OSTrails Train-the-Trainers Bootcamp is an intermediate-level course on OpenPlato for trainers and multipliers who want to support the adoption of OSTrails specifications and services. It covers the project’s core pillars — Data Management Plans, Scientific Knowledge Graphs and FAIR Assessment — as well as the reference architecture, interoperability frameworks, and practical use of the OSTrails tools and services.
OSTrails Train-the-Trainers Bootcamp is an intermediate-level course on OpenPlato for trainers and multipliers who want to support the adoption of OSTrails specifications and services. It covers the project’s core pillars — Data Management Plans, Scientific Knowledge Graphs and FAIR Assessment — as well as the reference architecture, interoperability frameworks, and practical use of the OSTrails tools and services.
The Open Science (OS) Policy Training Programme organized by OpenAIRE's Training Standing Committee as a hands-on, practice-oriented course designed to support the development and implementation of Open Science policies across institutional, national, and European contexts.
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.
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.
Welcome to the Open Access Publishing course! This course is designed for beginners and will guide you through the essential concepts and practical implementations of Open Access (OA) publishing.
The Open Science (OS) Policy Training Programme organized by OpenAIRE's Training Standing Committee as a hands-on, practice-oriented course designed to support the development and implementation of Open Science policies across institutional, national, and European contexts.
Καλώς ήρθατε στο μάθημα Quickstart στη Διαχείριση Ερευνητικών Δεδομένων - Ενδιάμεσο επίπεδο! Το μάθημα αυτό είναι σχεδιασμένο για όσους έχουν βασικές γνώσεις για τη Διαχείριση Ερευνητικών Δεδομένων σύμφωνα με τις αρχές FAIR. Απευθύνεται σε νέους και νέες ερευνητές/ριες που επιθυμούν να αποκτήσουν μια πρακτική εικόνα του αντικειμένου της διαχείρισης δεδομένων. Η βασική εξοικείωση με τη δομή ενός ερευνητικού έργου και την οργάνωση των δεδομένων του θα διευκολύνει την παρακολούθηση. Το μάθημα είναι κατάλληλο για όλα τα επιστημονικά πεδία.
Καλώς ήρθατε στο μάθημα Quickstart στη Διαχείριση Ερευνητικών Δεδομένων – Βασικό επίπεδο! Το εισαγωγικό αυτό μάθημα παρουσιάζει τις βασικές αρχές και πρακτικές της Διαχείρισης Ερευνητικών Δεδομένων σύμφωνα με τις αρχές FAIR. Απευθύνεται σε νέους και νέες ερευνητές/ριες χωρίς προηγούμενη εμπειρία, που θέλουν να αποκτήσουν μια πρώτη πρακτική εικόνα του αντικειμένου μέσα από μια σύντομη και ευέλικτη μαθησιακή εμπειρία. Βασική εξοικείωση με τη δομή ενός ερευνητικού έργου και την οργάνωση δεδομένων, όπως υπολογιστικά φύλλα ή αποτελέσματα αναλύσεων, μπορεί να διευκολύνει την παρακολούθηση. Το μάθημα είναι κατάλληλο για όλα τα επιστημονικά πεδία.
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.”
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.
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.
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.
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.
