Dataset Overview:
This dataset contains comprehensive player statistics for the 2024/25 Premier League season, scraped from FBref. It includes detailed performance metrics for approximately 500 players across all teams, making it a valuable resource for football analytics, machine learning models, and performance analysis.

Data Source:
Data is derived from FBref, a leading football statistics platform, collected on September 02, 2025. Credit to FBref for the original data. Compiled by [Your Name].

Columns Included:
- Player: Player's full name
- Nation: Player's nationality
- Position: Player's primary position (e.g., FW, MF, DF)
- Squad: Team name
- Age: Player's age
- Born: Year of birth
- Matches Played: Number of matches played
- Starts: Number of games started
- Minutes: Total minutes played
- 90s Played: Minutes divided by 90
- Goals: Total goals scored
- Assists: Total assists
- Goal+Assist: Combined goals and assists
- Penalty Goals: Goals from penalties
- Penalties Attempted: Total penalty attempts
- Yellow Cards: Number of yellow cards
- Red Cards: Number of red cards
- Expected Goals (xG): Expected goals based on shot quality
- Non-Penalty xG: xG excluding penalties
- Expected Assists (xAG): Expected assists
- Non-Penalty xG+xAG: Combined non-penalty xG and xAG
- Progressive Carries: Number of progressive carries
- Progressive Passes: Number of progressive passes
- Goals per 90: Goals per 90 minutes
- Assists per 90: Assists per 90 minutes
- Goal+Assist per 90: Combined G+A per 90 minutes
- Non-Penalty Goals per 90: Non-penalty goals per 90
- Non-Penalty G+A per 90: Non-penalty G+A per 90
- xG per 90: Expected goals per 90 minutes
- xAG per 90: Expected assists per 90 minutes
- xG+xAG per 90: Combined xG+xAG per 90
- Non-Penalty xG per 90: Non-penalty xG per 90
- Non-Penalty xG+xAG per 90: Non-penalty xG+xAG per 90

Limitations:
- Data reflects stats up to the scrape date (September 02, 2025) and may not include late-season updates.
- Scraped using automated tools; minor inconsistencies or missing data may exist.
- Subject to FBref's terms of use; please use responsibly and avoid commercial exploitation without permission.

Usage:
Ideal for statistical analysis, predictive modeling, or visualizing player performance. Users are encouraged to cite this dataset and FBref in any derived work. License: CC BY-4.0 (attribution required).

Contact:
For questions or updates, reach out to siddhraj761@gmail.com.

