13 datasets found
  1. Average hours per week of television viewing, by selected age groups

    • www150.statcan.gc.ca
    • ouvert.canada.ca
    • +2more
    Updated Dec 1, 2011
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    Government of Canada, Statistics Canada (2011). Average hours per week of television viewing, by selected age groups [Dataset]. http://doi.org/10.25318/2210009401-eng
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    Dataset updated
    Dec 1, 2011
    Dataset provided by
    Statistics Canadahttps://statcan.gc.ca/en
    Area covered
    Canada
    Description

    This table contains 39 series, with data for years 1998 - 2004 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada;Newfoundland and Labrador;Prince Edward Island;Nova Scotia; ...), Age group (3 items: Total population;Children 2 to 11 years;Teens 12 to 17 years)

  2. A

    ‘The Good Place Episode Data’ analyzed by Analyst-2

    • analyst-2.ai
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com), ‘The Good Place Episode Data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-the-good-place-episode-data-a2ab/674e93e5/?iid=006-114&v=presentation
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    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘The Good Place Episode Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bcruise/the-good-place-episode-data on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    Series created by: Michael Schur Number of seasons: 4 Number of episodes: 52 Original air dates: September 19, 2016 - January 30, 2020

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings. Note: Two of the two-part episodes are only included as one episode in the IMDb data, so these two rows will have missing ratings when the data files are combined.

    Motivation

    I wanted to share a dataset about The Good Place, one of my favorite TV shows to binge watch.

    --- Original source retains full ownership of the source dataset ---

  3. Lost Episodes

    • kaggle.com
    zip
    Updated Apr 3, 2022
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    Bill Cruise (2022). Lost Episodes [Dataset]. https://www.kaggle.com/datasets/bcruise/lost-episodes
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    zip(15214 bytes)Available download formats
    Dataset updated
    Apr 3, 2022
    Authors
    Bill Cruise
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Series created by: Jeffrey Lieber, J. J. Abrams, and Damon Lindelof Number of seasons: 6 Number of episodes: 121 Original air dates: September 22, 2004 – May 23, 2010

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

    Motivation

    I wanted to share a dataset about Lost, one of my favorite TV shows to binge watch.

  4. 30 Rock Episode Data

    • kaggle.com
    Updated Jan 14, 2022
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    Bill Cruise (2022). 30 Rock Episode Data [Dataset]. https://www.kaggle.com/datasets/bcruise/30-rock-episode-data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 14, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Bill Cruise
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Series created by: Tina Fey Number of seasons: 7 Number of episodes: 138 Original air dates: October 11, 2006 – January 31, 2013

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

    Motivation

    I wanted to share a dataset about 30 Rock, one of my favorite TV shows to binge watch.

  5. Numb3rs Episodes

    • kaggle.com
    zip
    Updated Mar 17, 2022
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    Bill Cruise (2022). Numb3rs Episodes [Dataset]. https://www.kaggle.com/bcruise/numb3rs-episodes
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    zip(15693 bytes)Available download formats
    Dataset updated
    Mar 17, 2022
    Authors
    Bill Cruise
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Series created by: Nicolas Falacci and Cheryl Heuton Number of seasons: 6 Number of episodes: 118 Original air dates: January 23, 2005 – March 12, 2010

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

    Motivation

    I wanted to share a dataset about Numb3rs, one of my favorite TV shows to binge watch. It's a show about using Math to solve crimes. Who doesn't want that job?

  6. A

    ‘Breaking Bad Episode Data’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Feb 13, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Breaking Bad Episode Data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-breaking-bad-episode-data-0d65/3bfe39bb/?iid=006-314&v=presentation
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    Dataset updated
    Feb 13, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘Breaking Bad Episode Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bcruise/breaking-bad-episode-data on 13 February 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    Series created by: Vince Gilligan Number of seasons: 5 Number of episodes: 62 Original air dates: January 20, 2008 – September 29, 2013

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

    Motivation

    I wanted to share a dataset about Breaking Bad, one of my favorite TV shows to binge watch.

