100+ datasets found
  1. t

    Visual Storytelling Dataset (VIST) - Dataset - LDM

    • service.tib.eu
    Updated Dec 16, 2024
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    (2024). Visual Storytelling Dataset (VIST) - Dataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/visual-storytelling-dataset--vist-
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    Dataset updated
    Dec 16, 2024
    Description

    The Visual Storytelling Dataset (VIST) consists of 10,117 Flickr albums and 210,819 unique images. Each sample is one sequence of 5 photos selected from the same album paired with a single human constructed story, where each story is comprised of mostly one sentence per image.

  2. VIST(Visual Storytelling)

    • opendatalab.com
    zip
    Updated Sep 21, 2022
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    Facebook AI Research (2022). VIST(Visual Storytelling) [Dataset]. https://opendatalab.com/OpenDataLab/VIST
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    zipAvailable download formats
    Dataset updated
    Sep 21, 2022
    Dataset provided by
    Google Research
    Facebook AI Research
    Microsoft Research
    Johns Hopkins University
    Carnegie Mellon University
    University of Rochester
    Virginia Polytechnic Institute and State University
    Description

    We introduce the first dataset for sequential vision-to-language, and explore how this data may be used for the task of visual storytelling. The dataset includes 81,743 unique photos in 20,211 sequences, aligned to descriptive and story language. VIST is previously known as "SIND", the Sequential Image Narrative Dataset (SIND).

  3. bloom-vist

    • huggingface.co
    Updated Dec 6, 2022
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    SIL Global - AI (2022). bloom-vist [Dataset]. https://huggingface.co/datasets/sil-ai/bloom-vist
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    Dataset updated
    Dec 6, 2022
    Dataset provided by
    SIL Globalhttp://www.sil.org/
    Authors
    SIL Global - AI
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This version of the Bloom Library data is developed specifically for the Visual Story Telling (VIST) task. It includes data from 363 languages across 36 language families, with many of the languages represented being extremely low resourced languages.

  4. Video Storytelling Dataset

    • zenodo.org
    • data.niaid.nih.gov
    tar, txt
    Updated Jan 24, 2020
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    Junnan Li; Yongkang Wong; Qi Zhao; Mohan S. Kankanhalli; Junnan Li; Yongkang Wong; Qi Zhao; Mohan S. Kankanhalli (2020). Video Storytelling Dataset [Dataset]. http://doi.org/10.5281/zenodo.2383739
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    tar, txtAvailable download formats
    Dataset updated
    Jan 24, 2020
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Junnan Li; Yongkang Wong; Qi Zhao; Mohan S. Kankanhalli; Junnan Li; Yongkang Wong; Qi Zhao; Mohan S. Kankanhalli
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Video Storytelling is a dataset for generating text story/summarization for videos containing social events. It consists of 105 videos from four categories: birthday, camping, Christmas and wedding. For each video, we provide at least 5 human-written stories.

    • Videos are contained in the .tar file with their corresponding category name.
    • Text stories are contained in Text.tar.
    • In each txt file, the first line is the video id. The start and end time (in seconds) of each sentence is also given.
    • test_id.txt provides the id for videos in the test set

    Please cite the following paper if you use the Video Storytelling dataset in your work (papers, articles, reports, books, software, etc):

    • Video Storytelling: Textual Summaries for Events. J. Li, Y. Wong, Q.Zhao, M. Kankanhalli. IEEE Transactions on Multimedia.
  5. t

    Visual Story-Telling dataset (VIST) - Dataset - LDM

    • service.tib.eu
    Updated Dec 2, 2024
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    (2024). Visual Story-Telling dataset (VIST) - Dataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/visual-story-telling-dataset--vist-
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    Dataset updated
    Dec 2, 2024
    Description

    Visual Story-Telling dataset (VIST) is the only publicly accessible dataset for storytelling problems. It comprises 210,819 distinct images that can be found in 10,117 different albums on Flickr and is arranged in sets of five different images.

