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228
datasets available to search
ShareScore release 0.9.0
Dataset results
228 results for “contours”
Carillon Mitral Contour System® for Reducing Functional Mitral Regurgitation
ClinicalTrials.gov study NCT02325830. IPD Sharing: NO. Countries: 8. Publications: 4.
Stapled TransAnal Rectal Resection (STARR) With Contour® TranstarTM
ClinicalTrials.gov study NCT00673400. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Restylane Perlane Lidocaine for Correction of Midface Volume Deficit and/or Midface Contour Deficiency
ClinicalTrials.gov study NCT03097783. IPD Sharing: NO. Countries: 1. Publications: 1.
Contours of the caregiver experience: Social resources and health behaviors in caregiving partners of persons with a spinal cord injury
Open the record for dataset details and reuse information.
Fluorescent (C)LSM image sequences of Dictyostelium discoideum (Ax2 - LifeAct mRFP) for cell track and cell contour analysis
Open the record for dataset details and reuse information.
In-sensor multilevel image adjustment for high-clarity contour extraction using adjustable synaptic phototransistors
Open the record for dataset details and reuse information.
Regional E-Atlas of the Greater Phoenix Region: Particulate Matter (2.5) pollution contours
These data represent the spatial distribution of one-year average particulate matter (2.5) concentration as pollution contours. Concentration is in micrograms per cubic meter.
Data for: Neonicotinoid retention and transport in a maize cropping system with contour prairie strips
<p>All data used in <em>Neonicotinoid retention and transport in a maize cropping system with contour prairie strips</em>.</p>
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined.
Dataset with square plots across Sierra Nevada (Spain) where the contours of all juniper shrubs were annotated as polygons using centimetric GPS and very high resolution aerial and satellite RGB images
<p><strong>This dataset is a shapefile of 767 polygons describing the contours of Juniperus communis L. and Juniperus sabina L. shrubs for the year 2021 in rectangular plots across Sierra Nevada. The coordinates of the polygons were obtained from a field work campaign with a differential centimetric GPS, and their contours were drawn manually in QGIS using the Google Earth satellite image for 2020 and the PNOA aerial image for the 2020. </strong></p> <p><strong>This dataset also contains an excel file describing the features of each polygon: the polygon centroid coordinates, the type of species, the sexgender, the morphotype, the damage in the vegetation cover estimated in the field and telematically, certainty of the digitalization with QGIS and also if the differential centimetric GPS used belongs to the University of Granada or the University of Almeria. </strong></p>
Capillaries_contour_lines_and_entrance_coordinates
<p>Dataset containing the information on the contour lines and entrance coordinates for the two capillaries (test and control) for each experimental replicate analyzed in the article: <strong>The distinctive chemotactic responses of three marine herbivore protists to DMSP and related compounds.</strong> </p> <p>Nomenclature:</p> <p>pipette_properties + _+ inital letter of the specie + _ + 1 letter identifying the compound + concentration in uM+ _ + experimental replicate number </p> <p>for example: pipette_properties_G_D20_R3 stands for "<em>Gyrodinium dominans</em>, DMSP, 20 uM, third replicate"</p> <p>can be either a .txt or .json file</p>
Contoured Altitude by Frequency over Time (CFADT) Animation for Metereologic Radar Data
<p>The animation depicts a 24h space-time contoured frequency by altitude over time visualisation (CFADT) derived from radar-metereologic data recorded by the x-band radar station of the South African Weather Service for the Liebenbergvlei in the Freestate, South Africa on December 31 2001. The temporal resolution (z) is 5 Minutes, starting on 2001-12-31 00:00:00 hours, ending on 2001-12-31 23:55:00 hours. The depicted reflectivity data was recorded in dBZ. The spatial resolution (x) is 1km, covering a radius of 200km from the radar station. The elevation resolution(y) is 1km, from 1 to 18km of elevation above ground. The transversly arranged red panes mark the time stamps of 6:00, 12:00, and 18:00 hours. The perpendicular arranged red pane seperates weak reflectivities (left) versus high reflectivities (right), which indicate heavy precipitation, if they occur on low elevations above ground.</p> <p>Data processing was done in GRASS v6.x, visualisation was done in Paraview.</p>
Contour Comparison Example Data
