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...

The database for this study (Briganti et al. 2018; the same for the Braun study analysis) was composed of 1973 French-speaking students in several universities or schools for higher education in the following fields: engineering (31%), medicine (18%), nursing school (16%), economic sciences (15%), physiotherapy, (4%), psychology (11%), law school (4%) and dietetics (1%). The subjects were 17 to 25 years old (M = 19.6 years, SD = 1.6 years), 57% were females and 43% were males. Even though the full dataset was composed of 1973 participants, only 1270 answered the full questionnaire: missing data are handled using pairwise complete observations in estimating a Gaussian Graphical Model, meaning that all available information from every subject are used.

The feature set is composed of 28 items meant to assess the four following components: fantasy, perspective taking, empathic concern and personal distress. In the questionnaire, the items are mixed; reversed items (items 3, 4, 7, 12, 13, 14, 15, 18, 19) are present. Items are scored from 0 to 4, where “0” means “Doesn’t describe me very well” and “4” means “Describes me very well”; reverse-scoring is calculated afterwards. The questionnaires were anonymized. The reanalysis of the database in this retrospective study was approved by the ethical committee of the Erasmus Hospital.

Size: A dataset of size 1973*28

Number of features: 28

Ground truth: No

Type of Graph: Mixed graph

The following gives the description of the variables:

Feature FeatureLabel Domain Item meaning from Davis 1980

001 1FS Green I daydream and fantasize, with some regularity, about things that might happen to me.

002 2EC Purple I often have tender, concerned feelings for people less fortunate than me.

003 3PT_R Yellow I sometimes find it difficult to see things from the “other guy’s” point of view.

004 4EC_R Purple Sometimes I don’t feel very sorry for other people when they are having problems.

005 5FS Green I really get involved with the feelings of the characters in a novel.

006 6PD Red In emergency situations, I feel apprehensive and ill-at-ease.

007 7FS_R Green I am usually objective when I watch a movie or play, and I don’t often get completely caught up in it.(Reversed)

008 8PT Yellow I try to look at everybody’s side of a disagreement before I make a decision.

009 9EC Purple When I see someone being taken advantage of, I feel kind of protective towards them.

010 10PD Red I sometimes feel helpless when I am in the middle of a very emotional situation.

011 11PT Yellow sometimes try to understand my friends better by imagining how things look from their perspective

012 12FS_R Green Becoming extremely involved in a good book or movie is somewhat rare for me. (Reversed)

013 13PD_R Red When I see someone get hurt, I tend to remain calm. (Reversed)

014 14EC_R Purple Other people’s misfortunes do not usually disturb me a great deal. (Reversed)

015 15PT_R Yellow If I’m sure I’m right about something, I don’t waste much time listening to other people’s arguments. (Reversed)

016 16FS Green After seeing a play or movie, I have felt as though I were one of the characters.

017 17PD Red Being in a tense emotional situation scares me.

018 18EC_R Purple When I see someone being treated unfairly, I sometimes don’t feel very much pity for them. (Reversed)

019 19PD_R Red I am usually pretty effective in dealing with emergencies. (Reversed)

020 20FS Green I am often quite touched by things that I see happen.

021 21PT Yellow I believe that there are two sides to every question and try to look at them both.

022 22EC Purple I would describe myself as a pretty soft-hearted person.

023 23FS Green When I watch a good movie, I can very easily put myself in the place of a leading character.

024 24PD Red I tend to lose control during emergencies.

025 25PT Yellow When I’m upset at someone, I usually try to “put myself in his shoes” for a while.

026 26FS Green When I am reading an interesting story or novel, I imagine how I would feel if the events in the story were happening to me.

027 27PD Red When I see someone who badly needs help in an emergency, I go to pieces.

028 28PT Yellow Before criticizing somebody, I try to imagine how I would feel if I were in their place

More information about the dataset is contained in empathy_description.html file.

Analysis of ‘Young People Survey’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/miroslavsabo/young-people-survey on 13 February 2022.

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

Introduction

In 2013, students of the Statistics class at "https://fses.uniba.sk/en/">FSEV UK were asked to invite their friends to participate in this survey.

• The data file (responses.csv) consists of 1010 rows and 150 columns (139 integer and 11 categorical).
• For convenience, the original variable names were shortened in the data file. See the columns.csv file if you want to match the data with the original names.
• The data contain missing values.
• The survey was presented to participants in both electronic and written form.
• The original questionnaire was in Slovak language and was later translated into English.
• All participants were of Slovakian nationality, aged between 15-30.

The variables can be split into the following groups:

• Music preferences (19 items)
• Movie preferences (12 items)
• Hobbies & interests (32 items)
• Phobias (10 items)
• Health habits (3 items)
• Personality traits, views on life, & opinions (57 items)
• Spending habits (7 items)
• Demographics (10 items)

Research questions

Many different techniques can be used to answer many questions, e.g.

