> Achieving AI Readiness in Scientific Research
Leveraging ZONTAL for Data Standardization and Sustainability
AI-driven scientific research is only as effective as the quality, consistency, and accessibility of its data. Challenges such as disparate data formats, fragmented experimental results, and inconsistent metadata impede AI readiness. To maximize AI’s potential, organizations must move beyond siloed data environments and adopt a structured data strategy that ensures interoperability, automation, and long-term sustainability.
This white paper explores how organizations can:
- Standardize and unify scientific data for AI-driven insights
- Enhance metadata to improve accessibility, interoperability, and reusability
- Integrate LIMS, ELNs, and other lab systems to eliminate data silos
- Leverage digital preservation and automation for AI-driven research
- Optimize workflows and ensure compliance with industry regulations
Organizations that invest in data standardization, governance, and automation will improve efficiency, enhance collaboration, and drive scientific innovation.
Download your copy to explore strategies for AI-ready data management.
