Public health-related decision-making on policies aimed at controlling the COVID-19 pandemic outbreak depends on complex epidemiological models that are compelled to be robust and use all relevant available data. This data article provides a new combined worldwide COVID-19 dataset obtained from official data sources with improved systematic measurement errors and a dedicated dashboard for online data visualization and summary. The dataset adds new measures and attributes to the normal attributes of official data sources, such as daily mortality, and fatality rates. We used comparative statistical analysis to evaluate the measurement errors of COVID-19 official data collections from the Chinese Center for Disease Control and Prevention (Chinese CDC), World Health Organization (WHO) and European Centre for Disease Prevention and Control (ECDC). The data is collected by using text mining techniques and reviewing pdf reports, metadata, and reference data. The combined dataset includes complete spatial data such as countries area, international number of countries, Alpha-2 code, Alpha-3 code, latitude, longitude, and some additional attributes such as population. The improved dataset benefits from major corrections on the referenced data sets and official reports such as adjustments in the reporting dates, which suffered from a one to two days lag, removing negative values, detecting unreasonable changes in historical data in new reports and corrections on systematic measurement errors, which have been increasing as the pandemic outbreak spreads and more countries contribute data for the official repositories. Additionally, the root mean square error of attributes in the paired comparison of datasets was used to identify the main data problems. The data for China is presented separately and in more detail, and it has been extracted from the attached reports available on the main page of the CCDC website. This dataset is a comprehensive and reliable source of worldwide COVID-19 data that can be used in epidemiological models assessing the magnitude and timeline for confirmed cases, long-term predictions of deaths or hospital utilization, the effects of quarantine, stay-at-home orders and other social distancing measures, the pandemic’s turning point or in economic and social impact analysis, helping to inform national and local authorities on how to implement an adaptive response approach to re-opening the economy, re-open schools, alleviate business and social distancing restrictions, design economic programs or allow sports events to resume.

This book is written for statisticians, data analysts, programmers, researchers, teachers, students, professionals, and general consumers on how to perform different types of statistical data analysis for research purposes using the R programming language. R is an open-source software and object-oriented programming language with a development environment (IDE) called RStudio for computing statistics and graphical displays through data manipulation, modelling, and calculation. R packages and supported libraries provides a wide range of functions for programming and analyzing of data. Unlike many of the existing statistical softwares, R has the added benefit of allowing the users to write more efficient codes by using command-line scripting and vectors. It has several built-in functions and libraries that are extensible and allows the users to define their own (customized) functions on how they expect the program to behave while handling the data, which can also be stored in the simple object system.For all intents and purposes, this book serves as both textbook and manual for R statistics particularly in academic research, data analytics, and computer programming targeted to help inform and guide the work of the R users or statisticians. It provides information about different types of statistical data analysis and methods, and the best scenarios for use of each case in R. It gives a hands-on step-by-step practical guide on how to identify and conduct the different parametric and non-parametric procedures. This includes a description of the different conditions or assumptions that are necessary for performing the various statistical methods or tests, and how to understand the results of the methods. The book also covers the different data formats and sources, and how to test for reliability and validity of the available datasets. Different research experiments, case scenarios and examples are explained in this book. It is the first book to provide a comprehensive description and step-by-step practical hands-on guide to carrying out the different types of statistical analysis in R particularly for research purposes with examples. Ranging from how to import and store datasets in R as Objects, how to code and call the methods or functions for manipulating the datasets or objects, factorization, and vectorization, to better reasoning, interpretation, and storage of the results for future use, and graphical visualizations and representations. Thus, congruence of Statistics and Computer programming for Research.

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.

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 outdoor recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 3.0 protection status (by GAP Status Code) and public access status for various land unit boundaries (Protected Areas Database of the United States 3.0 Vector Analysis and Summary Statistics). Summary statistics are also available to explore and download (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 ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster GIS analysis files are also available for combination with other raster data (Protected Areas Database of the United States (PAD-US) 3.0 Raster Analysis). The PAD-US 3.0 Combined Fee, Designation, Easement feature class in the full inventory, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class (Protected Areas Database of the United States (PAD-US) 3.0, https://doi.org/10.5066/P9Q9LQ4B), was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

The Source Code Hosting Services market has become an essential pillar for software development in today's fast-paced digital landscape. With the growing complexity of software projects and the demand for collaboration among distributed teams, source code hosting services enable developers to securely store, manage,

Building strong quantitative skills prepares undergraduate biology students for successful careers in science and medicine. While math and statistics anxiety can negatively impact student learning within biology classrooms, instructors may reduce this anxiety by steadily building student competency in quantitative reasoning through instructional scaffolding, application-based approaches, and simple computer program interfaces. However, few statistical programs exist that meet all needs of an inclusive, inquiry-based laboratory course. These needs include an open-source program, a simple interface, little required background knowledge in statistics for student users, and customizability to minimize cognitive load, align with course learning outcomes, and create desirable difficulty. To address these needs, we used the Shiny package in R to develop a custom statistical analysis application. Our “BioStats” app provides students with scaffolded learning experiences in applied statistics that promotes student agency and is customizable by the instructor. It introduces students to the strengths of the R interface, while eliminating the need for complex coding in the R programming language. It also prioritizes practical implementation of statistical analyses over learning statistical theory. To our knowledge, this is the first statistics teaching tool where students are presented basic statistics initially, more complex analyses as they advance, and includes an option to learn R statistical coding. The BioStats app interface yields a simplified introduction to applied statistics that is adaptable to many biology laboratory courses.

