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Abstract The field of connectomics aims to reconstruct the wiring diagram of Neurons and synapses to enable new insights into the workings of the brain. Reconstructing and analyzing the Neuronal connectivity, however, relies on many individual steps, starting from high‐resolution data acquisition to automated segmentation, proofreading, interactive data exploration, and circuit analysis. All of these steps have to handle large and complex datasets and rely on or benefit from integrated visualization methods. In this state‐of‐the‐art report, we describe visualization methods that can be applied throughout the connectomics pipeline, from data acquisition to circuit analysis. We first define the different steps of the pipeline and focus on how visualization is currently integrated into these steps. We also survey open science initiatives in connectomics, including usable open‐source tools and publicly available datasets. Finally, we discuss open challenges and possible future directions of this exciting research field.
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Ferret: Reviewing Tabular Datasets for Manipulation
Abstract 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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- Award ID(s):
- 1751238
- PAR ID:
- 10426691
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Computer Graphics Forum
- Volume:
- 42
- Issue:
- 3
- ISSN:
- 0167-7055
- Page Range / eLocation ID:
- p. 187-198
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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