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Interview Template

In the following you will find the interview template for the CESAER RDM Engineering interviews.

Research Data management in engineering disciplines – CESAER interview template

The interviews are carried out on behalf of the CESAER group (Conference of European Schools for Advanced Engineering Education and Research), within the Task Force Open Science (TFOS), group Research Data Management. Our aim 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.

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.

PROCEDURE – Checklist for the interviewer

  • As an interviewer, you are asked to select participants (researchers) working within engineering projects.
  • The survey is estimated to require approximately 45-60 minutes to complete, but may take more or less time for each individual interview.

CONFIDENTIALITY:

Please provide information that the data from this survey will be analyzed in aggregate; individual responses to questions will not be available to anyone outside of the Task Force Open Science at CESAER.

QUESTIONS on interview procedure:  If you have questions or comments about this interview format, please feel free to contact the principal investigators,

Alastair Dunning, Head Research Data Services & 4TU.Centre for Research Data, TU Delft Library, Email: a.c.dunning@@@tudelft.nl 

Angelina Kraft, Head Research Data Management, Technische Informationsbibliothek (TIB) German National Library of Science and Technology, Email: angelina.kraft@@@tib.eu

Background information:

Name of the person(s) interviewed:

University:

Faculty:

Department:

ORCID:

Introduction

  • Introduce survey – undertaking this survey as part of CESAER and in context of FAIR principles
  • Explain the purpose of the interview: we want to understand your data sharing and publishing practices in order to help define the FAIR principles for your discipline
  • If possible, we would like to see one of your datasets to help us get a better understanding
  • When we gathered all the interviews we will be back in touch to see if you agree how we have interpreted the FAIR principles
  • Please note that the following questions serve as a guideline for the interviewer – depending on the scientist and their respective research background, not all questions have to be asked. The following questions do not cover a deep data analysis, but rather aim to to gather case studies how RDM is applied across various engineering disciplines.

 

1) Background: On research, and type, amount & storage of data

  • Could you tell me a little bit about your current research projects? What area of engineering do they cover?
  • Could you tell me a little bit about the types of data which you are creating?
    • In what format is this data? (proprietary / open?)
    • Are there different versions of the data (primary, cleaned, analysed)?
    • Does the data have an incidental nature (e.g. experiment or measurement campaign) or is it growing over time (e.g. temperature sensor)? Or is it based on simulations?
  • Storage requirements (expected and current)
    • What is the volume of data you are working with (approximate size of your files)?
      • Is the data you are working on in any way sensitive or confidential?
        • If yes, is the data protected/encrypted in any way?
      • Are there any privacy considerations in relation to your data? (human participants data, need for secure storage, anonymisation etc.)
  • Are you working together with other parties? (also ask for commercially sensitive information)
    • If you are, do you know who is the owner of the data? Are there special circumstances with regard to ownership for this project?

 

2) Sharing and Publishing Data (Findable and Accessible)

  • Is it common practice to publish data?
  • Do you publish your data? (ie available for others on the web)
  • If yes:
    • What part of the lifecycle is the data from? Eg raw data from sensors?  Processed data? data connected to a publication?
    • Where is it published?
    • Does it take much work for you? Or another in your team?   
    • Are the datasets described in a specific way (ie have specific metadata)?
  • Are there preferred repositories in your discipline for sharing data? Do you make use of them?
  • Do you share your data in other ways? (eg via email)?
  • Do you need specific software to read and analyse the data?

 

3) Structuring and Standardising Data (Re-Usable and Interoperable)

  • Looking at the data you create as part of your research, it is structured according to agreed standards? (irrespective of whether the data is published or not)
    • Eg , are there particular data fields that must be included?
    • Are there particular ways that data should be expressed (simple example: dd/mm/yyyy)
  • Could a researcher in another group re-use your data easily?
  • Do you ever re-use data from other sources?
    • If yes
    • How do you find that data? What licence does it come with, if any? How do you cite it? (ask for actual example)
    • Is it structured according to common standards in your discipline?
    • Do you have to do any further work on that data so that you can analyse it?

 

4) Broader Questions

  • More generally,  have you ever heard about new data publishing requirements from funders?
  • Before today, have you heard about the FAIR principles?
  • What are your general feelings about managing and publishing research data ?
  • In your opinion, what are the main barriers to data publication?
  • In your opinion, what could encourage researchers towards more data sharing via trusted platforms?

 

5) Wrap up – IMPORTANT

  • Can you share an actual dataset you have created? (So that we can investigate if it works in accordance with FAIR principles)
  • Would you like to know the outcome of the interviews? Agree to contact them again?
  • Results will be published in Autumn 2018
  • Thanks!

 

FAIR data case studies

FAIR data habits of Mechanical & Material Engineering Researcher – Case Study

Engineering Sector: Mechanical and Materials Engineering
 

Community preferences:

MP4
TIFF
DICOM  Format
Adobe File Formats
Excel Spreadsheet
MRI File Formats
CT File Formats
Overall FAIRness: ORANGE
F No
A No
I Partially
R Yes

Overall FAIRness visualisation:

MME

Continue reading “FAIR data habits of Mechanical & Material Engineering Researcher – Case Study”

FAIR data case studies

FAIR data habits of Civil and Environmental Engineering Researcher – Case Study

Engineering Sector: Civil and Environmental Engineering
 

Community preferences:

GitHub
Matlab
Paraview
Programming Tools
Sourcefiles C++
Python Scripts
VTK files
DropBox
Overall FAIRness: ORANGE
F Partially
A Partially
I Partially
R Partially

Overall FAIRness visualisation:

CEE

Continue reading “FAIR data habits of Civil and Environmental Engineering Researcher – Case Study”

FAIR data case studies

FAIR data habits of Process Engineering Researcher – Case Study

Engineering Sector: Process Engineering
 

Community preferences:

Excel Spreadsheet
Matlab
Use Case Specific Hardware / Equipment
Proprietary File Formats
TXT
CSV
File Server
Cloud Services
Overall FAIRness: ORANGE
F No
A No
I No
R Partially

Overall FAIRness visualisation:

PE

Continue reading “FAIR data habits of Process Engineering Researcher – Case Study”
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