Dataset

This dataset was created by Alanoud Awaji

Contents

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

Due to the closure of school sites during the coronavirus pandemic, the Active Lives Children and Young People survey was adapted to allow at-home completion. This approach was retained into the academic year 2022-23 to help maximise response numbers. The at-home completion approach was actively offered for secondary school pupils, and allowed but not encouraged for primary pupils.

The adaptions involved minor questionnaire changes (e.g., to ensure the wording was appropriate for those not attending school and enabling completion at home) and communication changes. For further details on the survey changes, please see the accompanying User Guide document. Academic years 2020-21, 2021-22 and 2022-23 saw a more even split of responses by term across the year, compared to 2019-20, which had a reduced proportion of summer term responses due to the disruption caused by Covid-19.

The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets have been provided:

1) Main dataset: this file includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child’s activity levels; they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_gross - Csplan files are available for SPSS users who can utilise them).

2) Year 1-2 dataset: This file includes responses directly from children in school years 1-2, providing their attitudinal responses (e.g., whether they like playing sport and find it easy). Analysis can also be carried out into feelings towards swimming, enjoyment of being active, happiness, etc. Weighting is required when using this dataset (wt_gross / wt_gross - Csplan files are available for SPSS users who can utilise them).

3) Teacher dataset: This file includes responses from the teachers at schools selected for the survey. Analysis can be carried out to determine school facilities available, the length of PE lessons, whether swimming lessons are offered, etc. Since December 2023, Sport England has provided weighting for the teacher data (‘wt_teacher’ weighting variable).

For further information, please read the supporting documentation before using the datasets.

Abstract copyright UK Data Service and data collection copyright owner.

The Active Lives Children and Young People Survey, which was established in September 2017, provides a world-leading approach to gathering data on how children engage with sport and physical activity. This school-based survey is the first and largest established physical activity survey with children and young people in England. It gives anyone working with children aged 5-16 key insight to help understand children's attitudes and behaviours around sport and physical activity. The results will shape and influence local decision-making as well as inform government policy on the PE and Sport Premium, Childhood Obesity Plan and other cross-departmental programmes. More general information about the study can be found on the Sport England Active Lives Survey webpage and the Active Lives Online website, including reports and data tables.

The Active Lives Children and Young People Survey, 2017-2018 commenced during school academic year 2017 / 2018. It ran from autumn term 2017 to summer term 2018 and excludes school holidays. The survey identifies how participation varies across different activities and sports, by regions of England, between school types and terms, and between different demographic groups in the population. The survey measures levels of activity (active, fairly active and less active), attitudes towards sport and physical activity, swimming capability, the proportion of children and young people that volunteer in sport, sports spectating, and wellbeing measures such as happiness and life satisfaction. The questionnaire was designed to enable analysis of the findings by a broad range of variables, such as gender, family affluence and school year.

The following datasets are available:

1) Main dataset includes responses from children and young people from school years 3 to 11, as well as responses from parents of children in years 1-2. The parents of children in years 1-2 provide behavioural answers about their child's activity levels, they do not provide attitudinal information. Using this main dataset, full analyses can be carried out into sports and physical activity participation, levels of activity, volunteering (years 5 to 11), etc. Weighting is required when using this dataset (wt_gross / wt_set1.csplan).

2) Year 1-2 pupil dataset includes responses from children in school years 1-2 directly, providing their attitudinal responses (e.g. whether they like playing sport and find it easy). Analysis can be carried out into feelings towards swimming, enjoyment for being active, happiness etc. Weighting is required when using this dataset (wt_gross / wt_set1.csplan).

3) Teacher dataset includes responses from the teachers at schools selected for the survey. Analysis can be carried out into school facilities available, length of PE lessons, whether swimming lessons are offered, etc. Weighting was formerly not available, however, as Sport England have started to publish the Teacher data, from December 2023 we decide to apply weighting to the data. The Teacher dataset now includes weighting by applying the ‘wt_teacher’ weighting variable.

For further information about the variables available for analysis, and the relevant school years asked survey questions, please see the supporting documentation. Please read the documentation before using the datasets.

Latest edition information

For the second edition (January 2024), the Teacher dataset now includes a weighting variable (‘wt_teacher’). Previously, weighting was not available for these data.

