From June 2022 until December 2023, the ELIXIR Interoperability Platform worked with seven different ELIXIR Communities to compile and present nine distinct interoperability stories. The diverse set of stories showcased case studies and use cases for enhanced data reuse across the ELIXIR network, and explored the tools, processes, workflows, databases and approaches the different communities took to achieve impact through interoperable data sharing. The report ELIXIR Interoperability Stories recaps the key messages from those interoperability stories, identifies some common themes and technical solutions, and provides a synopsis of the main lessons learned from these stories.
Common FAIRification Process
Define goals
Determine the purpose and expected outcome of FAIRification, in terms of desired usability of the research asset that isn’t currently possible.
| Resource | Relevance |
|---|---|
| User journeys as means of effectively demonstrating value and engaging stakeholders |
Enabling user journeys categorised as either real-world case studies; business focused use cases; or task focused use cases that are individually optimised for their actors |
| Single Cell Omics Community Interoperability Showcase |
Define goals that could be achieved when data was aligned and distributed within the community in a manner that complied with a lightweight standardised model |
| Metabolomics Community Use Case |
Not provided |
Examine requirements
Assess the current state of the activity against the FAIRification goal, including available tools, software, expertise, budget, and time constraints.
| Resource | Relevance |
|---|---|
| Metabolomics Community Use Case |
Not provided |
Design & implement
Define and deliver on practical, achievable objectives across one or more release cycles to realise the overall FAIRification goal.
| Resource | Relevance |
|---|---|
| Metabolomics Community Use Case |
Not provided |
Review & report
Review outcomes and assess success against original goals, also disseminating lessons learned to support future FAIRification activities.
| Resource | Relevance |
|---|---|
| User journeys as means of effectively demonstrating value and engaging stakeholders |
Key learning captured in a library of community use cases |
| Metabolomics Community Use Case |
Not provided |
FAIRification template
Outlines a general course of action in eight steps with links to related capabilities across the dimensions Hosting, Format and Content
Obtain the research object
This step establishes how the project team will obtain the research object and the metadata, documentation, dependencies and provenance required to be able to analyse and work on it.
Research object access
Ensure that the FAIRification team is permitted and technically able to reach the research object and its associated metadata.
Examples of concerns:
- Open, registered or controlled access
- Authentication and authorisation
- Data-access applications and approvals
- Secure-environment requirements
- API or repository availability
- Applicable conditions affecting acquisition and project use
| Resource | Relevance |
|---|---|
| Intrinsically Disordered Protein Community Interoperability Showcase: Exploiting Bioschemas Markup In this presentation, the audience heard how the intrinsically disordered protein community had identified a challenge in discovery and integration of data that spans several experimental fields studying protein structure and function, using methods that are domain specific. As such, there are no comprehensive single resources containing all IDPs. Therefore, researchers wishing to see the combined knowledge from resources such as ModiDB, DisProt and PED encountered an onerous integration task. As a solution, the IDP community proposed the creation of a central knowledge graph that could be asked common queries. To achieve this goal, the IDP described their use of bioschemas, JSON-LD, SPARQL, OpenAIRE and FAIRsharing to assemble a knowledge graph that can be queried by researchers seeking a consolidated, integrated view of data from across the IDP community. |
Cascade mapping from FT 1: easier/better access to data via W3C standardised SPARQL endpoints (ideally machine interpretable) instead of custom APIs (for humans to implement/adapt to) |
| Plant Sciences Community Showcase |
Cascade mapping from FT 1, 7: hosting capabilities showcased with FAIRDARE |
Research object retrieval
Ensure that the required research object, version, components and metadata can be selected, obtained/downloaded and verified by the FAIRification team.
Considerations relating to research object retrieval, eg query language, results representation and exporting capabilities
| Resource | Relevance |
|---|---|
| Intrinsically Disordered Protein Community Interoperability Showcase: Exploiting Bioschemas Markup In this presentation, the audience heard how the intrinsically disordered protein community had identified a challenge in discovery and integration of data that spans several experimental fields studying protein structure and function, using methods that are domain specific. As such, there are no comprehensive single resources containing all IDPs. Therefore, researchers wishing to see the combined knowledge from resources such as ModiDB, DisProt and PED encountered an onerous integration task. As a solution, the IDP community proposed the creation of a central knowledge graph that could be asked common queries. To achieve this goal, the IDP described their use of bioschemas, JSON-LD, SPARQL, OpenAIRE and FAIRsharing to assemble a knowledge graph that can be queried by researchers seeking a consolidated, integrated view of data from across the IDP community. |
Cascade mapping from FT 1: easier/better access to data via W3C standardised SPARQL endpoints (ideally machine interpretable) instead of custom APIs (for humans to implement/adapt to) |
| Plant Sciences Community Showcase |
Cascade mapping from FT 1, 7: hosting capabilities showcased with FAIRDARE |
Adopt a domain model
This step establishes a shared understanding of the types of research objects, components, domain concepts and relationships involved in the FAIRification activity, and selects or defines a domain model to guide the work.
