The Uniform Appraisal Dataset (UAD) Aggregate Statistics Data File and Dashboards are the nation’s first publicly available datasets of aggregate statistics on appraisal records, giving the public new access to a broad set of data points and trends found in appraisal reports. The UAD Aggregate Statistics for Enterprise Single-Family, Enterprise Condominium, and Federal Housing Administration (FHA) Single-Family appraisals may be grouped by neighborhood characteristics, property characteristics and different geographic levels.DocumentationOverview (10/28/2024)Data Dictionary (10/28/2024)Data File Version History and Suppression Rates (12/18/2024)Dashboard Guide (2/3/2025)UAD Aggregate Statistics DashboardsThe UAD Aggregate Statistics Dashboards are the visual front end of the UAD Aggregate Statistics Data File. The Dashboards are designed to provide easy access to customized maps and charts for all levels of users. Access the UAD Aggregate Statistics Dashboards here.UAD Aggregate Statistics DatasetsNotes:Some of the data files are relatively large in size and will not open correctly in certain software packages, such as Microsoft Excel. All the files can be opened and used in data analytics software such as SAS, Python, or R.All CSV files are zipped.

The table UT- Uniform Data is part of the dataset L2 Uniform Voter Data, available at https://redivis.com/datasets/5dss-024bh1rkk. It contains 1521328 rows across 779 variables.

R code for the simulation-based Rao test. (R 5 kb)

The Uniform Facility Data Set (UFDS) was designed to measure the scope and use of drug abuse treatment services in the United States. The survey collects information from each privately- and publicly-funded facility in the country that provides substance abuse treatment as well as from state-identified facilities that provide other substance abuse services. Data are collected on a number of topics including facility operation, services provided (assessment, therapy, testing, health, continuing care, special programs, transitional services, community outreach, ancillary), type of treatment, numbers of clients, and various client characteristics. The main objective of the UFDS is to produce data that can be used to assess the nature and extent of substance abuse treatment services, to assist in the forecast of treatment resource requirements, to analyze treatment service trends, to conduct national, regional, and state-level comparative analyses of treatment services and utilization, and to generate the National Directory of Drug and Alcohol Abuse Treatment Programs and its on-line equivalent, the Substance Abuse Treatment Facility Locator http://findtreatment.samhsa.gov/.This study has 1 Data Set.

The table VA- Uniform Data is part of the dataset L2 Uniform Voter Data, available at https://redivis.com/datasets/5dss-024bh1rkk. It contains 5743196 rows across 779 variables.

Uniform distribution and ratio of two uniforms.

Since 1930, the Federal Bureau of Investigation has compiled the Uniform Crime Reports (UCR) to serve as periodic nationwide assessments of reported crimes not available elsewhere in the criminal justice system. Law enforcement agencies contribute reports either directly or through their state reporting programs. Each year, summary data are reported in four types of files: (1) Offenses Known and Clearances by Arrest, (2) Property Stolen and Recovered, (3) Supplementary Homicide Reports (SHR), and (4) Police Employee (LEOKA) Data. The Offenses Known and Clearances by Arrest data files include monthly data on the number of Crime Index offenses reported and the number of offenses cleared by arrest or other means. The counts include all reports of Index crimes (excluding arson) received from victims, officers who discovered infractions, or other sources.

Uniform Labels

## Overview

Uniform Labels is a dataset for object detection tasks - it contains Uniform annotations for 502 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

The UNIFORM CRIME REPORTING PROGRAM DATA: SUPPLEMENTARY HOMICIDE REPORTS, 2015 (SHR) provide detailed information on criminal homicides reported to the police. These homicides consist of murders; non-negligent killings also called non-negligent manslaughter; and justifiable homicides. UCR Program contributors compile and submit their crime data by one of two means: either directly to the FBI or through their State UCR Programs. State UCR Programs frequently impose mandatory reporting requirements which have been effective in increasing both the number of reporting agencies as well as the number and accuracy of each participating agency's reports. Each agency may be identified by its numeric state code, alpha-numeric agency ("ORI") code, jurisdiction population, and population group. In addition, each homicide incident is identified by month of occurrence and situation type, allowing flexibility in creating aggregations and subsets.

