Learning Paths Explained

Available learning paths
As a demonstration of how Learning Paths can be implemented, the following three options are available:
  1. FAIRify yourself - Read more
  2. FAIRify your data - Read more
  3. FAIRify your infrastructure - Read more
To find the associated Learning Resources, select the appropriate Learning Path filter on the search form.
The ENVRI-FAIR work package on Training and capacity building set out to create training materials and offer training activities addressing the needs and requirements of the project partners in their work to progressively understand, integrate and implement an increased FAIRness of their data and related services. The main goal of this flexible and agile approach was to assist the staff of the ENVRIs with focussed and to-the-point educational resources, often addressing an immediate need arising from project-internal discussions. Nevertheless, we have put together some examples of learning paths in order to illustrate how some of the materials we have been indexing in the Training Catalogue can be combined by different learner categories.
What is a learning path?
Learning paths or (pathways) represent the chosen route taken by a learner through a range of learning activities. As the learner progresses along a path, they build up knowledge and proficiency step-by-step. Importantly, while the components of a path (and their order) can be recommended by a tutor, the ultimate decisions on content can be seen as being with the learner. The figure below illustrates how three learners navigate through a landscape of learning resources by following their individual learning paths. These paths are designed by combining topics, relevant learning outcomes and other constraining factors (level, type of resource, cost…). In the figure, time is along the vertical axis, while the circles represent learning resources of different kinds; the blue circles indicate that sometimes these may be parts of composite resources, such as summer schools or study programmes.
Learning path descriptions

FAIRify yourself - Introduction to the FAIR principles for RI staff

Target group: all staff, but with focus on the researchers who ‘generate’ the data.

Learning outcome(s): Along this path a number of training resources are presented that gives you an introduction to the FAIR principles and presents an overview why FAIR data are important for research and researchers. In steps along the path, you will obtain detailed information about the so-called Data Life Cycle which is only a circle when data is made accessible for re-use. The path ends with material from the ENVRI-FAIR summer school on FAIRness: how you can test if your data are FAIR enough.

Components in suggested reading order:

1. “FAIR for beginners” from the Danish e-Infrastructure Cooperation (DeIC)
Learning outcome: you will be able to present an overview of the FAIR principles

2. “FAIR Data - ARDC” from the Australian Research Data Commons
Learning outcome: after further examining the principles of FAIR data, you will be able to discuss their implementation based on examples from different resources.

3. “DataOne Best Practices of Data Management” from the US DataONE collaboration
Learning outcome: You will be able to describe the Data Life Cycle and its most relevant aspects, connecting FAIR principles to data management

4. “2019 International Summer School ‘Data FAIRness in Environmental and Earth Science Infrastructures: theory and practice’”, specifically the subsections

  • Data Fairness by Erik Schultes
  • Assessing FAIRness by Margareta Hellstrom and Barbara Magagna
  • ENVRI-FAIR Procedure to extract FAIR Answers from FAIR Questionnaires by Barbara Magagna

Learning outcome: you will be able to illustrate concepts and solutions to evaluate data FAIRness

FAIRify your data - Principles, technologies and recommendations on how to achieve FAIRness in practice

Target group: IT staff, developers and researchers who manage and process data

Learning outcome(s): YThis Path focuses on principles, approaches and frameworks that support professionals producing and (re-)using data to adopt solutions, technologies and recommendations towards enriching data resources, advance their FAIR implementation and ultimately achieve FAIRness in practice.

Components in suggested reading order:

1. “2021 ENVRI Community International Winter School on Data FAIRness”, specifically the subsections

  • Semantics
  • VREs, data analysis and visualisation
  • Data access tools
  • Cloud computing for developing and operating data management services

Learning outcome: After examining semantic navigation, Jupyter environments for visualisation and data discovery, resource access tools and cloud computing, you will be able to discuss solutions and technologies to enrich data resources and advance their FAIRness.

2. “FAIR Implementation Profile (FIP) workshop (GO-FAIR & ENVRI-FAIR)”

Learning outcome: You can introduce the FIP approach, discussing its ontology and the FAIR enabling resources (technical solutions, implementation strategies, standards and models) which address each of the FAIR Guiding principles.

3. "Provenance tracing in ENVRI Research Infrastructures"

Learning outcome: You will be able to present the theoretical background of provenance, its rationale and values, how it may be recorded, and how it interrelates with FAIRness.

4. “ENVRI-FAIR workshop on the RDA InteroperAble Descriptions of Observable Property Terminologies (I-Adopt) framework”

Learning outcome: You will be able to describe the RDA I-ADOPT framework and discuss how to use it as a way to compile clear and unambiguous definitions of variables in a standardised way, advancing FAIRness in many ways.

FAIRify your infrastructure - Policies for FAIR web services

Target group: managers and staff in supporting positions in RIs aiming to implement FAIR principles in the work and presentation of the infrastructure’s data and services.

Learning outcome(s): The main theme of this path is the formulation of the policies needed for making data and services FAIR. You will receive an overview of drivers and contexts for policies and tips for how to write and curate them. Once the policies are defined, step 4 will give you examples of different web services that can be developed for your infrastructure. Step 5 introduces important non-technological aspects of setting up operational services, such as eliciting user requirements, providing documentation and tutorials, and preparing for validation and evaluation of services.

Components in suggested reading order:

1. “1st ENVRI-FAIR policy workshop for the ENVRI community Research Infrastructures”

Learning outcome: you will be able to motivate the need for the different policies, and connect formal policy definitions with their practical implementation using examples from EPOS.

2. “ENVRI-FAIR 2nd policy workshop for the ENVRI community Research Infrastructures”

Learning outcome: ou will examine the ENVRI Policy Framework, presenting some of the key policy drivers relevant to ENVRI data service providers and discussing the necessary requirements and best practises for ENVRI RIs policies.

3. “3rd ENVRI-FAIR Policy Workshop: from policy, to guidelines to implementation”

Learning outcome: You can discuss the main aspects of creating policies (also examining the three key documents related to policy) and guidelines from policy statements and their consequent IT implementation.

4. “4th ENVRI-FAIR Policy Workshop: from policy, to guidelines to implementation”

Learning outcome: You will examine the progress made on policies in ENVRI and define a roadmap, an integrated plan for policy, and the policy target for ENVRI RIs and ENVRI-Hub.

5. “2022 ENVRI Community International Summer School: developing FAIR web services”

Learning outcome: you will examine approaches and solutions to designing and developing user-centred interfaces for services, validating and evaluating them and fostering reusability/interoperability among them.