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EGI Inspired Magazine 2

EGI Inspired Magazine 1

Boost Your IT Service Management with FitSM Training!

EGI Check-in: The Backbone of Secure and Seamless Access for Research Infrastructures

What’s Been Going on in the “EOSCverse”, the Universe of EOSC?

Bringing Cultural Heritage to Life in 3D: the Experience of EUreka3D

Integrated AI-based Tools for Ecosystem Monitoring: Applications in Fish Detection and Artificial Reef Impact Assessment

Pangeo@EOSC: Enabling Open, Reproducible, and Scalable Big Data on the EOSC

RI-SCALE and the Future of Research Infrastructure Data Exploitation

EGI: Embracing the Brisbane Statement’s Vision for Research Infrastructures

GreenDIGIT: Pioneering Sustainable Digital Services for Research Infrastructures

Teaching Neuroimaging Data Analysis in the Cloud using NeuroDesk


<h1 class="wp-block-heading" id="h-the-landscape">The Landscape</h1>



<p>Researchers rely on a wide range of tools to analyse data and address complex research questions. However, in areas like neuroimaging analysis, training researchers to use these tools presents challenges. Many tools are Linux-based and difficult to install due to complex (and often conflicting) software tools, libraries, versions, and dependencies.</p>



<p>A key challenge in workshops and teaching scenarios is creating standardised computing environments for participants. Ensuring consistent setups can be time-consuming and error-prone, often leading to delays and frustration for both instructors and learners. This setup difficulty detracts from the core learning objectives, and it can hinder follow-up work, as trainees may struggle to reproduce their workshop computing environments on their own.</p>



<p>As fields such as imaging analysis also demand significant computational power and storage capacities, workshops are often forced to use simplified analysis and datasets because of the limitations of participants&#8217; computers.</p>



<h1 class="wp-block-heading" id="h-what-is-neurodesk">What is Neurodesk? </h1>



<p>To overcome these challenges, we developed Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Led by Aswin Narayanan and Steffen Bollmann at the University of Queensland, the Neurodesk project, recently featured in <a href="https://www.nature.com/articles/s41592-023-02145-x">Nature Methods</a>, involves more than 50 global researchers and is supported by a platform grant of the Australian Research Data Commons (ARDC) and a Wellcome Discretionary Award as part of the Chan Zuckerberg Initiative (CZI). The Neurodesk project is also supported by cloud resources from organisations across the world, such as EGI, the Jetstream2 cloud, and AWS.</p>



<p><a href="http://www.neurodesk.org">Neurodesk</a> includes a browser-accessible virtual desktop (Figure 1), a command-line interface, and computational notebooks (Figure 2), allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers, and the cloud.&nbsp;</p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1024" height="596" src="https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1-1024x596.png" alt="" class="wp-image-33635" srcset="https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1-1024x596.png 1024w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1-300x175.png 300w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1-768x447.png 768w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1-1536x894.png 1536w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1-1200x698.png 1200w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk1.png 1600w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>



<p><em>The interactive desktop environment enables students to work with all software tools interactively in a full Linux desktop environment.</em></p>



<figure class="wp-block-image size-large"><img loading="lazy" decoding="async" width="1024" height="522" src="https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2-1024x522.png" alt="" class="wp-image-33636" srcset="https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2-1024x522.png 1024w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2-300x153.png 300w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2-768x392.png 768w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2-1536x783.png 1536w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2-1200x612.png 1200w, https://cdn.egi.eu/app/uploads/2025/02/NeuroDesk2.png 1600w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure>



<p><em>The JupyterLab environment enables students to work with Jupyter notebooks and work through exercises with code and documentation.</em></p>



<p>In the backend of Neurodesk sits a modular and open analysis environment consisting of a continuous integration system to build neuroimaging software containers automatically. By providing a separate container for each neuroimaging software package, Neurodesk enables a fully reproducible environment while avoiding dependency conflicts between tools. The system is kept lightweight by mounting containers on the fly from a worldwide network of distributed and scalable software distribution service nodes running <a href="https://cernvm.cern.ch/fs/">CernVMFS</a>. One of these CVMFS stratum one nodes is also provided through an <a href="https://www.egi.eu/article/egi-and-neurodesk-partner-to-facilitate-reproducible-neuroimaging-research/">EGI collaboration</a>. </p>



<p>For cloud deployment, we built on the<a href="https://z2jh.jupyter.org/en/stable/" target="_blank" rel="noreferrer noopener"> Zero to JupyterHub project</a>, which uses Kubernetes to deploy JupyterHub. The project incorporated the CVMFS CSI driver to handle Neurodesk’s software distribution. This setup can also be deployed using <a href="https://k3s.io/">K3s</a>. This lightweight Kubernetes distribution only requires a single virtual machine in the simplest case, avoiding the need for a full Kubernetes setup, which can be resource-intensive and difficult to maintain on OpenStack. We also employ the <a href="https://longhorn.io/">Longhorn project</a> to create a distributed storage overlay. This storage overlay allows us to add multiple compute nodes to the K3s cluster, transparently handle user home directories moving between compute nodes, have shared working directories accessible from all nodes, and use the automated backup functionality.</p>



<h1 class="wp-block-heading" id="h-neurodesk-play">Neurodesk Play </h1>



<p>One example of this setup is hosting a freely available portal for learning about Neuroimaging data analysis called <a href="https://play.neurodesk.org/">Neurodesk Play</a>. This allows European researchers to quickly explore Neurodesk and learn about Neuroimaging data analysis without installing any software on their computers. We are also developing an <a href="https://www.neurodesk.org/example-notebooks/intro.html">example gallery</a> where we collect common analyses and workflows and show how these workflows can be run in Neurodesk.&nbsp;</p>



<p>Another example of this setup was recently deployed for a university course taught by Dr Vinod Kumar at the University of Tuebingen. In this course, 25 students will work with Neuroimaging software throughout the Winter semester (from October 2024 to mid-February 2025). In this setup, we are using two of the largest available compute nodes on CESNET-MCC in a K3s cluster. The students also have, in addition to their home directories, a shared working directory where they can collaborate on analyses.</p>