Main Topics:

Topics covered in the Active Lives Children and Young People Survey include:

• Sport and physical activity participation
• Well-being
• Health

Athletic Skill Demand Ranking for 60 Sports

An ESPN Expert-Rated Comparison

By Corey Hermanson [source]

About this dataset

Welcome to the Toughest Sport dataset! Here we are bringing you a complete breakdown of 60 sports and their demands for 10 distinct skills that make up athletic capabilities. We collected data from 8 expert panelists and asked them to rate each sport on a scale of 1-10 for every skill ranging from Strength and Speed to Nerve, Hand-eye Coordination, and more. By totalling up the opinions of our experts, we have created an overall degree of difficulty score for each sport in the dataset between 1-100. If you're curious as to which sports require what skill sets, or if you're wondering which is the toughest sport across all ten skillsets - this is your place! Get ready to explore how athleticism guides our understanding of what makes 'Toughest Sport'!

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How to use the dataset

This dataset contains the rankings of 60 sports across 10 skills, as well as the total score and rank of each sport. It is intended to provide an overview of the relative athleticism required across a variety of different competitive sports and can be used to identify which physical attributes are most important in each sport.

To use this dataset, you will need to understand what the different sports measured by this data set represent. Sports like skiing, boxing, wrestling, and football all require very different mental and physical abilities in order to compete successfully. For example, Alpine Skiing will require greater skill related to speed, agility and power than Cross Country running does; while Swimming may rely more heavily on durability than Football does. Once familiar with the included sports then it becomes easier to utilize the scores assigned for each skill in order identify which skills might benefit a particular athlete most when considering a new athletic challenge.

The dataset also provides useful information about how difficult it might be for any one individual athlete or competitor if they were looking at taking up one particular sport from scratch versus another with similar momentum when compared in terms of its overall scores across 10 areas relating specifically as they relate athletics. This could help indicate whether that athlete has a better chance or worse chance when competing against others who may have trained in or specialised within their chosen field longer or shorter than themselves respectively before stepping onto this same playing field together; simply by comparing total Athletics Skill Demand (ASD) Numbers over between both their desired sporting choices (the higher number representing higher difficulty).

Research Ideas

• Identifying the most requested athletic traits by sport. By analyzing the data, one can uncover patterns within certain sports that require certain skills or abilities more than others.
• Determining which sports offer the best opportunity for balance and development of all skillsets by athletes. Specifically, this dataset could be used to identify which sports encourage holistic athletic development and what combination of skill demands those particular sports have in common.
• Developing an optimal training program for athletes interested in perfecting their craft in a particular sport by geotargeting regions with more advanced competition based on their specific skill needs relative to that region’s average competition level as demonstrated by this dataset

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: toughestsport.csv | Column name | Description | |:--------------|:---------------------------------------------------------| | SPORT | Name of the sport. (String) | | END | Endurance score for the sport. (Integer) | | STR | Strength score for the sport. (Integer) | | PWR | Power score for the sport. (Integer) | | SPD | Speed score for the sport. (Integer) | | AGI | Agility score for the sport. (Integer) | | FLX | Flexibility score for the sport. (Integer) | | NER | Nerve score for the sport. (Integer) | | DUR | Durability score ...

Context

This dataset was collected by me, along with my friends during my college days. The dataset mostly contains data from my friends and family members. This dataset has the survey data for the type of fitness practices that people follow.

Acknowledgements

This dataset wouldn't be here without the help of my friends. So, thanks to them!

What is in the dataset

1. Name of the person attending the survey
2. Gender of the person attending the survey
3. Age of the person attending the survey
4. How important is an exercise to you on the scale of 1 to 5
5. How do you describe your current level of fitness? - Perfect, Very good, Good, Average, Unfit
6. How often do you exercise? - Every day, 1 to 2 times a week, 2 to 3 times a week, 3 to 4 times a week, 5 to 6 times a week, never
7. What barriers, if any, prevent you from exercising more regularly? (Select all that applies) - I don't have enough time, I can't stay motivated, ill become too tired, I have an injury, I don't really enjoy exercising, I exercise regularly with no barriers
8. What forms of exercise do you currently participate in? (Select all that applies) - Walking or jogging, gym, swimming, yoga, Zumba dance, lifting weights, team sport, I don't really exercise
9. Do you exercise _? - Alone, With a friend, With a group, Within a class environment, I don't really exercise
10. What time of the day do you prefer to exercise? - Early morning, afternoon, evening
11. How long do you spend exercising per day? - 30 min, 1 hour, 2 hours, 3 hours and above, I don't really exercise
12. Would you say, you eat a healthy balanced diet? - Yes, No, Not always
13. What prevents you from eating a healthy balanced diet, if any? (Select all that applies) - Lack of time, Cost, Ease of access to fast food, Temptation, and cravings, I have a balanced diet
14. How healthy do you consider yourself on a scale of 1 to 5?
15. Have you recommended your friends to follow a fitness routine? - Yes, No
16. Have you ever purchased fitness equipment? - Yes, No
17. What motivates you to exercise? (Select all that applies) - I want to be fit, I want to increase muscle mass and strength, I want to lose weight, I want to be flexible, I want to relieve stress, I want to achieve a sporting goal, I'm not really interested in exercising.

Swimming_stream1

## Overview

Swimming_stream1 is a dataset for object detection tasks - it contains People annotations for 1,000 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

Participation opportunities per inhabitant for 7-20 years in the municipality of LOK-assisted sports clubs engaged in swimming sports. Participants are the sum of the number of participants present in RF approved meetings for which the association has applied for LOK support (State Local Activity Support). Locomotive support is provided for activities for boys and girls aged 7-20. Support is also given in some cases to people older than 20 years, but this is excluded here. A meeting is a leader-led group activity geared towards the association’s sporting activities. Data is available according to gender breakdown.

ABSTRACT As people’s lives become better and better, more and more citizens are no longer satisfied with simple food-and-clothing problems, but gradually move towards the spiritual life they are yearning for. Among this, physical health is also an important part of it. So, at this stage, a lot of people will go swimming to exercise their body and mind. In this paper, the integration algorithm was used, and the self-learning ability of the integration algorithm was used. This algorithm was used to study the swimming posture model. This model can play an important role in the teaching of traditional swimming posture.

PUBG Data Description

In a PUBG game, up to 100 players start in each match (matchId). Players can be on teams (groupId) which get ranked at the end of the game (winPlacePerc) based on how many other teams are still alive when they are eliminated. In game, players can pick up different munitions, revive downed-but-not-out (knocked) teammates, drive vehicles, swim, run, shoot, and experience all of the consequences -- such as falling too far or running themselves over and eliminating themselves. You are provided with a large number of anonymized PUBG game stats, formatted so that each row contains one player's post-game stats. The data comes from matches of all types: solos, duos, squads, and custom; there is no guarantee of there being 100 players per match, nor at most 4 players per group.

File descriptions data.csv - 151MB

Data fields - DBNOs- Number of enemy players knocked. - assists- Number of enemy players this player damaged that were killed by teammates. - boosts - Number of boost items used. - damageDealt- Total damage dealt. Note: Self inflicted damage is subtracted. - headshotKills- Number of enemy players killed with headshots. - heals- Number of healing items used. - Id- Player’s Id - killPlace- Ranking in match of number of enemy players killed. - killPoints- Kills-based external ranking of players. (Think of this as an Elo ranking where only kills matter.) If there is a value other than -1 in rankPoints, then any 0 in killPoints should be treated as a “None”. - killStreaks- Max number of enemy players killed in a short amount of time. - kills - Number of enemy players killed. - longestKill - Longest distance between player and player killed at time of death. This may be misleading, as downing a player and driving away may lead to a large longestKill stat. - matchDuration - Duration of match in seconds. - matchId - ID to identify matches. There are no matches that are in both the training and testing set. - matchType - String identifying the game mode that the data comes from. The standard modes are “solo”, “duo”, “squad”, “solo-fpp”, “duo-fpp”, and “squad-fpp”; other modes are from events or custom matches. - rankPoints - Elo-like ranking of players. This ranking is inconsistent and is being deprecated in the API’s next version, so use with caution. Value of -1 takes the place of “None”. - revives - Number of times this player revived teammates. - rideDistance - Total distance traveled in vehicles measured in meters. - roadKills - Number of kills while in a vehicle. - swimDistance - Total distance traveled by swimming measured in meters. - teamKills - Number of times this player killed a teammate. - vehicleDestroys - Number of vehicles destroyed. - walkDistance - Total distance traveled on foot measured in meters. - weaponsAcquired - Number of weapons picked up. - winPoints - Win-based external ranking of players. (Think of this as an Elo ranking where only winning matters.) If there is a value other than -1 in rankPoints, then any 0 in winPoints should be treated as a “None”. - groupId - ID to identify a group within a match. If the same group of players plays in different matches, they will have a different groupId each time. - numGroups - Number of groups we have data for in the match. - maxPlace - Worst placement we have data for in the match. This may not match with numGroups, as sometimes the data skips over placements. - winPlacePerc - The target of prediction. This is a percentile winning placement, where 1 corresponds to 1st place, and 0 corresponds to last place in the - - match. It is calculated off of maxPlace, not numGroups, so it is possible to have missing chunks in a match.

