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453
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Dataset results
453 results for “Dynamic dataset”
Dataset and software for "Time-resolved imaging of three-dimensional nanoscale magnetization dynamics"
<p>Dataset and software for "Time-resolved imaging of three-dimensional nanoscale magnetization dynamics", Donnelly, C., Finizio, S., Gliga, S. <em>et al.</em> Time-resolved imaging of three-dimensional nanoscale magnetization dynamics. <em>Nat. Nanotechnol.</em> (2020). https://doi.org/10.1038/s41565-020-0649-x</p>
Dataset in the manscript titiled Magnetic field effect on plume dynamics and optical emission of laser-produced tungsten plasma in vacuum
<p>This is the data in the manuscript titiled "Magnetic field effect on plume dynamics and optical emission of laser-produced tungsten plasma in vacuum"submitted to POP </p>
CESM coupled simulation dataset with new (Mod_cp) and original (F09) dynamical coupling scheme from atmosphere, ocean and ice.
<p>1. Coupled models simulate air-sea interaction by coupling components models together. However, in state-of-the-art models such as CESM, the atmospheric model contains dynamic core and physic parameterization that are not differentiated during air-sea interaction. The dynamics-physics (D-P) coupling in CAM causes the prognostic winds of the dynamic core be interpolated onto non-staggered locations. We propose a new dynamical coupling scheme (denoted as Mod_cp) that eliminates the extra interpolation during D-P coupling for the atmosphere-ocean interaction, reducing the numerical diffusion during the dynamics-physics coupling for air-sea interactions.</p> <p>2. Using new and original dynamical coupling scheme, two coupled simulations (100 years) were conducted. Climatology of atmosphere (cam), ocean (ocn) and sea ice (ice) model simulations are archived here (last 50 years). The resolution is around 1 latitude x 1 longitude.</p> <p>3. B1850_mod_cp denotes the simulation with new dynamical coupling scheme, B1850_f09_g16 denotes the simulation with original scheme.</p>
Dataset on the dynamics of shallow wakes in gravel-bed floodplains: results of the field experiments on the Tagliamento River, Italy
<p>Shallow wakes in fluvial environments are hydrodynamic structures produced by in-stream obstructions such as boulders, log jams, patches of riparian and aquatic vegetation. Dynamics of wakes affect a range of biotic and abiotic processes including dispersal of aquatic organisms, mixing of pollutants, sediment deposition and therefore geomorphological changes in rivers. Despite their importance, fundamental mechanisms of shallow wakes development are not fully understood due to lack of experimental data collected in natural environments and free of scaling effects compared to laboratory studies. Present dataset provides explicit information on hydrodynamics of shallow wake flows produced by solid and porous instream obstructions. Controlled field experiments with 30 runs were conducted at the side-branch of the gravel-bed Tagliamento River in the North-Eastern Italy in the period from July till October 2019. During the experimental campaign we varied characteristics of the instream obstructions (their diameter, solid volume fraction, submergence and porosity at the leading edge) and approach velocity. Each run included: (1) measurements of mean velocity and turbulence in the longitudinal (25-30 sampling locations) and lateral transects (8-10 transects with 14 sampling locations) of the constructed experimental channel where model obstructions were placed; (2) detailed surveys of the free surface topography; and (3) flow visualizations and recordings of the wakes patterns using a drone. The dataset is intended to be used for understanding fundamental mechanisms of shallow wakes development in natural fluvial environments, examination of scaling effects and verification of numerical models.</p>
Dataset_2020_Biogeosciences_Protists and collembolans alter microbial community composition, C dynamics and soil aggregation in simplified consumer - prey systems
<p>Dataset associated to the publication: Erktan, A., Rillig, M.C., Carminati, A., Jousset, A., Scheu, S. (2020) Protists and collembolans alter microbial community composition, C dynamics and soil aggregation in simplified consumer - prey systems, Biogeosciences, accepted the 27/08/2020.</p>
Dataset to study "Social dynamics of values, taboos, and perceived threats around sacred groves in Kurdistan, Iran"
<p>This dataset includes the raw data of a survey of 205 local residents in Kurdistan province, Iran. The "Dataset" sheet presents respondents' socio-demographic characteristics (distance to Baneh city, age group, gender, formal education, traditional lifestyle) and Likert-scale answers (ranging from 1 to 5) on agreement with several statements on values, taboos, and perceived threats around sacred groves in Kurdistan. The full statements are cited in the "Statements" sheet. The complete method is described in: Plieninger, T., Quintas-Soriano, C., Torralba, M., Samani, M.K. & Shakeri, Z. (2020): Social dynamics of values, taboos, and perceived threats around sacred groves in Kurdistan, Iran. People and Nature, in press.</p>
Dynamic encoding of social threat and spatial context in the hypothalamus - calcium activity recordings and behavioural dataset
<p>Social aggression and avoidance are defensive behaviors expressed by territorial animals in a manner appropriate to spatial context and experience. The ventromedial hypothalamus controls both social aggression and avoidance, suggesting that it may encode an general internal state of threat modulated by space and experience. Here we show that neurons in the mouse ventromedial hypothalamus are activated both by the presence of a social threat as well as by a chamber where social defeat previously occurred. Moreover, under conditions where the animal could move freely between a home and defeat chamber, firing activity emerged that predicted the animal's position, demonstrating the dynamic encoding of spatial context in the hypothalamus. Finally, we found that social defeat induced a functional reorganization of neural activity as optogenetic activation could elicit avoidance after, but not before social defeat. These findings reveal how the hypothalamus dynamically encodes spatial and sensory cues to drive social behaviors.</p>
