> <li>Initial monolayer structure relaxations (4NPaG/4NPbG_monolayer_relaxation_every_fifth.extxyz). Every fifth geometry from the relaxation.</li> <li>Results from NequIP minima hopping for monolayer structures as trajectory files (alpha/beta_minima_hopping_nequip.traj). Contains also protonated NADG structures (alpha_protonated_minima_hopping_nequip.traj).</li> <li>Final NADG and NBDG monolayer structures with Hartree potentials and STM images simulated with FHI-aims (Final_NADG/NBDG_hartree_potential.cube, Final_NADG/NBDG_stm_01.cube) with the z-maps (Final_NADG/NBDG_stm_z_map.cube) for creating STM image contrast.</li> </ol>
5D-NP-FABTECH_ALD - Open Dataset for: "ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic-inorganic structures"
<p>This is the open dataset for the paper: "L. Demelius, L. Zhang, A. M. Coclite and M. D. Losego, ZnO vapor phase infiltration into photo-patternable polyacrylate networks for the microfabrication of hybrid organic–inorganic structures, <em>Mater. Adv.</em>, 2024, <strong>5</strong>, 8464–8474."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Indicative distribution map for Ecosystem Functional Group M4.1 Submerged artificial structures
<p>This archive contains indicative distribution maps and profiles for <strong>M4.1 Submerged artificial structures</strong>, a ecosystem functional group (EFG, level 3) of the <a href="https://global-ecosystems.org/">IUCN Global Ecosystem Typology</a> (v2.0). Please refer to Keith <em>et al.</em> (2020) for details.</p> <p>The descriptive profiles provide brief summaries of key ecological traits and processes, maps are indicative of global distribution patterns, and are not intended to represent fine-scale patterns. The maps show areas of the world containing major (value of 1, coloured red) or minor occurrences (value of 2, coloured yellow) of each ecosystem functional group. Minor occurrences are areas where an ecosystem functional group is scattered in patches within matrices of other ecosystem functional groups or where they occur in substantial areas, but only within a segment of a larger region. Given bounds of resolution and accuracy of source data, the maps should be used to query which EFG are likely to occur within areas, rather than which occur at particular point locations. Detailed methods and references for the maps are included in the profile (xml format).</p>
17O-EPR determination of the structure and dynamics of copper single-metal sites in zeolites
<p><strong>Description of the dataset: </strong></p> <ul> <li><strong>Data type</strong>: Experimental spectroscopic measurements, computer simulation and analysis</li> <li>Files are with filename extensions: <strong>DSC</strong>, <strong>DAT</strong>, <strong>spc</strong>, <strong>par</strong>, <strong>m</strong>, <strong>f34</strong>,<strong> xyz</strong>, <strong>out</strong>, <strong>in</strong></li> <li>Information on <strong>origin of the data</strong>:</li> </ul> <ul> <li>EPR spectroscopic measurements with filename extensions <strong>DSC</strong>, <strong>DTA</strong>,<strong> spc </strong>and<strong> par.</strong></li> <li>EPR spectroscopic simulation and analyses with filename extension <strong>m</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> <li>Geometry information of cluster models is stored in <strong>xyz</strong> files in ASCII format.</li> </ul> <ul> <li>X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with SHQ cavity produced by Bruker.</li> <li>Q-band and X-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 (v4.2.1) code.</li> <li><strong>If t</strong> <ul> <li>Files in <strong>PARACAT_WP3_20210625_01_CW</strong> folder includes X-band CW-EPR spectroscopic measurements; original data are in DTA/DSC and spc/par formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_02_HYSCORE</strong> folder includes HYSCORE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_03_ESE</strong> folder includes ESE spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_04_ENDOR</strong> folder includes ENDOR spectroscopic measurements; original data are in DTA/DSC formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_05_MATLAB</strong> folder includes computer simulations/analyses of the EPR measurements; data are in m formats.</li> <li>Files in <strong>PARACAT_WP3_20210625_06_DFT </strong>folder includes periodic and cluster DFT computation inputs, outputs and geometries in ASCII format.</li> </ul> </li> </ul> <ul> <li><strong>Information on</strong>: <ul> <li>specialized abbreviations: <strong>EPR</strong> – Electron Paramagnetic Resonance, <strong>CW</strong> – Continuous Wave EPR, <strong>ESE</strong> – Electron Spin Echo detected EPR, <strong>HYSCORE</strong> – HYperfine Sublevel CORrelation spectroscopy, <strong>ENDOR</strong> – Electron Nuclear DOuble Resonance spectroscopy, <strong>DFT </strong>– Density Functional Theory, <strong>CHA </strong>– Chabazite, zeolite topology.</li> <li>definitions of variables: <strong>Magnetic field, Temperature.</strong></li> <li>units of measurement: <strong>Gauss (G), K, degree (°), milliTesla (mT)</strong>.</li> <li>abbreviations: <strong>6MR, 8MR </strong>are the Cu docking sites; <strong>2Al-3NN</strong>, <strong>2Al-2NN</strong>, <strong>1Al</strong> are the different aluminium distributions analysed; <strong>1w</strong>, <strong>2w</strong>, <strong>3w, 4w</strong> indicates the number of water ligands considered in the models; <strong>eq</strong> and <strong>ax</strong> indicates equatorial and axial ligands. Periodic DFT computations with filename extension <strong>.f34</strong> include structural/symmetry information of optimized structure. Molecular cluster DFT computations with filename extension <strong>.in</strong>/<strong>.out</strong>/<strong>.xyz</strong> are inputs, outputs, and structure of cluster models.</li> </ul> </li> </ul>
Structure and dynamics of water confined in cylindrical nanopores with varying hydrophobicity
<p>Supporting data for Phil. Trans. R. Soc. A 379: 20200403 (2021)</p> <p><a href="https://doi.org/10.1098/rsta.2020.0403">http://doi.org/10.1098/rsta.2020.0403</a></p>
Data supporting the study "An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021))
<p>Data supporting the figures and findings presented in the study <strong>"An organic crystalline state in ageing atmospheric aerosol proxies: spatially resolved structural changes in levitated fatty acid particles" by Milsom et al. (2021), <em>Atmos. Chem. Phys..</em></strong></p>
GFN2-xTB structures of iCOM adsorbed on a cluster model of water molecules derived from a periodic model of crystalline ice
<p>This dataset contains the atomic coordinates in the <a href="http://www.moldraw.unito.it/">.</a>xyz format of the GFN2-xTB optimized structures of 20 iCOMs adsorbed at the surface of a cluster of 84 water molecules mimicking the periodic model of crystalline water icy grain as described by Ferrero, S.; Zamirri, L., Ceccarelli, C.; Witzel, A.; Rimola, A.; Ugliengo, P. ApJ, (2020) 904:11. For all considered structures we also provided a specific file in the Gaussian format with the computed harmonic frequencies. Each file can be easily converted in input for the variety of quantum mechanical programs, like VASP, QE, Gaussian 16 etc.</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.