Making Your Research Data FAIR

Overview

The huge amount of generated research data has urged the scientific community to consider developing efficient Research Data Management Strategies with an “Open Research Data” philosophy and implementing robust Data Management Plans (DMP) for research projects. Making research data FAIR - Findable, Accessible, Interoperable and Reusable ¹ - provides many benefits, including to increase the visibility and to improve the reproducibility, reuse, and the confidence towards the data ^2-4, as well as to enable new research questions and collaborations.

This two-day workshop will provide you with the means to make your data FAIR through theoretical concepts and hands-on sessions. Please note that the module 4 will be optional, as it will focus specifically on sensitive data.

It will be given by researchers and professionals involved in Research Data Management at ELIXIR Switzerland, SIB/Vital-IT and FBM-UNIL/CHUV.

¹ Wilkinson, M., Dumontier, M., Aalbersberg, I. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). DOI: https://doi.org/10.1038/sdata.2016.18

² Baker, M. 1,500 scientists lift the lid on reproducibility. Nature 533, 452–454 (2016). DOI: https://doi.org/10.1038/533452a

³ Begley, C G, and Ioannidis, J. PA. “Reproducibility in science improving the standard for basic and preclinical research.” Circulation research. 2015; 116.1: 116-126. DOI: 10.1161/CIRCRESAHA.114.303819

⁴ Asher Mullard, “Preclinical cancer research suffers another reproducibility blow” Nature Reviews Drug Discovery 21, 89 (2022). DOI: https://doi.org/10.1038/d41573-022-00012-6

Audience

This workshop is addressed to scientists and clinicians in the biomedical field who are involved, at several possible levels, in Research Data Management and would like to know how to make data compliant with RDM good practices and the FAIR principles.

Learning outcomes

At the end of the course, the participants are expected to know:

how to optimize the organization of their data and choose the most suitable file formats,
what are ontologies, how to choose them, how and when to create a new one,
how to document their data by generating a readme file and using appropriate metadata,
how to select FAIR data repositories and deposit data there,
how to perform risk assessment for sensitive data (optional module 4),
how to de-identify / anonymize sensitive data (optional module 4).

Prerequisites

Knowledge / competencies

This course is designed for participants who already have basic notions of Research Data Management and FAIR principles and would like to apply them on their data.

Basic knowledge of UNIX would be a desirable addition. Therefore, we suggest you explore our UNIX fundamentals e-learning module.

Technical

You are required to bring your own laptop.

Program Schedule (CET time zone)

Day 1 (9:00 – 17:00)

Module I: Data Type & Organization

In this module, we will provide participants with good practices in file management such as data entry validation, folders organization, file naming, file format, and versioning. In particular, the participants will learn how to choose appropriate file formats for sharing, and what is important in data entry validation / data cleaning.

Module II: Ontologies as controlled vocabularies

How to make your research data better understandable by others, and consequently, more reusable? In this module, to answer this question, we will learn how to choose and apply ontologies as controlled vocabularies. Moreover, we will also provide guidelines on how to choose an appropriate vocabulary along the FAIR principles and how to FAIRify existing ones.

Day 2 (9:00 – 17:00)

Module III: 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. Presented resources include specialized metadata standards (Datacite, OME, DDI, MIAME), domain-specific repositories, as well as a user-friendly automated approach to creating readme files.

Module IV: Data Protection (Optional)

This module focuses on equipping participants with the skills and knowledge needed to handle sensitive information effectively. It covers anonymizing and de-identifying research data to ensure privacy and compliance with ethical and legal guidelines. Participants will learn to assess risks, remove identifiable information, and use privacy-preserving data sharing tools.

Application

Registration is now open, click on the green button APPLY at the top of this page.

The registration fees for academics are 100 CHF and 500 CHF for for-profit companies.

You will be informed by email of your registration confirmation. Upon reception of the confirmation email, participants will be asked to confirm attendance by paying the fees within 5 days.

Applications close on [15/11/2023]. Deadline for free-of-charge cancellation is set to [15/11/2023]. Cancellation after this date will not be reimbursed. Please note that participation in SIB courses is subject to our general conditions.

Venue and Time

This course will take place at the University of Lausanne (Metro M1 line, Sorge station).

The course will start at 9:00 and end around 17:00. Precise information will be provided to the participants in due time.

Additional information

Organizers

Vassilios Ioannidis, PhD - Lead Computational Biologist at SIB/Vital-IT; Spécialiste Donnée de recherche - FAIR at UNIRIS UNIL
Cécile Lebrand, PhD - Head of Open Science service at FBM UNIL/CHUV; Spécialiste Donnée de recherche at UNIRIS UNIL
Grégoire Rossier, PhD - Training Manager & Project Manager at SIB/Vital-IT & SIB/Training.

Trainers

Vassilios Ioannidis, PhD - Lead Computational Biologist at SIB/Vital-IT; Spécialiste Donnée de recherche - FAIR at UNIRIS UNIL
Grégoire Rossier, PhD - Training Manager & Project Manager at SIB/Vital-IT & SIB/Training.
Tarcisio Mendes de Farias, PhD – Knowledge Representation Manager at SIB/Vital-IT.
Sabine Österle, Dr. sc. ETH Zürich, Team Lead Data Interoperability Data coordination center of Swiss Personalized Health Network at SIB.
Cécile Lebrand, PhD - Head of Open Science service at FBM UNIL/CHUV; Spécialiste Donnée de recherche at UNIRIS UNIL
Céline Racine, Unisanté, Center for Primary Care and Public Health, University of Lausanne.
Prof. Jean Louis Raisaro, Head of Clinical Data Science Group, Biomedical Data Science Center, Directorate for Innovation and Clinical Research, Lausanne University Hospital.
Prof. Aleksandar Vjestica, Research Group Leader at Center for Integrative Genomics, Faculty of Biology and Medicine, University of Lausanne.

Coordination: Grégoire Rossier

You are welcome to register to the SIB courses mailing list to be informed of all future courses and workshops, as well as all important deadlines using the form here.

SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.

For more information, please contact training@sib.swiss.