Jean Iaquinta

• Iaquinta, Jean & Fouilloux, Anne Claire Mireille (2025). Unlocking the Potential of Containers in Scientific Computing to Achieve Bitwise Reproducibility, Portability and Performance. Communications in Computer and Information Science (CCIS). ISSN 1865-0929. Fulltekst i vitenarkiv Vis sammendrag

  The modern form of containers, as popularly known through platforms like Docker, Singularity (Apptainer), Podman or Charliecloud, to cite only a few, began to take shape over a decade ago. However, the fundamentals behind containerization and actual benefits of containers in scientific computing remain largely unclear to a vast majority of users. In fact, there is a significant gap between simplistic “Hello world” examples found online and real scientific applications. Also, rumors suggest that achieving satisfactory performances on supercomputers across multiple nodes is impossible. The aim of this paper is to explain how containers can leverage the potential of high-speed networks for inter-node communications with UCX on Fram and Betzy (from the Norwegian national e-infrastructure provider). It is also shown how to achieve near-native performance on LUMI (EuroHPC’s flagship) despite a “Slingshot-11” interconnect and proprietary library. Results obtained in the standard OSU Micro-Benchmarks tests for latency and bandwidth, and with a fully-fledged climate model, demonstrate that containerized applications work just as well as their bare-metal counterparts, are portable and provide bit-for-bit reproducibility on different platforms. Containers are therefore highly recommended to minimize deployment and porting issues i) for AaaS (Applications as a Service) coming with all the necessary software environment (rather than source code only); and ii) so that HPC users do not have to rely on anybody to install what they need and can be operational within minutes whilst still getting top performance

• Fouilloux, Anne Claire Mireille; Iaquinta, Jean; Gupta, Alok Kumar; Struthers, Hamish; Landgren, Oskar Andreas & Dwarakanath, Prashanth [Vis alle 8 forfattere av denne artikkelen] (2023). Building on Communities to Further Software Sustainability. Computing in science & engineering (Print). ISSN 1521-9615. 25(3), s. 84–88. doi: 10.1109/MCSE.2023.3318749. Fulltekst i vitenarkiv Vis sammendrag

  The Nordic e-Infrastructure Collaboration on Earth System Modeling Tools is a small community comprising members with diverse backgrounds, skills, and interests. Largely dependent on temporary staff to develop, operate, and maintain large scientific codes, this community devised strategies to enhance software reusability and sustainability. These strategies include collaborating with other communities for support, adopting Open Science as well as findable, accessible, interoperable, and reusable principles to optimize resource usage, growing essential knowledge within the community, and setting up a community of practice to facilitate onboarding and offboarding. The strategies also promote inclusiveness, foster external collaboration, and recognize technical contributions.

• Fouilloux, Anne Claire Mireille; Trasatti, Elisa; Foglini, Federica; Coca-Castro, Alejandro & Iaquinta, Jean (2023). FAIR Research Objects for realizing Open Science with the RELIANCE EOSC project. Research Ideas and Outcomes (RIO). doi: 10.3897/rio.9.e108765. Fulltekst i vitenarkiv Vis sammendrag

  The numerous benefits of Open Science (OS) and of the four FAIR foundational principles - Findable, Accessible, Interoperable and Reusable - are increasingly valued in academia, although what OS and FAIR entail is still largely misunderstood. In such conditions, putting into practice OS and applying the FAIR principles is challenging and underrated. However, realising OS is perfectly within our grasp provided that an infrastructure supporting the management of the research lifecycle is available. ROHub (https://www.rohub.org/) is a Research Object (RO) management platform implementing three complementary technologies: Research Objects, Data Cubes and Text Mining services. ROHub enables researchers to collaboratively manage, share and preserve their research while they are still working on it (rather than after the work is finished). In this paper, three communities from Earth Sciences, namely Geohazards, Sea Monitoring and Climate Change, demonstrate how ROHub helped them to understand each other and to work openly and, more importantly, how communities of practice play an important role in facilitating reuse and interdisciplinary collaboration. These findings are illustrated with several use cases from these various communities.

