Data Management for Researchers

The Data Management for Researchers learning path is designed to support researchers in developing strong, practical skills across the entire data lifecycle. It introduces key elements of good data management practice, including FAIR principles, Data Management Plans (DMPs), metadata standards, data licensing, data publishing and sharing, and the ethical considerations involved in responsible data handling.

The development of the Data Management for Researchers learning path was a collaborative effort involving ELIXIR members from across Europe. It was initiated during a BioHackathon project and subsequently refined through ongoing contributions from the ELIXIR Research Data Management Community.

ELIXIR Learning Paths

The Data Management for Researcher is an ELIXIR Learning Path. A distinctive feature of ELIXIR learning paths is that they are composed of training materials developed by multiple institutions, showcasing resources produced by diverse groups across Europe. Each topic in this learning path brings together materials from different providers, and as a result, some similarities between resources is expected.

Note: If you have training materials you would like to see included in this learning path, please contact the authors of this learning path.

Licence: Creative Commons Attribution Non Commercial No Derivatives 4.0 International

Keywords: Data Management

Authors: Toni Hermoso Pulido, Helena Schnitzer, Alexia Cardona

Contributors: Aída Moure Fernánde, Alexia Cardona, Alice Barker, Cristian Castro, Daniel Wibberg, Eva Alloza, Hana Marcetic, Helena Schnitzer, Jessica Lindvall, Karla Vizcarra, Katarzyna Kamieniecka, Khaled Jumah, Krzys Poterlowicz, Loredana Le Pera, Mijke Jetten, Munazah Andrabi, Oscar Reina, Phil Reed, Saliha Zenboudji-Beddek, Stephen Fox, Toni Hermoso Pulido, Xenia Peres-Sitja, Zoi Litou

Scientific topics: Data management

Status: Active

Target audience: Researchers

Prerequisites:

  • A scientific degree or equivalent knowledge required for the necessary academic and critical thinking skills.
  • A foundational understanding of general research concepts, designs, and methodologies.
  • Comfort using standard computer operating systems and office applications.
  • Proficiency in organising and navigating local files/folders and data.

Learning objectives:

  • Understand the importance of Research Data Management and apply best practices for organising, documenting, preserving, and sharing research data.
  • Create and maintain a Data Management Plan (DMP) that meets funder and project needs.
  • Use metadata, vocabularies, ontologies, and standards to ensure clear, consistent and interoperable data description.
  • Apply the FAIR principles using suitable tools, repositories, and workflows.
  • Navigate essential legal, ethical, licensing, and sensitive-data requirements, including consent, anonymisation, preservation, and controlled access.

2

FAIR data

• beginner 3 materials

This section introduces the FAIR principles and shows how to apply them in real research workflows. Through short lessons, practical examples and tool overviews, learners discover how FAIR practices improve data quality, transparency and reuse across the life sciences domain.

4

Metadata

• beginner 2 materials

This section introduces the role and importance of metadata in research data management and its impact on making data FAIR. Learners explore metadata standards, controlled vocabularies, and ontologies, and learn how to apply them to describe their data effectively.

5

Licensing data

• beginner 1 material

This section offers the basic knowledge and tools to effectively manage licensing within bioinformatics research. Specifically, the included presentation provides guidance on understanding different types of licenses for both software and data, detailing their legal and practical implications. The goal is for participants—including researchers, bioinformaticians, developers, and data stewards—to learn best practices for selecting and appropriately applying licenses.

6

Data Publishing

• beginner 1 material

This section guides on the importance of preserving, publishing, and sharing research data for reproducibility and future reuse. It encompasses using long-term repositories, ensuring high-quality metadata and consistent naming, and selecting appropriate, non-proprietary file formats. Recommendations and examples of relevant repositories are presented to increase visibility, facilitate collaboration, and fulfill FAIRification and funder requirements.

7

Ethical aspects of RDM

•• intermediate 1 material

This section provides an outline of responsible data sharing in biomedical research beyond technical FAIR principles to encompass complex ethical, legal, and societal issues. It highlights the relevance of consent and anonymisation, the importance of participant engagement, and the need for equitable governance, especially in global collaborations. It emphasises that ethical data sharing is a continuous, context-dependent process requiring multidisciplinary solutions.

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