Context

This dataset contains 2021-2022 football player stats per 90 minutes. Only players of Premier League, Ligue 1, Bundesliga, Serie A and La Liga are listed.

• 2022-2023 Football Player Stats
• 2021-2022 Football Team Stats
• 2022-2023 Football Team Stats

Content

+2500 rows and 143 columns. Columns' description are listed below.

• Rk : Rank
• Player : Player's name
• Nation : Player's nation
• Pos : Position
• Squad : Squad’s name
• Comp : League that squat occupies
• Age : Player's age
• Born : Year of birth
• MP : Matches played
• Starts : Matches started
• Min : Minutes played
• 90s : Minutes played divided by 90
• Goals : Goals scored or allowed
• Shots : Shots total (Does not include penalty kicks)
• SoT : Shots on target (Does not include penalty kicks)
• SoT% : Shots on target percentage (Does not include penalty kicks)
• G/Sh : Goals per shot
• G/SoT : Goals per shot on target (Does not include penalty kicks)
• ShoDist : Average distance, in yards, from goal of all shots taken (Does not include penalty kicks)
• ShoFK : Shots from free kicks
• ShoPK : Penalty kicks made
• PKatt : Penalty kicks attempted
• PasTotCmp : Passes completed
• PasTotAtt : Passes attempted
• PasTotCmp% : Pass completion percentage
• PasTotDist : Total distance, in yards, that completed passes have traveled in any direction
• PasTotPrgDist : Total distance, in yards, that completed passes have traveled towards the opponent's goal
• PasShoCmp : Passes completed (Passes between 5 and 15 yards)
• PasShoAtt : Passes attempted (Passes between 5 and 15 yards)
• PasShoCmp% : Pass completion percentage (Passes between 5 and 15 yards)
• PasMedCmp : Passes completed (Passes between 15 and 30 yards)
• PasMedAtt : Passes attempted (Passes between 15 and 30 yards)
• PasMedCmp% : Pass completion percentage (Passes between 15 and 30 yards)
• PasLonCmp : Passes completed (Passes longer than 30 yards)
• PasLonAtt : Passes attempted (Passes longer than 30 yards)
• PasLonCmp% : Pass completion percentage (Passes longer than 30 yards)
• Assists : Assists
• PasAss : Passes that directly lead to a shot (assisted shots)
• Pas3rd : Completed passes that enter the 1/3 of the pitch closest to the goal
• PPA : Completed passes into the 18-yard box
• CrsPA : Completed crosses into the 18-yard box
• PasProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
• PasAtt : Passes attempted
• PasLive : Live-ball passes
• PasDead : Dead-ball passes
• PasFK : Passes attempted from free kicks
• TB : Completed pass sent between back defenders into open space
• PasPress : Passes made while under pressure from opponent
• Sw : Passes that travel more than 40 yards of the width of the pitch
• PasCrs : Crosses
• CK : Corner kicks
• CkIn : Inswinging corner kicks
• CkOut : Outswinging corner kicks
• CkStr : Straight corner kicks
• PasGround : Ground passes
• PasLow : Passes that leave the ground, but stay below shoulder-level
• PasHigh : Passes that are above shoulder-level at the peak height
• PaswLeft : Passes attempted using left foot
• PaswRight : Passes attempted using right foot
• PaswHead : Passes attempted using head
• TI : Throw-Ins taken
• PaswOther : Passes attempted using body parts other than the player's head or feet
• PasCmp : Passes completed
• PasOff : Offsides
• PasOut : Out of bounds
• PasInt : Intercepted
• PasBlocks : Blocked by the opponent who was standing it the path
• SCA : Shot-creating actions
• ScaPassLive : Completed live-ball passes that lead to a shot attempt
• ScaPassDead : Completed dead-ball passes that lead to a shot attempt
• ScaDrib : Successful dribbles that lead to a shot attempt
• ScaSh : Shots that lead to another shot attempt
• ScaFld : Fouls drawn that lead to a shot attempt
• ScaDef : Defensive actions that lead to a shot attempt
• GCA : Goal-creating actions
• GcaPassLive : Completed live-ball passes that lead to a goal
• GcaPassDead : Completed dead-ball passes that lead to a goal