<p></p>



<h1 class="wp-block-heading" id="h-what-is-pangeo">What is Pangeo?</h1>



<p>The exponential growth of data across scientific fields presents substantial opportunities and challenges. In geoscience, vast volumes of Earth observation data require sophisticated, scalable analysis techniques to extract valuable insights. When these techniques are open, reproducible, and adaptable, they accelerate reusability and foster cross-disciplinary innovation. The era of big data has revolutionised geoscience, with Earth observation satellites and other sources generating vast amounts of data daily. This influx offers unprecedented opportunities to gain insights into Earth’s systems, from climate change and ecosystem dynamics to natural disasters, resource management, and climate adaptation strategies. However, the volume and complexity of these data streams present significant challenges for researchers. Managing, processing, and analysing such vast datasets requires sophisticated computational tools and scalable methodologies to meet modern geoscience demands. These challenges are not unique to geoscience but resonate across multiple scientific domains as the demand for data-driven methodologies grows.</p>



<p>Recognising these challenges, EGI has played a crucial role in supporting the deployment of Pangeo@EOSC, providing the underlying computing and storage infrastructure that powers this innovative platform. Through projects like EGI-ACE and C-SCALE, EGI has facilitated seamless integration of the Pangeo ecosystem within the European Open Science Cloud (EOSC), ensuring that European researchers gain equitable access to scalable computational resources for big data geoscience. By leveraging EGI Cloud Compute, EGI Online Storage, and EGI Check-in for secure access, Pangeo@EOSC has been able to provide researchers with a powerful, federated environment for analysing and visualising large-scale datasets. This partnership is a key step in enabling open, reproducible, and collaborative research, fostering scientific advancements across multiple disciplines.</p>



<p><a href="https://pangeo.io/">Pangeo</a> is a collaborative, open-source project revolutionising big data analysis in geoscience. It provides tools and infrastructure for scientists to access, analyse, and visualise massive datasets. Initially US-centric, the Pangeo@EOSC project is bringing this powerful platform to Europe via the European Open Science Cloud (EOSC). This initiative not only improves data accessibility and streamlines research workflows for European geoscientists but also fosters open science practices.&nbsp; Furthermore, Pangeo@EOSC’s flexible design has potential applications beyond geoscience, offering a valuable resource for other data-intensive fields like bioimaging, astrophysics, and AI/machine learning, promoting interdisciplinary collaboration and innovation.</p>



<h1 class="wp-block-heading" id="h-pangeo-deployment-on-eosc">Pangeo Deployment on EOSC</h1>



<p>Pangeo@EOSC significantly advances open science by addressing key challenges researchers face in implementing FAIR principles. Integrating core Pangeo technologies like Jupyter notebooks, Dask, and Xarray within the European Open Science Cloud (EOSC) provides a much-needed, accessible platform for data-driven geoscience research. This addresses the previous lack of such resources in Europe, where researchers were hampered by geographically limited access to existing Pangeo deployments.&nbsp; Pangeo@EOSC fosters collaboration and empowers researchers to more easily share, access, and reuse data, directly supporting the core tenets of open science and accelerating scientific discovery.</p>



<p>In collaboration with EGI, the Pangeo Europe Community deployed a DaskHub comprising a Dask Gateway and JupyterHub, supported by a Kubernetes cluster within EOSC via the EGI Federation infrastructure. This deployment leverages EGI Check-in for user registration, enabling authenticated access to the Pangeo JupyterHub portal and underlying compute infrastructure. Additionally, it utilises EGI Cloud Compute and EGI Online Storage to efficiently distribute computational tasks across a scalable platform, with intermediate results stored for further analysis.</p>



<figure class="wp-block-image size-full"><img loading="lazy" decoding="async" width="877" height="510" src="https://cdn.egi.eu/app/uploads/2025/02/Pangeo-dask.png" alt="" class="wp-image-33656" srcset="https://cdn.egi.eu/app/uploads/2025/02/Pangeo-dask.png 877w, https://cdn.egi.eu/app/uploads/2025/02/Pangeo-dask-300x174.png 300w, https://cdn.egi.eu/app/uploads/2025/02/Pangeo-dask-768x447.png 768w" sizes="(max-width: 877px) 100vw, 877px" /></figure>



<p><em>Deployment of Pangeo platform with Dask Gateway on EOSC</em></p>



<p>These resources enable users to tackle realistic, large-scale data analysis problems, enhancing their ability to work with tools like Xarray and Dask in a real-world context. The collaborative environment also fosters interaction among researchers, Research Software Engineers, and Open Science practitioners, eliminating the need for individual infrastructure setup and package installation, ultimately advancing a culture of knowledge sharing and Open Science.</p>



<h1 class="wp-block-heading" id="h-pangeo-community-of-practice">Pangeo Community of Practice</h1>



<p>The Pangeo@EOSC initiative is not only a technical deployment but also a vibrant community of practice fostering collaboration and knowledge sharing among researchers, educators, engineers, and developers. This collaborative environment drives innovation, leading to the development of new tools like the xDGGS package for geospatial data processing, crucial for initiatives like Destination Earth. Beyond tool development, Pangeo@EOSC emphasises best practices for open science, training researchers and partnering with resources like the Environmental Data Science Book (EDS Book) to promote the creation and publication of FAIR, reusable Jupyter Notebooks. By championing these practices and providing a space for collaboration, Pangeo@EOSC strengthens the European research ecosystem and empowers researchers to build upon each other&#8217;s work, accelerating scientific progress.</p>



<p>Pangeo Training Infrastructure as a Service (PTIaaS) revolutionises Pangeo training by providing a dedicated, instantly usable, and scalable environment. Instead of participants struggling with setup, PTIaaS offers a pre-configured Pangeo JupyterHub instance tailored for training, complete with necessary resources, software, and sample data. This allows instructors to focus on teaching and participants to immediately engage with hands-on exercises and collaborative learning. This on-demand and customisable infrastructure is freely available via a simple request form, removing the technical barriers to Pangeo education and ensuring a consistent, reproducible learning experience for all.</p>



<h1 class="wp-block-heading" id="h-next-steps">Next Steps</h1>



<p>Pangeo Europe and Pangeo@EOSC have effectively created a collaborative ecosystem that Pangeo Europe and Pangeo@EOSC have successfully established a collaborative ecosystem for large-scale data analysis, advancing open science. Building on this success and its expanding cross-disciplinary reach, particularly in bioimaging and cosmology, the next steps involve several key areas. Pangeo will prioritise leadership in federated computing and green computing practices, including optimising workflows and supporting GPU acceleration for computationally intensive research. Further integration with EOSC resources and a focus on interoperability are crucial, along with tailored training programmes to broaden user adoption. Showcasing domain-specific use cases in diverse fields like climate resilience and bioimaging will demonstrate practical value. Finally, building strategic partnerships across sectors and investing in FAIR data practices will ensure long-term sustainability and maximise the impact of the Pangeo ecosystem.</p>


