- Helps with
- Findability, accessibility, interoperability, and reusability of data, metadata, and infrastructure.
- Best for
- Use as the outcome framework and assessment lens across the full R&D lifecycle.
- Watch out
- Principles describe desired behavior, not a single technical architecture or conformance test.
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Find the standard that fits the work in front of you—not just the acronym you already know.
17 of 54 profiles · Select up to three to compare.
Plan
- Helps with
- Protocol-to-analysis clinical and nonclinical research data, including acquisition, tabulation, analysis, and submission structures.
- Best for
- Best for regulated studies and traceable submission packages; less natural for early discovery or raw instrument output.
- Watch out
- Conformance is detailed and version-sensitive; transformations can preserve structure while losing source context.
Evidence & profile details
- Helps with
- Experimental design, sample characteristics, protocols, assay technologies, and sample-to-data relationships.
- Best for
- Multi-omics and multi-assay study metadata at the boundary between experiment design and repository submission.
- Watch out
- Rich metadata entry is labor-intensive and local templates can drift without governance and validation.
Evidence & profile details
- Helps with
- A family of interoperable biological and biomedical ontologies governed by principles for openness, scope, identifiers, relations, and maintenance.
- Best for
- Semantic annotation, knowledge graphs, terminology normalization, and cross-dataset integration.
- Watch out
- Coverage and maintenance vary by ontology; overlap, versioning, and term-selection policy still require local stewardship.
Evidence & profile details
- Helps with
- Persistent and resolvable identifiers, predictable metadata retrieval, and typing for machine-actionable digital objects.
- Best for
- Long-horizon infrastructure design for object-level interoperability across repositories and automated agents.
- Watch out
- The available documentation is explicitly incomplete and should not be treated as a comprehensive final specification.
Evidence & profile details
- Helps with
- Standards-agnostic biomedical concept definitions plus implementation artifacts such as SDTM Dataset Specializations and value-level metadata.
- Best for
- Computable clinical concepts that connect protocol, collection design, tabulation metadata, and downstream automation.
- Watch out
- Content is informative and incrementally curated; concept, specialization, terminology, and downstream standard versions must be pinned together.
Evidence & profile details
- Helps with
- Identification of medicinal products, pharmaceutical products, substances, dose forms, routes, units, and packages across the product lifecycle.
- Best for
- Regulated medicinal-product master data, cross-system product identity, and jurisdictional submissions such as EMA SPOR/PMS.
- Watch out
- The suite spans multiple ISO standards and amendments; implementation scope, identifiers, and timelines vary by jurisdiction and are still evolving.
Evidence & profile details
- Helps with
- Standardized biomedical data-use permission terms for matching controlled-access datasets to research purposes.
- Best for
- Consent-aware discovery, data access review, and machine-readable permitted-use conditions in genomics and health research.
- Watch out
- Ontology matching cannot resolve jurisdiction, contract, consent nuance, expiry, or downstream duties without authoritative policy and human governance.
Evidence & profile details
- Helps with
- Govern, Map, Measure, and Manage functions for addressing AI risks across organizations and system lifecycles.
- Best for
- Governance overlay for intended use, accountability, risk measurement, release decisions, and ongoing monitoring.
- Watch out
- Voluntary and use-case agnostic; it does not prescribe life-science schemas, legal compliance, or quantitative acceptance thresholds.
Evidence & profile details
- Helps with
- Tabular sample-to-data relationships, biological and technical factors, replicates, instruments, acquisition context, and proteomics experimental design.
- Best for
- Proteomics studies that need an explicit, machine-readable mapping from biosamples and factors to raw and processed mass-spectrometry files.
- Watch out
- It does not encode downstream statistical-analysis parameters or results. Working-branch templates and rules can move ahead of the final PSI specification, so release and validator versions must be pinned.
Evidence & profile details
- Helps with
- Minimum information for interpretable and reproducible microarray and high-throughput sequencing studies, including design, samples, raw and processed data, and protocols.
- Best for
- A submission and publication completeness gate for functional-genomics studies, especially when preparing repository records and supporting data.
- Watch out
- These are minimum-information checklists rather than one executable schema. Stewardship is legacy, and newer assay classes such as single-cell data require current repository guidance and additional profiles.
Evidence & profile details
- Helps with
- Reusable indicators, priorities, maturity levels, and evaluation guidance for assessing data and metadata against the FAIR principles.
- Best for
- Use as the evidence rubric that turns FAIR from an aspiration into a repeatable release and improvement assessment.
- Watch out
- It is not a certification, and locally adapted scoring or weighting means totals from different assessment tools are not automatically comparable.
Evidence & profile details
- Helps with
- Machine-readable concepts for data and processing, purposes, legal bases, parties, recipients, rights, risks, controls, technologies, AI, and jurisdiction-specific laws.
- Best for
- Privacy and data-protection context for human, genomic, clinical, real-world, and AI datasets where DUO alone is too narrow.
- Watch out
- DPV is a vocabulary, not legal advice or an enforcement engine; local authority, consent, contracts, and jurisdiction-specific interpretation remain controlling.
Evidence & profile details
- Helps with
- Policies containing permissions, prohibitions, duties, parties, assets, constraints, inheritance, and conflict strategies.
- Best for
- Machine-readable usage conditions for datasets and distributions, including research-purpose, redistribution, attribution, retention, and temporal or jurisdictional constraints.
- Watch out
- A syntactically valid policy does not prove the assigner has authority, make the policy legally enforceable, or provide the system that evaluates and enforces it.
Evidence & profile details
StandardISO/IEC 5259
ISO/IEC 5259 Data Quality for Analytics and Machine Learning
PlanAcquireHarmonizeExchangeLearn + reuse
- Helps with
- Terminology and examples, data-quality measures, management requirements, a process framework, and a governance framework for analytics and ML data.
- Best for
- The quality-management spine for training, validation, and evaluation data used in life-science analytics and ML.
- Watch out
- The normative publications are not freely available, the series is cross-domain, and it does not provide life-science thresholds, domain semantics, or regulatory approval.
Evidence & profile details
- Helps with
- Structured, audience-aware summaries of dataset origins, collection and annotation, intended use, evaluation context, ethical considerations, and decisions affecting downstream performance.
- Best for
- Human-facing readiness and release documentation for clinical, imaging, omics, laboratory, and real-world ML datasets.
- Watch out
- There is no single mandatory schema or conformance test; narrative claims require linked evidence, ownership, review, and update controls.
Evidence & profile details
- Helps with
- A computable study definition spanning objectives, endpoints, eligibility, interventions, schedule of activities, amendments, estimands, and protocol content.
- Best for
- Upstream protocol facts that must flow consistently into study-build, registry, document, and downstream data systems.
- Watch out
- USDM is a model and reference architecture—not an EDC, submission dataset, or proof that generated documents comply with every regulator—and every linked terminology and API version must be pinned.