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.
367
datasets available to search
ShareScore release 0.9.0
Dataset results
367 results for “Structural Dynamics”
Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis - Additional Files
<p>Additional files from the manuscript titled "Domain-specific model selection for structural identification of the Rab5-Rab7 dynamics in endocytosis" published in BMC Systems Biology:</p> <ul> <li>Data containing delimited time points and measurements used for fitting the different model structures</li> <li>Supplementary material containing additional figures and tables</li> <li>Archive containing the complete library, the incomplete model and the task used for modeling the Rab5-Rab7 switch</li> </ul> <p> </p>
Dataset and Figures for article: Ivanov P.Ch., Yuen, A., Perakakis, P., (2014). Impact of stock market structure on intertrade time and price dynamics. PLoS ONE 9(4): e92885
<p>Dataset and Figures for article: Ivanov P.Ch., Yuen, A., Perakakis, P., (2014). Impact of stock market structure on intertrade time and price dynamics. PLoS ONE 9(4): e92885</p>
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </p>
Research data supporting: "Machine learning of microscopic structure-dynamics relationships in complex molecular systems"
<p>This repository contains the set of data and the code to reproduce the results shown in "Machine learning of microscopic structure-dynamics relationships in complex molecular systems" published on Machine Learning: Science and Technology (DOI: 10.1088/2632-2153/ad0fa5).</p>
Native MS dataset for: "Insights into the pathogenesis of primary hyperoxaluria type I from the structural dynamics of alanine:glyoxylate aminotransferase variants"
<p>Native mass spectrometry dataset used in: <strong>Insights into the pathogenesis of primary hyperoxaluria type I from the structural dynamics of alanine:glyoxylate aminotransferase variants.</strong> Pavla Vankova, Juan Luis Pacheco-Garcia, Dmitry S. Loginov, Atanasio Gómez-Mulas, Alan Kádek, José Manuel Martín-Garcia, Eduardo Salido, Petr Man and Angel L. Pey. FEBS Letters (2024)</p> <p><strong>Description:</strong></p> <p>Native mass spectrometry (MS) analysis verifying the oligomeric state of alanine:glyoxylate aminotransferase (AGT) protein and its P11L and I340M (LM) polymorphism and LM G170R mutation variants in primary hyperoxaluria type I.</p> <p><strong>Sample processing:</strong></p> <p>AGT protein as well as its LM and LM G130R mutants were buffer exchanged into 150 mM aqueous ammonium acetate solution (pH 7.5, MS-grade, Sigma-Aldrich) through six cycles of tenfold dilution and re-concentration using centrifugal concentrators Vivaspin 500 (30 kDa cut-off, <em>Sartorius</em>). Desalted proteins were introduced into a Synapt G2Si mass spectrometer (Waters) via static nanoelectrospray ionization from in-house prepared gold-coated borosilicate glass capillaries Kwik-Fil 1B120F-4 (<em>World Precision Instruments</em>). Protein concentration in samples was determined by 280 nm absorbance measurements using DeNovix DS-11 spectrophotometer. Samples were diluted in ammonium acetate and electrosprayed at 1 and 2 µM concentration. The mass spectrometer was carefully tuned for best signal quality and intensity, while keeping ion activation and unfolding minimal. Namely, electrospray voltage was kept at 1.3 kV, source desolvation temperature 80°C, sampling cone 80 V and 10 V collision voltage with 6 ml/min flow of argon in the trap region for thermalization of ions. Quadrupole was operated in a broadband transmission mode up to 8000 m/z while the spectra were acquired in mass range 500 – 20000 m/z. Spectra were externally mass recalibrated using known masses of caesium iodide clusters.</p> <p><strong>Data processing:</strong></p> <p>Raw mass spectra were averaged over 75 scans and further processed in Waters MassLynx 4.1. The averaged spectra were exported for ZENODO deposition as plain in plain m/z vs intensity .txt files as well uploaded as part of the .raw file format of the whole analysis (including initial metadata) with scan descriptions and parameter changes described in a stand-alone .txt descriptor file.</p>
Experimental data for "Exact inversion of partially coherent dynamical electron scattering for picometric structure retrieval"
Open the record for dataset details and reuse information.
