User journey: RDM life cycle
Learn about how FAIRsharing's integrations provide rich content to tools across the research data life cycle.
This user story follows Alice, a clinical multi-omics researcher, through each stage of the research data life cycle — showing how FAIRsharing's manually curated content powers the tools she relies on, from writing her Data Management Plan (DMP) through to demonstrating the FAIRness of her deposited data.
FAIRsharing Across the Research Data Life Cycle: an illustrative user journey
FAIRsharing is more than a registry you visit once to find a standard or a database. It is a trusted point of truth — a manually curated knowledgebase and knowledge graph whose content actively powers tools and services that researchers, data stewards, and funders rely on every day. This page follows a researcher called Alice in her journey through the research data life cycle to show how FAIRsharing's curated metadata is important at each stage, reducing administrative burden and helping to ensure data is genuinely FAIR.
Meet Alice
Our hypothetical colleague on this journey is Alice, a postdoctoral clinical researcher managing a multi-centre study that generates both proteomics and metabolomics data. She knows her data needs to be FAIR — her funder requires it and the journals she wants to publish in expect it — but "FAIR" is a broad concept. She needs to know:
Which standards to use for her data types
How to implement them correctly
Where to deposit her data to satisfy funder and journal requirements
Whether she can demonstrate her data's FAIRness in a transparent and reusable way
FAIRsharing helps you get answers these questions both via its user interface and by powering the tools that answer them at every step of the journey.
Phase 1 — Plan: Building a FAIR Data Management Plan
"First I need to create a data management plan. But there are so many different requirements: from my institute, my funder, and the proteomics and metabolomics communities. How can I clearly state my intent? How can I build FAIR compliance into my project from the beginning?"
Alice starts with the Data Stewardship Wizard (DSW), a tool for creating structured, machine-actionable Data Management Plans (DMPs). Rather than leaving Alice to search for the right resources herself, DSW queries the FAIRsharing API as she types, surfacing relevant records directly within her DMP form.
What Alice does in DSW, powered by FAIRsharing:
When asked "What policies do you plan to comply with?", Alice types "Genomic Science" and DSW returns a list of matching FAIRsharing policy records, including the Genomic Science Program (GSP) Information and Data Sharing Policy. She selects it, and its FAIRsharing DOI is embedded in her DMP — unambiguously and persistently identifying the policy she aligns with.
When asked "What database/repository do you use?", she types "metabolomics" and DSW returns a list of FAIRsharing database records, including Metabolomics Workbench. Each suggestion is drawn directly from FAIRsharing's curated content, complete with description and persistent identifier.
When asked "What standard do you use?", she types "MIAPE mass" and DSW returns the relevant MIAPE modules curated in FAIRsharing, including MIAPE: Mass Spectrometry and MIAPE: Mass Spectrometry Informatics.
In a single DMP session, Alice has covered all three FAIRsharing registries — standards, databases, and policies — each linked by persistent identifiers. She has provided the standards and databases that will follow her throughout her project.
FAIRsharing's role: The FAIRsharing API provides DSW with as-you-type autocomplete for standards, databases, and policies. Each selected resource is linked to its FAIRsharing record via a globally unique persistent identifier (DOI), improving identifiability, connectivity, and machine actionability of the DMP.
Read more: Joining up the research data management dots
Phase 2 — Plan / Collect: Confirming Best Practices with RDMkit
"I'm nearly ready to finalise my DMP and begin data collection. I would like to understand community best practices for the standards and databases I've included in my DMP. How can I confirm that they are exactly what I need?"
As Alice finalises her plan and moves towards data collection, she consults the RDMkit — ELIXIR's practical guide to research data management across life science domains. The RDMkit Proteomics domain page gives her descriptive guidance on best practices: what to consider, what questions to ask, and how to approach sharing and preservation of proteomics data.
But when it comes time to examine and select the exact standards and databases relevant to her work, the RDMkit points her directly to FAIRsharing. Resources described in RDMkit domain pages are unambiguously linked to their FAIRsharing records, and RDMkit automatically populates and keeps its domain collections in FAIRsharing up to date, ensuring bi-directional connectivity between the two resources.
This means that the "how-to" guidance in RDMkit is always anchored to the "gold standard" specification in FAIRsharing. For Alice, this means she can trust that the standards and databases she is considering are current, community-endorsed, and correctly described — without needing to independently verify each one.
RDMkit provides the context (the why and how); FAIRsharing provides the specification (the what).
FAIRsharing's role: FAIRsharing hosts the RDMkit Proteomics Domain collection, which is automatically populated and updated by RDMkit. Resources listed on RDMkit domain pages link directly to their FAIRsharing records, ensuring guidance and specification remain in sync.
Phase 3 — Process / Analyse: Technical Recipes from the FAIR Cookbook
"I want to make sure that my data is understandable to others, to enable the reusability aspects of FAIR. Are there concrete instructions I can follow?"
Alice now needs practical, technical guidance on how to make her data interoperable and reusable. She finds this in the ELIXIR FAIR Cookbook — a collection of over 80 recipes covering FAIRification processes across omics, pre-clinical, and clinical research areas.
