Percentage of flights arriving on-time. A flight is on-time if it arrives within 15 minutes of the schedule arrival time. Data are available for those carriers that had at least 1% of domestic enplanements in the previous year. The last 25 months of data include only carriers that reported in each of the last 25 months to retain comparability. Earlier data includes all reporting carriers. A scheduled operation consists of any nonstop segment of a flight. The Bureau of Transportation Statistics air collects performance data from U.S. air carriers and international carriers operating within the U.S.

This dataset contains the Department of Finance Performance Statistics on Assembly Written Questions .

The Quarterly Life Insurance Performance Statistics publication provides industry aggregate summaries of financial performance, financial position, solvency, capital adequacy and management capital, as well as details of the performance of individual product groups.

Google Gemini Statistics: In 2023, Google unveiled the most powerful AI model to date. Google Gemini is the world’s most advanced AI leaving the ChatGPT 4 behind in the line. Google has 3 different sizes of models, superior to each, and can perform tasks accordingly. According to Google Gemini Statistics, these can understand and solve complex problems related to absolutely anything. Google even said, they will develop AI in such as way that it will let you know how helpful AI is in our daily routine. Well, we hope our next generation won’t be fully dependent on such technologies, otherwise, we will lose all of our natural talent! Editor’s Choice Google Gemini can follow natural and engaging conversations. According to Google Gemini Statistics, Gemini Ultra has a 90.0% score on the MMLU benchmark for testing the knowledge of and problem-solving on subjects including history, physics, math, law, ethics, history, and medicine. If you ask Gemini what to do with your raw material, it can provide you with ideas in the form of text or images according to the given input. Gemini has outperformed ChatGPT -4 tests in the majority of the cases. According to the report this LLM is said to be unique because it can process multiple types of data at the same time along with video, images, computer code, and text. Google is considering its development as The Gemini Era, showing the importance of our AI is significant in improving our daily lives. Google Gemini can talk like a real person Gemini Ultra is the largest model and can solve extremely complex problems. Gemini models are trained on multilingual and multimodal datasets. Gemini’s Ultra performance on the MMMU benchmark has also outperformed the GPT-4V in the following results Art and Design (74.2), Business (62.7), Health and Medicine (71.3), Humanities and Social Science (78.3), and Technology and Engineering (53.00).

This statistic displays the performance management efficiency benefits as a result of Big Data in the United Kingdom (UK) from 2015 to 2020, by industry. It was estimated that manufacturing would benefit most from improved performance management due to Big Data implementation. In contrast to the 2.25 billion British pounds in benefits of that sector, the estimated benefits of the insurance sector amounted to 86 million British pounds.

Fortnite Player Performance

Understanding Player Performance with Granular Data

By Kristian Reynolds [source]

About this dataset

This dataset contains 88 end-game Fortnite statistics, giving a comprehensive look at player performance over the course of 80 games. Discover the time of day, date, mental state and more that contribute to winning strategies! Measure success across eliminations, assists, revives, accuracy percentage, hits scored and head shots landed. Explore distance traveled and materials gathered or used to gauge efficiency while playing. Examine damage taken versus damage dealt to other players and structures alike. Use this data to reveal peak performance trends in Fortnite gameplay

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How to use the dataset

This dataset is a great resource for analyzing and tracking the performance of Fortnite players. It contains 88 end game stats that provide insights into player performance, such as eliminations, assists and revives. This dataset can help you gain a better understanding of your own performance or another player’s overall effectiveness in the game.

• Analyzing Performance: This dataset can be used to analyze your own or other players’ overall performance in Fortnite across multiple games by looking at statistics like eliminations, assists, revives and head shots (by looking at comparisons between different games).
• Tracking Performance: The dataset also has valuable data that enables you to track any changes in performance over time since it includes data on when the games were played (Date) as well as when they ended (Time of Day). This can be used to measure progress or stagnation in your play over time by comparing different stats like accuracy and distance traveled per game.
• Improving Performance: By combining this data with other information about gear and character builds, one can use this information to look for patterns between successful playstyles across multiple matches or build an optimal loadout for their particular playstyle preferences or intentions see what works best their intended approach

Research Ideas

• Using this dataset to develop player performance indicators that can be used to compare players across games. The indicators can measure each player's ability in terms of eliminations, assists, headshots accuracy and other data points.
• Establishing correlations between the mental state and performance level of a player by analyzing how their stats vary before and after playing under different mental states.
• Analyzing the relationship between overall game performance (such as placement) and specific statistics (such as materials gathered or damage taken). This could provide useful insights into what aspects of gameplay are more important for high-level play in Fortnite

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: Fortnite Statistics.csv | Column name | Description | |:-------------------------|:--------------------------------------------------------------| | Date | Date of the game. (Date) | | Time of Day | Time of day the game was played. (Time) | | Placed | Player's placement in the game. (Integer) | | Mental State | Player's mental state during the game. (String) | | Eliminations | Number of eliminations the player achieved. (Integer) | | Assists | Number of assists the player achieved. (Integer) | | Revives | Number of revives the player achieved. (Integer) | | Accuracy | Player's accuracy in the game. (Float) | | Hits ...

Analysis of ‘ Student Performance Data Set’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/larsen0966/student-performance-data-set on 28 January 2022.

