Welcome to the RAISE Open Research Data Management Programme!
Skill Level: Beginner
Course Overview
Welcome to the RAISE Open Research Data Management Programme!
Course Content
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Latest Courses
Updated: Jan 2026
Open Science
Abstract This module provides participants with practical skills to implement reproducibility tools and strategies in research or institutional workflows. It is structured around TIER2’s seven pilots, including: Reproducibility Management Plans (RMPs); Reproducible Workflows (life & computer sciences); Checklists for Computational Social Science; Reproducibility Promotion Plans for Funders (policy templates); a Reproducibility Monitoring Dashboard (tracking reusability of outputs); Editorial Workflows to improve data sharing; and an Editorial Reference Handbook for Reproducibility and FAIRness (publisher checks). It emphasizes hands-on exercises/discussions to help learners apply tools and foster transparency and reliability in research Authors Eleni Adamidi; Panagiotis Deligiannis; Nikos Foutris; Thanasis Vergoulis; Fakhri Momeni; Sarah Sajid; Joeri Tijdink; Barbara Leitner; Alexandra Bannach-Brown; Friederike Elisabeth Kohrs; Petros Stavropoulos; Stefania Amodeo; Haris Papageorgiou; Thomas Klebel; Eva Kormann; Matthew Cannon; Allyson Lister; Susanna-Assunta Sansone; Rebecca Taylor-Grant Language English Keywords reproducibility; computational reproducibility; editorial guidelines; FAIR principles; monitoring dashboard; open science; policy development; reproducibility checklists; reproducibility management; reproducible workflows; research evaluation; research integrity; research transparency; scientific publishing License CC BY-SA 4.0 International Target audience Researchers, research organizations, funders, publishers Prerequisites None Learning outcomes By the end of this module, participants will be able to implement tools and practices developed or extended through the seven pilots of the project, to enhance research reproducibility.”
Updated: Dec 2025
Open Science
AuthorsJoeri TijdinkBarbara Leitner LanguageEnglish Keywordsopen science, reproducibility, replication, qualitative research LicenseCC BY-SA 4.0 International Target audienceFunders, funding institutions PrerequisitesNone Abstract This module provides an overview of a funder-focused policy document offering practical recommendations to promote reproducibility. It explains how funders can adopt clear definitions, create incentive structures, and implement evaluation and monitoring processes that strengthen reproducibility practices within their funding programmes. It also summarises best practices gathered through stakeholder workshops and introduces a reproducibility promotion plan designed to support funders in improving research quality and accountability. Learning outcomes By the end of this module, learners will be able to: Recognise the importance of reproducibility for funders, including its role in improving trust, research quality, and return on investment. Implement key recommendations that help funders embed reproducibility into policies, incentives, and monitoring workflows. Develop a provisional plan for enhancing reproducibility within their organisation, drawing on lessons learned from pilot funder collaborations.
Updated: Dec 2025
Open Science
Authors Eva Kormann Tony Ross-Hellauer LanguageEnglish KeywordsTools, practices, interventions, state of the evidence, open science, reproducibility LicenseCC BY-SA 4.0 International Target audienceResearchers, funders, publishers, research support staff, open science trainers PrerequisitesNone Abstract This module introduces key tools and best practices that enhance transparency and reproducibility across the research lifecycle. It provides an overview of the research process and highlights common sources of bias or error before presenting a suite of practical interventions—including preregistration, data management plans, open lab notebooks, open-source analysis tools (e.g., R, Python, Jupyter/Quarto, Docker), and data and code sharing practices. The module also covers templates for documenting deviations from preregistration, reporting guidelines and checklists such as those offered by the EQUATOR Network, as well as preprints and good peer-review practices. A 20-minute video presentation and supporting materials illustrate how these tools apply across different epistemic contexts and summarise the current state of evidence on which interventions are most effective in strengthening research integrity and accountability. Learning outcomes By the end of this module, learners will be able to: Understand key concepts – Explain the importance of transparency and reproducibility in research and their role in supporting scientific integrity. Identify tools and practices – Recognise a range of practices that promote transparency, such as preregistration, data sharing, and reporting guidelines. Evaluate effectiveness – Assess current evidence on which reproducibility-enhancing interventions most effectively improve research quality. Apply best practices – Describe how to implement practical tools, including preregistration templates and transparent reporting checklists. Make informed decisions – Select and apply appropriate tools and workflows to improve rigor, accountability, and openness in their own research.
