- 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.
Profile library
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Search by task, domain, or lifecycle stage, then check each standard's fit and limits.
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- 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.
- Helps with
- Modular resources, profiles, terminology bindings, and APIs for electronic healthcare data exchange.
- Best for
- Operational exchange at system boundaries, eSource acquisition, registries, and clinical-research integrations.
- Watch out
- Base FHIR conformance does not imply conformance to a named research IG; local profiles can diverge, and FHIR is not an analysis-ready warehouse.
- Helps with
- Relational structure, conventions, and standardized vocabularies for longitudinal observational health data.
- Best for
- Multi-source cohort analytics, patient-level prediction, characterization, and network studies after ETL.
- Watch out
- ETL is expensive, source nuance can be compressed, and vocabulary maintenance is an ongoing operational dependency.
- Helps with
- Analytical instrument data, contextual metadata, semantic models, ontologies, and long-lived data packaging.
- Best for
- Vendor-neutral lab data acquisition, instrument integration, archive, and cross-technique reuse.
- Watch out
- ADF/ADM and ASM do not share one access path; technique coverage, converter behavior, model versions, and extension governance must be pinned. AFO openness does not make the whole stack open.
- 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.
- 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.
- Helps with
- An OWL 2 ontology for interoperable provenance using entities, activities, agents, and qualified relationships.
- Best for
- Cross-system lineage, transformation history, audit evidence, and knowledge-graph provenance.
- Watch out
- The model is intentionally generic; useful provenance requires a scoped profile, identifier policy, and capture instrumentation.
- Helps with
- Life-science profiles over Schema.org for datasets, tools, workflows, samples, proteins, genes, and related Web resources; status varies by profile.
- Best for
- Web-scale discovery and lightweight metadata publication alongside richer repository records.
- Watch out
- Profiles have different release states; markup improves discovery but is not a substitute for a domain data model.
- Helps with
- A JSON-LD metadata document that aggregates data, code, workflows, people, instruments, and contextual entities into a research object.
- Best for
- Portable dataset packages, workflow exchange, preservation, publication, and handoff between repositories and compute environments.
- Watch out
- Core conformance is deliberately lightweight; interoperability depends on shared profiles and validation beyond the base crate.
- Helps with
- An RDF vocabulary for interoperable catalogs of datasets, data services, distributions, dataset series, versions, and qualified relations.
- Best for
- Enterprise and federated data catalogs, cross-repository discovery, and standardized catalog APIs.
- Watch out
- DCAT describes catalog resources, not the internal scientific schema; useful deployment needs a domain profile and controlled vocabularies.
- Helps with
- Composable APIs for identifying and retrieving data, submitting workflows, and enabling federated genomic analysis.
- Best for
- Cloud and federated genomics where data access and computation must work across heterogeneous repositories.
- Watch out
- The APIs solve infrastructure interoperability, not dataset semantics, consent harmonization, or analytical comparability on their own.
- Helps with
- Machine-readable dataset metadata, resources, record structure, ML semantics, provenance, and usage-policy extensions.
- Best for
- The final mile from governed data product to portable, loadable ML dataset across tools and repositories.
- Watch out
- A newer cross-domain standard; life-science conventions and BioCroissant profiles are still developing.
- 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 final specification.
- 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.
- Helps with
- Medical imaging information objects, encoding, media, services, workflow, security, terminology resources, and DICOMweb.
- Best for
- Clinical imaging acquisition, archive, exchange, and imaging-derived research datasets.
- Watch out
- Conformance is feature-specific; private tags, de-identification, modality variation, and AI cohort labels require explicit profiles and tests.
- 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.
- Helps with
- Human- and machine-readable case-level phenotypic, clinical, diagnosis, measurement, biosample, and genomic interpretation data.
- Best for
- Portable phenotype/genotype exchange for rare disease, cancer, registries, diagnostics, and computational analysis.
- Watch out
- Flexible optionality and ontology dependence require application-specific validation; it does not replace an EHR API, consent layer, or cohort warehouse.
- Helps with
- Cloud/object-store bioimaging data in Zarr v3 with axes, multiscales, transforms, labels, and high-content-screening plates.
- Best for
- Large multidimensional microscopy, high-content screening, multiscale visualization, and cloud-native image analysis.
- Watch out
- Pre-1.0 changes and transitional metadata remain; writer/viewer compatibility and round-trip preservation must be tested with the chosen toolchain.
- Helps with
- Vendor-neutral mass-spectrometry spectra plus acquisition, instrument, and processing metadata using controlled vocabulary terms.
