NFDI-MatWerk Summer School 2023 – Research Data Management in Materials Science and Engineering

Why is it important?

Enabling a consistent and unobstructed standardization of research data will lead to substantial improvements in efficiency and synergies/collaboration among individual researchers and well-designed IT systems. In the era of digital infrastructures, this measure holds paramount importance in shaping the future of research. For the data to be sustainable and reusable, it must adhere to the “FAIR Data Principles”, meaning it should be “Findable, Accessible, Interoperable, and Re-usable”.

The FAIR principles will be a cornerstone of the European Open Science Cloud (EOSC) implementation. Nowadays, Data Management Plans (DMPs) have become a requirement for project applications in Horizon Europe and are increasingly being demanded by various funding organizations, including the German Science Foundation (DFG), among others.  

The scientific consortium NFDI-MatWerk (National Research Data Infrastructure) focuses on the research area Materials Science & Materials Engineering (MSE). The central challenge is the digital representation of materials and their relevant process and load parameters. NFDI-MatWerk will provide in this summer school tailored information and solutions for dealing with research data in MSE.

Who can apply?

PhD-students and Post-Docs working in experimental and/or modeling/simulation in any field of materials science and engineering (with or without experience in RDM). Advanced Master Students are also welcome to apply.

The Summer School will take place:

📅 From Monday September 18th to Friday 22th, 2023

📍 At Graduate Center, Building C 9.3, Saarland University, Saarbrücken

Contents of the summer school
  • Introduction to FAIR principles
  • Introduction to programming language Python
  • Designing of a Research Data Management Plan for the own PhD
  • Introduction to Electronic Lab Notebooks and demonstrations-workshop
  • Fundamentals of scientific metadata: relationship between (digital) research data, metadata and knowledge, importance of metadata in today’s research, technologies and concepts related to structured machine-readable metadata
  • Best practice examples of application of FAIR principles in MSE
Target groups and required background

PhD-students and Post-Docs working in experimental and/or modeling/simulation in any field of materials science and engineering (with or without experience in RDM), maximum 20 places will be available. Basic knowledge of programming, for instance in Python, MatLab or similar programing languages would be beneficial. Participants should bring their own laptop.


Monday September 18th

09:30  – 10:00 Registration

10:00 – 11:00     Introduction of the summer school, FAIR-Principles, F. Mücklich

11:00 – 12:00     Presentation of the participants – Part I (5 minutes each, 3 slides):

  • Personal information: Who am I? What is my background? Where did I do my studies?
  • What is my current research topic?
  • What kind of data do I have? How do I deal with my data? What is my knowledge about research data management and FAIR principles? What do I expect from the summer school?

12:00  – 13:30     Lunch break (Mensa)

13:30 – 14:30     Presentation of the participants – Part II

14:30 – 15:00     Coffee break

15:00 – 17:00     Introduction to Python I, T. Dahmen

19:00 – Dinner (Stiefel Bräu)

Tuesday, September 19th:

9:00 – 10:30       Data Management Plan I, K. Grünwald

10:30 – 11:00     Coffee break

11:00 – 12:00     Data Management Plan II, K. Grünwald

12:00 – 13:30     Lunch break (Mensa)

13:30 – 15:00     ELNs – Introduction, different approaches, selection of ELNs, S. Brinckmann

15:00 – 15:30     Coffee break

15:30 – 17:00     Lab visits: DFKI, T. Dahmen

Wednesday, September 20th

9:00 – 10:30       Introduction to Python II, T. Dahmen

10:30 – 11:00     Coffee break

11:00 – 12:00     Introduction to Python III, T. Dahmen

12:00 – 13:30     Lunch break (Mensa)

13:30 – 15:00      Introduction to Python IV, T. Dahmen

15:00 – 15:30     Coffee Break

15:30 – 17:00    Lab visits: MECS, D. Britz, F. Soldera

17:30 – Get together (Building D3.3) – Pizza and Beer

Thursday, September 21st

9:30 – 11:30       ELNs – Demonstration, N. Carpi

11:30 – 13:00     Lunch break (Mensa)

13:00 – 17:00     Fundamentals of scientific metadata I, A. Azocar Guzman, A. Pirogov, S. Gerlich

Friday, September 22nd

9:00 – 12:30       Fundamentals of scientific metadata II, A. Azocar Guzman, A. Pirogov, S. Gerlich

12:30 – 13:30     Lunch break (Buffet in Campus Center)

13:30 – 15:00     Example of concrete application in MSE, U. Kerzel, P. Mathews

15:00      END

Referents and organizers
  • Introduction: F. Mücklich (Saarland University)
  • Meta Data: Abril Guzman, Silke Gerlich, Anton Pirogov (Forschungszentrum Jülich GmbH)
  • Python: T. Dahmen (DFKI Saarbrücken)
  • ELN – Steffen Brinkman (Forschungszentrum Jülich GmbH), Nicolas Carpi (eLabFTW)
  • Research Data Management Plan – Katharina Grünwald (RWTH University Aachen)
  • Example of a concrete applications in MSE – Ulrich Kerzel (RWTH University Aachen), Prince Mathews (Max Planck Institute for Iron Research)
  • Institute visit – DFKI: Tim Dahmen (DFKI, Saarbrücken)
  • Institute visit – MECS: D. Britz (MECS, Saarbrücken)
  • Organisation: F. Mücklich, F. Soldera, C. Pauly, Pranav Nayak (Saarland University)
Expected Learning Outcomes

Introduction – FAIR Principles

  • Can recognize and define the FAIR principles.
  • Can explain and interpret the FAIR principles.
  • Can apply the FAIR principles.

Data management plan

  • Describe the purpose of a data management plan (DMP) in relation to FAIR.
  • Create a DMP for their research project.
  • Differentiate between DMP and other project documents.
  • Utilize tools, guides, templates, and resources for DMP creation.

Meta data

  • Understand differences between and importance of data and metadata
  • Annotate research data with structured metadata
  • Find and evaluate suitable metadata frameworks
  • Use basic Markdown / JSON / XML
  • Apply suitable tools for metadata annotation
  • Understand importance of structured metadata and its benefits for overall scientific visibility
  • Get to know the basic concepts of PIDs, linked data and the semantic web


  • Understands the necessity of using ELNs
  • Identify key features of ELNs
  • Identify personal requirements for an ELN
Online Application
Information for speakers

The participation is free of charge.
The event will take place in English.
In case of further inquiries please contact: