UPDATE

This dataset got a lot of love from the community and I saw many people asking for an updated version, so I have uploaded the latest scraped and processed data ( as of 21/03/2021). Now it's super easy for anyone to get the latest dataset (Just use a single command), so in case you need bleeding-edge data, or you want to see the code, you can look here. Hope this solves all problems! If there are any issues with the data, please forgive me and write about it in the comments or raise an issue on github. I will pick it up 👍 Thank you everyone for the emails and messages. As usual, have fun! ❤️ 😁

Context

This is a list of every UFC fight in the history of the organisation. Every row contains information about both fighters, fight details and the winner. The data was scraped from ufcstats website. After fightmetric ceased to exist, this came into picture. I saw that there was a lot of information on the website about every fight and every event and there were no existing ways of capturing all this. I used beautifulsoup to scrape the data and pandas to process it. It was a long and arduous process, please forgive any mistakes. I have provided the raw files incase anybody wants to process it differently. This is my first time creating a dataset, any suggestions and corrections are welcome! Incase anyone wants to check out the work, I have all uploaded all the code files, including the scraping module here

Have fun!

Content

Each row is a compilation of both fighter stats. Fighters are represented by 'red' and 'blue' (for red and blue corner). So for instance, red fighter has the complied average stats of all the fights except the current one. The stats include damage done by the red fighter on the opponent and the damage done by the opponent on the fighter (represented by 'opp' in the columns) in all the fights this particular red fighter has had, except this one as it has not occured yet (in the data). Same information exists for blue fighter. The target variable is 'Winner' which is the only column that tells you what happened. Here are some column definitions:

Column definitions:

• R_ and B_ prefix signifies red and blue corner fighter stats respectively
• _opp_ containing columns is the average of damage done by the opponent on the fighter
• KD is number of knockdowns
• SIG_STR is no. of significant strikes 'landed of attempted'
• SIG_STR_pct is significant strikes percentage
• TOTAL_STR is total strikes 'landed of attempted'
• TD is no. of takedowns
• TD_pct is takedown percentages
• SUB_ATT is no. of submission attempts
• PASS is no. times the guard was passed?
• REV is the no. of Reversals landed
• HEAD is no. of significant strinks to the head 'landed of attempted'
• BODY is no. of significant strikes to the body 'landed of attempted'
• CLINCH is no. of significant strikes in the clinch 'landed of attempted'
• GROUND is no. of significant strikes on the ground 'landed of attempted'
• win_by is method of win
• last_round is last round of the fight (ex. if it was a KO in 1st, then this will be 1)
• last_round_time is when the fight ended in the last round
• Format is the format of the fight (3 rounds, 5 rounds etc.)
• Referee is the name of the Ref
• date is the date of the fight
• location is the location in which the event took place
• Fight_type is which weight class and whether it's a title bout or not
• Winner is the winner of the fight
• Stance is the stance of the fighter (orthodox, southpaw, etc.)
• Height_cms is the height in centimeter
• Reach_cms is the reach of the fighter (arm span) in centimeter
• Weight_lbs is the weight of the fighter in pounds (lbs)
• age is the age of the fighter
• title_bout Boolean value of whether it is title fight or not
• weight_class is which weight class the fight is in (Bantamweight, heavyweight, Women's flyweight, etc.)
• no_of_rounds is the number of rounds the fight was scheduled for
• current_lose_streak is the count of current concurrent losses of the fighter
• current_win_streak is the count of current concurrent wins of the fighter
• draw is the number of draws in the fighter's ufc career
• wins is the number of wins in the fighter's ufc career
• losses is the number of losses in the fighter's ufc career
• total_rounds_fought is the average of total rounds fought by the fighter
• total_time_fought(seconds) is the count of total time spent fighting in seconds
• total_title_bouts is the total number of title bouts taken part in by the fighter
• win_by_Decision_Majority is the number of wins by majority judges decision in the fighter's ufc career
• win_by_Decision_Split is the number of wins by split judges decision in the fighter's ufc career
• win_by_Decision_Unanimous is the number of wins by unanimous judges decision in the fighter's ufc career
• win_by_KO/TKO is the number of wins by knockout in the fighter's ufc career
• win_by_Submission is the number of wins by submission in the fighter's ufc career
• win_by_TKO_Doctor_Stoppage is the number of wins by doctor stoppage in the fighter's ufc career

Acknowledgements

• Inspiration: https://github.com/Hitkul/UFC_Fight_Prediction Provided ideas on how to store per fight data. Unfortunately, the entire UFC website and fightmetric website changed so couldn't reuse any of the code.

