Database consists of filing data for Top Hat plan notices for management and HCE's, who defer income until termination of employment, and are therefore exempt from ERISA.

A comparison table of popular database documentation tools, including supported DBMS, documentation formats, ease of use, customization options, and pricing.

List of Top Institutions of Journal of Database Management sorted by citations.

As of June 2024, the most popular open-source database management system (DBMS) in the world was MySQL, with a ranking score of ****. Oracle was the most popular commercial DBMS at that time, with a ranking score of ****.

A comparison table of SQL GUI client features across multiple database tools including dbForge Edge, MySQL Workbench, Beekeeper Studio, DBeaver, DataGrip, HeidiSQL, Navicat Premium, DBVisualizer, RazorSQL, and OmniDB.

This statistic shows the revenues from the leading big data vendors from 2014 to 2017. In 2017, IBM generated around **** billion U.S. dollars worth of revenue through big data services, software and hardware.

Leverage high-quality B2B data with 468 enriched attributes, covering firmographics, financial stability, and industry classifications. Our AI-optimized dataset ensures accuracy through advanced deduplication and continuous updates. With 30+ years of expertise and 1,100+ trusted sources, we provide fully compliant, structured business data to power lead generation, risk assessment, CRM enrichment, market research, and more.

Key use cases of B2B Data have helped our customers in several areas :

1. Boost Lead Generation & Sales Outreach : Target the right businesses with precise, segmented contact lists for cold calling, email marketing, and industry-specific campaigns.
2. Enhance CRM & Web Data for Smarter Engagement : Enrich CRM records with instant access to detailed company profiles, visitor identification, and continuous data updates.
3. Strengthen Risk Assessment & Fraud Prevention : Evaluate supplier reliability, assess credit risk, and prevent fraud with deep firmographic and financial insights.
4. Gain a Competitive Edge with Market Research : Analyse industry trends, benchmark competitors, and identify automation-ready sectors for strategic positioning.
5. Optimize B2B Strategies with AI-Powered Insights : Leverage structured, compliant data to drive smarter business decisions across sales, marketing, and operations.

Football Player Database – Top 5 European Leagues (Added Season 2025-2026)

A database of players from the top 5 leagues from the 1992-1993 season (Ligue 1 from 1995-1996), excluding goalkeeper statistics, with added columns for UEFA Champions League (UCL) appearances and individual awards. For seasons up to 2017-2018, with limited/reduced statistics. Source: https://fbref.com/en/

Player Info

• PlayerID – Unique identifier for the player
• Player – Player's full name
• Squad – Team/club the player belongs to
• League – League in which the player competes
• Nation – Player's nationality
• Pos – Playing position (e.g., FW, MF, DF)
• Age – Age during the season
• Born – Year of birth
• Season – Season of the data (e.g., 2022-2023)

Playing Time

• MP – Matches played
• Min – Minutes played
• Mn/MP – Minutes per match (average)
• Min% – Percentage of team minutes played
• Starts – Matches started
• Mn/Start – Minutes per start
• Subs – Appearances as a substitute
• Mn/Sub – Minutes per substitute appearance
• unSub – Unsubstituted appearances (played full match)
• 90s – Minutes played expressed in 90-minute units

Shooting / Scoring

• Sh – Total shots
• Sh/90 – Shots per 90 minutes
• SoT – Shots on target
• SoT% – Percentage of shots on target
• SoT/90 – Shots on target per 90 minutes
• G/Sh – Goals per shot
• G/SoT – Goals per shot on target
• Gls – Goals scored
• Ast – Assists
• G+A – Goals plus assists
• PK – Penalties scored
• PKatt – Penalty attempts
• PKcon – Penalties conceded
• OG – Own goals
• xG – Expected goals
• npxG – Non-penalty expected goals
• npxG/Sh – Non-penalty xG per shot
• G-xG – Goals minus expected goals (over- or underperformance)
• np:G-xG – Non-penalty goals minus non-penalty xG

