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3,435 results for “visualizations”
Dataset for a physical model characterizing visualization of the cervix during pelvic exams
<p>This dataset accompanies our manuscript draft: <em>A physical model for improving visualization of the cervix during pelvic exams: A steppingstone towards reducing disparities in women's health</em>.</p> <p><strong>Manuscript Draft Abstract</strong></p> <p>Pelvic exams are frequently complicated by collapse of the lateral vaginal walls, obstructing the physician’s view of the cervix. A commonly utilized method in the clinical setting, passed down from mentors to trainees, is repurposing either a condom or a glove as a sheath placed over the speculum blades to retract the lateral vaginal walls during the exam. Despite their regular use in clinical practice, little research has been done comparing the relative efficacy of these methods. Better visualization of the cervix can benefit patients by decreasing examination-related discomfort, aiding in cancer screening, and preventing the need to move the examination to the operating room under general anesthesia.</p> <p>This study presents a physical model that simulates vaginal pressure being exerted around a speculum. Using it, we then compare the efficacy of different condom types, glove materials, glove sizes, and methods of application onto the speculum.</p> <p>The results showed that condoms provided minimal lateral wall retraction, while vinyl-material gloves with the speculum placed into the third finger had the best lateral wall retraction. However, the nitrile-material gloves are overall preferred over the vinyl gloves as they provided adequate lateral wall retraction without applying a significant vertical compressive effect on the speculum, and thus had overall better cervical visualization. Glove size had minimal impact.</p> <p>This study serves as a guide for clinicians as they use tools commonly found in a clinical setting to perform difficult pelvic exams. We recommend that clinicians consider the use of a nitrile glove as a sheath around a speculum. Additionally, this study demonstrates proof-of-concept of a physical model that can quantitatively describe different materials on their ability to improve cervical visualization. This model can be used in future research with more speculum and material combinations, including with materials custom-designed materials for this purpose.</p>
How do Google News' top 100 sources visually represent the data centres' energy footprint?
<p><strong>By querying "data centres' energy footprint" on Google News in incognito mode, the candidate has selected and mapped the top 100 results according to the ranking on May 15, 2022. </strong></p>
Learned value modulates the access to visual awareness during continuous flash suppression
<p>Data from Experiment 1 and Experiment 2 are reported in separate files. </p> <p>Each line contains the mean suppression time of a target grating under continuous flash suppression expressed in seconds for one participant. </p> <p>Each column refers to a different condition:<br> HREV = visual stimuli associated with high monetary reward<br> LREV = visual stimuli associated with low monetary reward<br> base = baseline measurements before associative learning<br> P1 = first measurement after associative learning<br> P2 = second measurement after associative learning<br> P3 = third measurement after associative learning</p> <p>For experiment 1, a short (20 trials) associative learning recall session was performed between P1 and P2 and between P2 and P3.</p> <p> </p> <p> </p> <p> </p>
Visual and Auditory vection stimuli reduce motion sickness
<p>This is the raw data and the full data set from all of our participants included in the analysis for this experiment. </p> <p>The raw data represents the data recorded throughout the experience. Motion Sickness scores, performance on reading task, performance on attention task. </p> <p>While the full data set (Final1) additionally includes questionnaire data (SSQ, NASA TLX, IPQ,...) as well as demographic data of the participants. </p>
Data to Three-Dimensional Binocular Eye-Hand Coordination in Normal Vision and with Simulated Visual Impairment
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Maiello, G., Kwon, M. & Bex, P.J. (2018) Three-dimensional binocular eye--hand coordination in normal vision and with simulated visual impairment. <em>Experimental Brain Research</em>. https://doi.org/10.1007/s00221-017-5160-8</p>
IODP Expedition 379 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
IODP Expedition 371 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
Improved visualization of oral microbial consortia- Associated images
