Dataset Card for ap-stats

This dataset has been created with distilabel.

  Dataset Summary

This dataset contains a pipeline.yaml which can be used to reproduce the pipeline that generated it in distilabel using the distilabel CLI: distilabel pipeline run --config "https://huggingface.co/datasets/snsslss/ap-stats/raw/main/pipeline.yaml"

or explore the configuration: distilabel pipeline info --config… See the full description on the dataset page: https://huggingface.co/datasets/snsslss/ap-stats.

Dataset

This dataset was created by David

Released under Other (specified in description)

Contents

Context

I am an aspiring Freshman in a public high school. I am taking a bunch of AP Exams at the end of my sophomore year and this data will help me what to expect from the exams

Content

The first column contains the name of the exams, the second one is the points necessary to get a 5, the third one is the total points, and lastly what percentage points one needs to get a five.

Acknowledgements

Thanks a lot to albert.io to help me get this data.

Inspiration

College Board has inspired me to create this dataset.

Dataset

This dataset was created by Michael Lomuscio

Contents

Research Papers Collection

100 recent research papers from arXiv across 5 AI/data science topics.

  Dataset Overview

Total Papers: 100 (20 per topic) Source: arXiv API Format: PDF files Topics: Generative AI, Machine Learning, Statistics, Analytics, Computer Vision

  Topics & Queries

Generative_AI: cat:cs.LG AND (generative OR diffusion OR GAN OR transformer OR GPT) Machine_Learning: cat:cs.LG OR cat:stat.ML Statistics: cat:stat.TH OR cat:stat.ME OR cat:stat.AP… See the full description on the dataset page: https://huggingface.co/datasets/mahimaarora025/research_papers.

Dataset

This dataset was created by LINTU OOMMEN NIT AP

Contents

Context

This dataset was created by an AP Statistics class. The purpose was to learn about survey methodology and analysis using R.

The participants are all AP Statistics students from schools within the US.

The purpose of their study was to learn more about the habits and beliefs that young adults have regarding face masks.

Content

Below is a description of each variable. - Timestamp: The time and date that the respondent completed the survey. - Boarding: Whether the respondent was a day student or a boarding student. - Age: The age of the respondent. - Gender: The reported gender of the respondent. - ResidentialElder: Response to the question "Do you live with someone over the age of 65 years old?" - InteractedElder: Response to the question "Have you interacted with anyone over the age of 65 in the last month?" - Restaurant: The number of times the respondent reported eating at a restaurant within the last week. - PreventSpread: Response to the question "Do you believe that masks are effective at preventing the spread of the coronavirus?" - Reason: Response to the question "What is your primary reason for wearing a mask?" - Public: Response to the question "Do you wear a mask in public places when it is not required?"

Termly number and rate of suspensions and permanent exclusions and those pupils receiving one or more suspension in state-funded alternative provision settings (includes pupil referral units, AP free schools and AP academies).

Context

This dataset was created by an AP Statistics class. The purpose was to learn about survey methodology and analysis using R.

The participants are all AP Statistics students from schools within the US.

The purpose of their study was to learn more about the self-reported knowledge of current events among young adults.

Content

Below is a description of each variable. - Timestamp: The date and time that the response was recorded. - Gender: Self-reported gender of the respondent. - Age: Self-reported age of the respondent. - USCitizen: Response to the question "Are you a US citizen who has resided full time in the US for the last 5 years?" - Boarding: Self-reported residential status of the respondent. - NewsSource: Response to the question "What's your primary news source?" - HKProtests: Response to the question "How informed are you regarding the Hong Kong protests?" 0 = not informed and 5 = very informed. - BTaylor: Response to the question "How informed are you regarding the Breonna Taylor case?" 0 = not informed and 5 = very informed. - Explosion: Response to the question "How informed are you about the explosion that occurred in Beirut, Lebanon?" 0 = not informed and 5 = very informed. - SCNomination: Response to the question "How informed are you on the ongoing debate over the Supreme Court nomination in the US?" 0 = not informed and 5 = very informed.

Number of pupils in state-funded nursery, primary, secondary and special schools, non-maintained special schools, AP schools and independent schools by SEN provision, type of need and school type.

🛑 Note: please do not claim diagnostic performance of a model without a clinical study! This is not a kaggle competition dataset.

Disclaimer

This dataset is credited to ieee8023. Use this dataset for research purpose.

COVID-19 image data collection

We are building a database of COVID-19 cases with chest X-ray or CT images. We are looking for COVID-19 cases as well as MERS, SARS, and ARDS.

All images and data will be released publicly in this GitHub repo. Currently we are building the database with images from publications as they are images that are already available.