--- Dataset description provided by original source is as follows ---

If this Data Set is useful, and upvote is appreciated. This data approach student achievement in secondary education of two Portuguese schools. The data attributes include student grades, demographic, social and school related features) and it was collected by using school reports and questionnaires. Two datasets are provided regarding the performance in two distinct subjects: Mathematics (mat) and Portuguese language (por). In [Cortez and Silva, 2008], the two datasets were modeled under binary/five-level classification and regression tasks. Important note: the target attribute G3 has a strong correlation with attributes G2 and G1. This occurs because G3 is the final year grade (issued at the 3rd period), while G1 and G2 correspond to the 1st and 2nd-period grades. It is more difficult to predict G3 without G2 and G1, but such prediction is much more useful (see paper source for more details).

--- Original source retains full ownership of the source dataset ---

This dataset contains the Department of Justice Performance Statistics on Assembly Written Questions

National statistical systems are facing significant challenges. These challenges arise from increasing demands for high quality and trustworthy data to guide decision making, coupled with the rapidly changing landscape of the data revolution. To help create a mechanism for learning amongst national statistical systems, the World Bank has developed improved Statistical Performance Indicators (SPI) to monitor the statistical performance of countries. The SPI focuses on five key dimensions of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. This will replace the Statistical Capacity Index (SCI) that the World Bank has regularly published since 2004.The SPI focus on five key pillars of a country’s statistical performance: (i) data use, (ii) data services, (iii) data products, (iv) data sources, and (v) data infrastructure. The SPI are composed of more than 50 indicators and contain data for 186 countries. This set of countries covers 99 percent of the world population. The data extend from 2016-2023, with some indicators going back to 2004.For more information, consult the academic article published in the journal Scientific Data. https://www.nature.com/articles/s41597-023-01971-0.

For further details, please refer to https://documents.worldbank.org/en/publication/documents-reports/documentdetail/815721616086786412/measuring-the-statistical-performance-of-countries-an-overview-of-updates-to-the-world-bank-statistical-capacity-index

The Employee Performance Appraisal Software market has emerged as a critical component in today's competitive business environment, serving as an integral tool for organizations looking to enhance employee engagement and optimize workforce productivity. These software solutions facilitate the systematic evaluation o

The High Performance Workstation (HPW) market has emerged as a critical component in various industries, providing the computational power required for intensive applications such as scientific simulations, 3D rendering, data analysis, and software development. With its ability to handle complex tasks efficiently, H

This data set replaces the previous Verify Performance Dashboard on Gov.UK. The data contains the overall statistics covering Number of Verifications, Verification Success Rates and Number of Services Using Verify.

Zambia ZM: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data was reported at 62.501 NA in 2023. This records an increase from the previous number of 61.215 NA for 2022. Zambia ZM: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data is updated yearly, averaging 60.449 NA from Dec 2016 (Median) to 2023, with 8 observations. The data reached an all-time high of 62.501 NA in 2023 and a record low of 53.248 NA in 2016. Zambia ZM: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Zambia – Table ZM.World Bank.WDI: Governance: Policy and Institutions. The SPI overall score is a composite score measuing country performance across five pillars: data use, data services, data products, data sources, and data infrastructure.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;

Table S6. Performance statistics on memory usage. a. Memory usage performance statistics on PacBio data of ten methods using five SR coverages (unit: G). b. Memory usage performance statistics on ONT data of ten methods using five SR coverages (unit: G). (XLSX 17 kb)

This dataset contains monthly performance statistics for the Great Lakes-St. Lawrence Seaway system.

This dataset contains the monthly average on time performance (OTP) percentage by route and service day type (weekday, Saturday, and Sunday/Holiday service). A bus is considered on time if it is no more than one minute early or five minutes late to a timepoint.

Port Authority has an OTP goal of 73% for bus and 80% for rail service.

Starting in October 2018, Port Authority moved to a different OTP recording system called Clever. OTP data from the Clever system is more accurate because it uses more timepoints; the previous system excluded a large portion of data from OTP processing due to minor technical issues with rider counts on certain trips.

The Mon Incline is not included in this dataset because it does not have a schedule. Service runs every 15 minutes.

OTP only goes back as far as November 2018 for the "T" light rail line because the railcars did not have Automated Vehicle Locators installed until then.

Performance Arts (Theaters, Festivals and Exhibitions) and Celebrations (Paintings and Drawings Displayed for the Artists)

Key Statistics on Business Performance and Operating Characteristics of the Information and Communications Sector - Table 640-75101 : Principal Statistics for All Establishments in the Information and Communications Sector

Tanzania TZ: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data was reported at 67.334 NA in 2022. This records an increase from the previous number of 63.459 NA for 2021. Tanzania TZ: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data is updated yearly, averaging 62.364 NA from Dec 2016 (Median) to 2022, with 7 observations. The data reached an all-time high of 67.334 NA in 2022 and a record low of 58.021 NA in 2018. Tanzania TZ: Statistical Performance Indicators (SPI): Overall Score: Scale 0-100 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Tanzania – Table TZ.World Bank.WDI: Governance: Policy and Institutions. The SPI overall score is a composite score measuing country performance across five pillars: data use, data services, data products, data sources, and data infrastructure. The new Statistical Performance Indicators (SPI) will replace the Statistical Capacity Index (SCI), which the World Bank has regularly published since 2004. Although the goals are the same, to offer a better tool to measure the statistical systems of countries, the new SPI framework has expanded into new areas including in the areas of data use, administrative data, geospatial data, data services, and data infrastructure. The SPI provides a framework that can help countries measure where they stand in several dimensions and offers an ambitious measurement agenda for the international community.;Statistical Performance Indicators, The World Bank (https://datacatalog.worldbank.org/dataset/statistical-performance-indicators);Weighted average;