• GcaDrib : Successful dribbles that lead to a goal
• GcaSh : Shots that lead to another goal-scoring shot
• GcaFld : Fouls drawn that lead to a goal
• GcaDef : Defensive actions that lead to a goal
• Tkl : Number of players tackled
• TklWon : Tackles in which the tackler's team won possession of the ball
• TklDef3rd : Tackles in defensive 1/3
• TklMid3rd : Tackles in middle 1/3
• TklAtt3rd : Tackles in attacking 1/3
• TklDri : Number of dribblers tackled
• TklDriAtt : Number of times dribbled past plus number of tackles
• TklDri% : Percentage of dribblers tackled
• TklDriPast : Number of times dribbled past by an opposing player
• Press : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball
• PresSucc : Number of times the squad gained possession withing five seconds of applying pressure
• Press% : Percentage of time the squad gained possession withing five seconds of applying pressure
• PresDef3rd : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball, in the defensive 1/3
• PresMid3rd : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball, in the middle 1/3
• PresAtt3rd : Number of times applying pressure to opposing player who is receiving, carrying or releasing the ball, in the attacking 1/3
• Blocks : Number of times blocking the ball by standing in its path
• BlkSh : Number of times blocking a shot by standing in its path
• BlkShSv : Number of times blocking a shot that was on target, by standing in its path
• BlkPass : Number of times blocking a pass by standing in its path
• Int : Interceptions
• Tkl+Int : Number of players tackled plus number of interceptions
• Clr : Clearances
• Err : Mistakes leading to an opponent's shot
• Touches : Number of times a player touched the ball. Note: Receiving a pass, then dribbling, then sending a pass counts as one touch
• TouDefPen : Touches in defensive penalty area
• TouDef3rd : Touches in defensive 1/3
• TouMid3rd : Touches in middle 1/3
• TouAtt3rd : Touches in attacking 1/3
• TouAttPen : Touches in attacking penalty area
• TouLive : Live-ball touches. Does not include corner kicks, free kicks, throw-ins, kick-offs, goal kicks or penalty kicks.
• DriSucc : Dribbles completed successfully
• DriAtt : Dribbles attempted
• DriSucc% : Percentage of dribbles completed successfully
• DriPast : Number of players dribbled past
• DriMegs : Number of times a player dribbled the ball through an opposing player's legs
• Carries : Number of times the player controlled the ball with their feet
• CarTotDist : Total distance, in yards, a player moved the ball while controlling it with their feet, in any direction
• CarPrgDist : Total distance, in yards, a player moved the ball while controlling it with their feet towards the opponent's goal
• CarProg : Carries that move the ball towards the opponent's goal at least 5 yards, or any carry into the penalty area
• Car3rd : Carries that enter the 1/3 of the pitch closest to the goal
• CPA : Carries into the 18-yard box
• CarMis : Number of times a player failed when attempting to gain control of a ball
• CarDis : Number of times a player loses control of the ball after being tackled by an opposing player
• RecTarg : Number of times a player was the target of an attempted pass
• Rec : Number of times a player successfully received a pass
• Rec% : Percentage of time a player successfully received a pass
• RecProg : Completed passes that move the ball towards the opponent's goal at least 10 yards from its furthest point in the last six passes, or any completed pass into the penalty area
• CrdY : Yellow cards
• CrdR : Red cards
• 2CrdY : Second yellow card
• Fls : Fouls committed
• Fld : Fouls drawn
• Off : Offsides
• Crs : Crosses
• TklW : Tackles in which the tackler's team won possession of the ball
• PKwon : Penalty kicks won
• PKcon : Penalty kicks conceded
• OG : Own goals
• Recov : Number of loose balls recovered
• AerWon : Aerials won
• AerLost : Aerials lost
• AerWon% : Percentage of aerials won

