Defining FAIR Objectives for a project

Status

In progress

FAIR elements

Pre-FAIRification
Findability
Accessibility
Interoperability
Reusability

There are no prerequisites defined for this lesson plan.

After completing this lesson plan, the participants are capable of:

Maturity Model (FAIR Evaluator and its metrics, FIP, FAIRplus Dataset Maturity Indicators)

E.g., evaluate your existing dataset maturity by assigning a DSM indicator to your project data. Then make a set of objectives that will lead your project from its existing to its ideal maturity level to move along the spectrum of data maturity and FAIR. This will also provide a good exercise in awareness of what the next levels are. This will also provide a good exercise in awareness of what the next levels are.

Use tooling from goal modelling to define better objectives

Topic, definition and scope

Defining FAIR Objectives for a research / scientific project as a part of data management plan creation.

Summary of Tasks / Actions

Identify where you are in the maturity model
Identify your goals / what do you want to achieve…
Make a plan (e.g. DMP FAIR part and / or FIP) - possible link to Topic 1.4, although this is about policies and not DMPs?
- Identify bottlenecks for your plan / reflection points: where does my project crew need training? More resources? More personnel?
…

Materials / Equipment

Data Stewardship wizard? https://ds-wizard.org/
**Would the list of existing DMP tools and assessment tools help, like this at FAIRassist? https://fairassist.org **
Need a tool such as DIP4F for FAIRification Objective (e.g. cancer data produced in the project is Findable for the patients community; cancer data produced in the project is reusable by the research community; connect rd data so that we have enough data to run disease natural history studies; accelerate new treatments discovery; Leo’s email) …

Additional resources

Ten Simple Rules for Creating a Good Data Management Plan arrow_outward Michener WK PLoS Comput Biol 11 (10) : e1004525 (2015)
Data management arrow_outward European Commission

Pauline l'Hénaff

Bruna Vieira

Cesar Bernabe

Anne-Françoise Adam-Blondon

Allyson Lister

Fieke Schoots

Pradeep Eranti

The terms4FAIRskills project has created a formalised terminology that describes the competencies, skills and knowledge associated with making and keeping data FAIR.

Data steward Data manager principal investigator researcher	wants competency in	data management develop fair and open research strategy understanding fair and open research challenges in your organization knowledge of theories underlying fair implementation knowledge to choose fair data handling approaches appropriate to the research phenomena
Online documentation	confers competency about	data management develop fair and open research strategy understanding fair and open research challenges in your organization knowledge of theories underlying fair implementation knowledge to choose fair data handling approaches appropriate to the research phenomena
Online documentation	confers knowledge about	data management plan
Online documentation	supports implementation of	the FAIR Principles
Online documentation	demonstrates how to fulfil the requirements for	fairplus dataset maturity model indicator