As a researcher, you may need to make your dataset Findable and
Accessible because of funder or institutional requirements.
Scenario Overview
You need to comply with your institution’s FAIR data policy and make your dataset available in the institutional catalog.
Complexity
Low to Medium
Key Experts
Data Steward
Outcome
Findable Dataset
Your Journey
🎯 Define FAIRification objectives
Start by clearly defining what you want to achieve.
You've identified your goal: making your dataset findable in the institutional catalog to comply with your organization's data policy. Having clear objectives helps you stay focused on what matters most.
Examples
Clinical Researcher
I need to register my clinical trial data in our institutional catalog before submitting my grant report. I'm aiming for the minimum metadata requirements our institution mandates.
Data Steward
I'm helping several research teams register their datasets. My goal is to create a smooth workflow that satisfies both our institutional policies and FAIR principles.
PhD Candidate
My supervisor wants me to make my research data available. I'm starting by understanding what 'findable' really means and identifying the essential steps I need to take.
Connect with someone who can guide you through the process.
You've reached out to your institutional data steward who will support you throughout this journey. Having expert guidance makes the process much smoother and helps you avoid common pitfalls.
Examples
Researcher at University Medical Center
I contacted our research data office and connected with a data steward. They're helping me understand what metadata I need and which catalog to use for registration.
Independent Researcher
Since my institution doesn't have a dedicated data steward, I reached out to the Health-RI helpdesk. They're providing remote guidance tailored to my situation.
Now you need to inventory the metadata you already have about your dataset. Things like title, description, and keywords, and identify what's still missing. This assessment helps you understand the work ahead.
Examples
Clinical Researcher
I have my research protocol with a detailed study description, but I need to check whether I have everything required: keywords, contact details, and properly documented access conditions.
Data Manager
I'm going through our data management plan to extract existing metadata. I also need to verify our licensing information and confirm we have the right consent documentation for sharing.
With your metadata complete, you're ready to register your dataset in the institutional catalog. This step makes your data discoverable to colleagues and collaborators who might benefit from it.
Examples
Researcher at University Medical Center
I'll use our institutional catalog's web interface to submit my dataset registration with all the metadata I've prepared.
Multi-center Study Coordinator
I need to register our dataset in multiple institutional catalogs across participating sites, making sure the information stays consistent everywhere.
Success! Your dataset can now be discovered by others.
Congratulations! Your dataset is now registered and findable in the catalog. Researchers looking for data like yours can now discover it and understand what you have available, opening doors for potential collaborations and data reuse.
💡 Apply metadata model
Consider using standardized metadata schemas.
Want to go further? Applying a standardized metadata model like Dublin Core, DataCite, or domain-specific schemas can significantly improve your data's discoverability across different systems and catalogs.
Examples
Data Steward
I want our metadata to work seamlessly with national and international catalogs, so I'm mapping our fields to the DataCite schema to ensure broad compatibility.
Document your data's internal structure in detail.
Take it even further by documenting the internal structure of your dataset, e.g., variable names, data types, relationships, and formats. This makes your data much more reusable and reduces the need for back-and-forth questions from potential users.
Examples
Database Administrator
I'm documenting our complete database schema so other researchers can understand the data structure independently, without needing to contact us for clarification.