Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
12
datasets available to search
ShareScore release 0.9.0
Dataset results
12 results for “Netflix”
La consommation de contenus sur Netflix chez les jeunes en France : une application de la théorie du comportement planifié.
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 325, année 2020, février-mars). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est la consommation de contenus sur Netflix. La population mère : les jeunes de 17 à 25 ans.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 3 variables de segmentation, le comportement de l’individu (2 items : fréquence et récence), l’intention comportementale (2 items dont 1 d’identité personnelle ; cf. infra pour la justification théorique), les croyances sur les bénéfices attendus (13 items), l’attitude (3 items), les croyances sur les freins perçus (9 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), 2 variables de signalétique (sexe et âge). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
Pivot subtitling of Korean dramas on Netflix Türkiye between 2015 and 2023
<p>This dataset complements the research article <em>Pivot Subtitling on Netflix: the Case of Squid Game </em>(Dallı, 2024) and provides comprehensive information on Korean dramas and their subtitlers on Netflix Türkiye from 2015 to 2023. It also includes an analysis report derived from the raw findings. The data was manually compiled in January 2024.</p> <p>The dataset comprises the following files:</p> <ol> <li><strong>turkish-subtitlers-of-korean-content-on-netflix:</strong> This file provides raw data on Korean dramas, including Netflix release date, title, episode count, assigned subtitlers, number of subtitlers involved, and translation methods (subtitling or dubbing).</li> <li><strong>pivot-subtitling-on-netflix_the-case-of-squid-game:</strong> This R Markdown file offers an analysis report examining the yearly trends of Korean dramas released on Netflix Türkiye and the average number of Turkish subtitlers per drama.</li> </ol> <p>This dataset is designed to help understand trends in the release of Korean dramas on Netflix, with a particular focus on Turkish subtitlers. It is valuable for researchers, practitioners, and industry stakeholders interested in Korean content and pivot subtitling, providing insights into the distribution dynamics and subtitling trends in Korean media.</p> <p>Keywords: pivot subtitling, pivot templates, pivot audiovisual translation, indirect translation, Korean dramas, Turkish subtitlers, <em>Squid Game</em>.</p> <p>Dallı, H. (2024). Pivot Subtitling on Netflix: the Case of Squid Game. <em>Journal of Audiovisual Translation</em>, <em>7</em>(1).</p>
Películas de Netflix (Noviembre 2024)
<p>El conjunto de datos recolectado contiene la información principal de un total de 1640 películas disponibles en la plataforma de streaming Netflix. El dataset cuenta con 9 columnas, la primera contiene el identificador de cada registro y las demás son variables que proporcionan información sobre el contenido.</p> <p>Entre los datos recolectados se encuentran: los títulos de las películas, su año de lanzamiento, su duración e información sobre las personas que han participado (directores, guionistas y reparto). Además se incluye un campo con la sinopsis y otro campo que categoriza al contenido por su género.</p> <p> </p>
Dataset from 10-K financial reports Netflix 2011 - 2022
<p>Dataset from Netflix's 10-K annual reports, which include externally audited data about financial activities of businesses based in the US. For a description of the data compiled see the .docx document. The code included was used in the following research:</p><p>Title: Evidence of diseconomies of scale in subscription-based video on demand services.<br><br>Abstract: This study provides evidence of diseconomies of scale in Netflix, a major subscription-based video on demand (SVOD) service provider. This contradicts the common belief in prevalent economies of scale for such e-businesses. We, however, rely on a comprehensive analysis of a dataset where we have collected and combined publicly available and audited financial data, mostly coming from Netflix's 10-K reports. In our analysis we employ several user-cost models, namely a baseline linear model, a power law model, an exponential model, and a logarithmic model. Such models often appear (in different variations) in economics literature, but are almost inexistent in the rhetoric around SVOD business models. Corroborating the applications of all these mathematical models on the financial data of Netflix identifies a super-linear increase in costs with expanding user basis, indicating the rising per-user costs that defines diseconomies of scale. These findings provide critical insights into SVOD service scalability, challenging prevailing assumptions and informing expectations about cost dynamics in this industry.</p>
