Researchers
About
The T4EU Open Science (OS) programme provides a comprehensive learning pathway for researchers to develop practical skills in Open Science practices, data management, and policy frameworks, aiming to foster transparency, collaboration, and accessibility in research.
The following Open Science courses for researchers will be carried out online starting in February 2026:
Organizer: University of Alicante
Instructor: Mario Guillo
About: Open Science represents a transformative paradigm in how research is done, shared, and evaluated: one that emphasizes transparency, accessibility, reproducibility, and the democratization of knowledge. This module introduces participants to the philosophical underpinnings, policies, tools, and practical skills of Open Science. Learners will explore concrete practices for data sharing, open software, open access publishing, pre-registration, reproducibility, and incentives & evaluation systems. The module also addresses challenges (ethical, institutional, cultural) and helps students apply Open Science practices in their own disciplines or projects. By the end, participants will be equipped to critically engage with Open Science debates, adopt best practices in their workflows, and contribute to a more open, equitable research ecosystem.
Syllabus: This is an introductory module designed for students and researchers across all disciplines, with no prior knowledge of Open Science required. After this 60-minute course, participants will understand what Open Science is and why it matters in modern research. They will be able to define its core principles—transparency, accessibility, reproducibility, and equity—recognize key practices such as open access publishing, open data, and citizen science, and reflect critically on both the opportunities and challenges of adopting Open Science in their own work.”
Links & resources
Organizer: Vytautas Magnus University
Instructor: Saulė Milčiuvienė.
About: In this module, we will examine metadata—structured information that describes, explains, locates, and facilitates the management of other data—and identifiers, which are unique, persistent labels that anchor and distinguish resources. Metadata takes several forms: Descriptive, such as titles, authors, and keywords, which support discovery and identification; Structural, which defines how complex objects are organized—for instance, linking chapters or sections; Administrative, which oversees resource provenance, creation, permissions, and preservation. Identifiers—such as DOIs and ORCIDs—provide persistent, language-neutral references to digital objects or individuals, ensuring reliable access, integrity, and interoperability across systems.
Syllabus: This is a basic module that requires no prior specialist knowledge and is open to everyone interested in the subject. The duration 45 minutes. Upon completing the course, students will be able to identify and classify different types of metadata (descriptive, structural, administrative), apply metadata schemas, and understand identifiers like DOIs.
Links & resources
Organizer: University of Primorska.
Instructors: Ana Slavec.
About: This course offers a comprehensive introduction to the principles and practices of research data management, with a particular focus on the FAIR (Findable, Accessible, Interoperable, Reusable) data principles and the development of effective Data Management Plans (DMPs). Participants will explore the full research data lifecycle, from creation and processing to sharing and preservation, while gaining insights into the strategic importance of data in the scientific process. Through practical exercises and tool-based learning, students will acquire the skills to design and implement robust DMPs that align with institutional and funder requirements. Emphasis will be placed on applying FAIR principles to enhance data discoverability and reuse, fostering a culture of openness and collaboration in research. By the end of the course, learners will be equipped to critically assess data sharing challenges and leverage opportunities for responsible and impactful data stewardship.
Syllabus:
1. Research Data Lifecycle
2. Data Management Plans
3. FAIR principles
4. Practical tools
Competencies:
- Understand the role of research data in the scientific
process. - Be able to apply the FAIR principles in the context of
research data management. - Gain practical knowledge of how to develop and
implement a DMP. - Develop critical thinking about challenges and opportunities in data sharing and reuse.
Learning objectives:
- Describe the key stages of the research data lifecycle.
- Explain the FAIR principles
- Identify the essential components of a high-quality
Data Management Plan. - Use selected tools to support data management
planning.
Links & resources
- Recommendations on FAIR metrics for EOSC, 2021
- European Open Science Cloud (EOSC) strategic implementation plan, 2019
- Herczog, M., Assante, M., Go FAIR Initiative, & others. (2020). FAIR Data Maturity Model
- Jones, S. (2018). Open data, FAIR data and RDM: The ugly duckling
- Wilkinson, M. D., et al. (2016). The FAIR guiding principles for scientific data management and stewardship.
Organizer: University of Trieste
Instructor: Daniele Albrizio.