• Print Progress Bar: https://gist.github.com/aubricus/f91fb55dc6ba5557fbab06119420dd6a To display progress of how much download is complete in the terminal

Info about me

You can check out who I am and what I do here

The National Social Life, Health and Aging Project (NSHAP) is the first population-based study of health and social factors on a national scale, aiming to understand the well-being of older, community-dwelling Americans by examining the interactions among physical health, illness, medication use, cognitive function, emotional health, sensory function, health behaviors, and social connectedness. It is designed to provide health providers, policy makers, and individuals with useful information and insights into these factors, particularly on social and intimate relationships. The National Opinion Research Center (NORC), along with Principal Investigators at the University of Chicago, conducted more than 3,000 interviews during 2005 and 2006 with a nationally representative sample of adults aged 57 to 85. Face-to-face interviews and biomeasure collection took place in respondents' homes. Round 2 interviews were conducted from August 2010 through May 2011, during which Round 1 Respondents were re-interviewed. An attempt was also made to interview individuals who were sampled in Round 1 but declined to participate. In addition, spouses or co-resident partners were also interviewed using the same instruments as the main respondents. This process resulted in 3,377 total respondents. The following files constitute Round 2: Core Data, Disposition of Round 1 Partner Data, Social Networks Data, Social Networks Update Data, Partner History Data, Partner History Update Data, Medications Data, Proxy Data, and Sleep Statistics Data. Included in the Core files (Datasets 1 and 2) are demographic characteristics, such as gender, age, education, race, and ethnicity. Other topics covered respondents' social networks, social and cultural activity, physical and mental health including cognition, well-being, illness, history of sexual and intimate partnerships, and patient-physician communication, in addition to bereavement items. Data were also collected from respondents on the following items and modules: social activity items, physical contact module, sexual interest module, get up and go assessment of physical function, and a panel of biomeasures, including weight, waist circumference, height, blood pressure, smell, saliva collection, and taste. The Disposition of Round 1 Partner files (Datasets 3 and 4) detail information derived from Section 6A items regarding the partner from Round 1 within the questionnaire. This provides a complete history for respondent partners across both rounds. The Social Networks files (Datasets 5 and 6) contain one record for each person identified on the network roster. Respondents who refused to participate in the roster or who did not identify anyone are not represented in this file. The Social Networks Update files (Datasets 7 and 8) detail respondents' current relationship status with each person identified on the network roster. The Partner History file (Dataset 9) contains one record for each marriage, cohabitation, or romantic relationship identified in Section 6A of the questionnaire, including a current partner in Round 2 but excluding the partner from Round 1. The Partner History Update file (Dataset 10) details respondents' current sexual partner information, as well as marital and cohabiting status. The Medications Data file (Dataset 11) contains records for items listed in the medications log. The Proxy Data files (Datasets 12 and 13) contain information from proxy interviews administered for Round 1 Respondents who were either deceased or whose health was too poor to participate in Round 2. The Sleep Statistics Data files (Dataset 14 and 15) provide information on actigraphy sleep variables. NACDA also maintains a Colectica portal with the NSHAP Core data across rounds 1-3, which allows users to interact with variables across rounds and create customized subsets. Registration is required.

Abstract This dataset was supplied to the Bioregional Assessment Programme by a third party and is presented here as originally supplied. Metadata was not provided and has been compiled by the Bioregional Assessment Programme based on the known details at the time of acquisition This dataset was sourced from the ABS website: www.abs.gov.au. It did not include comprehensive metadata. Australian Bureau of Statistics (ABS) Catalogue Number 2006.0 - Census of Population and Housing: Working Population Profile, 2011 Second Release This dataset contains the ABS 2011 Working Population profile for 6 LGA's (Local Government Areas) in the Cooper subregion. These include Barcoo, Bulloo, Quilpie, Diamantina, Longreach Regional and Unincorporated SA. The ABS dataset series is described as follows: The Community Profile Series contains six separate profiles providing information on key Census characteristics relating to persons, families and dwellings and covering most topics on the Census form. The profiles are excellent tools for researching, planning and analysing small and large geographic areas. They enable comparisons to be made between different geographic areas. The available profiles for the 2011 Census are: Basic Community Profile (BCP), Place of Enumeration Profile (PEP), Aboriginal and Torres Strait Islander Peoples (Indigenous) Profile (IP), Time Series Profile (TSP), Expanded Community Profile (XCP) and the Working Population Profile (WPP). The Working Population Profile (WPP) contains 23 tables of key Census characteristics of employed persons. The data are based on where people work. The profile includes data on hours worked, industry of employment, occupation, qualifications and method of travel to work etc. ## Dataset History The Working Population Profile spreadsheets within this dataset were downloaded from: http://www.abs.gov.au/websitedbs/censushome.nsf/home/map. Each LGA spreadsheet was searched for, and downloaded separately. ## Dataset Citation Australian Bureau of Statistics (2013) ABS Cooper Local Government Areas (LGA) Working Population Profile 2011 Census. Bioregional Assessment Source Dataset. Viewed 07 February 2017, http://data.bioregionalassessments.gov.au/dataset/65261aa9-38b8-4031-b10d-4170929f1477.

