Overly.Honest.Editor

@editoratlarge.bsky.social

#Openscience ❤️&👻; incrementalist; Cptn Grumblepants; thought follower; unbelievable little shit; self-serving internet bawbag; occasional Jorts; Grumpytits McGee. I will not just & I can't even. Skeets CC By.

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Wild story of #retractions of Max Planck papers... That being said, "copyright bot is to blame" sounds like nonsense. It's not how retractions anywhere work #OverlyConfusedEditor A bit surprised the question about how was not asked (given the bland comment from SN). www.science.org/content/arti...

Why have papers by one of history’s most famous physicists been retracted?

Springer Nature has removed two studies by Max Planck. A bot may be to blame

science.org

Wild story of #retractions of Max Planck papers... That being said, "copyright bot is to blame" sounds like nonsense. It's not how retractions anywhere work #OverlyConfusedEditor A bit surprised the question about how was not asked (given the bland comment from SN). www.science.org/content/arti...

Why have papers by one of history’s most famous physicists been retracted?

Springer Nature has removed two studies by Max Planck. A bot may be to blame

science.org

So much to unpack in this story. But if you think this will change the landscape for the better, think again. Editor Cat-zhang is already told by colleagues in CN that due to these scandals students are asked to sign declarations of sole responsibility for data / www.nature.com/articles/d41...

‘Student Geng’ ignites research-integrity scandal in China after calling out senior academics

Video blogger’s viral accusations of data manipulation in Nature journals have sparked intense debate and speedy institutional investigations.

nature.com

It's now 4½ years since I first thought of writing it, and 3½ years since I actually started writing it, but today I finally got around to submitting the preprint. Say hello to SPIV. 🧵

SPIV analysis: A heuristic meta-method for detecting non-random patterns in study data
Nicholas J. L. Brown, PhD
Kyle Sheldrick, MD, PhD
Ben W. Mol, MD, PhD
Lyle C. Gurrin, PhD

Please address correspondence to nicholasjlbrown@gmail.com.

Abstract
The increasing prevalence of open data is changing the ways in which forensic meta-science is done. Previously it was possible to infer what had or had not taken place only by examining its published summary statistics. Today meta-researchers wishing to reanalyse published results can also examine author-supplied individual participant data (IPD) to identify irregularities, either because they are directly published or because an editor has asked the authors to supply them. Here, we discuss some of the ways in which one might go about trying to detect anomalies in typical datasets in the biomedical or social sciences by examining the relationship between consecutive participant records within a dataset. We refer to these as “SPIV analyses”, where SPIV stands for Sequences in Purportedly Independent Values. SPIV is based on the principle that the data records for consecutive participants in a randomized study — and, potentially, some study designs that do not use randomisation — should be independent of each other for the great majority of measured variables. Using SPIV techniques, we can identify data that do not satisfy this condition, perhaps as a result of having been falsified or generated manually by unscrupulous researchers.