Data Train Advanced

This module is specifically engineered as a foundational component to establish the requisite competencies in Data Literacy and Research Data Management (RDM). It systematically introduces participants¡ªregardless of disciplinary background¡ªto the critical concepts underlying contemporary data governance and utilization. The curriculum addresses the fundamental structure of RDM, key principles such as FAIR data, and the essential introductory concepts of Data Science. Furthermore, a significant focus is placed on the Ethical, Legal, and Social Aspects (ELSA) of data handling, fostering a critical and responsible approach to data use, sharing, and privacy considerations. Upon completion, participants will possess the essential skills to manage, document, and interpret data, thereby recognizing the pervasive importance of robust data practices in modern research and industry.

Main module contents:

1. Research Data Management (RDM) Basics
2. Introduction to Data Science
3. Ethical, Legal and Social Aspects of Data Handling

Upon successful completion of this module, participants will be able to: 

• Understand fundamental concepts of research data management (RDM) and data science.
• Identify key principles such as FAIR data and ethical data handling.
• Recognize the importance of data literacy in modern research and industry applications.
• Apply basic RDM practices such as data documentation, metadata standards, and version control.
• Develop a critical approach to data use, sharing, and privacy considerations. 

Focus: Fundamentals of data literacy, research data management (RDM), and introductory data science concepts.
Target Group: Suitable for participants of all disciplines, no prior knowledge required.
Objective: Equip participants with essential data skills to manage, interpret, and responsibly use data.
Assessment: Own presentation (e.g., Poster, Talk) at an RDM-related event organized by the U Bremen Research Alliance. Will be specified at the beginning of the module (pass/fail).
Offered: Annually Jan-June

Module Speaker: Prof. Dr. Iris Pigeot - Faculty 3 Mathematics and Computer Science, Leibniz Institute for Prevention Research and Epidemiology (BIPS)

> Apply here

This module transitions directly from theory into hands-on Data Stewardship application. Designed for those who have mastered the fundamentals of Module 1, the focus is squarely on implementation: turning RDM best practices into routine operations. Participants will develop practical technical and organizational skills, including data management and version control using basic programming, ensuring research reproducibility and effective data management.

Main module contents:

This module aims to equip participants with essential data skills to manage and responsibly use data. It focuses on hands-on application of data stewardship methods.

Upon successful completion of this module, participants will be able to: 

• Apply hands-on skills in data stewardship.
• Implement RDM best practices, including metadata management and reproducibility.
• Manage data and version control using basic programming techniques.
• Work collaboratively in data-focused projects and communicate findings effectively.

Focus: Hands-on application of research data management methods.
Target Group: Participants who completed Module 1.
Objective: Provide practical training in Data Stewardship, ensuring participants develop technical and organizational skills.
Assessment: RDM application in ([Peer-] Reviewed) publication (e.g. DMP, preregistration, dataset, etc.; pass/fail)
Offered: Every second year, July-Dec (even years)

Module Speaker: Prof. Dr. Frank Oliver Gl?ckner - Faculty 5 Geosciences, Alfred Wegener Institute, Helmholtz Center for Polar and Marine Research (AWI), PANGAEA

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This module is an intensive, application-focused component designed to transition foundational data literacy into practical Data Science proficiency. Building upon the knowledge base established in Module 1, the curriculum is centered on developing technical and analytical skills. Participants will engage in hands-on data analysis utilizing machine learning methods, learning to robustly visualize and interpret data to derive meaningful results. The core objective is to ensure proficiency in the execution of data science methods and the effective communication of analytical findings.

Main module contents:

This module aims to equip participants with essential data skills to analyze, interpret, and responsibly use data. It focuses on hands-on application of data science methods.

Upon successful completion of this module, participants will be able to: 

• Apply hands-on skills in data science.
• Conduct data analysis using basic programming techniques.
• Visualize and interpret data to support decision-making.
• Work collaboratively in data-focused projects and communicate findings effectively.

Focus: Hands-on application of data science methods.
Target Group: Participants who completed Module 1.
Objective: Provide practical training in Data Science, ensuring participants develop technical and organizational skills.
Assessment: Data Science application in ([Peer-] Reviewed) publication (e.g. original research, code, AI model, toolbox, etc.; pass/fail)
Offered: Every second year, July-Dec (odd years)

Module Speaker: Prof. Dr. Rolf Drechsler - Faculty 3 Mathematics and Computer Science, German Research Center for Artificial Intelligence (DFKI)

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This module represents a comprehensive synthesis of technical and organizational expertise, providing integrated, hands-on training in both Data Stewardship and Data Science applications. Designed for participants who have completed Module 1, the curriculum is focused on operationalizing the entire data lifecycle. Participants will master RDM best practices¡ªincluding reproducibility and data management ¡ª while simultaneously gaining proficiency in data analysis, programming, and visualization. This dual focus ensures the development of highly capable research professionals who can responsibly manage and interpret data projects with confidence.

Main module contents:

This module aims to equip participants with essential data skills to manage, interpret, and responsibly use data. It focuses on hands-on application of data stewardship and data science methods.

Upon successful completion of this module, participants will be able to:

• Apply hands-on skills in data stewardship and data science.
• Implement RDM best practices, including metadata management and reproducibility.
• Manage data and version control using basic programming techniques.
• Conduct data analysis using basic programming techniques.
• Visualize and interpret data to support decision-making.
• Work collaboratively in data-focused projects and communicate findings effectively.

Focus: Hands-on application of research data management and data science methods.
Target Group: Participants who completed Module 1.
Objective: Provide practical training in both Data Stewardship & Data Science, ensuring participants develop technical and organizational skills.
Assessment: Application of RDM and Data Science in ([Peer-] Reviewed) publication (pass/fail)
Offered: Annually, July-Dec

Module Speaker: Prof. Dr. Iris Pigeot - Faculty 3 Mathematics and Computer Science, Leibniz Institute for Prevention Research and Epidemiology (BIPS)

> Apply here