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61 results for “visual scale”
TBPos: Dataset for Large-Scale Precision Visual Localization (database files)
<p>Large-scale dataset for visual localization, provided in the format of the well-known InLoc dataset (Taira et al, 2018). Contains co-registered RGB point clouds and a script for generating the rest of the 'database' files for visual localization by the InLoc algorithm. Note: query images are provided in a separate repository.</p>
Dataset for visualizing the atomic-scale origin of metallic behavior in Kondo insulators
<p>This dataset contains the raw data files and analysis steps used to produce the figures in the manuscript "Visualizing the atomic-scale origin of metallic behavior in Kondo insulators" Science 379, 1214–1218 (2023)</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs human dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://github.com/bhklab/ToxicoGx">https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
Figure 3. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 3. - TimelineThis Gantt chart provides an estimate of the relative timing and duration for achieving each of the Aims.
Figure 1. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 1. - First pass at a VPDRS static graphicFigure 1 corresponds to the first self-administered MDS-UPDRS question:1.7 SLEEP PROBLEMS.Over the past week, have you had trouble going to sleep at night or staying asleep through the night? Consider how rested you felt after waking up in the morning.0: Normal: No problems.1: Slight: Sleep problems are present but usually do not cause trouble getting a full night of sleep.2: Mild: Sleep problems usually cause some difficulties getting a full night of sleep.3: Moderate: Sleep problems cause a lot of difficulties getting a full night of sleep, but I still usually sleep for more than half the night.4: Severe: I usually do not sleep for most of the night."
Figure 2. from: Visual Parkinson's Disease Rating Scale: A Universal Iconic Questionnaire for Epidemiological Studies in India - Research Ideas and Outcomes 2: e8834 (03 May 2016) https://doi.org/10.3897/rio.2.e8834
Figure 2. - Prototype for the mobile phone appThis screen shows a pre-release version of Node, which will support the VPDRS/UPDRS modules. Here we present a means by which a person administering a questionnaire can securely log into and manipulate patient information locally and through cloud services and lastly an example clinician-administered UPDRS question.
Data and analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in Vanessa cardui"
<p>Butterfly scale data and data analysis codes for "In vivo visualization of butterfly scale cell morphogenesis in <em>Vanessa cardui</em>."</p> <p> </p> <p>It is recommended to download all files and folders into a single root folder for use in MATLAB.<br> This code was prepared for use in MATLAB R2019b, and some scripts or functions require the Image Processing Toolbox.</p>
Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data
<p>This dataset is provided for the paper "Flickr Africa: Examining Geo-Diversity in Large-Scale, Human-Centric Visual Data".</p> <p>Please refer to the readme in the zipped file for additional documentation.</p> <p>The zipped file contains two CSV files for every country in Africa obtained by queries "[country name]" and "[country name + people]".</p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 4. Radial visualization of scores across the EPQ–R scales for clustered data about all the participants
<p>On the other hand, the division of data points by gender might not be the only useful strategy when visually inspecting the analyzed sample in a coordinate system. Numerous clustering algorithms may be used to determine which data points share similar scores across the EPQ–R scales, i.e., which data points belong to the same cluster of similar entities based on their corresponding EPQ–R scores. A radial visualization in which data points were organized into three clusters is given in Figure 4. Each cluster is marked by a different color: cluster 1 by red, cluster 2 by green, and cluster 3 by blue. </p>
BRAIN Journal-Personality Questionnaires as a Basis for Improvement of University Courses in Applied Computer Science and Informatics-Figure 3. Radial visualization of scores across the EPQ–R scales for the male and female participants
<p>The radial visualization in Figure 3 depicts each participating student as a dot whose color indicates the gender of the student, blue for male students (M) and red for female students (F). The position of a dot in the visualization is determined by the scores of the associated student on the four EPQ–R scales. The radial overview may provide a much clearer outline of clustering within the analyzed group. Although there are only five female students, they are concentrated in a relatively narrow area within the radial coordinate system</p>
Multidimensional scaling informed by F-statistic: Visualizing microbiome for inference
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Data to "Near-optimal combination of disparity across a log-polar scaled visual field"
<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., Chessa, M., Bex, P. J., & Solari, F. (2020) Near-optimal combination of disparity across a log-polar scaled visual field. <em>PLOS Computational Biology, 16</em>(4), e1007699. <a href="https://doi.org/10.1371/journal.pcbi.1007699">https://doi.org/10.1371/journal.pcbi.1007699</a></p>
Visualization of particle-resolved simulations of aerosols on the regional scale