• Clustering: Given the music preferences, do people make up any clusters of similar behavior?
• Hypothesis testing: Do women fear certain phenomena significantly more than men? Do the left handed people have different interests than right handed?
• Predictive modeling: Can we predict spending habits of a person from his/her interests and movie or music preferences?
• Dimension reduction: Can we describe a large number of human interests by a smaller number of latent concepts?
• Correlation analysis: Are there any connections between music and movie preferences?
• Visualization: How to effectively visualize a lot of variables in order to gain some meaningful insights from the data?
• (Multivariate) Outlier detection: Small number of participants often cheats and randomly answers the questions. Can you identify them? Hint: [Local outlier factor][1] may help.
• Missing values analysis: Are there any patterns in missing responses? What is the optimal way of imputing the values in surveys?
• Recommendations: If some of user's interests are known, can we predict the other? Or, if we know what a person listen, can we predict which kind of movies he/she might like?

Past research

• (in slovak) Sleziak, P. - Sabo, M.: Gender differences in the prevalence of specific phobias. Forum Statisticum Slovacum. 2014, Vol. 10, No. 6. [Differences (gender + whether people lived in village/town) in the prevalence of phobias.]

• Sabo, Miroslav. Multivariate Statistical Methods with Applications. Diss. Slovak University of Technology in Bratislava, 2014. [Clustering of variables (music preferences, movie preferences, phobias) + Clustering of people w.r.t. their interests.]

Questionnaire

MUSIC PREFERENCES

1. I enjoy listening to music.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I prefer.: Slow paced music 1-2-3-4-5 Fast paced music (integer)
3. Dance, Disco, Funk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Folk music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Country: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Classical: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. Musicals: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Pop: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Metal, Hard rock: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Punk: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Hip hop, Rap: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
13. Reggae, Ska: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
14. Swing, Jazz: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
15. Rock n Roll: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
16. Alternative music: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
17. Latin: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
18. Techno, Trance: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
19. Opera: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

MOVIE PREFERENCES

1. I really enjoy watching movies.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. Horror movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
3. Thriller movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
4. Comedies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
5. Romantic movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
6. Sci-fi movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
7. War movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
8. Tales: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
9. Cartoons: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
10. Documentaries: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
11. Western movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)
12. Action movies: Don't enjoy at all 1-2-3-4-5 Enjoy very much (integer)

HOBBIES & INTERESTS

1. History: Not interested 1-2-3-4-5 Very interested (integer)
2. Psychology: Not interested 1-2-3-4-5 Very interested (integer)
3. Politics: Not interested 1-2-3-4-5 Very interested (integer)
4. Mathematics: Not interested 1-2-3-4-5 Very interested (integer)
5. Physics: Not interested 1-2-3-4-5 Very interested (integer)
6. Internet: Not interested 1-2-3-4-5 Very interested (integer)
7. PC Software, Hardware: Not interested 1-2-3-4-5 Very interested (integer)
8. Economy, Management: Not interested 1-2-3-4-5 Very interested (integer)
9. Biology: Not interested 1-2-3-4-5 Very interested (integer)
10. Chemistry: Not interested 1-2-3-4-5 Very interested (integer)
11. Poetry reading: Not interested 1-2-3-4-5 Very interested (integer)
12. Geography: Not interested 1-2-3-4-5 Very interested (integer)
13. Foreign languages: Not interested 1-2-3-4-5 Very interested (integer)
14. Medicine: Not interested 1-2-3-4-5 Very interested (integer)
15. Law: Not interested 1-2-3-4-5 Very interested (integer)
16. Cars: Not interested 1-2-3-4-5 Very interested (integer)
17. Art: Not interested 1-2-3-4-5 Very interested (integer)
18. Religion: Not interested 1-2-3-4-5 Very interested (integer)
19. Outdoor activities: Not interested 1-2-3-4-5 Very interested (integer)
20. Dancing: Not interested 1-2-3-4-5 Very interested (integer)
21. Playing musical instruments: Not interested 1-2-3-4-5 Very interested (integer)
22. Poetry writing: Not interested 1-2-3-4-5 Very interested (integer)
23. Sport and leisure activities: Not interested 1-2-3-4-5 Very interested (integer)
24. Sport at competitive level: Not interested 1-2-3-4-5 Very interested (integer)
25. Gardening: Not interested 1-2-3-4-5 Very interested (integer)
26. Celebrity lifestyle: Not interested 1-2-3-4-5 Very interested (integer)
27. Shopping: Not interested 1-2-3-4-5 Very interested (integer)
28. Science and technology: Not interested 1-2-3-4-5 Very interested (integer)
29. Theatre: Not interested 1-2-3-4-5 Very interested (integer)
30. Socializing: Not interested 1-2-3-4-5 Very interested (integer)
31. Adrenaline sports: Not interested 1-2-3-4-5 Very interested (integer)
32. Pets: Not interested 1-2-3-4-5 Very interested (integer)

