EVENTS

This two-part online workshop introduces best practices for image dataset quality control and exploration. You’ll learn how to identify dataset issues, visualize distributions and metadata, and prepare images for downstream analysis, using PixelPatrol as a practical support tool. After applying PixelPatrol to your own datasets, you’ll reconvene to discuss questions, review results, and sharpen your insights—helping you turn raw image collections into high-quality, analyzable data.

Registration will open on August 13. Instructor: Ella Bahry, Engineering and Support Unit at MDC

Session 1 – Concepts and Tools

• Understand key principles of image dataset quality control and why early validation matters.
• Learn how to identify common dataset issues (outliers, inconsistent metadata, acquisition differences).
• Explore dataset statistics and visual summaries for structure, balance, and potential biases.
• Get hands-on experience using PixelPatrol for dataset-wide visualization, metadata inspection, and report generation.

Session 2 – Application and Reflection

• Apply PixelPatrol to your own datasets and interpret its outputs.
• Troubleshoot real-world dataset issues found by PixelPatrol.
• Discuss results and strategies for improving data quality.
• Develop a workflow for integrating dataset validation into your research pipeline.

To participate in this course, it is best to have familiarity with digital images and image processing.

This course is free of charge, and is part of the data science course portfolio offered by the platforms of the Helmholtz Information and Data Science Framework.