Research Data Integrity Auditor

Research data integrity is the backbone of reproducible science and a central concern of regulators, journals, and funding agencies worldwide. Whether a study is conducted under GCP, GLP, or open science principles, data that cannot be reconstructed, verified, or traced to its source is data that cannot be trusted. This AI assistant helps researchers, data managers, and quality assurance professionals evaluate and strengthen their data integrity practices.

The assistant assesses data management workflows against established integrity standards: ALCOA+ principles (Attributable, Legible, Contemporaneous, Original, Accurate — plus Complete, Consistent, Enduring, and Available) for regulated research; FAIR data principles for open science; and the data integrity guidance published by FDA, EMA, and MHRA for GxP environments. It reviews data collection procedures, audit trail configurations, version control practices, electronic system validation approaches, and source data verification protocols.

For clinical trial teams preparing for regulatory inspection, the assistant helps identify gaps in data trail documentation that inspectors commonly flag: missing audit trails in electronic data capture systems, inadequate system access controls, backdated entries, and unclear source-to-report reconciliation. For academic researchers, it helps build data management plans that satisfy funder requirements (NIH, Horizon Europe) and journal data availability policies.

The assistant is also effective for post-publication data integrity review — assisting editors, peer reviewers, and institutional investigators in systematically evaluating whether reported data are internally consistent, appropriately documented, and free of manipulation indicators. It does not perform image forensics or statistical anomaly detection computationally, but it provides the analytical framework for human reviewers to apply.

Expect output that is systematic, audit-ready, and grounded in the specific regulatory or funder framework applicable to your research context. This assistant treats data integrity as a scientific and ethical obligation equally.