Spotlight on Research


<h1 class="wp-block-heading" id="h-applying-ai-to-marine-ecosystems-monitoring">Applying AI to Marine Ecosystems Monitoring</h1>



<p>Artificial intelligence has emerged as a transformative tool in various scientific fields, particularly marine ecosystem monitoring. Evaluating fish populations and other marine organisms is crucial for understanding biodiversity, abundance, and biomass, which are vital for promoting sustainable fishing practices and protecting marine habitats. Traditionally, these tasks were carried out manually, requiring extensive observation of thousands of images to identify and count fish specimens &#8211; a process that was not only labour-intensive but also time-consuming. A comparison between AI and human methodologies <a href="https://doi.org/10.3389/fmars.2020.00429">has demonstrated</a> that computer vision methods significantly improve the accuracy and efficiency of these assessments.</p>



<p>Here, we present our experience using AI techniques for fish detection and classification and evaluating an artificial reef (constructed as part of the SLAGREEF project). Using the <a href="https://github.com/ultralytics/ultralytics">YOLO (You Only Look Once)</a> object detection model, image analysis is optimised to assess the effectiveness of AI in automatically identifying marine species. AI accelerates the process, enabling more efficient monitoring of environmental impacts and ecosystem health.</p>



<p>In this context, it&#8217;s essential to acknowledge the transformative potential of AI, as it improves both the efficiency and accuracy of ecosystem monitoring. This technological advancement facilitates a more streamlined approach to assessing fish populations, ultimately supporting the development of sustainable environmental management practices.</p>



<h1 class="wp-block-heading" id="h-ai-in-practice">AI in Practice</h1>



<p>We used YOLOv8 to detect fish in various images obtained from the OBSEA underwater observatory and other monitoring platforms. Based on convolutional neural networks (CNN), this model divides each image into a grid, where each cell predicts multiple bounding boxes and class probabilities, achieving fast and accurate detection. We trained five YOLOv8 architectures on the dataset (Nano, Small, Medium, Large, and Extra Large), which included images from the <a href="http://hdl.handle.net/2117/411256">SLAGREEF project</a> and the <a href="https://minka-sdg.org/observations">MINKA platform</a>.</p>



<p>We fine-tuned our image recognition system by testing different settings. We experimented with things like the learning rate (how quickly the system learns), data augmentation techniques &#8211; ways to enhance the training images (like rotating or resizing them), and the images&#8217; size. Key metrics for evaluating the model include Intersection over Union (IoU), Precision, Recall, and mean Average Precision (mAP):</p>



<ul class="wp-block-list">
<li>IoU measures the accuracy of predicted bounding boxes</li>



<li>Precision and Recall assess the model&#8217;s ability to make accurate positive predictions comprehensively.</li>



<li>mAP provides a holistic evaluation of precision across various IoU thresholds (mAP@0.5 or mAP@0.95)</li>
</ul>



<p>The following figure illustrates this process.&nbsp;&nbsp;</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><a href="https://cdn.egi.eu/app/uploads/2025/02/imagine1.png"><img loading="lazy" decoding="async" width="371" height="237" src="https://cdn.egi.eu/app/uploads/2025/02/imagine1.png" alt="" class="wp-image-33777" srcset="https://cdn.egi.eu/app/uploads/2025/02/imagine1.png 371w, https://cdn.egi.eu/app/uploads/2025/02/imagine1-300x192.png 300w" sizes="(max-width: 371px) 100vw, 371px" /></a></figure></div>


<p class="has-text-align-center"><em>Schematic representation of the combinations used in each training.&nbsp;</em></p>



<p>Once we achieved the first satisfactory results (e.g., with mAP between 0.7 and 0.9), we used the model to automatically label new images, augmenting the quantity and quality of the training dataset. This results in a semi-automatic labelling tool, leveraging the model&#8217;s precision to expand the dataset efficiently. Figure 2 shows the steps carried out.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><a href="https://cdn.egi.eu/app/uploads/2025/02/imagine2.png"><img loading="lazy" decoding="async" width="405" height="195" src="https://cdn.egi.eu/app/uploads/2025/02/imagine2.png" alt="" class="wp-image-33776" srcset="https://cdn.egi.eu/app/uploads/2025/02/imagine2.png 405w, https://cdn.egi.eu/app/uploads/2025/02/imagine2-300x144.png 300w" sizes="(max-width: 405px) 100vw, 405px" /></a></figure></div>


<p class="has-text-align-center"><em>Following steps to label the images in a semi-automatic way.</em></p>



<p>We combined two use cases from the SLAGREEF and iMagine projects.</p>



<p>The SLAGREEF project, conducted at the <a href="http://doi.org/10.1109/ACCESS.2020.2973771">OBSEA observatory</a>, implemented artificial biotopes 3D-printed from recycled materials. The OBSEA camera captures images of these biotope structures at regular intervals, which we then analyse using the <a href="https://dashboard.cloud.imagine-ai.eu/marketplace/modules/obsea-fish-detection">YOLOv8 model</a> finetuned in the iMagine project to detect and classify species.</p>



<p>We used a twofold approach:&nbsp;</p>



<ol class="wp-block-list">
<li>Scientific Image Analysis: configuring YOLOv8 for high detection accuracy, we analysed 50,000 images in 3 hours—a significant improvement compared to the manual method, which required one year to analyse 30,000 photos.</li>



<li>Real-time Monitoring: we configured the system to perform real-time video inference (25 frames per second), allowing live streaming with predictions. Although this setup offers lower accuracy, it provides a quick and useful tool for real-time visualisation.</li>
</ol>



<figure class="wp-block-embed aligncenter is-type-video is-provider-youtube wp-block-embed-youtube wp-embed-aspect-16-9 wp-has-aspect-ratio"><div class="wp-block-embed__wrapper">
<iframe loading="lazy" title="AI-based fish detection and classification trials at OBSEA" width="500" height="281" src="https://www.youtube.com/embed/SEw79Gd6m5o?feature=oembed" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen></iframe>
</div></figure>



<p>Based on our experience, we can confirm that the use of YOLOv8 in marine image analysis has proven extremely effective, with substantial accuracy in species identification, enabling reliable monitoring of the ecological impact of the artificial reef. Also, the comparison between manually and AI-labeled images highlights the efficiency of AI, which requires only seconds to process each image instead of minutes.</p>



