Hands-on with Research Data Management in Chemistry

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