Good data management practices rely on structured metadata, standardized file organization, version control, experiment and sample traceability, secure storage, and reproducible workflows.

While spreadsheets such as Excel are widely used in research, they are prone to manual errors, inconsistent metadata, duplicate entries, and limited traceability. Non-standardized file organization and transferring data via external hard drives can further lead to data fragmentation, version confusion, and data loss.

Dedicated data and metadata management platforms address these challenges by improving standardization, reproducibility, collaboration, and long-term data accessibility.

This overview supports researchers, research groups, core facilities, and research IT staff in identifying tools suited to their workflows and infrastructure needs. It provides a starting point for comparing and implementing sustainable data management solutions.

Our goal is to raise awareness of mature platforms that can simplify research workflows, improve data quality, and support FAIR research practices.