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390 results for “after movie”
studyforrest_movie_denoised
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An fMRI dataset in response to "The Grand Budapest Hotel", a socially-rich, naturalistic movie
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MOBO: The MOvie and BOok reviews dataset
<p>The <strong>MOBO</strong> dataset.</p> <p>The <em><strong>MOvie and BOok reviews dataset</strong></em> is a collection made up of movie and book reviews, paired with their related plots.<br> The reviews come from different publicly available datasets: the Stanford's IMDB movie reviews [1], the GoodReads [2] and the Amazon reviews dataset [3]. With the help of 15 annotators, we further labeled more than 18,000 reviews' sentences (~6000 per corpus), marking the sentence polarity (<em>Positive</em>, <em>Negative</em>), or whether a sentence describes its corresponding movie/book <em>Plot</em>, or none of the above (<em>None</em>). In the <code>dataset</code> folder, we have shared an excerpt of the annotated sentences for each dataset.</p>
Movies of mouse oocyte maturation in transmitted light
<p>This dataset has been presented in our paper "An interpretable and versatile machine learning approach for oocyte phenotyping", in bioRxiv.</p> <p>It contains 466 movies of mouse oocytes maturation acquired in transmitted light every 3 min. Spatial resolution is 0.227 µm/pixel.</p>
MA14KD [AGGREGATED] Dataset: Visual Attraction of Movie Trailers
<p><strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>181 aggregated VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication:</p> <p><em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em></p>
MA14KD [ORIGINAL] Dataset: Visual Attraction of Movie Trailers
<p><strong>MA14KD</strong> (Movie Attract 14K Dataset) provides a set of <strong>10 VISUAL features </strong>extracted from <strong>14074 movie</strong> <strong>and tv series trailers</strong>. The movie IDs are in agreement with the movie IDs provided by another rating dataset that also contains movie genres and tags (see the description within the file). More details can be found in the following publication:</p> <p><em>Farshad B. Moghaddam, Mehdi Elahi, Reza Hosseini, Christoph Trattner, Marko Tkalcic, <strong>Predicting Movie Popularity and Ratings with Visual Features</strong>, IEEE SMAP’19, 9-10 June 2019, Larnaca, Cyprus</em></p>
Large Movie Review Dataset
<p>IMDB dataset having 50K movie reviews for natural language processing or Text analytics.<br> This is a dataset for binary sentiment classification containing substantially more data than previous benchmark datasets. We provide a set of 25,000 highly polar movie reviews for training and 25,000 for testing. So, predict the number of positive and negative reviews using either classification or deep learning algorithms.<br> For more dataset information, please go through the following link,<br> <a href="http://ai.stanford.edu/~amaas/data/sentiment/">http://ai.stanford.edu/~amaas/data/sentiment/</a>.</p>
A studyforrest extension, an annotation of spoken language in the German dubbed movie ``Forrest Gump'' and its audio-description (validation analysis)
<p>This component contains the data of the analysis that we ran as a validation of the annotation of speech spoken in the research cut (Hanke et al., 2016) of the movie "Forrest Gump" (Zemeckis, 1994) and its audio-description. The corresponding paper is hosted on github (https://github.com/psychoinformatics-de/studyforrest-paper-speechannotation) and published in f1000research (https://doi.org/10.12688/f1000research.27621.1).</p>
UnityMol demo movie showing custom user-added menus
<p>This video provides more detailed supportive information about using Unitymol.</p> <p> </p> <p>1) start up UnityMol</p> <p>2) activate the functionality to remote control Unitymol</p> <p>3) edit the provided example script menu-spike1.py to include the right filepath</p> <p>4) execute menu-spike1.py with python</p> <p>5) first there is only one button, allowing you to load the scene</p> <p>6) once loaded, several customized views are available through dedicated buttons</p>
Sentiment analysis in Galaxy with IMDB movie review dataset
<p>IMDB movie review sentiment classification dataset (Andrew L. Maas, Raymond E. Daly, Peter T. Pham, Dan Huang, Andrew Y. Ng, and Christopher Potts. (2011). Learning Word Vectors for Sentiment Analysis. The 49th Annual Meeting of the Association for Computational Linguistics (ACL 2011)). For more information please refer to: https://ai.stanford.edu/~amaas/data/sentiment/<br> <br> The IMDB dataset was modified as follows to prepare it for use in a Galaxy Training Tutorial (https://training.galaxyproject.org/):<br> <br> The top 50 words are excluded (mostly stop words). Included the next 10,000 top words. Reviews are limited to 500 words max (Longer reviews trimmed and shorter reviews are padded). 25,000 reviews are used for training and testing each. Files are in tsv (tab separated value) format to be consumed by Galaxy (www.usegalaxy.org). </p>
Top 120+ popular movies 2023 from RottenTomatoes
<p>[ENG] The file contains information about popular movies on the webpage Rotten Tomatoes. We suggest using R or Python to work with the dataset. The dataset has not been cleaned, so spaces, NaN values and unmatched variables types may be present.</p><p>[CAT] El fitxer conté informació sobre pel·lícules populars a la pàgina web Rotten Tomatoes. Suggerim utilitzar R o Python per a treballar amb el conjunt de dades. El conjunt de dades no s'ha netejat, de manera que els espais, els valors NaN i els tipus de variables que no coincideixn poden estar presents.</p><p>[ESP] El fichero contiene información sobre películas populares en la página web Rotten Tomatoes. Sugerimos utilizar R o Python para trabajar con el conjunto de datos. El conjunto de datos no se ha limpiado, de forma que los espacios, los valores NaN y los tipos de variables que no coincidan pueden estar presentes.</p><p> </p>
Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files
<p>This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy. The preprint manuscript associated with these movies is </p> <p>Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan</p> <p>Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain</p> <p>bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a></p> <p>Movie Legends</p> <p>Movie 1: 3D view of the entire slice showing key structures.<br>3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting<br>ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie.</p> <p><br>Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus.<br>The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained<br>nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa.<br>0:47s — Innervation of the outer boundary of the Bowman’s Capsules.</p> <p><br>Movie 3: Neuro-nephron connectivity.<br>The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and<br>2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal<br>(convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D<br>relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures.<br>1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation.</p> <p><br>Movie 4: Neurovascular – nephron patterns in the medulla.<br>The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D;<br>0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult<br>medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in<br>2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting<br>Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution.</p> <p><br>Movie 5: Network motifs.<br>Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2.<br>0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring<br>hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network<br>containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif.</p> <p><br>Movie 6: LSFM movie of pediatric kidney<br>3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels.<br>0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations.</p> <p><br>Movie 7: Neuronephron connectivity time course<br>Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec —<br>Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult.<br>1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network<br>with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.</p> <p> </p> <p>Movie 8: Mother Glomeruli</p> <p>Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.</p> <p> </p> <p>Movie 9: Segmentations</p> <p>Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.</p> <p> </p> <p>metadata_analyzed_images:</p> <p>Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).</p>
Alkali-silica reaction. A multi-disciplinary approach. Supplementary Materials: Movie file.
<p>Movie file being part of the Supplementary Materials document of the manuscript with the same title and submitted to the <a href="https://letters.rilem.net/index.php/rilem">RILEM Technical Letters</a>.</p>
Supplementary movies and figures for geophysical flows impacting a flexible barrier system
<p>The supplementary movies S1, S2, and S3 (presented in Figs. 1 and 2) show typical debris flow, debris avalanche, and rock avalanche impacting a flexible ring net barrier with vint = 6 m/s, respectively.</p> <p>As a supporting figure for Figs. 3b, 3c and 3d, Fig. S1 presents free surfaces of flowing layers and boundaries of dead zones measured at peak impacts for (a ~ d) DF, (e ~ h) DA and (i ~ l) RA cases near the slow-to-fast transitions.</p> <p>As a supplementary figure for Fig. 4, Fig. S2 presents the detailed barrier load-deformation cures until the peak barrier load is reached. It compares three Fr-dependent load-deflection modes of a flexible ring net barrier measured in all rock avalanche, debris avalanche and debris flow cases.</p>
ORPHEUS The Movie (MPEG-H audio track)
<p>This is an experimental version of the mid length version of the final ORPHEUS project movie<br> (as published here <a href="https://youtu.be/AW2EB7-zxf4">ORPHEUS-Audio on Youtubel</a>) <br> It has been authored and encoded using MPEG-H audio, thus including these NGA features:</p> <ul> <li>6 presets (perspectives): Main - Underground Narrator - Narrator App EN - Narrator App De - Underground App EN - Underground App DE</li> <li>the narrator level can be adjusted in all presets</li> </ul> <p>Mind: Successful audio playback requires a MPEG-H compatible device!</p>
Supporting Movies from: Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California
