FBM Doctoral School Courses

Workshop: “From Bench to FAIR Data: Practical Approaches to Research Data Management in Life Science and Clinical Research”

• Post date22 October 2025
• Post categoriesIn Courses

Course Structure

The course is organized into three sessions: two full-day in-person workshops (Days 1 and 3) and one self-paced practical session in between.

The in-person sessions are divided into four modules that combine theory with practical exercises using real data examples and institutional tools.

During the first three modules, all participants will work together to cover the organization and management of research data, the creation of clear documentation and standardized metadata using institutional tools, and the principles of FAIR data sharing and long-term preservation.

The fourth module will take place in two parallel sessions:

• The first group will include participants working with sensitive or clinical data, focusing on de-identification, restricted data sharing, and confidentiality management.
• The second group will target participants working in life science research, deepening their understanding of FAIR data sharing in specialized repositories (e.g., ENA, BIA) and the use of metadata standards specific to life science data.

Between the two in-person workshop days, participants will complete a self-paced practical exercise at home, applying the concepts learned to their own datasets. This work will then be discussed and consolidated during the final in-person sessions

Overview

The exponential growth of research data in life sciences and clinical research has made it crucial to implement robust Research Data Management (RDM) strategies based on Open Research Data practices.

Applying the FAIR principles (Findable, Accessible, Interoperable and Reusable) brings major benefits: improving data visibility, reproducibility, reusability, and credibility, while enabling new scientific collaborations and research questions.

This two-day theoretical and hands-on workshop, co-organized by the Data Stewardship BioMed Unit (DSBU) and the FBM Doctoral School, offers a practical and comprehensive introduction to RDM and the FAIR principles in the context of life sciences and clinical research.

Through a mix of lectures and guided exercises, participants will learn to manage their data according to FAIR and reproducible research standards and to use institutional and open-source tools to support these practices.

Target Audience

This workshop is reserved for members of the Faculty of Biology and Medicine (FBM) at the University of Lausanne (UNIL) and the University Hospital of Lausanne (CHUV).

• PhD students in life sciences or clinical research handling digital research data
• Postdoctoral researchers and data managers, including those fulfilling data management roles in research laboratories
• For PIs, shorter and tailored training sessions are organized separately for their research groups, focusing on the specific data types and research themes of individual groups. PIs interested in customized workshops related to their own research are encouraged to contact us.

Learning Outcomes

By the end of the course, participants will have acquired both theoretical and practical skills to implement FAIR and reproducible Research Data Management (RDM) practices throughout the research lifecycle.

Participants will learn to:

• Organize research data efficiently and select appropriate file formats for FAIR sharing and long-term preservation
• Develop and update Data Management Plans (DMPs) using the Data Stewardship Wizard (DSW)
• Document datasets with README files and metadata using DataSquid^@DSBU for semi-automated data documentation
• Integrate and harmonize metadata across systems and deposit datasets in FAIR-compliant repositories (e.g., FBM Zenodo Community, ENA for sequencing data, BIA for imaging data, or the Horus Dataset Catalog CHUV for sensitive clinical data)
• Assess and mitigate risks for sensitive data, applying appropriate security and de-identification measures, and ensuring compliance with ethical and legal standards
• Understand the principles of long-term data preservation via tape-based archiving solutions

Course Structure

Each module combines a theoretical introduction with hands-on exercises using real data examples and institutional tools.

The course is structured over three main sessions with two in person (Days 1 and 3) and one self-paced practical session in between.

Day 1 – Thursday, 27 November 2025 (Full day from 9:15 to 17:30, in person)

Module I: Data Types & Organization

Participants will be introduced to best practices in data and file management across the research lifecycle.

Topics include:

• Data entry validation, folder structure, file naming and file formats
• Selecting sustainable file formats for sharing and preservation
• Creating personalized DMP using Data Stewardship Wizard (DSW) tool

Module II: Data Documentation

During this module, participants will enhance their data documentation skills through metadata and README files, using tools to facilitate efficient data organization, storage, retrieval, and sharing.

Participants will learn how to:

• apply metadata standards
• create structured README files using DataSquid^@DSBU 

Day 2 – Independent work (self-paced practicals)

Between the two in-person sessions, participants will dedicate half a day to practicing with Data Stewardship Wizard (DSW) tool and DataSquid^@DSBUon their own research data.

This independent work, carried out on the participants’ own time, allows them to apply concepts introduced during the first day.

Questions and issues raised during this time will be addressed collectively in the final session through demonstrations and targeted feedback.

