Research ethics: 3 ways to blow the whistle

Abstract

re more people doing wrong or are more people speaking up? Retractions of scientific papers have increased about tenfold during the past decade, with many studies crumbling in cases of high-profile research misconduct that ranges from plagiarism to image manipulation to outright data fabrication. When worries about somebody's work reach a critical point, it falls to a peer, supervisor, junior partner or uninvolved bystander to decide whether to keep mum or step up and blow the whistle. Doing the latter comes at significant risk, and the path is rarely simple. Some make their case and move on; others never give up. And in what seems to be a growing trend, anonymous watchdogs are airing their concerns through e-mail and public forums. Here, Nature profiles three markedly different stories of individuals who acted on their suspicions. Successful or otherwise, each case offers lessons for would-be tipsters. WAYS TO BLOW THE WHISTLE Reporting suspicions of scientific fraud is rarely easy, but some paths are more effective than others.

1000 successes as CDDIS reaches 1000 published papers!

“…Industry has already alerted our community. These ‘hot' topics are highly debated in other, more appropriate settings 6 yet we do, and will further ensure, that such important issues stay among the forefront of our editorial agenda.…”

Association Claims in the Sequencing Era

“…The Economist recently published an article suggesting that science is not as self-correcting as many assume [22,23], and journals such as Nature and Science are publishing columns on scientific misconduct, peer-review, and other issues that contribute to the reporting of inaccurate results [24,25,26,27,28]. The last four years have also seen several published studies on the prevalence of false discoveries and misconduct, indicating that as many as “1% of published papers are fraudulent” (about 20,000 papers each year) [25].…”

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“…While image manipulation is quite transparent, data fabrication is much more difficult to detect unless statistical analysis of the distribution of the data points reveals anomalies (Yong et al . ). Ultimately, it is a ‘failure to repeat’ that throws doubt on the veracity of published data.…”