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• Iaquinta, Jean; Fouilloux, Anne Claire Mireille & Ragan-Kelley, Benjamin (2025). Climate Modeling with Conda and Containers to Improve Computational Resource Usage while Achieving Native Performance and Reproducibility. Fulltekst i vitenarkiv Vis sammendrag

  Integrating High-Performance Computing (HPC) and cloud computing in climate sciences is difficult, due to intricate hardware/software, compatibility, performance and reproducibility issues. Here, we address these challenges in a user-friendly way by leveraging the Conda ecosystem and containers. Containerization allows to match or exceed native performance on HPC while ensuring bit-for-bit reproducibility for deterministic algorithms and similar processor architectures. This approach simplifies deploying climate models across different platforms; for example, CESM 2.2.2 (Community Earth System Model) provides on various clusters throughputs in simulated years per computational day within +/- 1% of bare-metal performance for simulations spanning thousands of processors. Exclusively using generic Conda packages for MPI (Message Passing Interface) applications was challenging in HPC. Although OpenMPI included UCX (Universal Communication X) and OFI (Open Fabric Interface), it lacked UCC (Unified Collective Communication) and wasn't optimized by default for high-performance networks like InfiniBand, RoCE (Remote Direct Memory Access over Converged Ethernet) and HPE (Hewlett Packard Enterprise) Slingshot-11, often defaulting to TCP/IP (Transmission Control Protocol/Internet Protocol) or failing. After updating Conda-Forge’s OpenMPI and MPICH feedstocks, we are adding MVAPICH and ParaStationMPI support to PnetCDF, HDF5, NetCDF-C, NetCDF-Fortran and ESMF (Earth System Modeling Framework) libraries critical for modellers, alongside libFabric and openPMIx (Process Management Interface - Exascale). This incidentally exposed ABI (Application Binary Interface) compatibility issues. Now, MPI toolchains featuring major UCX/OFI/PMIx versions ensure consistent operation across different hosts without affecting numerical results. Using the same Conda environment inside a container, and no hardware-specific optimization, preserves bitwise reproducibility. OMB (Ohio State University Micro-Benchmark) tests for latency, bandwidth and other metrics help confirm if optimal performance can be achieved or not. Such developments enable climate scientists to focus on addressing scientific questions rather than sorting out software dependencies and technical problems. One can write code on a laptop then effortlessly scale to cloud or supercomputers, and seamlessly run climate simulations somewhere then continue these wherever compute resources are available without worrying about discontinuities. This also releases expensive HPC resources for production instead of wasting them for training, learning, development or testing which can be performed comfortably elsewhere, without job scheduling constraints, in the very same software environment. Conda has primarily been developed with a focus on compatibility which limits its suitability in highly performance-sensitive applications where locally optimized builds of specific key components are paramount, typically in climate modeling. Additionally, instead of relying on local engineers to install and maintain host software, Conda users can benefit from the work of thousands of open-source contributors who continuously update and test the entire ecosystem. This strategy fits the session's theme by providing a framework where cloud resources can be utilized for big data without compromising the performance or rigor of HPC environments. Conda and container technologies ought to change how climate scientists approach software management, focusing on ease of use, scalability and reproducibility, thereby potentially altering practices within the field to improve usage of computational resources and leverage community efforts to remain at the forefront.

• Fouilloux, Anne Claire Mireille & Iaquinta, Jean (2025). JupyterGIS - Real-time framework, QJIS compatibility and powerful Python integration. Fulltekst i vitenarkiv Vis sammendrag

  This tutorial introduces JupyterGIS, a web-based, collaborative GIS platform integrated with Jupyter notebooks. Participants will learn to edit geospatial data, visualize raster and vector layers, apply symbology, and use the Python API for spatial analysis. We will explore real-time collaboration features such as shared document editing, live cursor tracking, and geolocated comments. The session also demonstrates JupyterGIS integration with QGIS. Learning Objectives: - Understand the core features of JupyterGIS and how it facilitates collaborative GIS workflows. - Learn how to load and analyze raster and vector datasets in JupyterGIS. - Apply symbology and filtering tools to geospatial data. - Use the Python API for automating spatial analysis. - Explore real-time collaboration features, includ...

