In the crowded landscape of European-funded initiatives, your AI project might be innovative, disruptive and socially impactful, but unless people know about it, it won’t matter. Effective dissemination and communication (D&C) are not an afterthought - they’re a cornerstone of project success. Here’s a strategic guide to doing it right, based on best practices and real insights from the field.

1. Define a clear communication strategy

Start early. A strong D&C plan should be developed at the project’s inception. Define your tone of voice, outline your project’s marketing funnel (from awareness to advocacy) and align activities with key milestones. A good plan is more than a Gantt chart; it’s a roadmap that keeps your messaging consistent and impactful throughout the project’s lifecycle.

2. Know your audience

One-size-fits-all doesn’t work. Segment your stakeholders (policy makers, researchers, industry players and the general public) and craft tailored personas. What do they care about? How do they behave online? What do you want them to think, feel or do after encountering your content?

Once you understand your audiences, match each with the most effective channels - LinkedIn for researchers and industry? TikTok for young citizens? Newsletters for public authorities?

3. Build a strong visual identity

Your project is a brand. Develop a consistent visual identity with logos, colours, fonts and templates. Package this in a brandbook that all partners can use. A polished visual identity boosts recognition and professionalism - two things the public loves.

4. Use the right digital channels

Digital communication isn’t just about being present online - it’s about being strategic and engaging. Start by maintaining an up-to-date and accessible project website or landing page that is mobile-friendly, with clear navigation and features to allow for user engagement. This is your digital home base, where visitors can find everything from objectives and partners to events, deliverables and news.

Next, choose the right social media platforms to reach your audiences(e.g., LinkedIn, X (Twitter), YouTube, Instagram, etc.) and tailor your content accordingly. 

Diversify the formats used in these digital channels to keep it engaging and digestible:

• Videos: Short explainers, interviews, event highlights or storytelling mini-docs bring your project to life.

• Animations: Perfect for simplifying complex AI concepts or illustrating use cases.

• Infographics, booklets and factsheets: Summarise key messages or results in a visual and shareable way.

• Newsletters and blogs: Offer regular updates and deep dives into project progress.

Go beyond just publishing content. Engage actively by responding to comments, reposting relevant content and interacting with thematic communities or clusters.

By using the full spectrum of digital tools - websites, multimedia, and interactive formats - you can significantly increase your project’s visibility and stakeholder engagement.

5. Tell stories, not just facts

Storytelling humanises your project. Highlight real-world benefits, showcase testimonials from beneficiaries and share behind-the-scenes moments. Show empathy. Be transparent. Celebrate not just your successes but also your challenges and lessons learned.

6. Involve stakeholders from day one

Don’t wait for the final event to talk to stakeholders. Involve them through surveys, workshops or regular updates. Co-creation fosters trust and ensures your work stays relevant to the people it aims to serve.

7. Prioritise accessibility and inclusivity

Speak clearly. Use plain language, accessible design and multilingual materials. Make sure your content meets accessibility standards, especially for those with disabilities. When it comes to websites, integrating tools like the UserWay Accessibility Widget can make a big difference. This plugin allows users to adjust contrast, text size, reading aids and more, enhancing usability for people with disabilities and aligning your site with WCAG standards. Inclusivity is not just a checkbox - it broadens your impact.

8. Monitor, evaluate, adapt

Set KPIs for communication (website traffic, social media engagement, newsletter clicks, etc.), and track them consistently. Collect feedback and be ready to pivot. Regularly ask your partners for information about the D&C actions they have been implementing. A data-informed D&C strategy is more effective and helps justify your efforts to the EU.

9. Collaborate with other projects

Don’t work in a vacuum. Identify synergies with sister projects and join forces for events, campaigns or publications. This not only expands your audience, but it also builds stronger networks and shares the workload.

10. Go beyond the project lifetime

Sustainability in communication matters. Plan for post-project visibility: keep your website alive, archive key materials, and ensure results remain accessible. Collaborate with newsletters and platforms with high traffic and take advantage of all the European Commission channels. This enhances the long-term value of your outputs.