Day 3 – Thursday, 4 December 2025 (Full day from 9:15 to 17:30, in person)

Module III: Metadata Integration and FAIR Repositories

This module focuses on the practical application of FAIR data-sharing workflows, from metadata integration and documentation to dataset deposition and publication.

Participants will be introduced to long-term data preservation and archiving.

Through demonstrations and hands-on exercises, participants will practice how to:

• Identify the right repository for their research data and prepare and structure metadata for deposition using FAIRShare Explorer
• Upload, describe, and publish datasets on specialized repositories such as FBM Zenodo Community, ENA and BioImage Archive.
• Reuse, copyright, and license data.
• Ensure secure long-term preservation through tape-based archiving.

Module IV

Two parallel sessions will take place during this module. The first targets participants handling sensitive data, while the second will be for researchers in Life Sciences to practice depositing data on specialized repositories.

Module IV – Orientation Data Protection for Researchers handling Personal and Sensitive Data

The Data protection module will provide practical guidance on managing and sharing sensitive data in compliance with ethical, legal, and institutional frameworks.

Participants will learn to:

• Assess data sensitivity and apply risk mitigation strategies
• Use de-identification techniques to enable secure data reuse
• Metadata sharing for sensitive datasets using the HORUS Dataset Catalog (CHUV) developed by the Data Science Group – DSI.

Module IV – Orientation Life Science for Researchers at UNIL

The Life science module will provide deepen practices of FAIR data sharing within specialized repositories (e.g., ENA, BioImage Archive).

Participants will gain hands-on experience in:

• Depositing sequencing and imaging data in repositories such as ENA and BioImage Archive, using either participants’ own data or a provided dataset
• Exploring DataSquid and its integration within UNIL’s DCSR infrastructures, with a deeper practical focus on data documentation for short-term and long-term storage (LTS), to preserve, secure, and safeguard their research data

Evaluation and Credits for PhD Students

1 ECTS credit awarded upon:

• Active participation during the workshop
• Short presentation of practical outcomes
• Completion of the independent work (no final written exam)

Teachers

• Cécile Lebrand, PhD, Head of the DSBU, FBM UNIL–CHUV
• Vassilios Ioannidis, PhD, Lead Computational Biologist – FAIR Data Specialist, DSBU, FBM UNIL–CHUV
• Julien Dénéréaz, PhD, Biomedical Data Scientist, DSBU, FBM UNIL–CHUV

Requirements

• Participants must bring their own laptop.
• They should also come with a dataset from their own research that needs to be documented, as well as a clear understanding of the instruments, software, and data acquisition workflows used in their laboratory.
• This will allow participants to apply the concepts and tools presented during the workshop directly to their own research context.

References & Resources

• Wilkinson, M. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). DOI: 10.1038/sdata.2016.18
• Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016). DOI: 10.1038/533452a
• Begley, C.G. & Ioannidis, J.P.A. Reproducibility in science: improving the standard for basic and preclinical research. Circ. Res. 116(1):116–126 (2015). DOI: 10.1161/CIRCRESAHA.114.303819
• DSBU Website: Link
• FAIR Data Sharing & Open Research Data Support for FBM UNIL-CHUV Researchers
• RDMkit – The ELIXIR Research Data Management Kit
• DataSquid© – FBM Tool for Metadata & README Files
• FBM Zenodo Community

Registration

Registration via the DSBU contact form

Participation is free of charge.

FBM PhD course open to Postdoctoral researchers and data managers

Co-organized by the DSBU, University Center for Primary Care and Public  and the FBM Doctoral School

Thursday 27 November & Thursday 4 December 2025
 – In-person sessions

From 9:15 to 17:30

Room 2144, Géopolis Building, Quartier Mouline – University of Lausanne (Metro M1 line, Mouline station)

Course Structure

The course is organized into three sessions: two full-day in-person workshops (Days 1 and 3) and one self-paced practical session in between.

The in-person sessions are divided into four modules that combine theory with practical exercises using real data examples and institutional tools.

During the first three modules, all participants will work together to cover the organization and management of research data, the creation of clear documentation and standardized metadata using institutional tools, and the principles of FAIR data sharing and long-term preservation.

The fourth module will take place in two parallel sessions:

• The first group will include participants working with sensitive or clinical data, focusing on de-identification, restricted data sharing, and confidentiality management.
• The second group will target participants working in life science research, deepening their understanding of FAIR data sharing in specialized repositories (e.g., ENA, BIA) and the use of metadata standards specific to life science data.