Acknowledgements

Data from Football Reference. Image from UEFA Champions League.

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The USDA-NRCS Soil Series Classification Database contains the taxonomic classification of each soil series identified in the United States, Territories, Commonwealths, and Island Nations served by USDA-NRCS. Along with the taxonomic classification, the database contains other information about the soil series, such as office of responsibility, series status, dates of origin and establishment, and geographic areas of usage. The database is maintained by the soils staff of the NRCS MLRA Soil Survey Region Offices across the country. Additions and changes are continually being made, resulting from on going soil survey work and refinement of the soil classification system. As the database is updated, the changes are immediately available to the user, so the data retrieved is always the most current. The Web access to this soil classification database provides capabilities to view the contents of individual series records, to query the database on any data element and produce a report with the selected soils, or to produce national reports with all soils in the database. The standard reports available allow the user to display the soils by series name or by taxonomic classification. The SC database was migrated into the NASIS database with version 6.2. Resources in this dataset:Resource Title: Website Pointer to Soil Series Classification Database (SC). File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/nrcs/detail/soils/survey/class/data/?cid=nrcs142p2_053583 Supports the following queries:

• View Classification Data by Series Name
• Create Report for a List of Series (with download option)
• Create Report by Query (with download option)
• Create National Report (with download option)
• Soil Series Name Search

Investigator(s): Federal Judicial Center The purpose of this data collection is to provide an official public record of the business of the federal courts. The data originate from 100 court offices throughout the United States. Information was obtained at two points in the life of a case: filing and termination. The termination data contain information on both filing and terminations, while the pending data contain only filing information. For the appellate and civil data, the unit of analysis is a single case. The unit of analysis for the criminal data is a single defendant.Years Produced: Updated bi-annually with annual data.

Our vast historical bar database supports multi asset tick, n-minute and EOD time series price data from major markets. We provide Rest API or custom file services to meet requirements.

Connect with us and provide your request details in order to work out a data solution that meets your requirements.

These data are from marine filtered seawater samples collected at a nearshore station in Monterey Bay, CA. They have undergone metabarcoding for the 18S V9 region. A selection of samples from this plate were included in the publication "Environmental DNA reveals seasonal shifts and potential interactions in a marine community" (Djurhuus et al., 2020). Samples were collected by CTD rosette and filtered by a peristaltic pump system.

Illumina MiSeq metabarcoding data was processed in the following steps: (1) primer sequences were removed through atropos (Didion et al., 2017), (2) reads were denoised, ASV sequences inferred, paired reads merged and chimeras removed through Dada2 (Callahan et al., 2016), (3) taxonomic ranks were assigned through blastn searches to NCBI GenBank's non-redundant nucleotide database (nt) with hits filtered by lowest common ancestor algorithm within MEGAN6 (Huson et al., 2016). Furthermore, post-MEGAN6 filtering was performed to ensure only contigs with a hit of ≥97% sequence identity were annotated to the species level and only contigs with a hit of ≥95% sequence identity were annotated to the genus level. Annotations were elevated to the next highest taxonomic level for contigs that failed these conditions.

Data are presented in two comma-separated values files: occurrence.csv, and DNADerivedData.csv. The former contains the taxonomic identification of each ASV observed and its number of reads, in addition to relevant metadata including the location the water sample was taken, references for the identification procedure, and links to archived sequences. The latter contains the DNA sequence of each ASV observed, in addition to relevant metadata including primer information and links to detailed field and laboratory methods. This data set was transformed from its native format into a table structure using Darwin Core and DNA Derived Data Extension term names as column names.

References:

Djurhuus, A, Closek, CJ, Kelly, RP et al. (2020). Environmental DNA reveals seasonal shifts and potential interactions in a marine community. Nat Commun 11, 254. https://doi.org/10.1038/s41467-019-14105-1

Didion JP, Martin M, Collins FS. (2017) Atropos: specific, sensitive, and speedy trimming of sequencing reads. PeerJ 5:e3720 https://doi.org/10.7717/peerj.3720

Callahan, B., McMurdie, P., Rosen, M. et al. (2016) DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods 13, 581–583 . https://doi.org/10.1038/nmeth.3869

Huson DH, Beier S, Flade I, Górska A, El-Hadidi M, Mitra S, Ruscheweyh HJ, Tappu R. (2016) MEGAN community edition-interactive exploration and analysis of large-scale microbiome sequencing data. PLoS computational biology. Jun 21;12(6):e1004957.

Time Series Databases (TSDB) Software For BFSI Sector Market size was valued at USD 106.74 Million in 2023 and is projected to reach USD 235.99 Million by 2030, growing at a CAGR of 10.53% from 2024 to 2030.

Global Time Series Databases (TSDB) Software For BFSI Sector Market Overview

The need to handle and analyze time-stamped data in various industries, including finance, led to the emergence of time series databases. Traditional relational databases needed better suited for efficiently managing large volumes of time-series data. The banking, financial services, and insurance (BFSI) sector is undergoing a data revolution driven by the exponential growth of time-series data. This data, which captures trends and changes over time, is invaluable for everything from understanding customer behavior to managing risk and making investment decisions. As a result, the demand for robust and scalable time series databases (TSDBs) is skyrocketing in the BFSI sector.

