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453 results for “Dynamic dataset”
Dataset for molecular dynamics simulations of coalescence of Pd and AuPd clusters
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Dataset to develop the Generalized Linear Models in "THE CRITICAL ROLE OF HYDROLOGICAL DISTANCE IN SHAPING NUTRIENT DYNAMICS ALONG THE WATERSHED-LAKE CONTINUUM"
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Dataset for AR article: New particle formation dynamics in the central Andes: contrasting urban and mountaintop environments
<p>Dataset for Article New particle formation dynamics in the central Andes:<br>contrasting urban and mountaintop environments (Aliaga et al. 2024; Aerosol research)</p> <p>It contains four key data files with clustered model output and measurement data from FLEXPART simulations and in situ observations. Details on each file are provided below:</p> <p>1. flx_clust20_alto.nc</p> <p> - Description: Contains clustered output from FLEXPART simulations at EAC site .</p> <p><br>2. flx_clust20_chc.nc</p> <p> - Description: Stores the clustered output from FLEXPART simulations at the CHC site.</p> <p><br>3. particle_number_size_dist.nc</p> <p> - Description: Provides particle number concentrations measured at CHC and EAC sites, utilizing instruments NAIS and DMPS.</p> <p><br>4. hourly_data.nc</p> <p> - Description: dataset with all relevant parameters, including meteorological variables, cloud fraction, chemical composition, and particle concentration across various sizes.</p> <p> </p>
Dataset (IX) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>Well-tempered metadynamics simulation data of compounds <strong>1 </strong>and<strong> 2</strong> of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
Dataset (X) related to publication: Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors
<p>Well-tempered metadynamics simulation data of compounds <strong>1 </strong>and<strong> 2</strong> of the related to the publication Pantsar et al.: <em>Decisive Role of Water and Protein Dynamics in Residence Time of p38a MAP Kinase Inhibitors.</em></p> <p>Individual .zip files contain raw-desmond trajectories (-out.cms files and trj-files).</p> <p>All datasets related to this publication:</p> <p><a href="https://doi.org/10.5281/zenodo.4568113">https://doi.org/10.5281/zenodo.4568113</a>(compound <strong>1</strong>; dataset: I)</p> <p><a href="https://doi.org/10.5281/zenodo.4572444">https://doi.org/10.5281/zenodo.4572444</a> (compound <strong>1</strong>; dataset: II)</p> <p><a href="https://doi.org/10.5281/zenodo.4561797">https://doi.org/10.5281/zenodo.4561797</a>(compound <strong>2</strong>; dataset: III)</p> <p><a href="https://doi.org/10.5281/zenodo.4563896">https://doi.org/10.5281/zenodo.4563896</a> (compound <strong>2</strong>; dataset: IV)</p> <p><a href="https://doi.org/10.5281/zenodo.5563359">https://doi.org/10.5281/zenodo.5563359</a> (<strong>SB203580</strong>; dataset: V)</p> <p><a href="https://doi.org/10.5281/zenodo.5563655">https://doi.org/10.5281/zenodo.5563655</a> (<strong>SB203580</strong>; dataset: VI)</p> <p><a href="https://doi.org/10.5281/zenodo.5564118%20">https://doi.org/10.5281/zenodo.5564118 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564208%20">https://doi.org/10.5281/zenodo.5564208 </a>(compound <strong>1</strong> simulated in compound <strong>2</strong> metastable state <strong>2-<em>S</em><sub>3</sub></strong>; dataset: VIII)</p> <p><a href="https://doi.org/10.5281/zenodo.5564586">https://doi.org/10.5281/zenodo.5564586</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: IX)</p> <p><a href="https://doi.org/10.5281/zenodo.5570882">https://doi.org/10.5281/zenodo.5570882</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: X)</p> <p><a href="https://doi.org/10.5281/zenodo.5571352">https://doi.org/10.5281/zenodo.5571352</a> (well-tempered metadynamics simulations of compounds <strong>1</strong> and <strong>2</strong>; dataset: XI)</p> <p>The datasets include original Desmond raw-trajectories (datasets I–VIII), PDB-coordinates for the energy minimized metastable state derived structures (datasets II, IV and VI) and raw-trajectories of the well-tempered metadynamics simulations (dataset IX–XI).</p>
A dataset to support dynamical modelling of the thermal dynamics of a super-insulated building
<p>This work consisted of controlled heating experiments for the purpose of control-oriented dynamical modelling. The building studied is the LivingLab at NTNU, which is a super-insulated single-family house. The dataset contains full details of the measurements, as well as preprocessed (reduced) data which can be readily used as input for dynamical modelling investigations.</p>
Spatial datasets for Victorian kelp dynamics
<p class="MsoNormal"><strong><span>Aim:</span></strong><span> Kelp forests throughout temperate regions of the world serve as foundation species that play a critical role in sustaining the health and function of marine ecosystems but are experiencing declines in abundance due to loss in resilience as the ocean clim</span><span>ate changes. Ocean warming along southeast Australia</span> has already been linked to dramatic losses of kelp species and is contributing to the range expansion and population increases of two species of sea urchin. The purpose of this research is to understand the impact of multiple stressors on the decline in kelps in this region.