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35 results for βVideo networkβ
Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections - Supplementary videos
<p>We regard ROIs, regions of interest, from a human spleen specimen in single (four ROIs) and double (three ROIs) staining. The ROIs with the same number correspond to each other. Below we map references in manuscript (<strong>bold</strong>) to file names in this repository (<em>italics</em>).</p> <ul> <li>File <em>colour-deconvolution.png</em> – settings of colour deconvolution in Fiji for double staining.</li> <li>File <em>comments to videos.odt</em> – a commentary to S3[c,d] Video.</li> <li><strong>S1a,b Video to S3a,b Video</strong>: files <em>video_[1,2,3][a,b].mov</em> – sequence of section with single (a) and double (b) staining for ROI 1 to 3 in the main text.</li> <li><strong>S1c Video to S3c Video</strong>: files <em>video_[1,2,3]c.mov</em> – video of the reconstruction, single staining, special blood vessels highlighted.</li> <li><strong>S1d Video to S4d Video</strong>: files <em>video_[1,2,3]d.mov</em> – an overview video of the reconstruction, double staining.</li> <li><strong>S4 Video</strong>: file <em>video_4.mov</em> – quality control in virtual reality.</li> <li><strong>S1 Figure</strong>: a supplementary figure <em>fig_S1.tiff</em> and its caption <em>fig_S1_legend.odt</em></li> <li><strong>S2 Figure</strong>: a supplementary figure <em>fig_S2.tiff</em> and its caption <em>fig_S2_legend.odt</em></li> </ul> <p>This data corresponds to the publication "Capillary networks and follicular marginal zones in the human spleen. Three-dimensional models based on immunostained serial sections" by B. S. Steiniger, C. Ulrich, M. Berthold, M. Guthe, and O. Lobachev, 2017.</p>
Data and code for article "Nature reserve customized method of photo and video camera traps materials processing using two-stage neural network approach"
<p><strong>DESCRIPTION</strong> π</p> <p>"data" folder directory contains the datasets for classification and detection. </p> <ol> <li>The detection dataset has <strong>YOLOv5 format</strong> and contains three classes <strong>[tigers, leopards, empty]</strong>. The class empty is about <strong>10%</strong> of the total data. The leopard and tiger classes contain <strong>3500</strong> images each. The entire amount of data for the detection task is <strong>7600</strong> images.</li> <li>The classification dataset contains two classes <strong>[tigers, leopards]</strong>. Images for classification are cropped images from the detection task using bounding boxes. Each class has <strong>3500</strong> images</li> </ol> <p> </p> <p>The "weights" folder contains pretrained models for classification and detection tasks. </p> <ul> <li>The detector weights were pre-trained on <strong>231k</strong> images from camera traps located throughout Russia.</li> <li>The classifier weights were pre-trained on <strong>416k</strong> images that were cropped with <strong>bounding boxes</strong> from photographs for the detection task. Some of the images for the classification task were taken from the <strong>Internet</strong>. The classifiers were trained for <strong>29 classes</strong>.</li> <li>You can also find folder <strong>tigers_vs_leopards</strong> in both the detection and classification directory, where there are weights that have been trained on a part of the camera trap images available at the link below.</li> </ul> <p><em>Classification weights</em></p> <ol> <li>EfficientNetv2-M</li> <li><strong>ResNeSt-101e</strong> (π RECOMMENDED)</li> <li>ResNet-101d</li> <li>ReXnet-100</li> <li>SeResNet-152d</li> </ol> <p><em>Detection weights</em></p> <ol> <li>YOLOR-W6-1280</li> <li>YOLOX-X-640</li> <li>YOLOv5-X-640</li> <li>YOLOv5-X-1280</li> <li>YOLOv5-M6-1280</li> <li><strong>YOLOv5-L6-1280</strong> (π RECOMMENDED)</li> </ol> <p>Read README.md file for more details</p>
