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.
274
datasets available to search
ShareScore release 0.9.0
Dataset results
274 results for “wakes”
Dynamic corticothalamic modulation of the somatosensory thalamocortical circuit during wakefulness
<p>Data and code to accompany "Dynamic corticothalamic modulation of the somatosensory thalamocortical circuit during wakefulness" by Dimwamwa, E. D., Pala, A., Chundru, V., Wright, N. C., Stanley, G. B., under review.</p>
Data set used in article: Model Predictive Control for Wake Redirection in Wind Farms: a Koopman Dynamic Mode Decomposition Approach
<p>Step-wise yaw deflection in 2 wind turbines in SOWFA. More information in the article.</p>
xAI Ship Wakes in Sentinel-2 L2A images
<h2><strong>xS2Wakes: A dataset for xAI of Wakes in S-2 (L2A).</strong></h2><h3><strong>Summary</strong></h3><p>The dataset is derived from Sentinel-2 Level-2A (L2A) satellite images and focuses on the marine domain over Danish fjords. It provides a comprehensive collection of ship wakes and background clutter (referred to as "no_<i>wake</i>_crop") for remote sensing applications. The dataset has undergone post-processing through the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm with a clip limit value of 0.12 and a tile size of 16x16. The dataset comprises four spectral bands: B2, B3, B4, and B8.</p><h3><strong>Importance and Relevance to Remote Sensing Community</strong></h3><h4>Multifaceted Applications of Wake Detection</h4><p>Ship wake detection serves as a cornerstone in a multitude of domains that are critical to both human and environmental well-being:</p><p><strong>Navigational Safety</strong>: Understanding ship wakes can provide insights into water currents and traffic patterns. This is vital for ensuring the safe passage of marine vessels, particularly in narrow straits and busy ports.</p><p><strong>Environmental Monitoring</strong>: The study of ship wakes can reveal the influence of vessels on aquatic ecosystems. For instance, excessive wake turbulence can lead to coastal erosion and can disrupt marine habitats.</p><p><strong>Maritime Surveillance</strong>: Wake detection plays a crucial role in maintaining maritime security. Tracking the wakes of vessels can help in identifying illegal activities such as smuggling or unauthorized fishing.</p><h3><strong>Specifications</strong></h3><ul><li><strong>Data Source</strong>: Sentinel-2 L2A</li><li><strong>Region of Interest</strong>: Danish fjords</li><li><strong>Classes</strong>: Wake, No-Wake</li><li><strong>Number of Samples</strong>:<ul><li>Wake: 123</li><li>No-Wake: 150</li></ul></li><li><strong>Spectral Bands</strong>: B2 (Blue), B3 (Green), B4 (Red), B8 (NIR)</li><li><strong>Post-Processing</strong>: CLAHE (Clip Limit = 0.12, Tile Size = 16x16)</li><li><strong>Average Wake Chip Size</strong>: 390x351 pixels</li><li><strong>Average No-Wake Chip Size</strong>: 380x390 pixels</li></ul><h3><strong>Wake Detection and Analysis</strong></h3><h4>Traditional Methods and Their Limitations</h4><p>Traditionally, the process of ship wake detection has largely been a manual endeavor or employed simplistic statistical algorithms. Analysts would sift through satellite or aerial images to identify ship wakes, a process that is both time-consuming and prone to human error. Even automated statistical methods often lack the robustness needed to differentiate between true wakes and false positives, such as aquatic plants or natural water disturbances.</p><h4>Role of xAI (Explainable AI) in Wake Identification</h4><p>The introduction of explainable AI (xAI) techniques brings another layer of sophistication to wake analysis. While traditional machine learning models may offer high performance, they often act as "black boxes," making it difficult to understand how they arrive at a certain conclusion. In a critical domain like navigational safety or maritime surveillance, the ability to interpret and understand model decisions is indispensable. xAI methods can make these machine learning models more transparent, providing insights into their decision-making processes, which in turn can aid in fine-tuning or fully trusting the models.