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3,655 results for “Structural data”
Accompanying dataset for : "Nappe oscillations on free-overfall structures, data from laboratory experiments "
<p>This dataset accompanies the manuscrpit "Nappe Oscillations on Free-Overfall Structures: Data from Laboratory Experiments " submitted to Scientific Data.</p>
Integrative determination of atomic structure of mutant huntingtin exon 1 fibrils implicated in Huntington disease — data files
<div>This zenodo entry contains MD and solid-state NMR data files for the paper:</div> <div> </div> <div><strong><em>Mahdi Bagherpoor Helabad et al. (2024) Integrative determination of atomic structure of mutant huntingtin exon 1 fibrils implicated in Huntington disease</em></strong></div> <div> </div> <h2> </h2> <h2>MD datasets and code</h2> <div>We provide here (in <strong>MD_simulations_data_codes.zip</strong>) the MD simulations files for the MD runs and also data, and their respective codes, shown in the figures of the above papers.</div> <div> </div> <div>Data file structure: </div> <p><strong>MD_data </strong></p> <ul> <li>The MD simulation run files for three fully periodic systems—PolyQ15 and HTTex1—include the following: .gro files for both minimization and final structures, production .tpr files, force field parameters, GROMACS .mdp files, and position and dihedral restraint files. <ul> <li>fully_periodic_systems</li> <li>polyQ15</li> <li>HTTex1</li> </ul> </li> </ul> <p><strong>Figs_Data_Codes</strong></p> <div> <ul> <li>The data and in-house Python scripts associated with creating the figures: <ul> <li>Fig2B_S4 for Figure 2B and Supplementary Figure 4</li> <li>Fig2D_S5 for Figure 2D and Supplementary Figure 5</li> <li>Fig3C_S10 for Figure 3C and Supplementary Figure 10</li> <li>Fig4_S12_S13_S14 for Figure 4C and Supplementary Figures 12–14</li> <li>Fig6C for Figure 6C</li> <li>FigS3B_S6 for Supplementary Figures 3B and 6</li> <li>FigS8 for Supplementary Figure 8</li> <li>FigS9_S11 for Supplementary Figures 9–11</li> <li>FigS16_to_S21 for Supplementary Figures 16–21</li> <li>FigS22 for Supplementary Figure 22</li> <li>readMe.txt <div> </div> </li> </ul> </li> </ul> </div> <div><strong>Fig6_c_barplot_data.xlsx</strong></div> <div> <ul> <li>Excel file with data plotted in Figure 6C.</li> </ul> <p><strong>N17_SecStr_convergence.xlsx</strong></p> <div> <ul> <li>Excel file with convergence data for N17 domain.</li> </ul> </div> </div> <h2>Solid-state NMR data</h2> <div>We provide here the solid-state NMR spectrum files for the data shown in figures of the above paper.</div> <div> </div> <div>Data file structure:</div> <div> </div> <div><strong>SSNMR_data_listing_20241011a.txt</strong></div> <div> <ul> <li>text file describing the ssNMR data files</li> </ul> </div> <div><strong>SSNMR_data.zip</strong></div> <ul> <li>Figure_1 - data for Figure 1F</li> <li>Figure_5 - data for Figure 5</li> <li>Figure_6 - data for Figure 6</li> <li>Figure_S7 - data for Figure 2G and Supplementary Figure 7</li> <li>Figure_S15 - NMR data for HDX ssNMR of fibrils – Supplementary Figure 15</li> </ul> <div><strong>Fig6_b_barplot_data.xlsx</strong></div> <div> <ul> <li>Excel file with data plotted in Figure 6B, based on previously reported results (DOI 10.1038/ncomms15462)</li> </ul> </div> <div> </div> <div>Data are provided in either Bruker Topspin format, or in NMRPIPE format (ft2 extension).</div> <div>Experimental parameters are described in the published paper and its Supplementary Information files. In general, these are all data from magic-angle-spinning (MAS) NMR studies of intact amyloid fibrils made with isotope labeled HTTex1 fibrils. Experimental types include 2D CP-DARR, 2D TOBSY, 2D HETCOR spectra as well as relaxation measurements. Aside from NMR datafiles, also documents with interpreted and integrated data are included, used to make data curves in the figure (e.g. for Prism software).</div> <div> </div> <div> </div> <div> </div>
Data for "Resolving Structures of Paramagnetic Systems in Chemistry and Materials Science by Solid-State NMR: the Revolving Power of Ultra-Fast MAS"
<p>Raw NMR data</p>
Data from: Structured inhibitory activity dynamics in new virtual environments
