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11,506 results for “Dynamics”
Scalable dynamic characterization of synthetic gene circuits - Data
<p>This dataset contains supporting information and data for the preprint: <a href="https://www.biorxiv.org/content/10.1101/635672v2.abstract">Scalable dynamic characterization of synthetic gene circuits</a>.</p> <p>The dataset contains the following 3 files:</p> <ul> <li>DynamicCharacterizationGenbanks.zip: This zip contains Genbank files that describe the sequence information of the genetic circuits that have been built and characterized in this manuscript. </li> <li>DynamicCharacterizationWrapper.zip: This zip contains the models and data files of the case studies that run using the Dynamic Characterization wrapper CLI tool. </li> <li>GECWebserver.zip: This zip contains the models and data files of the case studies that run using the GEC (Genetic Engineering of Cells) Web server tool.</li> </ul>
Figure 4 from: Balestra V, Lana E, Carbone C, De Waele J, Manenti R, Galli L (2021) Don't forget the vertical dimension: assessment of distributional dynamics of cave-dwelling invertebrates in both ground and parietal microhabitats. Subterranean Biology 40: 43-63. https://doi.org/10.3897/subtbiol.40.71805
Figure 4 Biodiversity indices for wall and ground cave fauna.
The dynamic interaction between DNA polymerase beta and eta with an RNA-DNA hybrid
<p>The videos have shown that DNA polymerase beta (5TBB) exhibited dynamic interaction with a 1 nt-gap substrate containing an RNA template through the formation of base-pairing between the incoming nucleotide with the RNA template base. In contrast, DNA polymerase eta (4J9N) exhibited dynamic interaction with an open RNA template with disrupted base-pairing.</p>
Single-Cell RNA-seq Uncovers Dynamic Processes Orchestrated by RNA-Binding Protein DDX43 in Chromatin Remodeling during Spermiogenesis
<p>All datasets include analyzed scRNA-seq, peak files called from eCLIP-seq datasets, and spliced/unspliced readouts of single cell.</p>
Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials. Glycine and Acetylacetone Data.
<p>Data generated and analyzed in <strong>M.Kulichenko <em>et. al</em>. Uncertainty Driven Dynamics for Active Learning of Interatomic Potentials. <em>Nat. Comput. Sci </em>(2023).</strong></p> <ul> <li>Glycine data sets (MD-AL and UDD-AL).</li> <li>Acetylacetone MD and UDD trajectories.</li> <li>See README.md for details.</li> </ul> <p> </p> <p> </p> <p> </p>
Replication package for: House price dynamics, optimal LTV limits and the liquidity trap
<p>This package contains all the code necessary to replicate the figures in Ferrero, A., Harrison, R. and Nelson, B. (forthcoming) "House price dynamics, optimal LTV limits and the liquidity trap", Review of Economic Studies.</p>
Land change dynamics in the National Natural Park System of Colombia (2000-2018)
<p>This dataset contains final and intermediate products for analysis of Land Use and Land Cover Change in the National Natural Park System of Colombia from 2000 to 2018.</p> <p>The R scripts used to produce this dataset are available at: <a href="https://doi.org/10.5281/zenodo.7562104">https://doi.org/10.5281/zenodo.7562104</a>.</p> <p>The original Landsat multi-year composites were produced using the <a href="https://www.cde.unibe.ch/research/projects/a_tool_for_satellite_image_preprocessing_and_composition/index_eng.html">Google Earth Engine Image Pre-processing Tool</a>.</p>
Dataset for the simulated response of vegetation composition and carbon dynamics to variations in drought-heat occurrence
<p>Dataset consisting of model runs from six dynamic global vegetation models each using six different scenarios with varying drought-heat signatures as input. Each zip file contains all the scenario runs for one model.</p>
A comparison and coupling of two novel sensors for intertidal bed-level dynamics observation
<p>Raw datasets for paper "A comparison and coupling of two novel sensors for intertidal bed-level dynamics observation" published on Limnology and Oceanography: Methods</p>
Membrane free-energy landscapes derived from atomistic dynamics explain nonuniversal cholesterol-induced stiffening - Sample files
<p><strong>Initial and final snapshots of atomistic simulations of curved lipid bilayers</strong></p> <p>Each PDB file is named according to lipid composition and number of lipid molecules per leaflet.</p> <p>To reduce file size, solvent molecules were excluded and multiple snapshots of the same system zipped together.</p> <p>Reference: <a href="https://doi.org/10.1101/2023.02.02.525347">https://doi.org/10.1101/2023.02.02.525347</a></p>
Zeondo dataset for Probing the limits of the phonon quasi-particle picture: Transitions between underdamped and overdamped dynamics in CsPbBr3
<p>Zeondo dataset for Probing the limits of the phonon quasi-particle picture: The transition from underdamped to overdamped dynamics in CsPbBr3</p>
M3CropSeg: A Multi-platform, Multi-temporal, and Multi-resolution Remote Sensing Dataset for Crop Semantic to Instance and Dynamic Segmentation.
