This graph shows how the impact factor of ^ is computed. The left axis depicts the number of papers published in years X-1 and X-2, and the right axis displays their citations in year X.

Duration: Under 60 seconds Question scope: 1-7 questions Ideal question set: 3-5 core items Decision cadence: Quick wins within 24-72 hours, larger changes for weekly reviews

Optimal KPI Count: 3-7 per team Blended CAC Target Example: $450 Blended CAC Guardrail Example: $600 Revenue Attribution Finalization: T+5 days CPL Variance Example: 18% above target CPC Increase Example: 22%

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

aAll data sets were collected from a single crystal.bValues in the parentheses are for the highest-resolution shell.

This dataset reflects is for the Individual Shelter & Rescue Statistics that were reported in 2018 for the 2017 Calendar year. Although PACFA requires this data to be submitted and takes all care possible to ensure the validity of this data, we do not control, and therefore guarantee, the complete accuracy, completeness and availability of data. PACFA believes this information to be within ± 4% margin of error. The CDA-PACFA is not responsible for any issues that may arise from the use of this data.

Abstract

Within the frame of PCBS' efforts in providing official Palestinian statistics in the different life aspects of Palestinian society and because the wide spread of Computer, Internet and Mobile Phone among the Palestinian people, and the important role they may play in spreading knowledge and culture and contribution in formulating the public opinion, PCBS conducted the Household Survey on Information and Communications Technology, 2014.

The main objective of this survey is to provide statistical data on Information and Communication Technology in the Palestine in addition to providing data on the following: -

· Prevalence of computers and access to the Internet. · Study the penetration and purpose of Technology use.

Geographic coverage

Palestine (West Bank and Gaza Strip) , type of locality (Urban, Rural, Refugee Camps) and governorate

Analysis unit

Household. Person 10 years and over .

Universe

All Palestinian households and individuals whose usual place of residence in Palestine with focus on persons aged 10 years and over in year 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame The sampling frame consists of a list of enumeration areas adopted in the Population, Housing and Establishments Census of 2007. Each enumeration area has an average size of about 124 households. These were used in the first phase as Preliminary Sampling Units in the process of selecting the survey sample.

Sample Size The total sample size of the survey was 7,268 households, of which 6,000 responded.

Sample Design The sample is a stratified clustered systematic random sample. The design comprised three phases:

Phase I: Random sample of 240 enumeration areas. Phase II: Selection of 25 households from each enumeration area selected in phase one using systematic random selection. Phase III: Selection of an individual (10 years or more) in the field from the selected households; KISH TABLES were used to ensure indiscriminate selection.

Sample Strata Distribution of the sample was stratified by: 1- Governorate (16 governorates, J1). 2- Type of locality (urban, rural and camps).

Sampling deviation

-

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey questionnaire consists of identification data, quality controls and three main sections: Section I: Data on household members that include identification fields, the characteristics of household members (demographic and social) such as the relationship of individuals to the head of household, sex, date of birth and age.

Section II: Household data include information regarding computer processing, access to the Internet, and possession of various media and computer equipment. This section includes information on topics related to the use of computer and Internet, as well as supervision by households of their children (5-17 years old) while using the computer and Internet, and protective measures taken by the household in the home.

Section III: Data on persons (aged 10 years and over) about computer use, access to the Internet and possession of a mobile phone.

Cleaning operations

Preparation of Data Entry Program: This stage included preparation of the data entry programs using an ACCESS package and defining data entry control rules to avoid errors, plus validation inquiries to examine the data after it had been captured electronically.

Data Entry: The data entry process started on 8 May 2014 and ended on 23 June 2014. The data entry took place at the main PCBS office and in field offices using 28 data clerks.

Editing and Cleaning procedures: Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

Response rate

Response Rates= 79%

Sampling error estimates

There are many aspects of the concept of data quality; this includes the initial planning of the survey to the dissemination of the results and how well users understand and use the data. There are three components to the quality of statistics: accuracy, comparability, and quality control procedures.

Checks on data accuracy cover many aspects of the survey and include statistical errors due to the use of a sample, non-statistical errors resulting from field workers or survey tools, and response rates and their effect on estimations. This section includes:

Statistical Errors Data of this survey may be affected by statistical errors due to the use of a sample and not a complete enumeration. Therefore, certain differences can be expected in comparison with the real values obtained through censuses. Variances were calculated for the most important indicators.

Variance calculations revealed that there is no problem in disseminating results nationally or regionally (the West Bank, Gaza Strip), but some indicators show high variance by governorate, as noted in the tables of the main report.

Non-Statistical Errors Non-statistical errors are possible at all stages of the project, during data collection or processing. These are referred to as non-response errors, response errors, interviewing errors and data entry errors. To avoid errors and reduce their effects, strenuous efforts were made to train the field workers intensively. They were trained on how to carry out the interview, what to discuss and what to avoid, and practical and theoretical training took place during the training course. Training manuals were provided for each section of the questionnaire, along with practical exercises in class and instructions on how to approach respondents to reduce refused cases. Data entry staff were trained on the data entry program, which was tested before starting the data entry process.