• Iaquinta, Jean & Fouilloux, Anne Claire Mireille (2025). Making Earth System Modelling and Observation Workflows Reproducible and Portable Across Federated High-Performance Computer and Cloud Platforms with Conda-based Containers. Fulltekst i vitenarkiv Vis sammendrag

  Earth System Modeling (ESM) workflows, which are critical for Earth Observation (EO) and climate research, are highly complex and demand substantial computational power. These workflows typically employ domain decomposition techniques, using a combination of MPI (Message Passing Interface) and OpenMP (Open Multi-Processing) to parallelize computations efficiently. For global high-resolution models, each processor handles a small column of data (e.g., latitude-longitude and vertical levels), enabling scalability on powerful supercomputers like the LUMI EuroHPC. Conversely, calculations for smaller domains can be performed on personal computers, underscoring the need for a portable approach that spans this computational spectrum. This work presents the use of Conda, an open-source package manager, to create reproducible, portable, and scalable computational environments for ESM workflows across federated HPC and cloud platforms. A key challenge was that default Conda MPI implementations were not optimized for the high-performance interconnects and architectures of modern supercomputers. They lacked in particular support for advanced communication libraries such as Universal Communication X (UCX) for Mellanox Infiniband-based clusters and Open Fabric Interfaces (OFI) for Intel Omni-Path and other RDMA-capable networks like the EuroHPC. To address this, we upgraded the MPI toolchains in Conda-Forge, ensuring compatibility and optimized performance on state-of-the-art HPC infrastructure. This approach significantly accelerates the development of new algorithms by allowing scientists to develop locally on their personal computers or even laptops, then seamlessly scale to the largest HPC systems without having to modify their codes and scripts due to differences in the software stack. This eliminates the typically time-intensive porting steps required to adapt workflows to HPC environments, drastically shortening the transition from research development to production. This process should not only accelerate innovation by streamlining the full software environment management and usage but also make it more accessible to non-specialists, empowering researchers to focus on scientific discovery rather than sorting out technical software compatibility issues. Conda environments seamlessly run on a range of platforms without relying on host software stacks or requiring privilege escalation. By integrating these environments into lightweight containers, it is possible to achieve performance that matches or even exceeds native configurations. Additionally, conda-based containers, viewed as single compressed files, reduce storage requirements and alleviate strain on storage and file systems. Through comprehensive case studies and benchmarks, we demonstrate how this approach supports reproducibility, scalability, and computational efficiency for ESM workflows. This work highlights the transformative potential of Conda and containerized solutions in enabling complex EO modeling, accelerating innovation, and fostering sustainable and scalable digital innovation across federated platforms.

• Iaquinta, Jean (2025). Deploying Conda container solutions in HPC, from Galaxy and Lumi to Olivia. Fulltekst i vitenarkiv
• Iaquinta, Jean; Fouilloux, Anne Claire Mireille; Tangaro, Marco Antonio; Zambelli, Federico; Luna-Valero, Sebastian & Grüning, Björn (2024). Galaxy's Future: Bridging Interoperability and Execution Challenges. Fulltekst i vitenarkiv
• Fouilloux, Anne Claire Mireille & Iaquinta, Jean (2024). FAIR: More Than Just a Buzzword – A Framework for Data Excellence. Fulltekst i vitenarkiv Vis sammendrag

