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11,506 results for “Dynamics”
IMU-Based Tip-Over Dataset for Space Exploration Rover Dynamics and Stability Analysis
<p>This dataset provides a comprehensive collection of Inertial Measurement Unit (IMU) sensor data captured from a space exploration rover under varying conditions of terrain, speed, and inclination. The primary goal of this dataset is to enable the study of dynamic stability, specifically the detection and analysis of tip-over events, which are critical for safe and efficient operation of autonomous rovers in extraterrestrial environments.</p> <p> </p> <p><em>This dataset is provided by the Robotics Innivation Center, DFKI GmbH.</em></p> <p><em>The grant was provided by Federal Ministry for Economic Affairs and Climate Action </em></p> <p><em>Grant number: 50RA2124</em></p>
Raw data to "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions"
<div> <p>This directory contains the data used to generate the numerical results in the work "Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions [1]".</p> <p>To get an overview of the organization of the directory and a description of the data we recommend the README.md file.</p> <p>[1]: P. Adelhardt, A. Duft and K. P. Schmidt, Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions, <a href="https://arxiv.org/abs/2408.13145">arXiv:2408.13145</a></p> <p> </p> </div>
Measured and analyzed raw data for publication "Phase dynamics of tunnel Al-based ferromagnetic Josephson junctions"(https://doi.org/10.1063/5.0211006)
<p>The dataset provided here reports raw data published in June 2024 (Phase dynamics of tunnel Al-based ferromagnetic Josephson junctions): current-voltage I-V characteristics as a function of the temperature T; switching current distributions (SCD) as a function of T and calculated mean switching currents, standard deviations and skewness from the SCDs and superconducting branch resistance R0 as a function of the temperature. All the data for magnetic and non-magnetic Josephson junctions have been acquired, as highlighted in the corresponding reference.</p>
Demonstrations for imitation learning for the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives"
<p>Demonstrations for the coathanger experiment in the paper "Fitting parameters of linear dynamical systems to regularize forcing terms in Dynamical Movement Primitives". https://elib.dlr.de/205110/</p>
Data accompanying manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'
<p>This upload contains the data which accompanies the manuscript: 'Antarctic subglacial topography mapped from space reveals complex mesoscale landscape dynamics'.</p> <p><strong>Metrics calculated for each of the 4269 50 km by 50 km regions</strong></p> <table> <tbody> <tr> <td>Filename (IFPA)</td> <td>Filename (Bedmachine)</td> <td>Filename (Bedmap3)</td> <td>Description</td> </tr> <tr> <td>x_ifpa.nc<br>y_ifpa.nc</td> <td> </td> <td> </td> <td>X and Y coordinates</td> </tr> <tr> <td>mean_ifpa.nc or ifpa_mean.nc</td> <td>bedmach_mean.nc</td> <td> </td> <td>Mean elevation (m)</td> </tr> <tr> <td> <p>ifpa_count.nc<br>ifpa_count_max_20.nc<br>ifpa_count_max_100.nc<br>ifpa_count_max_250.nc</p> </td> <td>bedmach_count.nc<br>bedmach_count_max_20.nc<br>bedmach_count_max_100.nc<br>bedmach_count_max_250.nc</td> <td> </td> <td> <p>The number of hills with a 50 m prominence within a 5 km neighbourhood <br>(or 20 m, 100 m, 250 m respectively)</p> </td> </tr> <tr> <td>ifpa_b1_5km.nc<br>ifpa_b1_thickness.nc</td> <td>bedmach_b1_5km.nc<br>bedmach_b1_thickness.nc</td> <td> </td> <td>The fourier fractal dimension for wavelengths greater than 5 km or the ice thickness respectively</td> </tr> <tr> <td>ifpa_std_deslope.nc<br>i_std_l.nc</td> <td>bedmach_std_deslope.nc<br>b_std_l.nc</td> <td> </td> <td>The standard deviation:<br>- with the best fit slope removed<br>- of some long wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_wav_max_power.nc</td> <td>bedmach_wav_max_power.nc</td> <td> </td> <td>The wavelength in the Fourier spectrum with the maximum power</td> </tr> <tr> <td>ifpa_rms_slope.nc<br>i_rms_slope_h.nc</td> <td>bedmach_rms_slope.nc<br>b_rms_slope_h.nc</td> <td> </td> <td>The RMS slope of:<br>- the bed elevation<br>- some short wavelength components of the fourier spectrum</td> </tr> <tr> <td>ifpa_rms_curvature.nc</td> <td>bedmach_rms_curvature.nc</td> <td> </td> <td>The