Updated: Dec 2025
Open Science
AuthorsSven Ulpts • Jesper W. Schneider LanguageEnglish KeywordsEpistemic diversity, relevance, feasibility, epistemology, reproducibility LicenseCC BY-SA 4.0 International Target audienceFunders, publishers, researchers, and anyone confronted with issues of reproducibility PrerequisitesNone Abstract This module examines the impact of epistemic diversity on reproducibility. It introduces the conceptual complexity surrounding reproducibility and related terms, explores how epistemic differences shape the feasibility and relevance of reproducibility across disciplines, and presents the Knowledge Production Modes (KPM) framework as a tool for assessing reproducibility in diverse research contexts. The module consists of three main parts and concludes with an assessment quiz. Learning outcomes By the end of this module, learners will be able to: Identify and understand the conceptual confusion surrounding reproducibility and related terms across and within disciplines. Analyze how different epistemic contexts affect the interpretation of reproducibility and its implications. Apply the Knowledge Production Modes (KPM) framework to assess the relevance and feasibility of reproducibility in diverse epistemic settings.
Updated: Dec 2025
Open Science
Abstract This short module briefly introduces the concept of reproducibility, its importance, and the challenges in achieving it. You will also learn about the TIER2 project, its goals, and how it supports reproducible research practices. AuthorsTony Ross-Hellauer LanguageEnglish Keywordsreproducibility, research integrity, TIER2 project, open science LicenseCC BY-SA 4.0 International Target audienceResearchers, research support staff, and other stakeholders interested in reproducible research practices PrerequisitesNone Learning outcomes By the end of this module, learners will be able to: Describe the basic concept of reproducibility in research. Explain why reproducibility is important for scientific credibility and trust. Identify key challenges that make reproducibility difficult to achieve. Summarize the goals of the TIER2 project and how it supports reproducible research practices.
Updated: Dec 2025
Open Science
Research, and how it is conducted, is ever-changing and requires those that are tasked in its support to also keep up-to-date in Research Data Management, Open Access and Open Science in general. OpenAIRE's train-the-trainer bootcamps aim at empowering trainers with the knowledge and the know-hows of Open Science so they can pass it onto the researchers and students themselves, and ultimately help create a more open, transparent and accessible research ecosystem. Learning objectives Develop an in-depth understanding of Open Science, Open Data, Data Management Plans (DMPs), Open Access publishing, Intellectual Property Rights (IPR), Horizon Europe requirements, OpenAIRE services, and the European Open Science Cloud (EOSC); Improving the skills to effectively communicate and teach these concepts to researchers, research support, students or any other stakeholders; Get familiar with various tools and software that support best practices in Open Science; Build confidence and gain the competencies to handle challenging questions, including those related with AI; Foster a community of trainers by providing a platform for networking and collaboration. Organisation The bootcamp will offer a dynamic and engaging learning experience, combining interactive live sessions in the mornings, networking opportunities and hands-on assignments. You will have the chance to hear from experts in the field and engage in discussions and sharing of your own experiences as a trainer. The networking dimension is a central component of the bootcamp's design, with a variety of activities including breakout sessions and group exercises. Additionally, the use of OpenPlato as a learning platform will facilitate the formation of what we hope to be long-lasting connections among the Open Science trainer community. Who should attend Librarians, data stewards, research support staff or any other professional stakeholders who are planning to deliver Open Science training to researchers, research support staff and/or students. This is an intermediate-level course. You should have, at the very least, some basic knowledge of the state-of-the-art, including awareness around research data management topics and have an intermediate knowledge of Open Access publishing. Geographically, there are no restrictions and we welcome applicants from beyond Europe! But please note that some of the topics might be more Euro-centric. Only 20 applicants will be selected for the intensive 5-day course, where participants will be encouraged to interact and collaborate with their peers during and after the course. Commitments This course will be free. However, you are expected to commit to the 5 working days of that week. There will be 3 hours of synchronous meetings in the morning (10:00 to 13:00 CEST) and you will have to dedicate time in the afternoons (2-3 hours) to review the materials, complete the individual assignments, participate in optional activities, meet and prepare a presentation with your group and overall interact with other participants for the duration of the course. You will also be asked to familiarise yourself and complete some short activities the week before the bootcamp. You are also expected to conduct at least one training on an Open Science topic in your institution, country or community within the following year. Selection criteria 1. Potential and motivation to conduct training on Open Science in your institution, country or community - 50%. 