Research data for "Predicting Dynamics from Structure in a Sodium Silicate Glass"
<p>This dataset supports the paper "Predicting Dynamics from Structure in a Sodium Silicate Glass".</p> <p>The following files are provided.</p> <p>File: dataset_800.zip</p> <p>- Pickle files for:</p> <ul> <li>400 Sodium silicate glass structures of 3000 atoms</li> <li>30 Trajectories sampled eight times at various timescales up to 1 ns for each if the 400 glass structures</li> </ul> <p>File: in.comb</p> <p>- Lammps inputfile used to generate simulation from with the data in dataset was sampled</p>
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Timing of short-term drought structures plant-herbivore dynamics
<p>Drought is an important stressor that affects plant growth, survival and physiology and, through plant responses, alters plant-herbivore interactions and herbivore population dynamics. Short-term drought can occur at different times during a growing season, affecting herbivore populations and plants at various stages of development and growth. As phenology influences drought response, drought timing could strongly structure plant-herbivore interactions. We grew common milkweed plants with or without its dominant aphid herbivore, allowing both plants and herbivore populations to develop over time as in a typical growing season. To determine how drought timing affects plants and aphid populations, we applied short-term (1 week) drought at different times. Plants and aphids were sensitive to drought timing, with a few weeks difference shifting the effects of drought from minimal to causing massive declines in plant growth, plant survival and herbivore population density. Aphid herbivory added to or overrode drought consequences in plants, with different plant fitness components responding distinctly to herbivory. Drought timing also affected whether plant and herbivore responses were parallel or divergent, suggesting that shifts in the seasonal timing of drought have the potential to disrupt plant-herbivore interactions and affect the larger ecological community. Our results show drought timing has important and disparate consequences for herbivores and their host plants and indicates the necessity of considering phenology and timing in assessing drought response.</p>
Vegetation structure and fuel dynamics in fire-prone, Mediterranean-type Banksia woodlands
<p>Increasing extreme wildfire occurrence globally is boosting demand to understand the fuel dynamics and fire risk of fire-prone areas. This is particularly pressing in fire-prone, Mediterranean climate-type vegetation, such as the Banksia woodlands surrounding metropolitan Perth, southwestern Australia. Despite an extensive wildland-urban interface and frequent fire occurrence, fuel accumulation and the spatial variation in fuel risk is not well quantified across the broad extent of this ecosystem. Using a space for time sampling approach to generate a chronosequence of time since fire, we selected sites that spanned across two distinct sandy soil types (Spearwood and Bassendean sands) and a rainfall gradient (550 to 750 mm north–south). We examined 82 sites in Banksia woodlands, southwestern Australia. Of the 82 sites, 44 burnt during the measurement period (2016 to 2021), which provided the opportunity for fuel measurements following fire (resulting in total N = 126). We wanted to answer two key questions: 1) How do measures of fuel load (mass) and arrangement (structure and continuity) vary across space and time, particularly with respect to time since the last fire? 2) How do biophysical drivers, such as soil type and rainfall, influence fuel accumulation and arrangement, and do these covariates improve litter fuel modelling beyond traditional asymptotic models? We found that fine surface fuel loads (litter and small twigs) differed between sand types, accumulating faster and reaching a higher peak on Spearwood sands (7–9 Mg ha−1) compared to Bassendean sands (6–7 Mg ha−1). Shrub layer fuel loads also accumulated faster on Spearwood sands than on Bassendean sands. While shrub layer fuels on Spearwood sands peaked at 14 years and declined thereafter, those on Bassendean sand did not decline over time but have lower overall connectivity. Total fine fuels (fine surface plus fine shrub layer fuels) had no significant decline over the same time period, on either sand type. Total fine fuel loads reached a peak of 9–10 Mg ha−1 between 13- and 20-years following fire, depending on the underlying sand type. Our quantitative fuel accumulation models confirmed the strength of time since fire as a predictor of hazard, but nonetheless included up to 40% unexplained variance. Importantly, while components fluctuated over time, the combined total of fine fuels did not decline with the long absence of fire, suggesting fire risk does not necessarily decrease in long unburned vegetation.</p>
Fig. 2 in Structure And Dynamics Of Crucians' Settlements (Cypriniformes, Сarassius) In Water Systems Of Eastern Ukraine
Fig. 2. Changes in C. gibelio's share in the selections of crucian carps from Eastern Ukraine depending on geographical latitude.
Fig. 4 in Structure And Dynamics Of Crucians' Settlements (Cypriniformes, Сarassius) In Water Systems Of Eastern Ukraine
Fig. 4. Distribution pattern and frequency of occurrence of the aboriginal C. сarassius (marked with red color) and the representatives of the group C. (superspecies auratus) (marked with green color) in river systems of Eastern Ukraine within the last 100 years: а — 1918–1958; b — 1972–1981; c — 1987–2003; d — 2006–2009.
