The Shared Savings Program Performance Year Financial and Quality Results data provides Medicare Shared Savings Program (Shared Savings Program) ACO-specific quality, expenditure, benchmark, and shared savings/loss metrics, as well as summarized beneficiary and provider information for each performance year of the Shared Savings Program. DISCLAIMER: This information is current as of the last update. Changes to Shared Savings Program ACO information occur periodically. Each Shared Savings Program ACO has the most up-to-date information about their organization. Consider contacting the Shared Savings Program ACO for the latest information. Contact information is available in the ACO Public Use File (PUF) and the ACO Participants PUF.

This page provides granteee-level program performance data and data analysis derived from performance reports submitted by grantees under the Educational Opportunity Centers (EOC) program.

This artifact contains performance test results from MongoDB’s internal performance testing system.

The data included here within is the associated model training results from the correlated paper "Distribution-Driven Augmentation of Real-World Datasets for Improved Cancer Diagnostics With Machine Learning". This paper focuses on using kernel density estimators to curate datasets by balancing classes and filling missing null values though synthetically generated data. Additionally

The objective of publishing departmental internal audit performance results, in the form of key compliance attributes, is to provide pertinent information to stakeholders (Canadians, parliamentarians) regarding the professionalism, performance and impact of the function in departments.

The dataset details the performance (division 1 and division 2) by percentage in districts across Uganda.

The performance review software market is experiencing robust growth, driven by the increasing need for efficient employee performance management and the adoption of cloud-based solutions. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several key factors. Firstly, businesses are recognizing the strategic importance of performance management in improving employee engagement, productivity, and overall business outcomes. Secondly, the shift towards remote and hybrid work models necessitates robust digital solutions for performance tracking and feedback. Finally, advanced features such as AI-powered analytics, personalized development plans, and automated workflows are enhancing the value proposition of performance review software, attracting a wider range of users. The market is segmented by deployment (cloud-based and on-premise), organization size (small, medium, and large enterprises), and industry vertical, with cloud-based solutions and large enterprises driving a significant portion of market growth. Competition in the performance review software market is intense, with several established players like Trakstar, BambooHR, Lattice, and newer entrants vying for market share. Key success factors include ease of use, integration with existing HR systems, robust reporting and analytics capabilities, and strong customer support. While the market faces challenges like data security concerns and the need for ongoing employee training, the overall outlook remains highly positive. The projected CAGR suggests that the market will surpass $5 billion by 2033, driven by continuous innovation, increased adoption in emerging markets, and the growing awareness of the importance of data-driven performance management strategies. The market's success will also hinge on vendors' ability to adapt to evolving employee expectations and offer increasingly personalized and engaging performance management experiences.

The Performance Confidence Index assesses the extent to which employees believe their organization has an outstanding competitive future, based on innovative, high-quality products and services that are highly regarded by the marketplace. The Performance Confidence Index is an average of the responses for the five items: Employees in my work unit meet the needs of our customers; Employees in my work unit contribute positively to my agency’s performance; Employees in my work unit produce high-quality work; Employees in my work unit adapt to changing priorities; Employees in my work unit achieve our goals.

Esports Performance Rankings and Results

Performance Rankings and Results from Multiple Esports Platforms

By [source]

About this dataset

This dataset provides a detailed look into the world of competitive video gaming in universities. It covers a wide range of topics, from performance rankings and results across multiple esports platforms to the individual team and university rankings within each tournament. With an incredible wealth of data, fans can discover statistics on their favorite teams or explore the challenges placed upon university gamers as they battle it out to be the best. Dive into the information provided and get an inside view into the world of collegiate esports tournaments as you assess all things from Match ID, Team 1, University affiliations, Points earned or lost in each match and special Seeds or UniSeeds for exceptional teams. Of course don't forget about exploring all the great Team Names along with their corresponding websites for further details on stats across tournaments!

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Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

Download Files First, make sure you have downloaded the CS_week1, CS_week2, CS_week3 and seeds datasets on Kaggle. You will also need to download the currentRankings file for each week of competition. All files should be saved using their originally assigned name in order for your analysis tools to read them properly (ie: CS_week1.csv).

Understand File Structure Once all data has been collected and organized into separate files on your desktop/laptop computer/mobile device/etc., it's time to become familiar with what type of information is included in each file. The main folder contains three main data files: week1-3 and seedings. The week1-3 contain teams matched against one another according to university, point score from match results as well as team name and website URL associated with university entry; whereas the seedings include a ranking system amongst university entries which are accompanied by information regarding team names, website URLs etc.. Furthermore, there is additional file featured which contains currentRankings scores for each individual player/teams for an first given period of competition (ie: first week).