<p>The following example shows the same image analysed manually and analysed by AI. The first image took 150 seconds, and the second one took only 5 seconds.</p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><a href="https://cdn.egi.eu/app/uploads/2025/02/imagine3.png"><img loading="lazy" decoding="async" width="311" height="174" src="https://cdn.egi.eu/app/uploads/2025/02/imagine3.png" alt="" class="wp-image-33773" srcset="https://cdn.egi.eu/app/uploads/2025/02/imagine3.png 311w, https://cdn.egi.eu/app/uploads/2025/02/imagine3-300x168.png 300w" sizes="(max-width: 311px) 100vw, 311px" /></a></figure></div>


<p class="has-text-align-center"><em><em>Images analysed manually.</em></em></p>


<div class="wp-block-image">
<figure class="aligncenter size-full"><a href="https://cdn.egi.eu/app/uploads/2025/02/imagine4.jpg"><img loading="lazy" decoding="async" width="308" height="173" src="https://cdn.egi.eu/app/uploads/2025/02/imagine4.jpg" alt="" class="wp-image-33774" srcset="https://cdn.egi.eu/app/uploads/2025/02/imagine4.jpg 308w, https://cdn.egi.eu/app/uploads/2025/02/imagine4-300x169.jpg 300w" sizes="(max-width: 308px) 100vw, 308px" /></a></figure></div>


<p class="has-text-align-center"><em>Image analysed with AI.</em></p>



<h1 class="wp-block-heading" id="h-ai-impact-on-marine-research">AI Impact on Marine Research</h1>



<p>Our results suggest that AI-based detection models can replace manual analysis, providing speed and consistent accuracy. The semi-automatic labelling feature also improves dataset quality, creating a continuous cycle of enhanced model performance.</p>



<p>Implementing the YOLOv8 model has significantly reduced analysis time without sacrificing accuracy and optimising research and environmental management efforts.</p>



<p>The effectiveness of semi-automatic labelling also highlights the potential of AI to continuously improve its own outcomes as new images are incorporated. These approaches broadly affect sustainable practices and biodiversity management in marine ecosystems.</p>



<hr class="wp-block-separator has-alpha-channel-opacity"/>



<p><em>The fish detection and classification activities presented above are part of the use case “<a href="https://www.imagine-ai.eu/case-study/ecosystem-monitoring-at-emso-sites-by-video-imagery" target="_blank" rel="noreferrer noopener">Ecosystem Monitoring at EMSO Sites by Video Imagery</a>” of the iMagine project.</em></p>



<p><em>This work received financial support from the Spanish State Plan for Scientific and Technical Research and Innovation 2021, under the SLAGREEF project (3D slag concrete manufacturing solutions for marine biotopes, TED2021-129760B-I00) and the iMagine project by the European Commission (HORIZON-INFRA-2021-SERV-01, grant agreement 101058625). This work utilised the EGI infrastructure with dedicated support from EGI-CESGA-STACK.</em></p>



<p>Research Infrastructures (RIs) serve as the backbone of modern scientific exploration, generating vast and complex datasets that drive innovation across multiple disciplines. With advancements in sensors, cameras, and digital technologies, RIs have become big data factories, producing and publishing ever-growing datasets in FAIR data repositories. However, many scientific communities struggle to fully exploit those datasets due to the lack of technical expertise, scalable computational resources, and ready-to-use environments. Without proper tools for data staging, analysis, and AI-driven insights, the potential of these datasets remains untapped.</p>



<p><strong>RI-SCALE</strong> is a newly funded EC project designed to bridge this gap by co-design, prototyping, and validating the Data Exploitation Platform (DEP). This powerful new RI technology will enhance scalable data accessibility and analysis. The DEP will provide research communities with integrated environments that bring together scientific data, AI tools, and high-performance computing, enabling deeper insights and groundbreaking discoveries. Built upon open-source components, open standards, and APIs, the DEP will ensure interoperability across various data holdings and computational environments. Initially, the platform will support environmental and health science RIs, with plans to expand across the broader RI landscape through strategic partnerships between scientific data providers, compute service providers, and AI experts.</p>



<p>The RI-SCALE project brings together four thematic RIs – <a href="http://The Brisbane Statement, released at the 2024 International Conference on Research Infrastructures, underscores the vital role of Research Infrastructures in addressing global challenges and advancing scientific discovery. Among its many important themes, the statement prominently acknowledges the transformative role of Digital Research Infrastructures, a recognition that is particularly significant for e-infrastructures like EGI.">EISCAT</a> (Atmospheric Physics), <a href="https://portal.enes.org/index.html">ENES </a>(Climate), <a href="https://www.bbmri-eric.eu">BBMRI</a> (Biobanking), and <a href="https://www.bbmri-eric.eu">Euro-BioImaging</a> (Imaging Resources), two compute providers from EGI – <a href="https://www.tuwien.at/en/">Technical University of Wien</a> and <a href="https://www.google.com/url?sa=t&amp;source=web&amp;rct=j&amp;opi=89978449&amp;url=https://tubitak.gov.tr/en&amp;ved=2ahUKEwjnzOq9xs2LAxXsJhAIHbf6CAsQFnoECAoQAQ&amp;usg=AOvVaw1BBUOppWrWajdFqYSdsJ13">TUBITAK</a>, and three Data Spaces – <a href="https://www.copernicus.eu/en">Copernicus</a>, <a href="https://destination-earth.eu">DestinE</a>, and <a href="https://cancerimage.eu">EUCAIM</a>.</p>



<p>In this article, we will examine two examples of how RIs, specifically EISCAT and ENES, will benefit from the DEP technology. These examples highlight the significant impact the Data Exploitation Platform will have on scalable data accessibility and analysis.</p>



<h1 class="wp-block-heading" id="h-the-value-of-dep-for-eiscat"><strong>The Value of DEP for EISCAT</strong></h1>



<p>EISCAT operates radar systems that focus on the ionosphere and space weather, gathering valuable data on Earth&#8217;s upper atmosphere and geospace. EISCAT_3D, an advanced radar facility currently under construction, will provide detailed, volumetric data of the upper atmosphere, including three-dimensional atmospheric dynamics. This facility will generate vast amounts of data, ranging from hundreds of terabytes to several petabytes annually.</p>



<p>However, gaining insights from this ever-growing data poses significant challenges, including limited compute resources and difficulties in sharing data effectively with the global research community. By expanding&nbsp; EISCAT’s infrastructure with DEP, these challenges can be overcome.</p>



<p>The Data Lifecycle Management capability of DEP will allow EISCAT’s datasets to be replicated on-demand across scalable e-infrastructure resources. This will enable EISCAT to store large datasets remotely, making it easier to access and share data with collaborators worldwide. Furthermore, the DEP&#8217;s cloud-based solutions will enhance AI model deployment, enabling EISCAT to use advanced machine learning techniques to process and analyse complex data more efficiently. This AI-driven approach could significantly improve real-time space weather prediction capabilities, or issue warnings to space satellite operators concerning dangerous space debris.</p>



