Data Sources in Biostatistics

Thomas W. MacFarland
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Thomas W. MacFarland: Nova Southeastern University, Office of Institutional Effectiveness and College of Computing and Engineering

Chapter Chapter 2 in Introduction to Data Science in Biostatistics, 2024, pp 101-145 from Springer

Abstract: Abstract The purpose of this lesson is to identify a few of the many data resources used in biostatistics. There has been such a proliferation of information, in many cases information that is easily, freely, and legally available to the public, that at times it may seem that there is an inundation of data, with data of many types, data in many formats, and data from many sources. From this abundance, the challenge is to select appropriate data resources, reliable data, valid data, truthful data, data that can be reasonably obtained, data in a usable format, data that can be legally used, etc. Some degree of knowledge about data resources and the quality of data from these sources will help contemporary data scientists so that time and efforts are used against desired data and not merely data that may seem to meet needs, but lack efficacy, whether for technical or other reasons. This lesson provides a gentle introduction to but a few of the many external resources that may interest those who work in biostatistics and use R to engage in data science activities. With experience, data scientists develop a personal collection of data resources, typically those resources associated with specific fields of study.

Keywords: 80-20 Rule; Analysis of Variance; Application Programming Interface (API); Base R; Beautiful graphics; Code book; Environmental Protection Agency (EPA); github; Good Programming Practice (gpp); Gross Domestic Product (GDP); National School Lunch Programs (NSLP); National Science Foundation (NSF); Our World in Data; p-value; R; Statistically significant difference; Student’s t-Test; tidyverse ecosystem; United States Bureau of Labor Statistics (BLS); United States Centers for Disease Control and Prevention (CDC); World Health Organization (WHO) (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/978-3-031-46383-9_2

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