Research Data Management in Life Sciences
BIO-649 / 2 crédits
Enseignant(s): Argento Nicolas, Dornier Rémy Jean Daniel, Kubilay Dilara Selin, Tremblay Christopher Claude Giuseppe, Varrato Francesco
Langue: Anglais
Remark: Bring your laptop to all sessions. We will also have an in-depth hands-on practical session on git, install git on your machine before the course and make sure it works (https://git-scm.com/install/)
Frequency
Every year
Summary
Introduction to Research Data Management in Life Sciences, covering data organization, documentation, workflows, publication, FAIR principles, tools, and best practices. Includes hands-on activities to improve and optimize participants' own data management workflows (used for evaluation)
Content
DAY 1: Foundations of Research Data Management
Main scope: Understand the core principles, tools, and best practices of Research Data Management (RDM) in Life Sciences research.
- Introduction to the course and Research Data Management (RDM) in Life Sciences
- Overview of the research data lifecycle and FAIR principles
- Introduction to ELNs, LIMS, workflows, SOPs, and laboratory data organization
- Examples of research workflows from laboratories and scientific platforms
- Identification of data-related activities in participants' own research projects
- Collaborative tools for research data documentation
- Best practices for data organization and documentation (metadata, file naming, READMEs, etc.)
- Introduction to Data Management Plans (DMPs) and strategic RDM practices
DAY 2: Data Publication and Workflow Mapping
Main scope: Learn how to publish, organize, and map research data workflows through practical hands-on activities.
- Introduction to research data publication and sharing practices
- Introduction to Git and version control concepts
- Hands-on workshop on publishing datasets to repositories
- Curation considerations and best practices for data publication
- Mapping and analyzing participants' own research data workflows
- Creation of workflow schemas describing data creation, storage, processing, and archiving
DAY 3: Improving and Sustaining RDM Practices
Main scope: Develop practical strategies to optimize, document, and sustain good RDM practices within research projects and teams.
- Introduction to anonymisation and practical considerations for Life Sciences research
- OMERO workshop and image/data management concepts
- Hands-on improvement of participants' research data workflows
- Development of a short RDM improvement plan/report for participants' own projects
- Identification of challenges, improvement areas, and implementation strategies for better RDM practices
- Course wrap-up, discussion, and feedback session
Note
The course is primarily intended for life science students; PhD students from other doctoral programs may also benefit from it.
Should you have any problem with the installation of git (https://git-scm.com/install/), please contact helpdesk.sv@epfl.ch and put "Git installation problem - SV EDMS Course" and you will get assistance.
Keywords
RDM, Data, Dataset, Software, Code, FAIR Data Principles, ELN, Open Science, Repositories, Metadata
Learning Prerequisites
Required courses
No learning prerequisites
Learning Outcomes
By the end of the course, the student must be able to:
- Define the data life cycle of their research
- Apply good research data management (RDM) practices
- Identify and understand the services available for data management
- Use efficiently research data management tools available to them during their research
Assessment methods
Written
Dans les plans d'études
- Nombre de places: 15
- Forme de l'examen: Ecrit (session libre)
- Matière examinée: Research Data Management in Life Sciences
- Cours: 15 Heure(s)
- Exercices: 10 Heure(s)
- Projet: 10 Heure(s)
- TP: 4 Heure(s)
- Type: optionnel