Identify research object types
Identify and describe the types of research objects and components included in the FAIRification activity.
Research object type identification informs the selection of appropriate domain models, metadata profiles, identifier schemes, standards, vocabularies and target hosting environments. It also helps determine which relationships and dependencies must be preserved.
Distinguish between:
- Research object types, such as datasets, software, workflows, models, notebooks, protocols, and compound research objects.
- Components, such as files, records, modules, workflow steps, metadata documents and referenced resources.
- Domain entity types, such as samples, organisms, participants, observations, assays, images, sequences and variables.
- Representation types, such as schemas, file formats and serialisations, which are examined and implemented in later steps.
A research object may have more than one type or contain several different types of components. Classification should therefore describe the composition of the research object set rather than force each object into a single category.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Model the domain: Learnings published on fairtracks.net and included as ELIXIR RIR |
| Single Cell Omics Community Interoperability Showcase |
Example of a domain with few standards and little convergence |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Model the domain: Learnings published on fairtracks.net and included as ELIXIR RIR |
Select identifier schemes
This step defines what needs to be assigned an identifier and selects appropriate identifier schemes. Established identifiers should be reused where possible, with new unique, persistent and resolvable identifiers introduced only where required.
Identifier minting
Create, assign and register new identifiers—unique, persistent and resolvable.
This capability is required when no suitable established identifier exists or when a new research object, version or independently identifiable component is created. It includes selecting an appropriate identifier scheme and issuing authority; preventing duplicate assignment; associating identifiers with the correct subjects and metadata; and supporting the required resolution, persistence, versioning and lifecycle arrangements.
Identifier minting would normally be provided by a repository, registry, institutional service or infrastructure provider rather than brought in by the FAIRification team itself.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
Identifier discovery and reuse
Recognise, assess, retain and apply established identifiers for research objects, components and domain entities.
This capability enables consistent identification across systems and communities. It relies on and preserves existing links, avoids duplicate identities and enables information about the same subject to be connected across research objects, repositories, services and workflows. Identifier reuse may depend on external authorities, registries, repositories, lookup services or community expertise. It includes determining what existing identifiers represent, whether they are suitable for reuse and how they should be used and referenced in the FAIRification activity.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
Convert to standard formats
This step establishes which community or domain standards, profiles, formats and serialisations will be used to represent the research object and applies and validates them in ways that support the intended uses.
Standards discovery and reuse
Find, assess and choose existing standards, profiles, formats and serialisations that can represent the research object in ways that support the FAIRification goal and intended uses.
This capability includes identifying standards used by relevant communities, determining what each standard covers and assessing whether it can represent the selected domain model, identifiers, metadata, components and relationships.
The capability can be provided through project expertise but also through authoritative guidelines from communities, standards registries, target repositories/hosting environments, standards organisations or other infrastructure providers.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Rare Disease Community Interoperability Showcase: examples from EJP RD |
reuse data standards: use existing RIRs where possible |
Developing standards
Create, profile, extend and maintain a specification where existing standards cannot adequately represent the research object or support the intended uses.
This can include creating a new standard, but it also includes less extensive approaches such as defining a community profile, adding constraints, developing an extension or proposing changes to an established standard. Standards development usually requires coordination beyond the immediate FAIRification team. Domain communities, intended users, standards maintainers, repositories, software developers and other implementers may need to participate. An important question to ask is whether the required development capability can be established through collaboration.
This capability should be considered only after relevant existing standards and profiles have been examined.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Galaxy: a story about interoperability |
Cascade mapping from FT 4: Apply data standards: Reuse whenever possible |
| Single Cell Omics Community Interoperability Showcase |
Cascade mapping from FT 4: Community-driven work to define standards and cross-mappings between standards through workshop programmes emerging |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Cascade mapping from FT 4: Developing a framework for multi-omic data analysis (transcriptomics, proteomics and metabolomics), e.g. integration of MetaboLights, UniProt, BIoModels, MetabolicAtlas, Rhea, Reactome, BIGG, and http://pickle.gr/ |
Applying standards
Create or transform research objects so that they conform to selected standards, profiles, formats and serialisations.