Primary Image: Singing Junco. A sketch of a junco singing on a pine tree branch, created by the lead author of this paper.

Recognizing the importance of transportation and the importance of objective statistics for transportation decision-making, Congress requires the Director of the Bureau of Transportation Statistics (BTS) of the U.S. Department of Transportation (USDOT) to provide the Transportation Statistics Annual Report (TSAR) each year to Congress and the President.1 BTS published the first TSAR in 1994. This 30th TSAR edition documents the conduct of the duties of BTS as called out in the statute.Source: https://rosap.ntl.bts.gov/view/dot/79039The Transportation Statistics Annual Report (TSAR) describes the Nation’s transportation system, the system’s performance, its contributions to the economy, and its effects on people and the environment. This report is based on information collected or compiled by the Bureau of Transportation Statistics (BTS), a principle Federal statistical agency at the U.S. Department of Transportation.Source: https://www.bts.gov/product/transportation-statistics-annual-reportThis upload contains xlsx files supporting the 2023 (https://rosap.ntl.bts.gov/view/dot/72943) and 2024 (https://rosap.ntl.bts.gov/view/dot/79039) TSARs.The two readme files were created for this upload and were not produced by the BTS.

This statistic depicts the most common sources of stress reported among U.S. teenagers in 2013. School was overwhelmingly the most commonly reported source of stress in teens, with ** percent noting it as a source. Stress can impact overall health despite a lack of awareness. High stress can weaken the immune system and cause exhaustion in the body. Work is one of the most common sources of stress for adults.

The Mapping of Statistical and Information Sources: Indicators Report compiles already existing indicators, but spread across different Portuguese statistical and information sources. The collection covers the period from 13 March 2020 to 5 May 2023, matching the chronological scope of the RePeME project - And after the pandemic? Recovery, continuities and changes in basic education (ISCED 1) in Portugal - which foresees the systematic collection of information for two time frames: 1) emergency/during the pandemic; 2) change/after the pandemic. The information is presented in a table, in four points: indicator, date (corresponding to the year), source of information with reference/link to the origin of the data and brief description of the result. Whenever necessary, supplementary information has been introduced in footnotes.

Global COVID-19 surveys conducted by National Statistical Offices. This dataset has several columns that contain different types of information. Here's a brief explanation of each column:

1.**Country**: This column likely contains the names of the countries for which the survey data is collected. Each row represents data related to a specific country.

2.**Category**: This column might contain information about the type or category of the survey. It could include categories such as healthcare, economic impact, public sentiment, etc. This helps in categorizing the surveys.

3.**Title and Link**: These columns may contain the title or name of the specific survey and a link to the source or webpage where more information about the survey can be found. The link can be useful for referencing the original source of the data.

4.**Description**: This column likely contains a brief description or summary of the survey's objectives, methodology, or key findings. It provides additional context for the survey data.

5.**Source**: This column may contain information about the organization or agency that conducted the survey. It's essential for understanding the authority behind the data.

6.**Date Added**: This column probably contains the date when the survey data was added to the dataset. This helps track the freshness of the data and can be useful for historical analysis.

With this dataset, you can perform various types of analysis, including but not limited to:

• Country-based analysis: You can analyze survey data for specific countries to understand the impact of COVID-19 in different regions.

• Category-based analysis: You can group surveys by category and analyze trends or patterns related to healthcare, economics, or public sentiment.

• Temporal analysis: You can examine how survey data has evolved over time by using the "Date Added" column to track changes and trends.

• Source-based analysis: You can assess the reliability and credibility of the data by considering the source of the surveys.

• Data visualization: Create visual representations like charts, graphs, and maps to make the data more understandable and informative.

Before conducting any analysis, it's essential to clean and preprocess the data, handle missing values, and ensure data consistency. Additionally, consider the research questions or insights you want to gain from the dataset, which will guide your analysis approach.