<p>Inhibition plays a powerful role in regulating network excitation and plasticity; however, the activity of defined interneuron types during spatial exploration remain poorly understood. Using two-photon calcium imaging, we recorded hippocampal CA1 somatostatin- and parvalbumin-expressing interneurons as mice performed a goal-directed spatial navigation task in new visual virtual reality (VR) contexts. Activity in both interneuron classes was strongly suppressed but recovered as animals learned to adapt the previously learned task to the new spatial context. Surprisingly, although there was a range of activity suppression across the population, individual somatostatin-expressing interneurons showed consistent levels of activity modulation across exposure to multiple novel environments, suggesting context-independent, stable network roles during spatial exploration. This work reveals population-level temporally dynamic interneuron activity in new environments, within which each interneuron shows stable and consistent activity modulation.</p>
Data from: Spatial and temporal genetic structure of a river-resident Atlantic salmon (Salmo salar) after millennia of isolation
The river-resident Salmo salar ("småblank") has been isolated from other Atlantic salmon populations for 9,500 years in upper River Namsen, Norway. This is the only European Atlantic salmon population accomplishing its entire life cycle in a river. Hydropower development during the last six decades has introduced movement barriers and changed more than 50% of the river habitat to lentic conditions. Based on microsatellites and SNPs, genetic variation within småblank was only about 50% of that in the anadromous Atlantic salmon within the same river. The genetic differentiation (FST) between småblank and the anadromous population was 0.24. This is similar to the differentiation between anadromous Atlantic salmon in Europe and North America. Microsatellite analyses identified three genetic subpopulations within småblank, each with an effective population size Ne of a few hundred individuals. There was no evidence of reduced heterozygosity and allelic richness in contemporary samples (2005–2008) compared with historical samples (1955–56 and 1978–79). However, there was a reduction in genetic differentiation between sampling localities over time. SNP data supported the differentiation of småblank into subpopulations and revealed downstream asymmetric gene flow between subpopulations. In spite of this, genetic variation was not higher in the lower than in the upper areas. The meta-population structure of småblank probably maintains genetic variation better than one panmictic population would do, as long as gene flow among subpopulations is maintained. Småblank is a unique endemic island population of Atlantic salmon. It is in a precarious situation due to a variety of anthropogenic impacts on its restricted habitat area. Thus, maintaining population size and avoiding further habitat fragmentation are important.
Data from: Long distance dispersal and genetic structure of natural populations: an assessment of the inverse isolation hypothesis in peat mosses
It is well accepted that the shape of the dispersal kernel, especially its tail, has a substantial effect on the genetic structure of species. Theory predicts that dispersal by fat-tailed kernels reshuffles genetic material and thus preserves genetic diversity during colonization. Moreover, if efficient long distance dispersal is coupled with random colonization, an inverse isolation effect is predicted to develop in which increasing genetic diversity per colonizer is expected with increasing distance from a genetically variable source. By contrast, increasing isolation leads to decreasing genetic diversity when dispersal is via thin-tailed kernels. Here we use a well-established model group for dispersal biology (peat mosses: genus Sphagnum) with a fat-tailed dispersal kernel, and the natural laboratory of the Stockholm archipelago to study the validity of the inverse isolation hypothesis in spore-dispersed plants in island colonization. Population genetic structure of three species (S. fallax, S. fimbriatum and S. palustre) with contrasting life histories and ploidy levels were investigated on a set of islands using microsatellites. Our data show (φ'st, AMOVA, IBD) that dispersal of the two most abundant species can be well approximated by a random colonization model. We find that genetic diversity per colonizer on islands increases with distance from the mainland for S. fallax and S. fimbriatum. By contrast, S. palustre deviates from this pattern, owing to its restricted distribution in the region affecting its source pool strength. Therefore, the inverse isolation effect appears to hold in natural populations of peat mosses and, likely, in other organisms with small diaspores.
