Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
308
datasets available to search
ShareScore release 0.9.0
Dataset results
308 results for “Dynamical systems”
DNA methylation dynamics associated with visual system remodeling during flatfish metamorphosis
GEO Series GSE291550. Scophthalmus maximus. 21 samples. Type: Methylation profiling by high throughput sequencing.
Dynamic expression of Erg controls fetal-to-adult maturation of the hematopoietic system [ChIP-seq]
GEO Series GSE269350. Mus musculus. 3 samples. Type: Genome binding/occupancy profiling by high throughput sequencing.
Transcriptome dynamics of hematopoietic stem cell formation revealed using a combinatorial Runx1/Ly6a reporter system
GEO Series GSE145638. Mus musculus. 127 samples. Type: Expression profiling by high throughput sequencing.
Distinct signature, origin and dynamics of macrophages in the peripheral and central nervous system (microarray)
GEO Series GSE144702. Mus musculus. 25 samples. Type: Expression profiling by array; Third-party reanalysis.
The SaeRS two-component system dynamically regulates cellular adhesion and invasion during group B Streptococcus infection
GEO Series GSE269249. Streptococcus agalactiae. 12 samples. Type: Expression profiling by high throughput sequencing.
Supplementary Data: Cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system
<p>The files in this record contain data for cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>cascade reservoir system data;</li> <li>Source code and results of the parallel dynamic programming algorithm program on the physical machine;</li> <li>Source code and results of the parallel dynamic programming algorithm program on the cloud virtual machine;</li> </ul>
Dynamic, IPSC-derived Hepatic Tissue Tri-Culture System for the Evaluation of Liver Physiology in vitro
<p>This repository is related to the research article entitled "Dynamic, IPSC-derived Hepatic Tissue Tri-Culture System for the Evaluation of Liver Physiology in vitro" by Benedikt Scheidecker <em>et al</em> 2024 <em>Biofabrication</em> 16 025037. DOI 10.1088/1758-5090/ad30c5.</p> <p>The dataset contains raw RNA sequencing data (FASTQ files generated by paired-end Illumina sequencing of nanoCAGE and Chromium Single Cell 3' Gene Expression libraries) and processed nanoCAGE data files (demultiplexed FASTQ files and sequence alignments in the BED12 file format) produced by the CAGEscan Nextflow pipeline (please see https://gitlab.com/mcfrith/cagescan-pipeline and https://github.com/oist/plessy_CAGEscan_Nextflow for usage details). The nanoCAGE gene expression table (nanoCAGE_exp_table_genes_hg38.csv) was created from BED12 files using the "CAGEr" software package available from Bioconductor. Single cell sequencing data were processed with the dedicated Cell Ranger pipeline available from 10x Genomics.</p> <p> </p> <p> </p> <p> </p>
Data for the preprint of "Layer-by-layer unsupervised clustering of statistically relevant fluctuations in noisy time-series data of complex dynamical systems"
<p>README: description of the files. </p> <p>This Zenodo repository contains all the data and original code necessary to reproduce the results of the paper https://doi.org/10.48550/arXiv.2402.07786. The code (continuously mantained and updated) is available open-source as a Python package at https://pypi.org/project/onion-clustering/ and on GitHub (https://github.com/matteobecchi/timeseries_analysis). </p> <p>The repository contains the folders "Fig1", "Fig2" etc, which contain the corresponding Datasets, together with the code to reproduce the figures. Additionally, the folder "FigS1 contains code and data for FigS1. </p> <p>The repository also contains the Supplementary Movies S1 to S4, in .mp4 format. </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>
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>
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>
A Clinical Study Exploring the Safety, Efficacy and Cell Metabolic Dynamics of Universal CD19 / 20 Car-t Cell Injection in Moderate to Severe Refractory Systemic Lupus Erythematosus
ClinicalTrials.gov study NCT07339332. IPD Sharing: NO. Countries: 1. Publications: 0.
A New Non-invasive Method to Assess and Measure Palatal Masticatory Mucosa Using Dynamic Navigation System
ClinicalTrials.gov study NCT05124418. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Systemic VEGF Protein Dynamics Following Intravitreal Injections of Ranibizumab Versus Aflibercept in Patients With Visual Impairment Due to Diabetic Macular Edema
ClinicalTrials.gov study NCT02258009. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Dynamic Neural Systems Underlying Social-emotional Functions in Older Adults
ClinicalTrials.gov study NCT05645835. IPD Sharing: NO. Countries: 1. Publications: 0.
Role of the Noradrenergic System in the Regulation of Learning Dynamics: Evaluation of the Effect of a Low-dose Selective Noradrenaline Reuptake Inhibitor (NOISYXETINE)
ClinicalTrials.gov study NCT07239791. IPD Sharing: YES. Countries: 1. Publications: 0.
Accuracy of Immediate Implant Placement Using Robotic System Versus Dynamic Navigation System in Anterior Maxillae: a Random Controlled Clinical Trial
ClinicalTrials.gov study NCT06895915. IPD Sharing: UNDECIDED. Countries: 0. Publications: 0.
Percutaneous Dynamic Stabilization (PDS) System Versus Fusion for Treating Degenerative Disc Disease
ClinicalTrials.gov study NCT00878579. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Assessing the Effect of Different Grades of Chondromalacia on Static and Dynamic Balance Using Biodex Balance System
ClinicalTrials.gov study NCT07014787. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Comparison of Dynamic Distraction Systems in Proximal Interphalangeal Joint Fractures
ClinicalTrials.gov study NCT04470349. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.