  Join our workshop to learn about the FAIR principles and get to know ROHub, a platform tailored for Research Objects (ROs). With ROHub, you can manage, share, and preserve your research materials more efficiently. It even has a built-in tool to guide you in improving the quality of your research. We'll walk you through real-world examples of how FAIR and ROHub can benefit you. Key points: ROHub organizes your research into one accessible spot, making it easier to manage. Plus, it uses clear labels so both you and computers can make sense of your work. Objectives: Introduction to the FAIR principles and how we can put them in practice.- Introduction to RoHub: Participants will gain a comprehensive understanding of RoHub's capabilities and how it facilitates collaborative research. RO Model Exploration: The workshop delves into the Research Object model, explaining how RoHub adheres to its principles. Hands-On session: Attendees will have the opportunity to explore and experience for themselves RoHub's features through practical demonstrations. Collaborative Research: The workshop emphasises the benefits of collaborative research and how RoHub can speed it up. FAIR Principles: Discussion on how RoHub aligns with the FAIR principles, ensuring research work is easily discoverable, accessible, and reusable. Learning outcomes Understand the FAIR principles and how to interpret them to put them in practice.- Get a better understanding of the concept of FAIR Digital Objects (FDOs) and Research Objects (RO-Crate). Get to know ROHub (an online and freely available Research Object Management Platform). Be able to explore existing Research Objects in ROHub. Understand how to use the ROHub's integrated FAIR assessment tool to check the FAIRness of your Research Objects and guide end-users to improve the FAIRness of their own research work. Understand how to create ROs and their lifecycle (live ROs, snapshots, archives) when aggregating diverse research artefacts.By the end of this workshop, you will understand the capabilities of Research Objects and how to harness them for more effective research management and collaboration, and for increasing the FAIRness of your research outputs e.g. data, software, workflows, etc.

• Stöckl, Vanessa; Grüning, Björn; Fouilloux, Anne Claire Mireille; Iaquinta, Jean & Coca-Castro, Alejandro (2024). Implementation of a reproducible pipeline for producing seasonal Arctic sea ice forecasts. Fulltekst i vitenarkiv
• Fouilloux, Anne Claire Mireille & Iaquinta, Jean (2024). Unlock the Power of EOSC: Navigating Services for Research Visibility and Impact. Fulltekst i vitenarkiv Vis sammendrag

  In today's dynamic research landscape, visibility and impact are paramount for researchers seeking to share their research work, foster collaboration, and drive innovation. The European Open Science Cloud (EOSC) stands as a transformative platform, offering an array of services and resources to empower researchers on their journey to Open Science for greater recognition and influence.Join us in this hands-on workshop, where we will demystify the EOSC ecosystem and guide you through the practical steps to make the most of its offerings. Our emphasis is on equipping you with actionable insights to navigate the jungle of EOSC services effectively, all with the aim of increasing your research visibility and impact.Duration 3 hours. Learning outcomes Understanding EOSC: Acquire a comprehensive overview of EOSC's role and significance within the open science paradigm. Recognize its pivotal function in facilitating seamless interactions between researchers, data, and services. Exploring EOSC Services: Navigate the diverse array of EOSC services designed to cater to the multifaceted requirements of the research community, encompassing domains such as data management, collaboration, and data sharing. Enhancing Research Visibility: Equip yourself with tactical approaches to augment the reach and visibility of your research through EOSC. Explore the myriad avenues available for disseminating data, publications, and projects within the EOSC framework. Citation Insights: Comprehend the intrinsic advantages of integrating EOSC services for bolstering citations and overall research recognition. Delve into established practices for effectively citing EOSC resources. Practical Application: Engage in hands-on activities designed to instill a robust understanding of how to adeptly utilize EOSC services, including the nuances of the enrollment process. Engagement with the EOSC Community: Initiate meaningful dialogues with the EOSC community, fostering collaborative ventures and gleaning insights from researchers who have adeptly leveraged EOSC. Connecting with the EOSC Community: Engage with the EOSC community and seize collaborative opportunities. Gain insights from peers who've effectively used EOSC in their research. By the workshop's end, you'll be equipped with the proficiency to adeptly navigate EOSC. Regardless of your research experience, this session will bolster your research's visibility and impact. Join us to harness EOSC's potential and amplify your research's recognition.

