Resnik David B, Elliott Kevin C, Soranno Patricia A, Smith Elise M
a National Institute for Environmental Health Sciences , National Institutes of Health , Research Triangle Park , North Carolina , USA.
b Lyman Briggs College , Michigan State University , East Lansing , Michigan , USA.
Account Res. 2017;24(6):344-358. doi: 10.1080/08989621.2017.1327813. Epub 2017 May 8.
In this commentary, we consider questions related to research integrity in data-intensive science and argue that there is no need to create a distinct category of misconduct that applies to deception related to processing, analyzing, or interpreting data. The best way to promote integrity in data-intensive science is to maintain a firm commitment to epistemological and ethical values, such as honesty, openness, transparency, and objectivity, which apply to all types of research, and to promote education, policy development, and scholarly debate concerning appropriate uses of statistics.
在本评论中，我们思考了与数据密集型科学中的研究诚信相关的问题，并认为没有必要设立一个适用于与数据处理、分析或解释相关欺骗行为的独特不当行为类别。促进数据密集型科学诚信的最佳方式是坚定秉持适用于所有类型研究的认识论和伦理价值观，如诚实、开放、透明和客观，并推动有关统计数据恰当使用的教育、政策制定和学术辩论。