</p><h4>Spectral Bands Selected</h4><p>The inclusion of four key spectral bands—B2, B3, B4, and B8—offers the scope for multi-spectral analysis. Different bands can capture varying features of water and wake textures, thereby offering a richer feature set for machine learning models. We use these spectral bands as referred to in [Liu, Yingfei, Jun Zhao, and Yan Qin. "A novel technique for ship wake detection from optical images." <i>Remote Sensing of Environment</i> 258 (2021): 112375.] </p><h4>Understanding Optical vs. SAR Imaging Modalities</h4><p>It is important to note the fundamental differences between wakes captured in Synthetic Aperture Radar (SAR) images and those in optical imagery. In SAR images, narrow-V wakes often arise due to Bragg scattering, a phenomenon that does not exist at optical wavelengths. In optical images, bright lines close to turbulent wakes are actually foams generated by the interaction between the surface horizontal flow of turbulent wakes and the surrounding background waves (Ermakov et al., 2014; Milgram et al., 1993; Peltzer et al., 1992). This can make the detection of wakes in optical images more challenging as there are usually no bright lines near turbulent wakes, and Kelvin arms may also show dark contrast. Methods that solely rely on searching for a trough and peak pair, taking the trough as the turbulent wake, would miss many actual wakes and could also result in the identification of false wakes.</p><h4>Contrast Enhancement</h4><p>The application of the CLAHE (Contrast Limited Adaptive Histogram Equalization) algorithm to this dataset allows for enhanced local contrast, enabling subtle features to become more pronounced. This significantly aids machine learning algorithms in feature extraction, thereby improving their ability to distinguish between complex patterns.</p><h4>Environment and Clutter Assessment</h4><p>In addition to wakes, the dataset contains samples labeled as "No-Wake," which include environmental clutter and clouds. These samples are crucial for training robust models that can differentiate wakes from similar-looking natural phenomena.</p>
The Effect of Incubator Covers on the Sleep-wake Cycles of Newborn Babies: A Randomized Controlled Study
ClinicalTrials.gov study NCT07128173. IPD Sharing: NO. Countries: 1. Publications: 6.
Non-sedation Versus Sedation With a Daily Wake-up Trial in Critically Ill Patients Receiving Me-chanical Ventilation - Effects on Cognitive Function
ClinicalTrials.gov study NCT02035436. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Study of the Effect of Daridorexant on Nighttime Body Posture, the Noise Level Required to Wake up, and the Ability to Remember Words Previously Presented
ClinicalTrials.gov study NCT05702177. IPD Sharing: NO. Countries: 1. Publications: 1.
Efficacy and Safety of Circadin for Non-24 Hour Sleep-Wake Disorder in Totally Blind Subjects
ClinicalTrials.gov study NCT00972075. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Characterization of Altered Waking States of Consciousness in Healthy Humans
ClinicalTrials.gov study NCT03853577. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Non-sedation Versus Sedation With a Daily Wake-up Trial in Critically Ill Patients Receiving Mechanical Ventilation - Effects on Physical Function
ClinicalTrials.gov study NCT02034942. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Prevalence of Epilepsy and Sleep Wake Disorders in Alzheimer Disease
ClinicalTrials.gov study NCT03617497. IPD Sharing: Not stated. Countries: 1. Publications: 44.
Efficacy and Safety of MRI-based Thrombolysis in Wake-up Stroke
ClinicalTrials.gov study NCT01525290. IPD Sharing: Not stated. Countries: 6. Publications: 18.
Wake Forest Post-ICU Telehealth (WFIT) Program
ClinicalTrials.gov study NCT04576065. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effects of Modafinil on Waking Function and on Sleep in Individuals With Primary Insomnia
ClinicalTrials.gov study NCT00124384. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Light for Renal Transplant Recipients Having a Sleep-Wake Dysregulation
ClinicalTrials.gov study NCT01256983. IPD Sharing: Not stated. Countries: 0. Publications: 1.
A Study to Assess the Wakefulness Promoting Effect, Safety, Tolerability, and Pharmacokinetics (PK) of LML134 in Shift Work Disorder
ClinicalTrials.gov study NCT03141086. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Severe Asthma Research Program - Wake Forest University
ClinicalTrials.gov study NCT01750411. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Method of Assessment of Driving Ability in Patients Suffering From Wakefulness Pathologies
ClinicalTrials.gov study NCT00916253. IPD Sharing: Not stated. Countries: 1. Publications: 31.
Electronic Self-monitoring on Regulation of the Sleep-wake Cycle to Reduce Relapse of Depression After Discharge
ClinicalTrials.gov study NCT02679768. IPD Sharing: YES. Countries: 1. Publications: 2.
Non-sedation Versus Sedation With a Daily Wake-up Trial in Critically Ill Patients Receiving Mechanical Ventilation
ClinicalTrials.gov study NCT01967680. IPD Sharing: Not stated. Countries: 3. Publications: 3.
Non-sedation Versus Sedation With a Daily Wake-up Trial in Critically Ill Patients Receiving Mechanical Ventilation - Effects on PTSD
ClinicalTrials.gov study NCT02040649. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.