  6. h

    bloom_vist

    • huggingface.co
    Updated Jun 14, 2024
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    SEACrowd (2024). bloom_vist [Dataset]. https://huggingface.co/datasets/SEACrowd/bloom_vist
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    Dataset updated
    Jun 14, 2024
    Dataset authored and provided by
    SEACrowd
    License

    https://choosealicense.com/licenses/cc/https://choosealicense.com/licenses/cc/

    Description

    BLOOM VIST is a visual storytelling of books that consists of 62 languages indigenous to SEA. This dataset is owned by Bloom, a free, open-source software developed by SIL International and associated with Bloom Library, app, and services. This dataset is released with the LICENSE family of Creative Commons (although each story datapoints has its licensing in more detail, e.g cc-by, cc-by-nc, cc-by-nd, cc-by-sa, cc-by-nc-nd, cc-by-nc-sa). Before using this dataloader, please accept the… See the full description on the dataset page: https://huggingface.co/datasets/SEACrowd/bloom_vist.

  7. r

    SEQUENTIAL STORYTELLING IMAGE DATASET (SSID)

    • researchdata.edu.au
    Updated 2023
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    Ajmal Mian; Ghulam Mubashar Hassan; Saeed Anwar; Zainy M. Malakan Aljawy; School of Physics, Maths and Computing (2023). SEQUENTIAL STORYTELLING IMAGE DATASET (SSID) [Dataset]. http://doi.org/10.21227/DBR9-DQ51
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    Dataset updated
    2023
    Dataset provided by
    The University of Western Australia
    IEEE DataPort
    Authors
    Ajmal Mian; Ghulam Mubashar Hassan; Saeed Anwar; Zainy M. Malakan Aljawy; School of Physics, Maths and Computing
    Description

    Visual storytelling refers to the manner of describing a set of images rather than a single image, also known as multi-image captioning. Visual Storytelling Task (VST) takes a set of images as input and aims to generate a coherent story relevant to the input images. In this dataset, we bridge the gap and present a new dataset for expressive and coherent story creation. We present the Sequential Storytelling Image Dataset (SSID), consisting of open-source video frames accompanied by story-like annotations. In addition, we provide four annotations (i.e., stories) for each set of five images. The image sets are collected manually from publicly available videos in three domains: documentaries, lifestyle, and movies, and then annotated manually using Amazon Mechanical Turk. In summary, SSID dataset is comprised of 17,365 images, which resulted in a total of 3,473 unique sets of five images. Each set of images is associated with four ground truths, resulting in a total of 13,892 unique ground truths (i.e., written stories). And each ground truth is composed of five connected sentences written in the form of a story.

  8. Data from: Design Techniques for COVID-19 Story Maps: A Quantitative Content...

    • tandf.figshare.com
    tiff
    Updated Mar 28, 2024
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    Timothy Prestby (2024). Design Techniques for COVID-19 Story Maps: A Quantitative Content Analysis [Dataset]. http://doi.org/10.6084/m9.figshare.20973486.v1
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    tiffAvailable download formats
    Dataset updated
    Mar 28, 2024
    Dataset provided by
    Taylor & Francishttps://taylorandfrancis.com/
    Authors
    Timothy Prestby
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Story maps have emerged as a popular storytelling device in recent years with cartographers and journalists leveraging geospatial web technologies to create unique spatial narratives. However, empirical research analyzing the design of story maps remains limited. Two recently proposed design frameworks provide promising avenues to characterize story maps in terms of elements of vivid cartography and techniques of map-based storytelling. In this article, I conducted a quantitative content analysis on 117 story maps of COVID-19 to operationalize map-based storytelling and vividness frameworks and to identify common design traits in contemporary story maps. My findings indicated that most story maps are longform infographics that use scrolling to advance the narrative. Stories applied a variety of attention, dosing, and mood techniques to enrich the storytelling experience. Story maps were primarily vivid through their use of color and novelty. Overall, most story maps utilized only a fraction of the map-based storytelling framework techniques. This research also demonstrated that it is challenging to analyze story maps based on these frameworks. Finally, this article improves the frameworks by proposing two new story map techniques and suggesting avenues of refinement.

  9. B

    Data from: The Illustrated Page: Analyzing Illustrations of Historical...