About: This webinar explores how open source and free (libre) software reshape digital innovation and autonomy. It covers economic foundations of open licensing opposed to the risks of vendor lock-in, and the freedoms to use, share, study, and modify code. Through real world examples participants will understand how open models foster transparency, interoperability, and sustainable development supported by communities, academia, public institutions and even private companies that regularly leverage on proprietary licenses.
Syllabus: After this session, students will be able to:
- Explain the principles of free (libre) software, including the freedoms to use, share, study, and modify code.
- Understand the economic and strategic implications of open licensing, particularly in contrast to vendor lock-in.
- Identify real-world examples of how open source models are applied in various sectors (e.g., public institutions, academia, private companies).
- Evaluate the benefits of open source models in promoting transparency, interoperability, and sustainable digital development.
- Engage critically with open source tools and platforms, understanding their potential and limitations.
Links & resources
Organizer: University of Trieste
Instructor: Stefano Martellos
About: Introduction to Citizen Science. Definition and history. Examples of citizen science projects across disciplines. Benefits and Challenges. Scientific, societal, and educational impacts Ethical considerations and data quality concerns. How to Participate. Platforms and tools for citizen science. Steps to contribute meaningfully. Case Studies Discussion.
Syllabus: by the end of this session, learners will be able to:
- Explain the concept and significance of citizen science.
- Identify real-world projects and platforms for participation.
- Evaluate potential challenges, including ethical and data reliability concerns.
- Participate effectively in a citizen science activity or project.
Learning objectives:
After this session, students will be able to:
- Define citizen science and describe its historical context.
- Recognize examples of citizen science projects in different fields.
- Analyze the benefits and challenges of citizen science.
- Engage with citizen science platforms or local projects
confidently.
Links & resources
- Bonney, R., et al. (2014). Next Steps for Citizen Science. Science, 343(6178),
1436–1437. - Dickinson, J. L., & Bonney, R. (Eds.). (2012). Citizen Science: Public Participation in Environmental Research. Cornell University Press.
- Online platforms: Zooniverse, iNaturalist, SciStarter
Organizer: Vytautas Magnus University
Instructors: Saulė Milčiuvienė and Rūta Petrauskaitė.
About: In this module the document and its guiding principles will be presented and discussed from several perspectives, mostly relevant for T4EU members institutional or joint research such as Transparency, scrutiny, critique and reproducibility, Flexibility and Sustainability. A focus on research data: their acquisition, management and FAIRness. A special attention will be given to the legal perspective and involvement of non-professional data collectors.
Syllabus: After this course, students will be able to critically assess research documents, apply guiding principles such as transparency, reproducibility, and sustainability, and manage data according to FAIR standards. They will understand legal aspects, engage with non-professional data collectors, and conduct institutional or collaborative research with enhanced scrutiny and flexibility
Links & resources
- Wilkinson, M. D. et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci. Data 3:160018 doi: 10.1038/sdata.2016.18 (2016).
Organizer: University of Jean Monnet
Instructor: Filippo A. E. Nuccio.
About: In this module we will see what recommendations concerning Open Access are proposed by the European Union and what obligations and rights are in force for everybody who obtains an ERC grant. We’ll see how to help researcher comply with these obligations and how to avoid typical traps that might cause friction between PI’s and publishing houses.
Syllabus:
This will be a 45 minutes class aimed both at researchers and librarians, whose main goal is to explain the main EU regulations. After following this class, participants will be able to enforce the Right Retention Strategy to overcome difficulties concerning ERC regulations and copyright cessions.
Links & resources
Organizer: University of Jean Monnet
Instructor: Filippo A. E. Nuccio.
About: In this module we will detail what the main publishing models for scientific articles (Green, Gold, Diamond, Bronze…) and what are the different Creative Commons Licences that can be enforced by authors.
Syllabus: This will be a 60 minutes class aimed both at researchers and librarians. After following this class, participants will be comfortable with the basic notions concerning Open Access Models and the different Creative Commons Licenses
Links & resources
Organizer: University of Trieste
Instructor: Jordan Piščanc and Davide Franch.