Passing

• Pass – Total passes attempted
• Cmp – Passes completed
• Cmp% – Pass completion percentage
• PassLive – Completed live-ball passes that lead to a shot attempt
• PassDead – Completed dead-ball passes that lead to a shot attempt
• KP – Key passes
• Att – Passes Attempted
• Crs – Crosses attempted
• CrsPA – Crosses that lead to a shot
• A-xAG – Assists minus expected assists from key passes
• xAG – xAG: Exp. Assisted Goals Expected Assisted Goals xG which follows a pass that assists a shot
• xA – Expected assists
• PPA – Passes Penalty Arena
• Live – Live-ball Passes
• Dead – Set-piece passes leading to shots
• FK – Free kicks attempted
• TB – Through balls
• Sw – Switches Passes that travel more than 40 yards of the width of the pitch
• TI – Throw-ins Taken
• CK – Corners
• In – Inswinging Corner Kicks
• Out – Outswinging Corner Kicks
• Str – Straight Corner Kicks
• Compl – Completed progressive passes
• Mis – Misplaced passes

Defensive Actions

• Tkl – Tackles
• TklW – Tackles won
• Tkl% – Tackle success percentage
• Tkld – Tackles attempted in defensive third
• Tkld% – Tackle success in defensive third
• Tkl+Int – Tackles plus interceptions
• Int – Interceptions
• Blocks – Shots blocked
• Clr – Clearances
• Fls – Fouls committed
• Recov – Ball recoveries

Defensive / Positional Coverage

• Def – Defensive actions in total
• Def 3rd – Defensive actions in defensive third
• Mid 3rd – Defensive actions in middle third
• Att 3rd – Defensive actions in attacking third
• Att Pen – Actions in penalty area
• Off – Passes Offside
• Dis – Dispossessions

Duels / Possession

• Won – Duels won
• Won% – Duels win percentage
• Lost – Duels lost

On/Off Metrics

• +/- – Team goal difference when player is on pitch
• +/-90 – Goal difference per 90 minutes
• On-Off – Impact on team goal difference
• onG – Goals scored by team while player is on pitch
• onGA – Goals conceded while player is on pitch
• onxG – Expected goals while on pitch
• onxGA – Expected goals against while on pitch
• xG+/- – xG difference while player is on pitch
• xG+/-90 – xG difference per 90 minutes

Chance Creation / Progressive Play

• SCA – Shot-creating actions
• SCA90 – Shot-creating actions per 90 minutes
• PrgC – Progressive carries
• PrgDist – Progressive distance carried
• PrgP – Progressive passes
• PrgR – Progressive runs
• Rec – Recoveries
• Carries – Ball carries
• CPA – Carries into penalty area
• Touches – Number of touches
• Dist – Total distance covered with the ball
• TotDist – Total distance covered overall
• PPM – Points per Match

Individual Awards

• Ballon d’or – Ballon d’Or wins
• European Golden Shoe – European Golden Shoe wins
• League Won – Domestic league titles won
• UCL_Won – UEFA Champions League titles won
• The Best FIFA Mens Player – FIFA Best Men’s Pla...

List of Top Schools of Database: the Journal of Biological Databases and Curation sorted by citations.

Context

IMDB (Internet Movie Database) is one of the most popular web sites, or databases, about movies, TV shows and similar. IMDB's Top 250 lists also important feature for considering good movies. Rankings are calculated with users' votes. For more IMDB's pollmaster account shares previous years IMDB Top 250 lists. Top 250 lists changes all the time, so that the lists are created for December 31st, midnight PST of that year.

Content

This dataset contains IMDB Top 250 lists from 1996 to 2020 with every movie's basic information; release year, ranking, score, stars, etc.

Acknowledgements

This data scraped from IMDB, and you can reach scraping part from here

Inspiration

Time travel... You can look into lists for last 25 years. Analyze best movies for voters, genre preferences, most successful directors, stars, ranking changings over time et cetera. There are lots of things to do. Be creative and visualize them.

Discover top-performing keywords for Database Management Software on BigCommerce. Analyze monthly growth rate rankings to discover trending search terms and capitalize on emerging opportunities for your store.

Citation metrics are widely used and misused. We have created a publicly available database of over 100,000 top-scientists that provides standardized information on citations, h-index, co-authorship adjusted hm-index, citations to papers in different authorship positions and a composite indicator. Separate data are shown for career-long and single year impact. Metrics with and without self-citations and ratio of citations to citing papers are given. Scientists are classified into 22 scientific fields and 176 sub-fields. Field- and subfield-specific percentiles are also provided for all scientists who have published at least 5 papers. Career-long data are updated to end-of-2020. The selection is based on the top 100,000 by c-score (with and without self-citations) or a percentile rank of 2% or above.

