FAIR Data in Engineering – Background

The aim of this task is to identify best practices and provide guidance through the development of principles and guidelines for FAIR and secure data in the engineering / technical sciences.

Read – Interview Template 
Read – Summary of First Findings on FAIR Data in Engineering

Case Studies

  1. FAIR data habits of Mechanical & Material Engineering Researcher – Case Study
  2. FAIR data habits of Civil and Environmental Engineering Researcher – Case Study
  3. FAIR data habits of Process Engineering Researcher – Case Study
  4. FAIR data habits of Wind Energy Researcher – Case Study
  5. FAIR data habits of a Molecular Thermodynamics Researcher – Case Study
  6. FAIR data habits in Applied Ergonomics and Design – Case Study

The  term ‘FAIR Data’ was introduced by the FORCE 11 group in 2016 and refers to guiding principles for data that is Findable, Accessible, Interoperable, and Reusable (FAIR). But what is the meaning behind the FAIR Principles and how can they be fulfilled? While the principles are deliberately vague in many places and offer scope for interpretation, the main objective of the FAIR Data Principles is the optimal preparation of research data for humans and machines.

This survey is designed to collect case studies and understand the views and needs of scientists from engineering and technical disciplines on research data management (RDM).

The case studies will be analysed regarding their relation to the FAIR principles, in order to enable infrastructures and repositories to better support FAIR data in engineering.

Task Leaders: Angelina Kraft (TIB Hannover), Alastair Dunning (TU Delft). Also Jasmin Bohmer (ex-TU Delft)

Dates: 2018 – 2019

Contributors: RWTH Aachen, TIB Hannover, University College Dublin, University of Stuttgart