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.

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).

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).

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.

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

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 main content of this dataset includes the number of activities and participants organized by various cultural and educational centers abroad, categorized by venue usage and hosting entities over the years.

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.

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 provides key performance indicators for several agencies disaggregated by community district, police precinct, borough or school district. Each line of data indicates the relevant agency, the indicator, the type of geographic subunit and number, and full fiscal year data points. This data is submitted by the relevant agency to the Mayor’s Office of Operations on an annual basis and is available on Operations’ website. For the latest available information, please refer to the Mayor's Management Report - Agency Performance Indicators dataset.

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.

This dataset shows the scheduled time, actual departure time, and information about delays for each Staten Island Ferry trip.

This dataset contains select monthly performance statistics that DOI regularly reports to the Mayor's Office of Operations for 2010 - 2015. This dataset includes several indicators that are cummulated for the Mayor's Management Reports, such as the including numbers of complaints received by the Agency and the numbers of arrests made. This dataset also includes monthly statistics on the Agency's outreach efforts (anticorrupion and whistleblower lectures) as well customer service indicators (such as the number of emails received by the Agency).

Student Performance Data

This dataset provides insights into various factors influencing the academic performance of students. It is curated for use in educational research, data analytics projects, and predictive modeling. The data reflects a combination of personal, familial, and academic-related variables gathered through observation or survey.

The dataset includes a diverse range of students and captures key characteristics such as study habits, family background, school attendance, and overall performance. It is well-suited for exploring correlations, visualizing trends, and training machine learning models related to academic outcomes.

Highlights:

Clean, structured format suitable for immediate use Designed for beginner to intermediate-level data analysis Valuable for classification, regression, and data storytelling projects

File Format:

Type: CSV (Comma-Separated Values) Encoding: UTF-8 Structure: Each row represents a student record

Applications

Student performance prediction Educational policy planning Identification of performance gaps and influencing factors Exploratory data analysis and visualization

This paper examines the impact of lagged performance on free agent contracts for players in the National Basketball Association. The main approach of the paper is twofold. The first piece investigates how past performance affects future performance in the two seasons after contract year and compares it to the impact previous performance has on contract terms for free agent players. The second piece investigates the rationality of free agent contracts in their entirety by comparing the impact of lagged performance on total accumulated production and total dollar value paid. The goal is to determine if performance prior to contract year is underweighted in contract decision-making relative to its predictive power of future performance. There is evidence that performance in years prior to contract year is overlooked in contract determination decisions by NBA general managers, and there is mild evidence that performance data two years prior to contract year are underweighted given their predictive power of future performance.

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

Table S3. Performance statistics on output rate. a. Output rate (%) performance statistics on PacBio data of ten methods using five SR coverages. b. Output rate (%) performance statistics on ONT data of ten methods using five SR coverages. (XLSX 19 kb)

Table S1. Performance statistics on sensitivity. a. Sensitivity performance statistics on PacBio data of ten methods using five SR coverages. b. Sensitivity performance statistics on ONT data of ten methods using five SR coverages. (XLSX 20 kb)

Table S2. Performance statistics on accuracy. a. Accuracy performance statistics on PacBio data of ten methods using five SR coverages. b. Accuracy performance statistics on ONT data of ten methods using five SR coverages. (XLSX 22 kb)