About: In this module we will detail the main Open solutions for IT Infrastrucutres that support archiving all OpenScience “products”: starting from Projects, DMP, DataSets but also Publication. The main focus will be on Open Infrastucutres (as definied in the Barcelona Declaration) like: DataVerse, Invenio, DSpace, Zenodo, Argos, etc. We will explore also the integrations of the Infrastuctures with OpenAire services.
Syllabus: This will be a 45 minutes class that requires no prior specialist knowledge and is open to everyone interested in the subject. Upon completing the course, students will be able to be aware of all the Infrastrucutres (especialy the Open one) that supports all the Open Science ecosystem. There will be a special focus about integrations of the Infrastuctures with OpenAire services.
Links & resources
Organizer: Vytautas Magnus University
Instructors: Saulė Milčiuvienė.
About: This course critically examines personal data protection in research, emphasizing normative frameworks, legal instruments, and ethical principles. Special attention is given to GDPR, data minimization, anonymization, and consent as conditions of lawful processing. Students analyze tensions between scientific knowledge production and privacy, developing competencies for responsible, rights-based research practices.
Syllabus: This will be a 45 minutes class that requires no prior specialist knowledge and is open to everyone interested in the subject. Upon completion, students will be able to critically interpret data protection laws and ethical frameworks, design research protocols ensuring GDPR compliance.
Links & resources
- Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation) (Text with EEA relevance)
- Directive (EU) 2019/1024 of the European Parliament and of the Council of 20 June 2019 on open data and the re-use of public sector information (recast)
- Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC (General Data Protection Regulation)
- Regulation (EU) 2023/2854 of the European Parliament and of the Council of 13 December 2023 on harmonised rules on fair access to and use of data and amending Regulation (EU) 2017/2394 and Directive (EU) 2020/1828 (Data Act)
Organizer: Saarland University
Instructors: Karolin Gieseler.
About: In this module, we will explore the why and how of preregistrations. We will briefly talk about researcher degrees of freedom and the replication crisis and how preregistrations can address threats to reproducible research. We will discuss key elements of preregistrations and how they can be implemented in different types of research.
Syllabus: This 60-minute introductory class is designed for students and early-career researchers, but is open to anyone interested in the topic. By the end of the course, participants will understand the context in which preregistrations emerged and have a basic understanding of them. They will also know how to begin using preregistrations in their own research.
Links & resources
Organizer: University of Trieste
Instructor: Stefano Martellos.
About: 1. Introduction to Open Science in Biodiversity Principles of Open Science (FAIR, open access, open methods, citizen science). The EU policy context (EU Biodiversity Strategy 2030, EOSC, Open Science Policy Agenda). 2. European & Global Infrastructures European RIs: LifeWatch ERIC, DiSSCo, eLTER, EMBRC. Data infrastructures: GBIF, OBIS/EurOBIS, EMODnet Biology, Copernicus, BISE/Natura 2000. Interoperability Standards, Tools & FAIR Practices Data standards (Darwin Core, ABCD, MIxS). FAIR principles in practice. Repositories and tools (Zenodo, OpenBiodiv, Pensoft journals). 4. Citizen Science & Participation in Biodiversity research Role of citizen science in biodiversity monitoring. Platforms (iNaturalist, eBird, observation.org, EU-Citizen.Science). Data flows into GBIF and EU policy. 5. Open Publishing & Knowledge Sharing Open Research Europe and open-access publishing. Biodiversity Data Journal and data papers. Open licensing, reproducible workflows, semantic publishing.
Syllabus:
By the end of the course, participants will be able to:
- Explain the relevance of Open Science for addressing biodiversity challenges.
- Identify the main European and global infrastructures supporting Open Science in biodiversity.
- Apply FAIR principles to biodiversity data and understand the use of standards like Darwin Core.
- Recognize the contribution of citizen science platforms and how their data supports research and policy.
- Choose appropriate venues (repositories, journals, platforms) for sharing biodiversity data and results openly.
Links & resources
Organizer: University of Jean Monnet
Instructor: Filippo A. E. Nuccio.
About: In this module we will focus on Open Access for the field of Mathematics. We will discuss who are the main publishers, their models, and the main Diamond Open Access alternatives available in Mathematics, with a special focus on Epijournals (aka Overlay Journals).
