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dc.contributor.authorLange, Devinen_US
dc.contributor.authorSahai, Shauryaen_US
dc.contributor.authorPhillips, Jeff M.en_US
dc.contributor.authorLex, Alexanderen_US
dc.contributor.editorBujack, Roxanaen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorSchreck, Tobiasen_US
dc.date.accessioned2023-06-10T06:16:44Z
dc.date.available2023-06-10T06:16:44Z
dc.date.issued2023
dc.identifier.issn1467-8659
dc.identifier.urihttps://doi.org/10.1111/cgf.14822
dc.identifier.urihttps://diglib.eg.org:443/handle/10.1111/cgf14822
dc.description.abstractHow do we ensure the veracity of science? The act of manipulating or fabricating scientifc data has led to many high-profle 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 fagged 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 fndings, 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.en_US
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.subjectCCS Concepts: Human-centered computing -> Information visualization; Human computer interaction (HCI)
dc.subjectHuman centered computing
dc.subjectInformation visualization
dc.subjectHuman computer interaction (HCI)
dc.titleFerret: Reviewing Tabular Datasets for Manipulationen_US
dc.description.seriesinformationComputer Graphics Forum
dc.description.sectionheadersVisual Analysis and Processes
dc.description.volume42
dc.description.number3
dc.identifier.doi10.1111/cgf.14822
dc.identifier.pages187-198
dc.identifier.pages12 pages


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  • 42-Issue 3
    EuroVis 2023 - Conference Proceedings

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