Clinical Research Data Management Guide
Practical, evidence-based guidance for designing, collecting, managing, protecting, and using clinical research data.
Standards, practice, technology, and evidence for investigators, research coordinators, data managers, informaticians, sponsors, and organizations conducting clinical research.
The disciplined work of turning a study protocol into complete, accurate, analysis-ready data about human participants — not enterprise or master data management, not IT database administration, and not the open-data "research data management" of libraries and repositories, which share the words but not the field. Read the full definition.
What we cover
How clinical research data management actually works — from protocol and CRF design through collection, cleaning, coding, lock, and archive.
What evidence tells us about data quality, capture methods, and research infrastructure — and what it does not establish.
How clinical research is funded and how changing regulation and policy shape the way research data is collected and shared.
How EDC systems, research data platforms, and the wider eClinical ecosystem support studies, sites, and sponsors — with structured, sourced research.
Every study runs on two interlocking cycles. The study lifecycle moves from design and build through recruitment, collection, monitoring and cleaning, coding and analysis, to close, archive, and sharing. The data lifecycle runs alongside it: define, acquire, validate, transform, protect, use, and preserve. Good data management keeps the two in step.
The study lifecycle
The data lifecycle
Technology, governance, and data management connect the two lifecycles.
A Clinical Research Data Management Guide framework. Technology, governance, and data management connect the two: every study-lifecycle stage produces or depends on data moving through the data lifecycle.
A Clinical Research Data Management Guide framework — explored in The Two Lifecycles of Clinical Research. Technology supports the work; investigators, coordinators, and data managers produce the evidence.
Don't begin with capability checkboxes. We group clinical research data systems into six primary-function categories. They describe what a system is built to do — not a ranking, and not a progression. The right fit depends on your studies, organization, and implementation capacity.
EDC, CTMS, eCOA, eConsent, eTMF — what each system does · See the technology landscape
How clinical research data management actually operates — drawing on regulation, study designs, funding structures, and real-world implementation at sites and sponsors.
How investigator-initiated academic research and industry-sponsored commercial trials differ in practice — funding, regulatory posture, staffing, tec…
The organizing framework of this publication: every study runs a study lifecycle and a data lifecycle at the same time, and data management and techn…
We review research relevant to clinical data management practice and distinguish what studies found from what they did not establish.
Research electronic data capture (REDCap) — A metadata-driven methodology and workflow process for providing …
Journal of Biomedical Informatics 42(2): 377–381, 2009
Good Clinical Data Management Practices (GCDMP)
Society for Clinical Data Management (SCDM); chapters revised on an ongoing basis, 2000–present
Our research assesses every product against the same structured taxonomy of clinical research data-management capabilities — each defined in plain language, with the questions to ask vendors.
We explain the standards and regulations that shape clinical research data — 21 CFR Part 11, ICH E6 (GCP), HIPAA, GDPR, the Common Rule, CDISC standards (CDASH, SDTM, ADaM, ODM), HL7 FHIR, the NIH Data Management and Sharing Policy, FAIR principles, SOC 2, and WCAG — with links to their authoritative sources. We don't create standards; we make them understandable.
21 CFR Part 11: What Research Teams and Software Buyers Should Understand · All standards
Curated organizations, publications, and events for people who work with clinical research data.
Upcoming: SCDM Annual Conference (external link) — Dates and location on the organizer's site, See organizer site
Practice & Standards International Council for Harmonisation (ICH) · January 6, 2025
This is the first top-to-bottom rewrite of GCP since 1996, and it speaks the language of data management directly: data governance and life-cycle data integrity are now …
Regulation U.S. Food and Drug Administration · October 2, 2024
Part 11 questions consume a disproportionate share of vendor-selection and audit conversations, and this guidance is now the reference point. It gives current, citable a…
Technology & Data U.S. Food and Drug Administration · December 22, 2023
Remote acquisition moves data management upstream: fitness-for-purpose, provenance, and transmission integrity get decided before first patient in, not fixed during clea…
We distinguish evidence from opinion — and when evidence is incomplete, conflicting, or contested, we say so. Our editorial policy.
Claims are sourced and verified. Profiles, comparisons, and articles carry citations and verification dates, not marketing copy. Our methodology.
Clinical Research Data Management Guide is published by QuesGen Systems, Inc., a developer of clinical research data-management technology. We disclose that relationship plainly and cover the whole market — including competitors — under one published framework. Read the disclosure.
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