Bonus: Use AI-on-Demand (AIoD) outreach services

Projects in the AI domain can amplify their communication through the AIoD platform. From social media promotion and newsletter features to press release distribution and event co-organisation, AIoD offers a structured ecosystem to ensure your project gets the attention it deserves.

You can:

• Create a profile on the AIoD platform.

• Upload relevant content, such as news, events or publications.

• Use its Website Builder.

• Request visibility support via the communication team.

Final thoughts

Dissemination and communication aren’t just about ticking boxes - they’re about ensuring your work leaves a mark. In an AI-driven world, how you talk about your innovation is just as important as the innovation itself.

As the saying goes: "If you built it, they won’t come... until you talk about it."

Publicly funded AI platforms are essential for fostering innovation, collaboration, and knowledge-sharing among diverse stakeholders. Our platform, AI-on-Demand, embodies the principles of openness, inclusivity, and transparency creating an open, inclusive ecosystem where researchers, policymakers, and industry leaders can work together to shape the future of AI in Europe.

At the heart of this initiative lies a dynamic and self-organising community. Working groups that are currently in development will form the foundation of collaboration, allowing members to engage in focused discussions, share knowledge, and develop projects together. These groups will operate with a level of autonomy, while a Steering Board will provide strategic oversight, ensuring that the platform aligns with long-term goals and with Europe’s strategic AI objectives and ethical frameworks. 

To facilitate meaningful engagement, the platform offers a variety of tools designed to support discussion, coordination, and technical development. A currently in-development Discourse Forum will serve as an open space where AIoD’s active community members can freely exchange ideas, share experiences, and discuss key AI-related topics. For working groups, dedicated collaboration spaces will provide essential resources, including channels for real-time communication, 

AIoD is also recruiting Ambassadors for the platform. We believe that these Ambassadors can offer their insights in this increasingly updating and changing AI space in Europe and give them a platform to share their knowledge and inform like-minded users who can assist with this, setting the stage for Europe to reach its potential in the AI space. Benefits include (but not limited to) collaboration with AI researchers and business leaders, become an early adopter and experimenter of applications and tools on the platform, and much, much more.

Behind the scenes, our dedicated Support Team is available to assist with any technical challenges, resource consumption issues, or the integration and onboarding of new assets and services. Whether users need help navigating the platform or troubleshooting specific challenges, the team is ready to provide guidance and support.

By actively becoming a community member of AIoD, you can contribute to shaping the future of AI in a truly open and collaborative environment. This is an opportunity to engage, innovate, and work together toward advancing AI research and applications for the benefit of all.

Get involved today!

Inquire about becoming a part of our Ambassador Programme, contribute to discussions, leverage tools to accelerate your projects, and much more. Visit aiod.eu to join – where innovation meets collaboration for the benefit of all.

In the rapidly evolving domain of cybersecurity, the imperative for intrusion detection systems is undeniable; yet, it is increasingly clear that to meet the ever-growing challenges posed by sophisticated threats, intrusion detection itself stands in need of the transformative capabilities offered by the explainable artificial intelligence (xAI). As this concept is still developing, it poses an array of challenges that need addressing. This paper discusses 25 of such challenges of varying research interest, encountered in the domain of xAI, identified in the course of a targeted study. While these challenges may appear as obstacles, they concurrently present as significant research opportunities. These analysed challenges encompass a wide spectrum of concerns spanning the intersection of xAI and cybersecurity. The paper underscores the critical role of xAI in addressing opacity issues within machine learning algorithms and sets the stage for further research and innovation in the quest for transparent and interpretable artificial intelligence that humans are able to trust. In addition to this, by reframing these challenges as opportunities, this study seeks to inspire and guide researchers towards realizing the full potential of xAI in cybersecurity.

EGI is proud to contribute to the AI-on-Demand platform (AIoD), striving to establish it as the European Open Science Commons for AI!

What is a Research Commons? 