<h1 class="wp-block-heading" id="h-the-value-of-dep-for-enes"><strong>The Value of DEP for ENES</strong></h1>



<p>ENES supports a large scientific community focused on climate modeling, particularly through the Coupled Model Intercomparison Project (CMIP), which provides crucial climate projections that inform global climate policy and research. CMIP6, the most recent iteration of the project, generates over 20 petabytes of data from global climate simulations. As the volume of climate-related data continues to grow, ENES faces challenges in providing researchers with efficient and scalable access to these datasets.</p>



<p>RI-SCALE’s DEP will address these challenges by offering a scalable solution for managing and processing CMIP data. By integrating DEP into ENES’s data infrastructure, researchers will benefit from an integrated, cloud-based platform that supports data orchestration, data transfer, and AI model development. The platform will allow researchers to analyse climate data more efficiently, leveraging AI frameworks that can accelerate data processing and enhance the quality of climate models.</p>



<p>The interoperability of DEP with various data formats and computational resources will enable ENES to tap into new computational power, such as national cloud systems, high-performance computing (HPC) resources, and external EuroHPC allocations. This will ensure that ENES can process large-scale climate simulations, enhancing both the precision and scalability of climate predictions.</p>



<p>Moreover, the AI frameworks integrated into DEP will facilitate the creation of new models and algorithms that can be used to improve the accuracy of climate simulations. AI tools within the platform will also help enhance the FAIR (Findable, Accessible, Interoperable, Reusable) nature of ENES data, promoting greater accessibility and usability for the broader research community.</p>



<h1 class="wp-block-heading" id="h-the-synergy-between-eiscat-and-enes"><strong>The Synergy Between EISCAT and ENES</strong></h1>



<p>Although EISCAT and ENES serve different aspects of atmospheric science—space weather and climate modeling, respectively—both RIs face similar challenges in managing and exploiting large-scale scientific data. The integration of DEP into both infrastructures will enable them to share data more effectively, improve the quality of their data, and integrate data across disciplines. For instance, the AI models developed for analysing space weather data from EISCAT could be adapted to enhance climate modeling in ENES, fostering cross-disciplinary insights.</p>



<p>By leveraging DEP’s modularity and extensibility, both EISCAT and ENES will be able to build on each other’s successes. The interoperability between these RIs will support a cross-disciplinary ecosystem that drives new research innovations and supports the broader scientific community.</p>



<h1 class="wp-block-heading" id="h-the-future-of-dep"><strong>The Future of DEP</strong></h1>



<p>The DEP is set to become an essential tool for a broad spectrum of data provider research infrastructures across Europe, expanding them with computational service offerings towards their users. With its focus on scalability, open-source principles, and AI-driven tools, DEPs will facilitate data valorisation, enabling RIs to maximise the value of their scientific data. Through the collaborative development of this platform, RI-SCALE will tackle a wide range of scientific and technical challenges. While initially benefiting EISCAT, ENES, BBMRI, and Euro-BioImaging, in future, the platform’s impact will extend across multiple research domains, providing scalable solutions for a wide range of RIs.</p>



<p>As DEP continues to evolve, it will support the sustainability of research infrastructures by providing energy-efficient solutions for AI model training and data processing, making it possible to handle even more complex datasets. With a focus on ensuring the long-term viability of these platforms, DEP will ensure that RIs can continue to scale and support research across multiple disciplines, through exploitational platforms jointly provisioned by RIs and e-infrastructures.&nbsp;</p>



<p></p>



<p>Lowering the environmental impact of digital services and technologies has to become a priority for both the operation of existing digital services and the design of future digital infrastructures. Indeed, digital infrastructures are responsible for 5 to 9 % of the world’s total electricity use and more than 2% of global emissions. Within the European Union, data centres accounted for 2,7 % of electricity demand in 2018 and will reach 3,21 % by 2030 if development continues on the <a href="https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=OJ%3AJOL_2023_231_R_0001&amp;qid=1695186598766" target="_blank" rel="noreferrer noopener">current trajectory</a>.</p>



<p><a href="https://greendigit-project.eu/" target="_blank" rel="noreferrer noopener">GreenDIGIT</a>, funded in the Horizon Europe ‘New Technologies for Research Infrastructures’ (INFRA-TECH) programme, runs between 2024-2026 to pilot and validate various technical and non-technical solutions that Research Infrastructures can adopt to lower the environmental impact of their digital services. GreenDIGIT brings together four digital RIs: EGI, SLICES, SoBigData and EBRAINS. These bring expertise across the whole spectrum of digital services, from IoT to networks, data repositories and compute centres. Thanks to the contribution of the RIs, GreenDIGIT will be able to enable the whole European RI landscape to lower its environmental impact related to the provisioning or consumption of digital services. GreenDIGIT focuses on three areas:</p>



<ol class="wp-block-list">
<li>Policy and Governance: Establishing and providing frameworks for measuring and managing the environmental impact of digital services through the entire RI lifecycle.&nbsp;</li>



<li>Resource Efficiency: Develop software tools, frameworks and infrastructures that support digital service providers and users in measuring and lowering their environmental impact through optimised operation and consumption.&nbsp;</li>



<li>Skills and Collaboration: Run community building and training programmes for RI communities to increase the general understanding, specific skills and collaboration about environmental impact lowering approaches and technologies.&nbsp;</li>
</ol>



<p>EGI contributes to the project with EGI Federation partners, such as CESNET (Czech Republic), CNRS (France), CSIC (Spain), and SZTAKI (Hungary), who work on several technical activities in the project, as described in the following:&nbsp;&nbsp;</p>



<ol class="wp-block-list">
<li>Define and set up a metrics collection infrastructure to support RIs in collecting, aggregating and analysing environmental impact-related metrics about digital services.&nbsp;</li>



<li>Establish a federated, digital scientific experiment (analysis) reproducibility system to support RIs in the reusing analysis results, minimising the need for re-executing calculations that have been already performed.</li>



<li>Expand several EGI Services, such as EGI Workload Manager (DIRAC technology), the EGI Notebooks service (JupyterHub technology) &#8211; and the AI4EOSC frameworks with intelligent brokering capabilities that consider the environmental footprint of compute cycles during resource selection.&nbsp;</li>