The history of TSDBs in the BFSI sector can be traced back to the early days of electronic trading when the need for high-speed data capture and analysis became apparent. Early TSDBs were often custom-built solutions designed to meet the specific needs of individual financial institutions. However, the rise of cloud computing and big data has led to a new generation of commercial TSDBs that are more affordable, scalable, and easier to use. The BFSI sector generates massive amounts of time-series data from transactions, market movements, customer behavior, and operational systems. Traditional relational databases struggle to handle this data efficiently, making TSDBs essential for storage, retrieval, and analysis.

Regulations like Basel III and IFRS 17 necessitate comprehensive data storage and analysis capabilities. TSDBs facilitate efficient recordkeeping, risk management, and compliance reporting for BFSI institutions. Timely insights into market trends, customer behavior, and fraud detection are crucial for competitive advantage. TSDBs enable real-time data capture, analysis, and prediction, powering AI-driven applications for personalized banking, fraud prevention, and dynamic risk management.

A database providing detailed mortality and population data to those interested in the history of human longevity. For each country, the database includes calculated death rates and life tables by age, time, and sex, along with all of the raw data (vital statistics, census counts, population estimates) used in computing these quantities. Data are presented in a variety of formats with regard to age groups and time periods. The main goal of the database is to document the longevity revolution of the modern era and to facilitate research into its causes and consequences. New data series is continually added to this collection. However, the database is limited by design to populations where death registration and census data are virtually complete, since this type of information is required for the uniform method used to reconstruct historical data series. As a result, the countries and areas included are relatively wealthy and for the most part highly industrialized. The database replaces an earlier NIA-funded project, known as the Berkeley Mortality Database. * Dates of Study: 1751-present * Study Features: Longitudinal, International * Sample Size: 37 countries or areas

The Ocean Data Inventory database is an inventory of all of the oceanographic time series data held by the Ocean Science Division at the Bedford Institute of Oceanography. The data archive includes about 5800 current meter and acoustic doppler time series, 4500 coastal temperature time series from thermographs, as well as a small number (200) of tide gauges. Many of the current meters also have temperature and salinity sensors. The area for which there are data is roughly defined as the North Atlantic and Arctic from 30° - 82° N, although there are some minor amounts of data from other parts of the world. The time period is from 1960 to present. The database is updated on a regular basis.

Midyear population estimates and projections for all countries and areas of the world with a population of 5,000 or more // Source: U.S. Census Bureau, Population Division, International Programs Center// Note: Total population available from 1950 to 2100 for 227 countries and areas. Other demographic variables available from base year to 2100. Base year varies by country and therefore data are not available for all years for all countries. For the United States, total population available from 1950-2060, and other demographic variables available from 1980-2060. See methodology at https://www.census.gov/programs-surveys/international-programs/about/idb.html

25616 Global export shipment records of Series with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The Series Database Statistics provides statistical information of the different areas of economic activity related to the competences of the Ministry, allowing both monitoring of industrial activity such as the analysis of recent trends. Includes more than 7,000 indicators both cyclical and structural nature. The primary sources of information are the Ministry itself, Eurostat, INE, Bank of Spain and the Ministry of Economy.

The global Open Source Time Series Database (TSDB) market size was valued at USD 447.17 million in 2025 and is projected to reach USD 1,922.95 million by 2033, growing at a CAGR of 19.9% from 2025 to 2033. The growing adoption of IoT devices, the increasing need for real-time data analysis, and the rise of the Industrial Internet of Things (IIoT) are driving the growth of the Open Source TSDB market. Cloud-based TSDBs are expected to witness the fastest growth during the forecast period due to their scalability, cost-effectiveness, and ease of use. IoT industry is the largest application segment, and the financial industry is expected to witness the fastest growth during the forecast period. North America held the largest market share in 2025, and Asia Pacific is expected to register the highest CAGR during the forecast period. The key players in the Open Source TSDB market include InfluxData, Timescale, Prometheus, OpenTSDB, VictoriaMetrics, and QuestDB.

The U.S. Geological Survey (USGS) Oceanographic Time-Series Measurements Database contains oceanographic observations made as part of studies designed to increase understanding of sediment transport processes and associated ocean dynamics. This report describes the instrumentation and platforms used to make the measurements; the methods used to process and apply quality-control criteria and archive the data; and the data storage format. The report also includes instructions on how to access the data from the online database at https://stellwagen.er.usgs.gov/.

