Ratio: The number of students among all student survey respondents who watch television for more than 3 hours per day on an average school day.

Definition: The percentage of students who watch television or play video/computer games and use the internet for a specified number of hours per day on an average school day.

Data Sources:

1) New Jersey Student Health Survey, Office of Student Support Services, Division of Student Services and Career Readiness, New Jersey Department of Education

2) High School Youth Risk Behavior Survey Data, Centers for Disease Control and Prevention, http://nccd.cdc.gov/youthonline/

History: MAR 2017: Chart and table titles corrected to read as "More Than 3 Hours Per Day." They were erroneously labeled previously as "2 or Less Hours Per Day."

Tuvalu TV: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 13.900 % in 2010. Tuvalu TV: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 13.900 % from Dec 2010 (Median) to 2010, with 1 observations. The data reached an all-time high of 13.900 % in 2010 and a record low of 13.900 % in 2010. Tuvalu TV: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tuvalu – Table TV.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

SUPERSEDED - The dataset was originally published with a doi in February 2020 but has been superseded by an updated version. This DOI has been superseded in May 2021 by https://doi.org/ 10/gc9t due to corrections applied to the dataset. Nephrops norvegicus are common around the Irish coast, occurring in geographically distinct sandy or muddy areas where the sediment is suitable for construction of their burrow dwellings. The Marine Institute carries out Underwater TV surveys annually of commercially important Nephrops stocks. This dataset provides quality assured estimates of Nephrops burrow densities over the known spatial and bathymetric distribution of the FU (functional unit) 22: the “Smalls” Nephrops ground. From 2006 to 2011 (UWTV) stations set at 3.0 nautical mile spacing over the known distribution. From 2012 onwards a randomised isometric grid of underwater television (UWTV) stations set at 4.5 nautical mile spacing. Underwater TV Survey reports for this Nephrops stock are available at: http://hdl.handle.net/10793/1428 Also available is the ICES Cooperative Research Reports which details use of UWTV surveys to assess Nephrops stock:https://tinyurl.com/ices-nephrops GIS shapefiles of FU22 and the “Smalls” Nephrops grounds are provided. This dataset covers the period of 2006 and is ongoing. One hundred percent of the survey grid was covered in all years except in 2015, where 83 percent of the grid was covered. These 7 stations in 2015 could not be completed due to very poor or nil visibility conditions encountered at seabed. For these stations density estimates were filled-in using and average of historic values within 2nmi (buffer2estimated). Dataset fields are Nephrops Functional Unit Number; Survey Code; Year; UWTV station number; Date-Start of UWTV track; Time_Start of UWTV track; Date-End of UWTV track; Time_End of UWTV track; Decimalised longitude and latitude midpoint of the UWTV station track; Adjusted density (Nephrops burrows/m²) ;Length in metres of the UWTV station track; Field of View of camera system in metres; Total Nephrops burrow count; Nephrops Fishing Ground Name; Source of positional data to calculate UWTV station track (USBL sled GPS, SHIP GPS, Layback, estimated GPS, buffer2estimated); Camera system used (SD = standard analogue system, HD = high definition system); Data Extraction method (SQL, MSAccess); Data Status (Final for analysis); Research Vessel Name; Correction Factor (Density / Correction Factor = Adjusted Density) and Depth (metres). Entries with NA means data is not available. None

Suggested Citation: Doyle, Jennifer. (2020) VERSION SUPERSEDED - Nephrops Underwater TV Survey FU22 The "Smalls". Marine Institute, Ireland. doi:10/dk22.

This dataset includes statistics about durations between two consecutive subtitles in 5,000 top-ranked IMDB movies. The dataset can be used to understand how dialogue is used in films and to develop tools to improve the watching experience. This notebook contains the code and data that were used to create this dataset.

Dataset statistics:

• Average duration between subtitles
• Average duration between subtitles with a duration greater than 10, 30, 60, 120, and 300 seconds
• Maximum duration between subtitles
• Percentage of duration between subtitles from the runtime

Dataset use cases:

• Understanding how dialogue is used in movies, such as the average duration of a dialogue scene and how the duration of dialogue varies between different genres
• Developing tools to improve the watching experience by adjusting the playback speed of dialogue scenes
• Evaluating the effectiveness of tools like the VLC extension mentioned below

Data Analysis:

The next histogram shows the distribution of movie runtimes in minutes. The mean runtime is 99.903 minutes, the maximum runtime is 877 minutes, and the median runtime is 98.5 minutes.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3228936%2F5c78e4866f203dfe5f7a7f55e41f69d0%2Ffig%201.png?generation=1696861842737260&alt=media" alt="">