Syllabus: This will be a 45 minutes class mainly aimed at mathematicians, at every stage of their career, from PhD onwards. After following this class, participants will have a clear view on the policy of the main mathematical publishers and will acquire familiarity with the existing diamond OA options opened to them.
Links & resources
- HALId:ujm-04133052
- https://DOI:10.4171/NEWS/114/2
Organizer: Vytautas Magnus University
Instructors: Rūta Petrauskaitė.
About: The module is meant to present status quo in the field from the point of view of Open S, its developments, possibilities and limitations. A special attention will be drawn to copyright issues and compliance with the ES requirements for personal data. The problem of dealing with human- and AI made texts with be touched upon also.
Syllabus: The lecture will be oriented towards students and young researchers interested and text-based and text derived data: students in humanities and social sciences.
Links & resources
Organizer: Saarland University
Instructors: Karolin Gieseler.
About: This course critically examines the development and impact of open science practices in psychology. Starting with the replication crisis and its implications for psychological research, the course provides an overview of the challenges that became apparent in the early 21st century. Then, we will explore how open science emerged as a solution to these problems, including preregistration and registered reports. Finally, we will examine the empirical evidence assessing the effectiveness of open science practices in improving research credibility and reproducibility.
Syllabus: Anyone interested in the topic is welcome to take this class. By the end of the course, participants will understand the historical context and key events that led to the replication crisis in psychology. They will also be able to evaluate how well open science practices can address the problems identified in the replication crisis.
Links & resources
Organizer: University of Primorska.
Instructors: Ana Slavec.
About: Open Science principles are increasingly shaping the way research is conducted, shared, and evaluated. Survey data, as a rich source of empirical evidence across disciplines, presents unique opportunities and challenges in this context. This presentation explores how survey data can be made more open and reusable while maintaining ethical standards and data protection. We discuss key aspects such as informed consent, anonymization techniques, metadata standards, and data documentation. Drawing on examples from recent projects, we highlight best practices for sharing survey instruments, datasets, and codebooks. The session aims to foster dialogue on balancing transparency with privacy, and to encourage researchers to adopt open science practices that enhance the reproducibility and impact of survey-based research.
Syllabus
- Survey Design and Documentation
- Ethical and Legal Considerations
- Data Management and Sharing
- Reproducibility and Transparency
Competencies:
- Be able to plan and design surveys for openness.
- Prepare and anonymize survey data for public sharing.
- Document survey methodology using standardised metadata.
- Share analysis scripts and documentation alongside datasets.
Learning objectives:
- Ability to prepare a data management plan for survey data.
- Identify ethical and legal considerations in sharing survey data.
- Select appropriate repositories and licenses for publication.
- Use tools and workflows that support reproducibility
Links & resources
Organizer: Universidade Católica Portuguesa
Instructors: Maria Perdigão and Patrícia Carvalho.
About: This course provides an introduction to Open Science practices in health research, emphasizing transparency, reproducibility, and accessibility in research. Key topics include study registration and preregistration to minimize bias, Open Access publishing to increase dissemination, and data and code sharing to enable replication and validation. Participants will also learn about preprints and registered reports, as well as educational initiatives that integrate Open Science into health research programs. Challenges such as data privacy, cultural resistance, and resource limitations are discussed, along with strategies to address them. The course highlights future directions, including the use of AI and digital tools, policy development, and interdisciplinary collaboration. By the end, participants will understand how Open Science practices can improve research quality and foster a more collaborative and transparent scientific community.
Syllabus: The course Open Science in health research lasts 90 minutes and is aimed at health sciences researchers and postgraduate students. Participants will gain practical skills and competencies to implement Open Science practices in their research, including study registration, data sharing, Open Access publishing, and the use of preprints and registered reports. They will also develop strategies to address common challenges and explore tools and approaches that support transparency, reproducibility, and collaboration in health research.
Links & resources
- Demystifying Open Science in health psychology and behavioral medicine: a practical guide to Registered Reports and Data Notes
- Design and validation of a conceptual model regarding impact of open science on healthcare research processes
- Open Science vs. Responsible Science: Balancing Transparency and Security
