NFDI-MatWerk / AMASE Spring School 2024 – 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.

This activity will be Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under the National Research Data Infrastructure – NFDI 38/1 – project number 460247524 and by the Erasmus+ Programme of the European Union (AMASE – EMJMD GA 619784).

The Spring School will take place:

📅 From April 10th – 12th 2024

📍 Saarland University, Campus E1.7, 66123 Saarbrücken

Contents of the Spring School
  • Introduction to FAIR principles
  • 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
  • Introduction to Electronic Lab Notebooks (ELNs)
  • Designing of a Research Data Management Plan (RDMP)
  • Best practice examples in Materials Science and Engineering
Target groups and required background

Master students of the AMASE program, doing or starting their master thesis in experimental and/or modeling/simulation in 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.

Application

Online application providing a transcript of records of the AMASE programe and a short motivation letter (max 1 page) indicating topic of master thesis, current situation for dealing with research data and expectations for the summer school, as well as information about which operating system they use (Windows, Linux, Mac). The 20 candidates will be selected based on their performance in AMASE and the reasons for participating.

Conditions

EUSMAT will cover the accommodation of AMASE Students (if needed) in Saarbrücken, some meals, as well as part of the travel costs according to the following figures: up to 300 € for students from LTU, 200€ for students from MUL, 200 € for students from UPC and UNIPD, 60 € for students from UL. Only real costs up to the mentioned values and upon presenting the tickets can be reimbursed. Participants will receive a Certificate of Participation and can be awarded up to 1 CP for the Transversal Skills courses. Participants will have to fulfill a questionnaire for evaluating the event.

Tentative program

Tuesday, April 9th

Arrival at the hotel in Saarbücken, Germany.

Wednesday, April 10th

09:00  – 09:15 Registration

09:15 – 10:00     Introduction of the summer school, FAIR-Principles

10:00 – 10:30 Coffee break

10:30 – 12:00      Fundamentals of scientific metadata I

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

13:30 – 15:00      Fundamentals of scientific metadata II

15:00 – 15:30      Coffee break

15:30 – 17:00      Introduction to Python

19:00 – Dinner (Stiefel Bräu)

Thursday, April 11th

09:00 – 10:30      Fundamentals of scientific metadata III

10:30 – 11:00      Coffee break

11:00 – 12:00      Fundamentals of scientific metadata IV

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

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

15:00 – 15:30      Coffee break

15:30 – 16:30      ELN – Demonstration of examples           

18:30 – …            Bowling and dinner (Bowling arena)

Friday, April 12th

9:00 – 10:30        Data Management Plan I

10:30 – 11:00      Coffee break

11:00 – 12:00      Data Management Plan II

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

13:30 – 15:00       Best practice examples in materials science and engineering

15:00 – END

Referents
  • Introduction: F. Mücklich
  • Meta Data: Silke Gerlich, Forschungszentrum Jülich, Pranav Nayak, Christoph Pauly, Saarland University
  • ELN: Hanna Tsybenko, Forschungszentrum Jülich
  • Research Data Management Plan: Katharina Grünwald, RWTH Aachen
  • Best practice examples in MSE: Gargi Shankar Nayak, Saarland University
Contents and expected outcomes

Introduction – FAIR Principles

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

Fundamentals of Scientific Metadata for Materials Scientists

Have you ever struggled to make sense of scientific data provided by a collaborator – or even understanding your own data 5 months after publication? Do you see difficulties in meeting the data description requirements of your funding agency? Do you want your data to have lasting value, but don’t know how to ensure that?

Precise and structured description of research data is key for scientific exchange and progress – and also for the recognition of your effort in data collection. The solution: make your data findable, accessible, interoperable and reusable by describing them with metadata.

You will learn:

  • about the differences between and the importance of data & metadata
  • to annotate your research data with structured metadata
  • to find and evaluate a suitable metadata framework and data repository
  • to use basic Markdown / JSON / XML
  • which tools are already available to level up your metadata annotation game
  • why structured metadata is important and how it can increase your scientific visibility.

Electronic Lab Notebooks ELNs: your digital assistants in materials research

Using electronic laboratory notebooks (ELNs) is a standard practice in industrial labs, and now, more and more labs in academia have switched to them, too! Instead of writing protocols and notes by hand, with ELNs, you can document your work digitally, link those records to scientific data, and keep it all stored on a single centralized platform. Moreover, ELNs include features to search, visualize, analyze, export, and share data and metadata. As such, research workflows become much more transparent and reproducible, and scientists spend much less time and effort on their documentation. Do your research smarter, not harder!

  • Challenges in paper laboratory notebook-keeping
  • The need for digital documentation in modern research
  • Specific needs for data management in materials science
  • ELNs and their key features
  • Making the data more FAIR with the use of ELNs
  • Practical tips for selecting the right ELN
  • Best practices for keeping track of data and metadata in ELNs

The second session is a live demonstration of PASTA-ELN, a highly adaptive ELN for experimental materials science. Together, we will explore its key features and create our example project to learn how research workflows can be streamlined using ELNs.

Data Management Plans (DMP)

In the section „Data Management Plans (DMP)“ participants will learn how to approach a research project while considering research data management and the given infrastructure at their institution before they start a new project. Participants have to create a „data map“ of their research group / institution. Afterwards they will get an overview about Data Management Plans in general as well as about different tools (like RDMO, Data Steward Wizzard, etc.). Long term goal for the participants and for the NFDI-MatWerk consortium is to raise the awareness within the research community for RDM before a project starts. In the second part of the section participants will fill out a DMP for their project, discuss the outcome and benefits and issues they see with the tool.

Participants should be able to create a DMP autonomously and realize the benefit of planning and thinking beforehand.

APPLY HERE!

Application to NFDI-MatWerk Spring School

  • Max. file size: 2 MB.
    (Only PDF are accepted – Max. size: 2MB)
  • Max. file size: 2 MB.
    (Only PDF are accepted, max. 1 page – Max. size: 2MB) Indicate topic of PhD, current situation for dealing with research data and expectations for the summer school.
Tentative program, further details will be informed soon.

IMPORTANT!
The participation is free of charge.
The event will take place in English.
In case of further inquiries please contact: office@eusmat.net

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