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4 results for “TV series”
TV Series - Networks of characters
<p><strong>Description. </strong>This repository contains three sets of files related to the social networks of characters in the following episodes of 3 TV series:</p> <ul> <li><em>Breaking Bad</em> (BB): S01--S03;</li> <li><em>Game of Thrones</em> (GoT): S01--05;</li> <li><em>House of Cards</em> (HoC): S01--S02.</li> </ul> <p>The three separate Graphml files (`.graphml` extension) contain the static, cumulative, conversational networks at the end of the set of episodes considered. Three images (`.jpg` extension) of the resulting static, cumulative, conversational networks at the end of the first two seasons are also provided.<br><br>The compressed archives (`.tgz` extension) provide snapshots of the conversational network of characters (Graphml format) in every scene. These files come in three flavors:</p> <ol> <li><em>Narrative smoothing</em> based networks (denoted `ns`).</li> <li><em>Time-slice</em> based networks, where all interactions are agglomerated every <em>10 scenes </em>(denoted `'ts10`).</li> <li><em>Time-slice </em>based networks, where all interactions are agglomerated every <em>40 scenes </em>(denoted `ts40`).</li> </ol> <p>The video files (`.mp4` extension) contain short animations of the dynamic networks of characters as they evolve over the whole set of episodes considered:</p> <ul> <li>The nodes are represented by the names of the corresponding characters and the distance between two character names is inversely proportional to the weight of the corresponding edge: the closer they are at some point of the story, the more they interact then.</li> <li>The size of each character name is proportional to the local strength of the corresponding character at the moment considered.</li> <li>The color of each character name corresponds to his community at any moment.</li> </ul> <p>Each snapshot in these animations is based on our <em>narrative smoothing</em> approach.</p> <p>This dataset was used in the following articles:</p> <ol> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Narrative smoothing: dynamic conversational network for the analysis of TV Series plots,” in <em>2nd International Workshop on Dynamics in Networks (DyNo/ASONAM)</em>, 2016, pp. 1111–1118. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01276708">hal-01276708</a>⟩ DOI: <a href="http://doi.org/10.1109/ASONAM.2016.7752379">10.1109/ASONAM.2016.7752379</a></li> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction de réseaux dynamiques conversationnels par lissage narratif,” in <em>7ème Conférence sur les modèles et lánalyse de réseaux : approches mathématiques et informatiques</em>, 2016. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01385215">hal-01385215</a>⟩</li> <li>X. Bost, V. Labatut, S. Gueye, and G. Linarès, “Extraction and analysis of dynamic conversational networks from TV series,” in <em>Social Network Based Big Data Analysis and Applications</em>, Springer, 2018, pp. 55–84. ⟨<a href="https://hal.archives-ouvertes.fr/hal-01543938">hal-01543938</a>⟩ DOI: <a href="http://doi.org/10.1007/978-3-319-78196-9_3">10.1007/978-3-319-78196-9_3</a></li> </ol> <p><strong>Citation. </strong>If you use this dataset, please cite the article [1]</p> <p><br><code>@InProceedings{Bost2016,</code><br><code> author = {Bost, Xavier and Labatut, Vincent and Gueye, Serigne and Linarès, Georges},</code><br><code> title = {Narrative smoothing: dynamic conversational network for the analysis of {TV} Series plots},</code><br><code> booktitle = {2nd ASONAM International Workshop on Dynamics in Networks},</code><br><code> year = {2016},</code><br><code> pages = {1111-1118},</code><br><code> address = {San Francisco, US},</code><br><code> publisher = {IEEE Publishing},</code><br><code> doi = {10.1109/ASONAM.2016.7752379},</code><br><code>}</code></p>
Serial Speakers: a Dataset of TV Series