- Best for
- Raw-to-open conversion and exchange of MS spectra across proteomics and metabolomics toolchains.
- Watch out
- Conversion can lose vendor-specific detail; XML is large, and mzML does not capture the full cross-sample design, identifications, or quantification results.
- Helps with
- RDF terms for quality dimensions, metrics, measurements, policies, certificates, and annotations.
- Best for
- Publishing the evidence behind data-quality claims in catalogs, knowledge graphs, and governed dataset releases.
- Watch out
- DQV does not define universal quality metrics or decide fitness for use; projects must define and justify their own measurements and thresholds.
- 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.
- Helps with
- Shapes for validating RDF graphs against structural and semantic constraints, with machine-readable validation reports.
- Best for
- Executable conformance checks for linked-data metadata, profiles, and knowledge graphs.
- Watch out
- Passing shapes proves only the encoded constraints; it does not prove scientific truth, completeness, ontology fitness, or relational table quality.
- 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.
StandardSAM/BAM · CRAM · VCF/BCF
GA4GH HTS Format Specifications
AcquireHarmonizeExchangeLearn + reuse
- Helps with
- Sequence alignments, compressed reference-oriented reads, variant calls, binary encodings, standard tags, and associated indexes.
- Best for
- The base interchange layer for sequencing alignments and variant-call datasets across pipelines, archives, and analysis tools.
- Watch out
- Format conformance does not establish sample identity, consent, reference correctness, variant normalization, QC, or pipeline reproducibility. FASTQ has no formal hts-specs definition.
- 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.
- Helps with
- Dataset layout, filenames, participants, sessions, acquisition metadata, events, coordinate systems, and derivatives across MRI, PET, EEG, MEG, iEEG, microscopy, and related modalities.
- Best for
- Human- and machine-readable packaging of neuroimaging and behavioral studies for validation, sharing, and reproducible analysis.
- Watch out
- Passing BIDS validation proves encoded structure, not image quality, biological plausibility, complete metadata, de-identification, or analysis validity. Draft BEPs are not released specification content.
- Helps with
- Neurophysiology acquisition and processed data, time series, events, stimuli, behavior, electrophysiology, optical physiology, devices, and experimental metadata.
- Best for
- Session-level packaging and reuse of complex neurophysiology experiments where synchronized signals and experiment context must remain together.
- Watch out
- Extensions and optional fields can fragment interoperability; storage and API compatibility must be tested, and schema validity does not establish signal quality or biological correctness.
- Helps with
- Mass-spectrometry identification results, search inputs, peptide-spectrum matches, peptides, proteins, scores, thresholds, and crosslinking support.
- Best for
- Detailed, tool-independent exchange of proteomics identification evidence for post-processing, validation, archiving, and repository submission.
- Watch out
- The XML model is complex; older versions remain in operational use, and producer/consumer support varies by feature. It does not encode the full sample design or statistical analysis.
- 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.
- Helps with
- Tab-delimited summaries of mass-spectrometry-derived proteins, peptides, spectra, small molecules, features, identifications, and quantitative values.
- Best for
- Accessible, computational result exchange when consumers need a concise table rather than the complete identification or quantification evidence model.
- Watch out
- The two branches are not interchangeable, and a summary cannot reconstruct the full processing history or detailed evidence. Tool support must be checked against the exact flavor and version.
- Helps with
- Computational biological model structure and mathematics through SBML, plus simulation setup, model changes, algorithms, tasks, outputs, and data references through SED-ML.
- Best for
- Reproducible exchange and execution of systems-biology models and simulation experiments across compatible tools.
- Watch out
- SBML package and tool support varies, and a syntactically reproducible simulation does not prove biological validity, parameter identifiability, or agreement with experimental evidence.
- Helps with
- Vendor-neutral XML structures for analytical samples, experiment steps, methods, result series, audit trails, digital signatures, and technique-specific definitions.
- Best for
- Open analytical-instrument exchange and archival where a supported AnIML technique definition captures the required method and result semantics.
- Watch out
- The official schema remains Draft 0.90. Technique coverage, current conformance tooling, vendor support, and independent production adoption are unverified and must not be implied.
- Helps with
- XML exchange of experimental information about compound synthesis, reactions, products, procedures, measurements, and compound testing.
- Best for
- Cross-system exchange of medicinal-chemistry synthesis and testing records that are not covered by instrument-only or generic study metadata.
- Watch out
- The public repository has no packaged releases and showed no verified maintenance after 2021. Current adoption, extension governance, validator support, and successor plans are unverified.
- 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.
