175 R.L. Richesson, J.E. Andrews (eds.), Clinical Research Informatics, Health Informatics, DOI 10.1007/978-1-84882-448-5_10, © Springer-Verlag London Limited 2012 Abstract Every scientist knows that research results are only as good as the data upon which the conclusions were formed. However, most scientists receive no training in methods for achieving, assessing, or controlling the quality of research data— topics central to clinical research informatics. This chapter covers the basics of collect and process research data given the available data sources, systems, and people. Data quality dimensions specific to the clinical research context are used, and a framework for data quality practice and planning is developed. Available research is summarized, providing estimates of data quality capability for common clinical research data collection and processing methods. This chapter provides researchers, informaticists, and clinical research data managers basic tools to plan, achieve, and control the quality of research data. Keywords Clinical research data • Data quality • Research data collection • Processing methods • Informatics • Management of clinical data • Data accuracy Clinical Research Data Processes and Relationship to Data Quality Data quality is foundational to our ability to human research. Data quality is so impor- tant that an Institute of Medicine report [1] was written on the topic. Further, two key thought leaders in the quality arena, W. E. Deming and A. Donabedian, specifically addressed data quality [2–4]. Failing to plan for data quality is an implicit assumption that errors will not occur. Emphasizing that failing to plan for data quality further threatens data quality by inhibiting the detection of errors when they do occur, Stephan Arndt et al. state, M. Nahm, Ph.D. Informatics, Duke Translational Medicine Institute, Duke University, 2424 Erwin Road, Durham, NC 27705, USA e-mail: meredith.nahm@duke.edu Chapter 10 Data Quality in Clinical Research Meredith Nahm