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Dataset results
16 results for “energy detection”
Benchmark for energy efficient obstacle detection on head mounted wearable for the vision impaired
<p>Here we present a novel benchmark dataset with the associated challenge, that is to detect obstacles based on head-mounted sensors and lightweight wearable devices to assist Blind and Visually Impaired individuals (BVIs) navigate in indoor environments. The challenge encompasses three objectives: (1) as accurately as possible to detect the obstacles on the pathway that likely lead to a collision; (2) as durably as possible on a given amount of battery power for the detection algorithm or model to run; (3) as reliably as possible to compensate natural head turns so nearby objects would not trigger false alarms. The data provided in the benchmark are collected from the following head mounted sensors: (i) nine low-cost ultrasonic sensors; (ii) one high-end ultrasonic sensor with a larger detection range but higher power consumption; (iii) a 9-Degrees of Freedom (DOF) Inertial Measurement Unit (IMU). The resulting dataset consists of more than 188,000 unique sequences obtained from multiple subjects walking in three different indoor scenarios. This benchmark is to facilitate and encourage accurate yet fast obstacle detection solutions that can really benefit BVIs. </p>
Buoy-based detection of low-energy cosmic-ray neutrons (Seelhausener See, July 15 to Dec 02, 2014)
<p>Contains two resources used in Schrön & Rasche et al. (2024):</p> <ol> <li><strong>Raw</strong> measurement data files from the buoy detector. Column names are provided in the header of the files. For detailed information about column names and descriptions, see the readme.</li> <li><strong>Processed</strong> measurement data of the buoy detector. Data has been stored as CSV files, the column names are described in `Buoy.csv.readme`. Additional PDF files show the corresponding plots. Two versions of data are provided: <ol> <li><strong>Buoy-1h</strong> contains data aggregated to 1 hour, and</li> <li><strong>Buoy-1h-mavg25</strong> contains the same data but the neutrons underwent a moving average filter with a window size of 25 (1 day).</li> </ol> </li> </ol> <p>Processing has been performed using Corny v0.8.2 (<a title="Corny" href="https://git.ufz.de/CRNS/cornish_pasdy">git.ufz.de/CRNS/cornish_pasdy</a>) with the configuration file <code>Buoy-1h.cfg</code>.</p>
Satellite-based shoreline detection: macrotidal high-energy coasts dataset
<p>This dataset accompanies the article by Konstantinou <em>et al.</em> (2022) titled ‘Satellite-based shoreline detection: macrotidal high-energy coasts’. This study assesses the ability of existing satellite image analysis technology to capture shoreline position change at relevant magnitudes and timescales for two different coastal environments in the United Kingdom. It addresses the influence of tidal elevation and wave-induced water-level fluctuations at two sites representing end members of beach morphological type in a region of low satellite useability (high cloud cover combined with low image availability). The study uses 14 years of monthly topographic surveys at two macrotidal sites in the UK, combined with modelled wave data and harmonic tidal predictions to investigate the influence of tidal elevation and wave action on SDS accuracy.</p> <p><strong>Description</strong></p> <p>The dataset consists of two matlab files each containing the information listed below for the two test sites (Slapton Sands (SLSdata); Perranporth (PPTdata)):</p> <ul> <li>dnum: date of satellite image capture in matlab datenum format</li> <li>sat: name of satellite (L5=Landsat 5; L7=Landsat 7; L8=Landsat 8; S2=Sentinel-2)</li> <li>transects: a structure containing the following variables:</li> </ul> <ul> <li>profName – the name of the survey profile</li> <li>startEast; startNorth; endEast; endNorth: OSGB coordinates of the start (landward) and end (seaward) of the transect line</li> <li>startUTMlat; startUTMlon; endUTMlat; endUTMlon: WGS84-UTM30 coordinates of the start (landward) and end (seaward) of the transect line</li> <li>orientation: profile orientation</li> <li>shoreOrient: shoreline orientation</li> <li>transAngle: profile angle to shore-normal.</li> </ul> <ul> <li>transTimeSeries: time series of the intersection of the SDW at each transect in three columns that include chainage (m), longitude, latitude.</li> <li>profData: a nested structure containing the survey data at organised by profile including survey date, OSGB and WGS84-UTM30 coordinates of surveyed points.</li> </ul>
Data release for "Measurements of muon-antineutrino and muon-neutrino+muon-antineutrino charged-current cross-sections without detected pions nor protons on water and hydrocarbon at mean antineutrino energy of 0.86 GeV"
<p>This data release is associated with the publication "Measurement of charged-current cross-sections on water and hydrocarbon without detected pions nor protons using the T2K anti-neutrino beam at an off-axis angle 1.5 degrees". It is available in <a href="https://doi.org/10.1093/ptep/ptab014">Progress of Theoretical and Experimental Physics</a> and <a href="https://arxiv.org/abs/2004.13989">arXiv:2004.13989 [hep-ex]</a>.<br><br>The data release contains:</p> <ul> <li>The "histograms.root" file contains several histograms related to the cross-sections. <ul> <li>flux_numubar_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>flux_numu_* -> 1D histogram with the flux prediction at the WAGASCI module or the Proton Module of the T2K experiment.</li> <li>Err_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numubar cross-sections and their uncertainties.</li> <li>Err_numu_numubar_* -> 1D TGraphAsymmErrors with the measured flux-integrated numu+numubar cross-sections and their uncertainties.</li> <li>xsec_numubar_* -> 1D histogram with the predicted flux-integrated numubar cross-sections by NEUT (5.3.3).</li> <li>xsec_numu_numubar_* -> 1D histogram with the predicted flux-integrated numu+numubar cross-sections by NEUT (5.3.3).</li> </ul> </li> <li>The "Covariance_Matrix_Numubar.root" file contains the covariance matrix for the flux-integrated numubar cross-sections, considering all the uncertainties.</li> <li>The "Covariance_Matrix_Numu+Numubar.root" file contains the covariance matrix for the flux-integrated numu+numubar cross-sections, considering all the uncertainties.</li> <li>The "flux" file contains the (anti-)muon neutrino flux prediction at the WAGASCI module or the Proton Module of the T2K experiment</li> </ul>
Does pre-sorting by colour using visible and high-energy violet light improve the detection of plant species in honey bee pollen baskets?