Figure 5 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 5 - Example of a scientific name entry.
Figure 1 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 1 - A typical species record from the checklist.
Figure 4 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 4 - Data correctly aligned with columns.
Figure 6 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 6 - Synonym records (highlighted) added and linked.
Figure 2 from: Remsen D, Knapp S, Georgiev T, Stoev P, Penev L (2012) From text to structured data: Converting a word-processed floristic checklist into Darwin Core Archive format. PhytoKeys 9: 1-13. https://doi.org/10.3897/phytokeys.9.2770
Figure 2 - Taxon records imported into a database.
Data used in the publication: Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor
<p>These data support the results presented in the manuscript titled "Study on The Error Structure of Radar Reflectivity Using The Symmetric Rainrate Predictor". They consist of Observation minus Background data and rainrate data that are used to build the symmetric error model of reflectivity. We uploaded two data sets that support both original and revised manuscripts.</p>
Data from: Fragmentation shapes nest density and social structure but not genetic diversity of Temnothorax crassispinus (Formicidae)
<p><strong>This README accompanies data_fragmentation_T.crassispinus.xlsx</strong></p> <p> </p> <p><strong><em>Associate publication : </em></strong></p> <p> </p> <p>Fragmentation shapes nest density and social structure but not genetic diversity of <em>Temnothorax crassispinus</em> (Formicidae)</p> <p>M. Cordonnier, T. Lindner, J. Heinze</p> <p>Lehrstuhl für Zoologie / Evolutionsbiologie, Univ. Regensburg</p> <p> </p> <p>****************************** CONTENTS *******************************</p> <p>The data can be readily imported in any statistical package or spreadsheet program. Please, contact me if you need the file formatted in other ways.</p> <p> </p> <p>This file includes a description of the variables.</p> <p>***********************************************************************</p> <p>Variable names and descriptions</p> <p> </p> <p><strong>Sheet 1</strong></p> <p>ID nest: identity of the nest of the genotyped worker</p> <p>2MS46 to GT-1: complete genotype of the worker. Missing data: “-9”</p> <p>Q1_inter & Q2_inter: Q-values resulting from the Bayesian clustering (interspecific level)</p> <p>ID_inter: species identity based on genotype (TC: <em>Temnothorax crassispinus</em>, TN: <em>T. nylanderi</em>; H: intermediate)</p> <p>Q1_intra & Q2_intra: Q-values resulting from the Bayesian clustering (intraspecific level)</p> <p>ID_intra: population identity based on genotype (TC1: <em>Temnothorax crassispinus</em>, cluster1; TC2: <em>T. crassispinus</em>, cluster2)</p> <p>ID_sequencing_C1-J-2183/C2-N-3661: species identity based on sequencing for primers C1-J-2183/C2-N-3661 (TC: <em>Temnothorax crassispinus</em>, TN: <em>T. nylanderi</em>)</p> <p>ID_sequencing_LCO1490 ⁄ HCO2198: species identity based on sequencing for primers LCO1490 ⁄ HCO2198 (TC: <em>Temnothorax crassispinus</em>, TN: <em>T. nylanderi</em>)</p> <p> </p> <p><strong>Sheet 2</strong></p> <p>Idpatch: identity of the forest patch</p> <p>Latitude, longitude: geographic coordinates of the forest patch</p> <p>Date, Hour: Sampling time of the forest patch</p> <p>TC_Nest-density: number of <em>T. crassispinus </em>nests sampled per people in 30 minutes in the forest patch</p> <p>Prop_Queenright_nests: proportion of queenright nests in the forest patch</p> <p>Gen_div: averaged number of alleles per <em>T. crassispinus</em> nest in the forest patch</p> <p>Connect1 & Connect5: distance to the closest patch and mean distance to the five closest patches</p> <p>Nbneig200 & Nbneig400: number of neighboring forest patches at 200 and 400 meters from the focal forest patch</p> <p>area: size of the forest patch</p> <p>shape: shape of the forest patch (perimeter/surface)</p> <p> </p> <p> </p> <p>****************************** CONTACTING *****************************</p> <p>Contact me at:</p> <p> </p> <p>Marion Cordonnier</p> <p>e-mail: marion.cordonnier@hotmail.com</p> <p> </p> <p>***********************************************************************</p> <p> </p> <p> </p>
