A file containing all Misc Baseline Reports for 2018-2023 in their original format is available in the Attachments section below.

MEDLINE/PubMed annual statistical reports are based upon the data elements in the baseline versions of MEDLINE®/PubMed are available. For each year covered the reports include: total citations containing each element; total occurrences of each element; minimum/average/maximum occurrences of each element in a record; minimum/average/maximum length of a single element occurrence; average record size; and other statistical data describing the content and size of the elements.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

A file containing all Min/Max Baseline Reports for 2005-2023 in their original format is available in the Attachments section below. A second file includes a separate set of reports, made available from 2002-2017, that did not include OLDMEDLINE records. MEDLINE/PubMed annual statistical reports are based upon the data elements in the baseline versions of MEDLINE®/PubMed are available. For each year covered the reports include: total citations containing each element; total occurrences of each element; minimum/average/maximum occurrences of each element in a record; minimum/average/maximum length of a single element occurrence; average record size; and other statistical data describing the content and size of the elements.

The data shows the statistics of different item-wise reports on a cumulative yearly basis in states up to the sub-district level of Defence spending. It included 1) Ante Natal Care (ANC) - Antenatal care (ANC) is a means to identify high-risk pregnancies and educate women so that they might experience healthier delivery and outcomes. 2) Deliveries - The delivery of the baby by the pregnant women 3) Number of Caesarean (C-Section) deliveries - Caesarean delivery (C-section) is used to deliver a baby through surgical incisions made in the abdomen and uterus. 4) Pregnancy outcome & details of new-born - The records kept of the pregnancy outcome along with the details of new-born 5) Complicated Pregnancies - The different pregnancies that were not normal and had complications 6) Post Natal Care (PNC) - Postnatal care is defined as care given to the mother and her new-born baby immediately after the birth of the placenta and for the first six weeks of life 7) Reproductive Tract Infections/Sexually Transmitted Infections (RTI/STI) Cases - The records of reproductive tract infections along with the records of the sexually transmitted cases 8) Family Planning - The different methods used by families to keep track of family 9) CHILD IMMUNISATION - The records of child immunisation which are records of vaccination 10) Number of cases of Childhood Diseases (0-5 years) - The records of the number of cases of childhood diseases within the age of 5 years old 11) NVBDCP - The National Vector Borne Disease Control Programme (NVBDCP) is one of the most comprehensive and multi-faceted public health activities in the country and concerned with the prevention and control of vector-borne diseases, namely Malaria, Filariasis, Kala-azar, Dengue and Japanese Encephalitis (JE). 12) Adolescent Health - The record of the conditions of adolescent health 13 ) Directly Observed Treatment, Short-course (DOTS) - Directly observed treatment, short-course (DOTS, also known as TB-DOTS) is the name given to the tuberculosis (TB) control strategy recommended by the World Health Organization 14) Patient Services - Patient Services means those which vary with the number of personnel; professional and para-professional skills of the personnel; specialised equipment, and reflect the intensity of the medical and psycho-social needs of the patients. 15) Laboratory Testing - A medical procedure that involves testing a sample of blood, urine, or other substance from the body. Laboratory tests can help determine a diagnosis, plan treatment, check if the treatment works, or monitor the disease over time. 16) Details of deaths reported with probable causes - The reports of deaths recorded with possible reasons are given in a detail 17) Vaccines - The reports of vaccines which are recorded 18) Syringes - It is the number of syringes that are used and recorded 19) Rashtriya Bal Swasthaya Karyakram (RBSK) - Rashtriya Bal Swasthya Karyakram (RBSK) is an important initiative aiming at early identification and early intervention for children from birth to 18 years to cover 4 'D's viz. Defects at birth, Deficiencies, Diseases, Development delays, including disability. 20) Coverage under WIFS JUNIOR - The coverage of the Weekly Iron Folic Acid Supplementation Programme for children six to one 21) Maternal Death Reviews (MDR) - A maternal death review is cross-checking how the mother died. It provides a rare opportunity for a group of health staff and community members to learn from a tragic – and often preventable. 22) Janani Shishu Suraksha Karyakaram (JSSK)- This initiative provides free and cashless services to pregnant women, including normal deliveries and caesarean operations. It entitles all pregnant women in public health institutions to free and no-expense delivery, including caesarean section.