PHOBIAS

1. Flying: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
2. Thunder, lightning: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
3. Darkness: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
4. Heights: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
5. Spiders: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
6. Snakes: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
7. Rats, mice: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
8. Ageing: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
9. Dangerous dogs: Not afraid at all 1-2-3-4-5 Very afraid of (integer)
10. Public speaking: Not afraid at all 1-2-3-4-5 Very afraid of (integer)

HEALTH HABITS

1. Smoking habits: Never smoked - Tried smoking - Former smoker - Current smoker (categorical)
2. Drinking: Never - Social drinker - Drink a lot (categorical)
3. I live a very healthy lifestyle.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)

PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS

1. I take notice of what goes on around me.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
2. I try to do tasks as soon as possible and not leave them until last minute.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
3. I always make a list so I don't forget anything.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
4. I often study or work even in my spare time.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
5. I look at things from all different angles before I go ahead.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
6. I believe that bad people will suffer one day and good people will be rewarded.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
7. I am reliable at work and always complete all tasks given to me.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
8. I always keep my promises.: Strongly disagree 1-2-3-4-5 Strongly agree (integer)
9. **I can fall for someone very quickly and then

This feature service depicts the National Weather Service (NWS) watches, warnings, and advisories within the United States. Watches and warnings are classified into 43 categories.A warning is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. A warning means weather conditions pose a threat to life or property. People in the path of the storm need to take protective action.A watch is used when the risk of a hazardous weather or hydrologic event has increased significantly, but its occurrence, location or timing is still uncertain. It is intended to provide enough lead time so those who need to set their plans in motion can do so. A watch means that hazardous weather is possible. People should have a plan of action in case a storm threatens, and they should listen for later information and possible warnings especially when planning travel or outdoor activities.An advisory is issued when a hazardous weather or hydrologic event is occurring, imminent or likely. Advisories are for less serious conditions than warnings, that cause significant inconvenience and if caution is not exercised, could lead to situations that may threaten life or property.SourceNational Weather Service RSS-CAP Warnings and Advisories: Public AlertsNational Weather Service Boundary Overlays: AWIPS Shapefile DatabaseSample DataSee Sample Layer Item for sample data during Weather inactivity!Update FrequencyThe services is updated every 5 minutes using the Aggregated Live Feeds methodology.The overlay data is checked and updated daily from the official AWIPS Shapefile Database.Area CoveredUnited States and TerritoriesWhat can you do with this layer?Customize the display of each attribute by using the Change Style option for any layer.Query the layer to display only specific types of weather watches and warnings.Add to a map with other weather data layers to provide insight on hazardous weather events.Use ArcGIS Online analysis tools, such as Enrich Data, to determine the potential impact of weather events on populations.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.Additional information on Watches and Warnings.

N_ENJEU_PPRN_20100013_L_019 Linear stakes (seeed) of the PPRI 2013 Dordogne and tributaries to Nonards — Corrèze

Plans for the prevention of natural flood risk PPRI of the Dordogne basin and its tributaries of Argentat in Liourdres (15 communes) in Corrèze

Natural PPRs are established in accordance with Article L. 562-1 of the Environmental Code. In exposed areas, depending on the intensity of the risk, they shall define the prohibition zones for constructions and installations in order not to aggravate the risk, or, in areas where constructions and installations may be authorised, the implementation requirements.

The PPRi of the Dordogne basin and its tributaries from Argentat to Liourdres, approved on 30/10/2013, cover 15 municipalities (one PPRi per municipality). The rivers concerned are Dordogne, Maronne, Souvigne and Sagne and Filèle, Malefarge, Ménoire and Cerou. The hazard is derived from a modeling. The reference flood is the strongest history known for Souvigne, Sagne and Fidèle (crue of October 1960) and the centennial flood calculated for Dordogne, Maronne, Malefarge, Ménoire and Céroux. The purpose of the regulations is to prevent the risk to people and property. For this purpose, the flood-free territory is classified as red (inconstructible), dark blue (constructable under conditions for economic activities) or blue (constructible under conditions)

A PPRi is established for each municipality, i.e.: Argentat-sur-Dordogne, Hautefage, La-Chapelle-Saint-Géraud, Forgès, Saint-Chamant, Monceaux-sur-Dordogne, Bassignac-le-Bas, Reygades, Chenaillers-Mascheix, Brivezac (merged with Beaulieu-sur-Dordogne), Beaulieu-sur-Dordogne, Nonards, Altillac, Astaillac and Liourdres.