A dataset of well information and geospatial data was developed for 426 U.S. Geological Survey (USGS) observation wells in Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and Vermont. An extensive list of attributes is included about each well, its location, and water-level history to provide the public and water-resources community with comprehensive information on the USGS well network in New England and data available from these sites. These data may be useful for evaluating groundwater conditions and variability across the region. The well list and site attributes, which were extracted from USGS National Water Information System (NWIS), represent all of the active wells in the New England network up to the end of 2017, and an additional 45 wells that were inactive (discontinued or replaced by a nearby well) at that time. Inactive wells were included in the database because they (1) contain periods of water-level record that may be useful for groundwater assessments, (2) may become active again at some point, or (3) are being monitored by another agency (most discontinued New Hampshire wells are still being monitored and the data are available in the National Groundwater Monitoring Network (https://cida.usgs.gov/ngwmn/index.jsp). The wells in this database have been sites of water-level data collection (periodic levels and/or continuous levels) for an average of 31 years. Water-level records go back to 1913. The groundwater-level statistics included in the dataset represent hydrologic conditions for the period of record for inactive wells, or through the end of water year 2017 (September 30, 2017) for active wells. Geographic Information Systems (GIS) data layers were compiled from various sources and dates ranging from 2003 to 2018. These GIS data were used to calculate attributes related to topographic setting, climate, land cover, soil, and geology giving hydrologic and environmental context to each well. In total, the data include 90 attributes for each well. In addition to site number and station name, attributes were developed for site information (15 attributes); groundwater-level statistics through water year 2017 (16 attributes); well-construction information (9 attributes); topographic setting (11 attributes); climate (2 attributes); land use and cover (17 attributes); soils (4 attributes); and geology (14 attributes). Basic well and site information includes well location, period of record, well-construction details, continuous versus intermittent data collection, and ground altitudes. Attributes that may influence groundwater levels include: well depth, location of open or screened interval, aquifer type, surficial and bedrock geology, topographic position, flow distance to surface water, land use and cover near the well, soil texture and drainage, precipitation, and air temperature.

Abstract copyright UK Data Service and data collection copyright owner.

The Great Britain Historical Database has been assembled as part of the ongoing Great Britain Historical GIS Project. The project aims to trace the emergence of the north-south divide in Britain and to provide a synoptic view of the human geography of Britain at sub-county scales. Further information about the project is available on A Vision of Britain webpages, where users can browse the database's documentation system online.

The Great Britain Historical GIS Project has also produced digitised boundary data, which can be obtained from the UK Data Service Census Support service. Further information is available at census.ukdataservice.ac.uk

Main Topics:

The Great Britain Historical Database is a large database of British nineteenth and twentieth-century statistics. Where practical the referencing of spatial units has been integrated, data for different dates have been assembled into single tables.

The Great Britain Historical Database currently contains :

• Statistics from the 1861 Census and the Registrar General's reports, 1851-1861
  
• Employment statistics from the census, 1841-1931
  
• Demographic statistics from the census, 1841-1931
  
• Mortality statistics from the Registrar General's reports, 1861-1920
  
• Marriage statistics from the Registrar General's reports, 1841-1870
  
• Trade union statistics for the Amalgamated Society of Engineers (ASE), 1851-1918
  
• Trade union statistics for the Amalgamated Society of Carpenters and Joiners (ASCJ), 1863-1912
  
• Official poor law statistics, 1859-1915 and 1919-1939
  
• Wage statistics, 1845-1906
  
• Hours of work statistics, 1900-1913
  
• Small debt statistics from county courts, 1847-1913 and 1938
  

There are six tables in this part of the Great Britain Historical Database :

Tu_pc holds monthly local unemployment statistics for engineers (January 1902-December 1914), shipbuilders (January 1902-December 1914), printers (February 1902-December 1914) and carpenters and joiners (May 1901-October 1905). For details of the districts used please see the documentation.

Div23_38 holds annual data taken from the official tabulations of the Ministry of Labour's Local Unemployment Index from 1923 to 1938 for eight divisions.

Lui holds quarterly data taken from the official tabulations of the Ministry of Labour's Local Unemployment Index from January 1927 to July 1939.

Lui_gaz provides locational information for the exchange areas listed in lui.

Un_1928 holds information on registered unemployment at labour exchanges for 30th January 1928.

Un_1933 holds information on registered unemployment at labour exchanges for 23rd January 1933.

Please note: this study does not include information on named individuals and would therefore not be useful for personal family history research.

