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Updated: Jan 11, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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A Visual Dataset for Anomaly Detection in Self-Driving Laboratories.

Shiwei Lin1, Zesen Chen2, Xiaobin Jia2

  • 1Department of Computer Science and Technology, Tsinghua University, Beijing, China.

Scientific Data
|November 14, 2025
PubMed
Summary
This summary is machine-generated.

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Researchers created a new dataset for anomaly detection in self-driving laboratories. This resource aids in developing intelligent systems for monitoring and decision-making in automated scientific experiments.

Area of Science:

  • Robotics and Automation
  • Artificial Intelligence
  • Materials Science

Background:

  • Self-driving laboratories (SDLs) enhance scientific discovery via automation, but anomaly detection remains difficult due to process complexity.
  • Challenges include identifying anomalies in events, object states, and environmental conditions within SDLs.

Purpose of the Study:

  • To introduce a novel dataset specifically designed for process anomaly detection in scientific experiments within an SDL context.
  • To facilitate research in visual anomaly detection and automated monitoring systems for SDLs.

Main Methods:

  • A dataset was constructed from a fully automated Polydimethylsiloxane (PDMS) synthesis workflow using collaborative robots.
  • Images were captured from first-person perspectives using end-effector cameras on mobile and fixed robotic arms.

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Driving Simulation in the Clinic: Testing Visual Exploratory Behavior in Daily Life Activities in Patients with Visual Field Defects
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  • The dataset comprises 1,671 images and 2,788 image-text pairs, including step-specific descriptions, anomaly labels, and region-level annotations across 11 checkpoints and 14 viewpoints.

    • Main Results:

    • The dataset supports diverse visual anomaly detection tasks: image-text classification, anomaly type recognition, localization, grounded captioning, and attribution analysis.
    • It provides a practical resource for developing intelligent monitoring and automated decision-making in SDLs.

    Conclusions:

    • This dataset is a valuable resource for advancing anomaly detection in automated scientific research.
    • It enables the development of more robust and intelligent systems for self-driving laboratories, improving experimental reliability and discovery speed.