Several measures were taken to avoid non-sampling errors. These included editing of questionnaires before data entry to check field errors, using a data entry application that does not allow mistakes during the process of data entry, and then examining the data by using frequency and cross tables. This ensured that data were error free; cleaning and inspection of the anomalous values were conducted to ensure harmony between the different questions on the questionnaire.

The sources of non-statistical errors can be summarized as: 1. Some of the households were not at home and could not be interviewed, and some households refused to be interviewed. 2. In unique cases, errors occurred due to the way the questions were asked by interviewers and respondents misunderstood some of the questions.

This is a monthly report on publicly funded community services for children, young people and adults using data from the Community Services Data Set (CSDS) reported in England for April 2018. The CSDS is a patient-level dataset providing information relating to publicly funded community services for children, young people and adults. These services can include health centres, schools, mental health trusts, and health visiting services. The data collected includes personal and demographic information, diagnoses including long-term conditions and disabilities and care events plus screening activities. It has been developed to help achieve better outcomes for children, young people and adults. It provides data that will be used to commission services in a way that improves health, reduces inequalities, and supports service improvement and clinical quality. Prior to October 2017, the predecessor Children and Young Peoples Health Services (CYPHS) Data Set collected data for children and young people aged 0-18. The CSDS superseded the CYPHS data set to allow adult community data to be submitted, expanding the scope of the existing data set by removing the 0-18 age restriction. The structure and content of the CSDS remains the same as the previous CYPHS data set. Further information about the CYPHS and related statistical reports is available in the related links below. References to children and young people covers records submitted for 0-18 year olds and references to adults covers records submitted for those aged over 18. Where analysis for both groups have been combined, this is referred to as all patients. These statistics are classified as experimental and should be used with caution. Experimental statistics are new official statistics undergoing evaluation. They are published in order to involve users and stakeholders in their development and as a means to build in quality at an early stage. More information about experimental statistics can be found on the UK Statistics Authority website. We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the survey in the related links to provide us with any feedback or suggestions for improving the report.

Hydrographic and Impairment Statistics (HIS) is a National Park Service (NPS) Water Resources Division (WRD) project established to track certain goals created in response to the Government Performance and Results Act of 1993 (GPRA). One water resources management goal established by the Department of the Interior under GRPA requires NPS to track the percent of its managed surface waters that are meeting Clean Water Act (CWA) water quality standards. This goal requires an accurate inventory that spatially quantifies the surface water hydrography that each bureau manages and a procedure to determine and track which waterbodies are or are not meeting water quality standards as outlined by Section 303(d) of the CWA. This project helps meet this DOI GRPA goal by inventorying and monitoring in a geographic information system for the NPS: (1) CWA 303(d) quality impaired waters and causes; and (2) hydrographic statistics based on the United States Geological Survey (USGS) National Hydrography Dataset (NHD). Hydrographic and 303(d) impairment statistics were evaluated based on a combination of 1:24,000 (NHD) and finer scale data (frequently provided by state GIS layers).

This publication provides the timeliest picture available of people using NHS funded secondary mental health, learning disabilities and autism services in England, excluding those who are solely in contact with Talking Therapies. This information will be of use to people needing access to information quickly for operational decision making and other purposes. More detailed information on the quality and completeness of these statistics is available in the Data Quality section, as well as within the Data Coverage and Data Quality VODIM and Integrity files available under 'Resources'. Please note, the methodology for MHS30f - Attended contacts in the RP with community mental health services for adult and older adults with severe mental illness has been updated to account for both the team ID recorded in the contact and referral tables. This is inline with other metrics that are similar. This brings this metric inline with other similar metrics but there maybe minor methodological differences that mean that summing the totals from other metrics may not match the values presented in this metric.

The Crime Statistics Agency (CSA) is responsible for processing, analysing and publishing Victorian crime statistics, independent of Victoria Police.

The CSA aims to provide an efficient and transparent information service to assist and inform policy makers, researchers and the Victorian public.

The legal basis for the Crime Statistics Agency is the Crime Statistics Act 2014, which provides for the publication and release of crime statistics, research into crime trends, and the employment of a Chief Statistician for that purpose.

Under the provisions of the Act, the Chief Statistician is empowered to receive law enforcement data from the Chief Commissioner of Police and is responsible for publishing and releasing statistical information relating to crime in Victoria.

The number and rate of recorded offences in Victoria.

Data Classification - http://www.crimestatistics.vic.gov.au/home/about+the+data/classifications/

Glossary and Data Dictionary - http://www.crimestatistics.vic.gov.au/home/about+the+data/data+dictionary/

FBI National Incident-Based Reporting System (FBI NIBRS) crime data for Albany-Lebanon, OR Metropolitan Statistical Area (MSA), including incidents, statistics, demographics, and agency information across multiple jurisdictions.