The United States Geological Survey (USGS) - Science Analytics and Synthesis (SAS) - Gap Analysis Project (GAP) manages the Protected Areas Database of the United States (PAD-US), an Arc10x geodatabase, that includes a full inventory of areas dedicated to the preservation of biological diversity and to other natural, recreation, historic, and cultural uses, managed for these purposes through legal or other effective means (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/protected-areas). The PAD-US is developed in partnership with many organizations, including coordination groups at the [U.S.] Federal level, lead organizations for each State, and a number of national and other non-governmental organizations whose work is closely related to the PAD-US. Learn more about the USGS PAD-US partners program here: www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards. The United Nations Environmental Program - World Conservation Monitoring Centre (UNEP-WCMC) tracks global progress toward biodiversity protection targets enacted by the Convention on Biological Diversity (CBD) through the World Database on Protected Areas (WDPA) and World Database on Other Effective Area-based Conservation Measures (WD-OECM) available at: www.protectedplanet.net. See the Aichi Target 11 dashboard (www.protectedplanet.net/en/thematic-areas/global-partnership-on-aichi-target-11) for official protection statistics recognized globally and developed for the CBD, or here for more information and statistics on the United States of America's protected areas: www.protectedplanet.net/country/USA. It is important to note statistics published by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas (MPA) Center (www.marineprotectedareas.noaa.gov/dataanalysis/mpainventory/) and the USGS-GAP (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-statistics-and-reports) differ from statistics published by the UNEP-WCMC as methods to remove overlapping designations differ slightly and U.S. Territories are reported separately by the UNEP-WCMC (e.g. The largest MPA, "Pacific Remote Islands Marine Monument" is attributed to the United States Minor Outlying Islands statistics). At the time of PAD-US 2.1 publication (USGS-GAP, 2020), NOAA reported 26% of U.S. marine waters (including the Great Lakes) as protected in an MPA that meets the International Union for Conservation of Nature (IUCN) definition of biodiversity protection (www.iucn.org/theme/protected-areas/about). USGS-GAP released PAD-US 3.0 Statistics and Reports in the summer of 2022. The relationship between the USGS, the NOAA, and the UNEP-WCMC is as follows: - USGS manages and publishes the full inventory of U.S. marine and terrestrial protected areas data in the PAD-US representing many values, developed in collaboration with a partnership network in the U.S. and; - USGS is the primary source of U.S. marine and terrestrial protected areas data for the WDPA, developed from a subset of the PAD-US in collaboration with the NOAA, other agencies and non-governmental organizations in the U.S., and the UNEP-WCMC and; - UNEP-WCMC is the authoritative source of global protected area statistics from the WDPA and WD-OECM and; - NOAA is the authoritative source of MPA data in the PAD-US and MPA statistics in the U.S. and; - USGS is the authoritative source of PAD-US statistics (including areas primarily managed for biodiversity, multiple uses including natural resource extraction, and public access). The PAD-US 3.0 Combined Marine, Fee, Designation, Easement feature class (GAP Status Code 1 and 2 only) is the source of protected areas data in this WDPA update. Tribal areas and military lands represented in the PAD-US Proclamation feature class as GAP Status Code 4 (no known mandate for biodiversity protection) are not included as spatial data to represent internal protected areas are not available at this time. The USGS submitted more than 51,000 protected areas from PAD-US 3.0, including all 50 U.S. States and 6 U.S. Territories, to the UNEP-WCMC for inclusion in the WDPA, available at www.protectedplanet.net. The NOAA is the sole source of MPAs in PAD-US and the National Conservation Easement Database (NCED, www.conservationeasement.us/) is the source of conservation easements. The USGS aggregates authoritative federal lands data directly from managing agencies for PAD-US (https://ngda-gov-units-geoplatform.hub.arcgis.com/pages/federal-lands-workgroup), while a network of State data-stewards provide state, local government lands, and some land trust preserves. National nongovernmental organizations contribute spatial data directly (www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-data-stewards). The USGS translates the biodiversity focused subset of PAD-US into the WDPA schema (UNEP-WCMC, 2019) for efficient aggregation by the UNEP-WCMC. The USGS maintains WDPA Site Identifiers (WDPAID, WDPA_PID), a persistent identifier for each protected area, provided by UNEP-WCMC. Agency partners are encouraged to track WDPA Site Identifier values in source datasets to improve the efficiency and accuracy of PAD-US and WDPA updates. The IUCN protected areas in the U.S. are managed by thousands of agencies and organizations across the country and include over 51,000 designated sites such as National Parks, National Wildlife Refuges, National Monuments, Wilderness Areas, some State Parks, State Wildlife Management Areas, Local Nature Preserves, City Natural Areas, The Nature Conservancy and other Land Trust Preserves, and Conservation Easements. The boundaries of these protected places (some overlap) are represented as polygons in the PAD-US, along with informative descriptions such as Unit Name, Manager Name, and Designation Type. As the WDPA is a global dataset, their data standards (UNEP-WCMC 2019) require simplification to reduce the number of records included, focusing on the protected area site name and management authority as described in the Supplemental Information section in this metadata record. Given the numerous organizations involved, sites may be added or removed from the WDPA between PAD-US updates. These differences may reflect actual change in protected area status; however, they also reflect the dynamic nature of spatial data or Geographic Information Systems (GIS). Many agencies and non-governmental organizations are working to improve the accuracy of protected area boundaries, the consistency of attributes, and inventory completeness between PAD-US updates. In addition, USGS continually seeks partners to review and refine the assignment of conservation measures in the PAD-US.