The dataset and code provides an update to previously released version 1 data under https://doi.org/10.17632/btchxktzyw.1; The version 2 dataset is based on the May 06, 2020 snapshot from Scopus and is updated to citation year 2019 available at https://doi.org/10.17632/btchxktzyw.2

This version (3) is based on the Aug 01, 2021 snapshot from Scopus and is updated to citation year 2020.

European Football Leagues Database 2023-2024

Overview This dataset provides comprehensive information about the top 5 European football leagues for the 2023-2024 season. It includes detailed statistics about matches, players, teams, coaches, referees, and more, making it an invaluable resource for sports analysts, researchers, and football enthusiasts.

Dataset Description Leagues Covered: - English Premier League - Spanish La Liga - German Bundesliga - Italian Serie A - French Ligue 1

Database Schema

The database follows a normalized schema design with proper relationships between tables. Here's a simplified view of the main relationships:

leagues
 ↑
teams → matches ← referees
 ↓     ↑
players   scores
 ↑
coaches

Usage Examples

SQL Queries

Here are some example SQL queries to get you started:

1. Get all matches for a specific team: sql SELECT m.*, t1.name as home_team, t2.name as away_team FROM matches m JOIN teams t1 ON m.home_team_id = t1.team_id JOIN teams t2 ON m.away_team_id = t2.team_id WHERE t1.team_id = [team_id] OR t2.team_id = [team_id];

2. Get current league standings: sql SELECT t.name, s.* FROM standings s JOIN teams t ON s.team_id = t.team_id WHERE s.league_id = [league_id] ORDER BY s.points DESC;

3. Get top scorers: sql SELECT p.name, p.team_id, COUNT(*) as goals FROM scores s JOIN players p ON s.scorer_id = p.player_id GROUP BY p.player_id, p.name, p.team_id ORDER BY goals DESC;

Python Example

import pandas as pd
import sqlite3

# Connect to the SQLite database
conn = sqlite3.connect('sports_league.sqlite')

# Read data into pandas DataFrames
matches_df = pd.read_sql('SELECT * FROM matches', conn)
players_df = pd.read_sql('SELECT * FROM players', conn)
teams_df = pd.read_sql('SELECT * FROM teams', conn)

# Analyze data
team_stats = matches_df.groupby('home_team_id')['home_team_goals'].agg(['mean', 'sum'])

Applications

This dataset can be used for: 1. Match outcome prediction 2. Player performance analysis 3. Team strategy analysis 4. Historical trend analysis 5. Sports betting research 6. Fantasy football insights 7. Statistical modeling 8. Machine learning projects

Data Files:

1. matches.csv

   • Match ID, Date, Home Team, Away Team
   • Final Score, Half-time Score
   • Stadium, Referee
   • League and Season information

2. players.csv

   • Player ID, Name, Position
   • Date of Birth, Nationality
   • Team affiliation
   • Personal details

3. teams.csv

   • Team ID, Name, Founded Year
   • Stadium information
   • League affiliation
   • Coach information
   • Team crest URL

4. coaches.csv

   • Coach ID, Name
   • Team affiliation
   • Nationality

5. referees.csv

   • Referee ID, Name
   • Nationality
   • Matches officiated

6. stadiums.csv

   • Stadium ID, Name
   • Location
   • Capacity

7. standings.csv

   • Current league positions
   • Points, Wins, Draws, Losses
   • Goals For/Against
   • Form and Performance metrics

8. scores.csv

   • Detailed match scores
   • Goal statistics
   • Match events

9. seasons.csv

   • Season information
   • League details
   • Year

10. sports_league.sqlite

    • Complete database in SQLite format
    • All tables and relationships included
    • Ready for immediate use

Data Quality

• Data is sourced from football-data.org API
• Regular weekly updates
• Consistent format across all leagues
• Complete historical record for the 2023-2024 season
• Verified and cleaned data

License

This dataset is released under the Creative Commons Zero v1.0 Universal license

Updates and Maintenance

• Dataset is updated weekly
• Last update: March 20, 2024
• Check the version history for detailed changes

Contributing

If you find any issues or have suggestions for improvements, please: 1. Open an issue on the dataset's GitHub repository 2. Submit a pull request with your proposed changes 3. Contact the maintainer directly

Acknowledgments

• Data provided by football-data.org
• Community contributions and feedback
• Open-source tools and libraries used in data collection and processing

Github

Project: https://github.com/kaimg/Sports-League-Management-System

.TOP Whois Database, discover comprehensive ownership details, registration dates, and more for .TOP TLD with Whois Data Center.