RMS curvature of the bed elevation</td> </tr> <tr> <td> </td> <td>source.nc</td> <td> </td> <td>The method used to calculate the bed topography (Bedmachine only)</td> </tr> <tr> <td> </td> <td> </td> <td>mean_nearest.nc</td> <td>The mean distance from each IFPA grid point to the nearest Bedmap3 data point</td> </tr> <tr> <td> </td> <td> </td> <td>bedmap3_count.nc</td> <td>The number of Bedmap3 data points within the region</td> </tr> </tbody> </table> <p> </p> <p><strong>Datasets required for plotting</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>Groundingline_Antarctica_v2.shp</td> <td>Antarctic grounding line </td> </tr> <tr> <td>ECR_features.shp</td> <td>Outline of significant features within the example regions chosen</td> </tr> <tr> <td>IFPA_bed.nc</td> <td>OLD VERSION of IFPA bed topography map for Antarctica</td> </tr> <tr> <td> <p>IFPA_bed_C50.nc</p> </td> <td>IFPA bed topography map for Antarctica (without radar correction)</td> </tr> </tbody> </table> <p><strong>To plot the figures, you will either require the following datasets: </strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> </tr> <tr> <td>IFPA_figures_data.zip</td> <td>Additionally data to plot figures 1,6,8 and 9 </td> </tr> <tr> <td> <p>HA_data.csv<br>HB_data.csv<br>RSB_data.csv</p> </td> <td>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected), IFPA (not radar corrected), Bedmachine v3, and ice-penetrating radar profiles</td> </tr> <tr> <td> <p>HA_data_ifpa.csv<br>HB_data_ifpa.csv<br>RSB_data_ifpa.csv</p> </td> <td> <p>For Highland A, Highland B and Recovery Subglacial Basin<br>IFPA (radar corrected) map for the region crossed by the ice-penetrating radar profile</p> </td> </tr> </tbody> </table> <p><strong>or, the figures can be regenerated using the following datasets, which are available at the listed DOIs</strong></p> <table> <tbody> <tr> <td>Filename</td> <td>Description</td> <td>Reference</td> <td>DOI</td> </tr> <tr> <td>GaplessREMA100.nc</td> <td>Gapless REMA Antarctica dataset at 100m resolution</td> <td>Dong et al. (2022)</td> <td>10.1016/j.isprsjprs.2022.01.024</td> </tr> <tr> <td>BedMachineAntarctica-v3.nc</td> <td>MEaSURES BedMachine Antarctica bed topography map version 3</td> <td>Morlighem et al. (2020)</td> <td>10.5067/FPSU0V1MWUB6</td> </tr> <tr> <td>antarctica_ice_velocity_450m_v2.nc</td> <td>ITSLIVE Antarctic velocity map</td> <td>Gardner et al. (2019)</td> <td>10.5067/6II6VW8LLWJ7</td> </tr> <tr> <td>antarctic_ice_vel_phase.nc</td> <td>MEaSURES Antarctic velocity map</td> <td>Mouginot et al. (2019)</td> <td>10.5067/PZ3NJ5RXRH10</td> </tr> <tr> <td>UTIG_2010_ICECAP_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the UTIG Icecap survey</td> <td>Wright et al. (2012)</td> <td>10.1029/2011JF002066</td> </tr> <tr> <td>BAS_2012_ICEGRAV_AIR_BM3.csv</td> <td>Bed elevation from airborne radar from the BAS Icegrav survey</td> <td>Forsberg et al. (2018)</td> <td>10.1144/SP461.17</td> </tr> </tbody> </table> <p><strong> </strong></p>
How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study
<p>The data necessary to independently reproduce the MD simulations and the first part of the simulation trajectories reported in the paper "<strong>How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study</strong>", by Lemmens et al.</p> <p>Due to Zenodo data deposition limits, every 10th frame of the MD simulation trajectories is included. Due to Zenodo deposition limits, MD trajectory files for this paper are also available at 10.5281/zenodo.14260246.</p>
Data sets and code for "Suprachiasmatic Nucleus-wide Estimation of Oscillatory Temporal Dynamics" (Yao et al, 2024)
<ul> <li>Data from iDISCO clearing and scanning of three adult mouse suprachaismatic nuclei. Brains are labeled as b1, b2, and b3. Each lobe of the SCN is recorded in a separate csv file. Animals were sacrificed at ZT 19. </li> <li>Data for PER2::LUC recordings of ix adult mouse suprachaismatic nuclei. For each slice there are two files: the time series data (labeled "slice-[orientation]-time-series-#" and the coordinates of the pixels represented (labeled "slice-[orientation]-pixel-coords-#."</li> <li>Code in MATLAB to perform phase extraction, linear modeling, phase estimation, and dynamical simulation.</li> </ul>