<div class="article-section__content en main"> Premise <p>Pollen collected by honey bees from different plant species often differs in color, and this has been used as a basis for plant identification. The objective of this study was to develop a new, low-cost protocol to sort pollen pellets by color using high-energy violet light and visible light to determine whether pollen pellet color is associated with variations in plant species identity.</p> Methods and Results <p>We identified 35 distinct colors and found that 52% of pollen subsamples (<em>n</em> = 200) were dominated by a single taxon. Among these near-pure pellets, only one color consistently represented a single pollen taxon (Asteraceae: Cichorioideae). Across the spectrum of colors spanning yellows, oranges, and browns, similarly colored pollen pellets contained pollen from multiple plant families ranging from two to 13 families per color.</p> Conclusions <p>Sorting pollen pellets illuminated under high-energy violet light lit from four directions within a custom-made light box aided in distinguishing pellet composition, especially in pellets within the same color.</p> </div>
Does pre-sorting by colour using visible and high-energy violet light improve the detection of plant species in honey bee pollen baskets?
Open the record for dataset details and reuse information.
Data from: Towards a better understanding of avian collisions in wind energy facilities using automatic detection systems
Open the record for dataset details and reuse information.
Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification
<p>We uploaded the dataset of included patients of manuscript: Petrillo A, Fusco R, Vallone P, Filice S, Granata V, Petrosino T, Rosaria Rubulotta M, Setola SV, Mattace Raso M, Maio F, Raiano C, Siani C, Di Bonito M, Botti G. Digital breast tomosynthesis and contrast-enhanced dual-energy digital mammography alone and in combination compared to 2D digital synthetized mammography and MR imaging in breast cancer detection and classification. Breast J. 2020 May;26(5):860-872. doi: 10.1111/tbj.13739. Epub 2019 Dec 30. PMID: 31886607.</p>
Juno Stellar Reference Unit (SRU) image data for high energy ion detections during perijoves 22-35
<p>This is the Juno Stellar Reference Unit (SRU) image data for high energy ion detections during perijoves 22 to 35.</p>
Prospective Evaluation of High Resolution Dual Energy Computed Tomographic Imaging, Noninvasive (Liquid) Biopsies, and Minimally Invasive Surgical Surveillance for Early Detection of Mesotheliomas in
ClinicalTrials.gov study NCT04431024. IPD Sharing: YES. Countries: 1. Publications: 0.
Data Anomaly Detection in Cyber-Physical Energy Systems
<p>The data is created in an agent-system, used for controlling distributed energy systems. The agents follow the Lightweight Power Exchange Protocol to negotiate whenever an agent detects a planning problem. The protocol is explained in "A lightweight distributed software agent for automatic demand—supply calculation in smart grids" by Veith, Steinbach and Windeln. Different kinds of anomalies were created by manipulating an agent accordingly: anomalies in the values of the exchanged messages and anomalies in the communication behavior. The implementation of the power exchange used for the data generation can be found here: https://gitlab.com/mango-agents/mango-library/-/tags/Integration_of_the_LPEP.</p> <p>Anomalies in the values are manipulated by 500 % of the original value (dataset 500_p.csv).</p> <p>Anomalies in the communication behavior are anomalously started every minute for 8996 seconds in the future (dataset 1m_8996_50p.csv) and every 15 minutes for 2012 seconds in the future (15m_2012_50p.csv).</p> <p>For anomalies in the communication topology, an agent was chosen to which selected agent does not send any messages, although the agent is part of the neighborhood (Topology anomalies/agent_removed.csv). Furthermore, an agent was manipulated to send messages to an agent which is normally not part of its neighborhood (Topologie anomalies/agent_added.csv).</p> <p>Normal data is also given.</p>
Diagnostic Performance of Dual Energy CT for the Detection of Gallbladder Gallstones
ClinicalTrials.gov study NCT05704907. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
An Investigational Scan (Dual Energy CT) in Detecting Gastrointestinal Carcinoid Tumors
ClinicalTrials.gov study NCT04993261. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Dual-energy CT in Detecting Bone Marrow Edema of Vertebral Compression Fractures
ClinicalTrials.gov study NCT01281826. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility of Multi-Energy Digital Radiography Detector for Lung Lesions Detection
ClinicalTrials.gov study NCT03528733. IPD Sharing: NO. Countries: 1. Publications: 0.
Diagnostic Pilot Study of Dual Energy Absorptiometry in the Detection of Osteopenia or Osteoporosis in Patients With Thalassemia Major
ClinicalTrials.gov study NCT00006138. IPD Sharing: Not stated. Countries: 0. 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.