REFERENCE: PUBG Finish Placement Prediction (Kernels Only)

This is a starcraft gaming data set. This gives traits about different professional players playing for a particular clan in different league. This data set can be a benchmark dataset which can be used to pool talented players and to answer different questions like:

Q. Determine what are the most important features that could help predict a player’s rank? Interpret your results for a general audience (coaching staff, pro players, etc)

Q. Your team’s Starcraft2 coaching staff loved your project! They think this is perfect for scouting rising stars. Using your discoveries from (3), create a function to find players who should be given a chance to become professionals. Explain why your set of players make sense.

Q. Hypothetically, if you were to move forward with creating a fully-fledged model to predict LeagueIndex, what model(s) would you consider and why? (Don’t actually implement anything!)

Routine reinspection of over 537 public pools and treated water aquatic facilities in the US State of Kentucky, totalling over 2,000 inspections per year. Inspections include spas (hot tubs), pools, wave pools, splash pads, theme park pools, and special purpose pools (such as dive pools) etc. Purpose of inspections is included in this data set.For more information on Louisville Metro’s aquatic inspection program and policies, see:https://louisvilleky.gov/government/health-wellness/swimming-poolsData will be updated weekly. Each week the data will be posted as a rolling five year table. That is the new data will be a set that includes inspections from the day of posting minus five years.Data Dictionary:Field Name-DefinitionInspectionID-Unique Identifier for the inspectionFacility ID-Unique Identifier for the FacilityFacility Name-Name of the FacilityFacility Address-Location of the FacilityFacility Address 2-Location of the FacilityFacility City-Location of the FacilityFacility State-Location of the FacilityFacility Postal Code-Location of the FacilityFacility County-Location of the FacilityVenue Type- POOL-Any natural or artificial body or basin of water which is modified, improved, constructed or installed for the purpose of public swimming or bathing under the control or any person and includes but is not limited to the following: beaches, swimming pools, wave pools, competition swimming pools, diving pools, water slides and spray pools. HOT TUB/SPA-A special facility designed for recreational and therapeutic use, and which is not drained, cleaned, or refilled after each individual use. It may include, but not limited to, units designed for hydrojet circulation, hot water, cold water, mineral bath, air induction bubbles, or any combination thereof. Common terminology for a spa includes but not limited to, therapeutic pool, hydrotherapy pool, whirlpool, hot spa WADING POOL-A pool intended only for small children. The maximum depth is less than twenty-four (24) inches. OTHER-Any other swimming facility not specifically definedInspection Date-Date the inspection was performedInspection Score-"value between 0-100%, 86% without critical issue is a passing facility.A facility facility may have a score of 0 if the purpose of the inspection was ""Other"""Inspection Purpose- ROUTINE: Used to record routine inspection on an establishment and other facilities. FOLLOW-UP: Used to record all follow-up inspections as a result of a previous inspection. COMPLAINT: Used to record the investigation of a complaint received by the agency for regulated establishments, nuisances and the initial investigation for an animal bite. OTHER: The inspections are not given a numerical score. This inspection type used to record monitoring inspections for swimming pools. "Inspection Passed-TRUE: Inspection Score >= 86% without any critical violations.No Imminent Health Hazards-TRUE: no conditions are present that required of immediate closure of facility.Disinfectant-Type of disinfectant used at the facility, either BROMINE or CHLORINEFree Chlorine-measured in parts per million (PPM), the amount of free Chlorine in the body of water.Free Bromine-measured in parts per million (PPM), the amount of free Bromine in the body of water.pH-The amount of acidity measured in the body of water.Enclosure-TRUE: Facility Enclosure: adequate, self closing gate, good repair or locked if no lifeguard on duty.Main Drain Visible-TRUE: Turbidity: the water is clear enough to see the main drain at the bottom of the body of water.Safety Equipment"-TRUE: First Aid, Safety Equipment, Spa Timer Switch, Telephone: readily accessible, adequate, maintained, good repairPOOLS and/or SPAS could include elevated lifeguard chair, ring buoy, life pole/shepherds crook, backboard with straps, first aid kit. "Disinfectant Level-TRUE: CHLORINE disinfectant levels maintained between 1.0 and 2.5ppm for pools or slides, 2.0 and 3.0ppm for spas. BROMINE disinfectant levels maintained between 1.0 and 2.5ppm for pools or slides, 3.0ppm for spas. Anything outside this range is considered a violation. When only a shallow end reading is taken or shallow and deep end reading the shallow end value is used. If only a deep end reading is taken that value is used.pH Balance-TRUE: pH value tested between 7.2 and 7.8, anything outside this range is considered a violation.Inspection Notes-Description of violations noted at the facility.Health and Wellness Protects and promotes the health, environment and well being of the people of Louisville, providing health-related programs and health office locations community wide.Contact:Gerald KaforskiLMPHWDataTeam@Louisvilleky.gov