Video-Audio Neural Network Ensemble For Comprehensive Screening Of Autism Spectrum Disorder in Young Children (Openpose ADOS Dataset)
<p>Here, we share a de-identify subsample of the data used in the <a href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0308388">research article</a>, that will allow interested scientists to test the <a href="https://github.com/AutismBrainBehavior/Video-Neural-Network-ASD-screening">shared code</a>, as well as, develop alternatives for achieving better prediction accuracy. We have prepared a subsample of pose estimation videos for the first 10 minutes of ADOS examination videos for each of the 160 children including in the current study (80 ASD and 80 TD, 80 Training set and 80 Testing set).</p> <p>With this subset of the full dataset, our trained model achieved an accuracy of 68.75% over 80 videos (40 ASD & 40 TD) by training the Visual Geometry Group 16 Long short term memory recurrent neural network (VGG16 LSTM RNN) over 80 training videos (40 ASD & 40 TD) at 64 batch size and 120 epochs.</p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 4. Augmented Reality with video movie and social media (a vertical loom in front of two reconstructed kilns and a wall of a Roman villa rustica)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea HasnaΕ. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages. </p>
BRAIN Journal-Participative Teaching with Mobile Devices and Social Networks for K-12 Children-Figure 5. Fiber artist Alexandra Rusu (NUA) working at a Roman vertical loom (video movie)
<p>The third stage was represented by the 3D virtual reconstruction process of the historical contexts, in our case a prehistoric village and a complete Roman villa rustica, with the help of students from the Design Department, NUA, coordinated by Professor Arch. Andreea HasnaΕ. The AR application was created and tested on two commercial AR platforms, Layar and Junaio, and recently moved on the Aurasma platform (https://www.aurasma.com/). The POIs were augmented with the 3D virtual reconstructions, and also with 2D images and videos representing 3D virtual tours and technological processes (Figures 3, 4, 5). The AR application was connected to teachers’ emails and to Twitter, Facebook and Google+ project’s pages</p>
Video Documentation for the Giant Human Sorting Network event in Vienna, Austria, September 19, 2019
<p>This video is a supplementary material for the article "Large and Parallel Human Sorting Networks" by Stefan Szeider, to appear in the proceeedings of CMSC 2024, the 7th Conference on Creative Mathematical Sciences Communication, Springer Verlag, 2024.</p> <p> </p>
MHG4SNA: Images and videos of social networks on arthurian romances
<p><strong>Description</strong></p> <p>This dataset contains images and videos of social networks (static and dynamic graphs) based on character annotations of middle high german arthurian romances.</p> <p>All images and videos are part of my dissertation.</p> <p>The networks can be created in gephi using the gefx files provided in the following repositories:</p> <ul> <li>zenodo: <a href="https://doi.org/10.5281/zenodo.7544004">10.5281/zenodo.7544004</a> </li> <li>and github: <a href="https://github.com/NoraKet/MHG4SNA/tree/v1.0.0">https://github.com/NoraKet/MHG4SNA/tree/v1.0.0</a></li> </ul> <p> </p> <p><strong>Texts</strong></p> <ul> <li>Wolfram von Eschenbach: 'Parzival', in: Wolfram von Eschenbach: Werke, ed. by Karl Lachmann, 5th edition, Berlin 1891, pp. 11–388.</li> <li>Hartmann von Aue: 'Erec', ed. by Albert Leitzmann continued by Ludwig Wolff, 7th edition by Kurt Gärtner, Tübingen 2006 (Altdeutsche Textbibliothek 39).</li> <li>Hartmann von Aue: 'Iwein', ed. by G. F. Benecke and K. Lachmann, revised by Ludwig Wolff, 7th edition, part 1: Text, Berlin 1968.</li> </ul> <p> </p> <p><strong>Files</strong></p> <p>The network graphs are created using gephi.