Students' Academic Performance Dataset (xAPI-Edu-Data)

Data Set Characteristics: Multivariate

Number of Instances: 480

Area: E-learning, Education, Predictive models, Educational Data Mining

Attribute Characteristics: Integer/Categorical

Number of Attributes: 16

Date: 2016-11-8

Associated Tasks: Classification

Missing Values? No

File formats: xAPI-Edu-Data.csv

Source:

Elaf Abu Amrieh, Thair Hamtini, and Ibrahim Aljarah, The University of Jordan, Amman, Jordan, http://www.Ibrahimaljarah.com www.ju.edu.jo

Dataset Information:

This is an educational data set which is collected from learning management system (LMS) called Kalboard 360. Kalboard 360 is a multi-agent LMS, which has been designed to facilitate learning through the use of leading-edge technology. Such system provides users with a synchronous access to educational resources from any device with Internet connection.

The data is collected using a learner activity tracker tool, which called experience API (xAPI). The xAPI is a component of the training and learning architecture (TLA) that enables to monitor learning progress and learner’s actions like reading an article or watching a training video. The experience API helps the learning activity providers to determine the learner, activity and objects that describe a learning experience. The dataset consists of 480 student records and 16 features. The features are classified into three major categories: (1) Demographic features such as gender and nationality. (2) Academic background features such as educational stage, grade Level and section. (3) Behavioral features such as raised hand on class, opening resources, answering survey by parents, and school satisfaction.

The dataset consists of 305 males and 175 females. The students come from different origins such as 179 students are from Kuwait, 172 students are from Jordan, 28 students from Palestine, 22 students are from Iraq, 17 students from Lebanon, 12 students from Tunis, 11 students from Saudi Arabia, 9 students from Egypt, 7 students from Syria, 6 students from USA, Iran and Libya, 4 students from Morocco and one student from Venezuela.

The dataset is collected through two educational semesters: 245 student records are collected during the first semester and 235 student records are collected during the second semester.

The data set includes also the school attendance feature such as the students are classified into two categories based on their absence days: 191 students exceed 7 absence days and 289 students their absence days under 7.

This dataset includes also a new category of features; this feature is parent parturition in the educational process. Parent participation feature have two sub features: Parent Answering Survey and Parent School Satisfaction. There are 270 of the parents answered survey and 210 are not, 292 of the parents are satisfied from the school and 188 are not.

(See the related papers for more details).

Attributes

1 Gender - student's gender (nominal: 'Male' or 'Female’)

2 Nationality- student's nationality (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

3 Place of birth- student's Place of birth (nominal:’ Kuwait’,’ Lebanon’,’ Egypt’,’ SaudiArabia’,’ USA’,’ Jordan’,’ Venezuela’,’ Iran’,’ Tunis’,’ Morocco’,’ Syria’,’ Palestine’,’ Iraq’,’ Lybia’)

4 Educational Stages- educational level student belongs (nominal: ‘lowerlevel’,’MiddleSchool’,’HighSchool’)

5 Grade Levels- grade student belongs (nominal: ‘G-01’, ‘G-02’, ‘G-03’, ‘G-04’, ‘G-05’, ‘G-06’, ‘G-07’, ‘G-08’, ‘G-09’, ‘G-10’, ‘G-11’, ‘G-12 ‘)

6 Section ID- classroom student belongs (nominal:’A’,’B’,’C’)

7 Topic- course topic (nominal:’ English’,’ Spanish’, ‘French’,’ Arabic’,’ IT’,’ Math’,’ Chemistry’, ‘Biology’, ‘Science’,’ History’,’ Quran’,’ Geology’)

8 Semester- school year semester (nominal:’ First’,’ Second’)

9 Parent responsible for student (nominal:’mom’,’father’)

10 Raised hand- how many times the student raises his/her hand on classroom (numeric:0-100)

11- Visited resources- how many times the student visits a course content(numeric:0-100)

12 Viewing announcements-how many times the student checks the new announcements(numeric:0-100)

13 Discussion groups- how many times the student participate on discussion groups (numeric:0-100)

14 Parent Answering Survey- parent answered the surveys which are provided from school or not (nominal:’Yes’,’No’)

15 Parent School Satisfaction- the Degree of parent satisfaction from school(nominal:’Yes’,’No’)

16 Student Absence Days-the number of absence days for each student (nominal: above-7, under-7)

The students are classified into three numerical intervals based on their total grade/mark:

Low-Level: i...

This public dataset was created by the Social Security Administration and contains all names from Social Security card applications for births that occurred in the United States after 1879. Note that many people born before 1937 never applied for a Social Security card, so their names are not included in this data. For others who did apply, records may not show the place of birth, and again their names are not included in the data. All data are from a 100% sample of records on Social Security card applications as of the end of February 2015. To safeguard privacy, the Social Security Administration restricts names to those with at least 5 occurrences. This public dataset is hosted in Google BigQuery and is included in BigQuery's 1TB/mo of free tier processing. This means that each user receives 1TB of free BigQuery processing every month, which can be used to run queries on this public dataset. Watch this short video to learn how to get started quickly using BigQuery to access public datasets. What is BigQuery .

