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 dataset contains the results of Medicare Shared Savings Program Performance Accountable Care Organizations (ACO) coverage for years 2013, 2014 and 2015.

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).

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.

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.

The table Building Performance Data is part of the dataset U.S. Building Performance Database, available at https://cmu.redivis.com/datasets/8yz5-3vqbyynqy. It contains 439555 rows across 32 variables.

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

The percent of cases referred to The Department of Juvenile Services Intake that are diverted to pre-court supervision, as well as the percent of DJS Committed youth successfully released without a new delinquent adjudication or conviction within a year of release.

Each year, County departments and agencies report performance data on core activities for public viewing on the County’s website. This dataset contains these reports for all past years starting in 2018. recordKey: A unique identifier consisting of, respectively, a code for the department and the numbers of the goal, objective, and measureGoal: Encompasses one or more objectivesObjective: A subdivision of a goal, encompasses one or more measuresTimeframe: Either Calendar Year or Fiscal Year. For example, the 2023 fiscal year began on July 1, 2022, and ended on June 30, 2023.Measure: The specific result being measuredMeasure Type: Resource (Input); Workload, Demand, Production (Output); Efficiency; Quality; or Impact (Outcome)Units: Number; Percentage; Average; or DollarsYear (for example ‘2018): The amount reported by the department for the listed measure in this fiscal or calendar year

Each year, County departments and agencies report performance data on core activities for public viewing on the County’s website. This dataset contains these reports for all past years starting in 2019. recordKey: A unique identifier consisting of, respectively, a code for the department and the numbers of the goal, objective, and measureGoal: Encompasses one or more objectivesObjective: A subdivision of a goal, encompasses one or more measuresTimeframe: Either Calendar Year or Fiscal Year. For example, the 2023 fiscal year began on July 1, 2022, and ended on June 30, 2023.Measure: The specific result being measuredMeasure Type: Resource (Input); Workload, Demand, Production (Output); Efficiency; Quality; or Impact (Outcome)Units: Number; Percentage; Average; or DollarsYear (for example ‘2018): The amount reported by the department for the listed measure in this fiscal or calendar year

This dataset tracks the updates made on the dataset "Performance Year 2019 Medicare Shared Savings Program Accountable Care Organizations – SNF Affiliates" as a repository for previous versions of the data and metadata.

As of 2024, hedge funds following a quant strategy had the lowest net mean performance over a one-year period at **** percent. In comparison, ***** and five-year performance rates were higher at **** and **** percent, respectively. However, despite being higher, these rates were relatively mediocre, ranking as the second-lowest mean rate in a ***** and five-year performance period.

The data shows the proportion of employees awarded a performance rating in relation to grade, gender, working pattern, disability, ethnicity, religious belief, sexual orientation, age and distribution by location. The performance year is from 1 April to 31 March.

Note: Find data at source. ・ This dataset was curated from an office building constructed in 2015 in Berkeley, California, which includes whole-building and end-use energy consumption, HVAC system operating conditions, indoor and outdoor environmental parameters, and occupant counts. The data was collected in three years from more than 300 sensors and meters for two office floors (each 2,325 m2) of the building. A three-step data curation strategy is applied to transform the raw data into the research-grade data: (1) cleaning the raw data to detect and adjust the outlier values and fill the data gaps; (2) creating the metadata model of the building systems and data points using the Brick schema; (3) describing the metadata of the dataset using a semantic JSON schema. This dataset can be used for various types of applications, including building energy benchmarking, load shape analysis, energy prediction, occupancy prediction and analytics, and HVAC controls to improve understanding and efficiency of building operations for reducing energy use, energy costs, and carbon emissions.

Abstract

"The Building Performance Database (BPD) is the nation's largest dataset of information about the energy-related characteristics of commercial and residential buildings. The BPD combines, cleanses and anonymizes data collected by federal, state and local governments, utilities, energy efficiency programs, building owners and private companies, and makes it available to the public" (Lawrence Berkeley National Laboratory, 2022). Data curated by Carnegie Mellon University Libraries.

Methodology

Data were combined across the datasets listed on the BPD website (Menu button -%3E Public Datasets -%3E List of Files).

Usage

• The raw data were collected from public sources by the BPD.

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• The BPD-curated data are for public use.
• Data from multiple years are included.

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• The building identifiers in the id column may be duplicated across data sources.

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The data shows the year-wise performance of institutional sectors at current prices over the time period from 2011-2012 to 2015-2016.

Note: # indicates 1st Revised Estimates (New Series) * indicates 2nd Revised Estimates (New Series)

This table shows overall ATCEMS response interval performance for entire fiscal years. Data in the table is broken out by incident response priority and service area (City of Austin or Travis County).

The net performance of hedge funds had a ****** rate of distribution over the one-year period than that of the five-year period. Upon review of the assets under management, weighted net performance over the five-year period aside from quant funds performance rates did not drop below **** percent. In contrast, the rate of distribution of one-year performance period was much wider, ranging from under *** percent to above ** percent. The overall best-performing hedge funds were those that implemented a multi-strategy investment method. This strategy allocates funds across multiple sub-strategies and asset classes, resulting in highly diverse portfolios.

Interactive chart showing the YTD daily performance of the Dow Jones Industrial Average stock market index. Performance is shown as the percentage gain from the last trading day of the previous year.

IDEA Part B State Performance Plan and Annual Performance Report, Fiscal Year 2010 (IDEA Part B SPP/APR FY2010), is a study that is part of the IDEA Part B State Performance Plan and Annual Performance Report (IDEA Part B SPP/APR) program; program data is available since 2002-03 at https://www2.ed.gov/fund/data/report/idea/partbspap/allyears.html. IDEA Part B SPP/APR FY2010 (https://idea.ed.gov/explore/home) is a cross-sectional study that collects State Performance Plans that evaluate the state lead agency�s efforts to implement the requirements and purposes of Part B of the Individuals with Disabilities Education Act and describe how the lead agency will improve such implementation. The study also collects Annual Performance Reports which report annually to the public on the intervention service programs located in the state on the targets in the lead agency�s performance plan. They study�s response rate was 100 percent. Key statistics produced from IDEA Part B SPP/APR FY2010 are children age 3-21 participating in intervention programs and effectiveness of such programs, and other performance indicators.