{"id":335,"date":"2026-07-02T20:06:26","date_gmt":"2026-07-02T20:06:26","guid":{"rendered":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/?post_type=chapter&p=335"},"modified":"2026-07-06T14:14:04","modified_gmt":"2026-07-06T14:14:04","slug":"1-the-biomedical-data-journey-research-process-infrastructure-and-ethics","status":"publish","type":"chapter","link":"https:\/\/openpub.libraries.rutgers.edu\/dsarm12\/chapter\/1-the-biomedical-data-journey-research-process-infrastructure-and-ethics\/","title":{"raw":"The Biomedical Data Journey: Research Process, Infrastructure, and Ethics","rendered":"The Biomedical Data Journey: Research Process, Infrastructure, and Ethics"},"content":{"raw":"

Reading Objectives<\/h2>\r\n

After reading this chapter, you should be able to:<\/p>\r\n\r\n

    \r\n \t
  1. Explain how a broad research topic becomes a research question, hypothesis, and study design<\/strong>, including the roles of explanatory, outcome, demographic, and contextual variables.<\/li>\r\n \t
  2. Distinguish observational from experimental studies<\/strong> and explain why observational associations, including those observed over time, do not by themselves establish causation.<\/li>\r\n \t
  3. Describe the biomedical data journey<\/strong> from study design and participant encounters through data capture, processing, documentation, controlled access, analysis, and responsible reuse.<\/li>\r\n \t
  4. Explain how data infrastructure shapes research data<\/strong>, including the roles of participants, caregivers, staff, study sites, instruments, laboratories, identifiers, secure transfer, storage, and quality-control procedures.<\/li>\r\n \t
  5. Interpret the basic structure and documentation of a biomedical dataset<\/strong>, including DataFrames, units of observation, participant and visit identifiers, metadata, data dictionaries, codebooks, provenance, and data states.<\/li>\r\n \t
  6. Apply foundational principles of ethical and responsible data use<\/strong>, including privacy, confidentiality, re-identification risk, controlled access, data-use agreements, secure storage and deletion, responsible interpretation of race and ethnicity variables, and appropriate citation and acknowledgment.<\/li>\r\n \t
  7. Explain why reproducible notebook workflows and synthetic data support responsible learning and analysis<\/strong>, while distinguishing reproducibility from correctness, causal validity, or replication.<\/li>\r\n<\/ol>\r\n
    \r\n

    Key Terms<\/h2>\r\n

    Biomedical data journey; data governance; data infrastructure; hypothesis; research question; study design; variable.<\/p>\r\n\r\n<\/div>\r\n

    Introduction: From Research Questions to Analytic Datasets<\/h2>\r\nSubstance use and related harms are often reduced to matters of personal choice or morality. Such narratives overlook the many influences that shape behavior and health, including development, family and peer environments, stress, opportunity, policy, biology, and access to resources. When people who use substances are blamed for \"bad choices,\" important social and structural factors remain invisible and stigma grows. This course begins from a broader, evidence\u2011based view - questions about addiction and health require attention to multiple influences and careful data collection.\r\n\r\n[caption id=\"attachment_38\" align=\"aligncenter\" width=\"400\"]\"Anti-drug Figure 1.1. \u201cSay \u2018No\u2019 to crack and other drugs\u201d poster featuring McGruff the Crime Dog. Contributor: United States Department of Education. Source: National Library of Medicine (History of Medicine), via Wikimedia Commons. Rights status: No known copyright restrictions.[\/caption]\r\n\r\nScientific inquiry starts with questions rather than assumptions. Researchers ask how elements of a young person\u2019s environment relate to later outcomes\u2014for example, how neighbourhood social support or sleep quality relates to substance use, or how stress and peer relationships shape development. Such questions cannot be answered by inspecting a spreadsheet alone. They call for a process that spans study design, measurement, data collection, curation, analysis, and ethical governance. Data do not appear spontaneously; they are created through an organised journey.\r\n\r\nA value in a dataset might originate in a youth\u2019s survey response, a caregiver interview, a computer\u2011based task, a laboratory analysis of a specimen, or a brain\u2011imaging session. That value may be collected by a research coordinator, interviewer, technician, or clinician. It may be checked for quality, assigned an identifier, linked to documentation, transferred through secure systems, and stored under rules that limit who can access it and how it may be used. Every row in a dataset has a history.\r\n\r\nBefore researchers can analyze data, they must make a series of deliberate decisions - decide what they want to understand; design a study that can address the question; determine which people and experiences to include; select ways to measure important concepts; protect participants and their information; and document how the resulting data should be interpreted. These choices shape every later step and must be revisited when interpreting results.\r\n\r\nThe biomedical data journey<\/strong> provides a framework for tracing this process. It highlights four connected stages: (1) framing and designing the study; (2) recruiting participants and collecting data; (3) processing, checking, and documenting the data; and (4) governing, analyzing, reporting, and responsibly reusing data. Each stage builds on those that precede it. A change in recruitment, measurement, or documentation influences what analysts can do later. Figure 1.2 illustrates these stages, reminding us that the rows and columns seen in a DataFrame are the end product of a long chain of human, technical, and ethical decisions.\r\n\r\n[caption id=\"attachment_331\" align=\"aligncenter\" width=\"1672\"]\"A Figure 1.2. The Biomedical Data Journey: Four Connected Stages.[\/caption]\r\n\r\nThroughout this course we will return to the Adolescent Brain Cognitive Development (ABCD) Study as a running example. ABCD is a large, multisite longitudinal study that follows more than 11,000 children across 21 sites for a decade, collecting data on health, behaviour, cognition, social environment, substance use, biological measures, and brain development. Its scale and multimodal design enable questions that no single survey, clinic, or laboratory could answer. At the same time, its complexity means that analysts must know what each variable represents, who provided it, when and how it was collected, what the possible values mean, whether data are missing, and what the study design can and cannot support. Sensitive information about young people, families, health, substance use, genetics, and brain development carries ethical responsibilities.\r\n\r\nThis chapter establishes foundational concepts for DSARM\u00a01. You will learn how broad topics become research questions, how theory leads to constructs, measures, and variables, and how hypotheses are formulated and tested. You will learn the difference between exploratory and confirmatory questions, and between explanatory, outcome, demographic, and contextual variables. You will examine study designs (observational versus experimental, cross\u2011sectional versus longitudinal), sampling and recruitment, and the basic structure of a DataFrame. You will see why observational studies can identify important patterns but do not alone establish causation. You will explore the physical and digital infrastructure that makes data possible, the documentation that enables interpretation, and the ethical principles that govern responsible data use.\r\n