Context

IHME United States Mortality Rates by County 1980-2014: National - All. (Deaths per 100,000 population)

To quickly get started creating maps, like the one below, see the Quick Start R kernel.

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How the Dataset was Created

This Dataset was created from the Excel Spreadsheet, which can be found in the download. Or, you can view the source here. If you take a look at the row for United States, for the column Mortality Rate, 1980*, you'll see the set of numbers 1.52 (1.44, 1.61). Numbers in parentheses are 95% uncertainty. The 1.52 is an age-standardized mortality rate for both sexes combined (deaths per 100,000 population).

In this Dataset 1.44 will be placed in the named column Mortality Rage, 1989 (Min)* and 1.61 is in column named Mortality Rate, 1980 (Max)* . For information on how these Age-standardized mortality rates were calculated, see the December JAMA 2016 article, which you can download for free.

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Reference

JAMA Full Article

Video Describing this Study (Short and this is worth viewing)

Data Resources

How Americans Die May Depend On Where They Live, by Anna Maria Barry-Jester (FiveThirtyEight)

Interactive Map from healthdata.org

IHME Data

Acknowledgements

This Dataset was provided by IHME

Institute for Health Metrics and Evaluation 2301 Fifth Ave., Suite 600, Seattle, WA 98121, USA Tel: +1.206.897.2800 Fax: +1.206.897.2899 © 2016 University of Washington

Dataset showed that out of 460 respondents, 298 (64.8 %) used popular science or entertainment content as a distraction from the war, while 162 (35.2 %) did not do so. Of those who answered in the affirmative, 27.5% preferred popular science programs, 37.2% watched entertainment content, and 35.3% watched both. Among the 143 surveyed men, 87 (60.8%) needed distraction from news about the war, while 56 (39.2%) did not. Among the 317 surveyed women, 206 (65.0%) used popular science or entertainment programs as a distraction, while 111 (35.0%) did not

N_ZONE_REG_PPRN_20100015_S_019 Regulatory zones of PPRI 2013 Dordogne and tributaries in Saint Chamant — Corrèze

Plans for the prevention of natural flood risk PPRI of the Dordogne basin and its tributaries of Argentat in Liourdres (15 communes) in Corrèze

Natural PPRs are established in accordance with Article L. 562-1 of the Environmental Code. In exposed areas, depending on the intensity of the risk, they shall define the prohibition zones for constructions and installations in order not to aggravate the risk, or, in areas where constructions and installations may be authorised, the implementation requirements.

The PPRi of the Dordogne basin and its tributaries from Argentat to Liourdres, approved on 30/10/2013, cover 15 municipalities (one PPRi per municipality). The rivers concerned are Dordogne, Maronne, Souvigne and Sagne and Filèle, Malefarge, Ménoire and Cerou. The hazard is derived from a modeling. The reference flood is the strongest history known for Souvigne, Sagne and Fidèle (crue of October 1960) and the centennial flood calculated for Dordogne, Maronne, Malefarge, Ménoire and Céroux. The purpose of the regulations is to prevent the risk to people and property. For this purpose, the flood-free territory is classified as red (inconstructible), dark blue (constructable under conditions for economic activities) or blue (constructible under conditions)

A PPRi is established for each municipality, i.e.: Argentat-sur-Dordogne, Hautefage, La-Chapelle-Saint-Géraud, Forgès, Saint-Chamant, Monceaux-sur-Dordogne, Bassignac-le-Bas, Reygades, Chenaillers-Mascheix, Brivezac (merged with Beaulieu-sur-Dordogne), Beaulieu-sur-Dordogne, Nonards, Altillac, Astaillac and Liourdres.

A. SUMMARY This dataset lists intersections that have at least one continental crosswalk and meet the following criteria: Installed after January 1st, 2015 Installed before January 1st, 2015 and on the high injury network Continental crosswalks are marked with bold, wide stripes to indicate safe places for pedestrians to cross the road. Their high-visibility design helps alert drivers and cyclists to watch for people crossing. B. HOW THE DATASET IS CREATED Locations of continental crosswalks collected at the intersection level. Pre-2015 data was collected in the summer of 2019 as a one-time effort to locate every intersection with continental crosswalks on the city's High Injury Network. Crosswalks painted post-2015 are collected as part of Vision Zero data reporting. "Shops reports" are used as the data source. Shops reports include data citywide. Crosswalks marked "UNDETERMINED" in the "CONTINENTAL" field may or may not have continental crosswalks and require additional scrutiny. These two data sources were joined with an intersection nodes layer to create the feature class. The dataset is made available by SFMTA via their ArcGIS server/ feature server. C. UPDATE PROCESS The dataset is updated by MTA quarterly and published to the Open Data Portal automatically. D. HOW TO USE THIS DATASET This dataset includes: (1) all continental crosswalks citywide that were installed after 1/1/2015, and (2) all continental crosswalks that were installed before 12/31/2014 on the High Injury Network. It does not include continental crosswalks off the High Injury Network that were painted before 2015.

