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• Setting up workflows, tools, and platforms to facilitate reproducible data analysis
• Implementing a (meta)data documentation standard relevant to your research domain
• Choosing among and working with data management tools available at TU Delft
• Scoping and trialling suitable solutions for (shared) data storage, access, back-ups, and collection
• Structuring and preparing data for deposition in a research data repository such as 4TU.ResearchData

• Discussing general guidelines for building and deploying research software
• Enhancing existing software through code and repository reviews, restructuring, modularisation, and applying best practices for quality control, extensibility, and maintainability
• Setting up software testing and automation
• Publishing, packaging, distribution, and release of software for reproducible research

• Working remotely with servers and virtual environments 
• Using HPC clusters and performing parallel computations
• Automating components of your research workflow related to reading, processing, and producing data for your analysis
• Compiling and running MPI-parallelized software
• Providing advice on HPC-compatible algorithms and applications
• Working with Linux/Bash

• Sharing and implementing best practices for documenting data and code
• Generating metadata based on discipline-specific standards
• Setting up reproducible and containerised environments

• Connecting you to departmental software developers and data managers
• Sharing information about the roles and requirements to create local Research Software Engineer or Data Manager capacity, and offering mentorship and guidance to the person in this role
• Helping you organize and work collaboratively with your research group using digital tools like GitLab

Check out the descriptions of some of our projects on the page with examples.