- Helps with
- Identifiers, creators and contributors, titles, publisher, dates, resource types, versions, rights, funding, subjects, geolocation, and typed relations among research outputs.
- Best for
- Canonical publication metadata for datasets and related software, workflows, projects, instruments, and publications receiving DataCite DOIs.
- Watch out
- It describes and cites a research output but does not specify its internal scientific schema, validate quality, or enforce access and reuse conditions.
- 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.
- 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.
- Helps with
- A system bill of materials spanning software, builds, AI models, datasets, identities, provenance, integrity, licenses, security findings, and relationships.
- Best for
- Reproducible and reviewable supply-chain records for an AI release that combines scientific data, preprocessing code, dependencies, models, and licenses.
- Watch out
- It is a broad BOM model rather than a scientific metadata profile; generated inventories require verification, and license metadata does not authorize use of sensitive human data.
- Helps with
- A JSON descriptor for a coherent collection of resources, plus Data Resource, Table Schema, and Table Dialect specifications.
- Best for
- Lightweight packaging and validation of assay exports, reference tables, tabular analysis results, and other file-based data products.
- Watch out
- It does not supply biological semantics, full provenance, privacy policy, repository trust, or ML-specific intended-use and bias documentation.
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.
- 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.
- Helps with
- Runtime and design-time events for jobs, runs, and datasets, with extensible facets for source code, schemas, versions, quality metrics, assertions, and other lineage context.
- Best for
- Operational lineage instrumentation across ETL, ELT, laboratory, feature-engineering, and model-data pipelines.
- Watch out
- Lineage is only as complete as its instrumentation; inconsistent naming, missing events, facet-version drift, and backend retention can leave an incomplete history.
Validation standardCoreTrustSeal
CoreTrustSeal Trustworthy Data Repositories Requirements
ExchangeLearn + reuse
- Helps with
- Repository organizational infrastructure, digital-object management, technology, security, designated-community service, continuity, curation, and long-term preservation.
- Best for
- Repository selection and assurance where life-science data must remain authentic, understandable, accessible, and reusable over time.
- Watch out
- Certification concerns a repository and its declared scope, not the scientific quality, ethics, or AI fitness of every deposited dataset; certification is time-bound.
- 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. It is not an EDC, submission dataset, or proof that generated documents comply with every regulator. Every linked terminology and API version must be pinned.
- Helps with
- Vendor-neutral exchange and archival of study metadata, subject data, administrative data, reference data, and audit information.
- Best for
- EDC, eClinical, archive, and study-operation transfers where the full operational study record must remain portable alongside submission tables.
- Watch out
- ODM v2 is not backward-compatible in several areas, XML is the currently supplied schema serialization, and schema validity does not prove semantic or regulatory fitness.
TerminologyLOINC
Logical Observation Identifiers Names and Codes
AcquireHarmonizeExchangeLearn + reuse
- Helps with
- Identifiers for laboratory tests, clinical observations, survey instruments, panels, and answer lists.
- Best for
- Normalizing what was measured or observed across laboratories, EHRs, FHIR, CDISC, and real-world-data models.
- Watch out
- A LOINC code does not by itself preserve local method nuance, result quality, unit correctness, or mapping confidence, and implementations must track deprecated and replacement concepts.
- Helps with
- Polyhierarchical clinical concepts, descriptions, relationships, reference sets, and compositional semantics for detailed healthcare meaning.
- Best for
- Conditions, findings, procedures, anatomy, organisms, and other clinical concepts requiring more semantic depth than flat billing classifications.
- Watch out
- Licensing and distribution vary by territory, national extensions can diverge, and post-coordination support is not uniform across systems.
- Helps with
- A machine-processable syntax and semantics for units, prefixes, compound units, and conversions.
- Best for
- Quantitative values that must survive movement across instruments, laboratories, FHIR resources, CDISC datasets, and analytical stores.
- Watch out
- Syntactic validity does not prove that a unit is clinically appropriate, that a numerical value is plausible, or that arbitrary units are mutually convertible.
- Helps with
- Hierarchical coding of adverse events, medical history, indications, investigations, product issues, and related regulatory medical concepts.
- Best for
- Clinical-trial safety coding, individual case safety reports, aggregate safety analyses, and regulatory pharmacovigilance exchange.
- Watch out
- MedDRA is licensed, version-sensitive, and multiaxial; a coded term does not establish seriousness, expectedness, relatedness, or causality.
- Helps with
- Medicinal-product names, ingredients, countries, marketing authorization holders, strengths, ATC classifications, and drug groupings for medication coding.