This resource describes the zoning associated with a Cyngetic Interest Grouping (GIC) in the department of Orne. The “Groupement d’Intéêt Cynégétique” does not cover any particular legal regime. They represent a group of people who have grouped together to carry out game management actions in a given geographical area. The establishment of a Cynegetic Interest Grouping (GIC) is due solely to the will of the holders of hunting rights (associations, individuals, etc.) to coordinate actions in favour of a species, either reintroduced or in a precarious situation and whose staff must be restored in order to allow for future levies. Third parties can integrate these ICMs, such as the FDC (Departmental Hunters Federation) which provide interesting technical or administrative support. These good management practices are in turn beneficial to other species. This approach also makes it possible to associate other users of the territory with the practice of hunting, as was the case with the GIC de la Sainte-Victoire (Bouches-du-Rhône) where the afluence of tourists requires an appropriate management of game.

Essex EEG Movie Memory Dataset

Authors: Ana Matran-Fernandez and Sebastian Halder

Description

This dataset contains raw electroencephalography (EEG) signals recorded from 27 participants while watching 10-second long clips extracted from movies that they had previously watched. For each clip, participants were asked whether they recognised the movie it belonged to, and if so, whether they remembered having watched it previously or not. If a participant reported recognising or remembering a clip, it was shown a second time to capture (via a mouse click) time annotations of the instants that prompted this recognition.

EEG

EEG data were acquired with a BioSemi ActiveTwo system with 64 electrodes positioned according to the international 10-20 system. The sampling rate was 2048 Hz.

Stimuli

The clips used in the study were originally annotated in terms of their memorability by Cohendet et al (see References). This dataset can be requested from the authors.

Example code

We have prepared an example script to demonstrate how to load the EEG data into Python using MNE and MNE-BIDS packages. This script is located in the 'code' directory.

References

Romain Cohendet, Karthik Yadati, Ngoc Q. K. Duong, and Claire-Hélène Demarty. 2018. Annotating, Understanding, and Predicting Long-term Video Memorability. In Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval (ICMR '18). Association for Computing Machinery, New York, NY, USA, 178–186. https://doi.org/10.1145/3206025.3206056

References

Appelhoff, S., Sanderson, M., Brooks, T., Vliet, M., Quentin, R., Holdgraf, C., Chaumon, M., Mikulan, E., Tavabi, K., Höchenberger, R., Welke, D., Brunner, C., Rockhill, A., Larson, E., Gramfort, A. and Jas, M. (2019). MNE-BIDS: Organizing electrophysiological data into the BIDS format and facilitating their analysis. Journal of Open Source Software 4: (1896).https://doi.org/10.21105/joss.01896

Pernet, C. R., Appelhoff, S., Gorgolewski, K. J., Flandin, G., Phillips, C., Delorme, A., Oostenveld, R. (2019). EEG-BIDS, an extension to the brain imaging data structure for electroencephalography. Scientific Data, 6, 103.https://doi.org/10.1038/s41597-019-0104-8

Synchronized movement is a ubiquitous feature of dance and music performance. Much research into the evolutionary origins of these cultural practices has focused on why humans perform rather than watch or listen to dance and music. In this study, we show that movement synchrony among a group of performers predicts the aesthetic appreciation of live dance performances. We developed a choreography that continuously manipulated group synchronization using a defined movement vocabulary based on arm swinging, walking and running. The choreography was performed live to four audiences, as we continuously tracked the performers’ movements, and the spectators’ affective responses. We computed dynamic synchrony among performers using cross recurrence analysis of data from wrist accelerometers, and implicit measures of arousal from spectators’ heart rates. Additionally, a subset of spectators provided continuous ratings of enjoyment and perceived synchrony using tablet computers. Granger causality analyses demonstrate predictive relationships between synchrony, enjoyment ratings and spectator arousal, if audiences form a collectively consistent positive or negative aesthetic evaluation. Controlling for the influence of overall movement acceleration and visual change, we show that dance communicates group coordination via coupled movement dynamics among a group of performers. Our findings are in line with an evolutionary function of dance–and perhaps all performing arts–in transmitting social signals between groups of people. Human movement is the common denominator of dance, music and theatre. Acknowledging the time-sensitive and immediate nature of the performer-spectator relationship, our study makes a significant step towards an aesthetics of joint actions in the performing arts.

This dataset contains time-series for performer and spectator variables for all four performances.

