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.


Reading material

Detailed Course Schedule


Monday

9.30 - 10.45 Block 1. Introduction to FAIR and GO FAIR and FAIR Data Stewardship

    Topics:

  • The evolution of FAIR and GO FAIR
  • The GO FAIR Implementation Networks and Service Provider consortium
  • The GO FAIR Data Stewardship as a new profession
  • The cost and benefits of FAIR: use cases

    Objectives:

  • Understand the history and background of FAIR
  • Have a high level overview of the GO FAIR ecosystem and its stakeholders
  • Understand the concepts of FAIR Data Stewardship and related roles
  • Develop a perspective on FAIR Data Stewardship as a new profession
  • Understand the context of the FAIR Data Stewardship course

10.45 - 11.15 Coffee/tea break

11.15 - 12.30 Block 1.2. Introduction to FAIR Data Stewardship

    Topics:

  • FAIR Data Stewardship the old way
  • Data management versus data stewardship
  • Data stewardship: getting systematic
  • A. Research and data cycles
  • B. Data stewardship planning
  • C. The FAIR principles high over
  • D. FAIR Maturity Indicators
  • E. Meta data for Machines
  • F. Reference implementations

    Objectives:

  • Understand the concepts of FAIR Data Stewardship and related roles
  • Learn to approach Data stewardship in a systematic way

12.30 - 1.15 Lunch break

1.30 - 2.45 Block 1.3 - FAIR Data Stewardship a new profession

    Topics:

  • Capacity challenges
  • The 7 canonical steps of FAIRification
  • The FAIR Service Provider Consortium
  • FAIR tooling ecosystem
  • Offering and courses
  • Stakeholder driven implementation
  • Safeguarding of knowledge
  • Data Stewardship roles
  • Data stewardship in organizations

    Objectives:

  • Understand the aspects of the FAIR Data Stewardship profession
  • Understand the different FAIR Data Stewardship roles
  • Understand the available resources
  • Be able to (help) set up a FAIR Data Stewardship department

2.45 - 3.15 Coffee/tea break

3.15 - 5.00 Practising FAIR data (trainer Erik Schultes)

    Topics:

  • The GO FAIR matrix of best practices
  • Metadata for Machines in more detail
  • The FAIR Funders Pilot
  • Hands on assignments

    Objectives:

  • Understand the best practices available in the GO FAIR community
  • Understand the value of metadata templates
  • Understand the value of an automated request for proposal and response process
  • Be able to manually design a plan for FAIR Data Stewardship for an organization

Tuesday

9.00 - 10.30 Block 2.1 Introduction to Semantic Interoperability

    Topics:

  • The heterogeneous nature of today's reality;
  • The problem of sharing data;
  • Interoperability through time;
  • What is semantic interoperability?
  • How can semantics improve the current data situation?
  • What is ontology?
  • Types of ontologies
  • Spectrum of knowledge representation approaches

    Objectives:

  • Discuss current scenario of complexity and heterogeneity in the data space;
  • Provide an overview of the evolution of interoperability issues and their different levels;
  • Discuss the basics of semantic interoperability;
  • Introduce the concept of ontology.
  • Understand the core concepts of ontologies as globally shared knowledge-structures

10.30 - 11.00 Coffee/tea break

11.00 - 12.30 Block - 2.2 Introduction to Ontology Engineering

    Topics:

  • Ontology adequacy;
  • Types and individuals;
  • Generalisations;
  • Principle of identity;
  • Generalisation sets;
  • Roles;
  • Phases;
  • Formal and material relations;

    Objectives:

  • Introduce the discipline of Ontology Engineering;
  • Motivate how ontologies as conceptual model representations can be used to describe knowledge;
  • Discuss the relations between a conceptualisation and its model;
  • Understand the difference between types and individuals;
  • Understand the nature of identity;
  • Understand meta-properties of generalisation sets;
  • Understand the concept of roles;
  • Understand the concept of phases;
  • Understand the concepts of formal and material relations.