<li>Research, implement and validate a data centre automation service that monitors the use of computers, predicts incoming user requests then decides about switching on/off unused hardware to lower energy consumption without causing Quality-of-Service errors towards the users.</li>



<li>Research, implement and validate job schedulers that can choose the most suitable type of hardware resources and the most suitable time for job execution considering the environmental impact, the expected time of completion and the carbon content of the energy used by compute centres.</li>
</ol>



<p>These, and the other GreenDIGIT solutions will contribute to EGI’s ambition to lower its environmental footprint, without degrading or jeopardising its impact on science.</p>



<p>Imagine a world where museums and cultural institutions can easily share their treasures with a global audience, regardless of size or resources. This is the promise of <a href="https://eureka3d.eu/" target="_blank" rel="noreferrer noopener">EUreka3D (European Union’s REKonstructed content in 3D)</a>, a project supported by the European Union&#8217;s Digital Europe Programme.</p>



<p>EUreka3D, an EC-funded project which ended in December 2024, was designed to empower the Cultural Heritage (CH) sector in its digital transformation. Many institutions, particularly smaller ones, struggle with the complexities of modernisation, from updating internal workflows and enhancing digital asset use to retraining staff and developing new ways to document cultural artefacts. A key element of this transformation is the move towards a unified, cloud-based IT infrastructure. EUreka3D has established this infrastructure, creating a network that connects institutions and fosters collaboration. At the heart of this network is the EUreka3D DataHub, a virtual service centre providing access to essential resources like knowledge sharing, data storage, and high-performance computing. By leveraging existing research services like EGI Check-in, EGI DataHub, and cloud computing, the platform streamlines the entire lifecycle of digital objects, from creation and management to archiving, preservation, and sharing, with a special focus on 3D records.&nbsp;</p>



<h1 class="wp-block-heading" id="h-a-closer-look-at-the-eureka3d-datahub">A Closer Look at the EUreka3D DataHub</h1>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img loading="lazy" decoding="async" width="1024" height="683" src="https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-1024x683.png" alt="" class="wp-image-33091" srcset="https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-1024x683.png 1024w, https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-300x200.png 300w, https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-768x512.png 768w, https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-1536x1024.png 1536w, https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-2048x1365.png 2048w, https://cdn.egi.eu/app/uploads/2024/11/EUreka3D-DataHub-1200x800.png 1200w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></div>


<p>The EUreka3D platform and DataHub are designed to serve both cultural institutions and the public. The process begins with &#8220;Capture,&#8221; where specialised equipment like cameras, sensors, and laser scanners digitises physical objects.&nbsp;</p>



<p>This results in three types of data:</p>



<ul class="wp-block-list">
<li>File Data: The raw digital output, including 3D models, 2D images (textures), video, audio, and text.</li>



<li>Metadata: Descriptive information about the object itself.</li>



<li>Paradata: Details about the digitisation process.</li>
</ul>



<p>These data categories are then uploaded to the EUreka3D DataHub, which comprises three core components:</p>



<ul class="wp-block-list">
<li>Security: Robust authorisation and authentication measures, based on EGI Check-in, protect the 3D models and other data from unauthorised access while also facilitating community management.</li>



<li>Data Management: Powered by the EGI DataHub service and managed by EGI Federation Member CYFRONET, this component handles the storage, organisation, sharing, and publication of data.</li>



<li>Compute Power: Cloud-based virtual servers provided by the EGI Cloud&nbsp; Compute power the visualisation of 3D models, enabling interactive viewing through a dedicated 3D viewer.</li>
</ul>



<p>Once the data resides in the EUreka3D DataHub, it&#8217;s ready for &#8220;Delivery.&#8221;&nbsp; This can happen in two ways:</p>



<ul class="wp-block-list">
<li>Europeana Portal: This user-friendly platform allows the public to explore digitised cultural heritage.</li>



<li>Direct Download: Researchers and professionals can directly access and work with the data.</li>
</ul>



<p>By simplifying 3D digitisation and fostering data sharing, EUreka3D has started creating a more accessible and interconnected cultural heritage landscape. This initiative ensures that our shared history is preserved for future generations and made available to people around the globe.</p>



<h1 class="wp-block-heading" id="h-future-steps">Future Steps</h1>



<p>Building on the success of the EUreka3D project, EUreka3D-XR is taking cultural heritage a step further – into the immersive world of extended reality (XR). The project, starting soon, will increase the availability of 3D and XR experiences within the European data space.<br>EUreka3D-XR will leverage the foundation laid by EUreka3D, including its guidelines, recommendations, and DataHub, while also implementing capacity-building initiatives specifically focused on XR creation. The goal is to empower cultural institutions to create captivating and educational immersive experiences, making cultural content more accessible and engaging for researchers, students, tourists, creatives, and the public alike.</p>


Cutting-edge Technologies for Research


<p>The last year has been very busy for the European Open Science Cloud (EOSC), with concrete progress towards implementing an operational EOSC to realise the vision of a European Knowledge Commons. EOSC aims to achieve the digital transformation of research by implementing a “system of systems” for researchers in Europe to share, process, analyse and reuse FAIR research outputs within and across disciplines and borders. EGI has contributed significantly to the ongoing implementation of EOSC through its involvement in many of the main strands of activity which have taken place during 2024.</p>



<p>EOSC is envisaged to be implemented as a federation of EOSC Nodes. The EOSC Nodes will be the entry points to the EOSC Federation for end users, and will each offer research services and/or resources in compliance with common rules of the federation. The EOSC EU Node, formed as a result of a public procurement exercise, is the first EOSC Node. It recently became operational, being officially launched at the EOSC Symposium in Berlin on 22 October 2024. EGI Foundation is proud to be a contractor assisting with the implementation and operation of the EU Node, taking part in consortia including several EGI Federation members to deliver the EU Node’s federating (Core) services, and its infrastructure and application services.</p>



<p>Work to define EOSC Nodes and the EOSC Federation they will form together, continues with the shortlisting of candidate EOSC Nodes following a call for expressions of interest. The candidate Nodes selected early in 2025 will help to further define the requirements of an EOSC Node, how they will interact with the federating capabilities currently provided by the EU Node, and how Nodes will interact with one another within the EOSC Federation.</p>



