AI Data Readiness Assessment Is your lab ready to access the full power of AI tools?High-performing AI starts with a solid data foundation. Take 5 minutes (17 quick questions) to evaluate your lab’s data architecture against modern FAIR (Findable, Accessible, Interoperable, Reusable) data standards and key GxP/21 CFR Part 11 benchmarks. Your Lab’s True Digital Maturity Phase: Discover if your data architecture is operating as a legacy bottleneck, a transitional hybrid, or a true, AI-optimized discovery engine. Your High-risk Blind Spots: Pinpoint exactly where missing metadata, trapped data silos, or poor system interoperability are actively threatening your AI investments. Your Customized Priority Roadmap: Instantly access a tailored action plan. Depending on your score, you'll learn the precise next steps for your lab—whether that's establishing basic data stewardship or scaling advanced cross-departmental ML modeling and GxP-validated compliance. Answer each question based on your current laboratory setup to receive your customized insight summary. Data Silos and Accessibility Question Title * 1. Can researchers locate specific datasets from 6 months ago in under 30 seconds without asking a colleague? (Required.) Yes No Question Title * 2. Is data stored in a centralized repository (LIMS/ELN/Data Lakehouse) rather than on local instrument PCs or USB drives? (Required.) Yes No Question Title * 3. Does the system allow for automated data retrieval via APIs, or is manual export/import still required? (Required.) Yes No Metadata and Documentation Question Title * 4. Are globally unique persistent identifiers (PIDs) assigned to every new dataset automatically? (Required.) Yes No Question Title * 5. Do naming conventions follow a standardized lab-wide policy, or are they left to individual scientists' discretion? (Required.) Yes No Question Title * 6. Does every file include rich metadata (e.g., instrument model, method version, ambient temperature, and sample ID)? (Required.) Yes No System Interoperability Question Title * 7. Are instruments speaking the same language (e.g., converted from proprietary vendor formats to open standards like JSON, CSV, or ODT)? (Required.) Yes No Question Title * 8. Is there a business glossary/ontology in place to ensure terms like HCl and hydrochloric acid are recognized as the same entity across all systems? (Required.) Yes No Question Title * 9. Can your AI models pull data directly from your LIMS/ELN without 80% of the project time being spent on manual formatting? (Required.) Yes No Governance and Stewardship Question Title * 10. Has a Data Steward been formally assigned to oversee the quality and lifecycle of your scientific data domains? (Required.) Yes No Question Title * 11. Are there automated policies that mandate metadata creation at the point of data origination rather than retrospective cleanup? (Required.) Yes No Question Title * 12. Is there a clear authorization protocol defining who can access, edit, and share sensitive research data? (Required.) Yes No Reusability and Compliance Question Title * 13. Is every dataset accompanied by a standardized README detailing the provenance (origin and history) of the data? (Required.) Yes No Question Title * 14. Does your data remediation process maintain a GxP-validated audit trail for 21 CFR Part 11 or ISO 17025 integrity? (Required.) Yes No Question Title * 15. Can a collaborator or auditor verify your findings by accessing the raw data and the exact processing steps used? (Required.) Yes No Question Title * 16. Where should we send your results? (Required.) Question Title * 17. Please enter your information so we can personalize your results. (Required.) Get My Results