Between the two in-person workshop days, participants will complete a self-paced practical exercise at home, applying the concepts learned to their own datasets. This work will then be discussed and consolidated during the final in-person sessions

Overview

The exponential growth of research data in life sciences and clinical research has made it crucial to implement robust Research Data Management (RDM) strategies based on Open Research Data practices.

Applying the FAIR principles (Findable, Accessible, Interoperable and Reusable) brings major benefits: improving data visibility, reproducibility, reusability, and credibility, while enabling new scientific collaborations and research questions.

This two-day theoretical and hands-on workshop, co-organized by the Data Stewardship BioMed Unit (DSBU) and the FBM Doctoral School, offers a practical and comprehensive introduction to RDM and the FAIR principles in the context of life sciences and clinical research.

Through a mix of lectures and guided exercises, participants will learn to manage their data according to FAIR and reproducible research standards and to use institutional and open-source tools to support these practices.

Target Audience

This workshop is reserved for members of the Faculty of Biology and Medicine (FBM) at the University of Lausanne (UNIL) and the University Hospital of Lausanne (CHUV).

• PhD students in life sciences or clinical research handling digital research data
• Postdoctoral researchers and data managers, including those fulfilling data management roles in research laboratories
• For PIs, shorter and tailored training sessions are organized separately for their research groups, focusing on the specific data types and research themes of individual groups. PIs interested in customized workshops related to their own research are encouraged to contact us.

Learning Outcomes

By the end of the course, participants will have acquired both theoretical and practical skills to implement FAIR and reproducible Research Data Management (RDM) practices throughout the research lifecycle.

Participants will learn to:

• Organize research data efficiently and select appropriate file formats for FAIR sharing and long-term preservation
• Develop and update Data Management Plans (DMPs) using the Data Stewardship Wizard (DSW)
• Document datasets with README files and metadata using DataSquid^@DSBU for semi-automated data documentation
• Integrate and harmonize metadata across systems and deposit datasets in FAIR-compliant repositories (e.g., FBM Zenodo Community, ENA for sequencing data, BIA for imaging data, or the Horus Dataset Catalog CHUV for sensitive clinical data)
• Assess and mitigate risks for sensitive data, applying appropriate security and de-identification measures, and ensuring compliance with ethical and legal standards
• Understand the principles of long-term data preservation via tape-based archiving solutions

Course Structure

Each module combines a theoretical introduction with hands-on exercises using real data examples and institutional tools.

The course is structured over three main sessions with two in person (Days 1 and 3) and one self-paced practical session in between.

Day 1 – Thursday, 27 November 2025 (Full day from 9:15 to 17:30, in person)

Module I: Data Types & Organization

Participants will be introduced to best practices in data and file management across the research lifecycle.

Topics include:

• Data entry validation, folder structure, file naming and file formats
• Selecting sustainable file formats for sharing and preservation
• Creating personalized DMP using Data Stewardship Wizard (DSW) tool

Module II: Data Documentation

During this module, participants will enhance their data documentation skills through metadata and README files, using tools to facilitate efficient data organization, storage, retrieval, and sharing.

Participants will learn how to:

• apply metadata standards
• create structured README files using DataSquid^@DSBU 

Day 2 – Independent work (self-paced practicals)

Between the two in-person sessions, participants will dedicate half a day to practicing with Data Stewardship Wizard (DSW) tool and DataSquid^@DSBUon their own research data.

This independent work, carried out on the participants’ own time, allows them to apply concepts introduced during the first day.

Questions and issues raised during this time will be addressed collectively in the final session through demonstrations and targeted feedback.

Day 3 – Thursday, 4 December 2025 (Full day from 9:15 to 17:30, in person)

Module III: Metadata Integration and FAIR Repositories

This module focuses on the practical application of FAIR data-sharing workflows, from metadata integration and documentation to dataset deposition and publication.

Participants will be introduced to long-term data preservation and archiving.

Through demonstrations and hands-on exercises, participants will practice how to:

• Identify the right repository for their research data and prepare and structure metadata for deposition using FAIRShare Explorer
• Upload, describe, and publish datasets on specialized repositories such as FBM Zenodo Community, ENA and BioImage Archive.
• Reuse, copyright, and license data.
• Ensure secure long-term preservation through tape-based archiving.

Module IV

Two parallel sessions will take place during this module. The first targets participants handling sensitive data, while the second will be for researchers in Life Sciences to practice depositing data on specialized repositories.