<p><span><span>In this study, we visualize the evolution of aerosol particles over Northern California using simulations from the WRF-PartMC model. We present two days of results, centering the numerical grid on the Bay Area with a horizontal resolution of 4-by-4-kilometers. Each grid cell contains approximately 5,000 computational particles to represent the aerosol.</span></span></p> <p><span><span>Much like snowflakes, aerosol particles exhibit immense diversity in size and chemical composition, continuously evolving as they travel through the atmosphere. Our particle-resolved simulation captures these processes with unprecedented detail, including the aerosol mixing state. This study is pioneering in its use of particle-resolved methods to simulate aerosols on a regional scale.</span></span></p> <div></div> <div> <div> <div></div> </div> </div> <p> </p>
Data from: Navigating the scales of diversity in subtropical and coastal fish assemblages ascertained by eDNA and visual surveys
<p>Environmental DNA (eDNA) metabarcoding emerges as a powerful method, allowing a more exhaustive investigation of fish fauna than any other methods. Yet, the general use of eDNA as a replacement of traditional methods such as visual surveys or physical sampling remains debatable. Therefore, a prior understanding of eDNA's spatial resolution is necessary. This study aimed to compare the assessments of fish diversity at three spatial scales by eDNA, underwater visual census (UVC), and diver-operated video (DOV) surveys across 21 reef sites in northern Taiwan. The specific objectives were to explore the regional species pool (γ-diversity), reveal spatial patterns of fish assemblages (β-diversity), and disentangle the relationships between fish assemblages and benthic composition (α-diversity). At the γ-diversity level, a total of 438 marine fish species were detected across methods. eDNA exhibits an extraordinary power to explore the regional species pool given sufficient replication, a power which is unachievable by DOV and UVC. At the β-diversity level, all the methods successfully revealed the same spatial patterns of beta diversity and the distance decay of similarity in fish assemblages. At the α-diversity level, none of the methods is capable of investigating the entire resident fish fauna, but visual surveys are more suitable for scrutinizing interactions between fish and benthos. Instead of undiscriminatingly recommending a combination of eDNA with traditional survey methods, we suggest implementing specific surveys in accordance with the ecological questions of interest.</p>
Dataset from 'Klever, L., Beyvers, M., Fiehler, K., Mamassian, P., & Billino, J. (2023). Cross-modal metacognition: Visual and tactile confidence share a common scale. Journal of Vision, 23(5): 3, 1-16. doi: https://doi.org/10.1167/jov.23.5.1
<p>We provide two files:<br> - "data.txt": data file containing the data of our final sample (N=54) on which analyses are based<br> - "column_description.txt": description file providing information on the content of each column in the data file</p> <p>___________________________________________<br> For further questions, please contact:<br> jutta.billino[at]psychol.uni-giessen.de</p>
Data from: Navigating the scales of diversity in subtropical and coastal fish assemblages ascertained by eDNA and visual surveys
Open the record for dataset details and reuse information.
Data from: Plasticity contributes to a fine-scale depth gradient in sticklebacks’ visual system
Open the record for dataset details and reuse information.
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets
<p>This page links to the data associated with the publication "ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets ". The data have been curated and analyzed using our open-source R package, ToxicoGx (https://github.com/bhklab/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca). Please see the included DOIs below, or download the .csv file which contains the names, dates and DOIs of all datasets listed here.</p> <p>The TGGATES data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023.<br> <br> Data:</p> <ul> <li>TGGATEs humanldh (<a href="https://doi.org/10.5281/zenodo.3762812">https://doi.org/10.5281/zenodo.3762812</a>)</li> <li>TGGATEs humandna (<a href="https://doi.org/10.5281/zenodo.4024859">https://doi.org/10.5281/zenodo.4024859</a>)</li> <li>TGGATEs ratldh (<a href="https://doi.org/10.5281/zenodo.3762817">https://doi.org/10.5281/zenodo.3762817</a>)</li> <li>TGGATEs ratdna (<a href="https://doi.org/10.5281/zenodo.4024918">https://doi.org/10.5281/zenodo.4024918</a>)</li> </ul> <p>This Drug Matrix data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413.</p> <p>Data:</p> <ul> <li>Drug Matrix (<a href="https://doi.org/10.5281/zenodo.3766569">https://doi.org/10.5281/zenodo.3766569</a>)</li> </ul>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs rat dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="https://github.com/bhklab/ToxicoGx">https://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</p>
ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets (TGGATEs human dataset)
<p>This data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023. The data have been curated and analyzed using our open-source R package, <em>ToxicoGx</em> (<a href="http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html">http://bioconductor.org/packages/devel/bioc/html/ToxicoGx.html</a>), and are available publicly in the <em>ToxicoDB </em>web application (<a href="http://www.toxicodb.ca">www.toxicodb.ca</a>).</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.