Retraction: Persistent social interactions beget more pronounced personalities in a desert-dwelling social spider

Laskowski, Kate L.; Modlmeier, Andreas P.; DeMarco, Alex E.; Coleman, Anna; Zhao, Katherine; Brittingham, Hayley A.; McDermott, Donna R.; Pruitt, Jonathan N.

doi:10.1098/rsbl.2020.0062

Cited by 3 publications

(1 citation statement)

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“…Consistent with the published notices of retraction to articles 1 & 2 [53,54], the tests employed in the present study flagged issues in the data which, upon closer inspection, contained inexplicable duplications. In the case of both articles, retractions were issued just less than 6 years after the publication of the original articles.…”

Section: Discussionsupporting

confidence: 82%

Investigating and preventing scientific misconduct using Benford’s Law

Eckhartt

Ruxton

2023

Res Integr Peer Rev

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Integrity and trust in that integrity are fundamental to academic research. However, procedures for monitoring the trustworthiness of research, and for investigating cases where concern about possible data fraud have been raised are not well established. Here we suggest a practical approach for the investigation of work suspected of fraudulent data manipulation using Benford’s Law. This should be of value to both individual peer-reviewers and academic institutions and journals. In this, we draw inspiration from well-established practices of financial auditing. We provide synthesis of the literature on tests of adherence to Benford’s Law, culminating in advice of a single initial test for digits in each position of numerical strings within a dataset. We also recommend further tests which may prove useful in the event that specific hypotheses regarding the nature of data manipulation can be justified. Importantly, our advice differs from the most common current implementations of tests of Benford’s Law. Furthermore, we apply the approach to previously-published data, highlighting the efficacy of these tests in detecting known irregularities. Finally, we discuss the results of these tests, with reference to their strengths and limitations.

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Section: Discussionsupporting

confidence: 82%

Investigating and preventing scientific misconduct using Benford’s Law

Eckhartt

Ruxton

2023

Res Integr Peer Rev

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Ensuring data access, transparency, and preservation: mandatory data deposition for Behavioral Ecology and Sociobiology

Bakker

Traniello

2020

Behav Ecol Sociobiol

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Ferret: Reviewing Tabular Datasets for Manipulation

Lange

Sahai

Phillips

et al. 2023

Computer Graphics Forum

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How do we ensure the veracity of science? The act of manipulating or fabricating scientific data has led to many high‐profile fraud cases and retractions. Detecting manipulated data, however, is a challenging and time‐consuming endeavor. Automated detection methods are limited due to the diversity of data types and manipulation techniques. Furthermore, patterns automatically flagged as suspicious can have reasonable explanations. Instead, we propose a nuanced approach where experts analyze tabular datasets, e.g., as part of the peer‐review process, using a guided, interactive visualization approach. In this paper, we present an analysis of how manipulated datasets are created and the artifacts these techniques generate. Based on these findings, we propose a suite of visualization methods to surface potential irregularities. We have implemented these methods in Ferret, a visualization tool for data forensics work. Ferret makes potential data issues salient and provides guidance on spotting signs of tampering and differentiating them from truthful data.

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Retraction: Persistent social interactions beget more pronounced personalities in a desert-dwelling social spider

Cited by 3 publications

References 0 publications

Investigating and preventing scientific misconduct using Benford’s Law

Investigating and preventing scientific misconduct using Benford’s Law

Ensuring data access, transparency, and preservation: mandatory data deposition for Behavioral Ecology and Sociobiology

Ferret: Reviewing Tabular Datasets for Manipulation

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