12.30 - 1.30 Lunch break

1.30 - 3.00 Block 2.3 Semantic Web and Linked Data (1)

    Topics:

  • Word Wide Wide vs Semantic Web
  • Graphs and Linked Data
  • RDF syntaxes
  • SPARQL
  • OWL

    Objectives:

  • Understand the difference between the Web of documents and the Web of concepts (WWW vs Semantic Web)
  • Understand the goals and benefits of the Semantic Web
  • Understand the concept and structure of Linked Data
  • Understand how to create RDF triples and publish data as Linked Data
  • Recognise the various syntaxes of RDF
  • Practice the creation of triples and simple graphs
  • Understand SPARQL, the RDF querying language
  • Practice querying Linked Data using SPARQL
  • Understand the basics of the Web Ontology Language (OWL)

3.00 - 3.30 Coffee/tea break

3.30 - 5.00 Block 2.3 Semantic Web and Linked Data (2) and hands-on assignments



Wednesday

9.00 - 10.30 Block 3.1 The FAIR Principles explained

    Topics:

  • All FAIR principles

    Objectives:

  • Understand each of the FAIR principles, their original intentions, motivations and why they are necessary

10.30 - 11.00 Coffee/tea break

11.00 - 12.30 Block 3.2 Towards FAIRness: FAIR Data Stewardship Plans

    Topics:

  • Data Stewardship/Management
  • Supporting tools for DS/DM
  • DMPs/DSPs vs FAIR principles
  • Main elements of "good" DS

    Objectives:

  • Explore the current requirements of data stewardship wizards
  • Understand how each facet of a DSP maps to each FAIR Principle
  • Understand main elements/concern for a good DS
  • Discuss how DS can be applied in organisations

12.30 - 1.30 Lunch break

1.30 - 3.00 Block 3.3 FAIRification process and supporting ecosystem (trainer Luiz Bonino)

    Topics:

  • FAIRification process and its steps
  • Supporting FAIR ecosystem

    Objectives:

  • Understand the steps of the FAIRification process
  • Discuss the elements of an ecosystem for supporting operations on FAIR data

3.00 - 3.30 Coffee/ tea break

3.30 - 5.00 Block 3.4 FAIR Tools - FAIRifier; Cleansing and sculpting data (trainer Luiz Bonino)

    Topics:

  • Data preparation using the FAIRifier
  • Semantic data modeling using the FAIRifier
  • FAIR data publication using the FAIR Data Point
  • Metadata creation using the FAIR Data Point

    Objectives:

  • Understand the functionality of the FAIRifier
  • Understand how to do basic data cleansing steps
  • Understand how to do basic data harmonization steps
  • Understands how to split/merge columns when necessary
  • Understand the steps required to cleanse a dataset
  • Understand how to publish data into the FAIR Data Point
  • Understand how to define the FAIR Data Point's metadata

Thursday
Morning topic: Data modeling from-scratch & querying

9.00 - 10-30 Block 4.1 Data modelling from scratch

    Topics:

  • Core Ontological Frameworks that are useful for FAIR
  • Considerations for GUIDs - Principle F1
  • Create FAIR data models from-scratch
  • Custom scripting to apply the model
  • “Push” the FAIR data into a server

    Core ontological frameworks

    Objectives:

  • Understand the different kinds of ontologies (domain, application, structural, upper)
  • Understand the DCAT and LDP models in some depth
  • Considerations for GUIDs

    Objectives:

  • Where are we going to publish?
  • How can we model GUIDs that are not overly complex within a domain? Document-fragments
  • What are the consequences of mixing document fragments and root GUIDs in the context of LDP?
  • Create semantic model from-scratch

    Objectives:

  • Understand a new dataset (dataset of crop pests)
  • build-out a data model from zero
  • A. ontology lookups
  • B. what to do with hard cases? Options for extending ontologies
  • Custom Scripting to apply that model

    Objectives:

  • understand a code walk-through in Ruby
  • A. understand the general features of RDF code libraries shared between most programming languages
  • B. understand the considerations for the Object position, and the detection of appropriate datatypes in automated conversion pipelines
  • Understand a data push, using LDP

10.30 - 11-00 Coffee/tea break

11.00 - 12.30 Block 4.2 Introduction to SPARQL and FAIR data

    Topics:

  • Querying FAIR Data

    Objectives:

  • Learn about SPARQL interfaces (Local and Yasgui)
  • Learn the basics of SPARQL using our data push
  • Learn filters
  • Learn “distinct”
  • learn ORDER BY
  • Understand SPARQL over-the-web and SPARQL “locally” on in-memory datasets

12.30 - 1.30 Lunch break
Afternoon topic: Metadata modeling

1.30 - 3.00 Block 4.3. Metadata modelling principles

    Topics:

  • The nature and purpose of FAIR metadata
  • Types of Metadata
  • Meta data structures (FAIR Accessors and LDP containers)