    --- Original source retains full ownership of the source dataset ---

  7. How I Met Your Mother Episodes Data

    • kaggle.com
    Updated Jan 2, 2022
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    Bill Cruise (2022). How I Met Your Mother Episodes Data [Dataset]. https://www.kaggle.com/datasets/bcruise/how-i-met-your-mother-episodes-data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jan 2, 2022
    Dataset provided by
    Kagglehttp://kaggle.com/
    Authors
    Bill Cruise
    License

    https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/

    Description

    Context

    Series created by: Carter Bays and Craig Thomas Number of seasons: 9 Number of episodes: 208 Original air dates: September 19, 2005 – March 31, 2014

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

    Motivation

    I wanted to share a dataset about How I Met Your Mother, one of my favorite TV shows to binge watch.

  8. A

    ‘Parks and Recreation Episode Data’ analyzed by Analyst-2

    • analyst-2.ai
    Updated Jan 28, 2022
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    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com) (2022). ‘Parks and Recreation Episode Data’ analyzed by Analyst-2 [Dataset]. https://analyst-2.ai/analysis/kaggle-parks-and-recreation-episode-data-f833/cb2388dc/?iid=008-116&v=presentation
    Explore at:
    Dataset updated
    Jan 28, 2022
    Dataset authored and provided by
    Analyst-2 (analyst-2.ai) / Inspirient GmbH (inspirient.com)
    License

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

    Description

    Analysis of ‘Parks and Recreation Episode Data’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/bcruise/parks-and-recreation-episode-data on 28 January 2022.

    --- Dataset description provided by original source is as follows ---

    Context

    Series created by: Greg Daniels and Michael Schur Number of seasons: 7 Number of episodes: 126 Original air dates: April 9, 2009 - February 24, 2015

    Content

    Data was acquired through downloading IMDb TV episodes datasets and scraping information from Wikipedia.

    Acknowledgements

    Thanks to IMDb, Wikipedia, and community curators.

    Use

    It should be easy to join these data files together on Title and Air Date fields to compare (for example) US viewers and IMDb ratings.

    Motivation

    I wanted to share a dataset about Parks and Recreation, one of my favorite TV shows to binge watch.

    --- Original source retains full ownership of the source dataset ---

  9. D

    Data from: Media-uitrusting, media-exposure en mediagebruik in Nederland,...

    • ssh.datastations.nl
    • narcis.nl
    pdf, tsv, xml, zip
    Updated Apr 24, 2024
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    K. Renckstorf; C.H. Arts; E. Hollander; P. Verschuren; K. Renckstorf; C.H. Arts; E. Hollander; P. Verschuren (2024). Media-uitrusting, media-exposure en mediagebruik in Nederland, 1989 - MASSAT 1989 [Dataset]. http://doi.org/10.17026/DANS-ZBF-T58Y
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    xml(2381), pdf(3751239), zip(22175), tsv(1971619), tsv(2203678)Available download formats
    Dataset updated
    Apr 24, 2024
    Dataset provided by
    DANS Data Station Social Sciences and Humanities
    Authors
    K. Renckstorf; C.H. Arts; E. Hollander; P. Verschuren; K. Renckstorf; C.H. Arts; E. Hollander; P. Verschuren
    License

    https://doi.org/10.17026/fp39-0x58https://doi.org/10.17026/fp39-0x58

    Area covered
    Netherlands
    Description

    What respondent experiences as most important in life / how many times respondent is watching tv, to which stations / motivations of respondent to watch and listen to tv and radio / watching alone or with other people / which kind of programs is respondent watching / if respondent using a video recorder / if respondent is member of a broadcasting corporation / how many radio's are used in house / how often respondent is listening to the radio, to which channels / r.'s attitude towards politics / if respondent is reading papers, and which papers about which subjects / how much time it takes to read a paper (for respondent) / which kind of books respondent reads / which kind of languages respondent speaks / which kind of subjects respondents interest, and how much / r.'s opinion about local news and local media / r.'s opinion about his-her financial situation, now and in future / r.'s opinion about living in this municipality / r.'s activities in free time / r.'s contacts with local people, family, neighbours and colleagues / r's makes time-table in quarters of an hour of exact activities.Background variables: basic characteristics/ residence/ housing situation/ household characteristics/ characteristics of parental family/household/ place of work/ occupation/employment/ income/capital assets/ education/ politics/ religion/ consumption of durables/ readership, mass media, and 'cultural' exposure/ organizational membership

  10. H

    Replication Data for: "Must Watch Propaganda: The Marginal Treatment Effect...