The validity of empirical research often relies upon the accuracy of self-reported behavior and beliefs. Yet, eliciting truthful answers in surveys is challenging especially when studying sensitive issues such as racial prejudice, corruption, and support for militant groups. List experiments have attracted much attention recently as a potential solution to this measurement problem. Many researchers, however, have used a simple difference-in-means estimator without being able to efficiently examine multivariate relationships between respondents' characteristics and their answers to sensitive items. Moreover, no systematic means exist to investigate role of underlying assumptions. We fill these gaps by developing a set of new statistical methods for list experiments. We identify the commonly invoked assumptions, propose new multivariate regression estimators, and develop methods to detect and adjust for potential violations of key assumptions. For empirical illustrations, we analyze list experiments concerning racial prejudice. Open-source software is made available to implement the proposed methodology.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

World health statistics 2023: monitoring health for the SDGs, sustainable development goals

Overview

The World health statistics report is the annual compilation of health and health-related indicators which has been published by the World Health Organization (WHO) since 2005.

The 2023 edition reviews more than 50 health-related indicators from the Sustainable Development Goals (SDGs) and WHO’s Thirteenth General Programme of Work (GPW 13)

The report summarizes the trends in life expectancy and causes of death, and reports on progress towards the health-related Sustainable Development Goals (SDGs) and associated targets.

https://cdn.who.int/media/images/default-source/ddi-department/world-health-statistics-report-2023/01-who_mca-danangkmc-vnm-(22-of-37).tmb-1366v.jpg?sfvrsn=cb53a2df_1" alt="test">

Annual rate of reduction in maternal and child mortality has dropped in recent years

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Without faster progress, no regions will achieve the SDG target for NCD mortality by 2030 – and half still won’t by 2048

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Total years of life lost due to COVID-19 by age-group

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Acknowledgements

This Dataset is created from https://www.who.int/ . If you want to learn more, you can visit the Website.

The goal of Alaska Energy Authority's (AEA) Power Cost Equalization program is to provide economic assistance to customers in rural areas of Alaska where the kilowatt-hour charge for electricity can be three to five times higher than the charge in more urban areas of the state. Approximately 30% of all kWh’s sold by the participating utilities are eligible for PCE. PCE fundamentally improves Alaska’s standard of living by helping small rural areas maintain the availability of communications and the operation of basic infrastructure and systems, including water and sewer, incinerators, heat and light. PCE is a core element underlying the financial viability of centralized power generation in rural communities. The Legislature established different functions for AEA and the Regulatory Commission of Alaska (RCA) under Alaska Statutes 42.45.100-170, which govern PCE program responsibilities.AEA determines eligibility of community facilities and residential customers and authorizes payment to the electric utility. Commercial customers are not eligible to receive PCE credit. Participating utilities are required to reduce each eligible customer’s bill by the amount that the State pays for PCE.RCA determines if a utility is eligible to participate in the program and calculates the amount of PCE per kWh payable to the utility. More information about the RCA may be found at www.state.ak.us/rca .Power Cost Equalization Program GuideFor more information and for questions about this data, see: AEA Power Cost Equalization.Source data: PCE Statistical Reports By Utility FY2019

Research protocol, including all procedures, sources of variables, and software syntax for statistical analysis.

This dataset includes live births, birth rates, and fertility rates by Hispanic origin of mother in the United States since 1989. National data on births by Hispanic origin exclude data for Louisiana, New Hampshire, and Oklahoma in 1989; New Hampshire and Oklahoma in 1990; and New Hampshire in 1991 and 1992. Birth and fertility rates for the Central and South American population includes other and unknown Hispanic. Information on reporting Hispanic origin is detailed in the Technical Appendix for the 1999 public-use natality data file (see ftp://ftp.cdc.gov/pub/Health_Statistics/NCHS/Dataset_Documentation/DVS/natality/Nat1999doc.pdf). SOURCES NCHS, National Vital Statistics System, birth data (see https://www.cdc.gov/nchs/births.htm); public-use data files (see https://www.cdc.gov/nchs/data_access/VitalStatsOnline.htm); and CDC WONDER (see http://wonder.cdc.gov/). REFERENCES National Office of Vital Statistics. Vital Statistics of the United States, 1950, Volume I. 1954. Available from: https://www.cdc.gov/nchs/data/vsus/vsus_1950_1.pdf. Hetzel AM. U.S. vital statistics system: major activities and developments, 1950-95. National Center for Health Statistics. 1997. Available from: https://www.cdc.gov/nchs/data/misc/usvss.pdf. National Center for Health Statistics. Vital Statistics of the United States, 1967, Volume I–Natality. 1969. Available from: https://www.cdc.gov/nchs/data/vsus/nat67_1.pdf. Martin JA, Hamilton BE, Osterman MJK, et al. Births: Final data for 2015. National vital statistics reports; vol 66 no 1. Hyattsville, MD: National Center for Health Statistics. 2017. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr66/nvsr66_01.pdf. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Drake P. Births: Final data for 2016. National Vital Statistics Reports; vol 67 no 1. Hyattsville, MD: National Center for Health Statistics. 2018. Available from: https://www.cdc.gov/nvsr/nvsr67/nvsr67_01.pdf. Martin JA, Hamilton BE, Osterman MJK, Driscoll AK, Births: Final data for 2018. National vital statistics reports; vol 68 no 13. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_13.pdf.