Historically in the biopharmaceutical setting, USP has been used to establish that a batch of drug product has acceptable content uniformity. More recently, alternative approaches such as the two one-sided parametric tolerance interval test (PTI-TOST) have been proposed to establish content uniformity. Traditionally, the PTI-TOST is implemented as a sequential, two-tiered test, under the generally accepted assumption that the data are independently and identically distributed. Since the material is sequenced through the manufacturing process over a period of time, there are conceptually arguable locations within each batch, for instance: beginning, middle, and end. In such a situation, a practitioner may wish to evaluate potential effects of these batch locations, for example, during process validation. If location (stratified) differences exist within the batch and if multiple samples are taken from each location, significant within-location correlations may be induced in the data. In such a case, the traditional PTI-TOST underestimates the total variability, thereby improperly boosting the power of the test method. When there is reason to believe that location variances exist, the batch may be evaluated using stratified sampling, and the location effect may be modeled. In this paper, a two-tiered PTI-TOST that accounts for both between-location and within-location variance components is introduced. Operating characteristic curves and practical advice are given to aid the practitioner’s uptake of the proposed method.

The table CO- Uniform Data is part of the dataset L2 Uniform Voter Data, available at https://redivis.com/datasets/5dss-024bh1rkk. It contains 3839981 rows across 779 variables.

• Raw first-order p-values collected for Dieharder, NIST STS, TestU01. The batteries were applied to random data (AES CTR mode).
• Simulated (based on first-order collections) second-order p-values for Dieharder, NIST STS.
• JSON with various computed test statistics over sets of p-values.
  

For crop production it is desirable for the mapping between genotype and phenotype to be consistent, such that an optimized genotype produces uniform sets of individual plants. Uniformity is strongly selected in breeding programs, usually automatically, as harvest equipment eliminates severely non-uniform individuals. Uniformity is genetically controlled, is known to be increased by interplant competition, and is predicted to increase upon abiotic stress. We mapped maize loci controlling genotype by environment interaction in plant height uniformity. These loci are different than the loci controlling mean plant height. Uniformity decreases upon combining two abiotic stresses, with alleles conferring greater uniformity in a single stress showing little improvement in a combined stress treatment. The maize B73 and Mo17 inbreds do not provide segregating alleles for improvement in plant height uniformity, suggesting that the genetic network specifying plant height has a past history of selection for robustness.

For explanation, see dataset titled "Spatial uniformity of EIZO CG247X".

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

Subscribers can find out export and import data of 23 countries by HS code or product’s name. This demo is helpful for market analysis.

This data collection contains a revised SMSA (Standard Metropolitan Statistical Area) aggregate version of the FBI's Uniform Crime Reports (UCR) statistics gathered from 1966-1976, in which original UCR agency records are combined to produce several types of crime rates, by SMSA, for eight crimes. The data were prepared by the Hoover Institution for Economic Studies of the Criminal Justice System, at Stanford University. The data in the file are an aggregation of all relevant law enforcement reporting agencies into 291 SMSAs, and corresponding approximate aggregations of crime rates and dispositions. Each record contains crime rates for one SMSA in one specific year, with data including annual statistics of eight index crimes, i.e., murder, manslaughter, rape, robbery, assault, burglary, larceny, and motor vehicle theft. Calculations include offense-based clearance rates (the number of clearances of juvenile clearances per reported offense), clearance-based rates (the number of persons charged per offense cleared by arrest), and charge-based rates (the number of persons whose cases were disposed in a particular manner per person charged). A related study is UNIFORM CRIME REPORTS, 1966-1976 (ICPSR 7676).

In fiscal year 2023, the sales value of businesses in the uniform rental market in Japan grew slightly by 1.4 percent compared to the previous year, reaching a market size of 92.5 billion Japanese yen. Establishments operating in the Japanese uniform rental market offer uniform supply, cleaning and repairing services directed at domestic businesses.

The table SD- Uniform Data is part of the dataset L2 Uniform Voter Data, available at https://redivis.com/datasets/5dss-024bh1rkk. It contains 581520 rows across 779 variables.

This study reexamined and recoded missing data in the Uniform Crime Reports (UCR) for the years 1977 to 2000 for all police agencies in the United States. The principal investigator conducted a data cleaning of 20,067 Originating Agency Identifiers (ORIs) contained within the Offenses-Known UCR data from 1977 to 2000. Data cleaning involved performing agency name checks and creating new numerical codes for different types of missing data including missing data codes that identify whether a record was aggregated to a particular month, whether no data were reported (true missing), if more than one index crime was missing, if a particular index crime (motor vehicle theft, larceny, burglary, assault, robbery, rape, murder) was missing, researcher assigned missing value codes according to the "rule of 20", outlier values, whether an ORI was covered by another agency, and whether an agency did not exist during a particular time period.