View current images and metadata

Current stats of PA, AP, and AP Supine views. Labels 0=No or 1=Yes. Data loader is here ``` COVID19_Dataset num_samples=136 views=['PA'] {'ARDS': {0.0: 131, 1.0: 5}, 'Bacterial Pneumonia': {0.0: 119, 1.0: 17}, 'COVID-19': {0.0: 46, 1.0: 90}, 'Chlamydophila': {0.0: 135, 1.0: 1}, 'Fungal Pneumonia': {0.0: 123, 1.0: 13}, 'Klebsiella': {0.0: 135, 1.0: 1}, 'Legionella': {0.0: 134, 1.0: 2}, 'MERS': {0.0: 136}, 'No Finding': {0.0: 135, 1.0: 1}, 'Pneumocystis': {0.0: 123, 1.0: 13}, 'Pneumonia': {0.0: 1, 1.0: 135}, 'SARS': {0.0: 125, 1.0: 11}, 'Streptococcus': {0.0: 123, 1.0: 13}, 'Viral Pneumonia': {0.0: 35, 1.0: 101}}

COVID19_Dataset num_samples=28 views=['AP', 'AP Supine'] {'ARDS': {0.0: 28}, 'Bacterial Pneumonia': {0.0: 28}, 'COVID-19': {0.0: 4, 1.0: 24}, 'Chlamydophila': {0.0: 28}, 'Fungal Pneumonia': {0.0: 28}, 'Klebsiella': {0.0: 28}, 'Legionella': {0.0: 28}, 'MERS': {0.0: 28}, 'No Finding': {0.0: 28}, 'Pneumocystis': {0.0: 28}, 'Pneumonia': {0.0: 4, 1.0: 24}, 'SARS': {0.0: 28}, 'Streptococcus': {0.0: 28}, 'Viral Pneumonia': {0.0: 4, 1.0: 24}} ```

Contribute

• We can extract images from publications. Help identify publications which are not already included using a GitHub issue (DOIs we have are listed in the metadata file). There is a searchable database of COVID-19 papers here, and a non-searchable one (requires download) here.

• Submit data to https://radiopedia.org/ or https://www.sirm.org/category/senza-categoria/covid-19/ (we can scrape the data from them)

• Provide bounding box/masks for the detection of problematic regions in images already collected.

• See CONTRIBUTING.md for more information on the metadata schema.

Formats: For chest X-ray dcm, jpg, or png are preferred. For CT nifti (in gzip format) is preferred but also dcms. Please contact with any questions.

Background

The 2019 novel coronavirus (COVID-19) presents several unique features. While the diagnosis is confirmed using polymerase chain reaction (PCR), infected patients with pneumonia may present on chest X-ray and computed tomography (CT) images with a pattern that is only moderately characteristic for the human eye Ng, 2020. COVID-19’s rate of transmission depends on our capacity to reliably identify infected patients with a low rate of false negatives. In addition, a low rate of false positives is required to avoid further increasing the burden on the healthcare system by unnecessarily exposing patients to quarantine if that is not required. Along with proper infection control, it is evident that timely detection of the disease would enable the implementation of all the supportive care required by patients affected by COVID-19.

In late January, a Chinese team published a paper detailing the clinical and paraclinical features of COVID-19. They reported that patients present abnormalities in chest CT images with most having bilateral involvement Huang 2020. Bilateral multiple lobular and subsegmental areas of consolidation constitute the typical findings in chest CT images of intensive care unit (ICU) patients on admission Huang 2020. In comparison, non-ICU patients show bilateral ground-glass opacity and subsegmental areas of consolidation in their chest CT images Huang 2020. In these patients, later chest CT images display bilateral ground-glass opacity with resolved consolidation [Huang 2020](https://www.thelancet.com/journals/la...

Purpose

This data was collected as part of an AP Stats class project. The class was studying the perception of school rules among students at a residential school.

Descriptions of Variables

• Handbook: How well do you know the school handbook?
• Gender: What is your Gender?
• Grade: What Grade are you in?
• Years: How long have you been at Rabun Gap?
• Boarding: Are you a Boarding or a Day Student?
• BDress: How often do you break these school rules? [Dress Code]
• BPhone: How often do you break these school rules? [Phone in Dinning Hall]
• BSkipClass: How often do you break these school rules? [Skipping Class]
• BSkipConvo: How often do you break these school rules? [Skipping Convocation/ Advisory/ Chapel]
• BCheat: How often do you break these school rules? [Cheating]
• BPlag: How often do you break these school rules? [Plagarism]
• BDorm: How often do you break these school rules? [Being in the wrong dorm]
• BCandy: How often do you break these school rules? [Taking more than one piece of candy]
• CDress: How likely do you think you are to get caught violating these rules? [Dress Code]
• CPhone: How likely do you think you are to get caught violating these rules? [Phone in Dinning Hall]
• CSkipClass: How likely do you think you are to get caught violating these rules? [Skipping Class]
• CSkipConvo: How likely do you think you are to get caught violating these rules? [Skipping Convocation/ Advisory/ Chapel]
• CCheat: How likely do you think you are to get caught violating these rules? [Cheating]
• CPlag: How likely do you think you are to get caught violating these rules? [Plagarism]
• CDorm: How likely do you think you are to get caught violating these rules? [Being in the wrong dorm]
• CCandy: How likely do you think you are to get caught violating these rules? [Taking more than one piece of candy]
• SDress: How severe do you feel the punishments for these violations are? [Dress Code]
• SPhone: How severe do you feel the punishments for these violations are? [Phone in Dinning Hall]
• SSkipClass: How severe do you feel the punishments for these violations are? [Skipping Class]
• SSkipConvo: How severe do you feel the punishments for these violations are? [Skipping Convocation/ Advisory/ Chapel]
• SCheat: How severe do you feel the punishments for these violations are? [Cheating]
• SPlag: How severe do you feel the punishments for these violations are? [Plagarism]
• SDorm: How severe do you feel the punishments for these violations are? [Being in the wrong dorm]
• SCandy: How severe do you feel the punishments for these violations are? [Taking more than one piece of candy]
• Demerits: How often do you receive demerits?
• Fear: Does the fear of demerit hall keep you from getting demerits?