<p>These images are associated to the paper <strong>"Improved visualization of oral microbial consortia" published in the Journal of Dental Research.</strong></p> <p>Sample author: Tabita Ramirez Puebla</p> <p>Images show microbial consortia from human tongue dorsum biofilm.</p> <p>Imaged in a confocal microscope (Zeiss LSM 780) with a spectral detector (32 channels).</p> <p>Objective: Plan-Apochromat 63X; N.A. 1.4; Oil </p> <p>Pixel size: 0.07 um x 0.07 um</p> <p>Image size (pixels): 2048 x 2048</p> <p>Optical section : 1 micron</p> <p> </p> <p><strong>File descriptions</strong></p> <p><strong>TIFF files in Image5D format. Files resulted from linear unmixing performed with Zeiss ZEN algorithm (ZEN Black) or using the non-linear least-squares function in MATLAB. Individual fluorophore and autofluorescence channels are presented. </strong><br>Fig2A_5D<br>Fig2C_5D<br>Fig4_zstack_5D (zstack with 12 optical slices)</p> <p><strong>jpg files of pseudocolored images</strong><br>Fig2A_jpeg<br>Fig2B_jpeg<br>Fig2C_jpeg<br>Fig2D_jpeg<br>Fig3A_jpeg_stack_RGB_tif (stack of 257 optical slices RGB images in tif format)<br>Fig3A_Montage20x13_jpeg (Montage of 257 optical slices)<br>Fig3B_jpeg<br>Fig3C_jpeg<br>Fig3D_jpeg<br>Fig4A_xy (view of xy plane)<br>Fig4A_xz_orthogonal (view of xy plane -> orthogonal representation of 12 optical slices)<br>Fig4A_yz_orthogonal (view of yz plane -> orthogonal representation of 12 optical slices)<br>Fig4B_jpeg<br>Fig4C_jpeg</p> <p><strong>Representative Zeiss .czi (raw files from LSM780 confocal microscope)</strong><br>Fig2A_raw (original czi file)<br>Fig2C_raw (original czi file)</p>
IODP Expedition 360 Visual core description
Descriptions of samples, generally at the section half and smear slide or thin section scale, were performed by shipboard scientists and recorded in the JRSO description software. Descriptive data for both macroscopic and microscopic examination were collected in a Microscoft Excel workbook by hole. A zip file of the entire expedition's observations is also available.
[Resumen visual] Condición de discapacidad y victimización por robo en pobladores de Perú
<p>Fundamentos: Alrededor del 15% de la población mundial tiene algún grado de discapacidad. La violencia y el crimen afectan primordialmente a la región de América Latina, especialmente al Perú. El objetivo de este estudio fue determinar la asociación entre la condición de discapacidad y la victimización por robo en pobladores peruanos, durante el 2017. </p> <p>Métodos: Se realizó un estudio transversal de análisis secundario de datos de la Encuesta Nacional Especializada sobre Victimización (ENEVIC) 2017. La variable independiente fue la condición de discapacidad y la variable dependiente fue la victimización por robo; además, se incluyeron variables de confusión. Para demostrar la asociación se realizó una regresión de Poisson y se calcularon razones de prevalencia (RP) con sus intervalos de confianza al 95% (IC95%).</p> <p>Resultados: Se incluyeron los registros de 32.199 peruanos de 18 o más años. Las personas con discapacidad tuvieron 24% menos probabilidad de ser víctimas de robo que las personas sin discapacidad (RP=0,76; IC95%: 0,61−0,95), ajustado por las variables de confusión. Sin embargo, esta asociación solo fue significativa en las mujeres, adultos mayores y en el estrato socioeconómico alto. </p> <p>Conclusiones: En el Perú, las personas con discapacidad tienen menor probabilidad de ser víctimas de robo que las personas sin discapacidad, aunque solamente si son mujeres, adultos mayores y provienen de un nivel socioeconómico alto. En los demás grupos poblacionales, las probabilidades de sufrir de este hecho de victimización serían semejantes entre las personas con y sin discapacidad.</p>
Data accompanying the master thesis: A neuronal model for visually evoked startle responses in schooling fish
<p>This dataset contains data that was generated and analyzed for the master thesis "A neuronal model for visually evoked startle responses". All related material, including analysis code, of the master thesis can be found at https://github.com/awakenting/master-thesis.</p>
3D printed map for blind or visually impaired people
<p>This data set is composed of three parts each having its proper origins, formats and rights. This data set was used to apply the methods of relief editing and image processing to facilitate the production of accessible documentation by having in hand an easy to use interface.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: dataset
<p>This dataset contains images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation, 3D visualization and geometric analysis is presented in the corresponding manuscript. Files are uploaded in 16bit .tif format and are named: mouseid_pressurelevel_stacknumber, with mouseid consisting of either Apoe (Apoe-deficient) or Bl (wild-type) and the mouse number, pressurelevel varies from P0 to P120 and stacknumber indicates which image from the stack has been uploaded.</p>
Synchrotron-based visualization and segmentation of elastic lamellae in the mouse carotid artery during quasi-static pressure inflation: 2D segmentations