<p>EGI Federation has expressed its interest in becoming an EOSC Node. EGI’s vision of its contribution to EOSC, in the broader context of the Global Open Research Commons, was elaborated in a <a href="https://doi.org/10.5281/zenodo.11128539" target="_blank" rel="noreferrer noopener">discussion paper</a> published in May 2024. Becoming an EOSC Node is very much in line with this vision. The EGI Federation could provide a European multi-site EOSC Node offering EOSC-dedicated compute and storage capacity tailored to the needs of data-intensive open science. The EGI EOSC Node would leverage the investments of national e-Infrastructures participating in the EGI Federation, to augment EOSC’s capabilities of scientific reproducibility and research data reuse. The EGI Node could either offer services directly to EOSC user communities (B2C), or indirectly to other EOSC nodes (B2B), complementing the EOSC EU Node and enhancing the value proposition of EOSC. Importantly, the EGI Node would be able to serve as a platform for resources from research infrastructures and communities which are not currently in a position to form their own EOSC Node.</p>



<p>In parallel with this, the EOSC Association, with the support of the EOSC Focus project, has led an initiative on behalf of the EOSC Tripartite to draft the first edition of the EOSC Federation Handbook. The Handbook is envisaged as a single reference document describing the EOSC Federation, its operational structure and responsibilities, its legal and governance framework, and its technical operations. A draft of the Handbook was subject of a community consultation during October and November, and the first complete edition is expected to be published during the next few months. EGI has been closely involved in the Handbook work, contributing to its writing and editing, drawing in part on the knowledge developed in the EGI-coordinated EOSC Beyond project which is piloting regional and thematic EOSC Nodes and Data Spaces, connecting national, regional and thematic infrastructures to the EOSC EU Node and developing a <a href="https://www.eosc-beyond.eu/article/concept-paper-eosc-federation-architecture-federating-capabilities-open-survey" target="_blank" rel="noreferrer noopener">concept for EOSC’s federation architecture and federating capabilities</a>. The contribution of this flagship project to the continued development of EOSC is complemented by EGI’s involvement in two EOSC Task Forces: co-chairing the Technical and Semantic Interoperability Task Force, and participating in the Health Data Task Force.</p>



<p>EOSC is an EU co-programmed partnership, whose founding documents include the EOSC SRIA (Strategic Research and Innovation Agenda) which defines the strategic objectives, guiding principles and expected impacts of EOSC. The SRIA includes a multi-annual roadmap (MAR) which defines high-level objectives and priority action areas the Partnership will take to deliver EOSC. The MAR is updated periodically, to keep it current and relevant to the development of EOSC and its evolving landscape. The draft MAR 2026-27 was the subject of a community consultation during September 2024 and the resulting updated roadmap was adopted at the EOSC Association General Assembly on 22 November. The European Commission uses the MAR as input in the drafting of EOSC-related calls in the Horizon Europe work programmes, ensuring that actions at the European level identified in the MAR are backed where appropriate with funding to ensure they are achieved. EGI Foundation has contributed to updates to the SRIA and MAR through its involvement in the EOSC Focus project task which supports these activities.</p>



<p>The 2024 EOSC Symposium was held in Berlin from 21-23 October under the patronage of the German Minister for Education and Research, Bettina Stark-Watzinger. NFDI, the German national mandated member of the EOSC Association, co-organised the Symposium and held an event for its community back-to-back with it, indicating its commitment to EOSC. At the Symposium, it was clear that EOSC has support from senior politicians and policymakers, including Ms Stark-Watzinger, Iliana Ivanova, the European Commissioner for Innovation, Research, Culture, Education and Youth, and Signe Ratso, the Deputy Director-General of DG Research and Innovation at the European Commission. They conveyed messages of strong EC and German national support for EOSC and the federated approach being implemented. Enrico Letta, former Italian prime minister and author of the recent <a href="https://www.consilium.europa.eu/media/ny3j24sm/much-more-than-a-market-report-by-enrico-letta.pdf" target="_blank" rel="noreferrer noopener">“Much More than a Market”</a> report which advocates for adding research, innovation and education as the “fifth freedom” of the EU Single Market, addressed the event to highlight his belief in the importance of EOSC for this flagship initiative.</p>


<div class="wp-block-image">
<figure class="aligncenter size-large"><img loading="lazy" decoding="async" width="1024" height="576" src="https://cdn.egi.eu/app/uploads/2025/02/EOSC-Symposium-2024_collage-1536x864-1-1024x576.jpg" alt="" class="wp-image-33719" srcset="https://cdn.egi.eu/app/uploads/2025/02/EOSC-Symposium-2024_collage-1536x864-1-1024x576.jpg 1024w, https://cdn.egi.eu/app/uploads/2025/02/EOSC-Symposium-2024_collage-1536x864-1-300x169.jpg 300w, https://cdn.egi.eu/app/uploads/2025/02/EOSC-Symposium-2024_collage-1536x864-1-768x432.jpg 768w, https://cdn.egi.eu/app/uploads/2025/02/EOSC-Symposium-2024_collage-1536x864-1-1200x675.jpg 1200w, https://cdn.egi.eu/app/uploads/2025/02/EOSC-Symposium-2024_collage-1536x864-1.jpg 1536w" sizes="(max-width: 1024px) 100vw, 1024px" /></figure></div>


<p>Credits: <a href="https://eosc.eu/news/2024/11/eosc-symposium-2024-moves-eosc-federation-toward-operations-phase/" target="_blank" rel="noreferrer noopener">EOSC Association</a>.</p>



<p>Looking ahead, the momentum of EOSC may be expected to increase in 2025 with the enrolment of the first wave of EOSC Nodes and the resumption of discussions in the EOSC tripartite governance about the post-2027 legal entity and business models for EOSC. The EOSC Association has been supported since 2022 by the EOSC Focus project, which provides additional resource and expertise to help in the execution and delivery of the Association’s many tasks. EGI Foundation staff have made a significant contribution to EOSC Focus, and will continue to do so in the EOSC Gravity project, which will take its place from June this year, focussing particularly on governance, funding and cost models.</p>



<p>EGI will build on the contributions it made last year, continuing to position EGI Federation’s services and expertise as key assets for EOSC, especially for the Federating Core, with the strategic aim of being a recognised foundation of EOSC.</p>



<p>The <a href="https://icri2024.au/about-icri/brisbane-statement/" target="_blank" rel="noreferrer noopener">Brisbane Statement</a>, a landmark declaration from the <a href="https://icri2024.au/" target="_blank" rel="noreferrer noopener">2024 International Conference on Research Infrastructures</a> in Brisbane, Australia, underscores the vital role of Research Infrastructures in addressing global challenges and advancing scientific discovery. Among its many important themes, it has rightly placed digital infrastructures at the forefront of global research collaboration. For EGI, a leading provider of advanced digital services for science and research, this recognition is not just welcome – it&#8217;s a validation of our long-held belief in the transformative power of digital tools.</p>