The Research Data Alliance (RDA) defines a Global Open Research Common (GORC) as “a global trusted ecosystem that provides seamless access to high-quality interoperable research outputs and services” and identifies the following components as essential elements of a commons:

1. Governance structures: Defines purpose, strategic development, values, policies and decision-making processes of the commons.
2. Rules of participation and access: Establishes the rights, obligations and accountability for stakeholders. 
3. Sustainability: Ensures long-term viability through funding models and operational agreements.
4. Engagement: Fosters collaboration among stakeholders to drive innovation and success.
5. Human capacity: Encompasses researchers, users, service providers and intermediaries contributing to the commons.
6. Interoperability and standards: Facilitates seamless integration of data and tools through agreed-upon protocols.
7. ICT infrastructure: Provides the backbone of the commons, including computing, storage, networking and Authentication and Authorisation Infrastructure (AAI).
8. Services and tools: Includes software, data management platforms, workflow automation and discovery services to accelerate research.
9. Research objects: Covers research outputs such as datasets, publications and software. 

The first five elements represent the social and human dimension essential to a thriving commons, aligned with Elinor Ostrom’s framework for the sustainable management of collective resources. Interoperability and Standards ensure FAIR (Findability, Accessibility, Interoperability, and Reusability) principles are upheld, while the last three elements form the core infrastructure researchers interact with. 

AIoD aligns with this model by offering a unified platform for accessing and sharing AI resources. The core infrastructure includes: 

• The AIoD Metadata catalogue: Fosters interoperability with the commons catalogue and discovery service on which other services, such as MyLibrary, can be built. 
• Cloud, HPC and Edge Infrastructures: Provision the compute resources directly accessed by researchers or indirectly accessed by research platforms such as AI Builder.
• AIoD Login: Provides the federated identity management and access service, ensuring seamless authentication across AIoD services. 

As an AI4Europe partner, EGI actively contributes to developing the AIoD platform, ensuring it meets the evolving needs of the research community. EGI aims to:

• Extend the compute and storage capacity available through the AIoD platform, leveraging EGI Federated Infrastructures.
• Aggregate user demand from EGI research communities and projects, facilitating broader adoption of AIoD services.
• Contribute expertise from the European Open Science Cloud (EOSC) and other major initiatives where the GORC model is being applied.
• Support AI democratisation by integrating open-source datasets, federated computing resources, and advanced AI tools, building on projects like interTwin, iMagine, RI-SCALE and EOSC Data Commons. 

The EGI Federation aspires to become a node of the AIoD Federation, offering dedicated federated compute and storage capacity from its member institutions. This will enhance the exploitation of AI research objects and services, further strengthening AIoD’s role as a collaborative AI resource hub.

Want to learn more about EGI, AIoD and how you can contribute?

Join us at EGI2025 and be part of shaping the future of AI research in Europe!

What Is Reproducibility? 

Reproducibility refers to the ability to repeat an experiment or computational analysis under similar conditions and achieve the same results. It ensures that scientific claims are verifiable and trustworthy. In the context of AI, reproducibility encompasses several dimensions: 

Repeatability involves re-executing experiments within the same environment, using the same code, data, and configuration, and obtaining consistent results. Replicability focuses on conducting experiments in a different environment or using slightly modified methods to verify findings. Generalizability assesses the extension of research findings to new datasets, tasks, or scenarios to evaluate their broader applicability. 

Reproducibility is more than a technical requirement; it is an ethical imperative that supports rigorous validation of research outputs and promotes scientific progress. 

Barriers to Reproducibility 

Despite its importance, reproducibility faces significant barriers, including technical, cultural, and systemic barriers.  

Technical barriers include dependency management, which refers to ensuring that all software dependencies, libraries, and configurations are adequately documented and preserved. Data accessibility is another challenge, as many datasets are proprietary, sensitive, or unavailable due to licensing restrictions, which limits their reuse. Hardware variability also presents an issue, as results may differ across hardware platforms, particularly in high-performance computing environments or when using specialized accelerators like GPUs. 