- Best for
- Concomitant medication, prior therapy, exposure, and pharmacovigilance drug coding across global clinical programs.
- Watch out
- Dictionary data require a subscription, ambiguous product names still need expert review, B3/C3 choices affect granularity, and up-versioning can change records and classifications.
- Helps with
- A relational representation of EHR, claims, prescribing, laboratory, patient-reported, and related data for distributed patient-centered research.
- Best for
- Analyses that must run consistently across PCORnet partners while each institution retains operational control of local data.
- Watch out
- The CDM deliberately preserves source values and does not itself impose all plausibility or consistency edits; ETL fidelity and study-specific fitness remain separate gates.
- Helps with
- Nineteen linked tables supporting standardized queries over enrollment, encounters, diagnoses, procedures, dispensing, prescribing, laboratory, death, patient-reported, and feature-engineering data.
- Best for
- FDA-style distributed active surveillance of drugs, biologics, devices, and vaccines using partner-held healthcare data.
- Watch out
- The public repository’s latest tag is v8.2.2 while the landing page still names v8.1.0 and v8.2.0 concurrently; the current partner deployment mix is not publicly verified.
Metadata profileVulcan RWD IG
HL7 Vulcan Retrieval of Real World Data for Clinical Research
AcquireExchange
- Helps with
- FHIR profiles and queries for finding cohorts and retrieving a minimal EHR-derived dataset for retrospective clinical research and potential regulatory use.
- Best for
- Research applications retrieving RWD from FHIR-capable EHRs through a named, testable research profile rather than unconstrained base resources.
- Watch out
- STU1 is limited to retrospective EHR RWD; prospective eSource, registries, payer data, downstream transformation, and analysis readiness are outside current scope.
- Helps with
- Portable JSON or YAML descriptions of command-line tools and data-intensive workflows, including typed inputs and outputs, requirements, dependencies, conditional steps, and scatter execution.
- Best for
- Reproducible bioinformatics and scientific workflows that must move across workstations, clusters, clouds, and compatible workflow engines.
- Watch out
- Runner behavior outside the specified execution model can vary, and CWL alone does not freeze containers, reference data, credentials, resource policies, or scientific assumptions.
- Helps with
- Contextual metadata about the source, environment, sampling, processing, and sequencing of genomes, metagenomes, marker genes, single amplified genomes, metagenome-assembled genomes, and uncultivated virus genomes.
- Best for
- Sequence and microbiome studies that need repository-ready sample context through a named checklist plus an environmental or host extension.
- Watch out
- The tagged release and the maintained main schema can diverge, while repository profiles may lag or alter requirements. Pin the schema commit, checklist, extension, term identifiers, and target repository profile.
- Helps with
- Algorithmic generation of normalized, layered chemical identifiers and compact InChIKeys from molecular structures for interoperable identification and search.
- Best for
- Chemical dataset deduplication, database linkage, structure-derived identifiers, and reproducible joins across discovery and laboratory systems.
- Watch out
- InChI is not a structure file, registry identifier, or experimental record. Mixtures, reactions, polymers, organometallics, and other extensions have distinct coverage and maturity.
- Helps with
- Structured communication of high-throughput sequencing analyses through provenance, usability, description, execution, parameters, inputs and outputs, error domains, attribution, and review metadata.
- Best for
- Bioinformatics analyses that need a stable, reviewable account for scientific exchange, regulated communication, or reproducibility assessment.
- Watch out
- BioCompute is descriptive rather than executable and does not prove analytical validity or scientific fitness. The IEEE edition, open schema, object version, and referenced workflow assets must be pinned together.
- Helps with
- Structured chemical test summaries for physicochemical properties, environmental fate, effects on biotic systems, human health effects, pesticide residues, analytical methods, efficacy, intermediate effects, use, and exposure.
- Best for
- Chemical safety and regulatory programs that exchange endpoint study summaries across databases, jurisdictions, and IUCLID-based workflows.
- Watch out
- The suite has no single version, and OHT fields are not themselves regulatory data requirements. Exact templates, schemas, picklists, test guidelines, IUCLID release, and jurisdictional profile must be frozen.
Metadata profileREMBI
Recommended Metadata for Biological Images
PlanAcquireHarmonizeExchangeLearn + reuse
- Helps with
- Study, study-component, biosample, specimen, image-acquisition, image-data, image-correlation, analysis, and annotation context for reusable biological images.
- Best for
- Biological imaging studies that need scientific context around raw images, processed images, derived annotations, and repository deposits.
- Watch out
- Version 1.5 is the BioImage Archive operational model and should not be presented as a universal canonical serialization. REMBI guidance does not provide one cross-repository conformance test.