Data from: Microcrystal Electron Diffraction (MicroED) Structure Determination of a Mechanochemically Synthesized Co-crystal not Affordable from Solution Crystallization
<p>Solid-state grinding can provide “mechano-distinctive” cocrystals that are not accessible from solutions. Herein, we demonstrate the structure determination of a powdered mechano-distinctive cocrystal of 2-aminopyrimidine and succinic acid in a 2:1 molar ratio using microcrystal electron diffraction.</p>
Data for "Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions"
<p>Data used in "<em>Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions</em>"<br> by F. Pellicani, D. Dal Ben, A. Perali, S. Pilati</p> <p>If you use these data or the python script for your research or other activities, please cite the corresponding journal article.</p> <p> </p> <p>====================</p> <p>Uncompressing the zipped file <em>DataSFUnicam.zip</em> provies the following files and folders:</p> <p><br> <strong>DataSFUnicam/</strong></p> <p> </p> <p> ExperimentalDataPDBFiles/<br> <em>This folder contains 2408 .pdb files of experimental complex structures. The files are named with a univocal code corresponding to the protein-ligand complex.</em></p> <p> </p> <p> ExperimentalDataXLSXFile.xlsx<br> <em>This Excel file reports the experimental protein-ligand chemical information. In the sheet named “Foglio1”, the first column contains the univocal code of the protein-ligand complex, the second column contains the experimentally measured pK_d.</em></p> <p> </p> <p> SyntheticDataPDBFiles/<br> <em>This folder contains the .pdb files of the synthetic complex structures. The .pdb files are grouped in 17 folders according to just as many target proteins. The folders are named after the corresponding protein. Each folder contains the .pdb files for the best position of each protein-ligand pair according to the MOE docking score. The files are named with a univocal code.</em></p> <p> </p> <p> SyntheticDataXLSXFiles/<br> <em> The folder contains 17 Excel files with the chemical information of the synthetic protein-ligand complexes. The files are named after the corresponding target protein. In the sheet named “Foglio1” of each .xlsx file, the first column contains a univocal code of the protein-ligand complex in each conformation, the second column contains an auxiliary numerical code corresponding to the protein-ligand pair, the third column contains the experimentally measured pK_i, and the fourth column contains the docking score provided by the MOE software.</em></p> <p>====================</p> <p>USER GUIDE FOR THE PYTHON SCRIPT</p> <p>Download and uncompress the zipped file "<em>SFUnicam.zip</em>" with a command like "<em>unzip SFUnicam.zip</em>". </p> <p>The following file structure is created:</p> <p><em>SFUnicam/</em></p> <p> <em>ComplexToBePredictedFolder/4ey5_30.pdb <br> MaxAssMatrix.npy<br> my_model<br> devStndSynt.npy<br> mediaSynt.npy<br> UnicamSF13prot.py<br> README.txt</em><br> <br> The subfolder "<em>ComplexToBePredictedFolder/</em>" contains the example PDB file "<em>4ey5_30.pdb</em>".</p> <p>-) To execute the script "<em>UnicamSF13prot.py</em>", Python 3 should be installed with the following libraries and sublibraries:<br> <em>Keras:<br> Regularizers<br> Sequential (keras.models)<br> Conv1D, Dense, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, AveragePooling1D (keras.layers)<br> Adam (keras.optimizers)<br> Numpy</em><br> <em>Tensorflow</em></p> <p>Operation:<br> -) Copy the .pdb file related to the protein-ligand complex whose affinity is to be predicted in the subfolder “<em>ComplexToBePredictedFolder/</em>”.