Definition
Data integrity refers to the accuracy, consistency, and reliability of research data throughout its lifecycle; data fabrication or falsification is the unethical act of creating or altering data to mislead results.

Introduction
Data are sacred in research. They represent truth observed through systematic effort. Tampering with them is equivalent to falsifying history. When integrity fails, entire disciplines suffer reputational and practical damage.

Explanation
Maintaining data integrity begins with accurate recording, secure storage, and transparent documentation. Researchers must keep original datasets, analysis logs, and software code for verification.
Fabrication (making up data) and falsification (manipulating results) are grave violations of research ethics. Even subtle acts—like omitting inconvenient results or misrepresenting statistical significance—distort reality.
Institutions today enforce strict data policies, encouraging open access and peer verification to deter misconduct. Technology now enables reproducible research, ensuring that findings can be independently validated.

Key Takeaways
Integrity is the soul of research—once lost, credibility cannot be regained.

Real-World Case
In 2011, Dutch social psychologist Diederik Stapel was found guilty of fabricating data in dozens of studies, leading to over 50 paper retractions. This scandal reshaped global academic protocols for data transparency and verification.
Reference: https://ori.hhs.gov