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Dataset results
368 results for “imaging system”
Whole-slide Image and CT Radiomics Based Deep Learning System for Prognostication Prediction in Upper Tract Urothelial Carcinoma
ClinicalTrials.gov study NCT06993779. IPD Sharing: NO. Countries: 1. Publications: 0.
Utility of Intra-operative Image Guidance System for Missing Metastases
ClinicalTrials.gov study NCT02806037. IPD Sharing: Not stated. Countries: 0. Publications: 1.
MR, Histologic And EM Imaging Of Intravenous Ferumoxytol In Central Nervous System (CNS) Inflammation
ClinicalTrials.gov study NCT00659776. IPD Sharing: Not stated. Countries: 1. Publications: 0.
COmparison of Clarus and Optos Ultrawide Field Imaging Systems for Geographic Atrophy
ClinicalTrials.gov study NCT05961332. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Clinical Decision Support System-mPDia for Neurodegenerative Parkinsonism Using MRI Images
ClinicalTrials.gov study NCT04334902. IPD Sharing: NO. Countries: 1. Publications: 0.
Pilot Clinical Evaluation of a Microwave Imaging System for Breast Cancer Detection
ClinicalTrials.gov study NCT05757427. IPD Sharing: NO. Countries: 1. Publications: 0.
[18F] FACBC and [18F] FLT PET Imaging in Central Nervous System Tumors
ClinicalTrials.gov study NCT00832598. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Data from: GLO-Roots: an imaging platform enabling multidimensional characterization of soil-grown roots systems
Open the record for dataset details and reuse information.
Data from: A multimodal image guiding system for Navigated Ultrasound Bronchoscopy (EBUS): a human feasibility study
Open the record for dataset details and reuse information.
VISSR/SMS-1 Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) Data V001 (VISSRSMS1L1AOIPS) at GES DISC
VISSRSMS1L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the first Synchronous Meteorological Satellite (SMS-1). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. There are three types of data files in this product: one contains IR data, one contains the IR grid information (blank before 1974/10/29), and another contains VIS data. Each data file is structured with an AOIPS label, followed by an IPD label, and then an optional 8 telemetry records followed by a set of data records. Visible data are typically 3904 pixels by either 4000 or 2000 scan lines (5 or 2.5 minute scenes respectively). IR data are typically 976 pixels by either 500 or 250 scan lines (5 or 2.5 minute scenes respectively). A full scan of the Earth was made every 20 minutes.The data were used to make 70mm film negatives and 9.5” positive prints on a Dicomed Image Recording System. Data for this product are available from 1974/07/01 through 1979/04/19 (with gaps plus no data between 1975/08/20 and 1979/02/17). The SMS-1 satellite was initially parked over the equator at longitude 45W on June 7, 1974 viewing the hemisphere below the satellite to support the GARP Atlantic Tropical Experiment (GATE). It was moved to its operational position at 75W on Nov 15, 1974 where it remained until GOES-1 was launched, after which SMS 1 was moved to 105W and placed in stand-by-mode as a backup to GOES-1 or SMS-2. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.This product was previously available from the NSSDC with the identifier ESAD-00018 (old ID 74-033A-01D).
VISSR/SMS-2 Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) Data V001 (VISSRSMS2L1AOIPS) at GES DISC
VISSRSMS2L1AOIPS is the Visible Infrared Spin-Scan Radiometer (VISSR) Level 1 Atmospheric and Oceanographic Image Processing System (AOIPS) data product from the second Synchronous Meteorological Satellite (SMS-2). There are typically three data files for a scene of the Earth with radiances that were measured in the visible (0.55 to 0.70 micrometer) and/or IR (10.5 to 12.6 micrometer) wavelengths with a spatial resolution of 0.9 and 8 km, respectively. Files also include time, geolocation, orbit, attitude, and telemetry information. There are three types of data files in this product: one contains IR data, one contains the IR grid information, and another contains VIS data. Each data file is structured with an AOIPS label, followed by an IPD label, and then an optional 8 telemetry records followed by a set of data records. Visible data are typically 3904 pixels by either 4000 or 2000 scan lines (5 or 2.5 minute scenes respectively). IR data are typically 976 pixels by either 500 or 250 scan lines (5 or 2.5 minute scenes respectively). A full scan of the Earth was made every 20 minutes.The data were used to make 70mm film negatives and 9.5” positive prints on a Dicomed Image Recording System. Data for this product are available from 1975/04/27 through 1980/08/22 (with gaps plus no data between 1975/07/31 and 1979/05/10). The SMS-2 satellite was initially parked over the equator at longitude 105W on Feb 22, 1975 viewing the hemisphere below the satellite. It was moved to its final operational position at 135W on Dec 19, 1975. The VISSR experiment was operated by the NOAA National Environmental Satellite Data and Information Service (NESDIS), as well as scientists from NASA Goddard Space Flight Center.This product was previously available from the NSSDC with the identifier ESAD-00095 (old ID 75-011A-04D).
