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453 results for “Dynamic dataset”
Dataset for "Modelling impacts of spatially variable erosion drivers on suspended sediment dynamics"
<p>Dataset for publication:</p> <p>Battista, P. Molnar, and P. Burlando. Modelling impacts of spatially variable erosion drivers on suspended sediment dynamics. Earth Surface Dynamics, 8(3):619{635, jul 2020a. ISSN 2196-632X. doi: 10.5194/esurf-8-619-2020.</p>
Analysis Titanic Survival through Graph-Based Node with Neo4j: Unraveling Social Dynamics (Dataset & Cypher)
Open the record for dataset details and reuse information.
"Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks": Dataset
<p>This repository contains the data accompanying the paper "<em>Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks</em>", by Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dedè and Alfio Quarteroni.</p> <p> </p> <p>The associated codes are available in the Github repository <a href="https://github.com/FrancescoRegazzoni/LDNets">https://github.com/FrancescoRegazzoni/LDNets</a></p>
Landslide dynamics dataset based on FNO
<p>These are 1D and 2D landslide numerical solution data sets using FNO model to solve real landslide dynamics process. Two of the files are 1D and 2D landslide datasets with an output time interval of 10s.</p>
Analysis software for StateSwitch dynamic (DataLad datasets)
<p>These directories contain the code (and on gin the associated data; available upon request) for the (pre-)processing of the multimodal data of the StateSwitch study.</p> <div> <div>Please cite the following references if you use these scripts:</div> <br> <div> <ul> <li>Kosciessa, J. Q., Lindenberger, U. & Garrett, D. D. Thalamocortical excitability modulation guides human perception under uncertainty. Nat. Commun. 12, 2430 (2021).</li> </ul> </div> <div> <ul> <li>Kosciessa, J. Q., Mayr, U., Lindenberger, U. & Garrett, D. D. Broadscale dampening of uncertainty adjustment in the aging brain. Nat. Commun. (in press, 2024).</li> </ul> <p> </p> </div> </div> <p><strong>Note</strong>: The code and data are shared as nested DataLad datasets. These provide the most flexible and efficient access (<em>recommended</em>). The archive attached to this database record provides a snapshot in December 2024, please note that repositories may have been updated since then. The public copy is hosted on <a href="https://git.mpib-berlin.mpg.de/LNDG/stateswitchage/stsw">https://git.mpib-berlin.mpg.de/LNDG/stateswitchage/stsw</a> and provides access to code and tools. The gin sibling (<a href="https://gin.g-node.org/StateSwitch/stsw">https://gin.g-node.org/StateSwitch/stsw</a>) contains the annexed (pre-)processed data. These are available upon request from kosciessa@mpib-berlin.mpg.de.</p> <p>Please note that content of subdatasets (i.e., subfolders) may be subject to alternative licenses.</p>
Dynamic regimes of the Greenland Ice Sheet emerging from interacting melt-elevation and glacial isostatic adjustment feedbacks - Dataset
<p>Research data for the publication "Dynamic regimes of the Greenland Ice Sheet emerging from interacting melt-elevation and glacial isostatic adjustment feedbacks" by Maria Zeitz, Jan M. Haacker, Jonathan F. Donges, Torsten Albrecht, and Ricarda Winkelmann, accepted for Earth System Dynamics in 2022.</p> <p> </p> <p>Find the output of the simulations with the Parallel Ice Sheet Model PISM in the pism_out* archives, the binary, the run scripts, the input data and the analysis scripts in the "other" archive.</p> <p> </p> <p> </p>
Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements Dataset
<p>Dataset containing simulation results and example input scripts for the paper "Molecular Dynamics Assessment of Primary Damage Efficiency for Fusion Relevant Elements".</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>
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>
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>
Systematic characterization of the composition and dynamics of processing body-associated mRNAs [dataset 3]
GEO Series GSE297900. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
DynaMO, a package identifying transcription factor binding sites in dynamical ChIPSeq/RNASeq datasets, identifies transcription factors driving yeast ultradian and mammalian circadian cycles
GEO Series GSE72263. Saccharomyces cerevisiae. 30 samples. Type: Expression profiling by high throughput sequencing; Genome binding/occupancy profiling by high throughput sequencing.
Systematic characterization of the composition and dynamics of processing body-associated mRNAs [dataset 1]
GEO Series GSE279168. Homo sapiens. 458 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 (bulk RNA-seq dataset)
GEO Series GSE149634. Mus musculus. 7 samples. Type: Expression profiling by high throughput sequencing.
Dynamic changes in genome-wide histone methylation and gene expression of soybean roots in response to salt stress (ChIP-seq dataset)
GEO Series GSE133573. Glycine max. 10 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Datasets for Combining Dynamic & Static Scheduling in High-level Synthesis
<p>Benchmark datasets for Combining Dynamic & Static Scheduling in High-level Synthesis.</p>
Dataset for "Global dominance of seasonality in shaping lake surface extent dynamics" (in review)
Open the record for dataset details and reuse information.
Dataset for Role of Initial Particle Deposition in Dataset for Collapse Dynamics and Deposition Morphology of Submarine Granular Flows using CFD-DEM Coupling Method
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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.