    • borealisdata.ca
    Updated Oct 9, 2025
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    Andrew Piper; Jiaming Jiang; Robert Budac (2025). The Illustrated Page: Analyzing Illustrations of Historical Children’s Books Using Citizen Science [Dataset]. http://doi.org/10.5683/SP3/KTSY9B
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Oct 9, 2025
    Dataset provided by
    Borealis
    Authors
    Andrew Piper; Jiaming Jiang; Robert Budac
    License

    CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
    License information was derived automatically

    Description

    Supporting data for the article "The Illustrated Page: Analyzing Illustrations of Historical Children’s Books Using Citizen Science" (CHR 2025)

  10. C

    Content Marketing Strategies for Mortgage Lenders: Beyond Generic Blog Posts...

    • my-pull-zone112.b-cdn.net
    Updated Jan 11, 2025
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    Casey Miller (2025). Content Marketing Strategies for Mortgage Lenders: Beyond Generic Blog Posts in 2025 [Dataset]. https://my-pull-zone112.b-cdn.net/crafting-compelling-content-marketing-for-colorado-4-20251209/index.html
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    Dataset updated
    Jan 11, 2025
    Dataset provided by
    Casey's SEO Tools
    Authors
    Casey Miller
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Time period covered
    2025
    Variables measured
    Content Length, Platform Diversity, Content Shareability, Maximum Video Duration, Content Production Quality, Audience Targeting Precision, Content Engagement Frequency, Lead Generation Effectiveness
    Measurement technique
    Industry best practices analysis, Digital marketing effectiveness measurement, Content performance tracking across social media platforms, Mortgage professional case study documentation, Borrower engagement pattern analysis, SEO performance evaluation for financial services
    Description

    Comprehensive analysis and strategic guide for mortgage lending professionals seeking to implement advanced content marketing strategies in 2025. This dataset provides detailed insights into video marketing, interactive content, visual storytelling, and specialized SEO techniques specifically tailored for the mortgage industry. The content addresses the evolution from generic blog posts to personalized, engaging content that builds trust with modern borrowers who expect authentic, helpful guidance through their home-buying journey.

  11. Data from: Enacting Algorithms: Evolution of the AlgoRythmics Storytelling

    • figshare.com
    txt
    Updated Jan 24, 2024
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    Zoltán Kátai; Pálma Rozália Osztián; David Iclanzan (2024). Enacting Algorithms: Evolution of the AlgoRythmics Storytelling [Dataset]. http://doi.org/10.6084/m9.figshare.25053356.v1
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    txtAvailable download formats
    Dataset updated
    Jan 24, 2024
    Dataset provided by
    Figsharehttp://figshare.com/
    figshare
    Authors
    Zoltán Kátai; Pálma Rozália Osztián; David Iclanzan
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    This dataset includes responses from 51 students who participated in a survey evaluating a short film used in Computer Science education, that portrayed three algorithmic approaches: ad-hoc, greedy, and dynamic programming. Using a 7-point Likert scale (-3 to 3), students rated statements about the film's characteristics and potential benefits. The questionnaire aimed to thoroughly capture students' perspectives on the film's attributes and educational impact.Items used in the survey.EF.Entertainment - The short film provided a high entertainment value.EF.ProductionValue - The short film had a high production value.EF.Premise - The premise (escape room) was intriguing.EF.Expressive - The short film was expressive.EF.Immersive - The short film was immersive.EF.Creative - The short film was creative.EF.Pacing The pacing of the story was appropriate.FA.Story-plot - I appreciate as important the presence of the story-plot.FA.LiveAction - I appreciate as important the use of live-action performances.FA.CameraWork - I appreciate as important the cut and switch of camera angles.FA.Atmosphere - I appreciate as important the mood and atmosphere.FA.Choreography - I appreciate as important the choreography.FA.Cinematography - I appreciate as important the depicted cinematography.FA.NonVerbal - I appreciate as important the facial expressions, body language of the actors.FA.SoundDesign I appreciate as important the sound design, narration and sound effects present.CB.Educational. The short film provided a high educational value.CB.Understanding The learning experience deepened my understanding of the subject.CB.Clarity The algorithmic strategies were clearly depicted.EB.Attention - The movie engaged my attention.EB.Curiosity - The movie engaged my curiosity.PU.Quicker - Using such short films during a class would enable me to learn and deepen algorithmic concepts more quickly.PU.Performance - Using such short films during a class would improve my learning performance and grades.PU.Efficiency - Using such short films could help me get the most out of my time while learning.PU.Knowledge - Using such short films may improve my knowledge.PU.Easier - Using such short films would make it easier to accomplish my learning tasks.PU.Overall Using such short films would be overall beneficial.PE.Enjoyable - The learning experience was enjoyable.PE.Exciting - The learning experience was exciting.PE.Pleasant - The learning experience was pleasant.PE.Interesting - The learning experience was interesting.PE.Immersive - The learning experience was immersive.C.Changes - The use of such short films may imply major changes in how I learn.C.Incorporation - It would be easy to incorporate such short films in my learning process.A.Worthwhile - Using similar educational short films to learn algorithmic concepts is a good idea.A.Positivity - I am positive towards using visual media to better understand algorithmic concepts.A.Appreciate - I would appreciate the availabilty of similar short films as learning instuments.A.WouldUse - If available, I would use such short films in my learning process.Eval.Use - I often use/used existing AlgoRythmics videos in my learning process.Eval.Comp - Overall, the short film approach (story-line, live-acting etc.) provides a richer and more valuable learning experience than the viewing of simple videos or animations.