S1_raw_images paper "Use of dual-flow bioreactor to develop a simplified model of nervous-cardiovascular systems crosstalk: a preliminary assessment
<p>Original uncropped and unadjusted images underlying all blot or gel results reported in a submission’s figures and Supporting Information files.</p>
Data and R script for publication: The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean
<p>This is a www.zenodo.org data and R script upload for publication:</p> <p> </p> <p>Schmid, M.S. et al., The LOKI underwater imaging system and an automatic identification model for the detection of zooplankton taxa in the Arctic Ocean. Methods in Oceanography (2016), http://dx.doi.org/10.1016/j.mio.2016.03.003</p> <p> </p> <p>Downloadable script: Script_Schmid_Mio_2016_data_upload.R</p> <p>Downloadable data: Schmid_2016_MIO_CGlac.csv</p>
[LS2N_IPI_DIBR_Videos] 3D-TV System with Depth-Image-Based Rendering Architectures, Techniques and Challenges
<h3>Presentation & Goal</h3> <p>The <strong>IRCCyN IVC DIBR Videos</strong> quality database contains <strong>102 video sequences</strong> of 6 seconds in 1024x768 resolution with between 15 and 30 frames per second. Individual votes and Mean Opinion Scores (MOS) obtained by an Absolute Category Rating with Hidden Reference (ACR-HR) experiment are provided.</p> <p>Three different multiview plus depth (MVD) sequences are considered in this database. The sequences are Book Arrival (1024x768, 16 cameras with 6.5cm spacing), Lovebird1 (1024x768, 12 cameras with 3.5 cm spacing) and Newspaper (1024x768, 9 cameras with 5 cm spacing). Seven DIBR algorithms processed the three sequences to generate, for each sequence, four new viewpoints.</p> <h4>Initial goal</h4> <p>This database was done to consider the reliability of usual assessment methods when evaluating virtual synthesized views in the multiview video context. Virtual views are generated from Depth Image Based Rendering (DIBR) algorithms. Because DIBR algorithms involve geometric transformations, new types of artifacts come up. The question regards the ability of commonly used methods to deal with such artifacts.</p> <h4>Possible usage</h4> <p>This video quality database can be used to test a video quality metric precision in a context of DIBR.</p> <p> </p> <p> </p> <h3>Dataset description</h3> <div> <p>We used Absolute Category Rating - Hidden Reference (<strong>ACR-HR</strong>) and as test methodology.</p> <p>We provide a <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/IRCCyN_IVC_DIBR_Videos_Scores.xls">spreadsheet</a> with the individual scores, the Mean Opinion Score (MOS) and the Differential Mean Opinion Score (DMOS) for each of the 102 Processed Video Sequences (PVS).</p> <p>The <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/Videos">PVS (avi videos)</a> are available on our FTP server and are in the AVI format.</p> <p>The information about the source contents, video tested generation, subjective experiment environment and available data are provided in a <a href="https://web.archive.org/web/20200219062815/ftp://ftp.ivc.polytech.univ-nantes.fr//IRCCyN_IVC_DIBR_Videos/IRCCyN_IVC_DIBR_Videos_Description_v1.0.pdf">description file</a>.</p> <h4>Source contents (SRC)</h4> <table> <tbody> <tr> <th> <h4>Source number</h4> </th> <td> <h4>Source name</h4> </td> <td> </td> <td> <h4>Description</h4> </td> </tr> <tr> <th>01</th> <td><strong>Book Arrival</strong></td> <td> </td> <td><u>Description</u> : A man in an office standing up to welcome another man. <br><u>Author</u> : Heinrich-Hertz-Institut. <br><u>Video frame rate</u> : 15Hz <br><u>Number of frames</u> : 100</td> </tr> <tr> <th>02</th> <td><strong>Lovebird1</strong></td> <td> </td> <td><u>Description</u> : A couple walking in front of a temple. <br><u>Author</u> : Electronics and Telecommunications Research Institute (ETRI). <br><u>Video frame rate</u> : 30Hz <br><u>Number of frames</u> : 150</td> </tr> <tr> <th>03</th> <td><strong>Newspaper</strong></td> <td> </td> <td><u>Description</u> : A man and a girl reading the newspaper around a table when a man coming behind them. <br><u>Author</u> : Gwangju Institute of Science and Technology (GIST). <br><u>Video frame rate</u> : 30Hz <br><u>Number of frames</u> : 200</td> </tr> </tbody> </table> <p> </p> <p> </p> <h3>Hypothetical Reference Circuits (HRC)</h3> <div> <p>The Processed Video Sequences (PVS) were created using the following Hypothetical Reference Circuits (HRC).