Figure 1: Histogram of the runtime in minutes

The next histogram shows the distribution of the percentage of gaps (duration between two consecutive subtitles) out of all the movie runtime. The mean percentage of gaps is 0.187, the maximum percentage of gaps is 0.033, and the median percentage of gaps is 327.586.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3228936%2F235453706269472da11082f080b1f41d%2Ffig%202.png?generation=1696862163125288&alt=media" alt="">

Figure 2: Histogram of the percentage of gaps (duration between two consecutive subtitles) out of all the movie runtime

The next histogram shows the distribution of the total movie's subtitle duration (seconds) between two consecutive subtitles. The mean subtitle duration is 4,837.089 seconds and the median subtitle duration is 2,906.435 seconds.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3228936%2F234d31e3abaf6c4d174f494bf5cb86fa%2Ffig%203.png?generation=1696862309880510&alt=media" alt="">

Figure 3: Histogram of the total movie's subtitle duration (seconds) between two consecutive subtitles

Example use case:

The Dynamic Adjustment of Playback Speed (DAPS), a VLC extension, can be used to save time while watching movies by increasing the playback speed between dialogues. However, it is essential to choose the appropriate settings for the extension, as increasing the playback speed can impact the overall tone and impact of the film.

The dataset of 5,000 top-ranked movie subtitle durations can be used to help users choose the appropriate settings for the DAPS extension. For example, users who are watching a fast-paced action movie may want to set a higher minimum duration between subtitles before speeding up, while users who are watching a slow-paced drama movie may want to set a lower minimum duration.

Additionally, users can use the dataset to understand how the different settings of the DAPS extension impact the overall viewing experience. For example, users can experiment with different settings to see how they affect the pacing of the movie and the overall impact of the dialogue scenes.

Conclusion

This dataset is a valuable resource for researchers and developers who are interested in understanding and improving the use of dialogue in movies or in tools for watching movies.

This dataset covers the period of 2013 and is ongoing. One hundred percent of the survey grid was covered in all years except in 2013, where fifty-six percent of the grid was covered (55 stations). Dataset fields are Nephrops Functional Unit Number; Survey Code; Year; UWTV station number; Date-Start of UWTV track; Time_Start of UWTV track; Date-End of UWTV track; Time_End of UWTV track; Decimalised longitude and latitude midpoint of the UWTV station track; Adjusted density (Nephrops burrows/m²) ;Length in metres of the UWTV station track; Field of View of camera system in metres; Total Nephrops burrow count; Nephrops Fishing Ground Name; Source of positional data to calculate UWTV station track (USBL sled GPS, SHIP GPS, Layback, estimated GPS); Camera system used (SD = standard analogue system, HD = high definition system); Data Extraction method (SQL, MSAccess); Data Status (Final for analysis); Research Vessel Name; Correction Factor (Density / Correction Factor = Adjusted Density) and Depth (metres). Entries with NA means data is not available.

Super Bowl Game Records

2019 Super Bowl Game Records

By Throwback Thursday [source]

About this dataset

This dataset provides comprehensive information about Super Bowl games that took place in 2019, including game details such as the winning team, losing team, venue, city, attendance, network that broadcasted the game, average number of viewers in the United States who watched the game, rating (representing the percentage of households with televisions that were tuned into the game), share (representing the percentage of households with televisions in use that were tuned into the game), and cost per 30-second advertisement. Additionally, this dataset includes specific details about each Super Bowl game such as the final score (in terms of winning team points minus losing team points), conference affiliations of both winning and losing teams, and any additional notes or information about each respective Super Bowl. All of these data points collectively provide a comprehensive overview of each recorded Super Bowl game from 2019

How to use the dataset

• Game details: The 'Game' column represents the number or identifier of the Super Bowl game. For example, '1' indicates it is the first Super Bowl game.

• Winning team: The 'Winning team' column lists the name of the team that won the Super Bowl game. For example, 'New England Patriots'.

• Winning Team Points: The 'Winning Team Points' column shows the number of points scored by the winning team in that particular game.

• Winning Team Conference: The 'Winning Team Conference' column indicates which conference (e.g., AFC or NFC) the winning team belongs to.

• Score: The 'Score' column displays a summary of the final score in each game, showcasing how many points were scored by both teams in this format - Winning Team Points - Losing Team Points.

• Losing team: Similar to winning teams, losing teams are listed under the 'Losing team' column.

• Losing Team Conference: This column represents which conference (e.g., AFC or NFC)the losing team belongs to.

• Venue and city: The columns 'Venue' and 'City' show where each Super Bowl game was played, respectively.

