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| Illecito nella ricerca× | Fabbricazione e Falsificazione di Dati× | |
|---|---|---|
| Campo | Etica della ricerca | Etica della ricerca |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine | 2005 | 2005 |
| Ideatore≠ | U.S. Office of Research Integrity (ORI) / National Science Foundation; International standards via COPE | U.S. Office of Research Integrity; definitions in federal policy 42 CFR 93 |
| Tipo | Standard | Standard |
| Fonte seminale≠ | U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Federal Register. link ↗ | U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Definitions of fabrication and falsification. link ↗ |
| Alias≠ | FFP, Research Fraud, Scientific Misconduct | FFP Data Violations, Data Integrity Violations |
| Correlati | 3 | 3 |
| Sintesi≠ | Research misconduct comprises intentional or reckless fabrication, falsification, or plagiarism in proposing, conducting, or reporting research. Formally defined by U.S. federal policy (42 CFR Part 93, Office of Research Integrity), misconduct is distinguished from honest error, negligence, and good-faith disagreements about research methods or interpretation. Misconduct undermines scientific integrity, harms subjects and institutions, wastes research resources, and erodes public trust in science. Allegations are investigated formally with due process; proven misconduct results in sanctions ranging from publication correction to career-ending bans. | Data fabrication and falsification are serious forms of research misconduct involving intentional misrepresentation of research data. Fabrication means inventing data that were never actually collected; falsification means altering authentic data to change the meaning. Both undermine scientific integrity, waste research resources, and can harm research subjects and the public. Federal policy (42 CFR Part 93) formally defines these violations; detection is improving through statistical analysis tools and data transparency practices; prevention requires robust data governance and culture of accountability. |
| ScholarGateInsieme di dati ↗ |
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