<p><strong>Description. </strong>This dataset consists of 3 TV series with manual annotations:</p> <ol> <li><em>Breaking Bad</em>: S01--S05 (file 'bb.json');</li> <li><em>Game of Thrones</em>: S01--08 (file 'got.json');</li> <li><em>House of Cards</em>: S01--S02 (file 'hoc.json').</li> </ol> <p>All three files are in `.json` format and contain TV Series annotated data.</p> <p>Each TV Series is defined by its name, A TV Series contains seasons, defined by their ids. Every season is made of episodes, defined by their ids, titles, duration and fps. Each episode contains two basic kinds of data: scenes and speech segments. Scenes are defined by starting points and are made of shots (Seasons 1 only).<br>A shot is defined by:</p> <ul> <li>Starting and ending positions.</li> <li>Recurring shot ids.</li> </ul> <p>The speech segments are defined by their:</p> <ul> <li>Starting and ending points.</li> <li>Textual content (here encrypted for copyright reasons).</li> <li>Speaker.</li> <li>Possible interlocutors (for the following episodes only: bb: S01E04, S01E06, S02E03, S02E04; got: S01E03, S01E07, S01E08; hoc: S01E01, S01E07, S01E11).</li> </ul> <p>All timestamps are expressed in seconds and are valid for the video files extracted from the commercial DVDs (PAL 25 FPS), with recaps (unannotated) included at the beginning of the <em>House of Cards</em> episodes.</p> <p>A comprehensive description of the dataset can be found in the following paper:</p> <ul> <li>X. Bost, V. Labatut, and G. Linarès, “Serial Speakers: a Dataset of TV Series,” in <em>12th Language Resources and Evaluation Conference</em>, Marseille, FR, 2020, pp. 4249–4257. ⟨<a href="https://hal.archives-ouvertes.fr/hal-02477736">hal-02477736</a>⟩</li> </ul> <p><strong>Source code.</strong> If you are interested in the textual content of the dataset, please consider using our text recovering tool on GitHub: <a href="https://github.com/bostxavier/Serial-Speakers">https://github.com/bostxavier/Serial-Speakers</a></p> <p><strong>Citation. </strong>If you use these data or source code, please cite the above article.</p> <p><br><code>@InProceedings{Bost2020,</code><br><code> author = {Bost, Xavier and Labatut, Vincent and Linarès, Georges},</code><br><code> title = {Serial Speakers: a Dataset of {TV} Series},</code><br><code> booktitle = {12\textsuperscript{th} Language Resources and Evaluation Conference},</code><br><code> year = {2020},</code><br><code> pages = {4249-4257},</code><br><code> address = {Marseille, FR},</code><br><code> url= {http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.524.pdf},</code><br><code>}</code></p>
Series y películas de TV y plataformas de streaming españolas lanzadas entre el año 2000 y 2022, valoradas por IMBD
<p>Este dataset recoge las valoraciones en IMDB, a fecha de publicación (11/04/2022), de las películas y series españolas emitidas desde el año 2000.</p>
jcpenney-mcs-series-vtr-component-tv
jcpenney-mcs-series-vtr-component-tv This model is hand-made using parametric techniques, image networks and some ray-traced label making HDA fun. #houdini #procedural Only the decals have UV's and textures, the rest is just form and function. This model is inspired greatly by a physical object that can be seen in a youtube video located here: https://www.youtube.com/watch?v=KPneS9ZdUB8 (We thank TechMoan for his wonderful coverage of #vintage and #retro electronics gear!! [disclaimer: not affiliated with TechMoan, just a fan]) please enjoy this lightweight, render-ready model - 100% made using parametric and procedural techniques in #sidefxhoudini [renders with #blender #cycles #b279] <a href="https://imgur.com/BF6kcxW"></a> <a href="https://imgur.com/ELGv3JS"></a> If you are interested in custom graphics work, please get in touch Thank you, see you soon, -Alan/Organic https://www.instagram.com/organiccomputer/ Source: Objaverse 1.0 / Sketchfab
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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OpenNeuro
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