Here are a few use cases for this project:

1. Pool Ball Tracking: This model can be used to track the movement and positions of pool balls in a game. This could be valuable for creating an automatic scoring system or for providing players with analytics post-game to improve strategizing and skill building.

2. Online Gaming: Within virtual pool or billiards games, the model could facilitate the identification of balls on various table layouts, aiding in the creation of more realistic gaming experiences.

3. Production Line Quality Control: In industries where pool balls are manufactured, the model could be used for quality control, automatically detecting and classifying pool balls based on their color. This would ensure that the manufacturing process is accurate and efficient.

4. Pool Tutoring Applications: Pool training mobile apps might use this model to analyze the user's performance based on their shot selection and ball positioning strategy. Over time, this kind of application could provide personalized coaching recommendations to help users improve their pool-playing skills.

5. Sports Live Broadcasting: In the sports broadcasting industry, this computer vision model could be utilized to detect pool balls in real-time during matches. This can lead to automated statistics generation for live commentary and more enhanced viewer experiences.

Here are a few use cases for this project:

1. Automated Toy Organization: The model can be deployed in a system for toy sortation, identifying and sorting balls in playrooms or toy shops based on their colors. The recognition of the 'robot' class indicates possibilities in sorting modern electronic toys too.

2. Assisted Surveillance in Water Parks: The model can be used in pool safety monitoring. If the pool bot is a remote-controlled robot that's placed in a pool, it can identify toys floating in the water and alert staff about potential hazards. Pool toys tagged with colored tags can be monitored to ensure that none are missing, thereby improving the safety of the swimming pool.

3. Prototype Detection in Robotic Competitions: Deployed in competitions where robots are required to identify and interact with objects labeled with specific tags. The 'pool_bot_v1' model can detect objects with a pink_tag, blue_tag, and distinguish between red_ball, yellow_ball, and a 'robot'.

4. Assisted Therapy Sessions: In therapeutic activities involving color recognition and sorting, the 'pool_bot_v1' could be used to assist therapists in tracking progress. It could signal how clients interact with objects of different colors and note the improvement over time.

5. Interactive Gaming Environment: Use the model in an augmented reality game where players control a bot that's tasked to find and interact with specific colored objects in a physical play area. Objectives could include locating the 'robot', gathering balls of a particular color, etc. The identified classes would make the pool_bot_v1 model suitable in meeting such game objectives.

This dataset includes the sampling results and the swimming advisories for the City of Ottawa beaches for the swimming season.Accuracy: No known errorsUpdate Frequency: Daily updates by noonAttributes: Beach Name, location, E.coli level, StatusA no-swimming advisory will be issued if bacteria levels are over 200 E. coli per 100mL of water tested for one day; or if bacteria levels are over 100 E. coli per 100mL of water tested on two or more consecutive days. When a no-swim advisory is in effect, people should not swim due to the risk of getting a skin, ear, throat or even gastro-intestinal illness.A 24-hour no-swim advisory may be in place at the beaches after significant rainfall.*Please note Ottawa Public Health collects beach water samples every day. The results take 18 to 24 hours to process in the laboratory, and as such, swim and no-swim advisories are based on sample results taken from the previous day.Contact: Blayr Kelly

The discharge of disinfected sewage from vessels carrying less than 16 people in the vicinity of recognised swimming beaches and monitored recreational waters during the summer period has been created in accordance with the Sewage Management Directive. The Discharge of Sewage from Certain Vessels into State Waters is a risk-based approach to sewage discharge which specifies where sewage may and may not be discharged from certain vessels into Tasmanian waters.