Cultural barriers stem from incentive structures, where academic and industrial research often prioritize novelty and publication over reproducibility, leading to insufficient efforts to document and share resources. Time constraints exacerbate the problem, as researchers face tight deadlines that limit their ability to fully document their methods and share reproducible artifacts. 

Systemic barriers arise from the lack of standards, as the absence of universal guidelines for documenting code, data, and experiments complicates reproducibility efforts. Insufficient infrastructure further hinders progress, as researchers may lack access to platforms and tools designed to facilitate reproducibility, such as version control systems or reproducibility benchmarks. 

Proposed Practices for Reproducibility 

To address these challenges, the AI4Europe Reproducibility Initiative advocates for a comprehensive approach to reproducibility that integrates cultural change, technical solutions, and policy frameworks. Our key proposals include cultural shifts, the recognition and rewards to encourage institutions and funding agencies to value reproducibility efforts, such as open science practices, through grants, awards, and promotions. Education and training are essential to equip researchers with the knowledge and skills to adopt reproducibility best practices through workshops, courses, and community engagement. 

Technical solutions emphasize the development and promotion of reproducibility platforms that simplify the process of documenting and sharing code, data, and computational environments. Examples include containerization technologies like Docker and platforms like Code Ocean or Open Science Framework. Standardized workflows, such as creating detailed README files, adopting FAIR (Findable, Accessible, Interoperable, Reusable) data principles, and using version control, are crucial. Benchmarking efforts encourage the creation of benchmark datasets and tasks to assess the reproducibility of new research contributions. 

Policy interventions focus on reproducibility requirements, with journals and conferences mandating the submission of reproducible artifacts alongside papers, including code, data, and detailed documentation. Infrastructure investment by governments and funding bodies is also essential to support reproducibility, such as cloud-based computing resources or centralized repositories. 

Conclusion 

Reproducibility is fundamental to scientific integrity and progress. The AI4Europe Reproducibility Initiative highlights that achieving reproducibility requires a multifaceted approach addressing technical, cultural, and systemic barriers. By fostering a culture of openness, investing in robust tools and infrastructure, and implementing supportive policies, the research community can ensure that scientific findings are reliable, impactful, and accessible to future generations. 

Article by Rafael Tolosana Calasanz, Universidad de Zaragoza (UNIZAR)

To reflect the associations between actors, stakeholders, and offerings of the European AI Ecosystem, the Metadata Catalogue must define and employ a comprehensive structure for defining these linkages and their meaning. What’s more, the platform must ensure interoperability, that is, facilitate information exchange with other environments and allow for the easy ingestion of relevant metadata from other platforms connected with AI tools and activities.

For this purpose, AIoD’s Metadata Catalogue relies on the AIoD Conceptual Model, a collection of semantic resources that specify the entities participating in the European AI ecosystem, the relations between them, and the properties that adequately describe all these entities. To model these, the model adopts the core notion of an AI Resource, which is further analysed in AI Assets (i.e. different types of technical and informational assets accessible and usable from the interested audience) and Ecosystem Resources (i.e. entities that are not assets per se but are closely related to them and can be sources of information or guidance, such as people and institutions, projects, and networks). Assets defined in the model include datasets, AI models, experiments, documentary, and multimedia resources, while Ecosystem Resources entail event information, project information, news items including job offerings and funding opportunities, as well as organizations and individuals active in the broader AI ecosystem.

For each of the aforementioned entities, an extensive set of properties is defined in order to provide all the information that can be useful for assessing the relevance of a resource for different use cases. When applicable, such information is further structured via the usage of the appropriate taxonomies for assigning values to the relevant properties, e.g. for specifying the core business sector where an organisation is active, the business function and specific business problem addressed by an AI model, and the research area to which a solution pertains.