    • dataverse.harvard.edu
    • search.dataone.org
    Updated Aug 11, 2023
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    Zeyang (Arthur) Yu (2023). Replication Data for: "Must Watch Propaganda: The Marginal Treatment Effect of Foreign Media among Always-Takers" [Dataset]. http://doi.org/10.7910/DVN/S1QAE2
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Aug 11, 2023
    Dataset provided by
    Harvard Dataverse
    Authors
    Zeyang (Arthur) Yu
    License

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

    Description

    Studies of political persuasion often use an exogenous encouragement as an instrument for persuasive messaging. However, for some people, such encouragement is insufficient, while for others, it is unnecessary. These individuals are excluded from methods that only estimate a treatment effect among compliers. Using the marginal treatment effect framework, we extend research finding that exposure to West German television increases support for communism. We find that, because of self-selection, for those who watch West German TV regardless of signal quality, i.e. always-takers, cutting off West German television would have increased support for communism. Our extrapolation shows that media choices reinforce, rather than mollify, political preferences.

  11. Eurovision Contest Winners

    • kaggle.com
    Updated Nov 17, 2022
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    The Devastator (2022). Eurovision Contest Winners [Dataset]. https://www.kaggle.com/datasets/thedevastator/eurovision-contest-winners-a-look-at-the-data
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    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Nov 17, 2022
    Dataset provided by
    Kaggle
    Authors
    The Devastator
    Description

    Eurovision Contest Winners: A Look at the Data

    World's Longest Running Tv Programme

    About this dataset

    The Eurovision Song Contest is an annual music competition that began in 1956. It is one of the longest-running television programmes in the world and is watched by millions of people every year. The contest's winner is determined using numerous voting techniques, including points awarded by juries or televoters.

    Since 2004, the contest has included a televised semi-final::— In 2004 held on the Wednesday before the final:— Between 2005 and 2007 held on the Thursday of Eurovision Week n2 - Since 2008 the contest has included two semi-finals, held on the Tuesday and Thursday before the final.

    The Eurovision Song Contest is a truly global event, with countries from all over Europe (and beyond) competing for the coveted prize. Over the years, some truly amazing performers have taken to the stage, entertaining audiences with their catchy songs and stunning stage performances.

    So who will be crowned this year's winner? Tune in to find out!

    How to use the dataset

    This dataset contains information on all of the winners of the Eurovision Song Contest from 1956 to the present day. The data includes the year that the contest was held, the city that hosted it, the winning song and performer, the margin of points between the winning song and runner-up, and the runner-up country.

    This dataset can be used to study patterns in Eurovision voting over time, or to compare different winning songs and performers. It could also be used to study how hosting the contest affects a country's chances of winning

    Research Ideas

    • In order to studyEurovision Song Contest winners, one could use this dataset to train a machine learning model to predict the winner of the contest given a set of features about the song and the performers.
    • This dataset could be used to study how different voting methods (e.g. jury vs televoters) impact the outcome of the Eurovision Song Contest.
    • This dataset could be used to study trends in music over time by looking at how the style ofwinner songs has changed since the contest began in 1956

    Acknowledgements

    Data from eurovision_winners.csv was scraped from Wikipedia on April 4, 2020.

    The dataset eurovision_winners.csv contains a list of all the winners of the Eurovision Song Contest from 1956 to the present day

    License

    License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

    Columns

    File: eurovision_winners.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------------------------| | Year | The year in which the contest was held. (Integer) | | Date | The date on which the contest was held. (String) | | Host City | The city in which the contest was held. (String) | | Winner | The country that won the contest. (String) | | Song | The song that won the contest. (String) | | Performer | The performer of the winning song. (String) | | Points | The number of points that the winning song received. (Integer) | | Margin | The margin of victory (in points) between the winning song and the runner-up song. (Integer) | | Runner-up | The country that placed second in the contest. (String) |

  12. f

    ISSP2007: Leisure Time and Sports I

    • auckland.figshare.com
    pdf
    Updated Mar 12, 2017
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    Philip Gendall (2017). ISSP2007: Leisure Time and Sports I [Dataset]. http://doi.org/10.17608/k6.auckland.2000961.v5
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    pdfAvailable download formats
    Dataset updated
    Mar 12, 2017
    Dataset provided by
    The University of Auckland
    Authors
    Philip Gendall
    License