Split the city's report data into smaller statistical units such as valid subcategories, locate the data with a fixed rule independent URL, provide JSON transmission format for different applications or software to collect and integrate data.

Contains a summary of regulatory, and supporting, data and information regarding the volume of water withdrawn, system capacity indications, and water quality tests performed in each of York Region's drinking water systems. It also contains a list of communities and municipalities that are served by the York Drinking Water System, and the various groundwater and surface water sources.

The ckanext-statsresources extension for CKAN provides the ability to generate and publish various statistics and reports from a CKAN instance as resources within datasets. It leverages the ckanext-report extension to create these reports and allows administrators to configure which reports are generated, in what format, and for which datasets. This extension automates the process of creating and updating statistical resources, making it easier to provide insights into the CKAN instance's data. Key Features: Automated Report Generation: Automatically generates statistical reports based on a configurable map of report names, formats, dataset IDs, and resource titles. Multiple Format Support: Supports exporting reports in various formats, as determined by the underlying ckanext-report extension (example: JSON and CSV). Configurable Report Options: Allows customization of report content by specifying options such as including or excluding private and draft datasets. Command-Line Interface (CLI): Provides paster commands to list available statistical resources and generate/update the corresponding resources. Access Control: Configure to limit access to the generated reports pages to only sysadmins. Dependency on ckanext-report: Built upon ckanext-report to handle underlying report creation, so ckanext-report needs to be installed first. Use Cases: Dataset Usage Monitoring: Regularly generate reports on dataset creation dates, providing insight into the growth of the CKAN instance's data catalog.

The Port Statistical Areas dataset was updated on June 05, 2025 from the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). USACE works with port authorities from across the United States to develop the statistical port boundaries through an iterative and collaborative process. Port boundary information is prepared by USACE to increase transparency on public waterborne commerce statistic reporting, as well as to modernize how the data type is stored, analyzed, and reported. A Port Statistical Area (PSA) is a region with formally justified shared economic interests and collective reliance on infrastructure related to waterborne movements of commodities that is formally recognized by legislative enactments of state, county, or city governments. PSAs generally contain groups of county legislation for the sole purpose of statistical reporting. Through GIS mapping, legislative boundaries, and stakeholder collaboration, PSAs often serve as the primary unit for aggregating and reporting commerce statistics for broader geographical areas. Per Engineering Regulation 1130-2-520, the U.S. Army Corps of Engineers' Navigation Data Center is responsible to collect, compile, publish, and disseminate waterborne commerce statistics. This task has subsequently been charged to the Waterborne Commerce Statistics Center to perform. Performance of this work is in accordance with the Rivers and Harbors Appropriation Act of 1922. Included in this work is the definition of a port area. A port area is defined in Engineering Pamphlet 1130-2-520 as: (1) Port limits defined by legislative enactments of state, county, or city governments. (2) The corporate limits of a municipality. The USACE enterprise-wide port and port statistical area feature classes per EP 1130-2-520 are organized in SDSFIE 4.0.2 format. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/2ngc-4984