</p> <p>The filenames indicate:</p> <ul> <li>the name of the text ("Erec", "Iwein", "Parzival"; "Parzival" consists of 16 books; it can be devided in the story of Parzival ("PARZ") and the story of Gawan ("GAW")); </li> <li>the filters "EK" and "EK2" which indicate if narrator's comments are included or excluded from the data;</li> <li>the filter "DS" which indicates if direct speech is included or excluded from the data;</li> <li>additional filters performed in gephi, for example removed characters ("noErec"), frequence filters ("filterfreq1": all characters that only occur one time are removed), selection of books ("Parzival_3-6": contains only books 3 to 6 from "Parzival"), "Egozentriert" indicates an egocentric network;</li> <li>for videos: the playback speed which can be set in gephi's settings.</li> </ul> <p> </p> <p>For information about the annotations please see <a href="https://doi.org/10.5281/zenodo.7544004">10.5281/zenodo.7544004</a>. </p> <p> </p> <p> </p>
Supplementary videos for "Active flow network generates molecular transport by packets: case of the endoplasmic reticulum"
<p>Videos showing simulated motion on the active flow network for different switching timescales. In particular, compare <span class="math-tex">\(\tau_{\text{switch}} = 3 \text{ s}\)</span> to <span class="math-tex">\(\tau_{\text{switch}} = 30 \text{ ms}\)</span>. The red bubbles are proportional to the number of particles present in a node. Initially, all particles are placed in a central source node.</p>
Video Supplement for Himes et al. (2024): "Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements"
<p>This archive contains the video supplement for</p> <p>Using neural networks for near-real-time aerosol retrievals from OMPS Limb Profiler measurements</p> <p>by Himes et al. (2024), submitted to Atmospheric Measurement Techniques. The file contains an animation of the V2.1 and NRT average retrieved extinction coefficient between 19.5--21.5 km at 997 nm for the 2024 Ruang eruptions.</p>
Validation Videos - Robotic System for Reproducible Mobile Networking Experimentation in Anechoic Chambers (Master Thesis)
<p><strong>Note on Robot's Referential:</strong></p> <p>The robot's referential can be inferred in the recording via the "Safety Position." The safety position is the same for both the Digital Model (Gazebo) and the Real Robot (Joint Position = [0.0, -1.57, 1.57, 0.0, 0.0, 0.0]).</p> <p>In the safety position, the robot is approximately aligned with the X-axis, with its end-effector on the positive side of the axis. The end-effector faces perpendicular to the Y-axis. The positive Z-axis points upwards towards the ceiling.</p> <p> </p>
Videos: Computing on actin bundles network
<p>These are videos of experiments described in the paper </p> <p>Andrew Adamatzky, Florian Huber, Florian HuberJörg Schnauß. Computing on actin bundles network (March, 2019).</p> <p>Abstract </p> <p>Actin filaments are conductive to ionic currents, mechanical and voltage solitons. These travelling localisations can be utilised in making the actin network executing specific computing circuits. The propagation of localisations on a single actin filament is experimentally unfeasible, therefore we propose a `relaxed' version of the computing on actin networks by considering excitation waves propagating on actin bundles. We show that by using an arbitrary arrangement of electrodes it is possible to implement two-inputs-one-output circuits. Frequencies of the Boolean gates' detection in actin network match an overall distribution of gates discovered in living substrates.</p>
Videos for "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground"
<p>Supplementary videos for the paper "Weather and climate forecasting with neural networks: using GCMs with different complexity as study-ground" by S. Scher and G. Messori, Geoscientific Model Development 2019</p>
Data from: A hands-on guide to use network video recorders, internet protocol cameras, and deep learning models for dynamic monitoring of trout and salmon in small streams
Open the record for dataset details and reuse information.