  12. D

    Digital Visual Content Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Aug 22, 2025
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    Data Insights Market (2025). Digital Visual Content Report [Dataset]. https://www.datainsightsmarket.com/reports/digital-visual-content-1409244
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Aug 22, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2026 - 2034
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Discover the booming digital visual content market! This in-depth analysis reveals key trends, growth projections (CAGR), major players (Shutterstock, Getty Images, Adobe), and regional insights from 2019-2033. Learn about the driving forces and challenges shaping this multi-billion dollar industry.

  13. D

    Digital Storytelling Platforms Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Jan 15, 2026
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    Data Insights Market (2026). Digital Storytelling Platforms Report [Dataset]. https://www.datainsightsmarket.com/reports/digital-storytelling-platforms-499379
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    pdf, ppt, docAvailable download formats
    Dataset updated
    Jan 15, 2026
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2026 - 2034
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Discover the booming digital storytelling platform market! Our analysis reveals a $3712 million market in 2025, growing at 7.8% CAGR through 2033. Learn about key drivers, trends, and top players like Adobe and Canva. Explore market segmentation and regional insights for informed business decisions.

  14. DataSheet1_COVID ISSUE: Visual Narratives About COVID-19 Improve Message...

    • frontiersin.figshare.com
    docx
    Updated May 30, 2023
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    Paige Brown Jarreau; Leona Yi-Fan Su; Elfy Chun-Lin Chiang; Shauna M. Bennett; Jennifer Shiyue Zhang; Matt Ferguson; Doryan Algarra (2023). DataSheet1_COVID ISSUE: Visual Narratives About COVID-19 Improve Message Accessibility, Self-Efficacy, and Health Precautions.docx [Dataset]. http://doi.org/10.3389/fcomm.2021.712658.s001
    Explore at:
    docxAvailable download formats
    Dataset updated
    May 30, 2023
    Dataset provided by
    Frontiers Mediahttp://www.frontiersin.org/
    Authors
    Paige Brown Jarreau; Leona Yi-Fan Su; Elfy Chun-Lin Chiang; Shauna M. Bennett; Jennifer Shiyue Zhang; Matt Ferguson; Doryan Algarra
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Visual narratives are promising tools for science and health communication, especially for broad audiences in times of public health crisis, such as during the COVID-19 pandemic. In this study, we used the Lifeology illustrated “flashcard” course platform to construct visual narratives about COVID-19, and then assessed their impact on behavioral intentions. We conducted a survey experiment among 1,775 health app users. Participants viewed illustrated (sequential art) courses about: 1) sleep, 2) what COVID-19 is and how to protect oneself, 3) mechanisms of how the virus works in the body and risk factors for severe disease. Each participant viewed one of these courses and then answered questions about their understanding of the course, how much they learned, and their perceptions and behavioral intentions toward COVID-19. Participants generally evaluated “flashcard” courses as easy to understand. Viewing a COVID-19 “flashcard” course was also associated with improved self-efficacy and behavioral intentions toward COVID-19 disease prevention as compared to viewing a “flashcard” course about sleep science. Our findings support the use of visual narratives to improve health literacy and provide individuals with the capacity to act on health information that they may know of but find difficult to process or apply to their daily lives.