Dataset: Netflix, Inc. (NFLX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Netflix Title Dataset
<p> </p> <p><a href="https://www.kaggle.com/datasets/shivamb/netflix-shows">https://www.kaggle.com/datasets/shivamb/netflix-shows</a></p> <p> </p> <p> </p> <p> </p> <p> </p>
G-DeGo, Integración listado Disney+, Netflix, IMDB
<p>Proyecto final de Integración de Datos, 2022. Facultad de Ingeniería, UdelaR.</p> <p>Integración de cuatro datasets obtenidos de la plataforma Kaggle:</p> <p>- titles.csv, dataset que incluye información sobre títulos encontrados en la plataforma de streaming Netflix. Autor: Victor Soeiro. Link: <a href="https://www.kaggle.com/datasets/victorsoeiro/netflix-tv-shows-and-movies?select=titles.csv">Netflix TV Shows and Movies | Kaggle</a></p> <p>- disney_plus_titles.csv, dataset que incluye información sobre títulos encontrados en la plataforma de streaming Disney+. Autor: Shivam Bansal. Link: <a href="https://www.kaggle.com/datasets/shivamb/disney-movies-and-tv-shows">Disney+ Movies and TV Shows | Kaggle</a></p> <p>- imdb_top_1000.csv, dataset que incluye información sobre las top 1000 películas listadas en IMDB, junto con su respectivo rating. Autor: Harshit Shankhdhar. Link: <a href="https://www.kaggle.com/datasets/harshitshankhdhar/tv-series-dataset">IMDB TV Series Dataset | Kaggle</a></p> <p>- series_data.csv, dataset que incluye información series de televisión listadas en IMDB, junto con su respectivo rating. Autor: Harshit Shankhdhar. Link: <a href="https://www.kaggle.com/datasets/harshitshankhdhar/imdb-dataset-of-top-1000-movies-and-tv-shows">IMDB Movies Dataset | Kaggle</a></p> <p>Resultados de la integración:<br> En el dataset TvShowsAndMoviesWithRating se encuentra el listado de películas y series de Netlix y Disney+, con la información correspondiente a en qué plataforma se encuentra cada una, y su respectivo rating en imdb (si lo tiene), además de su título, descripción, tipo, paises de producción, año de producción, certificación de edad y duración.</p> <p>El dataset provenance ilustra la procedencia de los datos en el dataset integrado.</p> <p> </p>
Gêneros de crime - Netflix Agosto de 2022 - Desenvolvimento em Novembro e Dezembro de 2022
<p>A ordenação de obras da coleção 1 exibe títulos a partir de uma primeira pesquisa após o login na plataforma, isto é, sem o fornecimento de informações sobre os possíveis interesses do usuário. Antes do resultado exibido na coleção 2, foram feitas pesquisas de filmes latino-americanos e de cinematografias nacionais: “latinos”, “filmes brasileiros”, “filmes argentinos” e “filmes mexicanos”. Em seguida, foi feita a pesquisa de “filmes de crime”, assim como na primeira coleção. No entanto, as obras exibidas, bem como a sua ordenação, foram diferentes. O exemplo de "<em>filmes de detetives</em>" (id.: 10745), por tratar de uma listagem com obras específicas, não mostrou dados diferentes a partir da interação do usuário com a plataforma.<br> <br> Os dados foram coletados em agosto de 2022, analisados e discutidos em contexto de pesquisa e em evento acadêmico. Seus possíveis impactos na pesquisa ainda estão em andamento.</p> <p> </p>
Netflix and Disney+ films and Shows in Spain
<p>This dataset contains data of Netflix and Disney+ films and Shows in Spain </p>
Netflix Movies and TV Shows Recommendation with Neo4j
<p>Recommendation for movies can help discover new and enjoyable movies. This study uses the Neo4j Graph Database to create a recommendation system using the Netflix Movie Dataset. The objective of this research is the development of a movie recommendation algorithm using the k-NN similarity algorithm and FastRP node embedding machine learning. The results have provided recommendations based on similar attributes, such as actors, directors, country, type, and rating.</p>
Pivot subtitling of Korean dramas on Netflix Türkiye between 2015 and 2022
<p>This list provides detailed information on the pivot subtitling of Korean dramas on Netflix Türkiye between 2015 and 2022. It includes essential details such as the titles, release years on Netflix, subtitlers, the number of subtitlers involved, and the available translation methods. This list has been compiled for the article ‘Pivot Subtitling on Netflix: the Case of Squid Game’ to be published in <em><a href="https://www.jatjournal.org/">Journal of Audiovisual Translation</a></em>.</p>
Netflix Streaming Services in the Operating Room During Total Knee Replacement - a Feasibility Study
ClinicalTrials.gov study NCT04469881. IPD Sharing: NO. Countries: 1. Publications: 0.
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.