For a comprehensive guide to this data and other UCR data, please see my book at ucrbook.comVersion 18 release notes:Adds data for 2021.Version 17 release notes:Adds data for 2020.Please note that the FBI has retired UCR data ending in 2020 data so this will be the last Offenses Known and Clearances by Arrest data they release. Changes .rda files to .rds. Please note that in 2020 the card_actual_pt variable always returns that the month was reported. This causes 2020 to report that all months are reported for all agencies because I use the card_actual_pt variable to measure how many months were reported. This variable is almost certainly incorrect since it is extremely unlikely that all agencies suddenly always report. However, I am keeping this incorrect value to maintain a consistent definition of how many months are missing (measuring missing months through card_actual_type, for example, gives different results for previous years so I don't want to change this). Version 16 release notes:Changes release notes description, does not change data.Version 15 release notes:Adds data for 2019.Please note that in 2019 the card_actual_pt variable always returns that the month was reported. This causes 2019 to report that all months are reported for all agencies because I use the card_actual_pt variable to measure how many months were reported. This variable is almost certainly incorrect since it is extremely unlikely that all agencies suddenly always report. However, I am keeping this incorrect value to maintain a consistent definition of how many months are missing (measuring missing months through card_actual_type, for example, gives different results for previous years so I don't want to change this). Version 14 release notes:Adds arson data from the UCR's Arson dataset. This adds just the arson variables about the number of arson incidents, not the complete set of variables in that dataset (which include damages from arson and whether structures were occupied or not during the arson.As arson is an index crime, both the total index and the index property columns now include arson offenses. The "all_crimes" variables also now include arson.Adds a arson_number_of_months_missing column indicating how many months were not reporting (i.e. missing from the annual data) in the arson data. In most cases, this is the same as the normal number_of_months_missing but not always so please check if you intend to use arson data.Please note that in 2018 the card_actual_pt variable always returns that the month was reported. This causes 2018 to report that all months are reported for all agencies because I use the card_actual_pt variable to measure how many months were reported. This variable is almost certainly incorrect since it is extremely unlikely that all agencies suddenly always report. However, I am keeping this incorrect value to maintain a consistent definition of how many months are missing (measuring missing months through card_actual_type, for example, gives different results for previous years so I don't want to change this).For some reason, a small number of agencies (primarily federal agencies) had the same ORI number in 2018 and I removed these duplicate agencies. Version 13 release notes: Adds 2018 dataNew Orleans (ORI = LANPD00) data had more unfounded crimes than actual crimes in 2018 so unfounded columns for 2018 are all NA. Version 12 release notes: Adds population 1-3 columns - if an agency is in multiple counties, these variables show the population in the county with the most people in that agency in it (population_1), second largest county (population_2), and third largest county (population_3). Also adds county 1-3 columns which identify which counties the agency is in. The population column is the sum of the three population columns. Thanks to Mike Maltz for the suggestion!Fixes bug in the crosswalk data that is merged to this file that had the incorrect FIPS code for Clinton, Tennessee (ORI = TN00101). Thanks for Brooke Watson for catching this bug!Adds a last_month_reported column which says which month was reported last. This is actually how the FBI defines number_of_months_reported so is a more accurate representation of that. Removes the number_of_months_reported variable as the name is misleading. You should use the last_month_reported or the number_of_months_missing (see below) var

Abstract

Most countries collect official statistics on energy use due to its vital role in the infrastructure, economy and living standards.

In Palestine, additional attention is warranted for energy statistics due to a scarcity of natural resources, the high cost of energy and high population density. These factors demand comprehensive and high quality statistics.

In this contest PCBS decided to conduct a special Energy Consumption in Transport Survey to provide high quality data about energy consumption by type, expenditure on maintenance and insurance for vehicles, and questions on vehicles motor capacity and year of production.

The survey aimed to provide data on energy consumption by transport sector and also on the energy consumption by the type of vehicles and its motor capacity and year of production.

Geographic coverage

Palestine

Analysis unit

Vehicles

Universe

All the operating vehicles in Palestine in 2014.

Kind of data

Sample survey data [ssd]

Sampling procedure

Target Population: All the operating vehicles in Palestine in 2014.

2.1Sample Frame A list of the number of the operating vehicles in Palestine in 2014, they are broken down by governorates and vehicle types, this list was obtained from Ministry of transport.

2.2.1 Sample size The sample size is 6,974 vehicles.

2.2.2 Sampling Design it is stratified random sample, and in some of the small size strata the quota sample was used to cover them.

The method of reaching the vehicles sample was through : 1-reaching to all the dynamometers (the centers for testing the vehicles) 2-selecting a random sample of vehicles by type of vehicle, model, fuel type and engine capacity

Mode of data collection

Face-to-face [f2f]

Research instrument

The design of the questionnaire was based on the experiences of other similar countries in energy statistics subject to cover the most important indicators for energy statistics in transport sector, taking into account Palestine's particular situation.