• Attendance : This column shows numbers associated with how many people attended a particular super bowl event

• Network : Indicates Television network for broadcasted super bowl

11.Average U.S viewers : It denotes average number of viewers in United States who watched a specific super bowl

12.Rating & Share : These represent data associated with watching percentage (Rating)and households televisions percanton tuned into a particular event(Share).

13.Cost Per 30s Ad: The 'Cost Per 30s Ad' column specifies the cost of a 30-second advertisement during the Super Bowl game in dollars.

14.Notes: The 'Notes' column includes additional notes or information about each Super Bowl game.

This dataset provides a comprehensive record of every Super Bowl game that took place in 2019. By analyzing these attributes, you can gain insights into team performance, viewer interest, and commercial aspects of the games. Use this guide to explore and analyze the dataset effectively for your analysis or research purposes

Research Ideas

• Analyzing the popularity and reach of the Super Bowl: With data on average U.S. viewers, rating, share, and cost per 30-second ad, this dataset can be used to analyze the Super Bowl's popularity and reach. By comparing these metrics across different games, one can assess how the viewership and interest in the Super Bowl has changed over time.
• Evaluating advertising effectiveness during the Super Bowl: The dataset includes information on the cost per 30-second ad during each Super Bowl game. This data can be used to analyze whether there is a correlation between ad costs and viewer ratings or share. It can also help marketers and advertisers understand how effective their advertisements were in reaching a wide audience during past Super Bowls.
• Studying game attendance trends: The dataset provides information on attendance at each Super Bowl game. By analyzing this data, one can identify trends in game attendance over the years and evaluate factors that may impact ticket sales such as venue location or teams competing in the game. This analysis could be useful for event organizers and stadium operators looking to optimize future hosting decisions for large-scale events like sports championships or music festivals

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset descrip...

Number and percentage of persons by household food security status, age group, sex, visible minority group, Indigenous group and immigration status, Canada and provinces.

Number of divorces and various divorce indicators (crude divorce rate, divorce rate for married persons, age-standardized divorce rate, total divorce rate, mean and median duration of marriage, median duration of divorce proceedings, percentage of joint divorce applications), by place of occurrence, 1970 to most recent year.

Nephrops norvegicus is a lobster also known as the Norway lobster, Dublin Bay prawn, langoustine or scampi. Nephrops are one of the most valuable demersal fisheries in Europe. They are common around the Irish coast, occurring in geographically distinct sandy or muddy areas where the sediment is suitable for construction of their burrow dwellings.

The Marine Institute conducts Underwater TV (UWTV) surveys annually of commercially important Norway lobster (Nephrops norvegicus) grounds following ICES Survey protocols( https://doi.org/10.17895/ices.pub.8014) . Nephrops grounds are managed and assessed across Europe as individual stocks as Functional Units (FUs). This survey dataset provides quality assured estimates of Nephrops burrow densities over the known spatial and bathymetric distribution grounds within FUs: 16,17,19, 22 and 20-21. This dataset covers the period of 2002 and is ongoing.

Functional Units: FU 16: Porcupine Bank Nephrops grounds. Survey series commenced in 2012. 100% of the survey grid was covered in all years except in 2012, where sixty-nine percent of the grid was covered (47 stations), and in 2015, no survey data are available due to research vessel breakdown. Survey design is a randomised isometric grid of UWTV stations at six nautical mile spacing. Water depth ranges from 290 to 585 metres.

FU 17: Aran, Galway Bay and Slyne Head Nephrops grounds. Survey series commenced in 2002. Survey 100% of the survey grid was covered in all years except in 2003 and 2008, no survey data for Slyne Head, and in 2022, no survey data for Aran and Slyne Head grounds, all due to logistical problems. Survey design is a randomised isometric grid of UWTV stations at 4.5 nautical mile spacing on the Aran grounds and random stratified for Galway Bay and Slyne Head grounds. Water depth ranges from 21 to 125 metres.

FU19: In 2006, 6 stations only were completed as an exploratory survey. The 2006 is provided in this dataset. There was no survey was carried out in years 2007 to 2010 due to time constraints. Survey design is a random stratified for the discrete Nephropsgrounds in this FU. Water depth ranges from 27 to 149 metres.

FU20-21: Labadie, Jones and Cockburn Banks Nephrops grounds. Survey series commenced in 2013. One hundred percent of the survey grid was covered in all years except in 2013, where 56% of the grid was covered (55 stations) and 2024 where 90 percent of the grid was covered (84 stations), all due to logistical problems. Survey design is a randomised isometric grid of UWTV stations at 6 nautical mile spacing. Water depth ranges from 73 to 149 metres.

FU22:The “Smalls” Nephrops grounds. Survey series commenced in 2006. Survey 100% of the survey grid was covered in all years except in 2015, where 83 percent of the grid was covered (33 stations). Survey design is a randomised isometric grid of UWTV stations at 4.5 nautical mile spacing. Water depth ranges from 74 to 145 metres.