    Why metadata

    Objectives:

  • Understand the principles are related to both data and metadata
  • Understand the purpose of metadata in FAIR
  • Types of metadata

    Objectives:

  • Understand the 7 types of metadata
  • Know some ontologies relevant to these different types
  • Know which principles are primarily associated with each type
  • The FAIR Accessor as a Metadata structure

    Objectives:

  • Understand the FAIR Accessor data model
  • Understand how this relates to LDP (relates to Core Ontological Frameworks Block 3.1)
  • Understand how this relates to DCAT (relates to Core Ontological Frameworks Block 3.1)
  • See the position of the DCAT SKOS Concept Scheme in the FAIR Accessor

3.00 - 3.30 Coffee/tea break

3.30 - 5.00 Block 4.4 - Interacting with the Linked Data Platform

    Topics:

  • Interacting with a FAIR metadata repository (Linked Data Platform in detail)
  • Create a FAIR metadata record by scripting (SKOS and DCAT)
  • Demonstrate value of FAIR data through a search

    Linked Data Platform in Detail

    Objectives:

  • Understand the basic concepts of REST
  • Understand how to create Containers and Resources in LDP from command-line
  • Decide where our SKOS Concept Scheme will “live” and select it’s URL
  • Create a SKOS Concept Scheme

    Objectives:

  • Understand what a SKOS Concept Scheme is, and its purpose
  • Learn the “SKOS Play” tool for easy conversion of Excel into SKOS
  • Push metadata

    Objectives:

  • POST the SKOS Concept Scheme to an LDP server
  • Push DCAT metadata that refers to that SKOS Scheme
  • Look at code that pushes additional metadata features - note the features that are critical for FAIR
  • A. NOTE: we will not add all features at this point - some will be added after the initial Evaluation step later
  • SPARQL using Named Graphs

    Objectives:

  • Competency in querying over the named graphs representing the metadata, SKOS, and data layers of the FAIR Accessor

Friday
Morning topic: Measuring FAIRness

9.00 - 10.30 Block 5.1 FAIR Maturity Indicators and their Compliance Tests

    Topics:

  • Are we FAIR yet: discuss measuring FAIRness
  • FAIR Maturity Indicators
  • FAIR Maturity Indicator tests

    FAIR Maturity Indicators

    Objectives:

  • Understand the existing core Maturity Indicators
  • Understand how to design a new MI (use “The 15th Metric” as an example?)
  • Understand the role of FAIRsharing in MI registration
  • FAIR Maturity Indicator Tests

    Objectives:

  • Understand how the core Maturity Indicator tests work
  • A. The Harvester
  • Understand how to interact with an MI test directly

10.30 - 11.00 Coffee/tea break

11.00 - 12.30 Block 5.2 FAIR Evaluation and leveraging its output

    Topics:

  • FAIR Maturity Indicator Evaluator (prototype)
  • Evaluate our published FAIR (meta)data record

    Evaluation our FAIR Accessor and dataset using The Evaluator

    Objectives:

  • Understand how The Evaluator works
  • Learn how to register a new Maturity Indicator Test
  • Learn how to register a Collection of Metrics
  • Learn how to initiate a new Evaluation
  • Learn how to improve your FAIRness score
  • Learn how to create a W3ID .htaccess record

12.30 - 1.30 Lunch break
Afternoon topic: what have we achieved?

1.30 - 3.00 Block 5.3 Dynamic data discovery and query federation

    Topics:

  • Leveraging FAIRness - FAIR data is self describing
  • Automated integration of your FAIR data with other people’s FAIR data

    Advanced SPARQL – exploring new datasets and federated query

    Objectives:

  • a) Patterns for exploration
  • b) Stepping through a complex dataset (EBI)
  • c) Stepping through a third-party dataset relevant to our sample data
  • i) food.data.gov website in the UK
  • d) Understand federated query
  • e) Consider options for integrating non-SPARQL data sources like LSID.
  • f) See a more complex federated query, using WikiData

3.00 - 3.30 Coffee/tea break

3.30 - 5.00 Block 5.4 Data Stewardship Plans and addressing Community Challenges

    Topics:

  • The FAIR Maturity Indicators (FMI) mapped to DSW


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
Poortgebouw
Rijnsburgerweg 10
2333 AA Leiden
The Netherlands


Other European and US venues are also available.


Upcoming Events (click event to book)