2. Previous experience as a trainer, particularly on topics related to Open Science- 50%. During registration, you will be asked to answer the following four questions (all compulsory): Why would you like to participate in this Open Science trainer bootcamp? (up to 200 words); Tell us a little about your experience or skills in Open Science? (up to 200 words); Tell us a little about your experience in training or running workshops? (up to 200 words). Which training events are you planning on organising within the year following the bootcamp? Tell us about the impact you are expecting to have within your institution, country and/or community (up to 200 words). Please take your time in answering these questions. The number of participants being limited, we will be deciding who participates based on your answers. Your answers will be pseudonymised for the reviewing process, so please try and not include any information that could allow us to identify you in these answers. Additional weighting criteria: Geographical balance (to establish a good representation of European regions with positive discrimination of eastern European participants). Gender (to ensure a good gender balance in the selection). Preference might be given to the OpenAIRE AMKE members.
Updated: Dec 2025
Open Science
Updated: Dec 2025
1. General Introduction This course provides a practical and comprehensive introduction to Responsible Research Assessment (RRA) why it matters, how it is implemented, and which tools and resources can support institutions and individuals in making the transition. You will begin by understanding the limitations of traditional evaluation systems and the global reform movements pushing for change. The course then guides you through the i+SCOPE methodology & guidelines, a structured approach that helps organisations define purpose, clarify values, identify meaningful contributions, and build transparent assessment protocols. From there, you will explore the GraspOS Catalogue, a central hub where tools, services, datasets, and guidelines for responsible assessment are brought together in one accessible place. You will discover concrete digital services including profile builders, indicator dashboards, citation tools, book-usage metrics, and metadata validators through real training demonstrations. The final module broadens your perspective with additional resources and initiatives outside the Catalogue, such as narrative CV frameworks, peer-mentoring approaches, and community-driven open infrastructures. By the end of the course, you will understand not only why assessment reform is essential but also how and where to take practical steps toward more fair, transparent, and meaningful evaluation practices. 2. Instructions for Learners Welcome to this course on Responsible Research Assessment (RRA).Before you begin, please follow these guidelines to get the most from your learning experience: Move through the modules in order, as each builds on the previous one. Watch the included videos and review linked slides and external resources they demonstrate real tools and processes. Reflect on the examples and consider how assessment practices operate in your own context. Complete the quizzes at the end of modules to check your understanding. If you are a trainer, make sure to watch all demonstrations beforehand so you can confidently guide participants through the tools and services. Remember that this course is practice-oriented: you will be encouraged to identify values, assess readiness, explore tools, and prepare for an assessment protocol of your own. This learning journey will help you move from awareness to action as you rethink and redesign research assessment. 3. Learning Outcomes By the end of this course, you will be able to: Explain what research assessment is and why traditional approaches are being reconsidered, recognising the motivations behind the shift toward more responsible, multidimensional evaluation. Understand the principles of Responsible Research Assessment, including the importance of context, transparency, and recognising diverse contributions. Describe how the i+SCOPE methodology works, guiding institutions to design evaluation processes that align with their values and objectives. Navigate the GraspOS Catalogue and identify relevant tools, services, and guidelines to support assessment activities in your context. Engage with additional external initiatives and resources that strengthen the transition to responsible assessment. Prepare for defining a context-specific assessment protocol using the frameworks and resources introduced in the course. 