The Carpentries Workshop: Introduction to the Unix Shell, Git, and GitLab

The DKZ.2R presents, as a part of our “Trainings” work package, a Carpentries Workshop on the 18th and 22nd of November on the topic of “Introduction to the Unix Shell, Git, and GitLab”. This is an official Carpentries Workshop and will be hosted on-site at RWTH Aachen University.

Workshop material is available online and will be presented by official Carpentries instructors, who will guide you through the concepts with the help of hands-on exercises and personalized support. The course is designed for beginners and is open to participants from all domains. No prior knowledge is required. If you are interested in taking part in the workshop, you can sign up here.

Seminar - Statistical and Machine Learning Methods

Course Description and Goals:

Statistical learning is a field that teaches students how to analyze and interpret data by applying statistical methods and machine learning algorithms to uncover patterns, make predictions, and gain insights from data. The syllabus includes:

• Statistical and machine learning methods, including linear and polynomial regression, logistic regression, and linear discriminant analysis.

• Model validation techniques such as cross-validation and bootstrap, model selection, and regularization methods (ridge and lasso).

Spring School 2025 in Aachen – Research Data Management in Materials Science and Engineering

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.