- Helps with
- The Essential 10 and Recommended Set for study design, sample size, inclusion and exclusion, randomization, blinding, outcome measures, statistics, animal characteristics, procedures, results, and interpretation.
- Best for
- Planning, recording, reporting, and reviewing in vivo experiments so readers can assess methodological rigor and reproduce the work.
- Watch out
- ARRIVE is a human-facing reporting guideline rather than a machine-readable study schema. Checklist completion does not establish ethical approval, statistical validity, or reproducibility.
Data model / schemaGA4GH VRS
GA4GH Variation Representation Specification
HarmonizeExchangeLearn + reuse
- Helps with
- Computable representations of molecular variation, supporting data classes, canonical serialization, and globally reproducible computed identifiers for interoperable variant exchange.
- Best for
- Diagnostic laboratories, research systems, federated networks, and knowledge bases that must recognize equivalent variants without prior identifier coordination.
- Watch out
- VRS does not replace VCF, HGVS interpretation, or reference governance. Public v2.1 snapshots contain trial-use classes and must not be represented as an approved release.
Metadata profileRDA maDMP
RDA DMP Common Standard for Machine-actionable Data Management Plans
PlanAcquireHarmonizeExchangeLearn + reuse
- Helps with
- A machine-actionable application profile for projects, datasets, distributions, contributors, funding, costs, repositories, licenses, identifiers, security, privacy, quality assurance, preservation, and technical resources across a data-management plan.
- Best for
- Planning and maintaining computable data-governance commitments that can move among researchers, funders, repositories, and infrastructure providers.
- Watch out
- The 2019 RDA endorsement and the later v1.2 repository release are distinct status claims; the profile does not impose funder rules, prove that declared actions occurred, or replace domain metadata.
- Helps with
- Frameworks and metamodels for registering metadata items with governed identification, naming, definitions, classification, mappings, registration status, administration, and dataset descriptions.
- Best for
- Enterprise or federated registries that must govern the meaning and lifecycle of data elements, concepts, value domains, classifications, and related metadata independently of a storage platform.
- Watch out
- The suite is an abstract metamodel rather than a ready-made catalog API; parts version independently, much normative text is not freely available, and useful deployment requires a scoped profile and governance process.
- Helps with
- A YAML data contract spanning fundamentals, schema, references, data-quality rules, support channels, pricing, teams, roles, service-level agreements, infrastructure, and extension properties.
- Best for
- A versioned producer-consumer agreement for operational datasets, tables, streams, or APIs where schema, ownership, quality expectations, and service levels must be testable together.
- Watch out
- ODCS is an evolving open industry standard rather than an ISO or W3C standard; its companion JSON Schema does not supersede the specification, and declared rules do not prove scientific fitness.
StandardISO/IEC 27560
ISO/IEC TS 27560 Consent Record Information Structure
PlanAcquireExchangeLearn + reuse
- Helps with
- An interoperable, open, and extensible information structure for consent records and receipts, including exchange and lifecycle management of consent for processing personally identifiable information.
- Best for
- Systems that must record what a person consented to, provide a receipt, exchange consent information, and manage changes or withdrawal over time.
- Watch out
- A conforming record does not prove that consent was informed, freely given, current, or the correct lawful basis; jurisdictional and ethical requirements remain controlling, and the standard is being revised.
Reference architectureOAIS
ISO 14721 Open Archival Information System Reference Model
PlanExchangeLearn + reuse
- Helps with
- Archive responsibilities, designated communities, ingest, archival storage, data management, preservation planning, administration, access, and submission, archival, and dissemination information packages.
- Best for
- Designing or evaluating repositories that accept responsibility for preserving information and keeping it understandable and available to a defined community over technological and organizational change.
- Watch out
- OAIS is a conceptual reference model, not software or certification; the term Open refers to standards development rather than unrestricted data access, and local preservation policies remain necessary.
Governance frameworkCARE
CARE Principles for Indigenous Data Governance
PlanAcquireHarmonizeExchangeLearn + reuse
- Helps with
- Collective Benefit, Authority to Control, Responsibility, and Ethics for the governance and use of Indigenous data and Indigenous Knowledge.
- Best for
- Any data activity involving Indigenous peoples, lands, resources, knowledge, or community interests where openness and reuse must be governed through rights, authority, benefit, and accountability.
- Watch out
- CARE is not a checklist that an external organization can self-certify; obligations are community and context specific, and implementation requires continuing participation by legitimate Indigenous authorities.