    2. From Question to Study Design<\/h2>\r\n

    2.1 From Broad Topics to Specific Questions<\/h3>\r\nHealth researchers often begin with a broad topic, such as youth development and substance use<\/strong>. A topic captures a general area of curiosity, but it does not yet specify what will be measured or how relationships will be tested.\r\n\r\nTo make progress, researchers refine that topic into a research question<\/strong>: an inquiry that identifies relationships, patterns, descriptions, or changes that can be investigated empirically.\r\n\r\nA scientific research question always includes variables<\/strong>. For example, instead of asking:\r\n
    How do teenagers grow up?<\/blockquote>\r\nAn addiction research project might ask:\r\n
    How is neighborhood social support related to later cannabis use among adolescents?<\/blockquote>\r\nThis question identifies a population, proposes a relationship between two variables, and invites data collection.\r\n\r\nBroad topics and research questions emerge from theory<\/strong> and prior evidence<\/strong>. Theory explains why certain relationships might exist. Constructs are the abstract concepts that theory proposes. Measures translate constructs into observable quantities. Variables are the measured values we analyze.\r\n\r\nEach step should be explicit:\r\n\r\nTheory \u2192 Construct \u2192 Measure \u2192 Variable<\/strong>\r\n\r\nFor instance, the construct stress<\/strong> might be measured by a validated questionnaire or a biomarker. A research question might then connect stress as an explanatory variable<\/strong> to another variable, such as sleep quality<\/strong>, as the outcome variable<\/strong>.\r\n\r\nWhen a relationship is expected, researchers often formulate a hypothesis<\/strong>: a specific, testable prediction that can be falsified.\r\n

    2.1.1 Exploratory and Confirmatory Questions<\/h4>\r\nResearch questions come in two main forms.\r\n\r\nExploratory questions<\/strong> ask broadly about patterns or associations without specifying which variables explain which outcomes. For example:\r\n
    What factors most influence substance use among urban high school students?<\/blockquote>\r\nExploratory work is common when relationships have not been extensively studied. It allows researchers to examine large datasets, identify patterns, and generate new hypotheses.\r\n\r\nConfirmatory questions<\/strong> test specific relationships derived from theory or prior evidence. For example:\r\n
    Does peer support reduce risk of substance use among urban high school students?<\/blockquote>\r\nThis confirmatory question proposes a direction of effect between a predictor and an outcome. It may lead to a hypothesis such as:\r\n
    Adolescents with higher peer support will show lower substance use frequency over the next year compared with those with lower peer support.<\/blockquote>\r\nExploratory questions are useful for discovery. Confirmatory questions enable rigorous testing of hypotheses.\r\n