4. Course Structure Module 1 : The Reform of Research Assessment & the Need for Transition Lesson Title: The Reform Research Assessment and the need for Transition Description:This lesson explains what research assessment is, how it influences decisions across the ecosystem (researchers, RPOs, RFOs), and why traditional systems based on publication counts and journal prestige fall short. It introduces the principles of RRA and presents global reform movements such as DORA, the Leiden Manifesto, and CoARA.Who Benefits:Researchers, research managers, institutional leaders, funders, and anyone seeking a clearer understanding of why assessment reform is needed.Estimated Duration: ~45 minutes (content + reading + quiz) Module 2 : Redesigning Research Assessment: How i+SCOPE Guides Contextual Change Lesson Title: Redesigning Research Assessment: How i+SCOPE Guides Contextual Change Description:This lesson introduces the i+SCOPE methodology as a structured way to translate values into assessment practices. Learners explore how to define purpose, clarify values, identify indicators, and document an assessment protocol. Key tools: the Assessment Protocol Portfolio (APP) and the Openness Profile (OP) are presented in depth. Who Benefits:Institutional assessment designers, RPOs, RFOs, research officers, and teams developing new evaluation processes.Estimated Duration: ~60 minutes (content + resources + quiz) Module 3 : Where to Find the Resources for Responsible Research Assessment Lesson Title: Where to Find the Resources for Responsible Research AssessmentDescription:This lesson explains why a central Catalogue is needed and introduces the GraspOS Catalogue as a practical entry point for tools, services, datasets, guidelines, and templates that support assessment reform. Learners discover the Data Registry, Tools Catalogue, Services Catalogue, and guidance materials. Who Benefits:Researchers, institutions, funders, librarians, digital infrastructure teams, and anyone looking for ready-to-use RRA resources.Estimated Duration: ~45 minutes (content + browsing + quiz) Module 4 : What You Can Find in the GraspOS Catalogue Lesson Title: What You Can Find in the GraspOS CatalogueDescription:This lesson provides video-based training on key services within the Catalogue, such as BIP! Scholar, OpenAIRE MONITOR, OpenCitations, OPERAS Metrics, PRISM, the OpenAIRE Metadata Validator, and the OpenAIRE Graph API. Learners see demonstrations of how each tool works and how it supports responsible assessment.Who Benefits:Researchers building holistic profiles, institutional analytics teams, library staff, assessment committees, and funders seeking transparent metrics.Estimated Duration: ~90 minutes (multiple videos + exercises + quiz) Module 5 : Additional Resources to Support Your RRA Transition Lesson Title: Additional Resources to Support Your RRA TransitionDescription:This final lesson highlights complementary initiatives not yet in the Catalogue such as PEP-CV, OPUS, Diamond Open Access infrastructures, and webinars on cultural and policy change. These resources expand the learner’s understanding of narrative CVs, peer mentoring, alternative publishing models, and broader systemic reform.Who Benefits:Individuals seeking a deeper understanding of the wider RRA movement policy makers, research offices, funders, senior managers, and reform advocates.Estimated Duration: ~60–75 minutes (videos + discussions + quiz)
Updated: Dec 2025
Welcome to the self-paced Science Communication course, designed to support researchers in developing the skills needed to engage effectively with non-expert audiences, the media, and policymakers. The course will guide you through the foundations of communicating science in today’s complex information environment, helping you strengthen your ability to convey your expertise clearly, responsibly, and confidently.
Updated: Nov 2025
Plan and follow your data Create machine actionable DMPs. Configure to best fit your discipline. Link to EOSC components out of the box. Share easily in your repository. Bring your Data Management Plans closer to where data are generated, analysed and stored.
Updated: Dec 2025
Open Science
This learning module explains the critical relationship between reproducibility and the trustworthiness of artificial intelligence (AI). It emphasizes the understanding of how reproducibility impacts credibility and explains the different levels of AI reproducibility that researchers may strive for. The module also elaborates on barriers, such as inconsistent data collection and lack of transparency, as well as drivers, like standardized practices and tools that can support reproducibility. Finally, it illustrates how the presented barriers and drivers interact and aims to foster an understanding of this interaction in order to enhance the reproducibility of AI systems, thereby leading to a more reliable and valuable research practice in the field of AI. By the end of this module, learners will be able to: Understand the relationship between reproducibility and the trustworthiness of AI Know the different levels of AI reproducibility Be aware of Barriers and Drivers of reproducibility Understand the relation of barriers, drivers, and degree of AI reproducibility
Updated: Nov 2025