Two recipes are particularly relevant:
Making an omics data matrix FAIR — a hands-on recipe for making tabular omics results machine-actionable, referencing terminologies including Chemical Entities of Biological Interest (ChEBI) and NCBI Taxonomy (NCBITaxon)
Mapping datasets to the CDISC-SDTM standard — an experience report on mapping clinical data to CDISC Study Data Tabulation Model (CDISC-SDTM)
Each recipe includes a dedicated FAIRsharing section that lists every standard and database mentioned in the recipe, linked directly to its FAIRsharing record. This allows Alice to verify the status, community adoption, and relationships of any resource mentioned — ensuring her analysis is built on a stable, well-described, interoperable foundation.
FAIRsharing's role: Every standard and database mentioned in a FAIR Cookbook recipe is unambiguously identified via its FAIRsharing record. This allows researchers to verify the health and adoption of the resources they rely on, and ensures that recipe guidance is grounded in well-described, community-endorsed specifications.
Phase 4 — Preserve / Share: Finding the Right Repository
"I want to make sure I share my results in a FAIR manner. My funder and the journal I want to publish in require deposition in community-endorsed repositories. How do I find the best repositories, and ensure they fit my data and the formats I've used?"
Alice is ready to share her results. Rather than searching generically for a repository, she returns directly to FAIRsharing, where the curated relationships between standards, databases, and policies make it straightforward to find a repository that is the right fit for her data.
Using the MOMSI Collection: Alice navigates to the Multi-Omics Metadata Standards Integration (MOMSI) Working Group Iterative Collection; a curated, RDA-endorsed collection of standards for multi-omics research. She can see that mzML and the MIAPE: Mass Spectrometry guidelines, which she identified during the planning phase, are recommended by this working group.
Using the Relation Graph: Following the relation graph for mzML, Alice can see exactly which databases implement the standards she has chosen. The graph reveals that PRIDE implements mzML, MIAPE-MS, MIAPE-MSI, and several other MIAPE modules — making it the most comprehensive choice for her proteomics data. She also sees links to additional policy records, confirming that EMBO Press and other journals recommend PRIDE and endorse the standards she has used.
Confirming policy alignment: The same graph shows Alice that journals including EMBO Press and publishers including Taylor and Francis recommend MIAPE guidelines in their data policies — linking the standards she chose at the planning stage, the repositories she as identified, and the journal requirements she needs to meet.
FAIRsharing's role: FAIRsharing's curated relationships among standards, databases, and policies allow Alice to trace a direct line from funder policy → recommended standard → implementing database. This is only possible because FAIRsharing manually curates not just records, but the connections between them.
Phase 5 — Reuse: Demonstrating FAIRness via FAIR Assessment
"As I prepare my final report, I need to list all research outputs in the final version of my DMP. My funders also want proof that I align with the FAIR definitions of the communities relevant to my project. How can I show the FAIRness of my data in a transparent and reusable way?"
As Alice prepares her final report, she needs to go beyond simply asserting that her data is FAIR — she needs to demonstrate it in a transparent, reproducible, and machine-actionable way. This is where FAIR assessment tools come in.
Tools such as F-UJI and FAIR Champion (part of the OSTrails ecosystem, developed with EOSC) query FAIRsharing's curated content as part of their evaluation process. The FAIRassist registry — hosted within FAIRsharing — contains benchmarks and metrics tailored to specific communities and use cases, including metrics relevant to Alice's domains. These benchmarks define exactly what "FAIR" means in practice for a given community, linking abstract FAIR principles to concrete, measurable tests.
When a FAIR assessment tool such as FAIR Champion evaluates Alice's dataset using a community-appropriate benchmark, the results are transparent, comparable, and reproducible — because the benchmark itself is registered in FAIRsharing with a DOI, and the metrics it uses are unambiguously defined. Alice can cite the benchmark used, the score achieved, and the version of the assessment — giving her funders exactly the verifiable, machine-actionable evidence they need.
FAIRsharing's role: FAIRsharing hosts the FAIRassist registry of FAIR benchmarks and metrics. FAIR assessment tools query this registry to access community-appropriate evaluation criteria. This ensures that FAIR assessment results are grounded in transparent, citable, and community-endorsed definitions — rather than ad hoc or subjective criteria.
Find registered benchmarks and metrics at FAIRassist (https://fairassist.org/registry), part of FAIRsharing, by filtering according to a number of common fields.
FAIRsharing at every stage
Alice's journey illustrates that FAIRsharing's value extends beyond direct engagement with the registry itself. Its manually curated, richly interlinked content acts as a backbone for the broader research data management ecosystem — powering the tools that researchers, data stewards, and funders rely on at every stage of the data life cycle.
Plan
Data Stewardship Wizard (DSW)
API-powered autocomplete for standards, databases, and policies in DMPs
Plan / Collect
RDMkit
Hosts and updates domain collections; provides specification behind guidance
Process / Analyse
FAIR Cookbook
Identifies and unambiguously links all standards and databases cited in recipes
Preserve / Share
FAIRsharing directly
Curated collections and relation graphs match standards to implementing databases and recommending policies
Reuse / Assess
OSTrails FAIR Champion / F-UJI
Provides curated content during FAIR evaluation; hosts the FAIRassist registry of community benchmarks and metrics used in FAIR evaluation
If you would like to explore how FAIRsharing can support your own tools or workflows, please contact us at contact@fairsharing.org.
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