  15. t

    Semantic Image-Text-Classes - Vdataset - LDM

    • service.tib.eu
    Updated Apr 23, 2019
    + more versions
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    (2019). Semantic Image-Text-Classes - Vdataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/luh-image-text-classes
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    Dataset updated
    Apr 23, 2019
    License

    Attribution-NonCommercial 3.0 (CC BY-NC 3.0)https://creativecommons.org/licenses/by-nc/3.0/
    License information was derived automatically

    Description

    This dataset is introduced by the paper "Understanding, Categorizing and Predicting Semantic Image-Text Relations". If you are using this dataset it in your work, please cite: @inproceedings{otto2019understanding, title={Understanding, Categorizing and Predicting Semantic Image-Text Relations}, author={Otto, Christian and Springstein, Matthias and Anand, Avishek and Ewerth, Ralph}, booktitle={In Proceedings of ACM International Conference on Multimedia Retrieval (ICMR 2019)}, year={2019} } To create the full tar use the following command in the command line: cat train.tar.part* > train_concat.tar Then simply untar it via tar -xf train_concat.tar The jsonl files contain metadata of the following format: id, origin, CMI, SC, STAT, ITClass, text, tagged text, image_path License Information: This dataset is composed of various open access sources as described in the paper. We thank all the original authors for their work. Pitt Image Ads Dataset: http://people.cs.pitt.edu/~kovashka/ads/ Image-Net challenge: http://image-net.org/ Visual Storytelling Dataset (VIST): http://visionandlanguage.net/VIST/ Wikipedia: https://www.wikipedia.org/ Microsoft COCO: http://cocodataset.org/#home

  16. D

    The framing of subjectivity: Point-of-view in a cross-cultural analysis of...

    • dataverse.nl
    csv, pdf
    Updated Jan 19, 2023
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    Neil Cohn; Neil Cohn; Irmak Hacımusaoğlu; Irmak Hacımusaoğlu; Bien Klomberg; Bien Klomberg (2023). The framing of subjectivity: Point-of-view in a cross-cultural analysis of comics - Data and Publication [Dataset]. http://doi.org/10.34894/NNPWI6
    Explore at:
    csv(25455), pdf(5441098), pdf(145533), csv(24649), csv(192157)Available download formats
    Dataset updated
    Jan 19, 2023
    Dataset provided by
    DataverseNL
    Authors
    Neil Cohn; Neil Cohn; Irmak Hacımusaoğlu; Irmak Hacımusaoğlu; Bien Klomberg; Bien Klomberg
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    In visual narratives like comics, the most overt form of perspective-taking comes in panels that directly depict the viewpoints of characters in the scene. We therefore examined these subjective viewpoint panels (also known as point-of-view panels) in a corpus of over 300 annotated comics from Asia, Europe, and the United States. In line with predictions that Japanese manga use a more “subjective” storytelling style than other comics, we found that more manga use subjective panels than other comics, with high proportions of subjective panels also found in Chinese, French, and American comics. In addition, panels with more “focal” framing, i.e. micro panels showing close ups and/or amorphic panels showing views of the environment, had higher proportions of subjective panels than panels showing wider views of scenes. These findings further show that empirical corpus analyses provide evidence of cross-cultural variation and reveal relationships across structures in the visual languages of comics.

  17. O

    Online Comic Generator Report

    • datainsightsmarket.com
    doc, pdf, ppt
    Updated Dec 9, 2025
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    Data Insights Market (2025). Online Comic Generator Report [Dataset]. https://www.datainsightsmarket.com/reports/online-comic-generator-1409617
    Explore at:
    ppt, doc, pdfAvailable download formats
    Dataset updated
    Dec 9, 2025
    Dataset authored and provided by
    Data Insights Market
    License

    https://www.datainsightsmarket.com/privacy-policyhttps://www.datainsightsmarket.com/privacy-policy

    Time period covered
    2025 - 2033
    Area covered
    Global
    Variables measured
    Market Size
    Description

    Explore the dynamic Online Comic Generator market's growth, key drivers, and future trends. Discover opportunities in personal, educational, and advertising comic creation.