</p> <p class="MsoNormal"><strong>Location:</strong> Coastal waters off Victoria, Australia</p> <p class="MsoNormal"><strong>Methods:</strong> <span>In this study, we use long-term (> 20 years) datasets on biological observations across Victorian waters to determine trends in coverage and the impact of multiple environmental variables (temperature, habitat, currents, waves, connectivity, urchin abundances) on two important kelps that serve as foundation species (<em>Phyllospora comosa</em> and <em>Ecklonia radiata) </em>using boosted regression trees. These models were then used to develop predictive distribution models for each species and also to predict areas of future risk. </span></p> <p class="MsoNormal"><strong><span>Results:</span></strong><span> We found that both kelp species are decreasing in percent coverage over time and multiple environmental variables, including increasing temperatures, intensifying wave energy, changes in currents and recruitment patterns, and increases in urchin populations are all contributing to the declines of kelps. Additionally, future projections of temperature and wave energy show that these species will likely continue to decrease across 71% of Victorian waters. </span></p> <p><strong><span>Main Conclusions:</span></strong><span><strong> </strong>This information can help to better manage these important foundation species by providing maps of their current and past distributions, along with projections of climate change, to target different areas for urchin culling or macroalgae restoration to reduce future losses.</span></p>
Prestorm atmospheric dynamic variable profiling dataset in Beijing as observed from the Radar wind profiler mesonet
<p>This dataset contains prestorm atmospheric dynamical variables of 30 minutes before rainfall onset in summer (June ~ August) for the period 2018–2019, which is determined by the measurements from the triangular mesonet of radar wind profile in Beijing. Each data file is stored in CSV format, containing the triangle area averaged divergence, vertical velocity and vorticity at 400、450、500、550, 600, 650, 700, 750, 775, 800, 825, 850, 875 and 900 hPa levels. The name for each data is formatted as RWP_YYYY_NNN hPa_Lead-MM min.csv, where YYYY refers to 2018 and 2019, NNN refers to 400、450、500、550, 600, 650, 700, 750, 775, 800, 825, 850, 875 and 900 hPa, and MM refers to 12, 18, 24, 30, 36 and 42 minutes prior to rainfall onset. </p>
Dataset for Interspecies interactions determine growth dynamics of biopolymer degrading populations in microbial communities.
<p>This is the dataset (containing raw and annotated image sets, scripts, and growth dynamics) </p>
Dataset I related to publication: Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations
<p>Dataset related to publication:<em> </em></p> <p><em>Leini, R., Kapp, J., Kopra, K. et al. Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations. Sci Rep <strong>15</strong>, 24843 (2025). https://doi.org/10.1038/s41598-025-07532-2</em></p> <ul> <li>.zip-files contain the raw Desmond trajectories of individual replica simulations of GDC-6036.</li> <li>WaterMap-output file for the representative conformation of GDC-6036.</li> </ul>
Dataset II related to publication: Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations
<p>Dataset related to publication:<em> </em></p> <p>Leini, R., Kapp, J., Kopra, K. <em>et al.</em> Binding modes of the KRAS(G12C) inhibitors GDC-6036 and LY3537982 revealed by all atom molecular dynamics simulations. <em>Sci Rep</em> <strong>15</strong>, 24843 (2025). https://doi.org/10.1038/s41598-025-07532-2</p> <ul> <li>.zip-files contain the raw Desmond trajectories of individual replica simulations of LY3537982.</li> <li>WaterMap-output file for the representative conformation of LY3537982</li> </ul>
Dynamic encoding of social threat and spatial context in the hypothalamus - calcium activity recordings and behavioural dataset
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Biogeography of telomere dynamics in a vertebrate - Datasets S1
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Spatial datasets for Victorian kelp dynamics
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Dataset for: Vibrometry as a noncontact alternative to dynamic and viscoelastic mechanical testing in cartilage
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Systematic characterization of the composition and dynamics of processing body-associated mRNAs [dataset 2]
GEO Series GSE279284. Homo sapiens. 131 samples. Type: Expression profiling by high throughput sequencing; Other.
Dynamic changes in genome-wide histone methylation and gene expression of soybean roots in response to salt stress (RNA-seq dataset)
GEO Series GSE133574. Glycine max. 6 samples. Type: Expression profiling by high throughput sequencing.
Integration of a multi-omics stem cell differentiation dataset using a dynamical model
GEO Series GSE93301. Mus musculus. 11 samples. Type: Expression profiling by high throughput sequencing.
Single cell mapping of breast tumor stroma reveals dynamically evolving compositions of cancer-associated fibroblasts along tumor progression (scRNA-seq dataset)
GEO Series GSE149635. Mus musculus. 50 samples. Type: Expression profiling by high throughput sequencing.
Replicating dynamic humerus motion using an industrial robot dataset
<p>This dataset encompasses all of the data utilized to replicate humeral kinematics, as captured via skin marker motion capture, on a FANUC M20ia industrial robot. The activities replicated include 119 jumping jacks, 15 jug lifts, 105 jogging trials, and 15 rapid internal rotation trials. An Optotrak Certus optical tracking system was utilized to record the kinematics of the humerus as actuated by the robot (also included in this dataset) in order to compare the robotically replicated trajectories against the motion capture trajectories. This dataset is intended to accompany the "Mocap to Robot" software package that provides an algorithmic pipeline for mapping motion capture trajectories to robotically replicated motion.</p> <p>Related publication can be found at: <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242005">https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0242005</a></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.