Analyzing Data Now that everything is set up on your end it’s time explore! You can dive deep into trends amongst universities or individual players in regards to specific match performances or standings overall throughout weeks of competition etc… Furthermore you may also jumpstart insights via further creation of graphs based off compiled date from sources taken from BUECTracker dataset! For example let us say we wanted compare two universities- let's say Harvard University v Cornell University - against one another since beginning of event i we shall extract respective points(column),dates(column)(found under result tab) ,regions(csilluminating North America vs Europe etc)general stats such as maps played etc.. As well any other custom ideas which would come along in regards when dealing with similar datasets!

Research Ideas

• Analyze the performance of teams and identify areas for improvement for better performance in future competitions.
• Assess which esports platforms are the most popular among gamers.
• Gain a better understanding of player rankings across different regions, based on rankings system, to create targeted strategies that could boost individual players' scoring potential or team overall success in competitive gaming events

Acknowledgements

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

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: CS_week1.csv | Column name | Description | |:---------------|:----------------------------------------------| | Match ID | Unique identifier for each match. (Integer) | | Team 1 | Name of the first team in the match. (String) | | University | University associated with the team. (String) |

File: CS_week1_currentRankings.csv | Column name | Description | |:--------------|:-----------------------------------------------------------|...

Provide annual audit results statistics (annual performance)

The Policy on Internal Audit and its associated Directive on Internal Audit came into force on April 1, 2017. The Directive on Internal Audit stipulates, “Departments must meet public reporting requirements as prescribed by the Comptroller General of Canada and using Treasury Board of Canada Secretariat prescribed platforms, including: Performance results for the internal audit function (A.2.2.3, A.2.2.3.1)”. In accordance with the Office of the Comptroller General’s request and with the Policy, we are pleased to provide Shared Service Canada’s Office of Audit and Evaluation key compliance attributes as defined by the Office of the Comptroller General guidance for the reporting period April 1 to June 30, 2018.

This dataset tracks the updates made on the dataset "Performance Year Financial and Quality Results" as a repository for previous versions of the data and metadata.

The dataset includes the predictive performance and profitability performance of each model. On the top panel, there are quarterly performance. On the bottom panel, there are yearly performance. Each sheet describes the results in each financial market.

This dataset tracks the updates made on the dataset "Performance Year Financial and Quality Results - Archived" as a repository for previous versions of the data and metadata.

Provide 113 Ministry of the Interior performance evaluation results list for free professional groups to inquire about

This is the anonymous reviewing version; the source code repository will be added after the review.

This dataset provides the results of measuring jetty with the 1,000 artificial regressions with Peass and with JMH. The creation of the artificial regressions and the measurement is defined here: https://anonymous.4open.science/r/jetty-evaluation-6F58/ (Repository named jetty-evaluation, GitHub link will be provided after review) An example regression is contained in https://anonymous.4open.science/r/jetty-experiments-202D We obtained these data from measurement on Intel Xeon CPU E5-2620 v3 @ 2.40GHz.

The dataset contains the following data:

• regression-results-peass-0.tar.xz (Results of the measurement with Peass, part 0)
• regression-results-peass-1.tar.xz (Results of the measurement with Peass, part 1)
• regression-results-peass-2.tar.xz (Results of the measurement with Peass, part 2)
• regression-results-peass-3.tar.xz (Results of the measurement with Peass, part 3)
• regression-results-jmh.tar.xz (Results of the measurement with JMH)
• tree-results.tar.xz (Metadata of the trees)

To get the data in a usable format, extract the peass data to one folder (the folder will be named $PEASS_RESULT_FOLDER):

mkdir peass
for file in *; do echo $file; tar -xf $file; done
for i in {0..3}; do mv $i/* .; done

This will yield to a folder containing 1000 folders named regression-$i, where each consists of

• deps.tar.xz: The regression test selection results
• logs.tar.xz: The logs of the test executions
• results: The traces of the regression test selection and a file named changes_*testcase.json, which contains statistical details of the measured performance change (if present)
• jetty.project_peass: Detailed measurement data and logs of individual JVM starts

To analyse the Peass results, run

cd scripts/peass
./analyzeChangeIdentification.sh $PEASS_RESULTS_FOLDER
./analyzeFrequency.sh $PEASS_RESULTS_FOLDER

This will take some time, since partial results need to be unpacked for analysis. The first script will create the following results:

and the second will yield the following results:

Correct Measurement: 587
Not selected changes: 146
Wrong measurement result: 267
Wrong analysis (should be 0): 0
Overall: 1000
Share of changed method on correct measurements: 0.109571 0.11238 32
Method call count on correct measurement: 15638.4 32853.7 32
Average tree depth on correct measurements: 1.1022 2.6875 32
Share of changed method on wrong measurements: 0.17692 0.180365 968
Method call count on wrong measurement: 711415 180438 968
Average tree depth on wrong measurements: 1.23239 2.42252 968

To analyze the JMH data, first extract the metadata (the folder will be named $TREEFOLDER):

tar -xf tree-results.tar.xz

Afterwards extract the JMH results (the folder will be named $JMH_RESULTS_FOLDER):

tar -xvf regression-results-jmh.tar.xz

This will yield a folder containing a measurement for each regression with two files:

• basic.json: The performance measurement result of the basic version
• regression-$i.json: The performance measurement result of the version containing the regression

Afterwards, run the analysis in the jetty-evaluation repository:

cd scripts/jmh
./analyzeFrequency.sh $JMH_RESULTS_FOLDER $TREEFOLDER

Since the regression are injected in the call tree of the benchmark, there are now unselected changes. The analysis will yield the following results:

Share of changed method on correct measurements: 0.184631 0.271968 587
Method call count on correct measurement: 14628.2 4979.6 587
Share of changed method on wrong measurements: 0.180981 0.235614 267
Method call count on wrong measurement: 14902.2 4333.58 267

This dataset shows the value modifier public use file (PUF) Performance Year 2016 (Payment Year 2018) by the Centers for Medicare and Medicaid Services (CMS).

The performance of the 109-Year Government Policy Implementation Plan is completed by self-assessment by various departments and bureaus of the government. After that, the Planning Office summarizes the achievement of the goals reported by each unit, and completes the 109-Year Government Performance Report.

According to a 2022 global review of studies published on the non-surgical applications of virtual reality in the area of education in healthcare, almost 30 percent published results which showed VR use led to reduced procedure or performance times. Furthermore, over 17 percent of studies on VR found increased performance.

Employee Performance Management System Market size was valued at USD 4.64 Billion in 2023 and is projected to reach USD 8.73 Billion by 2031, growing at a CAGR of 6.2% during the forecast period 2024-2031.

Global Employee Performance Management System Market Drivers

The market drivers for the Employee Performance Management System Market can be influenced by various factors. These may include:

Technological Progress: The market for employee performance management systems is heavily influenced by the quick development of technology. Advances in analytics, machine learning, and artificial intelligence allow businesses to automate performance review procedures, providing real-time feedback and insights. Performance management tools are now more easily available and scalable for companies of all sizes thanks to the incorporation of cloud-based solutions. Additionally, by enabling goal-setting and feedback-gathering while on the road, mobile applications improve employee engagement. These developments make it easier for businesses to conduct performance reviews and promote a continuous improvement culture, which helps them stay up to date with changing workplace dynamics.

A Greater Focus on Staff Involvement: The market for employee performance management systems is being driven in large part by an increasing emphasis on employee engagement. Employers are realizing more and more that dedicated, productive staff members are more likely to contribute to the success of the company as a whole. Systems for performance management make it easier to have continuous dialogues about progress and feedback, which fosters an atmosphere of openness and trust. Employee ambitions are in line with business objectives thanks to this technology's emphasis on personal growth. Through performance management systems, organizations are focusing more on creating meaningful employee experiences as a means of retaining top talent and lowering attrition rates.

Global Employee Performance Management System Market Restraints

Several factors can act as restraints or challenges for the Employee Performance Management System Market. These may include:

High Expenses of Implementation: The market for employee performance management systems is severely constrained by the high implementation costs. In addition to software solutions, organizations also need to engage in continuous maintenance, training initiatives, and infrastructure updates. Budgetary restrictions are a major issue for small and medium-sized businesses (SMEs), which makes it difficult for them to implement advanced performance management systems. This financial obstacle might force people to rely on antiquated techniques, which would impede growth and productivity as a whole. Furthermore, unstated expenses associated with system integration and customization may increase the financial strain and deter businesses from adopting more sophisticated performance management systems.

Opposition to Change: One significant barrier to the market for employee performance management systems is resistance to change among staff members and management. A lot of people are used to the old-fashioned ways of evaluating performance, which makes them wary of newly established systems. This resistance can take many different forms, such as an unwillingness to use new technologies or an attachment to antiquated methods. These difficulties may also be made worse by the leadership's poor communication on the advantages and features of the new technology. Organizations may find it difficult to accomplish intended results without the right buy-in from all stakeholders, which could ultimately undermine the efficacy of the performance management programs they implement.