This includes applying standards when a research object is first created and applying them retrospectively to existing research objects. It may involve restructuring content, converting formats, generating metadata, packaging components, preserving identifiers and provenance, and documenting transformation decisions.
The capability can be provided through data-processing tools, export services, repository submission workflows, conversion libraries, schemas, templates, and specialists in the implementation team.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Galaxy: a story about interoperability |
Cascade mapping from FT 4: Apply data standards: Reuse whenever possible |
| Single Cell Omics Community Interoperability Showcase |
Cascade mapping from FT 4: Community-driven work to define standards and cross-mappings between standards through workshop programmes emerging |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Cascade mapping from FT 4: Developing a framework for multi-omic data analysis (transcriptomics, proteomics and metabolomics), e.g. integration of MetaboLights, UniProt, BIoModels, MetabolicAtlas, Rhea, Reactome, BIGG, and http://pickle.gr/ |
Validating against standards
Assess whether a research object representation conforms to the requirements of a specified standard, version, profile or extension.
This can include syntactic, structural, schema-based, semantic, rules-based, completeness and cross-component validation. Different validation mechanisms may be required for different parts of a compound research object.
Validation establishes conformance with defined requirements. It does not by itself establish that the research object is FAIR, scientifically correct or suitable for every intended use.
The capability can be provided through official validators, schemas, repository submission checks, testing frameworks, quality-control workflows, community services or manual expert review.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Galaxy: a story about interoperability |
Cascade mapping from FT 4: Apply data standards: Reuse whenever possible |
| Single Cell Omics Community Interoperability Showcase |
Cascade mapping from FT 4: Community-driven work to define standards and cross-mappings between standards through workshop programmes emerging |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Cascade mapping from FT 4: Developing a framework for multi-omic data analysis (transcriptomics, proteomics and metabolomics), e.g. integration of MetaboLights, UniProt, BIoModels, MetabolicAtlas, Rhea, Reactome, BIGG, and http://pickle.gr/ |
Harmonise content elements
This step establishes which controlled vocabularies, terminologies and ontologies will be used to describe the research object and its associated metadata, and applies their terms in ways that support consistent interpretation and reuse.
Vocabulary discovery and selection
Find, assess and choose semantic resources that provide appropriate identifiers and descriptions for the concepts represented in the research object and its associated metadata.
This capability includes determining which concepts need controlled terms, identifying relevant community resources and evaluating whether those resources provide sufficient coverage, granularity, semantic precision and operational support.
The capability may be provided through project expertise, domain communities, vocabulary registries, lookup services, ontology portals, standards organisations, repositories or other FAIR-enabling resources.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 5: choose data vocabularies: selecting, developing and annotating with data vocabularies: use of DCAT, DCT, FOAF, EJP RD |
| Galaxy: a story about interoperability |
Cascade mapping from FT 5: Data vocabularies, 6. Transform data for interop: |
| all tools rendered interoperable by describing I/O and filetypes with EDAM, cross referenced with registries |
Vocabulary extension and development
Add or develop new concepts when established semantic resources do not adequately cover the requirements of the FAIRification activity.
This capability may involve requesting a new term from an existing authority, contributing corrections or relationships, creating a governed local extension, defining an application ontology or developing a new semantic resource.
The preferred sequence is to:
- Reuse a suitable existing term.
- Request a new term, definition or correction from the maintaining authority.
- Use an established extension mechanism.
- Create a governed local extension that reuses existing identifiers where possible.
- Combine or extract modules from compatible semantic resources.
- Develop a new vocabulary or ontology only where no suitable alternative exists.
Vocabulary development usually requires coordination with domain communities, intended users, ontology or terminology specialists, repositories and software implementers. The required capability may therefore be provided outside the immediate FAIRification team.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 5: choose data vocabularies: selecting, developing and annotating with data vocabularies: use of DCAT, DCT, FOAF, EJP RD |
| Galaxy: a story about interoperability |
Cascade mapping from FT 5: Data vocabularies, 6. Transform data for interop: |
| all tools rendered interoperable by describing I/O and filetypes with EDAM, cross referenced with registries |
Semantic annotation
Associate research objects, components, metadata elements and values with identifiable concepts from selected semantic resources.