No description is available. Visit https://dataone.org/datasets/f0cc7db9e91fc405d0ef8767e53df074 for complete metadata about this dataset.

1021 Global import shipment records of Series with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

THIS RESOURCE IS NO LONGER IN SERVICE, documented June 5, 2017. It has been merged with Cell Image Library. Database for sharing and mining cellular and subcellular high resolution 2D, 3D and 4D data from light and electron microscopy, including correlated imaging that makes unique and valuable datasets available to the scientific community for visualization, reuse and reanalysis. Techniques range from wide field mosaics taken with multiphoton microscopy to 3D reconstructions of cellular ultrastructure using electron tomography. Contributions from the community are welcome. The CCDB was designed around the process of reconstruction from 2D micrographs, capturing key steps in the process from experiment to analysis. The CCDB refers to the set of images taken from microscope the as the Microscopy Product. The microscopy product refers to a set of related 2D images taken by light (epifluorescence, transmitted light, confocal or multiphoton) or electron microscopy (conventional or high voltage transmission electron microscopy). These image sets may comprise a tilt series, optical section series, through focus series, serial sections, mosaics, time series or a set of survey sections taken in a single microscopy session that are not related in any systematic way. A given set of data may be more than one product, for example, it is possible for a set of images to be both a mosaic and a tilt series. The Microscopy Product ID serves as the accession number for the CCDB. All microscopy products must belong to a project and be stored along with key specimen preparation details. Each project receives a unique Project ID that groups together related microscopy products. Many of the datasets come from published literature, but publication is not a prerequisite for inclusion in the CCDB. Any datasets that are of high quality and interest to the scientific community can be included in the CCDB.

This is a compilation of stratigraphic constraints and relevant U-series ages from the Last Interglacial period taken from samples in the Bahamas, Turks and Caicos, and the east coast of Florida, USA. It has been exported from the World Atlas of Last Interglacial Shorelines (WALIS) database (https://warmcoasts.eu/world-atlas.html).

The Uniform Crime Reports National Time-Series Data, 1967-1980, include detailed criminal offense and clearance information submitted monthly by over 3,000 consistently reporting law enforcement agencies in the United States. These data, provided in annually pooled cross-sections, were processed at the Center for Applied Social Research, Northeastern University, Boston, Massachusetts to produce easily accessible and highly reliable time-series data on officially reported crime. Originally provided by the Federal Bureau of Investigation (FBI), these data exclude Uniform Crime Report (UCR) data from infrequently reporting law enforcement agencies. In general, only those agencies that submitted ten or more monthly reports in every year during 1967 through 1980 are included in this collection. The data include detailed breakdowns of offenses and clearances taken from disaggregated UCR Return A tapes. Of particular interest are weapon-specific robbery and assault variables, types of rape, burglary, larceny, and motor vehicle theft, and clearances by arrest (or other exceptional means) of adults and juveniles for each offense sub-type. Both monthly and annual counts of these are available. Finally, as an aid to the user, each agency is identified by its FBI "ORI Code" as well as a sequential case number produced and documented by ICPSR in the codebook's appendix. Cases also may be identified by geographic region, state, SMSA, county, population size and group, and frequency of reporting.

Rosalia Times Series Database

The BOKU (University of Natural Resources and Life Sciences Vienna) university demonstration forest Rosalia with an area of 950 ha has been used for research and education since 1875. In 2013 – upon an initiative of a group of researchers in various disciplines – it was decided to extend the so far mainly forestry oriented activities by implementing a hydrological experimental research watershed. The overall objective is to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere. More specifically, emphasis is on bridging the gap between point related measurements and effective values and parameters required for modelling flow and transport processes in watersheds.

2 Objectives

The main objectives for the research watershed are

• to collect data that support the study of transport processes in the system of soil, water, plants and atmosphere
• emphasis is on bridging the gap between point related measurements and effective values and parameters for modelling watersheds of various sizes
• to generate comprehensive reference information for research projects on future management and climate change impacts

Operation is planned for a period of at least 10 years using only internal resources of the university, to avoid potential interruptions due to project-based short-term availability of personal and financial resources.

The objective of this article is to present the research watershed, the data collected and to make these data accessible to the research community.