Data Quality Metrics

Note

für die Förderung und Unterstützung im Rahmen des Konsortiums NFDI4Ing bedanken. Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 442146713.

The authors; Hauke Dierend, Hendrik Görner and Rayen Hamlaoui would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.

Welcome to our comprehensive resource hub dedicated to quality metrics in various domains. This wiki serves as a guide to understanding, evaluating, and applying metrics across diverse fields, including:

FAIR Metrics: Detailed insights into Findability, Accessibility, Interoperability, and Reusability principles. General Quality Metrics: Essential concepts like Completeness, Accuracy, Consistency, and Timeliness. Image Quality Metrics: Evaluation methods such as Mean Squared Error and Peak Signal-to-Noise Ratio. Pointcloud Quality Metrics: Standards and examples for assessing 3D data quality.

Machine Learning Metrics:
Classification Metrics: Metrics like Accuracy, F1-Score, and Confusion Matrix for evaluating model performance.
Regression Metrics: Techniques for assessing predictive models.

Explore detailed explanations, hands-on examples, and code snippets that will empower you to work effectively with these metrics.

If you would like to make a scientific contribution to this site, feel free to clone the repository and create your own branch with a metric that is relevant to you.