<p>This dataset contains 2D segmentations of images that were obtained during quasi-static pressure inflation of mouse carotid arteries. Images were taken with phase propagation imaging at the X02DA TOMCAT beamline of the Swiss Light Source synchrotron at the Paul Scherrer Institute in Villigen, Switzerland. Scans of n=12 left carotid arteries (n-6 Apoe-deficient mice, n=6 wild-type mice, all on a C57Bl6J background) were taken at pressure levels of 0, 10, 20, 30, 40, 50, 70, 90 and 120 mmHg. For analysis we selected 75 images from the center of each stack (starting at the center of the stack, and skipping 2 of every three images in both cranial and caudal axial directions) for each sample and for each pressure level, resulting in a total of 75 x 12 x 9 = 8100 analyzed images from 108 different scans. Segmentation algorithm, 3D visualization and geometric analysis are presented in the corresponding manuscript. Files are uploaded in .jpg format and are named: lamella_slicenumber, with slicenumber varying from 1 to 8100. There is also a Matlab file, UndulationData_Zenodo.mat, in which all the relevant variables post analysis are stored. This file contains a variable called "myFiles", which contains the link between the slicenumbers used here and the original dataset that is published in Zenodo (.tif synchrotron images).</p>
Cancer related protein visualized by using Discovery Studio Visualizer
<p>Cancer related Protein visualized by using Discovery Studio Visualizer</p>
Visualization of guided elastic waves generated by SHPFP transducers
<p>DualSH-PFP_measurement.avi: velocity magnitude generated by the Dual SHPFP and measured by Laser Doppler vibrometry</p> <p>SH-PFP_measurement.avi: velocity magnitude generated by the original SHPFP and measured by Laser Doppler vibrometry</p> <p>DualSHPFP_simulation.avi: circumferential component of the velocity generated by the Dual SHPFP and determined by simulation</p> <p>SHPFP_simulation.avi: circumferential component of the velocity generated by the original SHPFP and determined by simulation</p> <p>All simulations and measurements are performed at a center frequency of 80 kHz.</p>
Dataset: Feedback contribution to surface motion perception in the human early visual cortex
<p><strong>Dataset</strong></p> <p>Dataset accompanying the manuscript "Feedback contribution to surface motion perception in the human early visual cortex" (<a href="https://doi.org/10.1101/653626">biorxiv</a>).</p> <p><strong>Description</strong></p> <p>fMRI data are arrange by subject (following BIDS convention). For each subject, there are subfolders for anatomical and functional MRI data.</p> <p>├── sub-01<br> │ ├── anat<br> │ │ └── ...<br> │ ├── func<br> │ │ └── ...<br> │ ├── func_se<br> │ │ └── ...<br> │ └── func_se_op<br> │ └── ...</p> <p>The subfolder 'anat' contains four images from the MP2RAGE sequence (among these, T1 and proton-density weighted images). The subfolder 'func' contains the functional data (GE EPI, T2* weighted) from the main experiment (i.e. the data from which the haemodynamic response was estimated, and on which statistical analysis was performed). The subfolders 'func_se' and 'func_se_op' contain SE EPI images with opposite phase encode polarity that were used for distortion correction. Moreover, for each image/timeseries there is a json file with metadata.</p> <p>Anatomical images have been masked anteriorly (defaced). Functional images are in coronal oblique orientation, covering early visual cortex.</p> <p>The folder 'stimuli' contains information on the stimuli used for retinotopic mapping, including timecourse models used for population receptive field mapping. (These files are included here because of their relatively large file size, which would make distribution via a git repository impractical.) The software used for the presentation of retinotopic mapping stimuli (and for the corresponding analysis) is available on <a href="https://github.com/ingo-m/pyprf">github</a>.</p> <p>For example videos of the main experimental stimuli, see <a href="https://doi.org/10.5281/zenodo.2583017">zenodo.2583017</a>. If you would like to reproduce the experimental stimuli, the respective PsychoPy code can be found on <a href="https://github.com/ingo-m/PacMan/tree/master/stimuli/experiment">github</a>.</p> <p>The exact timing of events during the experiments (rest & stimulus blocks, target events) can be found in FSL-style design matrices ("3 column format") on <a href="https://github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata">github.com/ingo-m/PacMan/tree/master/analysis/FSL_MRI_Metadata</a>.</p> <p><strong>Analysis</strong></p> <p>The analysis pipeline makes use of several MRI software packages (such as SPM and FSL for preprocessing, and CBS tools for cortical depth sampling). In order to facilitate reproducibility, the entire analysis was containerised using docker. Because of licensing issues, the docker images with the third-party software cannot be directly made available. However, the docker files and detailed instructions for the creation of the docker images are available on <a href="https://github.com/ingo-m/PacMan/tree/master/docker">github</a>.