Molecular dynamics trajectories of C3 H8 O molecule and its structural isomers
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p>------------------------------------------------</p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Screening routine for integrative dynamic structural biology using SAXS and intramolecular FRET and DEER-EPR on hGBP1 (human guanalyte binding protein 1)
<p>Initial and selected ensemble for major and minor species of the human guanalyte binding protein 1 with scripts for the reading routine to combine and analyse jointly SAXS, EPR and FRET data.</p>
Inferring global dynamics from local structure in liquid electrolytes.
<p>Molecular dynamics simulation data (obtained using the HOOMD-blue package) used to generate results in "Inferring global dynamics from local structure in liquid electrolytes". </p> <p>108 bulk electrolytic systems were simulated with twelve concentrations ranging from 0.0005 σ−3 to 0.05 σ−3 and nine Bjerrum lengths in the range 2.5 σ to 10.0σ. Each simulation consisted of cations, anions, and solvent, all of which are modelled as beads of diameter σ (the Lennard-Jones unit of distance) and unit mass. If the size of each bead is mapped to the size of a water molecule (2.75 Å), the concentration range of 0.0005 σ−3 to 0.05 σ−3 approximately corresponds to 0.04 M to 4 M in real units. </p> <p>For example, the system with concentration 0.01 σ−3 and Bjerrum length 4.0σ has the following relevant file names: </p> <p>"conc0.01_lb4.0.dcd" contains the MD trajectory, and "initial_config_conc0.01.gsd" contains the initial configuration.</p> <p>The jupyter notebook "sample_data_analysis.ipynb" illustrates how to load these trajectories as well as the computed molar conductivities for each system.</p> <p>"transport_coefficients.zip" contains the computed Onsager transport coefficients for each of the simulated systems. </p>
"Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data". Raw data and JANA refinement files.
<p><strong>Chirality and accurate structure models by exploiting dynamical effects in continuous-rotation 3D ED data</strong><br> 3D ED data sets of 5 compounds and JANA refinement files of 12 compounds</p> <p><strong>Relevant tools</strong><strong>:</strong></p> <ul> <li>PETS2: data reduction and analysis of electron diffraction patterns <ul> <li>Download program and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Palatinus, L. <em>et al.</em> Specifics of the data processing of precession electron diffraction tomography data and their implementation in the program PETS2.0. <em>Acta Cryst. B</em><strong>75</strong>, 512–522 (2019). <a href="https://doi.org/10.1107/S2052520619007534">DOI: 10.1107/S2052520619007534</a></li> </ul> </li> <li>JANA2006: crystal structure model refinement program <ul> <li>Download program from <a href="http://jana.fzu.cz/">http://jana.fzu.cz/</a> and access step-by-step tutorials at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a></li> <li>Results here were obtained with JANA2006. We recommend using JANA2020.</li> <li>Petricek, V., Dusek, M. & Palatinus, L. Crystallographic Computing System JANA2006: General features. <em>Z. Kristallogr.</em> <strong>229</strong>, 345–352 (2014). <a href="https://doi.org/10.1515/zkri-2014-1737">DOI: 10.1515/zkri-2014-1737</a></li> </ul> </li> <li>DYNGO: Bloch wave program, calculates dynamical diffraction intensities and derivatives <ul> <li>Program automatically included in JANA2006/JANA2020</li> <li>Palatinus, L., Petříček, V. & Corrêa, C. A. Structure refinement using precession electron diffraction tomography and dynamical diffraction: theory and implementation. <em>Acta Cryst. A</em><strong>71</strong>, 235–244 (2015). <a href="https://doi.org/10.1107/S2053273315001266">DOI: 10.1107/S2053273315001266</a></li> </ul> </li> </ul> <p><strong>3D ED data sets:</strong></p> <p>STW_HPM-1 (RT) was measured on a JEOL JEM-2100-LaB6 and diffraction patterns were recorded with an ASI Timepix detector. Another sample of STW_HPM-1 was measured at a temperature of 100 K after cryotransfer with a Titan Krios (CETA-D detector). The other data sets were measured on an FEI Tecnai G2 20 (Olympus SIS Veleta, CCD). Each data set contains the raw diffraction patterns (*.tif) and the basic input files needed to reproduce the data reduction with PETS2 as used in the associated publication (*.pts2, *.celllist, *.cenloc). Step-by-step tutorials are provided for quartz and glycine (and selected steps for abiraterone acetate) at <a href="http://pets.fzu.cz/">http://pets.fzu.cz/</a>.</p> <ul> <li>α-quartz, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>natrolite, stepwise continuous-rotation and precession-assisted (2 data sets from the same crystal)</li> <li>cobalt aluminophosphate (CAP), static ED patterns recorded in 0.1° steps (3 data sets from 2 crystals)</li> <li>abiraterone acetate, stepwise continous-rotation (5 data sets from 5 crystals)</li> <li>STW_HPM-1, continuous-rotation (1 data set, room temperature)</li> <li>STW_HPM-1, continuous-rotation (1 data set, <em>T</em> = 100 K, cryotransfer)</li> </ul> <p><strong>JANA refinement and CIF files:</strong></p> <p>CIF (Crystallographic Information Framework) files include two data items. The first is related to the dynamical and the second to the kinematical refinement. Relevant parameters and statistics specific for dynamical refinement are found in the field _refine_special_details.