List of Top Authors of Database Management sorted by citations.

The Alaska Geochemical Database Version 3.0 (AGDB3) contains new geochemical data compilations in which each geologic material sample has one best value determination for each analyzed species, greatly improving speed and efficiency of use. Like the Alaska Geochemical Database Version 2.0 before it, the AGDB3 was created and designed to compile and integrate geochemical data from Alaska to facilitate geologic mapping, petrologic studies, mineral resource assessments, definition of geochemical baseline values and statistics, element concentrations and associations, environmental impact assessments, and studies in public health associated with geology. This relational database, created from databases and published datasets of the U.S. Geological Survey (USGS), Atomic Energy Commission National Uranium Resource Evaluation (NURE), Alaska Division of Geological & Geophysical Surveys (DGGS), U.S. Bureau of Mines, and U.S. Bureau of Land Management serves as a data archive in support of Alaskan geologic and geochemical projects and contains data tables in several different formats describing historical and new quantitative and qualitative geochemical analyses. The analytical results were determined by 112 laboratory and field analytical methods on 396,343 rock, sediment, soil, mineral, heavy-mineral concentrate, and oxalic acid leachate samples. Most samples were collected by personnel of these agencies and analyzed in agency laboratories or, under contracts, in commercial analytical laboratories. These data represent analyses of samples collected as part of various agency programs and projects from 1938 through 2017. In addition, mineralogical data from 18,138 nonmagnetic heavy-mineral concentrate samples are included in this database. The AGDB3 includes historical geochemical data archived in the USGS National Geochemical Database (NGDB) and NURE National Uranium Resource Evaluation-Hydrogeochemical and Stream Sediment Reconnaissance databases, and in the DGGS Geochemistry database. Retrievals from these databases were used to generate most of the AGDB data set. These data were checked for accuracy regarding sample location, sample media type, and analytical methods used. In other words, the data of the AGDB3 supersedes data in the AGDB and the AGDB2, but the background about the data in these two earlier versions are needed by users of the current AGDB3 to understand what has been done to amend, clean up, correct and format this data. Corrections were entered, resulting in a significantly improved Alaska geochemical dataset, the AGDB3. Data that were not previously in these databases because the data predate the earliest agency geochemical databases, or were once excluded for programmatic reasons, are included here in the AGDB3 and will be added to the NGDB and Alaska Geochemistry. The AGDB3 data provided here are the most accurate and complete to date and should be useful for a wide variety of geochemical studies. The AGDB3 data provided in the online version of the database may be updated or changed periodically.

Top Tier Domains LLC Whois Database, discover comprehensive ownership details, registration dates, and more for Top Tier Domains LLC with Whois Data Center.

This statistic shows the leading vendors of big data and analytics software from 2015 to 2017. In 2017, Splunk was the largest big data and analytics software provider with ** percent of the market.

Variability in mean payment per physician, number of physicians, and aggregated payments for transactions in the Open Payments database, 2014–2018, for each top-category specialty available for allopathic and osteopathic physicians.

The Species of Greatest Conservation Need National Database is an aggregation of lists from State Wildlife Action Plans. Species of Greatest Conservation Need (SGCN) are wildlife species that need conservation attention as listed in action plans. In this database, we have validated scientific names from original documents against taxonomic authorities to increase consistency among names enabling aggregation and summary. This database does not replace the information contained in the original State Wildlife Action Plans. The database includes SGCN lists from 56 states, territories, and districts, encompassing action plans spanning from 2005 to 2022. State Wildlife Action Plans undergo updates at least once every 10 years by respective wildlife agencies. The SGCN list data from these action plans have been compiled in partnership with individual wildlife management agencies, the United States Fish and Wildlife Service, and the Association of Fish and Wildlife Agencies. The SGCN ...