Global hydrology and water quality data from 1980-2019, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<p>Global ~10km (5 arcmin) output data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual and monthly temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Discharge (m3 s-1)</li> <li>Channel storage (m3) </li> <li>Water temperature (K)</li> <li>Total dissolved solids (TDS) load (g s-1)</li> <li>Biological oxygen demand (BOD) load (g s-1)</li> <li>Fecal coliform (FC) load (million cfu s-1)</li> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Note. a minimum discharge threshold of 0.1 m3 s-1 was used when computing salinity (TDS), organic (BOD) and pathogen (FC) concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Thus, if the the average discharge for the month was below 0.1 m3 s-1, concentrations are not calculated (assigned as NA).</p> <p>In-stream water quality aggregated to 0.5 degree (i.e. 30 arcmin) spatial resolution (daily, monthly and annual) can be found at: <a href="https://zenodo.org/records/14675270">https://zenodo.org/records/14675270</a>. </p>
Global surface water quality data from 1980 - 2019, derived from the dynamical surface water quality model (DynQual) at 30 arcmin spatial resolution
<p>Global ~50km (30 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 1980-2019, with annual, monthly and daily temporal resolution. Simulations are made following the ISIMIP3a protocol (https://protocol.isimip.org/#/ISIMIP3a).</p> <p>Output data includes:</p> <ul> <li>Salinity; as indicated by TDS concentrations (mg l-1)</li> <li>Organic pollution; as indicated by BOD concentrations (mg l-1)</li> <li>Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml-1)</li> </ul> <p>Simulations were originally made at 5-arcmin resolution and aggregated to 30 arcmin 0.5 degree by summing the in-stream (routed) loadings and channel storage over the aggregated area (at daily, monthly and annual timesteps), and subsequently calculating in-stream concentrations. Please note the aggregation technique is provisional and thus the data is subject to change.</p> <p>Note. A minimum discharge threshold of 0.1 m3 s-1 was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.</p> <p>Hydrology and water quality simulations made at DynQuals native spatial resolution (5 arcmin) can be found at: <a href="https://zenodo.org/records/14673871">https://zenodo.org/records/14673871</a>.</p>
Global surface water quality datasets under uncertain climate and socio-economic change, derived from the dynamical surface water quality model (DynQual) at 5 arcmin spatial resolution
<pre>Global ~10km (5 arcmin) surface water quality data from the dynamical surface water quality model (DynQual) from 2005-2100, with annual and monthly temporal resolution. Simulations are made under three combined climate and socio-economic scenarios (SSP1-RCP2.6; SSP3-RCP7.0 and SSP5-RCP8.5) and using five general circulation model (GFDL-ESM4; UKESM1-0-LL; MPI-ESM1-2-hr; IPSL-CM6A-LR and MRI-ESM2-0), following the ISIMIP3b protocol (<a href="https://protocol.isimip.org/#/ISIMIP3b">https://protocol.isimip.org/#/ISIMIP3b</a>). Output data are provided at annual and monthly temporal resolution over WorldClim time periods (2005-2020; 2021-2040; 2041-2060; 2061-2080; 2081-2100). Output data includes: - Discharge (m<sup>3</sup> s<sup>-1</sup>) - Water temperature (K)<br>- Total dissolved solids (TDS) load (g s<sup>-1</sup>)<br>- Biological oxygen demand (BOD) load (g s<sup>-1</sup>)<br>- Fecal coliform (FC) load (million cfu s<sup>-1</sup>) - Salinity; as indicated by TDS concentrations (mg l<sup>-1</sup>) - Organic pollution; as indicated by BOD concentrations (mg l<sup>-1</sup>) - Pathogen/bacterial pollution; as indicated by FC concentrations (cfu 100ml<sup>-1</sup>)<br><br>Note. A minimum discharge threshold of 0.1 m<sup>3</sup> s<sup>-1</sup> was used when computing TDS, BOD and FC concentrations, as uncertainties in absolute values of water availabilities have large impacts on resulting in-stream concentrations. Concentrations in these gridcells are assigned as NA.<br><br>Full time series of these variables at 30 arcmin (0.5 degree) can be found at: <a href="https://zenodo.org/records/14677534">https://zenodo.org/records/14677534</a>.</pre>
Skogaryd data used for the paper: Evaluation of long-term carbon dynamics in a drained forested peatland using the ForSAFE-Peat Model.