- Helps with
- Datum-oriented descriptions of wide, long or event, multidimensional, key-value, and related data structures, plus foundational metadata and detailed process and provenance descriptions.
- Best for
- Cross-domain integration where the same information appears in different structures and each transformation must remain understandable from business process to machine-level operation.
- Watch out
- Version 1.0 is new and model rich; adoption and tooling are still scaling, and practical interoperability requires a constrained profile rather than implementing every class.
- Helps with
- Web-based schemas and protocols for entities to publish data, negotiate usage-controlled agreements, and access data within a federation of technical systems called a dataspace.
- Best for
- Governed exchange across organizational and cloud boundaries where discovery, contract negotiation, and transfer must interoperate without centralizing all data.
- Watch out
- DSP does not define scientific schemas, data quality, federation governance, identity assurance, or every transfer protocol; current implementation evidence is concentrated in the Eclipse dataspace ecosystem.
- Helps with
- A lifecycle-managed electronic submission package for CTD Modules 2 through 5, document metadata, replacement and reuse operations, and region-specific Module 1 content.
- Best for
- Regulatory applications, amendments, supplements, reports, and master files that must be assembled, validated, transmitted, and reviewed as one traceable dossier lifecycle.
- Watch out
- Module 1, controlled vocabularies, validation criteria, acceptance dates, and forward-compatibility support vary by regulator. ICH and regional packages must be pinned separately.
StandardICH E2B(R3)
ICH E2B(R3) Electronic Transmission of Individual Case Safety Reports: Data Elements and Message Specification
AcquireHarmonizeExchangeLearn + reuse
- Helps with
- Data elements, message structure, identifiers, acknowledgements, and implementation rules for transmitting individual case safety reports between pharmacovigilance systems and regulators.
- Best for
- Pre- and post-market adverse-event case intake, follow-up, exchange, regulatory reporting, reconciliation, and downstream safety surveillance.
- Watch out
- Adoption and business rules remain regional, and some authorities still accept or require E2B(R2) for selected product classes. A valid message does not establish seriousness, expectedness, relatedness, or causality.
Governance frameworkICH E6(R3)
ICH E6(R3) Guideline for Good Clinical Practice
PlanAcquireHarmonizeExchangeLearn + reuse
- Helps with
- Participant protection, reliable trial results, proportional quality management, data governance, source records and metadata, audit trails, computerized systems, oversight, and essential records.
- Best for
- Any regulated interventional clinical trial, including decentralized, pragmatic, or RWD-enabled designs that need an accountable quality and data-integrity operating model.
- Watch out
- E6(R3) is not a data schema or executable validator, and Step 4 publication does not mean identical implementation dates or legal requirements in every region.
Governance frameworkICH M14
ICH M14 General Principles on Planning, Designing, Analysing, and Reporting of Non-interventional Studies That Utilise Real-World Data for Safety Assessment of Medicines
PlanAcquireHarmonizeLearn + reuse
- Helps with
- Planning, data-source evaluation, study design, variable validation, bias and confounding assessment, analysis, sensitivity work, and reporting for non-interventional medicine-safety studies using RWD.
- Best for
- Regulatory pharmacoepidemiology and post-authorization safety studies where routinely collected data must support an explicit, reviewable evidence argument.
- Watch out
- The guideline is focused on non-interventional safety assessment, not every effectiveness question, and conformance cannot make an inadequate source complete or remove unmeasured confounding.
StandardSPL R8
HL7 Version 3 Standard: Structured Product Labeling, Release 8
HarmonizeExchangeLearn + reuse
- Helps with
- Machine-readable product labeling, product and package data, establishment and listing information, REMS content, and related regulatory document types.
- Best for
- Structured labeling and product-information submissions that must be validated, exchanged with FDA systems, published, indexed, or reused by downstream safety and decision-support services.
- Watch out
- FDA implementation rules, document types, controlled terminology, validation files, and schemas change independently from the HL7 normative release, and other regions use different product-information profiles.
- Helps with
- A concise, specialty-agnostic core patient summary and FHIR document profiles for essential clinical information such as problems, allergies, medications, results, procedures, and provenance.
- Best for
- Cross-border or unscheduled care, patient-mediated exchange, continuity-of-care summaries, and bounded clinical or research handoffs that need a named minimum-content contract.
- Watch out
- ISO 27269 does not define the summarization workflow, and the HL7 guide remains STU, is based on FHIR R4, and permits national derivations. A patient summary is a curated snapshot, not a longitudinal warehouse.