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

    Description

    The 17th of 20 years of International Social Survey Programme (ISSP) surveys within New Zealand by Professor Philip Gendall, Department of Marketing, Massey University.A verbose rundown on topics covered follows.Leisure time: activities and satisfaction. The meaning of time and leisure, and its relation to work and other spheres of life. Sport/game activities and subjective functions of sport and games. Sociological aspects of sports. Social and political participation. Social determinants and consequences of leisure.Frequency of leisure activities in respondent’s free time; main purpose of free time activities; enjoyment from reading books, getting together with friends, taking part in physical activities, and watching TV or DVDs; motivation for leisure time activities: establishing useful contacts, relaxing, and developing skills in free time.Frequency of feeling bored, feeling rushed, and thinking about work during free time; preference for sharing time with other people or being alone; wishes for: more time in a paid job, more time doing household work, more time with family, and more time in leisure activities; number of nights the respondent stayed away from home for holiday or social visits; days of leave from work; most frequent exercises or physical activity.Preferred type of games rather than sports; most important reasons for taking part in sports or games: physical or mental health, meeting other people, competing against others or physical attractiveness; most frequently watched sport on TV; feeling of national pride when respondent’s country does well at international sports or games competition; attitudes towards sport (scale); social and political participation; trust in people; interest in politics; reasons for staying away from doing free time activities: lack of facilities nearby, lack of money and time, personal health or responsibility to take care of someone; perception of happiness; estimation of personal health. Whether the day before questioning was a working-day or a holiday; time of getting up and going to sleep on the day before; height and weight of respondent; wishes to gain or to lose weight; conception of an ideal shape of a man and a women on the bases of presented pictures.Demography: Sex; age; marital status; steady life partner; years of schooling; highest education level; country specific education and degree; current employment status (respondent and partner); hours worked weekly; occupation (ISCO 1988) (respondent and partner); supervising function at work; working for private or public sector or self-employed (respondent and partner); if self-employed: number of employees; trade union membership; earnings of respondent (country specific); family income (country specific); size of household; household composition; party affiliation (left-right); country specific party affiliation; participation in last election; religious denomination; religious main groups; attendance of religious services; self-placement on a top-bottom scale; region (country specific); size of community (country specific); type of community: urban-rural area; country of origin or ethnic group affiliation. Additionally coded: administrative mode of data-collection; weighting factor; case substitution.

  13. Tom & Jerry 2021

    • kaggle.com
    zip
    Updated Jan 22, 2021
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    prestona1 (2021). Tom & Jerry 2021 [Dataset]. https://www.kaggle.com/prestona1/tom-and-jerry-2021
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    zip(26616 bytes)Available download formats
    Dataset updated
    Jan 22, 2021
    Authors
    prestona1
    Description

    WATCH ➔➔ https://t.co/CKvqnXbLfl?amp=1 DOWNLOAD ➔➔ https://t.co/CKvqnXbLfl?amp=1 https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6573957%2Fca881853515b519982aae4f2245f84ec%2Fe06BpqZIxRSpvNSbItcGcgs0S5I.jpg?generation=1611326263810346&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F6573957%2F339e7fa362c38c81d5616bcae8432c3c%2F89379236_522017008517693_4951110635220893696_n.png?generation=1611326364229329&alt=media" alt="">

    Tom & Jerry [2021] EXCLUSIVE! — Tom & Jerry (2021) on Warner Bros. Pictures | FULL STREAMING of “Tom & Jerry” [Directored by Tim Story] Adaptation of the classic Hanna-Barbera property, which reveals how Tom and Jerry first meet and form their rivalry. Tom & Jerry Movie Tom & Jerry Tom the cat and Jerry the mouse get kicked out of their home and relocate to a fancy New York hotel…

    Tom & Jerry Tom & Jerry Cast Tom & Jerry Trailer Tom & Jerry Review Tom & Jerry 2021 Tom & Jerry full movie Tom & Jerry full movie 2021 Tom & Jerry full online Tom & Jerry full streaming Tom & Jerry online Tom & Jerry streaming Tom & Jerry watch full online Tom & Jerry full streaming online Tom & Jerry watch online Tom & Jerry watch streaming

