ABSTRACT
Chapter 5 provides a practical overview of how research data are structured and documented, emphasizing why format and metadata matter for meaningful analysis. It first differentiates structured, semi-structured, and unstructured data: from neatly organized spreadsheets and SQL tables to flexible JSON files and raw text or multimedia. The chapter illustrates how each format requires appropriate handling – for example, statistical analysis on survey data versus text mining on interview transcripts – and warns that forcing complex information into simple tables can strip context. It then spotlights the “unsung hero” of data: metadata and thorough documentation. Researchers are introduced to standard metadata frameworks (like the Data Documentation Initiative [DDI] and Dublin Core) that define common descriptors for datasets. By adopting such standards and detailing variables, collection methods, and data transformations, scholars ensure that data remain interpretable and FAIR (findable, accessible, interoperable, and reusable) over time. The abstract underscores that meticulous metadata and format awareness are not optional niceties but fundamental to transparency, reproducibility, and future reuse of social science data.