Key Table Information.Table Title.Manufacturing: E-Commerce Statistics for the U.S.: 2022.Table ID.ECNECOMM2022.EC2231ECOMM.Survey/Program.Economic Census.Year.2022.Dataset.ECN Core Statistics Manufacturing: E-Commerce Statistics for the U.S.: 2022.Release Date.2025-01-23.Release Schedule.The Economic Census occurs every five years, in years ending in 2 and 7.The data in this file come from the 2022 Economic Census data files released on a flow basis starting in January 2024 with First Look Statistics. Preliminary U.S. totals released in January 2024 are superseded with final data shown in the releases of later economic census statistics through March 2026.For more information about economic census planned data product releases, see 2022 Economic Census Release Schedule..Dataset Universe.The dataset universe consists of all establishments that are in operation for at least some part of 2022, are located in one of the 50 U.S. states, associated offshore areas, or the District of Columbia, have paid employees, and are classified in one of nineteen in-scope sectors defined by the 2022 North American Industry Classification System (NAICS)..Methodology.Data Items and Other Identifying Records.Sales, value of shipments, or revenue ($1,000)E-Shipments value ($1,000) E-Shipments as percent of total sales, value of shipments, or revenue (%) Range indicating imputed percentage of total sales, value of shipments, or revenueDefinitions can be found by clicking on the column header in the table or by accessing the Economic Census Glossary..Unit(s) of Observation.The reporting units for the economic census are employer establishments. An establishment is generally a single physical location where business is conducted or where services or industrial operations are performed. A company or firm is comprised of one or more in-scope establishments that operate under the ownership or control of a single organization. For some industries, the reporting units are instead groups of all establishments in the same industry belonging to the same firm..Geography Coverage.The data are shown for the U.S. level only. For information about economic census geographies, including changes for 2022, see Geographies..Industry Coverage.The data are shown at the 2- through 3-digit 2022 NAICS code levels for the U.S. For information about NAICS, see Economic Census Code Lists..Sampling.The 2022 Economic Census sample includes all active operating establishments of multi-establishment firms and approximately 1.7 million single-establishment firms, stratified by industry and state. Establishments selected to the sample receive a questionnaire. For all data on this table, establishments not selected into the sample are represented with administrative data. For more information about the sample design, see 2022 Economic Census Methodology..Confidentiality.The Census Bureau has reviewed this data product to ensure appropriate access, use, and disclosure avoidance protection of the confidential source data (Project No. 7504609, Disclosure Review Board (DRB) approval number: CBDRB-FY23-099).To protect confidentiality, the U.S. Census Bureau suppresses cell values to minimize the risk of identifying a particular business’ data or identity.To comply with disclosure avoidance guidelines, data rows with fewer than three contributing firms or three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. More information on disclosure avoidance is available in the 2022 Economic Census Methodology..Technical Documentation/Methodology.For detailed information about the methods used to collect data and produce statistics, survey questionnaires, Primary Business Activity/NAICS codes, NAPCS codes, and more, see Economic Census Technical Documentation..Weights.No weighting applied as establishments not sampled are represented with administrative data..Table Information.FTP Download.https://www2.census.gov/programs-surveys/economic-census/data/2022/sector31/.API Information.Economic census data are housed in the Census Bureau Application Programming Interface (API)..Symbols.D - Withheld to avoid disclosing data for individual companies; data are included in higher level totalsN - Not available or not comparableS - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page.X - Not applicableA - Relative standard error of 100% or morer - Reviseds - Relative standard error exceeds 40%For a complete list of symbols, see Economic Census Data Dictionary..Data-Specific Notes.Data users who create their own es...

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.

This is the latest statistical publication of linked HES (Hospital Episode Statistics) and DID (Diagnostic Imaging Dataset) data held by the Health and Social Care Information Centre. The HES-DID linkage provides the ability to undertake national (within England) analysis along acute patient pathways to understand typical imaging requirements for given procedures, and/or the outcomes after particular imaging has been undertaken, thereby enabling a much deeper understanding of outcomes of imaging and to allow assessment of variation in practice. This publication aims to highlight to users the availability of this updated linkage and provide users of the data with some standard information to assess their analysis approach against. The two data sets have been linked using specific patient identifiers collected in HES and DID. The linkage allows the data sets to be linked from April 2012 when the DID data was first collected; however this report focuses on patients who were present in either data set for the period April 2015-February 2016 only. For DID this is provisional 2015/16 data. For HES this is provisional 2015/16 data. The linkage used for this publication was created on 06 June 2016 and released together with this publication on 07 July 2016.

Beginning in 2016, the Law Enforcement Management and Administrative Statistics (LEMAS) survey adopted a core and supplement structure. The LEMAS core has been conducted every 3 to 4 years since 1987 with approximately 3,200 local, county and state law enforcement agencies across the United States. Due to the breadth of the survey, detailed analysis of any specific law enforcement topic cannot be done with the LEMAS core. The LEMAS supplements are designed to fill this void by allowing for a more comprehensive examination on a key topic in law enforcement and are administered in between core years. The 2016 LEMAS Body-Worn Camera Supplement (LEMAS-BWCS) is the first supplement administered under the new structure.

Monthly Jobseekers Allowance unemployment data for North Yorkshire from the Office for National Statistics.

This data release contains the U.S. salient statistics and world production data extracted from the tables and figures of the USGS Mineral Commodity Summaries 2025 that give an overview of the U.S. mineral industry in 2024.