<br> -) Make sure the following files are in the same folder where the python script is:<br> <em>MaxAssMatrix.npy<br> mediaSynt.npy<br> devStndSynt.npy<br> my_model</em><br> -) Run the code using Python 3 with a command like "<em>python3.x UnicamSF13prot.py</em>".<br> -) Enter the name of the protein-ligand PDB file whose affinity is to be predicted (excluding the extension ".pdb").<br> -) Read the predicted affinity from screen.<br> </p> <p> </p>
Computational data on "A Model for the Rapid Assessment of Solution-Structures for 24-Atom Macrocycles: The Impact of β-Branched Amino Acids on Conformation"
<p>The archive contains 5 different folders:<br> <br> 1. mdp_files, that contains all the input files for the minimization, equilibration and molecular dynamics run (in gromacs format);<br> 2. topologies, that contains the equilibrated configurations and topology of the 4 systems studied (in gromacs format);<br> 3. trajectories, that contains the coordinates of the macrocycles during the metadynamics calculations and the relevant metadynamics output files (hills file and energy file);<br> 4. plumed_input, that contains the input file for the metadynamics calculations.<br> 5. gaussian, that contains input and output for the single-point charge calculations.</p>
Data from Imaging and structure analysis of ferroelectric domains, domain walls, and vortices by scanning electron diffraction
<p><strong>Direct electron detectors in scanning transmission electron microscopy give unprecedented possibilities for structure analysis at the nanoscale. In electronic and quantum materials, this new capability gives access to, for example, emergent chiral structures and symmetry-breaking distortions that underpin functional properties. Quantifying nanoscale structural features with statistical significance, however, is complicated by the subtleties of dynamic diffraction and coexisting contrast mechanisms, which often results in low signal-to-noise and the superposition of multiple signals that are challenging to deconvolute. Here we apply scanning electron diffraction to explore local polar distortions in the uniaxial ferroelectric Er(Mn,Ti)O<sub>3</sub>. Using a custom-designed convolutional autoencoder with bespoke regularization, we demonstrate that subtle variations in the scattering signatures of ferroelectric domains, domain walls, and vortex textures can readily be disentangled with statistical significance and separated from extrinsic contributions due to, e.g., variations in specimen thickness or bending. The work demonstrates a pathway to quantitatively measure symmetry-breaking distortions across large areas, mapping structural changes at interfaces and topological structures with nanoscale spatial resolution.</strong></p>
Research data for "Structure and Bonding in Amorphous Red Phosphorus"
<p>This dataset supports the paper: "Structure and Bonding in Amorphous Red Phosphorus". The paper is online here: https://doi.org/10.1002/anie.202216658. </p> <p>The following .xyz and .cif files are provided in a compressed .zip file:</p> <ul> <li>"Optimised_phosphorus_models.zip": the atomic structures of various phosphorus modifications, including three representative models of amorphous phosphorus, as well as black phosphorus, white phosphorus, violet phosphorus, fibrous phosphorus, and three phosphorus nanorods, which are all fully optimised using DFT with van der Waals corrections (i.e., PBE+D3).</li> </ul>
Scalable Micro-planned Generation of Discourse from Structured Data
<p>Dataset created </p>
Structured Data Collection of Patients Undergoing Liver Transplantation and Evaluated for Bone Mineral Metabolism
ClinicalTrials.gov study NCT06838702. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.