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研究诚信原则×数据伪造与篡改×
领域研究伦理研究伦理
方法族Process / pipelineProcess / pipeline
起源年份20072005
提出者Multiple (National Academies, NIH/ORI, ESOMAR, individual discipline standards)U.S. Office of Research Integrity; definitions in federal policy 42 CFR 93
类型FrameworkStandard
开创性文献National Academies of Sciences, Engineering, and Medicine. (2017). Fostering Integrity in Research. The National Academies Press. DOI ↗U.S. Office of Research Integrity. (2005). Public Health Service Policy on Research Misconduct. 42 CFR Part 93. Definitions of fabrication and falsification. link ↗
别名Responsible Conduct of Research, RCR, Research Ethics StandardsFFP Data Violations, Data Integrity Violations
相关43
摘要Research integrity encompasses the ethical and professional standards that guide responsible conduct in all aspects of research—from study design and data collection through analysis, reporting, and publication. The core principles—honesty, transparency, accountability, respect, and stewardship—ensure that research is trustworthy, reproducible, and contributes legitimate knowledge. These principles are universal across disciplines and are enforced through institutional policies, professional standards, and regulatory oversight. Violations of research integrity undermine scientific credibility and can harm subjects, institutions, and public trust.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.
ScholarGate数据集
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ScholarGate方法对比: Research Integrity Principles · Data Fabrication and Falsification. 于 2026-06-18 检索自 https://scholargate.app/zh/compare