Cleaning operations

The data processing stage consisted of the following operations: Editing and coding prior to data entry: all questionnaires were edited and coded in the office using the same instructions adopted for editing in the field.

Data entry: The survey questionnaire was uploaded on office computers. At this stage, data were entered into the computer using a data entry template developed in Access Database. The data entry program was prepared to satisfy a number of requirements: ·To prevent the duplication of questionnaires during data entry. ·To apply checks on the integrity and consistency of entered data. ·To handle errors in a user friendly manner. ·The ability to transfer captured data to another format for data analysis using statistical analysis software such as SPSS. Audit after data entered at this stage is data entered scrutiny by pulling the data entered file periodically and review the data and examination of abnormal values and check consistency between the different questions in the questionnaire, and if there are any errors in the data entered to be the withdrawal of the questionnaire and make sure this data and adjusted, even been getting the final data file that is the final extract data from it. Extraction Results: The extract final results of the report by using the SPSS program, and then display the results through tables to Excel format.

Response rate

80.7%

Sampling error estimates

Data of this survey may be affected by sampling errors due to use of a sample and not a complete enumeration. Therefore, certain differences are anticipated in comparison with the real values obtained through censuses. The variance was calculated for the most important indicators: the variance table is attached with the final report. There is no problem in the dissemination of results at national and regional level (North, Middle, South of West Bank, Gaza Strip).

Data appraisal

The survey sample consisted of around 6,974 vehicles, of which 5,631 vehicles completed the questionnaire, 3,652 vehicles from the West Bank and 1,979 vehicles in Gaza Strip.

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases, hospitalizations, and associated deaths that have been reported among Connecticut residents. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Hospitalization data were collected by the Connecticut Hospital Association and reflect the number of patients currently hospitalized with laboratory-confirmed COVID-19. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the daily COVID-19 update. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical examiner) using their best clinical judgment. Additionally, all COVID-19 deaths, including suspected or related, are required to be reported to OCME. On April 4, 2020, CT DPH and OCME released a joint memo to providers and facilities within Connecticut providing guidelines for certifying deaths due to COVID-19 that were consistent with the CDC’s guidelines and a reminder of the required reporting to OCME.25,26 As of July 1, 2021, OCME had reviewed every case reported and performed additional investigation on about one-third of reported deaths to better ascertain if COVID-19 did or did not cause or contribute to the death. Some of these investigations resulted in the OCME performing postmortem swabs for PCR testing on individuals whose deaths were suspected to be due to COVID-19, but antemortem diagnosis was unable to be made.31 The OCME issued or re-issued about 10% of COVID-19 death certificates and, when appropriate, removed COVID-19 from the death certificate. For standardization and tabulation of mortality statistics, written cause of death statements made by the certifiers on death certificates are sent to the National Center for Health Statistics (NCHS) at the CDC which assigns cause of death codes according to the International Causes of Disease 10th Revision (ICD-10) classification system.25,26 COVID-19 deaths in this report are defined as those for which the death certificate has an ICD-10 code of U07.1 as either a primary (underlying) or a contributing cause of death. More information on COVID-19 mortality can be found at the following link: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Mortality/Mortality-Statistics Data are reported d

Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

On October 15, 2013, Louisville Mayor Greg Fischer announced the signing of an open data policy executive order in conjunction with his compelling talk at the 2013 Code for America Summit. In nonchalant cadence, the mayor announced his support for complete information disclosure by declaring, "It's data, man."Sunlight Foundation - New Louisville Open Data Policy Insists Open By Default is the Future Open Data Annual ReportsSection 5.A. Within one year of the effective Data of this Executive Order, and thereafter no later than September 1 of each year, the Open Data Management Team shall submit to the Mayor an annual Open Data Report.The Open Data Management team (also known as the Data Governance Team is currently led by the city's Data Officer Andrew McKinney in the Office of Civic Innovation and Technology. Previously (2014-16) it was led by the Director of IT.Full Executive OrderEXECUTIVE ORDER NO. 1, SERIES 2013AN EXECUTIVE ORDERCREATING AN OPEN DATA PLAN. WHEREAS, Metro Government is the catalyst for creating a world-class city that provides its citizens with safe and vibrant neighborhoods, great jobs, a strong system of education and innovation, and a high quality of life; andWHEREAS, it should be easy to do business with Metro Government. Online government interactions mean more convenient services for citizens and businesses and online government interactions improve the cost effectiveness and accuracy of government operations; andWHEREAS, an open government also makes certain that every aspect of the built environment also has reliable digital descriptions available to citizens and entrepreneurs for deep engagement mediated by smart devices; andWHEREAS, every citizen has the right to prompt, efficient service from Metro Government; andWHEREAS, the adoption of open standards improves transparency, access to public information and improved coordination and efficiencies among Departments and partner organizations across the public, nonprofit and private sectors; andWHEREAS, by publishing structured standardized data in machine readable formats the Louisville Metro Government seeks to encourage the local software community to develop software applications and tools to collect, organize, and share public record data in new and innovative ways; andWHEREAS, in commitment to the spirit of Open Government, Louisville Metro Government will consider public information to be open by default and will proactively publish data and data containing information, consistent with the Kentucky Open Meetings and Open Records Act; andNOW, THEREFORE, BE IT PROMULGATED BY EXECUTIVE ORDER OF THE HONORABLE GREG FISCHER, MAYOR OF LOUISVILLE/JEFFERSON COUNTY METRO GOVERNMENT AS FOLLOWS:Section 1. Definitions. As used in this Executive Order, the terms below shall have the following definitions:(A) “Open Data” means any public record as defined by the Kentucky Open Records Act, which could be made available online using Open Format data, as well as best practice Open Data structures and formats when possible. Open Data is not information that is treated exempt under KRS 61.878 by Metro Government.(B) “Open Data Report” is the annual report of the Open Data Management Team, which shall (i) summarize and comment on the state of Open Data availability in Metro Government Departments from the previous year; (ii) provide a plan for the next year to improve online public access to Open Data and maintain data quality. The Open Data Management Team shall present an initial Open Data Report to the Mayor within 180 days of this Executive Order.(C) “Open Format” is any widely accepted, nonproprietary, platform-independent, machine-readable method for formatting data, which permits automated processing of such data and is accessible to external search capabilities.(D) “Open Data Portal” means the Internet site established and maintained by or on behalf of Metro Government, located at portal.louisvilleky.gov/service/data or its successor website.(E) “Open Data Management Team” means a group consisting of representatives from each Department within Metro Government and chaired by the Chief Information Officer (CIO) that is responsible for coordinating implementation of an Open Data Policy and creating the Open Data Report.(F) “Department” means any Metro Government department, office, administrative unit, commission, board, advisory committee, or other division of Metro Government within the official jurisdiction of the executive branch.Section 2. Open Data Portal.(A) The Open Data Portal shall serve as the authoritative source for Open Data provided by Metro Government(B) Any Open Data made accessible on Metro Government’s Open Data Portal shall use an Open Format.Section 3. Open Data Management Team.(A) The Chief Information Officer (CIO) of Louisville Metro Government will work with the head of each Department to identify a Data Coordinator in each Department. Data Coordinators will serve as members of an Open Data Management Team facilitated by the CIO and Metro Technology Services. The Open Data Management Team will work to establish a robust, nationally recognized, platform that addresses digital infrastructure and Open Data.(B) The Open Data Management Team will develop an Open Data management policy that will adopt prevailing Open Format standards for Open Data, and develop agreements with regional partners to publish and maintain Open Data that is open and freely available while respecting exemptions allowed by the Kentucky Open Records Act or other federal or state law.Section 4. Department Open Data Catalogue.(A) Each Department shall be responsible for creating an Open Data catalogue, which will include comprehensive inventories of information possessed and/or managed by the Department.(B) Each Department’s Open Data catalogue will classify information holdings as currently “public” or “not yet public”; Departments will work with Metro Technology Services to develop strategies and timelines for publishing open data containing information in a way that is complete, reliable, and has a high level of detail.Section 5. Open Data Report and Policy Review.(A) Within one year of the effective date of this Executive Order, and thereafter no later than September 1 of each year, the Open Data Management Team shall submit to the Mayor an annual Open Data Report.(B) In acknowledgment that technology changes rapidly, in the future, the Open Data Policy should be reviewed and considered for revisions or additions that will continue to position Metro Government as a leader on issues of openness, efficiency, and technical best practices.Section 6. This Executive Order shall take effect as of October 11, 2013.Signed this 11th day of October, 2013, by Greg Fischer, Mayor of Louisville/Jefferson County Metro Government.GREG FISCHER, MAYOR

