Research Data Management and Data Management Plan

This course is oversubscribed, from now on, any new application will be put onto the waiting list. Applicants will only be contacted if a place becomes available.

Please be aware that SIB has taken the decision to stream this course to contribute to slow down the transmission of the COVID-19.

Overview

Recent studies have shown that worldwide, between 51% and 89% of published life sciences research is not reproducible, with consequent losses estimated at around $100 billions/year in biomedical research (Chalmers et al., 2009; Freedman et al., 2015; Begley and Ioannidis, 2015). In particular, these studies have made clear that the research data associated with a publication are fundamental to validate the published analyses and results. Many causes contribute to this lack of reproducibility in life science studies such as a lack of rigor in data management and analysis. This extensive problem related to improper research management has urged scientists to consider developing efficient Data Management Plans (DMP) for their research projects, a need that is also reflected in the requirements of funding agencies, amongst which the Swiss National Science Foundation (SNFS) and Horizon 2020.

This course, given by researchers and professionals involved in Big Data management at SIB/Vital-IT as well as in Data Management Plan preparation at UNIL/CHUV, will provide you with effective support to produce high quality DMP complying with the guidelines established by funding agencies. Importantly, it will provide you with tools to generate robust data and excellent quality studies that are reproducible and reusable.

Sources of information

• 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
• Chalmers I, Glasziou P. Avoidable Waste in the Production and Reporting of Research Evidence. Lancet. 2009; 374(9683): 86–89. DOI: 10.1016/S0140-6736(09)60329-9
• Freedman LP, Cockburn IM, Simcoe TS. The Economics of Reproducibility in Preclinical Research. PLoS Biol. 2015;13(6): e1002165. DOI: 10.1371/journal.pbio.1002165

Schedule

Thursday 27 January 2022 - 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 Research Data Management (RDM) and especially two first steps concerning data collection and data documentation. During the exercises, you will directly apply what you have learned.

During the afternoon, you will learn additional steps of the RDM cycle concerning Ethics, Legal, Security issues, Data Preservation and Data Sharing. The remaining part of the course will be dedicated to demos and exercises on Data management, where you will learn how to share your published data on adapted repositories, such as Zenodo, and how to fill a DMP corresponding to your own research using the “SIB/Vital-IT DMP Canvas Generator tool”.

Friday 28 January 2022 - 09:00-12:30 CET

On this half day, you will continue to complete your DPMs and refine the information under the guidance of the teachers. Those who would like will be able to present their draft DMPs in order to concretize their work and generate productive discussions in the group.

Audience

The course is addressed to postgraduate students and researchers who plan to apply for SNFS funds and want to be trained on how to efficiently complete the Data Management Plan form. At the same time, this course aims at educating the participants on Data Management principles in general.

Learning objectives

At the end of the course you will be able to:

• put in place a DMP (data management plan) which fulfills the requirements of the funding agencies such as the SNFS and H2020, which require a DMP to be put in place
• manage in detail your research data, specifying how your data will be analysed, organised, stored, secured and shared
• specify the type of data that is going to be created and shared
• indicate the process to be followed in respect of the budget, intellectual property, and monitoring
• use the “SIB/Vital-IT DMP Canvas Generator tool” to make your own DMP template.

Prerequisites

Knowledge / competencies

To be involved in Life Sciences research.

Technical

You will need a laptop with a web browser installed.

Trainers

• Cécile Lebrand - Open Science advocate and information specialist, Bibliothèque Universitaire de Médecine, CHUV

• Vassilios Ioannidis - SIB/Vital-IT Competence Center, SIB Swiss Institute of Bioinformatics

• Grégoire Rossier - SIB/Vital-IT Competence Center & SIB Training, SIB Swiss Institute of Bioinformatics

Application

This course is oversubscribed, therefore applications are closed. Applicants in the waiting list will only be contacted if a place becomes available.

This course is free of charges. It is partially sponsored by ELIXIR-CONVERGE.

You will be informed by email of your registration confirmation.

Deadline for cancellation is set to 13 January 2022. Please note that participation to SIB courses is subject to our general conditions.

Venue and time

This course will only be streamed online. It will start at 9:00 CET and end around 17:00 CET. 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.

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

ELIXIR-CONVERGE is funded by the European Commission within the Research Infrastructures programme of Horizon 2020