StandardHL7 v2.9.1
Health Level Seven Standard Version 2.9.1: An Application Protocol for Electronic Data Exchange in Healthcare Environments
AcquireExchange
- Helps with
- Delimited event messages, segments, fields, datatypes, trigger events, acknowledgements, and conformance constructs for healthcare transactions including admissions, orders, observations, and results.
- Best for
- Source-system integration where EHR, laboratory, pharmacy, registration, or ancillary systems emit transactional HL7 v2 feeds that must be preserved and interpreted before harmonization.
- Watch out
- Many deployments use older releases and site-specific Z-segments, optionality, code sets, and interface agreements. Declaring HL7 v2 alone does not establish semantic or profile-level interoperability.
Metadata profileTMF Reference Model
CDISC Trial Master File Reference Model
PlanAcquireHarmonizeExchange
- Helps with
- A standardized taxonomy, artifact identifiers, zones, sections, levels, metadata, and filing conventions for essential clinical-trial records in sponsor and investigator files.
- Best for
- Trial record management, sponsor-CRO transfer, completeness assessment, inspection readiness, and reconstruction of trial conduct across organizations and systems.
- Watch out
- CDISC describes the model as adaptable rather than a formal enforceable standard. Company SOPs, jurisdictional requirements, and the upcoming successor model can change artifact selection and filing rules.
- Helps with
- Aggregate metadata for biobanks, sample and data collections, research resources, and networks, with separate individual-level components for samples, donors, and events.
- Best for
- Biobank directories, cohort discovery, federated catalogues, access negotiation, and metadata exchange across biospecimen networks.
- Watch out
- Core 3.0 is aggregate-level metadata, not a complete specimen record. Individual-level components and serializations version separately, and MIABIS does not by itself establish consent, sample quality, or one universal conformance test.
- Helps with
- Compact coded descriptions of important preanalytical variables for fluid and solid biospecimens, including source type, containers, processing delays, centrifugation, fixation, and storage conditions.
- Best for
- Biospecimen traceability, sample selection, cross-biobank exchange, retrospective comparability, and analysis of preanalytical effects.
- Watch out
- SPREC compresses selected variables and is not a complete process history, empirical quality score, or substitute for SOPs. Unknown, unavailable, and local conditions still require explicit handling.
- Helps with
- Requirements for biobank competence, impartiality, consistent operation, and quality control of biological material and associated data collections.
- Best for
- Biobank quality systems, capability assessment, accreditation preparation, partner qualification, and governance of materials intended for research and development.
- Watch out
- The current edition is under revision, so contracts and assessments must pin the exact edition. Its scope excludes biological material intended for food, feed, or therapeutic use.
Data model / schemaPDBx/mmCIF
PDBx/mmCIF Exchange Dictionary and Format
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- Helps with
- Structured macromolecular coordinate data and related entities, sequences, assemblies, experimental methods, refinement, citations, software, and archive metadata.
- Best for
- Deposition, validation, exchange, parsing, and reuse of experimentally determined macromolecular structures across wwPDB and structural-biology toolchains.
- Watch out
- The living dictionary is large and revisions can affect parsers, extensions, and optional categories. Syntactic conformance does not establish model quality, biological relevance, or comparability.
StandardFCS · MIFlowCyt · Gating-ML
Flow Cytometry Standards Stack
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- Helps with
- Flow-cytometry event data in FCS, minimum experiment reporting through MIFlowCyt, and machine-readable gate definitions through Gating-ML.
- Best for
- Reproducible flow-cytometry acquisition, publication, repository deposition, reanalysis, and exchange of gating strategies across compatible tools.
- Watch out
- Implementations can omit or interpret metadata differently, Gating-ML support is not universal, and the stack does not supply one governed cell-type ontology, panel model, calibration policy, or assay-quality threshold.
- Helps with
- Adaptive immune receptor repertoire study metadata, sample processing, raw-sequence references, rearrangements, repertoires, germline and genotype records, clones, single-cell data, reactivity, and receptor annotations.
- Best for
- AIRR-seq publication, repository submission, immune-repertoire exchange, federated discovery, reproducible analysis, and cross-tool interoperability.
- Watch out
- MiAIRR compliance establishes reporting completeness, not sequencing quality or biological validity. The v2.0 transition, optional fields, ontology versions, germline references, privacy, and repository profiles require separate controls.
- Helps with
- Uniform descriptions of DNA, RNA, and protein sequence variants using explicit reference sequences, coordinate systems, variant classes, and syntax.
- Best for
- Clinical reporting, variant databases, publications, laboratory systems, and exchange workflows that must communicate sequence-level variation unambiguously.
