Publications

The ‘Open Science and Evidence-Informed Decision Making’ (EIDM) Learning Path is a targeted Train-the-Trainer Initiative, developed under the Skills4EOSC project, specifically designed for policymakers, civil servants, knowledge brokers and all stakeholders interested in Open Science and decision making. The learning path addresses the pressing need to equip these professional profiles with the knowledge, skills, and competences to engage with and apply Open Science principles in policy development and evidence-informed decision making. The programme aimed to improve policy effectiveness, transparency and trust by equipping participants with practical skills and knowledge.
This training pathway is not a general-purpose curriculum but one tailored to the distinctive needs and roles of policy sector professionals. Recognising that little existing training material fully addressed these profiles, the development process combined a landscaping analysis of relevant initiatives with the creation of original, purpose-built content. The goal was to ensure that the training program responds directly to the real-world responsibilities, constraints, and opportunities faced by decision-makers and intermediaries in public administrations.

The S4E Quality Assurance Framework (QAF) is a practical, community-endorsed reference that helps improve the quality of Open Science training courses. It's not a rigid standard but a set of flexible guidelines and best practices to help course creators make sure their learning materials are clear, FAIR and aligned with good practices.
The S4E QAF promotes best practices and self-evaluation tools to ensure coherence between content and container: a course about Open Science should also embed Open Science principles in how it’s designed, presented, and shared.

This booklet presents a concise, practice-oriented version of the content developed in the Skills4EOSC D6.3 deliverable titled “Top 10 FAIR Data Things for AI and Health Technology”. It focuses on the Artificial Intelligence section of that document.
It is intended as a quick-access resource for researchers, data professionals, and developers working to apply FAIR principles (Findable, Accessible, Interoperable, Reusable) in Artificial Intelligence workflows, especially those involving machine learning models and datasets.
The 10 practices highlighted here were identified and validated through a collaborative, two-round Delphi study involving domain experts in ML/AI and FAIR. The aim is to share easy, practical steps to help make machine learning and AI models more FAIR. The materials of the Delphi study are available on Zenodo (link: https://zenodo.org/ records/16536643). In addition to the practices themselves, this booklet briefly summarises key reflections that emerged during a final community discussion, to provide context and indicate future directions. The work focuses on machine learning (ML) models because they are widely used across disciplines and offer a practical entry point for developing FAIR implementation guidelines. This focus also enabled collaboration with ongoing, well-aligned initiatives. While the study did not distinguish between types of ML (e.g., supervised or reinforcement learning) or delve into more complex models like deep learning, the approach and outcomes are intended to be broadly applicable across different AI model types.

Leaflet presenting the Building Blocks of a Data Steward Training Curriculum

This handbook is a condensed version of the Data Steward Network Starter Kit (Horton et al., 2023). It supports the creation, launch, and development of new professional and sustainable communities of practice in the field of data stewardship. Professional networks play a key role in the context of the Skills4EOSC Competence Centre Network, where they are considered tools for lifelong learning through peer exchange. This document brings together theoretical models and practical action points to help data stewards build and develop their communities effectively.

Coordinated set of guides, fact-sheets and FAQs on ELSI aspects of OS for civil servants and policy makers.

The Science4Policy Kit, developed by Skills4EOSC Task 3.3, is a practical resource offering real-life examples of Science4Policy initiatives, providing a rich repository of diverse case studies for Competence Centres to include in training activities, or to inspire similar initiatives in their local contexts. The Science4Policy Kit was created using a dual approach combining landscaping and survey methodologies to gather data on Science4Policy initiatives across multiple countries. These initiatives were categorised into ten distinct types, with representative examples featured in the Science4Policy Kit, highlighting practical tools and successful methodologies which serve as an inspiration for integrating science into policy-making processes.

This publication introduces the Minimum Viable Skillsets (MVS) methodology, which defines essential profiles for research data professionals—individuals whose roles involve creating, collecting, or using research data, often as evidence. Each MVS profile centres on key elements of a role that supports Open Science. This briefing outlines the purpose, scope, and structure of these profiles. It addresses questions from Competence Centres regarding how MVS profiles can be utilized in skills development and the collaborative approach behind their creation.

Open Science advocates for the democratisation of scientific research and its outputs, emphasising accessibility, transparency, and collaboration. This movement aims to enhance the quality, efficiency, and impact of scientific work, thereby fostering increased public trust and broader engagement. Central to this ecosystem is the Honest Broker, an entity (i.e. a person or a group of people) that plays a crucial role in bridging the gap between scientific research and policy-making. Honest Brokers act as neutral intermediaries, facilitating the use of scientific evidence in decision-making without pushing a particular agenda. They ensure that policy and decision-makers have access to reliable, authoritative, relevant scientific evidence, helping them to navigate the complexities of the Open Science resources. Open Science poses unique challenges and opportunities at the interface of science and policy. Access to vast amounts of scientific information can be overwhelming; hence, the Honest Broker’s role is crucial in making this information understandable and actionable.

Training is vital for the European Open Science Cloud (EOSC) to succeed as key EU Open Science research meta-infrastructure. It tackles the challenge of upskilling a large community of various stakeholders with the necessary competences to both build and use data and services. It is therefore important that the EOSC training community is able to efficiently collaborate ensuring the delivery of high-quality up-to-date trainings and learning opportunities. To help achieve this goal, Task 3 of Work Package 2 within the Skills4EOSC project has produced a methodology for developing FAIR-by-Design learning materials that will ensure maximum reusability of developed learning materials within the community and higher-quality materials.

This booklet is designed to offer policymakers insight into the essential connections between the United Nations Sustainable Development Goals (SDGs), Open Science, and Open Collections across various domains. It highlights how global policy goals are intrinsically linked to research initiatives that rely on Open Collections to provide evidence for decision-making. Policy measures to develop collections as research infrastructure are making collections more accessible useful for research and subsequently for evidence based policy and impactful decisions. Relevant use cases demonstrate how Open Science and Open Collections are fundamental to achieving the SDGs. We outline future directions for developing and expanding the necessary Open Access to collections.

This document is intended to be a collection of useful materials for the drafting of a privacy policy in research projects. Based on the experience gained in coordinating and participating in national and international projects, it was possible to identify the most problematic issues in the management and processing of personal data and, therefore, to write down forms that serve as models/templates to be used in concrete and realistic situations. The attachments in the following sections include the necessary and general requirements prescribed by current legislation and have been drafted to be adapted, once completed, to the specificities of the projects.