    2.1.2 Explanatory, Outcome, Demographic, and Contextual Variables<\/h4>\r\nResearchers classify variables by their role in a question.\r\n\r\nAn outcome variable<\/strong> is the focus of a question. It represents the result to be explained. It is sometimes called a dependent variable<\/strong> or response variable<\/strong>.\r\n\r\nAn explanatory variable<\/strong> is expected to influence the outcome. It is often called an independent variable<\/strong> or predictor<\/strong>.\r\n\r\nDemographic variables<\/strong> describe characteristics of participants, such as age, gender, race, or socioeconomic status.\r\n\r\nContextual variables<\/strong> capture features of the environment, such as family, neighborhood, school, policy, or historical context, that may shape outcomes.\r\n\r\nFor example, if a study asks how weekly cannabis use is related to academic performance, then weekly cannabis use<\/strong> is the explanatory variable and academic performance<\/strong> is the outcome variable. Participants\u2019 age and neighborhood deprivation could be demographic and contextual variables, respectively.\r\n\r\nThe ABCD Study<\/strong>, which follows more than 11,000 young people across 21 sites for ten years, provides many examples. A broad topic might be:\r\n
    Adolescent brain development and substance use.<\/blockquote>\r\nA research question could ask:\r\n
    How does sleep quality in early adolescence relate to later cognitive performance?<\/blockquote>\r\nIn this question, sleep quality<\/strong> is the explanatory variable. It might be measured by actigraphy or self-report. Cognitive performance<\/strong> is the outcome variable. It might be measured by standardized tests.\r\n\r\nDemographic and contextual variables could include age, sex, family income, and parental education. Because ABCD collects repeated measures over a decade, researchers can investigate change over time.\r\n
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    Table 2.1. Example of Turning a Broad Topic into a Study Design<\/th>\r\n<\/tr>\r\n
    Part of a Study<\/th>\r\nExample Question or Decision<\/th>\r\n<\/tr>\r\n<\/thead>\r\n
    Broad topic<\/strong><\/td>\r\nYouth development and substance use<\/td>\r\n<\/tr>\r\n
    Research question<\/strong><\/td>\r\nHow is early adolescent sleep quality related to later cannabis use?<\/td>\r\n<\/tr>\r\n
    Explanatory variable<\/strong><\/td>\r\nSleep quality (e.g., hours of sleep, sleep latency)<\/td>\r\n<\/tr>\r\n
    Outcome variable<\/strong><\/td>\r\nCannabis use frequency (e.g., days used in the past 30 days)<\/td>\r\n<\/tr>\r\n
    Contextual\/demographic variables<\/strong><\/td>\r\nAge, sex, family income, neighborhood factors<\/td>\r\n<\/tr>\r\n
    Study design<\/strong><\/td>\r\nLongitudinal observational study following participants across multiple years<\/td>\r\n<\/tr>\r\n
    Limitation<\/strong><\/td>\r\nObservational associations do not imply causation. Other factors, such as stress or peer influences, may confound the relationship.<\/td>\r\n<\/tr>\r\n<\/tbody>\r\n<\/table>\r\n

    2.2 Study Designs: Observational and Experimental<\/h3>\r\n

    A study design<\/strong> describes how a research question will be investigated.<\/p>\r\n

    In an experimental design<\/strong>, researchers manipulate an explanatory variable to observe its effect on an outcome. For example, researchers might randomly assign participants to receive either a new medication or a placebo. Randomization helps ensure that differences in outcomes are due to the manipulated variable rather than other factors.<\/p>\r\n

    In an observational design<\/strong>, researchers observe natural variation without deliberately changing participants' experiences or exposures. The ABCD Study<\/strong> is an observational longitudinal study: researchers follow participants over ten years and record naturally occurring experiences and outcomes.<\/p>\r\n

    Observational studies can identify associations and developmental trajectories, but they cannot by themselves demonstrate that one variable causes another.<\/p>\r\n

    Study designs also differ in whether they collect data at a single time point or across multiple time points.<\/p>\r\n

    A cross-sectional study<\/strong> collects data at one point in time.<\/p>\r\n

    A longitudinal study<\/strong> collects data across repeated visits, making it possible to examine change over time.<\/p>\r\n

    ABCD's longitudinal design allows researchers to assess how early exposures relate to later outcomes. However, even with longitudinal data, causal interpretations require caution because unmeasured factors may influence both exposures and outcomes.<\/p>\r\n\r\n

    2.3 Sampling, Recruitment, and Protocols<\/h3>\r\n

    Research questions are answered using data from a sample<\/strong>, which is a subset of individuals drawn from a larger population<\/strong>.<\/p>\r\n

    Sampling involves:<\/p>\r\n\r\n