Dataset fields are Nephrops Functional Unit Number; Survey Code; Year; UWTV station number; Date-Start of UWTV track; Time_Start of UWTV track; Date-End of UWTV track; Time_End of UWTV track; Decimalised longitude and latitude midpoint of the UWTV station track; Adjusted density (Nephrops burrows/m²) ;Length in metres of the UWTV station track; Field of View of camera system in metres; Total Nephrops burrow count; Nephrops Fishing Ground Name; Source of positional data to calculate UWTV station track (USBL sled GPS, SHIP GPS, Layback, estimated GPS); Camera system used (SD = standard analogue system, HD = high definition system); Data Status (Final for analysis); Research Vessel Name; Correction Factor (Density / Correction Factor = Adjusted Density) and Water Depth (metres).

Suggested Citation: Marine Institute. (2025) Nephrops (Nephrops norvegicus) Underwater TV Surveys, Marine Institute Ireland, 2002 - Present . Marine Institute, Ireland. doi:10/n4dj.

Differences in the number and proportion of persons with and without disabilities, by age group and gender, Canada, provinces and territories.

IntroductionPrior studies examining longitudinal patterns of television (TV) watching have tended to use analytical approaches which do not allow for heterogeneity in the variation of TV watching over time. In the current study, we used latent class analysis (LCA) to examine the relationships between television watching (from childhood to early adulthood) and body fat percentage (%) and mental health.MethodsData were collected from 2411 participants (50% female) from the Raine Study, a prospective birth cohort study in Australia. Participants were followed up over 15 years and answered questions about hours of TV watching per week at six time-points (5, 8, 10, 14, 17 and 20yrs). Trajectories of television watching were estimated using LCA and appropriate regression models used to test the association of television watching class with percentage body fat (measured by DXA) and mental health (DASS-21) at age 20. Physical activity was used as a covariate.ResultsThree distinct trajectories of TV watching were identified. Class 1 (47.4%) had consistently high (>14 hrs/wk) levels of TV watching, Class 2 (37.9%) was characterised by an increase in TV watching over adolescence and Class 3 (14.7%) had consistently lower (

Annual population estimates by marital status or legal marital status, age and sex, Canada, provinces and territories.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

Percentage of Canadians using a smartphone for personal use and selected habits of use during a typical day.

This modelled fluvial flood depth data was created for the 0.1% annual chance of flooding situations and was produced as a by-product from the 2004 generalised modelling project. The purpose of the generalised modelling project was to fill the gaps where there was no detailed local modelled data in 2004, in order to define the extents of Flood Zones for spatial planning.

A two-dimensional hydrodynamic model called JFlow was used to produce this modelled fluvial flood depth data on a 5x5m grid.

Since 2004, local detailed modelling has been used to replace this generalised modelling in many areas to define the extents of Flood Zones. However this depth dataset has not been updated.

INFORMATION WARNING: This data is not suitable for identifying whether an individual property will flood, for detailed decision making or for use in site specific Flood Risk or Strategic Flood Risk Assessments. Where this data is used for anything other than broad catchment or Shoreline Management Plan scale further evidence, verification and studies should be undertaken.

More recent, accurate and local detailed modelling depth data is available for many places. Please contact your local Environment Agency office to see if detailed modelling is available for your area of interest.

This metadata record is for Approval for Access product AfA238 Flood Zone Depth Grid Dataset 2004

Modelled fluvial flood depth data are available for the whole of England, however this data is for the 100x100km squared Ordnance Survey National Grid reference TV. If you are interested in data for another grid reference refer to the Ordnance Survey National Grid document linked below to find the relevant referencing code and search on Data.gov.uk again to download the data. https://www.ordnancesurvey.co.uk/docs/support/national-grid.pdf Attribution statement: © Environment Agency copyright and/or database right 2016. All rights reserved. Some features of this information are based on digital spatial data licensed from the Centre for Ecology & Hydrology © NERC (CEH). Defra, Met Office and DARD Rivers Agency © Crown Copyright. © Cranfield University. © James Hutton Institute. Contains OS data © Crown copyright and database right 2015. Land & Property Services © Crown copyright and database right.

Holes percentage using block size 8x8 and block size 16x16 tested dataset [38].

Number, rate and percentage changes in rates of homicide victims, Canada, provinces and territories, 1961 to 2023.

Percentage of persons aged 15 years and over by trust in media and main source of news, by gender, for Canada, regions and provinces.

Daily average time in hours and proportion of day spent on various activities by age group and sex, 15 years and over, Canada and provinces.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.