This capability includes selecting the correct concept, representing its identifier in the appropriate context and recording sufficient provenance to understand how and why the annotation was made.
In addition to a term from a vocabulary, an annotation usually also includes the subject being described and the relationship between the subject and the concept that the term represents. For example, stating that a research object “is about” a disease, “uses” a method or “has specimen type” a biological material expresses different meanings.
The capability may be provided through manual curation, data-entry systems, transformation workflows, text-mining or annotation tools, repository services or combinations of automated and expert processes.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 5: choose data vocabularies: selecting, developing and annotating with data vocabularies: use of DCAT, DCT, FOAF, EJP RD |
| Galaxy: a story about interoperability |
Cascade mapping from FT 5: Data vocabularies, 6. Transform data for interop: |
| all tools rendered interoperable by describing I/O and filetypes with EDAM, cross referenced with registries |
Vocabulary management
Maintain reliable and reproducible use of semantic resources over time.
For most FAIRification activities, this means managing the project’s use of externally maintained vocabularies: recording versions, monitoring changes, updating annotations and preserving reproducibility.
Where the project or community maintains a vocabulary, ontology, value set or extension, the capability additionally includes editorial governance, identifiers, releases, publication, support and long-term maintenance.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 5: choose data vocabularies: selecting, developing and annotating with data vocabularies: use of DCAT, DCT, FOAF, EJP RD |
| Galaxy: a story about interoperability |
Cascade mapping from FT 5: Data vocabularies, 6. Transform data for interop: |
| all tools rendered interoperable by describing I/O and filetypes with EDAM, cross referenced with registries |
Transform to match use cases
This step creates mappings, translations and alternative representations that allow the research object to meet integration and interoperability requirements of the primary use cases.
Identifier mapping
Establish and maintain documented relationships between identifiers used by different schemes, authorities, repositories or systems.
This capability allows integrations to recognise when identifiers refer to the same subject or to subjects connected through version, component, derivation, replacement or other defined relationships. Since mapping are not always assertions of an exact identity match, the relations should be stated explicitly:
- Exactly the same subject.
- Different records describing the same subject.
- Different versions or releases.
- A collection and one of its components.
- An original and a derived research object.
- A deprecated and replacement identifier.
- Closely related but distinct subjects.
The capability may be provided through authoritative registries, repository cross-references, identifier-resolution services, community mapping resources, lookup services or project-maintained mappings (that themselves can be shared).
| Resource | Relevance |
|---|---|
| Intrinsically Disordered Protein Community Interoperability Showcase: Exploiting Bioschemas Markup In this presentation, the audience heard how the intrinsically disordered protein community had identified a challenge in discovery and integration of data that spans several experimental fields studying protein structure and function, using methods that are domain specific. As such, there are no comprehensive single resources containing all IDPs. Therefore, researchers wishing to see the combined knowledge from resources such as ModiDB, DisProt and PED encountered an onerous integration task. As a solution, the IDP community proposed the creation of a central knowledge graph that could be asked common queries. To achieve this goal, the IDP described their use of bioschemas, JSON-LD, SPARQL, OpenAIRE and FAIRsharing to assemble a knowledge graph that can be queried by researchers seeking a consolidated, integrated view of data from across the IDP community. |
Cascade mapping from FT 6: transform data for increased interoperability via creation of knowledge graph |
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Rare Disease Community Interoperability Showcase: examples from EJP RD |
Cascade mapping from FT 6: transform data for increased interoperability by connecting resources via KGs/SPARQL endpoints |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 6: transform data for interoperability: mapping of identifiers and vocabularies to Bioschemas for further analysis and dissemination |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Cascade mapping from FT 6: Transform data for inter- operability: Convert metadata from avirous schema to Track Hub format and FAIRtracks metadata exchange format using Omnipy ETL flows |
Vocabulary alignment
Establish and maintain documented semantic relationships between concepts from different controlled vocabularies, terminologies, taxonomies or ontologies.
This capability allows the concepts used in the canonical representation to be interpreted or translated for communities and systems that use other semantic resources. Possible alignment relationships include:
- Exact match.
- Close match.
- Broader match.
- Narrower match.
- Related match.
- Context-dependent correspondence.
- Composite or one-to-many correspondence.
- No suitable match.