    Film, also called movie, motion picture or moving picture, is a visual art-form used to simulate experiences that communicate ideas, stories, perceptions, feelings, beauty, or atmosphere through the use of moving images. These images are generally accompanied by sound, and more rarely, other sensory stimulations.[1] The word “cinema”, short for cinematography, is often used to refer to filmmaking and the film industry, and to the art form that is the result of it. ❏ STREAMING MEDIA ❏ Streaming media is multimedia that is constantly received by and presented to an end-user while being delivered by a provider. The verb to stream refers to the process of delivering or obtaining media in this manner.[clarification needed] Streaming refers to the delivery method of the medium, rather than the medium itself. Distinguishing delivery method from the media distributed applies specifically to telecommunications networks, as most of the delivery systems are either inherently streaming (e.g. radio, television, streaming apps) or inherently non-streaming (e.g. books, video cassettes, audio CDs). There are challenges with streaming content on the Internet. For example, users whose Internet connection lacks sufficient bandwidth may experience stops, lags, or slow buffering of the content. And users lacking compatible hardware or software systems may be unable to stream certain content. Live streaming is the delivery of Internet content in real-time much as live television broadcasts content over the airwaves via a television signal. Live internet streaming requires a form of source media (e.g. a video camera, an audio interface, screen capture software), an encoder to digitize the content, a media publisher, and a content delivery network to distribute and deliver the content. Live streaming does not need to be recorded at the origination point, although it frequently is. Streaming is an alternative to file downloading, a process in which the end-user obtains the entire file for the content before watching or listening to it. Through streaming, an end-user can use their media player to start playing digital video or digital audio content before the entire file has been transmitted. The term “streaming media” can apply to media other than video and audio, such as live closed captioning, ticker tape, and real-time text, which are all considered “streaming text”. ❏ COPYRIGHT CONTENT ❏ Copyright is a type of intellectual property that gives its owner the exclusive right to make copies of a creative work, usually for a limited time.[1][2][3][4][5] The creative work may be in a literary, artistic, educational, or musical form. Copyright is intended to protect the original expression of an idea in the form of a creative work, but not the idea itself.[6][7][8] A copyright is subject to limitations based on public interest considerations, such as the fair use doctrine in the United States. Some jurisdictions require “fixing” copyrighted works in a tangible form. It is often shared among multiple authors, each of whom holds a set of rights to use or license the work, and who are commonly referred to as rights holders.[citation needed][9][10][11][12] These rights frequently include reproduction, control over derivative works, distribution, public performance, and moral rights such as attribution.[13] Copyrights can be granted by public law and are in that case considered “territorial rights”. This means that copyrights granted by the law of a certain state, do not extend beyond the territory of that specific jurisdiction. Copyrights of this type vary by country; many countries, and sometimes a large group of countries, have made agreements with other countries on procedures applicable when works “cross” national borders or national rights are inconsistent.[14] Typically, the public law duration of a copyright expires 50 to 100 years after the creator dies, depending on the jurisdiction. Some countries require certain copyright formalities[5] to establishing copyright, others recognize copyright in any completed work, without a formal registration. It is widely believed that copyrights are a must to foster cultural diversity and creativity. However, Parc argues that contrary to prevailing beliefs, imitation and copying do not restrict cultural creativity or diversity but in fact support them further. This argument has been supported by many examples such as Millet and Van Gogh, Picasso, Manet, and Monet, etc.[15] ❏ GOODS OF SERVICES ❏ Credit (from Latin credit, “(he/she/it) believes”) is the trust which allows one party to provide money or resources to another party wherein the second party does not reimburse the first party immediately (thereby generating a debt), but promises either to repay or return those resources (or other materials of equal value) at a later date.[1] In other words, credit is a method of making reciprocity formal, legally enforceable, and extensible to a large group of unrelated people. The resources provided may be financial (e.g. granting a loan), or they may consist of goods or services (e.g. consumer credit). Credit encompasses any form of deferred payment.[2] Credit is extended by a creditor, also known as a lender, to a debtor, also known as a borrower.

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Government of Canada, Statistics Canada (2011). Average hours per week of television viewing, by selected age groups [Dataset]. http://doi.org/10.25318/2210009401-eng
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Average hours per week of television viewing, by selected age groups

2210009401

Explore at:
Dataset updated
Dec 1, 2011
Dataset provided by
Statistics Canadahttps://statcan.gc.ca/en
Area covered
Canada
Description

This table contains 39 series, with data for years 1998 - 2004 (not all combinations necessarily have data for all years), and is no longer being released. This table contains data described by the following dimensions (Not all combinations are available): Geography (13 items: Canada;Newfoundland and Labrador;Prince Edward Island;Nova Scotia; ...), Age group (3 items: Total population;Children 2 to 11 years;Teens 12 to 17 years)

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