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
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.
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).
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.
You are required to bring your own laptop.
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, they 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 of how to propose new terms to existing ontologies and when to build new 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.
Registration will open in September.
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.
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.
Coordination: Grégoire Rossier
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Please note that participation in SIB courses is subject to our general conditions.
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