</p> <p>JANA files are provided for the dynamical and kinematical refinement at the stage after the final refinement cycle together with the original input files generated by PETS2. For quartz and natrolite, relevant files for the refinements against precession-assisted 3D ED data are included. For abiraterone acetate and limaspermidine, relevant files for the absolute structure determination are included.</p> <ul> <li>α-quartz</li> <li>albite</li> <li>mordenite</li> <li>natrolite</li> <li>STW_HPM-1</li> <li>cobalt aluminophosphate (CAP)</li> <li>CAU-36</li> <li>α-glycine</li> <li>carbamazepine</li> <li>(+)-limaspermidine</li> <li>abiraterone acetate</li> <li>MBBF4</li> </ul> <p>For the kinematical refinements based on more than one data set, the self-written tool "CompInt" (unpublished) was used. The tool can be found in the file "tool_scalehkl_compint.zip". Input (*.hkl, *.compint) and output files (*.scalehkl) are provided in the respective folder with the JANA files.</p> <p>Raw data sources of other data sets relevant for the associated publication are given in the SI of the associated publication.</p>
Structure and dynamics of enterovirus genotype networks
<p>Like all biological populations, viral populations exist as networks of genotypes connected through mutation. Mapping the topology of these networks and quantifying population dynamics across them is crucial to understanding how populations adapt to changes in their selective environment. The influence of mutational networks is especially profound in viral populations which rapidly explore their mutational neighborhoods via high mutation rates. Using a novel single-cell sequencing method, scRNAseq-Enabled Acquisition of mRNA and Consensus Haplotypes Linking Individual Genotypes and Host Transcriptomes (SEARCHLIGHT), we captured and assembled viral haplotypes from hundreds of individual infected cells to reveal the complexity of viral populations. We obtained these genotypes in parallel with host cell transcriptome information, enabling us to link host cell transcriptional phenotypes to the genetic structures underlying virus adaptation. Our examination of these structures reveals the common evolutionary dynamics of enterovirus populations and illustrates how viral populations reach through mutational 'tunnels' to span evolutionary landscapes and maintain connection with multiple adaptive genotypes simultaneously.</p>
Fig. 1 in The Dynamics Of Genetic Structure Of Round G O B Y N E O G O B I U S M E L A N O S T O M U S (Pa L L A S) Groupings In The Odessa Bay Of The Black Sea Utilizing Biochemical Marker Loci
Fig. 1. Frequencies of S-alleles by polymorphic locus Es2 in round goby groupings from different parts of the Odessa Bay. * – significant deviation of allele frequencies in round goby groupings from the south and the north part of the Odessa Bay (Р = 0,05); # – significant deviation of allele frequencies in round goby groupings in the south part of the Odessa Bay in 2015-2016 in comparison to 2013-2014 (Р = 0,05).
Fig. 2 in The Dynamics Of Genetic Structure Of Round G O B Y N E O G O B I U S M E L A N O S T O M U S (Pa L L A S) Groupings In The Odessa Bay Of The Black Sea Utilizing Biochemical Marker Loci
Fig. 2. Frequencies of S-alleles by polymorphic locus of myogene 3 in round goby groupings from different parts of the Odessa Bay * – significant deviation of allele frequencies in round goby groupings from the south and the north part of the Odessa Bay in 2013 and 2014 (Р = 0,05); # – significant deviation of allele frequencies in round goby groupings from the south part of the Odessa Bay in 2013-2014 in comparison to 2015 (Р = 0,05).
Structure and dynamics of plasma irregularities over the equatorial ionospheric region: A study using spaced receiver technique employing geostationary satellites' radio signals-Data set
<p>The study investigates the characteristic features of the ionospheric irregularities using spaced receiver technique. In the spaced receiver technique, we have used a trio of receivers separated by 40 and 100 m from each other. These receivers monitor scintillations patterns of the L1 signals transmitted by the geostationary satellites. The cross-correlation of the signals and the power spectral analysis yields the measure of characteristic features of the irregularities. The data folder contains the S4 index, drift velocity of the irregularities, powerspectral slopes and size of the irregularities observed on four days. The folder also contains the gnuscript used for plotting. </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.