Jordan JO: Death Rate: Crude: per 1000 People data was reported at 3.828 Ratio in 2016. This records a decrease from the previous number of 3.829 Ratio for 2015. Jordan JO: Death Rate: Crude: per 1000 People data is updated yearly, averaging 5.389 Ratio from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 15.800 Ratio in 1960 and a record low of 3.828 Ratio in 2016. Jordan JO: Death Rate: Crude: per 1000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank.WDI: Population and Urbanization Statistics. Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear. Subtracting the crude death rate from the crude birth rate provides the rate of natural increase, which is equal to the rate of population change in the absence of migration.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Protected areas are one of the most widespread and accepted conservation interventions, yet their  population trends are rarely compared to regional trends to gain insight into their effectiveness. Here, we leverage two long-term community science datasets to demonstrate mixed effects of protected areas on long-term bird population trends. We analyzed 31 years of bird transect data recorded by community volunteers across all major habitats of Stanford University’s Jasper Ridge Biological Preserve to determine the population trends for a sample of 66 species. We found that nearly a third of species experienced long-term declines, and on average, all species declined by 12%. Further, we averaged species trends by conservation status and key life history attributes to identify correlates and possible drivers of these trends. Observed increases in some cavity-nesters and declines of scrub-associated species suggest that long-term fire suppression may be a key driver, reshaping bird communit...,

From 1989 to 2020, volunteer observers conducted monthly surveys of six sectors within Stanford University's Jasper Ridge Biological Preserve (JRBP). Each survey consisted of a trail-based transect in which a group of observers walked the trail in the morning and counted all birds detected over roughly 3 hours. Observers recorded the number of each species seen or heard along the route, regardless of the distance to the bird. Over 31 years of surveys, 192 observers conducted 2,055 transects and recorded a total of 473,401 observations of 184 species (91% of JRBP’s documented avian richness). We used these data to estimate long-term avian population trends at JRBP. Prior to analy- sis, we performed extensive data cleaning, including the standardization of species names and observer identity. Unlikely species without notes or supporting information were removed from the analysis. All transects with fewer than seven species (n = 30) were considered incidental and removed. These transect..., , # Data and model code from: Mixed population trends inside a California protected area: evidence from long-term community science monitoring

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Here, we provide the R code used to model the abundance for each species in the Jasper Ridge Biological Preserve. We have also provided a spreadsheet with each species' life history traits, taxonomy, annual trends in the preserve, and annual trends in the surrounding region (BCR 32) from the North American Breeding Bird Survey. Finally, we have attached an R code that analyzes the trends for various life history traits and taxonomic families, compares trends within the protected area and in the surrounding region, and produces figures 2, 4, and 5 in the main manuscript and all supplementary material figures.

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Description of the data and file structure

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The JRBP_Transect_Data_Species.R file provides the code required to create a generalized linear mixed model for each species in R-INLA and extract the percent change in ab...

Annual indexes for major components and special aggregates of the Consumer Price Index (CPI), for Canada, provinces, Whitehorse, Yellowknife and Iqaluit. Data are presented for the last five years. The base year for the index is 2002=100.

Geographic information for Ontario Health (OH) Regions, and Home and Community Care Support Services (HCCSS) boundaries.

The HCCSS boundary file is maintained by Statistics Canada. A link is provided to the Statistics Canada website where you can download the HCCSS boundary file in a variety of formats.

Data includes:

• Ontario Health Region boundaries
• HCCSS boundaries
• LHIN office locations (historical)

This zip file contains the Code History Database for the United Kingdom as at May 2023. To download the zip file click the Download button.File includes updates to:• Updates in England to: Civil Parishes (E04), Electoral Wards/Divisions (E05), Unitary Authorities (E06), Non-metropolitan districts (E07), Counties (E10), National Parks (E26), Registration Districts (E28), Registration Sub-districts (E29), Sub Integrated Care Board Locations (E38), Non-Civil Parished Areas (E43), Local Resilience Forums (E48), Integrated Care Boards (E54), County Electoral Divisions (E58)• New Entities introduced: Areas of Outstanding Natural Beauty (E62), England Non-National Park Area (E65), Grouped Local Authority Districts (Parishes) (E66), Census Merged Local Authority Districts 2021 ((E67), Grouped Local Tier Local Authorities (E68) Non-Standard Geography Categories (K99)• Updates in Northern Ireland to: Small Areas (N00)• New Entities introduced: Data Zones (N20), Super Data Zones (N21) • Updates in Scotland to: National Parks (S21)• Updates in Wales to: National Parks (W18)• New Entities introduced: Areas of Outstanding Natural Beauty (W44), Grouped Local Authority Districts (Parishes) (W46), Grouped Lower Tier Local Authorities (W47)(File Size - 50 MB)