The capability may be provided through mappings published by vocabulary authorities, community alignment projects, ontology services, mapping registries, specialist tools or project-generated mapping sets.
| Resource | Relevance |
|---|---|
| Intrinsically Disordered Protein Community Interoperability Showcase: Exploiting Bioschemas Markup In this presentation, the audience heard how the intrinsically disordered protein community had identified a challenge in discovery and integration of data that spans several experimental fields studying protein structure and function, using methods that are domain specific. As such, there are no comprehensive single resources containing all IDPs. Therefore, researchers wishing to see the combined knowledge from resources such as ModiDB, DisProt and PED encountered an onerous integration task. As a solution, the IDP community proposed the creation of a central knowledge graph that could be asked common queries. To achieve this goal, the IDP described their use of bioschemas, JSON-LD, SPARQL, OpenAIRE and FAIRsharing to assemble a knowledge graph that can be queried by researchers seeking a consolidated, integrated view of data from across the IDP community. |
Cascade mapping from FT 6: transform data for increased interoperability via creation of knowledge graph |
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Rare Disease Community Interoperability Showcase: examples from EJP RD |
Cascade mapping from FT 6: transform data for increased interoperability by connecting resources via KGs/SPARQL endpoints |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 6: transform data for interoperability: mapping of identifiers and vocabularies to Bioschemas for further analysis and dissemination |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Cascade mapping from FT 6: Transform data for inter- operability: Convert metadata from avirous schema to Track Hub format and FAIRtracks metadata exchange format using Omnipy ETL flows |
Model mapping
Relate entities, properties, relationships, structures and constraints across domain, data, metadata or structural models.
This capability connects the canonical model and representation to the models expected by target communities, repositories, services, tools and workflows. Model mapping may describe conceptual correspondences, structural crosswalks or executable transformation rules.
Mappings are not limited to one-to-one equivalence. A source element may correspond to several target elements, several source elements may be combined, or a target value may need to be derived.
The capability may be provided through standards crosswalks, repository mappings, schemas, transformation specifications, ETL workflows, query languages or project-developed mapping artefacts.
| Resource | Relevance |
|---|---|
| Intrinsically Disordered Protein Community Interoperability Showcase: Exploiting Bioschemas Markup In this presentation, the audience heard how the intrinsically disordered protein community had identified a challenge in discovery and integration of data that spans several experimental fields studying protein structure and function, using methods that are domain specific. As such, there are no comprehensive single resources containing all IDPs. Therefore, researchers wishing to see the combined knowledge from resources such as ModiDB, DisProt and PED encountered an onerous integration task. As a solution, the IDP community proposed the creation of a central knowledge graph that could be asked common queries. To achieve this goal, the IDP described their use of bioschemas, JSON-LD, SPARQL, OpenAIRE and FAIRsharing to assemble a knowledge graph that can be queried by researchers seeking a consolidated, integrated view of data from across the IDP community. |
Cascade mapping from FT 6: transform data for increased interoperability via creation of knowledge graph |
| Plant Sciences Community Showcase |
Cascade mapping from FT 2–6: integration of multiple resources, data harmonisation |
| Rare Disease Community Interoperability Showcase: examples from EJP RD |
Cascade mapping from FT 6: transform data for increased interoperability by connecting resources via KGs/SPARQL endpoints |
| Mapping the DCAT-based EJP RD Metadata Model to Bioschemas for improved interoperability |
Cascade mapping from FT 6: transform data for interoperability: mapping of identifiers and vocabularies to Bioschemas for further analysis and dissemination |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Cascade mapping from FT 6: Transform data for inter- operability: Convert metadata from avirous schema to Track Hub format and FAIRtracks metadata exchange format using Omnipy ETL flows |
Deploy to hosting environments
This step deploys the research object, its alternative representations and any supporting resources to one or more hosting environments that meet the functional, operational, security and preservation requirements of the FAIRification activity.
Research object deposition
Persist, manage, expose and operate research objects, metadata, mappings, alternative representations and supporting services in an environment suitable for their intended uses.
This capability includes selecting or confirming a hosting environment, deploying the required objects and services, and establishing the storage, preservation, discovery, security, operational and sustainability arrangements needed to support them.