Module IV – Orientation Data Protection for Researchers handling Personal and Sensitive Data

The Data protection module will provide practical guidance on managing and sharing sensitive data in compliance with ethical, legal, and institutional frameworks.

Participants will learn to:

• Assess data sensitivity and apply risk mitigation strategies
• Use de-identification techniques to enable secure data reuse
• Metadata sharing for sensitive datasets using the HORUS Dataset Catalog (CHUV) developed by the Data Science Group – DSI.

Module IV – Orientation Life Science for Researchers at UNIL

The Life science module will provide deepen practices of FAIR data sharing within specialized repositories (e.g., ENA, BioImage Archive).

Participants will gain hands-on experience in:

• Depositing sequencing and imaging data in repositories such as ENA and BioImage Archive, using either participants’ own data or a provided dataset
• Exploring DataSquid and its integration within UNIL’s DCSR infrastructures, with a deeper practical focus on data documentation for short-term and long-term storage (LTS), to preserve, secure, and safeguard their research data
  

Evaluation and Credits for PhD Students

1 ECTS credit awarded upon:

• Active participation during the workshop
• Short presentation of practical outcomes
• Completion of the independent work (no final written exam)

Teachers

• Cécile Lebrand, PhD, Head of the DSBU, FBM UNIL–CHUV
• Vassilios Ioannidis, PhD, Lead Computational Biologist – FAIR Data Specialist, DSBU, FBM UNIL–CHUV
• Stéphanie Battini, PhD, Biomedical Data Scientist, DSBU, FBM UNIL–CHUV
• Julien Dénéréaz, PhD, Biomedical Data Scientist, DSBU, FBM UNIL–CHUV
• Clara Heiman, PhD, Data Steward, DSBU, FBM UNIL–CHUV
• Céline Racine, Head of the Documentation and Data Unit & Research Data Management Specialist, Research Support Division, University Center for Primary Care and Public

Requirements

• Participants must bring their own laptop.
• They should also come with a dataset from their own research that needs to be documented, as well as a clear understanding of the instruments, software, and data acquisition workflows used in their laboratory.
• This will allow participants to apply the concepts and tools presented during the workshop directly to their own research context.

References & Resources

• Wilkinson, M. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). DOI: 10.1038/sdata.2016.18
• Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016). DOI: 10.1038/533452a
• Begley, C.G. & Ioannidis, J.P.A. Reproducibility in science: improving the standard for basic and preclinical research. Circ. Res. 116(1):116–126 (2015). DOI: 10.1161/CIRCRESAHA.114.303819
• DSBU Website: Link
• FAIR Data Sharing & Open Research Data Support for FBM UNIL-CHUV Researchers
• RDMkit – The ELIXIR Research Data Management Kit
• DataSquid© – FBM Tool for Metadata & README Files
• FBM Zenodo Community

Registration

Registration via the DSBU contact form

Participation is free of charge.

Science Integrity in biomedical sciences : an open science approach/Intégrité scientifique en science biomédicale : une approche par le prisme de la science ouverte – ED 29/Série 3

Faculté de gestion: Ecole doctorale (FBM-DOCT)

Responsable(s): Cécile Lebrand, Sandrine Pernier
Période de validité: 2023 ->

Pas d’horaire défini.

Cours

Annuel

Langue(s) d’enseignement: anglais
Public: Oui
Crédits: 1

Objectif

En suivant le cycle de vie de la recherche, cette formation pratique permet, par le prisme de l’Open Science, de prendre connaissance des éléments contribuants au développement d’une science intègre et transparente. Un focus particulier est donné sur les directives et ressources propres à la Faculté de Biologie et Médecine à l’UNIL et CHUV mais les principes sont transposables à d’autres facultés dans les mêmes discipline.

Contenu

5 modules avec différents intervenants:

1. Intégrité scientifique : définition et principes. Contexte de la Faculté de Biologie et Médecine à l’UNIL et CHUV.
2. Cycle de la recherche : rigueur et transparence
3. Diffusion des résultats, processus de publication
4. Valoriser son travail: devoirs et responsabilités
5. Discussion – Clôture du cours

Evaluation

Travail personnel de préparation : Oui
Présentation personnelle : Oui
Test final : Non
Evaluation de la participation par le tuteur : Oui

Formation en 3 temps :

Jour 1: journée complète en présentiel
Jour 2: demie journée de travail en autonomie
Jour 3: demie journée en présentiel

Bibliographie

Will be done with the set of documents before de course

Exigences du cursus d’études

Aucun

Conditions d’accès

Inscription auprès de l’Ecole doctorale. Série 3.