<p>Earth observation (EO) provides various multi-platform, multi-temporal, and multiresolution<br> remote sensing imagery for dynamic monitoring of planet Earth, with<br> a wide variety of uses. Crop monitoring is a typical application, which involves<br> timely gathering of the information of crop types, boundaries, and dynamic changes<br> during the whole crop growth period. However, most of the existing datasets and<br> benchmarks focus on medium-resolution (≥ 10 m) classification of the main crop<br> type by using satellite image time series (SITS), where the individual boundaries<br> (parcels) and the dynamic changes of the crop cannot be obtained, due to the<br> limited spatial resolution and the lack of multi-season annotation. In this paper,<br> a multi-platform, multi-temporal, and multi-resolution (M3) remote sensing crop<br> segmentation dataset (M3CropSeg) is introduced for very high resolution (VHR, 1<br> m) crop semantic segmentation to instance segmentation and dynamic segmentation.<br> Specifically, M3CropSeg contains 16311 pairs of airborne VHR (1 m) and SITS<br> (10 m) images, with 45 crop types and 101k instance annotations, covering a<br> 26,000 km<sup>2</sup> area of California in the U.S. M3CropSeg has various challenges,<br> including M3 data fusion, class imbalance, fine-grained classification, and multilabel<br> classification. Three tracks are designed for M3CropSeg, i.e., M3 semantic<br> segmentation, M3 instance segmentation, and M3 dynamic segmentation, to obtain<br> high-resolution pixel-level, parcel-level, and multi-season crop types, respectively.<br> The corresponding benchmarks are also provided to address the above challenges,<br> along with a variety of experimental analyses.</p>
Three-step docking by WIPI2, ATG16L1 and ATG3 delivers LC3 to the phagophore: Molecular dynamics simulation data
<p>Atomistic molecular dynamics simulation data set accompanying manuscript "Three-step docking by WIPI2, ATG16L1 and ATG3 delivers LC3 to the phagophore".</p>
MetROMS model output for manuscript "Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf"
<p>This dataset contains output of the MetROMS ocean/sea-ice/ice-shelf model.</p><p>The output was used for analyses of the manuscript:</p><p>Huneke W. G. C., Hobbs W.R., Klocker A., Naughten K. A., 2023, Dynamic response to ice shelf basal meltwater relevant to explain observed sea ice trends near the Antarctic continental shelf, Geophysical Research Letters, 50, e2023GL105435, https://doi.org/10.1029/2023GL105435</p><p>The Github repository https://github.com/wghuneke/MetROMS_BasalMelt_Perturbation/tree/main contains analysis (python) scripts for processing and visualising the model output.</p><p>Contact wilma.huneke@anu.edu.au if you need further information.</p>
Code and Data for "Effective Statistical Control Strategies for Complex Turbulent Dynamical Systems"
<p>The code and data used in the paper "Effective Statistical Control Strategies for Complex Turbulent Dynamical Systems".</p>
Representative movies of dynamic changes in RlmI
<p>"RlmI_Trajectory1.mp4": Representative movie of "domain-III" closure in trajectory1</p> <p>"RlmI_Trajectory2.mp4": Representative movie of "domain-III" closure in trajectory2</p> <p>The movies was created by "VMD"</p>
Dataset for Dynamic Hand Gesture Recognition Systems
<p>Computer vision systems are commonly used to design touchless human-computer interfaces (HCI) based on dynamic hand gesture recognition (HGR) systems, which have a wide range of applications in several domains, such as gaming, multimedia, automotive, and home automation. However, automatic HGR is still a challenging task, mostly because of the diversity in how people perform the gestures. In addition, the number of publicly available hand gesture datasets is scarce; often, the gestures are not acquired with sufficient image quality, and the gestures are not correctly performed. In this data article, we propose a dataset of 27 dynamic hand gesture types acquired at full HD resolution from 21 different subjects, which were carefully instructed before performing the gestures and monitored when performing the gesture; the subjects had to repeat the movement in case the performed hand gesture was not correct, i.e., the authors of this paper that were observing the gesture found that it did not correspond to the exact expected movement and/or the camera recorded a viewpoint did not allow for a plain visualizing of the gesture. Each subject performed 3 times the 27 hand gestures for a total of 1701 videos collected and corresponding to 204120 video frames.</p> <p> </p> <p>In the following, we discuss the details of the provided datasets.</p> <p><strong>hand_gestures_dataset_videos.zip</strong> - This dataset contains the videos of the recorded hand gestures. The zip contains 27 main folders. Each main folder refers to a hand gesture class, for a total of 27 main folders named “class_xx”, where “xx” identifies the class from 01 to 27. Within each of the class folders, there are 21 sub-folders, one folder for each of the subjects that performed the hand gestures. These folders are named “Useryy_”, where “yy” identifies the user from 01 to 21. Each of the user folders contains three videos (.avi) corresponding to the three hand gestures performed by the user for each hand gesture class. The size of the full dataset is 21.34 GB.