  18. D

    Processing and understanding inferential techniques in visual narratives

    • dataverse.nl
    csv, html, pdf
    Updated Sep 1, 2022
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    Bien Klomberg; Bien Klomberg; Neil Cohn; Neil Cohn (2022). Processing and understanding inferential techniques in visual narratives [Dataset]. http://doi.org/10.34894/DTBW7M
    Explore at:
    html(202114), html(2453982), csv(71081), html(873894), html(631438), csv(55067), csv(13401), pdf(196077), html(122587), csv(31953), csv(156859), html(1982134), csv(81929), html(873101), csv(22279), html(768010), html(631434), csv(37240), html(1285743), csv(3931508), html(871995), csv(6064809), html(1336832), pdf(37415), csv(71254), html(749784)Available download formats
    Dataset updated
    Sep 1, 2022
    Dataset provided by
    DataverseNL
    Authors
    Bien Klomberg; Bien Klomberg; Neil Cohn; Neil Cohn
    License

    https://dataverse.nl/api/datasets/:persistentId/versions/2.1/customlicense?persistentId=doi:10.34894/DTBW7Mhttps://dataverse.nl/api/datasets/:persistentId/versions/2.1/customlicense?persistentId=doi:10.34894/DTBW7M

    Description

    This project examined the processing of bridging inferences in visual narratives, i.e. when readers need to infer information missing in a previous panel in a comic sequence. Rather than omitting the key event, this study replaced the climax of the scene with a variety of five inferential techniques, which implicitly express the unseen event while each balancing several underlying features that describe their informativeness. The main question asked to what extent the processing of these techniques differed. Two self-paced reading experiments measured viewing times as well as comprehensibility ratings; experiment 1 directly compared the five types and experiment 2 explored the effect of combining techniques. Additionally, this project explored the underlying features as predictors for viewing times and ratings.

  19. S

    Social Media Design Apps Report

    • marketreportanalytics.com
    doc, pdf, ppt
    Updated Jan 11, 2026
    + more versions
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    Market Report Analytics (2026). Social Media Design Apps Report [Dataset]. https://www.marketreportanalytics.com/reports/social-media-design-apps-75033
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    doc, pdf, pptAvailable download formats
    Dataset updated
    Jan 11, 2026
    Dataset authored and provided by
    Market Report Analytics
    License

    https://www.marketreportanalytics.com/privacy-policyhttps://www.marketreportanalytics.com/privacy-policy

    Time period covered
    2026 - 2034
    Area covered
    Global
    Variables measured
    Market Size
    Description

    The social media design app market is booming, projected to reach $701.3 million by 2033 with a 9.6% CAGR. Learn about key drivers, trends, and leading players like Canva and Adobe in this in-depth market analysis. Discover regional market shares and growth opportunities in this rapidly evolving sector.

  20. h

    databird-visuals

    • huggingface.co
    Updated Oct 24, 2025
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    Rasmus Rasmussen (2025). databird-visuals [Dataset]. https://huggingface.co/datasets/theprint/databird-visuals
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    Dataset updated
    Oct 24, 2025
    Authors
    Rasmus Rasmussen
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Description

    DATABIRD VISUALS

      Description
    

    This dataset contains 5755 entries focused on understanding color theory principles and their application in various fields including complimentary colors, working with color contrasts, color psychology, color and branding, color in fashion design, color grading and visual storytelling, associating colors with emotions, building a color palette, and the impact of black and white vs. color imagery. The data is provided in JSON format. Each… See the full description on the dataset page: https://huggingface.co/datasets/theprint/databird-visuals.

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(2024). Visual Storytelling Dataset (VIST) - Dataset - LDM [Dataset]. https://service.tib.eu/ldmservice/dataset/visual-storytelling-dataset--vist-

Visual Storytelling Dataset (VIST) - Dataset - LDM

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Dataset updated
Dec 16, 2024
Description

The Visual Storytelling Dataset (VIST) consists of 10,117 Flickr albums and 210,819 unique images. Each sample is one sequence of 5 photos selected from the same album paired with a single human constructed story, where each story is comprised of mostly one sentence per image.

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