<p>Dataset of abiotic and carbon exchange variables for Skogaryd drained afforested peatland. The dataset include measurements of soil temperature, ground water level, and carbon exhange as well as modelled carbon fluxes performed with the model ForSAFE-Peat </p>
GLC_FCS30D: the first global 30-m land-cover dynamic monitoring product with fine classification system from 1985 to 2022
<div> <p>GLC_FCS30D is the first global fine land cover dynamic product at a 30-meter resolution that adopts continuous change detection. It utilizes a refined classification system containing 35 land-cover categories and covers the time span from 1985 to 2022. Before the year 2000, the update cycle was every 5 years, while after 2000, it is updated annually. In specific, it developed by combining the continuous change detection method, local adaptive updating models and the spatiotemporal optimization algorithm from dense time-series Landsat imagery, and was validated to achieve an overall accuracy of 80.88% (±0.27%) for the basic classification system 10 major land-cover types) and 73.24% (±0.30%) for the LCCS level-1 validation system (17 LCCS land-cover types).</p> <p>The GLC_FCS30D has been compressed into 36 zip files, and<strong> <em>the details about the GLC_FCS30D can be found in the User's guides.</em></strong></p> </div>
PubMed-Temporal: A dynamic graph dataset with node-level features
Open the record for dataset details and reuse information.
Performance of wave function and Green's function methods for non-equilibrium many-body dynamics
<p>In this repository we have compiled 1-RDMs on a time grid obtained from various methods, namely, time-dependent full configuration interaction (TD-FCI), time-dependent coupled cluster (TD-CC), time-dpendent Hartree-Fock (TD-HF), Kadanoff-Baym Equations, and generalized Kadanoff-Baym approximation (GKBA). We have evaluated the 1-RDMs from Hubbard model in presence of an external drive. We have included a PySCF script to generate the integrals with a specific choice for various parameters. One can reproduce the HF results from that script. The Python script to evaluate various observables that we have analyzed in our article, namely, time-dependent dipole moment, Von-Neumann entropy are also added. </p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Temporary Allee effects among non-stationary recruitment dynamics in depleted gadid and flatfish populations
<p>We investigated whether low-abundance recruitment dynamics can change in time between compensation and depensation, the latter implying the presence of the Allee effect. For this, we studied the stock-recruitment time series of 17 gadid and flatfish populations in the RAM Legacy Stock Assessment Database using a Bayesian change point model. The recruitment dynamics were represented with the sigmoidal Beverton-Holt and the Saila-Lorda stock-recruitment models, and the parameters of the models were allowed to shift at a priori unknown change points.</p>
A Global Dataset of Standardized Moisture Anomaly Index Incorporating Snow Dynamics (SZIsnow) from 1948 to 2010
<p>The SZI<sub>snow</sub> dataset was calculated based on systematic physical fields from the Global Land Data Assimilation System Version 2 (GLDAS-2) with the Noah land surface model. This SZI<sub>snow</sub> dataset considers different physical water-energy processes, especially snow processes. The evaluation shows the dataset is capable of investigating different types of droughts across different timescales. The assessment also indicates that the dataset has an adequate performance to capture droughts across different spatial scales. The consideration of snow processes improved the capability of SZI<sub>snow</sub>, and the improvement is evident over snow-covered areas (e.g., Arctic region) and high-altitude areas (e.g., Tibet Plateau). Moreover, the analysis also implies that SZI<sub>snow</sub> dataset is able to well capture large-scale drought events across the world. This drought dataset has high application potential for monitoring, assessing, and supplying information on drought, and also can serve as a valuable resource for drought studies.</p>
Siderophores drive invasion dynamics in bacterial communities through their dual role as public good versus public bad
<p>Microbial invasions can compromise ecosystem services and spur dysbiosis and disease in hosts. Nevertheless, the mechanisms determining invasion outcomes often remain unclear. Here, we examine the role of iron-scavenging siderophores in driving invasions of <em>Pseudomonas aeruginosa</em> into resident communities of environmental pseudomonads. Siderophores can be "public goods" by delivering iron to individuals possessing matching receptors; but they can also be "public bads" by withholding iron from competitors lacking these receptors. Accordingly, siderophores should either promote or impede invasion, depending on their effects on invader and resident growth. Using supernatant feeding and invasion assays, we show that invasion success indeed increased when the invader could use its siderophores to inhibit (public bad) rather than stimulate (public good) resident growth. Conversely, invasion success decreased the more the invader was inhibited by the residents' siderophores. Our findings identify siderophores as a major driver of invasion dynamics in bacterial communities under iron-limited conditions.</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>
Solvent sites detection from molecular dynamics in mixed solvents
<p>In this zip file, you will find 3 molecular dynamics (MDs) that were run using the AMBER molecular dynamics package (https://ambermd.org/), and the necessary scripts to obtain the solvent sites.</p> <p>The MDs correspond to the kinase and rubredoxin domains of protein kinase G from Mycobacterium tuberculosis. </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.