Hands-on with Research Data Management in Chemistry
ChE-601 / 1 credit
Teacher(s): Borel Alain, Gabella Chiara, Varrato Francesco
Language: English
Remark: Next time Winter 24 (block)
Frequency
Every year
Summary
PhD students in Chemistry will learn hands-on Research Data Management (RDM) skills transferable to their research practices. They will contextualize their research into RDM best practices (day 1), discover appropriate tools (day 2) and work on a project (day 3) for the course accreditation
Content
DAY 1: RDM GOOD PRACTICES & EPFL SOLUTIONS
Main scope: PhD students will contextualize their current lab RDM practices in light of FAIR principles
- Contextualize the FAIR data principles in the chemical research field
- Discover the SNSF DMP as a guideline
- Differentiate between raw data, processed data and code
- Compare ELNs and other collaborative solutions
- Collaborative tools:
--- Collaborative writing tools (Authorea, Overleaf, HackMD, ...)
--- Electronic Lab Notebooks (EPFL ELN, SLIMs, OpenBis, ...)
--- Cloud storage solutions (Switch, EPFL GDrive, OwnCloud, ...)
- Data organization, file naming and documentation
- Discover metadata for research data
DAY 2: TOOLS HANDS-ON
Main scope: PhD students will discover software and platforms to improve their current RDM practices
- Data formats, exporting & conversion
- Differentiate between storage, back-up and preservation solutions
- Data reuse:
--- Discover the re3data.org
--- Data access & re-use from data repositories
- Versioning:
--- Git
- Data manipulation
--- Dataviz for publication
--- Open tools for data analysis
--- Data formats converters
Practical session: PhD students will model and present their current practices and workflows involving research data
DAY 3: PROJECT
Main scope: PhD students will discover further tools and concepts to plan their RDM activities and improve their research workflows
- Dealing with sensitive data, proprietary data and licensing
- Data publishing via data repositories, data archiving
- Computational chemistry workflows and tools
Practical session: PhD students will refine their workflow models and present them for peer-assessment and evaluation
- Pitch the RDM aspects of the research project
- Describe data generation & reuse
- Select relevant and applicable solutions for their project, such as:
--- storage & collaborative tools
--- documentation & metadata standards
--- repositories for data publication and archiving
Learning Outcomes
By the end of the course, the student must be able to:
- Define Data Life-Cycle of his/her research
- Identify Specific softwares
- Apply RDM good practices
In the programs
- Number of places: 15
- Exam form: Project report (session free)
- Subject examined: Hands-on with Research Data Management in Chemistry
- Lecture: 12 Hour(s)
- Exercises: 3 Hour(s)
- Practical work: 9 Hour(s)