Research Data Management for Junior Researchers: Essential Knowledge and Fundamental Steps

Over the last few years, research data and its good management have become increasingly important. Proper data management and publication of research data is often required by funding bodies (e.g., the SNSF or EC) as well as journals. It ensures reproducibility, it facilitates reuse by other researchers and paves the way for automated analysis and text mining. Articles containing data on average receive about 25% more citations. Moreover, as professionals, researchers can no longer risk the loss of a dataset, nor the confusion over the way they obtained their results. Research Data Management (RDM) enhances the necessary, transversal skills to boost and improve research outputs, while fostering collaborations. Whether researchers' interest lies in the challenges of digital humanities or the advancements of machine learning, for a career in academia or in industry, they need to be equally aware of the recent developments in RDM and ready to provide the data that underpin their analyses and research results.

Learning Outcomes:
This workshop will provide the participants with the essential knowledge and concrete examples to tackle these requirements and to manage the entire data life cycle covering both qualitative and quantitative research.

Ultimately, participants will be able to:

• Understand the latest developments in Open Science, especially FAIR principles
• Plan their research and ensure compliance with policies and funders' requirements, by writing a Data Management Plan (DMP)
• Use digital formats that improve collaborations and increase research reproducibility
• Organize and document their datasets, considering naming conventions and metadata standards
• Analyze and improve their own data workflow, considering storage solutions, security issues, collaborative sharing, and back-ups
• Improve the data workflow by integrating specific tools such as Electronic Lab Notebooks, surveying platforms, anonymization software, etc.
• Understand the pros and cons of various platforms for data publication, such as data repositories, code repositories, databanks, or data papers
• Tackle possible legal and ethical issues, with reference to privacy by-design and specific data masking techniques
• Understanding issues when handling personal and sensitive data
• Annotate a dataset and go through the publication procedure on Zenodo
• Identify and use the most appropriate data license for publishing their datasets