This dataset includes elevation-based probability and depth statistics for estimating inundation under various sea-level rise and high tide flooding scenarios in and around the National Park Service’s Biscayne National Park. For information on the digital elevation model (DEM) source used to develop these datasets refer to the corresponding spatial metadata file (Danielson and others, 2023). This data release includes results from analyses of two local sea-level rise scenarios for two-time steps — the Intermediate-Low and Intermediate-High for 2040 and 2080 from Sweet and others (2022). Additionally, this data release includes maps of inundation probability under the minor, moderate, and major hight tide flooding thresholds. We estimated the probability of an area being inundated under a given scenario using Monte Carlo simulations with 1,000 iterations. For an individual iteration, each pixel of the DEM was randomly propagated based on the lidar data uncertainty, while the sea-le ...

Trending YouTube Video Statistics for unique Video IDs with its Subscriber data

Context

Original dataset was adopted from below URL : Trending YouTube Video Statistics That data was collected for several countries : US(United states of America), GB(Great Britain), DE(Germany), CA(Canada), and FR(France). I chose the data for USA country only. I modified the dataset to analyze some hidden information. Such as, I removed duplicate video_id's and make use of them to retrieve some meaningful data. I removed some unrelated attributes.As per my requirement,I changed type/class of few attributes too. I derived some new attributes from existing once.And many other minor modifications.

All the modifications are done by R-Programming.

I also added a new feature called "subscriber" to the dataset. I collected all subscriber information from youtube.com ,process was automatically done by a python script,written by me.

Content

YouTube (the world-famous video sharing website) maintains a list of the top trending videos on the platform. According to Variety magazine, To determine the year's top-trending videos, YouTube uses a combination of factors including measuring users interactions (number of views, shares, comments and likes).Note that they’re not the most-viewed videos overall for the calendar year.

This dataset is the daily record from the top trending YouTube videos. Top 200 trending videos of a given day.

Original Data was collected during 14th November 2017 & 5th March 2018(though, data for January 10th & 11th of 2017 is missing) Original dataset was collected by Youtube API.

Subscriber column data scrapped by me on 13th March of 2018, through a automated python script. NA introduced in the column for videos those are removed by the Youtube due to copyright or other issue.

Acknowledgements

https://www.kaggle.com/datasnaek/

Inspiration

Analyzing what factors affect how popular a YouTube video will be.

Context

Road traffic open data provides street-level data for every junction-to-junction link on the motorway and 'A' road network, and for some minor roads in Great Britain.

Annual statistics are mostly presented in units of vehicle miles, which combines the number of vehicles on the road and how far they drive. Annual traffic statistics are compiled using data from around 8,000 roadside 12-hour manual counts, continuous data from automatic traffic counters, and data on road lengths.

The road traffic statistics team carry out a minor road traffic benchmarking exercise approximately every 10 years, with the aim to improve the accuracy of traffic estimates for minor roads. The results of the 2018 to 2019 exercise have been published and have resulted in revisions to the minor road traffic estimates covering 2010 to 2018. For more information about the minor roads benchmarking exercise, please refer to the documentation from the 2019 exercise.

Data disclaimer

Traffic figures at the regional and national level are robust, and are reported as National Statistics. However, DfT’s traffic estimates for individual road links and small areas are less robust, as they are not always based on up-to-date counts made at these locations. Where other more up-to-date sources of traffic data are available (e.g. from local highways authorities), this may provide a more accurate estimate of traffic at these locations.

Quality flags in data downloads

DfT’s road link level traffic estimates are calculated using a variety of methods, with some methods likely to produce more accurate estimates than others. The data tables available to download here contain a column - estimation_method – showing the method used to estimate traffic for each location and year. Figures having an estimation method of “Counted” are likely to be more accurate than those marked as “Estimated”, and the latter should be used with caution.

