ChE-601 / 1 crédit

Enseignant(s): Borel Alain, Gabella Chiara, Varrato Francesco

Langue: Anglais

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

Resources

Moodle Link

Dans les plans d'études

  • Nombre de places: 15
  • Forme de l'examen: Rapport de TP (session libre)
  • Matière examinée: Hands-on with Research Data Management in Chemistry
  • Cours: 12 Heure(s)
  • Exercices: 3 Heure(s)
  • TP: 9 Heure(s)
  • Type: optionnel

Semaine de référence

Cours connexes

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