Training the next generation of enablers of the Internet of FAIR Data & Services

Training for FAIR Data Program Managers, Data Stewards, Operators and Engineers

FAIR Data Stewardship & Management training

Overview - FAIR Data Stewardship, as a new profession, is rapidly gaining momentum. New requirements from national and international funders are driving the need for training of competent, professional data stewards and data managers with knowledge of the FAIR principles and their application. This course introduces the required knowledge and skills in a broader data stewardship context, including topics like semantic data modeling, metadata modeling, the FAIRification process, publishing FAIR Data Points, and other topics related to managing research project's data requirements. After completion of the course participants will be able to work with domain specialists in making their data FAIR and preserving them for re-use.

  • An introduction to GO FAIR and Data Stewardship
  • FAIR Data Stewardship and FAIR Data in Practice
  • Semantic Data Modeling and Ontologies
  • Semantic Web and Linked Data
  • FAIRification: Data
  • FAIRification: Processes
  • FAIRification: Metadata
  • The Fair Data Point (FDP) in practice
  • The value of FAIR data - Results for End Users
  • Putting FAIR into Practice

Who should attend - This course is aimed at librarians or data experts at universities, research institutions and R&D intensive companies who are dealing with the ever growing complexity of data integration. Currently data technicians/ICTers spend between 70 and 80 percent of their time on data wrangling such as dealing with format issues, identifiers, ontologies, massaging the data so that it is ready for big data analysis. For large organisations choosing to GO FAIR, integration and re-use of data sets becomes less labor intensive, leaving more time to dive into more complex data analysis answering research questions.

Duration - 5 day course starting at 10:00 on day 1 to accommodate travelling attendees. The morning of the first day covers the main topics that are discussed in the FAIR Awareness training. Attending the morning session on the first day is therefore not mandatory for attendees that have already followed the FAIR awareness training. The sessions contain regular breaks for coffee and networking.

Detailed Course Schedule


09:00-10:00 - Registration & Coffee

10:00-12:30 - An introduction to GO FAIR and Data Stewardship

  • Introduction and purpose of the course
  • The need for FAIR data
  • The history of the FAIR initiative
  • The internet of FAIR Data and Services
  • What is FAIR Data Stewardship
  • The purpose and goals
  • The FAIR Principles and Metrics


13:30-17:00 - FAIR Data Stewardship and FAIR Data in Practice

  • The need for high quality FAIR services
  • Elements of a FAIR Data Stewardship Department
  • Roles in a FAIR Data Stewardship Department
  • A FAIR Readiness Implementation Program
  • The FAIR Principles explained
  • The FAIR Metrics applied
  • The FAIR Community Challenges discussed
  • Resources for FAIR Data Steward


09:00-12:30 - Introduction to Semantic Data Modeling and Ontologies

  • What is semantic interoperability
  • How can it improve the current data situation
  • Ontological principles
  • Ontologies are computer-actionable artefacts


13:30-17:00 - Introduction to Semantic Web and Linked Data

  • The Semantic Web
  • Linked Data
  • Unique Identifiers
  • The FAIR principles explained
  • For each Principle what are the required actions


09:00-12:30 - FAIRification - Process and tooling

  • Overview of the process and tooling
  • Consider semantic models for the sample sett
  • onsider ‘core’ ontological frameworks (e.g. SIO, DCAT)
  • Where will the data “live” (repository)?
  • Considerations for GUPIDs for the FAIR data
  • Apply semantic model to data elements
  • Data transformation to FAIR Data
  • Repositories for data and metadata


13:30-17:00 - FAIRification - Processes and tooling (ctd)

  • Custom scripts to achieve data record transformation to FAIR Data
  • “Push” into the selected repository for data and metadata) = machine-readable knowledge graph
  • Open Refine FAIRifier tool
  • Sculpting and cleaning data
  • Export data without custom scripting


09:00-12:30 - FAIRification process - Metadata

  • The purpose of metadata
  • What should be included to be FAIR
  • Meta data structures (FAIR Accessors and LDP containers)
  • Create a FAIR metadata record by scripting
  • Push into a FAIR metadata repository


13:30-17:00 - FAIRification process - The Fair Data Point (FDP)

  • Introduction to the FDP
  • Create FDP record for a data set
  • Explore the FDP (interface and SPARQL)
  • Are we FAIR yet: discuss measuring FAIRness
  • The FAIR Maturity Indicators (FMI)
  • FMI Evaluator
  • Evaluate published data / metadata


09:00-12:30 - The value of FAIR data - Results for End Users

  • Query federation
  • More SPARQL if needed
  • Through a search or analysis demonstrate value
  • Discussion: what did we achieve?
  • Professional Analytics Tool: an example


13:30-16:00 - Putting FAIR into Practice

  • Scenarios and Plan of Action for follow up
  • Institutional FAIR Data Stewardship
  • Plans for Train-the-trainers
  • Next Steps

16:00-16:30 - Close

Contact Us

Contact us for more details or upcoming events. Private training sessions can be arranged for groups of 10 or more attendees.

Main Venue
Rijnsburgerweg 10
2333 AA Leiden
The Netherlands

Other European and US venues are also available.

Upcoming Events (click event to book)

San Diego: May: 28-31, 2019