From a technical standpoint, it is important to note the adoption of semantic web standards for expressing and deploying the model. These standards allow the effective introduction of the model in the platform’s development process, as there are multiple readily available tools and services for parsing and exploiting the model. Furthermore, semantic web standards enable the straightforward linkage of the model’s entities and terminology with important external standards used by data spaces, specialized sector-targeted communities, and important platforms like Google or Hugging Face. Moreover, any existing or future external platform and repository can be integrated with the AIoD Platform via connector applications that carry out the mapping between the AIoD model and the representation schema adopted by the external platform.

In summary, the AIoD Metadata Model and its application to the AIoD Metadata Catalogue ensures that the collected information from participants in the European AI Ecosystem is easily ingested, maintained, updated, and interlinked. Thus, it constitutes a critical factor for the evolution, sustainability, and continuous relevance and prominence of the platform.

For more information and for following the evolution of the model, please visit the dedicated GitHub repository that host the model and its documentation.

Article written by: Antonis Koukourikos, Costas Spyropoulos, NCSR Demokritos

Programme highlights and learning experience

The Winter School offered a rich learning environment where theory met practice. Participants began each day with lectures from leading experts covering diverse topics such as Fuzzy Logic Control, Task Planning, Knowledge Graphs, and Ethics in AI and Robotics. These sessions laid a strong foundation, preparing attendees for the practical exercises that followed.

In the afternoons, participants engaged in hands-on labs, using the AI-on-Demand platform to apply their learning to real-world challenges. These practical sessions encouraged collaboration and highlighted the importance of reproducibility in research, a key focus of the programme.

Ethics, user experience, and collaboration

Interactive workshops further enriched the experience, with discussions on the ethical implications of AI and robotics and the principles of designing user-friendly systems. These sessions emphasised the need for accessible and ethical solutions that serve diverse communities effectively.

The event fostered a collaborative atmosphere, bringing together participants from various academic and cultural backgrounds. This diversity not only enhanced the discussions but also underscored the global relevance of AI and robotics research.

Shaping the future of AI and robotics

The AI-on-Demand Winter School 2024 successfully bridged the educational gap between AI and robotics, equipping participants with the tools and perspectives needed to tackle the challenges of integration. By combining technical training with ethical and practical considerations, the programme prepared attendees to lead impactful research and innovation in these rapidly evolving fields. The event’s emphasis on collaboration and community-building ensures its lessons will resonate far beyond its conclusion, shaping the future of AI and robotics.

The Winter school brought together talented students from diverse backgrounds and provided a unique opportunity for them to explore the intersection of AI and robotics. Through hands-on lab sessions, participants delved into various technical topics, including Fuzzy Logic Control, Task Planning, and Knowledge Graphs. These practical exercises enabled students to grasp the complexities of the conceptual integration of AI and robotics, fostering a deeper understanding of the synergies between these two fields.

Beyond the technical aspects, the Winter school emphasized the importance of reproducibility in research. Students learned about the challenges of reproducing results in AI and robotics and how the AI-on-Demand platform can facilitate this process. By leveraging the platform's resources and tools, researchers can ensure the reliability and validity of their findings, ultimately advancing the field as a whole.

Furthermore, the Winter school tackled essential non-technical aspects of AI and robotics integration, including ethics and user experience (UX). Through an interactive session, students gained insights into the importance of considering the ethical implications of AI and robotics research. By analyzing the front page of the AI-on-Demand website, they also learned about UX principles and practices, recognizing the need to design systems that are not only functional but also user-friendly and accessible.

The AI-on-Demand Winter School on AI & Robotics demonstrated the value of interdisciplinary education and the potential of the AI-on-Demand platform to support researchers in their pursuit of innovative and impactful work. By providing a comprehensive and integrated learning experience, the Winter school empowered students to become the next generation of leaders in AI and robotics research.

By leveraging the platform's resources, tools, and community, researchers can accelerate their work, ensure reproducibility, and create innovative solutions.

Pioneering AI Workloads with Telenor’s AI Factory

The AI Factory represents Telenor’s commitment to creating an environment where AI solutions can be developed, tested, and deployed securely. With a focus on data privacy, the AI Factory empowers organisations to harness the full potential of AI while ensuring that data remains within European borders. For stakeholders in healthcare, public policy, and beyond, Telenor’s AI Factory delivers the robust, compliant infrastructure necessary to develop AI models that meet European standards for quality and trustworthiness.