</p> <p><strong>HGD_VideoFrames_class_XX.zip</strong> - These datasets contain the video frames extracted from the videos of the recorded hand gestures. Each zip file contains the video frames of a hand gesture class, for a total of 27 zip files named “HGD_VideoFrames_class_XX.zip”, where “xx” identifies the class from 01 to 27. Therefore, each zip file contains one of the 27 class folders. Within each of the class folders, there are 21 sub-folders, one folder for each of the subjects that performed the hand gestures. These folders are named “Useryy_”, where “yy” identifies the user from 01 to 21. Each of the user folders contains, in turn, 3 sub-folders, one folder for each of the three hand gestures performed for each hand gesture class. These sub-folders are named, respectively, “Useryy_1”, “Useryy_2”, and “Useryy_3”, and contain 120 video frames (.png) extracted from the corresponding video. The size of each zip file is about 9 GB.</p> <p><strong>hand_gesture_timing_stats.csv</strong> - This dataset contains timing information regarding the gestures performed by the subjects. The size of this dataset is 36 KB. It has 567 records plus the header. The meaning of the columns is as follows:</p> <ul> <li> <p><em>class</em>: hand gesture class, from 01 to 27.</p> </li> <li> <p><em>user</em>: user who performed the hand gestures, from 01 to 21.</p> </li> <li> <p><em>start_frame_1</em>: starting frame related to the first performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>end_frame_1</em>: ending frame related to the first performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>exec_time_1</em>: execution time (in seconds) related to the first performed hand gesture. It is computed as the difference between the ending frame and the starting frame divided by the 30 fps set for video recording.</p> </li> <li> <p><em>start_frame_2</em>: starting frame related to the second performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>end_frame_2</em>: ending frame related to the second performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>exec_time_2</em>: execution time (in seconds) related to the second performed hand gesture. It is computed as the difference between the ending frame and the starting frame divided by the 30 fps set for video recording.</p> </li> <li> <p><em>start_frame_3</em>: starting frame related to the third performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>end_frame_3</em>: ending frame related to the third performed hand gesture. It is a number between 0 and 119.</p> </li> <li> <p><em>exec_time_3</em>: execution time (in seconds) related to the third performed hand gesture. It is computed as the difference between the ending frame and the starting frame divided by the 30 fps set for video recording.</p> </li> <li> <p><em>mean_exec_time</em>: mean execution time (in seconds) for that related user and hand gesture. It is computed as the mean of the execution times computed for the three hand gestures performed by that user for that class.</p> </li> <li> <p><em>std_dev_exec_time</em>: standard deviation of the three execution times (in seconds) computed for the three hand gestures performed by that user for that class.</p> </li> <li> <p><em>total_mean_exec_time</em>: total mean execution time (in seconds) for that class. It is computed as the mean of all the execution times computed for the three hand gestures performed by all the users for that class.</p> </li> <li> <p><em>total_std_dev_exec_time</em>: total standard deviation of all the execution times (in seconds) for that class. It is computed as the standard deviation of all the execution times computed for the three hand gestures performed by all the users for that class. Note that in this case, the standard deviation has been computed by dividing by (N-1) as the entire population is considered.</p> </li> </ul> <p> </p> <p dir="ltr"><strong>If you make use of this dataset, please consider citing the following publication:</strong></p> <p dir="ltr">Fronteddu, G., Porcu, S., Floris, A., & Atzori, L. (2022). A dynamic hand gesture recognition dataset for human-computer interfaces. Computer Networks, 205, 108781.</p> <p dir="ltr">BibTex format:</p> <p>@article{fronteddu2022dynamic, title={A dynamic hand gesture recognition dataset for human-computer interfaces}, author={Fronteddu, Graziano and Porcu, Simone and Floris, Alessandro and Atzori, Luigi}, journal={Computer Networks}, volume={205}, pages={108781}, year={2022}, publisher={Elsevier}, doi = {https://doi.org/10.1016/j.comnet.2022.108781} }</p>
Dataset of traffic dynamics during the 2020 Silverado Wildfire Evacuation
<p>This dataset has been sourced from the Performance Measurement System of the California Department of Transportation. The data has been processed, analysed, presented in the paper.</p>
Effects of Dynamic Cupping on Shoulder Active Range of Motion of Senior Male Handball Athletes
ClinicalTrials.gov study NCT06226961. IPD Sharing: Not stated. Countries: 1. Publications: 0.
The Effect of Kinesiotaping on Sprint and Dynamic Balance in Children
ClinicalTrials.gov study NCT04979286. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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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)
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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.