Here are a few use cases for this project:

1. Sports Analytics: In pool/billiard games, the model could be used to analyze player strategies, track ball trajectories, or calculate hit accuracy by identifying the different balls on the table. This information can enhance player performance and provide valuable insights to coaches or commentators.

2. Augmented Reality Games: Developers can incorporate the model into augmented reality applications. Users could digitally interact with real-world pool games, adding a layer of customizability and interactivity, such as showing ball paths, predicting results, or more novel gaming concepts.

3. Robotic Pool/Billiard Player: Robot designers could use this model for developing pool/billiard robots. By recognizing different balls and areas of the table, the robot could strategize, take shots, and even compete with human players.

4. Quality Control in Manufacturing: Producers of pool/billiard equipment could use this model to automatically inspect the product's color, texture, and dimension—the model could identify whether painted balls have the right color, are properly numbered, and that tables meet specifications.

5. Pool/Billiard Game Tutoring App: For teaching and learning purposes, this model could be used in a tutoring app, highlighting different balls and explaining their importance, leading to the development of strategies and helping to visualize possible moves.

E.coli is a type of bacteria commonly found in the intestines of warm–blooded animals (including people). When found in freshwater, it can indicate the presence of pathogens associated with faecal contamination, from sources such as waste from humans and farmed animals such as sheep and cows. E.coli concentrations can vary due to differences in land use, climate, elevation, and geology. High E. coli concentrations may cause illness in humans and animals if ingested. This is an important consideration for human health, particularly where people use the river for swimming or boating. This dataset relates to the ""River water quality: bacteria (Escherichia coli)"" measure on the Environmental Indicators, Te taiao Aotearoa website.

We provide the first empirical analysis of pool hopping behavior among 15 mining pools throughout Bitcoin's history. Bitcoin mining is a critical activity that keeps the Bitcoin system secure, valid, and stable. Mining pools have emerged as major players that ensure that the Bitcoin system stays secure, valid, and stable. Individual miners join mining pools to benefit from a more stable and predictable income. Many questions remain open regarding how mining pools have evolved throughout Bitcoin's history and when and why miners join or leave mining pools. We propose a heuristic algorithm to extract the payout flow from mining pools and detect the pools' migration of miners. Our results showed that reward rules and pool fees influence miners' decisions to join, change, or exit from a mining pool, thus affecting the dynamics of mining pool market shares. Our analysis provides evidence that mining activity becomes an industry as miners' decisions follow classical economic rationale.

Beachwatch was established in 1989 in response to community concern about the impact of sewage pollution on human health and the environment at Sydney's ocean beaches. The programs provide regular and reliable information on beach water quality to enable people to make informed decisions about where and when to swim.

FishBase (31 000 Species, 270 600 Common names, 46 900 Pictures, 42 400 References, 1620 Collaborators, 33 million Hits/month). FishBase is an information system with key data on the biology of all fishes. Similar to an encyclopedia, FishBase contains different things for different people. For example, fisheries managers will dive into the largest existing compilation of population dynamics data; teachers and students will find numerous graphs illustrating basic concepts of fish biology; taxonomists will enjoy access to the November 2000 update of Eschmeyers (1998) Catalog of Fishes databases; conservationists will use the lists of threatened fishes for any given country (Hilton-Taylor 2000); policymakers may be interested in a chronological, annotated list of introductions to their country; research scientists, as well as funding agencies, will find it useful to gain a quick overview of what is known about a certain species; zoologists and physiologists will have the largest existing compilations of fish morphology, metabolism, gill area, brain size, eye pigment, or swimming speed at their fingertips; ecologists will likewise use data on diet composition, trophic levels, food consumption and predators as inputs for their models; geneticists will find the largest compilation of allele frequencies; the fishing industry will find proximate analyses, as well as processing recommendations for many marine species; anglers will enjoy a listing of all game fishes occurring in a particular country (IGFA 1994); and scholars interested in local knowledge will find more than 100,000 common names of fishes together with the language/culture in which they are used and comments on their etymology.