Skygard: Protecting Data and Privacy with Local Cloud Solutions

Skygard, another key initiative from Telenor, offers a home for the AI Factory in the sense of a local cloud solution that prioritises data sovereignty and regulatory compliance. As more organisations look to adopt AI technologies, the importance of secure and sovereign data storage cannot be overstated. Skygard enables Telenor’s customers to safely store and process sensitive data within local jurisdictions, providing them with the control and protection required in today’s data-driven world. This approach supports not only businesses but also public research institutions, ensuring that data privacy standards are upheld and European citizens’ data rights are protected.

Local Cloud: Empowering Europe’s AI Landscape with Reliable Compute and Communication

Telenor’s local cloud offering gives customers access to cutting-edge compute and communication facilities designed to handle even the most intensive AI workloads. This infrastructure supports the fast, reliable data processing needed for AI innovation, offering everything from high-performance computing to scalable storage solutions. By delivering these resources locally, Telenor allows organisations to manage AI projects efficiently while adhering to European data regulations.

Supporting the AI-on-Demand Platform as an Infrastructure Marketplace

In alignment with the European AI-on-Demand platform, Telenor’s offerings contribute to a robust marketplace where infrastructure providers can connect with AI developers and researchers in need of reliable, sovereign computing resources. The AIoD platform serves as a marketplace for providers like Telenor, enabling users of the AIoD platform to access secure local computing and communication solutions that meet the needs of Europe’s research infrastructure.

By participating in the AIoD and the AI4Europe project, Telenor is not only supporting the advancement of trustworthy, explainable AI but also empowering European organisations to innovate while upholding Europe’s high standards for data privacy and transparency. For infrastructure providers, the AIoD platform is an opportunity to collaborate, innovate, and contribute to a shared AI ecosystem that reflects European values.

Join Telenor on the AIoD Platform

Are you ready to take your AI projects to the next level with reliable, compliant infrastructure? Explore Telenor’s offerings directly via the AI Factory and later as an infrastructure offer available on the AI-on-Demand platform, where you’ll find local computing and communication resources tailored to European standards. Discover how Telenor is advancing Europe’s AI capabilities while ensuring data security, sovereignty, and collaboration across the continent.

Stereopsia offered the perfect stage to highlight the AIoD Platform’s role in advancing creativity and innovation. On the first day, F6S Innovation hosted a 30-minute workshop titled “AI on Demand: Empowering Creativity and Innovation.” Delivered by Cátia Sousa of F6S and Taniya Das of TUE, the session explored the platform’s potential to transform the way researchers, startups, and businesses harness the power of AI. The interactive format encouraged lively discussion, leaving participants inspired and eager to explore further.

Throughout the event, F6S Innovation also engaged with attendees at the EU Village booth. This dynamic space attracted a wide array of visitors, including researchers, SMEs, startups, and students, all eager to learn more about the AIoD Platform. The booth provided an excellent opportunity to demonstrate the platform’s tools, share its success stories, and invite new users to join the AIoD community. The enthusiastic response from participants highlighted the growing interest in accessible AI solutions and underscored the platform’s value in supporting European innovation.

Stereopsia’s broader agenda was equally impactful, featuring panels, networking sessions, and exhibitions that showcased cutting-edge advancements in XR and AI technologies. The event was a powerful reminder of the importance of collaboration and knowledge-sharing in driving the EU’s digital transformation goals. By bringing together diverse stakeholders, Stereopsia created an environment where ideas flourished, partnerships were formed, and progress was made towards a more connected and innovative future.

This experience reaffirms the importance of platforms like AIoD in empowering Europe’s innovators. Stereopsia 2024 not only highlighted the transformative potential of AI but also demonstrated the value of collective effort in addressing the challenges and opportunities of the digital age.

2025 is certain to bring even more opportunities to highlight the significance of European AI and its great impact.