Content

• https://roadtraffic.dft.gov.uk/about
• http://data.dft.gov.uk.s3.amazonaws.com/road-traffic/all-traffic-data-metadata.pdf

Acknowledgements

Road Trafic Statistics

Inspiration

• Street-level road traffic prediction
• EDA on roads at a national, regional and local authority level

This dataset provides an extensive overview of wildfire activity worldwide, capturing both the frequency and intensity of fires over a range of years. It is derived from the Global Wildfire Information System (GWIS) and integrates satellite imagery from MODIS and VIIRS. The dataset is adapted and processed by Our World in Data, including standardizations for global comparisons.

Key Features:

Entity: Represents countries, regions, or territories where the wildfire data is collected.

Code: ISO or custom country/region codes.

Year: The year of recorded wildfire data.

Annual Number of Fires: The total number of wildfires recorded in a given year for a specific entity.

Annual Area Burnt per Wildfire (in hectares): The average area burned by each wildfire during the year.

Dataset Highlights: Covers various regions, including individual countries (e.g., Afghanistan, Albania) and larger areas like Africa. Includes granular data for entities like Akrotiri and Dhekelia, and zero-incident regions like the Aland Islands and American Samoa. Captures annual trends from 2012 to 2025, demonstrating variability in both the number of wildfires and the average area burnt.

Modifications Made to the Dataset: One column's name was changed to "Annual area burnt per wildfire in hectares" to improve clarity and relevance.

Note: Recent California Los Angeles fires are not included in this dataset. Data for 2025 is incomplete as the year has just begun.

An unwanted column was removed to streamline the dataset and ensure only relevant data is included.

Data Source and Processing:

Original Source: Global Wildfire Information System (GWIS), providing weekly updates on fire activity and its environmental impact.

Processing: Adaptations by Our World in Data include standardizing names, converting units, and calculating derived indicators.

Citations:

Primary data retrieved from GWIS Seasonal Trends (https://gwis.jrc.ec.europa.eu/apps/gwis.statistics/seasonaltrend). Minor processing by Our World in Data.

Usage and Licensing: This dataset is open access under the Creative Commons BY license. Users may reproduce, distribute, and adapt the data with appropriate credit to the source.

Suggested Citation for Dataset: Global Wildfire Information System (2025); Global Wildfire Information System (2024) – with minor processing by Our World in Data. “Annual number of wildfires” [dataset]. Retrieved January 26, 2025, from https://ourworldindata.org/grapher/annual-number-of-fires?time=2025#explore-the-data.

This dataset provides valuable insights into global wildfire patterns, supporting analysis for environmental studies, policy-making, and disaster management planning.

GenASiSBasics provides Fortran 2003 classes furnishing extensible object-oriented utilitarian functionality for large-scale physics simulations on distributed memory supercomputers. This functionality includes physical units and constants; display to the screen or standard output device; message passing; I/O to disk; and runtime parameter management and usage statistics. This revision—Version 2 of Basics—makes mostly minor additions to functionality and includes some simplifying name changes.

This dataset contains major and trace element compositions for a sample of basalt recovered from Rangitoto Island, New Zealand. Both bulk rock and microanalytical observations are made and presented. Electron probe microanalysis (EPMA) results detail the compositions of individual phases within the basalt including olivine, clinopyroxene, plagioclase feldspar, glass, and polyphase microcrystalline groundmass. Laser ablation inductively-coupled plasma mass spectrometry (LA-ICP-MS) results detail trace element compositions for the glass as well as on select olivine grains. X-Ray fluorescence (XRF) data detailing major and minor element oxide concentrations were gathered using bulk rock powders. Additional tables detail the statistical analysis of the microanalytical data. Outliers within the glass and groundmass data populations were identified, and the summary statistics of this quality control processing are presented in a separate table. The trimmed glass and groundmass data are ...