Self-stabilizing Byzantine-resilient communication in dynamic networks (video)
Full video presentation of the paper: Self-stabilizing Byzantine-resilient communication in dynamic networks.<br><br>Appears in Session 1 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network (video)
Full video presentation of the paper: Echo-CGC: A Communication-Efficient Byzantine-tolerant Distributed Machine Learning Algorithm in Single-Hop Radio Network.<br><br>Appears in Session 2 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Video Figure: Intelligent Agents and Networked Buttons Improve Free-Improvised Ensemble Music-Making on Touch-Screens
<p>This video figure is an overview of our study comparing two designs for network communications between touch-screen musical instruments played in free-improvised ensemble performances.</p> <p>The video shows an overview of the touch-screen app (PhaseRings) used in the study and each of the interface conditions.</p> <p>The abstract of the paper relating to this figure is as follows:</p> <p>We present the results of two controlled studies of free-improvised ensemble music-making on touch-screens. In our system, updates to an interface of harmonically-selected pitches are broadcast to every touch-screen in response to either a performer pressing a GUI button, or to interventions from an intelligent agent. In our first study, analysis of survey results and performance data indicated significant effects of the button on performer preference, but of the agent on performance length. In the second follow-up study, a mixed-initiative interface, where the presence of the button was interlaced with agent interventions, was developed to leverage both approaches. Comparison of this mixed-initiative interface with the always-on button-plus-agent condition of the first study demonstrated significant preferences for the former. The different approaches were found to shape the creative interactions that take place. Overall, this research offers evidence that an intelligent agent and a networked GUI both improve aspects of improvised ensemble music-making.</p>
ATELIER. QoE and Net.KPIs for On-Demand video transmission in Mobile Networks (emu.) part 1
<p>This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.</p> <p>First part. Please download both parts of raw logs before unzipping.</p> <p>The logs contain network KPI information collected during the transfer of a video-stream using FFMpeg from the core net to a UE using SRS-RAN.<br>Each experiment contains the full logs with the full network traces, for a total of more than 4000 experiments.<br>The transferred and received videos are excluded due to the total weight.</p> <p>For a more detailed explanation on how the dataset has been generated, please refer to the associated article and GitHub repository.<br>Title of the article: “ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning”.</p>
ATELIER. QoE and Net.KPIs for On-Demand video transmission in Mobile Networks (emu.) part 2
<p>This dataset contains all the raw (and processed) information for thousands of experiments that transfer a video on-demand from a core network to a UE emulated through SRSRan inside docker containers. The dataset contains both the network statistics but also the evaluation of the QoE through VMAF for each experiment. In total, 3 different videos has been used with different characteristics and different network impairments has been applied.</p> <p>Second part. Please download both parts of raw logs before unzipping.</p> <p>The logs contain network KPI information collected during the transfer of a video-stream using FFMpeg from the core net to a UE using SRS-RAN.<br>Each experiment contains the full logs with the full network traces, for a total of more than 4000 experiments.<br>The transferred and received videos are excluded due to the total weight.</p> <p>For a more detailed explanation on how the dataset has been generated, please refer to the associated article and GitHub repository.<br>Title of the article: “ATELIER: Service Tailored and Limited-Trust Network Analytics Using Cooperative Learning”.</p>
Constructing a social-behavioral association network to study management impact on waterbird community ecology using digital video recording cameras
<p>Studying social behavior and species associations in ecological communities is challenging because it is difficult to observe the interactions in the field. Animal behavior is especially difficult to observe when selection of habitat and activities are linked to energy costs of long-distance movement. Migrating communities tend to be resource specific and prefer environments that offer more suitability for coexisting in a shared space and time. Given the recent advances in digital technologies, digital video recording systems are gaining popularity in wildlife research and management. We used digital video recording cameras to study social interactions and species-habitat linkages for wintering waterbird communities in shared habitats. Examining over 8,640 hours of video footages, we built tetrapartite social behavioral association network of wintering waterbirds over habitat (n=5) selection events in sites with distinct management regimes. We analyzed these networks to identify hub species and species role in activity persistence, and to explore the effects of hydrological regime on these network characteristics. Although the differences in network attributes were not significant at treatment level (<i>p</i> = 0.297) in terms of network composition and keystone species composition, our results indicated that network attributes were significantly different (<i>p </i>= 0.000, <i>r<sup>2</sup></i><sup> </sup>= 0.278) at habitat level. There were evidences suggesting that the habitat quality was better at the managed sites, where the formed networks had more species, more network nodes and edges, higher edge density, and stronger intra- and inter-species interactions. In addition, we also calculated the species interaction preference scores (SIPS) and behavioral interaction preference scores (BIPS) of each network. The results showed that species synchronize activities in shared space for temporal niche partitioning in order to avoid or minimize any potential competition for shared space.Β Our social network analysis (SNA) approach is likely to provide a practical use for ecosystem management and biodiversity conservation.</p>
Dataset: Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks
<p>Dataset of the <strong>Gauze detection and segmentation in minimally invasive surgery video using convolutional neural networks</strong> article.</p> <p>Further information is available in the README file.</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.