🟤Bioimaging Data Domain
Bioimaging Data Domain Overview
🧭 Table: RDMkit – Bioimaging Data Domain Overview
Category
Topic / Challenge
Best Practices / Solutions
Tools & External Resources
1. Introduction
Growing volumes of images & metadata are not adequately managed. Storage on personal devices is insufficient. Stakeholders lack coordination.
Involve biologists, facility managers, analysts, and IT support early. Define storage duration, costs, and infrastructure responsibilities.
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2. What Constitutes Bioimage Data
Images contain rich metadata beyond pixel arrays (e.g., microscope hardware, acquisition settings). PFFs (Proprietary File Formats) dominate the field.
Understand file structures and metadata content. Avoid disorganized metadata practices. Plan for long-term readability and standardization.
3. Data Management Challenges
• Large file numbers and sizes • High risk of file loss or misplacement • Disorganized metadata • User-dependence for structure
• Capture relevant metadata at acquisition • Avoid metadata overload • Implement consistent organizational structures • Address risks of personnel turnover
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4. Standard (Meta)Data Formats
No universal file format. PFFs hinder interoperability and data sharing.
• Use vendor-neutral formats (e.g., OME-TIFF, NGFF) • Consider open format availability before equipment purchase • Use conversion tools where needed
5. Metadata Models & Standards
Metadata capture often overlooked or inconsistent. Needs differ across modalities.
Adopt or align with community-driven models. Define consistent metadata schemas.
• REMBI • 4DN-BINA-OME • QUAREP-LiMi
6. Image Management Tools
Acquired data is rarely stored in centralized, searchable systems. Local storage hampers collaboration.
Use dedicated platforms for data management, annotation, sharing, and access control.
• OMERO • XNAT (medical imaging) • MyTardis • BisQue • Cytomine IMS • iRODS • B2SHARE
7. Data Sharing & Publication
Bioimaging datasets are large, with variable metadata quality. Not all repositories accept such data.
Choose repositories with high-volume, imaging-aware support. Prefer domain-specific archives when possible. Ensure persistent identifiers and clear licenses.
8. Data Discovery & Interoperability
Hard to discover tools, standards, or ontologies for specific imaging workflows.
Use registries to search for tools, standards, or databases. Apply ontologies for metadata enrichment.
9. Infrastructure Access & Support
Researchers may lack local infrastructure or support for scalable image data management.
Seek external services and hosted platforms that provide imaging and RDM capabilities.
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