Ferret: Reviewing Tabular Datasets for Manipulation
Ferret: Reviewing Tabular Datasets for Manipulation
复制标题
DOI：
10.1111/cgf.14822
复制
发表时间：
2023-06
影响因子：
2.5
通讯作者：
Devin Lange;Shaurya Sahai;J. M. Phillips;A. Lex
中科院分区：
文献类型：
--
作者：
Devin Lange;Shaurya Sahai;J. M. Phillips;A. Lex
如何保证科学的准确性？操纵或伪造科学数据的行为导致了许多备受瞩目的欺诈案件和撤回。然而，检测被操纵的数据是一项具有挑战性且耗时的奋进。由于数据类型和操作技术的多样性，自动检测方法受到限制。此外，自动标记为可疑的模式可以有合理的解释。相反，我们提出了一种细致入微的方法，让专家分析表格数据集，例如，作为同行评审过程的一部分，使用指导，交互式可视化方法。在本文中，我们分析了如何创建操作的数据集以及这些技术生成的工件。基于这些发现，我们提出了一套可视化方法来表面潜在的不规则性。我们已经在Ferret中实现了这些方法，Ferret是一个用于数据取证工作的可视化工具。Ferret突出了潜在的数据问题，并提供了发现篡改迹象并将其与真实数据区分开来的指导。
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.