</p> <table> <tbody> <tr> <th>HRC name</th> <th>HRC description</th> </tr> </tbody> <tbody> <tr> <td>cam_xx</td> <td>[No impairment - Reference sequence view 1]</td> </tr> <tr> <td>cam_yy</td> <td>[No impairment - Reference sequence view 2]</td> </tr> <tr> <td>cam_zz</td> <td>[No impairment - Reference sequence view 3]</td> </tr> <tr> <td>A1</td> <td>Fehn cropped : where the depth map is pre-processed by a low-pass filter. Borders are cropped, and then an interpolation is processed to reach the original size.</td> </tr> <tr> <td>A2</td> <td>Fehn interpolated : Borders are inpainted by the method proposed by Telea</td> </tr> <tr> <td>A3</td> <td>Tanimoto et al., it is the recently adopted reference software for the experiments in the 3D Video group of MPEG.</td> </tr> <tr> <td>A4</td> <td>Müller et al., proposed a hole filling method aided by depth information.</td> </tr> <tr> <td>A5</td> <td>Ndjiki-Nya et al., the hole filling method is a patch-based texture synthesis.</td> </tr> <tr> <td>A6</td> <td>Köppel et al., uses depth temporal information to improve the synthesis in the disoccluded areas.</td> </tr> <tr> <td>A7</td> <td>corresponds to the unfilled sequences (i.e. with holes).</td> </tr> <tr> <td>cam_xx_qp26</td> <td>Reference sequence view 1 coded by H.264 with a QP = 26</td> </tr> <tr> <td>cam_xx_qp34</td> <td>Reference sequence view 1 coded by H.264 with a QP = 34</td> </tr> <tr> <td>cam_xx_qp44</td> <td>Reference sequence view 1 coded by H.264 with a QP = 44</td> </tr> </tbody> </table> <p>There are several references (3 first lines of the table) for each content, because there are the 3 views used for each content. So there are 3 HRC with no impairment.</p> <p> </p> <p> </p> <h3>Experiment information</h3> <div> <table> <tbody> <tr> <td> <h4>Features</h4> </td> <td> <h4>Value</h4> </td> </tr> <tr> <td><strong>Highlights of this database</strong></td> <td>Depth Images Based Rendering</td> </tr> <tr> <td>Free-viewpoint</td> </tr> <tr> <td>video quality</td> </tr> <tr> <td><strong>Video</strong></td> </tr> <tr> <td><strong>Frame frequency</strong></td> <td>30 or 15Hz</td> </tr> <tr> <td><strong>Video format</strong></td> <td>1024x768</td> </tr> <tr> <td><strong>Video length</strong></td> <td>100, 150 or 200 frames</td> </tr> <tr> <td><strong>Video duration</strong></td> <td>5 to 7 seconds</td> </tr> <tr> <td><strong>Number of videos</strong></td> <td>102</td> </tr> <tr> <td><strong>Subjective test</strong></td> </tr> <tr> <td><strong>Observation distance</strong></td> <td>4H</td> </tr> <tr> <td><strong>Environment</strong></td> <td>ITU-R BT.500-11</td> </tr> <tr> <td><strong>Background luminance</strong></td> <td>25cd/m²</td> </tr> <tr> <td><strong>Methodology</strong></td> <td>ACR</td> </tr> <tr> <td><strong>Duration</strong></td> <td>30 minutes</td> </tr> <tr> <td><strong>Pre screening</strong></td> <td>Snellen, Ishihara</td> </tr> <tr> <td><strong>Post screening (Rejection method)</strong></td> <td>VQEG Multimedia</td> </tr> <tr> <td><strong>Observers</strong></td> </tr> <tr> <td><strong>Number of observers</strong></td> <td>32 naives</td> </tr> <tr> <td><strong>Display</strong></td> </tr> <tr> <td><strong>Display</strong></td> <td>TVLogic LMV401</td> </tr> <tr> <td><strong>Display resolution</strong></td> <td>Full HD (1920x1080 pixels)</td> </tr> <tr> <td><strong>Display frequency</strong></td> <td>60Hz</td> </tr> <tr> <td><strong>Display mode</strong></td> <td>progressive</td> </tr> <tr> <td><strong>Display luminance max</strong></td> <td>180cd/m²</td> </tr> </tbody> </table> </div> </div> </div>
Development of an IoT based smart potato leaf diseases monitoring and controlling system with image processing
Open the record for dataset details and reuse information.
Initial camera matrix values for AWI CANON aerial imaging system
<p>The file contains a camera matrix in Agisoft Metashape's format that can be used as initial parameters for a multi view reconstruction of aerial sea ice images recorded with the AWI CANON 14mm wide-angle camera (sensor.awi.de ID 4430). </p>
Evaluating the Effectiveness and Safety of Colon Capsule Endoscope System for Capturing and Viewing Colon Images
ClinicalTrials.gov study NCT05308511. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Feasibility Study on the VERAFEYE Imaging and Navigation System for Guided Catheter Ablation Procedures
ClinicalTrials.gov study NCT07271238. IPD Sharing: Not stated. Countries: 0. Publications: 0.
Demonstration of the "SPECT/CT System With CZT Detectors" Imaging Performance in a Clinical Setting
ClinicalTrials.gov study NCT03229772. IPD Sharing: NO. Countries: 1. Publications: 0.
Evaluation of Reliability of the Near-infrared Imaging System (VistaCam iX Proxi) in Comparison With Digital Radiographic Findings and ICDAS-II in Detection of Approximal Carious Lesions.
ClinicalTrials.gov study NCT03923192. IPD Sharing: UNDECIDED. 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.