• Iaquinta, Jean & Fouilloux, Anne Claire Mireille (2024). UHI-Stream: A User-Friendly, Cloud-Based Tool for Rapid Analysis of Urban Heat Island Effect Changes Anywhere On Earth. Fulltekst i vitenarkiv Vis sammendrag

  The term "Urban Heat Island" (UHI) effect describes the phenomenon where urban environments exhibit higher air temperatures than their rural counterparts, a difference that is especially pronounced at night. This effect arises from the greater capacity of urban materials and man-made structures, such as buildings and pavements, to absorb, store, and then re-radiate heat compared to natural materials and landscapes. First identified over two centuries ago, the UHI effect is still subject of research to understand, measure, and mitigate its impacts on society as a whole, and in particular on economic activities and public health. Although traditionally the prerogative of specialists, the UHI is also attracting increasing interest among citizens. However, not all have the necessary technical expertise or infrastructure access to source relevant data (from in-situ measurements, satellite remote sensing or numerical models), process it efficiently, synthesize it and interpret the changes over time or between different locations. The UHI-Stream tool was specifically developed to bridge this gap and quickly analyze temperature differences between two points anywhere on Earth's by leveraging EGI compute and storage resources (owned by CESNET) and ERA5-Land reanalysis data (available from 1950, as part of the Copernicus Climate Change Service). The corresponding hourly 2m air temperatures are streamed from S3 buckets, processed on-the-fly and visualized as annual heat-maps or animations spanning user-defined time-frames. Conveniently hosted on RoHub as a FAIR (Findable, Accessible, Interoperable, and Reusable) Executable Research Object, UHI-Stream is expected to be further converted into a Galaxy tool with a Graphical User Interface as part of the EuroScienceGateway project, potentially incorporating additional features to help users pinpoint representative urban and adjacent rural areas, or account for more grid cells. In summary, UHI-Stream is poised to become a valuable asset in urban climatology studies, enabling easier identification of UHI patterns and estimating climate impacts on a regional scale. The tool’s versatility in analyzing any two geographic points enhances its usefulness beyond the mere urban-rural context, allowing for comparative analyses of temperature changes across diverse locales, regardless of their relationship.

• Iaquinta, Jean & Fouilloux, Anne Claire Mireille (2024). Unveiling the Potential of Containers in Scientific Computing. Fulltekst i vitenarkiv
• Fouilloux, Anne Claire Mireille; Marasco, Pier-Lorenzo; Odaka, Tina; Mottram, Ruth; Zieger, Paul & Schultz, Michael [Vis alle 9 forfattere av denne artikkelen] (2023). Pangeo framework for training: experience with FOSS4G, the CLIVAR bootcamp and the eScience course. Fulltekst i vitenarkiv Vis sammendrag