Across all cultures, people dance. Yet, little is known about what function dance and the performing arts fulfill in society, or why TV shows such as "Strictly come dancing" are so popular. We propose that the appeal of dancing and watching dance partly lies in promoting and communicating successful cooperation between people. Research in social psychology has shown that when two people meet, they become more like each other. They imitate each others' accent, speech rate and syntax; they look at the same things and use the same words; they adopt similar postures, gesture alike and gently sway together. This behavioural coordination studied in social psychology seems to produce feelings of liking and affiliation between pairs of people. Similarly, when small groups of people interact, and move together, they also feel closer to each other and are more likely to cooperate. We will use dance as a means to study how moving together is linked to liking each other. Similarly, observing other people move together may produce aesthetic pleasure because it showcases successful social interactions. Our research aims to provide novel insights into the role that dance and the performing arts fulfill in modern society. In a set of experiments, we will test this hypothesis by inviting groups of people (non-dancers) to participate in "dance workshop" experiments that teach moving in synchrony. Rather than asking participants to just "do the same", we will work with professional dancers and choreographers to apply principles from dance and choreography to examine different ways of moving together. Following these workshops, we will assess cooperation, sympathy and liking between participants of the workshop and members of the audience. Performers and audience members will be equipped with small motion sensors and we will also record their electrical brain activity. This will allow us to link different ways of moving in synchrony (or asynchrony) to brain activity, cooperation and liking. In a follow-up functional neuroimaging experiment we will link aesthetic pleasure derived from observing collective human movement to specific brain mechanisms. We will also explore clinical applications of our research project. For example the perception of human movement is impaired in patients with autism. Training to move in synchrony might help to improve such deficits in recognizing other people's actions because it requires to carefully monitor how movements are performed. Similarly, increasing awareness of an action by moving in synchrony may boost memory for an already performed action. In obsessive-compulsive disorder compulsive checking involves a vicious circle in which more checking paradoxically leads to less confidence in memory and impairs attention. Increasing action awareness through "over performing" obsessive actions or moving in synchrony with others could thus reduce obsessive behaviours such as washing by making it easier to remember that the action was performed already. In summary our research project combines expertise in dance, social...

The first Media Barometer was conducted in 1979 and since then the survey has been carried out annually. The purpose is to explore how the Swedish population is using different media during an average day. In 2000 the respondents were asked about their usage of different media equipment such as television, text-television, radio, video recorder, CD-player/record player, and tape recorder, the day before the interview. Respondents using any of these equipments were asked about time spent using the equipment. For equipment not used the day before the respondents were asked when it was last used. Television watchers were asked about which channels they had watched. Video watchers were asked if they watched a recorded program, a rented movie or a movie they had bought. All respondents were asked if the had been reading any of the following the day before: morning paper, evening paper, weekly magazine, comics or any other magazine, or book. If so, they were asked how many and for how long period. Readers of morning and evening papers were asked if they read the printed version or the internet version. Book readers were also asked what kind of literature they were reading, and paper and magazine readers were asked about what kind of paper/magazine they read. Those respondents who answered that they did not read any paper, magazine or book the day before were asked when they last did so. The survey also includes detailed information on at what time the day before the respondent spent time reading morning paper, evening paper, listening to the radio or watching television. There is also more detailed information on which news magazines the respondent watched. The respondents also had to state what kind of advertisments they had been reading/looking to during the last week. A number of questions dealt with computer usage at home and at work respectively, and the usage of Internet at home and at work. Background variables includes information on age, gender, education, occupation, and household composition.

Purpose:

Describe the trends and changes in people's use of mass media.

DESCRIPTION

The dataset provided contains movie reviews given by Amazon customers. Reviews were given between May 1996 and July 2014.

Data Dictionary UserID – 4848 customers who provided a rating for each movie Movie 1 to Movie 206 – 206 movies for which ratings are provided by 4848 distinct users

Data Considerations - All the users have not watched all the movies and therefore, all movies are not rated. These missing values are represented by NA. - Ratings are on a scale of -1 to 10 where -1 is the least rating and 10 is the best.

Analysis Task - Exploratory Data Analysis:

Which movies have maximum views/ratings? What is the average rating for each movie? Define the top 5 movies with the maximum ratings. Define the top 5 movies with the least audience. - Recommendation Model: Some of the movies hadn’t been watched and therefore, are not rated by the users. Netflix would like to take this as an opportunity and build a machine learning recommendation algorithm which provides the ratings for each of the users.

Divide the data into training and test data Build a recommendation model on training data Make predictions on the test data

Who are the top YouTubers in Hong Kong?

According to the number of subscribers, these are the top 300 channels on YouTube.

HK's 300 most popular YouTube channels, together with the channel's name and the number of subscribers, are included in this dataset.

Interested in what Hong Kong People is watching?

Zeeshan-ul-hassan Usmani is to be credited as the source of inspiration.