The Sample Processing System market has emerged as a pivotal sector within the broader landscape of laboratory automation and biotechnology, providing essential solutions for the efficient handling and analysis of samples in various industries, including pharmaceuticals, diagnostics, and environmental testing. These

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The granola bars market size will grow up to $ 2.32 bn at a CAGR of 5% during 2021-2025.

This granola bars market analysis report entails exhaustive statistical qualitative and quantitative data on Product (Conventional and Organic), Distribution Channel (Offline and Online), and Geography (North America, Europe, APAC, South America, and MEA) and their contribution to the target market. View our sample report to gather market insights on the segmentations. Furthermore, with the latest key findings on the post COVID-19 impact on the market, available in this report, you can create successful business strategies to generate new sales opportunities.

What will the Granola Bars Market Size be in 2021?

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Granola Bars Market: Key Drivers and Trends

Based on our research output, there has been a positive impact on the market growth during and post COVID-19 era. The multiple health benefits of granola is notably driving the granola bars market growth, although factors such as harmful effects of few ingredients used in granola bars may impede market growth. To unlock information on the key market drivers and the COVID-19 pandemic impact on the granola bars market get your FREE report sample now.

This post-pandemic granola bars market report has assessed the shift in consumer behavior and identified and explored the upcoming trends, drivers, and challenges that will have a far-reaching effect on the market growth. Understanding market trends is essential for identifying new business opportunities across segmentations and geographies.

Who are the Major Granola Bars Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Clif Bar & Co.General Mills Inc.Kellogg Co.Mars Inc.McKee FoodsMondelez International Inc.PepsiCo Inc.Question NutritionThe Hain Celestial Group Inc.The Hershey Co.

The granola bars market is fragmented and the vendors are deploying various organic and inorganic growth strategies to compete in the market. Click here to uncover other successful business strategies deployed by the vendors.

Furthermore, to make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments. Buy the full granola bars market statistics report to understand the magnitude of the economic impact on each segment and recovery expectations.

For insights on complete key vendor profiles, download a free sample of the granola bars market forecast report. The profiles include information on the production, sustainability, and prospects of the leading companies. The report's vendor landscape section also provides industry risk assessment in terms of labor cost, raw material price fluctuation, and other parameters, which is crucial for effective business planning.

Which are the Key Regions for Granola Bars Market?

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US, UK, China, Germany, Canada, and Japan are the key markets for granola bars market in North America. Learn about the key, emerging, and untapped markets from our granola bars market size, share, & trends analysis report for targeting your business efforts toward promising growth regions. 40% of the market’s growth will originate from North America during the forecast period.

The report offers an up-to-date analysis of the geographical composition of the market. North America has been recording significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. Convenience of consuming granola bars on-the-go will facilitate the granola bars market growth in North America over the forecast period. To garner further competitive intelligence and regional opportunities in store for vendors, view our sample report.

What are the Revenue-generating Product Segments in the Granola Bars Market?

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The granola bars market share growth by the _ segment has been significant. This report provides insights on the impact of the unprecedented outbreak of COVID-19 on market segments. Through these insights, you can safely deduce transformation patterns in consumer behavior, which is crucial to gauge segment-wise revenue growth during 2021-2025 and embrace technologies to improve business efficiency.

This report provides thorough granola bars market segment analysis that will help you gauge the existing competition, demographics, lucrative target customer groups, solutions to improve sales and services, and other cruci

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