<div> <div> <div> <p>This repository includes supplementary movies from the manuscript titled, "Seismo-acoustic observations of crashing ocean waves: Investigating surf monitoring at Coal Oil Point Reserve, Santa Barbara, California," submitted to the Journal of Geophysical Research: Solid Earth.</p> <p> </p> <p>Movies S1 and S2. These two movies taken during array deployment 4 on October 20, 2023 show the NW tip of Coal Oil Point at the left of the field of view and Sands Beach northwest of that toward the right. Frames have the same figure layout as Figure 4 of the main text.</p> <p>Movie S3. Same as Movies S1 and S2 but with the NW tip of Coal Oil Point at the right of the field of view and Devereux Beach southeast of that toward the left.</p> </div> </div> </div>
Drosophila Larvae Tracking: movies of drosophila larvae communities
<h2>33 movies of drosophila larvae communities</h2> <p>The task associated to this dataset is tracking multiple drosophila larvae. Such a tracking is required in the quest to elucidate the genetic basis of Drosophila's behaviour. This dataset was used in the article "<a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/219478572/fiaschi_14_tracking.pdf" target="_blank" rel="noopener">Tracking indistinguishable translucent objects over time using weakly supervised structured learning</a>". We provide the raw data, an intermediate segmentation of the foreground and the gold standard used in the <a href="https://hci.iwr.uni-heidelberg.de/sites/default/files/publications/files/219478572/fiaschi_14_tracking.pdf">evaluation of that tracking algorithm</a>. </p>
Simulated movies with gaussian-shaped pHluorin signal intensity on the cell surface
<p>Synthetic data mimicking exocytic events across a wide range of features including normalized intensity, apparent size and decay mean lifetime. Numbers and spatial location of simulated events are randomly distributed over time.</p> <p>Events could have:</p> <p>* positive attribute: single exponential decay</p> <p>* negative attribute: constant signal for a random amount of time, damped sine decay signal +/- spatial displacement</p>
Supplementary Datasets and Movies for the Paper "Major California faults are smooth across multiple scales at seismogenic depth"
<p>Supplementary Datasets and Movies for the Paper<br> <strong>M</strong><strong>ajor California faults </strong><strong>are</strong><strong> smooth </strong><strong>across</strong><strong> multiple scales </strong><strong>at </strong><strong>seismogenic </strong><strong>depth</strong><br> by Anthony Lomax and Pierre Henry</p> <p>DOI: <a href="https://doi.org/10.26443/seismica.v2i1.324">https://doi.org/10.26443/seismica.v2i1.324</a></p> <p>Movies S1-2 Seismicity along the central San Andreas fault zone around Parkfield as Figure 1 in the main text. Shows rotating, lateral view around ~S40°E for (Movie S1) NCSS-DD and (Movie S2) NLL-SSST-coherence. See Figure 1 caption in the main text for key to figure elements.</p> <p>Movie S3-7 Animated, rotating later views of NLL-SSST-coherence relocations of seismicity other than Parkfield presented in the main paper and this supplement.<br> Movie_S1_Parkfield_2022_sect_DD_movie_20230401.mp4 Movie_S2_Parkfield_2022_sect_NLL-SSST-coherence_movie_20230401.mp4 Movie_S3_S_Calaveras_2022_NLL-SSST-coherence_movie_20230401.mp4 – Southern Calaveras Fault Zone<br> Movie_S4_Mendocino_2021_NLL-SSST-coherence_movie_20230401.mp4 – Cape Mendocino<br> Movie_S5_MountLewis_1986_NLL-SSST-coherence_movie_20230401.mp4 – Mount Lewis<br> Movie_S6_SW_SanFrancisco_NLL-SSST-coherence_movie_20230401.mp4 - Southwest of San Francisco<br> Movie_S7_Calipatria_2021_NLL-SSST-coherence_movie_20230401.mp4 - Calipatria sequence</p> <p>Datasets S1-5 CSV format catalogs of NLL-SSST-coherence relocation results presented in the main text.<br> CSV fields correspond to data in the <a href="http://alomax.net/nlloc/soft7.00/formats.html#_location_hypphs_">NLLoc Hypocenter-Phase file</a></p> <ul> <li>ds01_Parkfield_2004_NLL_SSST_coherence.csv - Parkfield</li> <li>ds02_S_Calaveras_2022_NLL_SSST_coherence.csv – Southern Calaveras Fault Zone</li> <li>ds03_Mendocino_2021_NLL_SSST_coherence,csv – Cape Mendocino</li> <li>ds04_MountLewis_1986_NLL_SSST_coherence.csv – Mount Lewis</li> <li>ds05_SW_SanFrancisco_2021_NLL_SSST_coherence.csv - Southwest of San Francisco</li> <li>ds06_Calipatria_2021_NLL_SSST_coherence.csv - Calipatria sequence</li> </ul> <p>File S1 (project_run_scripts.zip) Archive of run scripts and related set-up, configuration and other meta-data files for locations cases presented in this paper.</p>
Supplementary Movies and Source Data for: Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation
<p>Supplementary Movies and raw data for the manuscript: "Quantitative real-time in-cell imaging reveals heterogeneous clusters of proteins prior to condensation":</p> <p>Source_Data.zip: Supplementary Code, Supplementary Data and Weka Analysis</p> <p>Lan_supplementary_movies_AVI.zip: Supplementary movies as AVI</p> <p>Lan_supplementary_movies_MP4.zip: Supplementary movies as MP4</p> <p>Lan_raw_movies.zip: Raw TIFF stacks of the movies.</p> <p>Lan_supplementary_movies.zip: Old version of the movies.</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.