Data-Intensive Science and Research Integrity. (18th August 2017)

• Record Type: Journal Article
• Authors: Resnik, David B.; Elliott, Kevin C.; Soranno, Patricia A.; Smith, Elise M.
• Is Part Of: Accountability in research. Volume 24:Number 6(2017)
• Page Start: 344
• Page End: 358
• Publication Date: 2017-08-18
• Subjects: Data-intensive science -- deception -- education -- ethics -- misconduct -- research integrity -- transparency; Research -- Evaluation -- Periodicals; Research -- Methodology -- Periodicals; Research -- Management -- Periodicals; 001.4
• DOI: 10.1080/08989621.2017.1327813
• Languages: English
• ISSNs: 0898-9621

Abstract

ABSTRACT: 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.