</p> <p>If you would like to reproduce the analysis, the first step will be to create the docker images (which provide an exact copy of the system environment that was used to conduct the published analysis). There are two docker images, one for the main analysis (motion correction, distortion correction, GLM fitting; named "dockerimage_pacman_jessie"), and another one for the depth sampling (named "dockerimage_cbs"). Detailed instructions on how to create the docker images can be found <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_PacMan_Image_Jessie.txt">here</a> and <a href="https://github.com/ingo-m/PacMan/blob/master/docker/Info_Prepare_CBS_Image.txt">here</a>.</p> <p>Once you set up the docker images, the analysis can be run automatically. For each subject, there is one parent script for the main analysis (e.g. <a href="http://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_01.sh">~/analysis/20180118/metascript_01.sh</a> for subject 20180118) and a separate script for the depth sampling (e.g. <a href="https://github.com/ingo-m/PacMan/blob/master/analysis/20180118/metascript_03.sh">~/analysis/20180118/metascript_03.sh</a>). The only manual adjustments you should have to perform to reproduce the analysis is to change the file paths in the first section of these scripts ('pacman_anly_path' is the parent directory containing the analysis code, i.e. the git repository, and 'pacman_data_path' is the parent directory containing the MRI data). The main analysis (metascript_01.sh) should take about 24 h per subject on a workstation with 12 cores, and the depth sampling (metascript_02.sh) about 2 h. The analysis can be run on consumer-grade hardware, but some parts of the analysis may not run with less than 16 GB of RAM (recommended: 32 GB).</p> <p>Visualisations (e.g. cortical depth profiles and signal timecourses) and group-level statistical tests are implemented in <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">py_depthsampling</a>.</p> <p><strong>Further resources</strong></p> <p>Please refer to the research paper for more details: <a href="https://doi.org/10.1101/653626">https://doi.org/10.1101/653626</a></p> <p>The analysis pipeline can be found on <a href="https://github.com/ingo-m/PacMan">https://github.com/ingo-m/PacMan</a></p> <p>A separate repository contains the code used for visualisation of depth-sampling results: <a href="https://github.com/ingo-m/py_depthsampling/tree/PacMan">https://github.com/ingo-m/py_depthsampling/tree/PacMan</a></p> <p>Free & open source software package for population receptive field mapping: <a href="https://github.com/ingo-m/pyprf">https://github.com/ingo-m/pyprf</a></p> <p> </p>
Visual and inertial data for validation of gliding models of ornithopters
<p>This dataset contains data from different gliding flights with an ornithopter in low wind conditions. For each experiment, the inertial information is provided.</p> <p>Additionally, the flights have been recorded from three different points of view to track and triangulate its position. The videos are provided and the position of the camera has been determined using a Leica Total Station system with submillimeter accuracy. A sample of the 2D track of the ornithopter is provided for each video and experiment. The tracking along the three cameras are synchronized.</p> <p> </p> <p>------Camera Pose structure------</p> <p> </p> <p>Three cameras with four points: three to measure orientation and the last one the lens position. The last two points are the measured fall.<br> Camera 1 -> top left, bottom left and top right.<br> Camera 2 -> top left, top rigth and bottom right.<br> Camera 3 -> top left, bottom left and top right.<br> Then there are 14 rows. The pattern is: Point1, Point2, Point3 and Lens Position.</p> <p>------IMU structure------</p> <p>time, quaternion w, quaternion x, quaternion y, quaternion z, accelerometer x, accelerometer y, accelerometer z, Gyroscope x, Gyroscope y, Gyroscope z, magnetometer x ,magnetometer y ,magnetometer z<br> units: time->ms, accelerometer->g, gyroscope->ยบ/s</p> <p> </p>
Sidewalk Environment for Visual Navigation
<p>This dataset contains low and high resolution panoramic images, coordinates, labels and a connectivity graph. In order to run this simulated environment, you will need at least one copy of the panoramic images, which are available in low (84x224 pixels) or high resolution (1280x3840 pixels). For more information, visit https://mweiss17.github.io/SEVN/.</p>
Visual abstract for SAMPL6 logP Challenge
<p>This figure was created as a visual abstract for the SAMPL6 Part II logP Challenge, which was a blind computational prediction challenge for predicting octanol-water partition coefficients of kinase inhibitor fragment-like small molecules. This figure can possibly be used as a cover art for the special journal issue organized for this challenge. </p>
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.