<h1 class="wp-block-heading" id="h-acknowledging-the-critical-role-of-digital-infrastructures">Acknowledging the Critical Role of Digital Infrastructures</h1>



<p>The statement articulates a crucial point: as research becomes increasingly data-driven, digital infrastructure is no longer a supporting element, but a <em>foundational</em> pillar. It highlights the essential role of digital resources in enabling:</p>



<ul class="wp-block-list">
<li><strong>Data-intensive research:</strong> Managing and analysing the ever-expanding datasets that span scientific disciplines. Without robust digital infrastructure, researchers would be drowning in data, unable to extract meaningful insights.</li>



<li><strong>Global collaboration:</strong> Fostering seamless access and interoperability for researchers across geographical boundaries. Global challenges demand global solutions, and digital infrastructure is the bridge connecting researchers worldwide.</li>



<li><strong>Technological innovation:</strong> Driving advancements in areas like AI, big data analytics, and high-performance computing. These cutting-edge technologies are crucial for addressing complex scientific questions, and they rely heavily on robust digital foundations.</li>
</ul>



<p>EGI, as a key provider of advanced digital services for science and research, recognises this affirmation as a milestone in acknowledging the strategic importance of digital infrastructures in the global research ecosystem.&nbsp;&nbsp;</p>



<h1 class="wp-block-heading" id="h-fair-principles-and-open-science-cornerstones-of-our-approach">FAIR Principles and Open Science: Cornerstones of Our Approach</h1>



<p>The Brisbane Statement&#8217;s emphasis on FAIR (Findable, Accessible, Interoperable, Reusable) data principles strongly resonates with EGI&#8217;s long-standing commitment to open science. We believe that research data should be readily accessible and reusable, fostering transparency and accelerating discovery. Our initiatives, such as the EGI Federated Cloud and our high-performance computing resources, are designed to empower research infrastructures and research communities in general, to achieve these FAIR data goals. We also recognise the importance of secure, trustworthy, and equitable access, ensuring that all researchers can benefit from these digital resources.</p>



<h1 class="wp-block-heading" id="h-enabling-multidisciplinary-and-global-collaboration">Enabling Multidisciplinary and Global Collaboration</h1>



<p>The Brisbane Statement recognises that tackling global challenges—such as climate change, food security, and public health—requires multidisciplinary collaboration supported by robust digital infrastructures. EGI plays a pivotal role in enabling such collaborations through its global platforms that connect research communities, provide federated identity solutions, and offer scalable compute and storage capabilities for diverse types of applications, such as digital twins and digital spaces.&nbsp;&nbsp;</p>



<h1 class="wp-block-heading" id="h-preparing-for-the-future">Preparing for the Future</h1>



<p>As the statement calls for long-term strategic planning and investment in RIs, it aligns closely with EGI’s vision of developing a resilient, inclusive, and interoperable digital ecosystem.&nbsp;</p>



<p>EGI remains committed to:&nbsp;&nbsp;</p>



<ul class="wp-block-list">
<li>Supporting the skills and training needed to empower future generations of researchers and RI staff.</li>



<li>Facilitating the integration of emerging technologies to meet evolving research needs.</li>



<li>Strengthening partnerships in Europe and across continents to build a truly global digital infrastructure framework.&nbsp;&nbsp;</li>
</ul>



<p>The Brisbane Statement’s recognition of digital infrastructure as a cornerstone of research collaboration inspires EGI to continue innovating and expanding its services for RIs, its main user group. It is a clarion call for policymakers, funders, and RI managers to invest in digital infrastructures, ensuring it is well-resourced and strategically aligned with the broader goals of research and innovation.&nbsp;&nbsp;</p>



<p>By embracing the vision outlined in the Brisbane Statement, EGI and its partners are poised to lead the way in creating a digital infrastructure ecosystem that empowers global research to address today’s challenges and unlock the discoveries of tomorrow.&nbsp;&nbsp;</p>



<p></p>



<p>In the era of open science and global collaboration, Research Infrastructures rely on complex digital ecosystems that connect researchers and institutions and distribute resources worldwide. Ensuring secure, efficient, and scalable access to these environments is crucial. This is where EGI Check-in, an Identity and Access Management (IAM) service, comes into play—simplifying and strengthening access management for research infrastructures.</p>



<h1 class="wp-block-heading" id="h-why-is-iam-essential-for-research-infrastructures">Why is IAM essential for Research Infrastructures?</h1>



<p><strong>Federated and Unified Access</strong>: Researchers can authenticate using their institutional credentials (universities, research organisations, identity federations) without creating new accounts. This improves their user experience and reduces credential management overhead.</p>



<p><strong>Security and Compliance</strong>: EGI Check-in implements centralised access control policies, integrating standards such as x509, OAuth2, OpenID Connect, and SAML, ensuring compliance with security and data protection requirements.</p>



<p><strong>Alignment with the AARC Blueprint Architecture (BPA)</strong>: EGI Check-in follows the best practices defined by the Authentication and Authorisation for Research Collaboration (AARC) Blueprint Architecture, guaranteeing interoperability with other research infrastructures and fostering a federated, scalable, and policy-driven IAM approach.</p>



<p><strong>Technology powering the EOSC EU Node</strong>: EGI Check-in uses the same technology as the <a href="https://open-science-cloud.ec.europa.eu/">EOSC EU Node</a>, serving as the access gateway to EOSC Core services. This reinforces Check-in&#8217;s reliability and scalability in enabling access to pan-European research services.</p>



<p><strong>Granular Authorisation Management</strong>: EGI Check-in enables precise role-based access control (RBAC), ensuring that only the right users access the right resources.</p>



<p><strong>Seamless Integration with Research Services</strong>: EGI Check-in connects with key platforms for computing, data hubs, container orchestration, and cloud services, facilitating frictionless access to a rich ecosystem of tools.</p>



<p><strong>Scalability and Automation</strong>: The service supports many users and services while automating provisioning and de-provisioning, reducing administrative workload.</p>



<h1 class="wp-block-heading" id="h-the-future-of-research-is-secure-open-and-interoperable">The Future of Research is Secure, Open, and Interoperable.</h1>



<p>By adopting an IAM solution like EGI Check-in, Research Infrastructures align with AARC BPA&#8217;s various guidelines, ensuring a standardised and interoperable authentication and authorisation ecosystem. This approach enhances security, improves user experience, and reduces operational complexity, enabling researchers to focus on science rather than access management. Global collaboration demands simple yet secure access: IAM is the bridge between innovation and security.</p>



<p></p>



<p></p>


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