![\"High-level](\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/01\/domains.png\")
Figure 0.1. High-level overview of data domains represented in the ABCD Study.<\/strong> The study includes information on substance use, physical health, biospecimens, neurocognition, mental health, brain imaging, family and community contexts, environmental exposures, and emerging technologies. Linked data<\/em> refers to contextual information, such as education, environmental exposures, policy, cultural values, and discrimination, that can be connected to participant records. Not every participant has every measure at every visit, and available measures vary across data releases. Source: Adolescent Brain Cognitive Development Study. Study design and overview documentation. Retrieved June 15, 2026, from ABCD Study Documentation: Study Design<\/a>.<\/span><\/em>[\/caption]\r\nData do not simply appear in a file ready for analysis. Each value reflects decisions about study design, recruitment, consent, measurement, collection procedures, quality control, documentation, storage, access, and reuse. A survey response, laboratory result, genetic variant, or brain-imaging measure therefore has both a scientific meaning and a history.<\/p>\r\n
Figure 0.2 introduces the framework used throughout this course. Data infrastructure<\/strong> includes the people, equipment, settings, procedures, and materials that make research data possible. Data architecture<\/strong> includes the digital structures through which data are organized and analyzed, such as DataFrames, identifiers, linked datasets, longitudinal records, metadata, network data, and genomic variants. Ethics, governance, and regulation connect these domains by shaping what may be collected, how sensitive information is protected, and who may access or reuse it.<\/p>\r\n\r\n\r\n[caption id=\"attachment_356\" align=\"aligncenter\" width=\"1672\"] The goal of this course is to apply this framework to the data types and instruments represented in Figure 0.1. You will learn to connect research questions to measurable variables, interpret documentation and data structures, evaluate data quality and limitations, use Python to conduct transparent analyses, and communicate conclusions responsibly.<\/p>\r\n Although ABCD is a recurring example, students work with synthetic instructional datasets rather than restricted participant-level ABCD data. These datasets resemble selected features of real biomedical data, including repeated visits, multiple instruments, identifiers, missing-value codes, quality-control flags, and supporting documentation. They allow students to practice data analysis and reproducible workflow while protecting participant privacy.<\/p>\r\nThe course GitHub repository is available here:\r\n Biomedical research increasingly uses longitudinal datasets that follow participants over time and combine information from surveys, behavioral tasks, health measures, biospecimens, genetic data, environmental context, and neuroimaging. Bringing these sources together allows researchers to examine questions that a single survey, clinic, or laboratory could not answer alone, including how social environments, development, biology, family experiences, and policy conditions relate to health and substance use over time.<\/p>\n Throughout this textbook, the Adolescent Brain Cognitive Development (ABCD) Study serves as a central example. ABCD is a large, multisite study of young people that collects information across many connected areas of development, behavior, health, and environment. Figure 0.1 provides a high-level orientation to those data domains.<\/p>\n Data do not simply appear in a file ready for analysis. Each value reflects decisions about study design, recruitment, consent, measurement, collection procedures, quality control, documentation, storage, access, and reuse. A survey response, laboratory result, genetic variant, or brain-imaging measure therefore has both a scientific meaning and a history.<\/p>\n Figure 0.2 introduces the framework used throughout this course. Data infrastructure<\/strong> includes the people, equipment, settings, procedures, and materials that make research data possible. Data architecture<\/strong> includes the digital structures through which data are organized and analyzed, such as DataFrames, identifiers, linked datasets, longitudinal records, metadata, network data, and genomic variants. Ethics, governance, and regulation connect these domains by shaping what may be collected, how sensitive information is protected, and who may access or reuse it.<\/p>\n The goal of this course is to apply this framework to the data types and instruments represented in Figure 0.1. You will learn to connect research questions to measurable variables, interpret documentation and data structures, evaluate data quality and limitations, use Python to conduct transparent analyses, and communicate conclusions responsibly.<\/p>\n Although ABCD is a recurring example, students work with synthetic instructional datasets rather than restricted participant-level ABCD data. These datasets resemble selected features of real biomedical data, including repeated visits, multiple instruments, identifiers, missing-value codes, quality-control flags, and supporting documentation. They allow students to practice data analysis and reproducible workflow while protecting participant privacy.<\/p>\n The course GitHub repository is available here:<\/p>\n This textbook is designed to be used alongside:<\/p>\n![\"This](\"https:\/\/openpub.libraries.rutgers.edu:443\/wp-content\/uploads\/sites\/28\/2026\/01\/ChatGPT-Image-Jul-4-2026-09_39_47-AM.png\")
Figure 0.2. From Data Infrastructure to Data Architecture. Data infrastructure includes the physical and operational systems that produce research data. Data architecture includes the digital structures used to organize, link, document, and analyze those data. Ethics, governance, and regulation shape both systems through requirements for consent, privacy, security, controlled access, and responsible reuse.[\/caption]\r\nCompanion Statistics Text<\/h2>\r\nThis textbook is designed to be used alongside:\r\n\r\nVu, J., & Harrington, D. (2021). Introductory Statistics for the Life and Biomedical Sciences<\/em> (1st ed., Version August 8, 2021). OpenIntro.<\/a>\r\n
Module Pairing Schedule<\/h2>\r\n
\r\n\r\n
\r\n \r\nModule<\/th>\r\n DSARM module topic<\/th>\r\n Paired statistical concepts from Vu & Harrington<\/th>\r\n<\/tr>\r\n<\/thead>\r\n \r\n M1<\/td>\r\n The Biomedical Data Journey<\/td>\r\n -<\/td>\r\n<\/tr>\r\n \r\n M2<\/td>\r\n Measuring Addiction & Youth Substance Use<\/td>\r\n Exploratory data analysis; numerical and categorical summaries; graphical displays (\u00a71.2\u20131.7).<\/td>\r\n<\/tr>\r\n \r\n M3<\/td>\r\n Attitudes, Environments, Data Quality & Cleaning<\/td>\r\n -<\/td>\r\n<\/tr>\r\n \r\n M4<\/td>\r\n Behavioral Genetics & Addiction<\/td>\r\n Foundations of probability; rules of probability; random variables (\u00a72.1\u20132.2).<\/td>\r\n<\/tr>\r\n \r\n M5<\/td>\r\n Polygenic Traits & Addiction<\/td>\r\n Discrete and continuous probability distributions; binomial, normal, and Poisson distributions (\u00a73.1\u20133.4).<\/td>\r\n<\/tr>\r\n \r\n M6<\/td>\r\n Social Determinants of Addiction<\/td>\r\n Statistical inference; sampling variability; confidence intervals (\u00a74.1\u20134.2).<\/td>\r\n<\/tr>\r\n \r\n M7<\/td>\r\n Public Policy & Addiction<\/td>\r\n Hypothesis-testing framework; test statistics; Type I and Type II errors (\u00a74.3\u20134.4).<\/td>\r\n<\/tr>\r\n \r\n M8<\/td>\r\n Brain\u2019s Reward System & Addiction<\/td>\r\n One- and two-sided tests; interpreting p-values; decision rules (\u00a75.1\u20135.3).<\/td>\r\n<\/tr>\r\n \r\n M9<\/td>\r\n Cue-Based Habits & Impulse Control<\/td>\r\n Statistical power; one-way ANOVA; comparing multiple group means (\u00a75.4\u20135.6).<\/td>\r\n<\/tr>\r\n \r\n M10<\/td>\r\n Capstone<\/td>\r\n -<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>","rendered":" Introduction to Data Science & Addiction Research Methods<\/h2>\n
Companion Statistics Text<\/h2>\n