Dataset includes date and time, scoring, rushing, passing, turnovers, penalties, point spread, weather, and more. All games since September 1962 are included.

• Scoring by quarter, first down, third down, fourth down, and opponent penalty data begins in the 2004 season.
• Nebraska penalty data is incomplete before 1972-09-16 (Texas A&M).
• Time of possession data begins in 2012 and is complete from 2013-forward.
• Point spread data is mostly absent prior to the 1978 season. It's listed for most games from from 1978-forward.
• Betting total data is listed for most games from 2006-forward.
• Weather data is taken from the (now defunct) DarkSky API and Weather Underground. I've found temperature and humidity data to be fairly reliable but wind data is less so.

Column Labels:

• date – Date the game was played.
• time – Kickoff time (CT).
• season – Season the game was played.
• opp – Nebraska's opponent.
• site – Location the game was played (home, away, neutral-home, or neutral-away).
• conference – Whether it was a conference opponent (TRUE or FALSE).
• opp_rank – Opponent's rank going into the game. CFP ranking when available, otherwise AP ranking.
• ne_rank – Nebraska's rank going into the game. CFP ranking when available, otherwise AP ranking.
• result – Win (W), Loss (L), or Tie (T).
• opp_score – Opponent's score.
• ne_score – Nebraska's score.
• opp_score_q1 – Opponent's first quarter points.
• opp_score_q2 – Opponent's second quarter points.
• opp_score_q3 – Opponent's third quarter points.
• opp_score_q4 – Opponent's fourth quarter points.
• opp_score_ot – Opponent's overtime points.
• ne_score_q1 – Nebraska's first quarter points.
• ne_score_q2 – Nebraska's second quarter points.
• ne_score_q3 – Nebraska's third quarter points.
• ne_score_q4 – Nebraska's fourth quarter points.
• ne_score_ot – Nebraska's overtime points.
• opp_rush_att – Opponent's rushing attempts.
• opp_rush_yards – Opponent's rushing yards.
• ne_rush_att – Nebraska's rushing attempts.
• ne_rush_yards – Nebraska's rushing yards.
• opp_pass_comp – Opponent's passing completions.
• opp_pass_att – Opponent's passing attempts.
• opp_pass_yards – Opponent's passing yards.
• ne_pass_comp – Nebraska's passing completions.
• ne_pass_att – Nebraska's passing attempts.
• ne_pass_yards – Nebraska's passing yards.
• opp_first_downs – Opponent's first downs.
• ne_first_downs – Nebraska's first downs.
• opp_third_down_comp – Opponent's successful third down conversions.
• opp_third_down_att – Opponent's third down attempts.
• ne_third_down_comp – Nebraska's successful third down conversions.
• ne_third_down_att – Nebraska's third down attempts.
• opp_fourth_down_comp – Opponent's successful fourth down conversions.
• opp_fourth_down_att – Opponent's fourth down attempts.
• ne_fourth_down_comp – Nebraska's successful fourth down conversions.
• ne_fourth_down_att – Nebraska's fourth down attempts.
• opp_int – Opponent's interceptions thrown.
• opp_fum – Opponent's fumbles lost.
• ne_int – Nebraska's interceptions thrown.
• ne_fum – Nebraska's fumbles lost.
• opp_pen_num – Opponent's number of penalties.
• opp_pen_yards – Opponent's penalty yards.
• ne_pen_num – Nebraska's number of penalties.
• ne_pen_yards – Nebraska's penalty yards.
• opp_possession – Opponent's time of possession (MM:SS)
• ne_possession – Nebraska's time of possession (MM:SS)
• spread – Point spread. A negative value means Nebraska was favored.
• total – Betting total, i.e. Over/Under.
• temp – Temperature at kickoff (Fahrenheit).
• humidity – Relative humidity at kickoff (0.0 to 1.0).
• wind_speed – Wind speed at kickoff (mph)
• wind_bearing – Direction from which the wind was blowing at kickoff. Measured in degrees. North is 0 and values increase clockwise.