<p>Some test data extracted from the TCIA used for some examples to demonstrate how to compute contour comparison metrics.</p> <p>WARNING: This is sample/dummy data, only intended for demonstration purposes.</p> <p>This data is part of the LCTSC dataset provided by The Cancer Imaging Archive (TCIA) and is released under the Creative Commons Attribution 3.0 Unported License.</p> <p>Citations & Data Usage Policy</p> <p>Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution</p> <p>3.0 Unported License under which it has been published. Attribution should include references to</p> <p>the following citations:</p> <p>Dataset Citation</p> <p>Yang, Jinzhong; Sharp, Greg; Veeraraghavan, Harini ; van Elmpt, Wouter ; Dekker, Andre; Lustberg, Tim; Gooding, Mark. (2017). Data from Lung CT Segmentation Challenge. The Cancer Imaging Archive. http://doi.org/10.7937/K9/TCIA.2017.3r3fvz08</p> <p>Publication Citation</p> <p>Yang, J. , Veeraraghavan, H. , Armato, S. G., Farahani, K. , Kirby, J. S., Kalpathy‐Kramer, J. , van Elmpt, W. , Dekker, A. , Han, X. , Feng, X. , Aljabar, P. , Oliveira, B. , van der Heyden, B. , Zamdborg, L. , Lam, D. , Gooding, M. and Sharp, G. C. (2018), Autosegmentation for thoracic radiation treatment planning: A grand challenge at AAPM 2017. Med. Phys.. . doi: 10.1002/mp.13141</p> <p>TCIA Citation</p> <p>Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. (paper)</p>
The potential vorticity contours
<p>Anomalies and contours of potential vorticity (PV) at 330 and 350 K for extreme precipiation events in Siberia and Mongolia</p> <p>The anomalies of PV are shown by filling, at 330 K (upper figure) and 350 K (lower figure). The black dots correspond to the precipitation areas.</p>
Real-time Welding Sparks Detection on Construction Sites us-ing Contour Detection and Deep Learning
<p>One of the primary causes of fires at construction sites is welding sparks. Fire detection systems utilizing computer vision technology offer a unique opportunity to monitor fires in construction sites. However, little effort has been made to date in regards to real-time tracking of small sparks that can lead to major fires at construction sites. In this study, a novel method is proposed to detect welding sparks in real time contour detection with deep learning parameter tuning. An automatic parameter tuning algorithm employing a convolutional neural network was developed to identify the optimum hue-saturation-value. Additional filtering methods regarding non-welding zone and contour area-based filter, were also newly developed to enhance prediction accuracy of welding sparks. The method was evaluated using 230 welding sparks images and 104 videos. The results obtained from the welding images indicate that the suggested model for detecting welding sparks achieves a precision of 74.45% and a recall of 63.50% when noise images, such as flashing and reflection light, were removed from the dataset. Furthermore, our findings demonstrate that the proposed model is effective in capturing the number of welding sparks in the video dataset, with a 95.2% accuracy in detecting the moment when the number of welding sparks reaches its peak. These results highlight the potential of automated welding sparks detection for enhancing fire surveillance at construction sites.</p>
Zr–O Ab Initio Training Data Created by Molecular Dynamics, Contour Exploration, and Dimer Searches
<p> These density functional theory calculations span a diverse set of structures in the Zr–O system which was used as machine-learned interatomic potential (MLIP) training data. This data set was used to benchmark different structural evolution methods (molecular dynamics, contour exploration, and dimer searches) for the quality and accuracy of MLIPs trained on them. The data is provided in the .traj format from ASE. Along with data set used in our publication, we provide a large set of extra unused data and a small Python script example for parsing the data set. The set contains 120,068 structures which contain a total of 3,154,158 atoms.</p> <p>For more details, please see our paper:<br> Michael J Waters and James M Rondinelli, <em>J. Phys.: Condens. Matter</em> <strong>34</strong> 385901(2022) (<a href="https://dx.doi.org/10.1088/1361-648X/ac7f73">https://dx.doi.org/10.1088/1361-648X/ac7f73</a>)</p>
Buccal Soft Tissue Contour Changes After Immediate Implant Placement with or Without Connective Tissue Graft
ClinicalTrials.gov study NCT04309006. IPD Sharing: YES. Countries: 1. Publications: 37.
Comparison of Different Portable Tonometers (Icare Pro, TONO-Pen AVIA, Perkins Tonometer, PASCAL Hand Held Dynamic Contour Tonometer)
ClinicalTrials.gov study NCT01325324. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Cardiac Output by Nine Different Pulse Contour Algorithms
ClinicalTrials.gov study NCT02438228. IPD Sharing: Not stated. Countries: 1. Publications: 7.
The Influence of 3D Surgical Template on the Contour of Bone Augmentation, in Patient With Labial Alveolar Ridge Defect and Simultaneous Implantation
ClinicalTrials.gov study NCT06312384. IPD Sharing: NO. 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.