IoTeX is a decentralized crypto system, a new generation of blockchain platform for the development of the Internet of things (IoT). The project team is sure that the users do not have such an application that would motivate to implement the technology of the Internet of things in life. And while this will not be created, people will not have the desire to spend money and time on IoT. The developers of IoTeX decided to implement not the application itself, but the platform for creation. It is through the platform that innovative steps in the space of the Internet of things will be encouraged. Learn more... This dataset is one of many crypto datasets that are available within the Google Cloud Public Datasets . As with other Google Cloud public datasets, you can query this dataset for free, up to 1TB/month of free processing, every month. Watch this short video to learn how to get started with the public datasets. Want to know how the data from these blockchains were brought into BigQuery, and learn how to analyze the data? Scopri di più

This dataset is for object detection task of the blue-ringed octopus (one of the most venomous animals in the world). With this dataset, I hope people can become more familiar with the blue ringed octopus and be aware of its dangers

I collected the images and labeled them myself (for a competition). I have less experience in collecting datasets, so I cannot guarantee the quality of this dataset. I trained a yolov7 object detection model with this data and got a mean average precision of 0.987 (with an IoU threshold of 0.5) .

About Datasets:

• Consists of 316 images with each label in the Pascal Voc format
• No pre-processing or image augmentation
• Not separated into train and test
• To use as an image classification, just delete its xml/label file
• Made in August-September 2022

How I Collect the Data:

I didn't go into the field to take these images, instead I took them from Google, some also from screenshots of some Youtube videos: - https://www.youtube.com/watch?v=MBHjo6UaHzk&t=62s - https://www.youtube.com/watch?v=c4BoYORmgSM - https://www.youtube.com/watch?v=DSdq8XFQdKo - https://www.youtube.com/watch?v=64mY1klkf4I&t=215s - https://www.youtube.com/watch?v=C0DOusbGWbU - https://www.youtube.com/watch?v=mTnmw5o4vRI - https://www.youtube.com/watch?v=bejKAB2Eazw&t=317s - https://www.youtube.com/watch?v=emisZUHJAEA - https://www.youtube.com/watch?v=6b_UYwyWI6E - https://www.youtube.com/watch?v=vVamzP52qwA - https://www.youtube.com/watch?v=3Bt1LvpZ1Oo

I also played around with the ai text to image generator to create multiple images and manually choose which one is acceptable (r_blue_ringed_octopus_100 - r_blue_ringed_octopus_110 , you can remove it if you want). After collecting the images, I do the labeling my self.

2008 European Adult Social Contact and Frequency Networks

Examining Dynamics of Contact in Different Environments

By [source]

About this dataset

This dataset provides valuable insights into the social contact patterns and frequency of contacts between adults in Europe in 2008. It includes a host of features such as age estimates, gender, home life, work, school, transport and leisure activities. The dataset also covers an array of contact frequencies such as regular meetings with family or friends, physical contact with people outside the household and overall duration spent together. Each data point provides an all-encompassing view of social interactions in adult networks between 2008 - all contributing to our understanding of human behaviour across different European contexts!

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How to use the dataset

This dataset aims to measure social contact and the frequency of contact between adults in Europe in 2008. Through this dataset, you can observe how different factors like age, gender, and occupation can predict social interaction. The columns provided in the dataset helps us to analyze how these factors affect the duration and frequency of contacts.

In order to use this dataset effectively, we need to pay close attention to all of the available variables. For example, looking at cnt_age_exact gives us an exact age for each contact person in a particular network or community. Similarly, cnt_age_est_min provides an estimated minimum age while cnt_age_est_max estimates a maximum age range for these contacts. Additionally, both phys_contact and frequency multi tell us about physical contacts that were established with other people as well as their relative durations (duration multi).

Finally, observing the values for cnt home/work/school help uncover how many contacts were made at each associated location on average; furthermore it is possible to see what kind of settings tend to encourage more person-to-person interactions by measuring the number of contacts there are at each site or domain (i.e.: cnt leisure). This data set then takes these observations one step further by delving into other locations such as transport which could potentially hold more meaningful insight into communication rates between groups within society! Thus it is possible not only quantify communication rate but also make connections that may have otherwise been missed without such an expansive source

Research Ideas

• Using the exact and estimated age ranges, gender, contact frequency and duration data, this dataset can be used to analyze differences in social contact patterns between different age groups and genders in Europe.
• The contact data could also be used to study the prevalence of physical contacts between adults in various locations (e.g. home environments, schools or workplaces) as well as to model transmission patterns of infectious diseases through these social networks.
• Additionally, the cnt_home and cnt_work columns could be studied separately to analyze the effect of working from home on people’s social contacts with other family members or peers at work respectively

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: 2008_Mossong_POLYMOD_contact_common.csv | Column name | Description | |:--------------------|:----------------------------------------------------------------------------| | cnt_age_exact | The exact age of the person contacted. (Integer) | | cnt_age_est_min | The estimated lower end age of the person contacted. (Integer) | | cnt_age_est_max | The estimated upper end age of the person contacted. (Integer) | | cnt_gender | The gender of the person contacted. (String) | | cnt_home | The number of contacts made at home. (Integer) | | cnt_work | The number of contacts made at work. (Integer) | | cnt_school | The number of contacts made at school. (Integer) ...