- Watch out
- One biological variant can have several valid descriptions against different references or transcripts. HGVS does not encode pathogenicity, evidence strength, observed genotype, or cohort frequency, and syntax validity alone does not establish biological correctness.
Standardrefget + SeqCol
GA4GH refget Sequences and Sequence Collections
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- Helps with
- Content-derived identifiers and retrieval for individual reference sequences, plus identification, lookup, and comparison of sequence collections such as genomes, transcriptomes, and proteomes.
- Best for
- Genomic pipelines, archives, variant systems, and federated services that must prove which reference sequence or assembly collection an analysis used.
- Watch out
- Content identity does not establish that a reference is authoritative, biologically appropriate, complete, or suitable for a particular analysis. Sequence Collections is newer, so service compatibility and collection conventions require explicit testing.
- Helps with
- Federated queries across genomic variants and related individuals, biosamples, cohorts, runs, analyses, filters, result granularity, and handover links.
- Best for
- Privacy-aware discovery of relevant genomic and biomedical datasets before a researcher begins a separate authorization and access process.
- Watch out
- Beacon is a discovery protocol, not a complete authorization, consent, or privacy guarantee. Record-level and count responses can disclose sensitive information, and local schemas, filters, and handovers can still diverge.
- Helps with
- Ticket-based retrieval of complete or region-scoped read and variant data in BAM, CRAM, VCF, and BCF formats through GET or POST requests and service metadata.
- Best for
- Remote genomic analysis and visualization that need selected genomic regions or full datasets without copying an entire source object first.
- Watch out
- The API does not define source-file semantics, consent, authorization policy, reference aliases, or QC. Servers may transcode data, and bearer tokens or returned ticket URLs require careful handling.
- Helps with
- An encrypted genomic file format using envelope encryption and recipient keys while retaining random access to protected data.
- Best for
- Sensitive genomic files that must remain encrypted during storage, transfer, and analysis workflows without always decrypting the entire object.
- Watch out
- Crypt4GH does not manage identity, consent, authorization decisions, key custody, revocation, or audit policy. Authorized users can still create unencrypted copies after decryption.
Metadata profileExpmeta
GA4GH Experiments Metadata Checklist
PlanAcquireHarmonizeExchangeLearn + reuse
- Helps with
- Minimum experiment-level properties for high-throughput sequencing, focused on library preparation, sequencing instruments, run context, protocols, and relevant identifiers.
- Best for
- Repositories, laboratories, and federated discovery systems that need comparable descriptions of WGS, RNA-seq, methylation, and other sequencing experiments.
- Watch out
- The current scope excludes biological sample descriptors, clinical data, downstream processing, and analysis. A completed checklist is not a reproducibility record, and the checklist does not require one canonical serialization.
Quality vocabularyWGS QC
GA4GH Whole Genome Sequencing Quality Control Standards
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- Helps with
- Formally defined quality-control metrics, exchange structures, reference implementations, unit tests, and benchmarking resources for short-read germline whole-genome sequencing.
- Best for
- Genome programs and laboratories that need comparable WGS QC calculations and machine-readable evidence across institutions, pipelines, and repositories.
- Watch out
- The initial scope is short-read germline WGS. Standardized metrics do not supply universal pass thresholds or establish clinical validity, contamination absence, representativeness, or suitability for every downstream analysis.
Reference architectureAnnData ecosystem
Single-cell and Spatial Data Ecosystem
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- Helps with
- Annotated single-cell matrices, multimodal containers, repository metadata requirements, ontology-bound annotations, and spatial images, labels, points, shapes, tables, and coordinate systems.
- Best for
- An implementable analysis and publication stack for single-cell, multi-omic, and spatial-omics data when the exact component, encoding, schema, and ontology releases can be pinned.
- Watch out
- This is a fast-moving software ecosystem rather than a single governed standard. Container validity does not guarantee consistent preprocessing, cell annotation, biological comparability, or lossless conversion to Seurat or other ecosystems.
Data model / schemaOME Model / OME-TIFF
OME Data Model and OME-TIFF
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- Helps with
- Biological-image pixels and metadata including dimensionality, acquisition hardware and settings, experiments, annotations, regions of interest, OME-XML serialization, and the OME-TIFF pixel container.
- Best for
- Microscopy exchange, migration, preservation, and metadata-aware conversion where OME-TIFF compatibility is valuable; use OME-NGFF separately for cloud-native chunked arrays.
- Watch out
- Conversion may omit vendor-specific fields, and the current schema namespace dates to 2016. OME-TIFF is not optimized for every very large or cloud-native workload and does not replace study-level experimental metadata.