Dataset for "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"
<p>Dataset to reproduce results of "Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control"</p> <p> </p> <p><strong>Abstract</strong>: Recent approaches for modelling dynamics of physical systems with neural networks enforce Lagrangian or Hamiltonian structure to improve prediction and generalization. However, when coordinates are embedded in high-dimensional data such as images, these approaches either lose interpretability or can only be applied to one particular example. We introduce a new unsupervised neural network model that learns Lagrangian dynamics from images, with interpretability that benefits prediction and control. The model infers Lagrangian dynamics on generalized coordinates that are simultaneously learned with a coordinate-aware variational autoencoder (VAE). The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane. By inferring interpretable Lagrangian dynamics, the model learns physical system properties, such as kinetic and potential energy, which enables long-term prediction of dynamics in the image space and synthesis of energy-based controllers.</p>
Biogeography of telomere dynamics in a vertebrate - Datasets S1
<p>Telomere length variation has been implicated in processes of ecological and evolutionary importance in a wide range of organisms. However, while the temporal component of this variation has been the subject of much research, we do not know yet whether a spatial component exists within species in telomere dynamics. Here, we investigated for the first time whether the biogeographic history of populations, a key driver of eco-evolutionary processes, can influence telomere dynamics. Based on a one-year longitudinal common-garden experiment in a tree frog species we found that co-specific populations can show striking differences in telomere dynamics, not explained by distinct environmental conditions experienced by individuals through time. Indeed, the populations did not differ in telomere length at the beginning of the study, yet they did after one year under standard conditions. We observed stability of telomere length over time within historically stable populations, but remarkable elongation (31.2% on average) within populations arisen during a recent range expansion. Our results suggest the intriguing scenario that non-equilibrium processes, such as range expansions, might promote plasticity in the molecular machinery regulating telomere dynamics.</p>
Datasets associated with the manuscript "Discovering SARS-CoV-2 neoepitopes and the associated TCR-pMHC recognition mechanisms by combining single-cell sequencing, deep learning, and molecular dynamics simulation techniques"
<p>meta_data_TCR-pMHC_from_STCRDab.tsv, TCR-pMHC structures used for contacts analysis.</p><p>tcr_gliph_input_sars2.tsv, input files (TCR sequences and related information) used for clustering TCRs targeting SARS-CoV-2 epitopes and epitope-unknown TCRs.</p><p>tcr_gliph_input_non-sars2.tsv, input files used for clustering TCRs targeting non-SARS-CoV-2 epitopes and epitope-unknown TCRs.</p><p>tcr_gliph_output*, output files from the GLIPH software, including the recognized TCR clusters by GLIPH (convergence-group.txt), the linkage information of TCR clusters (clone-network.txt), and the recognized motif in TCR clusters (kmer.txt).</p><p>md_trajs.tar, structures and MD simulation trajectories of TCR-614-pMHC and TCR-204-pMHC complexes.</p>
The structure and dynamics of scientific collaborations in Europe (PhD. Dataset)
<p>The dataset comprises articles used in the qualitative analysis within the doctoral thesis entitled The Structure and Dynamics of Scientific Collaborations in Europe (Pințoiu-Mihăilă Bianca-Elena, 2023). These articles capture networks of authors and co-authors who have published on COVID-19 between 2020 and 2021. They also encompass information regarding the relationship between the number of prevention measures recommended by researchers and those implemented by the Romanian, Italian, or Swedish governments. The articles were found on the Web of Science using a search algorithm incorporating keywords such as COVID-19 and [country], publication year (for 2020 and 2021), document type (articles), Web of Science Category (Multidisciplinary Science, Sociology, and all other categories containing the term "policy"), sorted by country.</p>
DYNAMIC Tanzania Quality of Care Cluster RCT dataset from the 2nd cross-sectional survey
Open the record for dataset details and reuse information.
Dataset related to the article: Economic Resilience and the Dynamics of Capital Stock
Open the record for dataset details and reuse information.
Datasets for Computational Fluid Dynamics
Open the record for dataset details and reuse information.
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engines that take AMBER input.</p>
Datasets for "The dynamic evolution of back-arc basins affected by double subduction"
<p>Data packs for numerical model results and codes to visualize the data</p> <p> </p>
Dataset for article titled 'Quantifying 3D time-resolved kinematics and kinetics during rapid granular compaction, Part I: quasistatic and dynamic deformation regimes'
<p>Dataset containing the raw X ray CT data, experimental XPCI images and input files for Abaqus simulations for the article titled ' Quantifying 3D time-resolved kinematics and kinetics during rapid granular compaction, Part I: quasistatic and dynamic deformation regimes' . The README file in Part1.zip provides information on the data.</p>
Picturing global substorm dynamics in the magnetotail using low-altitude ELFIN measurements and data mining-based magnetic field reconstructions (SST19 Reconstruction Output Dataset for Review)
<p>The dataset containing SST19 reconstruction output data accompanying the GRL submission for "Picturing global substorm dynamics in the magnetotail using low-altitude ELFIN measurements and data mining-based magnetic field reconstructions"</p>
2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 7/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains a sample of the input data for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</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.