This series of courses, given by our DSBU unit in collaboration with SIB/Vital-IT will provide Swiss researchers with the knowledge and the tools to generate robust data and excellent quality studies that follow the FAIR principles. This courses will also provide you with effective support to build high quality DMP complying with the guidelines established by funding agencies.
Workshop 1: Open and reusable research: boost the value of your data
Details
Overview
The exponential growth of data has urged the scientific community to consider developing efficient FAIR Research Data Management (RDM) strategies with an “Open Data” philosophy and implementing robust Data Management Plans (DMP) for research projects. Adopting best RDM practices including FAIR principles (Findable, Accessible, Implementable, Reusable)1 not only enhances the value of your data by boosting visibility, reproducibility, and reuse, but also increases confidence in your findings2-4 and opens up opportunities for new collaborations. These practices are mandated by funding agencies such as the Swiss National Science Foundation (SNFS) and Horizon Europe, as well as by leading publishing platforms.
This course, given by researchers and professionals involved in Research Data Management and in Data Management Plan preparation at ELIXIR-CH, SIB/Vital-IT and DSBU/FBM-UNIL/CHUV, will provide you with the knowledge and the tools 5 to generate robust data and excellent quality studies that follow the FAIR principles. This course will also provide you with effective support to build high quality DMP complying with the guidelines established by funding agencies.
Sources of information
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
5 RDMkit: RDMkit The ELIXIR Research Data Management Kit.2022. https://rdmkit.elixir-europe.org/index.html.
Schedule
First day- 09:00-17:00 CET
At first, participants will be introduced to the notion of research reproducibility and to the need for a Data Management Plan (DMP) preparation, an evolving document reporting how the research data will be managed during and after a research project. You will learn best practices in FAIR Research Data Management (RDM) with a focus on data collection and data documentation. During the exercises, participants will directly apply what they have learned.
In the afternoon, you will learn additional steps of the RDM cycle concerning ethics, legal, security issues, data preservation and data sharing, as well as an overview of the FAIR principles. During the exercises you will learn how to share your data on suitable repositories, such as Zenodo.
Second day- 09:00-17:00 CET
On the second day, participants will gain hands-on experience creating Data Management Plans (DMPs) tailored to their research using the effective and collaborative Data Stewardship Wizard (DSW) tool. They will also have the opportunity to share and receive feedback on their draft DMPs through group discussions.
Audience
The course is addressed to PhD students, postdocs and researchers involved in life sciences and clinical research.
Learning objectives
At the end of the course you will be able to:
- Manage the main steps of your research rata using best practices and guidelines (RDM)
- Understand the FAIR guiding principles and Open Data foundations
- Understand the requirements of a Data Management Plan (DMP)
- Use the DSW tool to complete your own DMP
Prerequisites
Knowledge / competencies
To be involved in Life Sciences or clinical research.
Technical
You will need a laptop with a web browser installed.
Trainers
- Cécile Lebrand – Head of the Data Stewardship Biomed Unit (DSBU) at FBM UNIL- CHUV
- Vassilios Ioannidis – Lead Computational Biologist – FAIR specialist at SIB/Vital-IT and DSBU/FBM UNIL-CHUV
- Grégoire Rossier – Training Manager at SIB Training & Project Manager at SIB/Vital-IT
Application
While participants are registered on a first come, first served basis, exceptions may be made to ensure diversity and equity, which may increase the time before your registration is confirmed.
Deadline for cancellation is set to 09/10/2024.
You will be informed by email of your registration confirmation.
Venue and time
This course will ONLY be held in person at the University of Lausanne (Metro M1 line, Sorge station). No online streaming will be offered.
It will start at 9:00 and end around 17:00 on each day.
Precise information will be provided to the participants before the course.
Additional information
Coordination: Valeria Di Cola, SIB Training Group
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.
Please note that participation in SIB courses is subject to our general conditions.
SIB abides by the ELIXIR Code of Conduct. Participants of SIB courses are also required to abide by the same code.
Workshop 2: Making your Research Data Fair
Details
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 12/11/2024. Deadline for free-of-charge cancellation is set to 12/11/2024. 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 To be announced later.
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.