The capability may be provided by an external repository, institutional infrastructure, community platform, secure environment, commercial provider or project-operated service. The FAIRification team does not necessarily have to operate the environment itself.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 1, 7: hosting capabilities showcased with FAIRDARE |
| Galaxy: a story about interoperability |
Cascade mapping from FT 7: Data hosting, versioning and transfer: all tools are annotated and exportable (workflows, Bioschemas, RO-crate…) |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Cascade mapping from FT 7: Host your data: Submit to Zenodo, index in Track Hub Registry, discover via TrackFind and interfaces integrated into Galaxy and GSuite |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Cascade mapping from FT 7: Promote/develop MetaboLights as deposition database |
Research object versioning
Identify, relate, preserve and manage changes to deployed research objects and their associated metadata, mappings, representations and services.
This capability allows users and machines to distinguish a changing research object from a particular reproducible version and to understand how versions, releases, components and derived representations relate.
The capability may be provided by a repository, version-control system, package registry, storage service, workflow platform or project-operated versioning process.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 1, 7: hosting capabilities showcased with FAIRDARE |
| Galaxy: a story about interoperability |
Cascade mapping from FT 7: Data hosting, versioning and transfer: all tools are annotated and exportable (workflows, Bioschemas, RO-crate…) |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Cascade mapping from FT 7: Host your data: Submit to Zenodo, index in Track Hub Registry, discover via TrackFind and interfaces integrated into Galaxy and GSuite |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Cascade mapping from FT 7: Promote/develop MetaboLights as deposition database |
Research object transfer
Move, ingest, synchronise or register research objects and associated information between environments securely, completely and verifiably.
This capability may involve transferring files or packages, migrating databases or object stores, depositing through a repository interface, synchronising environments or registering an object in place without copying it.
Transfer is distinct from retrieval. Retrieval concerns what a user or service can select and receive. Transfer concerns the operational movement or ingestion of research objects between source, staging and target environments.
Transfer is not required where the research object remains in place and the existing environment is upgraded to fulfil the target role.
| Resource | Relevance |
|---|---|
| Plant Sciences Community Showcase |
Cascade mapping from FT 1, 7: hosting capabilities showcased with FAIRDARE |
| Rare Disease Community Interoperability Showcase: examples from EJP RD |
rich data export options explored |
| Galaxy: a story about interoperability |
Cascade mapping from FT 7: Data hosting, versioning and transfer: all tools are annotated and exportable (workflows, Bioschemas, RO-crate…) |
| FAIRtracks: FAIRtracks and Omnipy - FAIRtracks interoperability story |
Cascade mapping from FT 7: Host your data: Submit to Zenodo, index in Track Hub Registry, discover via TrackFind and interfaces integrated into Galaxy and GSuite |
| Metabolomics Community & Interoperability Platform - On which subjects could they collaborate? |
Cascade mapping from FT 7: Promote/develop MetaboLights as deposition database |
Activate sharing and reuse
This step authorises and activates the release of the validated research object deployment under appropriate rights, access and privacy conditions, and provides the documentation, support and monitoring needed for its intended reuse.
Rights and reuse conditions
Authorise and communicate the terms under which a research object and its components may be accessed, used, modified, combined and redistributed.
This capability includes declaring rights holders and release authorities; evaluating licences, contracts and third-party terms; selecting suitable licences or rights statements; defining permitted uses; and communicating the resulting conditions to people and machines.
The capability may be provided by rights holders, legal advisers, research organisations, repositories, data-access committees, ethics or information-governance functions, community authorities or other authorised decision-makers.
Privacy and disclosure control
Identify, assess and manage the risk that releasing a research object, metadata or associated information could expose personal, confidential, sensitive or otherwise restricted information.
This capability is broad and complex and its nature will vary greatly depending on the research object and intended use. For example, anonymisation may be inappropriate or insufficient where the information is highly distinctive, where external information creates linkage risks or where removing sufficient detail would undermine the intended use.
The capability may be provided by information-governance, data-protection, statistical-disclosure, ethics, security or domain specialists and by authorised data-access or release bodies.
Release management
Define, approve, activate, document and manage a specific release of the research object.
This capability controls when and what is being released through which channels and interfaces, which version and representations are included, where they are available, who may access them and which conditions apply.
The capability may be provided through repository deposition and approval workflows, institutional release procedures, community governance, project release management or combinations of these.
Hosting
Capabilities of the environment(s) where the research objects are hosted and made available for access and distributions. Exchange formats for the research objects are covered under Formats & representation, while APIs, indexing and query interfaces are covered here.
Formats
What is reported in the Dataset (data) & the Dataset Descriptor (metadata)
Content
What is reported in the research object set (research object) & the research object set Descriptor (metaresearch object).