  The ever increasing number of scientific datasets made available by authoritative data providers (NASA, Copernicus, etc.) and provided by the scientific community opens new possibilities for advancing the state of the art in many areas of the natural sciences. As a result, researchers, innovators, companies and citizens need to acquire computational and data analysis skills to optimally exploit these datasets. Several educational programs dispense basic courses to students, and initiatives such as “The Carpentries” (https://carpentries.org/) complement this offering but also reach out to established researchers to fill the skill gap thereby empowering them to perform their own data analysis. However, most researchers find it challenging to go beyond these training sessions and face difficulties when trying to apply their newly acquired knowledge to their own research projects. To this regard, hackathons have proven to be an efficient way to support researchers in becoming competent practitioners but organising good hackathons is difficult and time consuming. In addition, the need for large amounts of computational and storage resources during the training and hackathons requires a flexible solution. Here, we propose an approach where researchers work on realistic, large and complex data analysis problems similar to or directly part of their research work. Researchers access an infrastructure deployed on the European Ocean Science Cloud (EOSC) that supports intensive data analysis (large compute and storage resources). EOSC is a European Commission initiative for providing a federated and open multi-disciplinary environment where data, tools and services can be shared, published, found and re-used. We used jupyter book for delivering a collection of FAIR training materials for data analysis relying on Pangeo EOSC deployments as its primary computing platform. The training material (https://pangeo-data.github.io/foss4g-2022/intro.html, https://pangeo-data.github.io/clivar-2022/intro.html, https://pangeo-data.github.io/escience-2022/intro.html) is customised (different datasets with similar analysis) for different target communities and participants are taught the usage of Xarray, Dask and more generally how to efficiently access and analyse large online datasets. The training can be completed by group work where attendees can work on larger scale scientific datasets: the classroom is split into several groups. Each group works on different scientific questions and may use different datasets. Using the Pangeo (http://pangeo.io) ecosystem is not always new for all attendees but applying Xarray (http://xarray.pydata.org) and Dask (https://www.dask.org/) on actual scientific “mini-projects” is often a showstopper for many researchers. With this approach, attendees have the opportunity to ask questions, collaborate with other researchers as well as Research Software Engineers, and apply Open Science practices without the burden of trying and failing alone. We find the involvement of scientific computing research engineers directly in the training is crucial for success of the hackathon approach. Feedback from attendees shows that it provides a solid foundation for big data geoscience and helps attendees to quickly become competent practitioners. It also gives infrastructure providers and EOSC useful feedback on the current and future needs of researchers for making their research FAIR and open. In this presentation, we will provide examples of achievements from attendees and present the feedback EOSC providers have received.

• Eynard-Bontemps, Guillaume; Iaquinta, Jean; Luna-Valero, Sebastian; Caballer, Miguel; Paul, Frederic & Fouilloux, Anne Claire Mireille [Vis alle 9 forfattere av denne artikkelen] (2023). Pangeo@EOSC: deployment of PANGEO ecosystem on the European Open Science Cloud. Fulltekst i vitenarkiv Vis sammendrag

  Research projects heavily rely on the exchange and processing of data and in this context Pangeo (https://pangeo.io/), a world-wide community of scientists and developers, thrives to facilitate the deployment of ready to use and community-driven platforms for big data geoscience. The European Open Science Cloud (EOSC) is the main initiative in Europe for providing a federated and open multi-disciplinary environment where European researchers, innovators, companies and citizens can share, publish, find and re-use data, tools and services for research, innovation and educational purposes. While a number of services based on Jupyter Notebooks were already available, no public Pangeo deployments providing fast access to large amounts of data and compute resources were accessible on EOSC. Most existing cloud-based Pangeo deployments are USA-based, and members of the Pangeo community in Europe did not have a shared platform where scientists or technologists could exchange know-how. Pangeo teamed up with two EOSC projects, namely EGI-ACE (https://www.egi.eu/project/egi-ace/) and C-SCALE (https://c-scale.eu/) to demonstrate how to deploy and use Pangeo on EOSC and emphasise the benefits for the European community. The Pangeo Europe Community together with EGI deployed a DaskHub, composed of Dask Gateway (https://gateway.dask.org/) and JupyterHub (https://jupyter.org/hub), with Kubernetes cluster backend on EOSC using the infrastructure of the EGI Federation (https://www.egi.eu/egi-federation/). The Pangeo EOSC JupyterHub deployment makes use of 1) the EGI Check-In to enable user registration and thereby authenticated and authorised access to the Pangeo JupyterHub portal and to the underlying distributed compute infrastructure; and 2) the EGI Cloud Compute and the cloud-based EGI Online Storage to distribute the computational tasks to a scalable compute platform and to store intermediate results produced by the user jobs. To facilitate future Pangeo deployments on top of a wide range of cloud providers (AWS, Google Cloud, Microsoft Azure, EGI Cloud Computing, OpenNebula, OpenStack, and more), the Pangeo EOSC JupyterHub deployment is now possible through the Infrastructure Manager (IM) Dashboard (https://im.egi.eu/im-dashboard/login). All the computing and storage resources are currently supplied by CESNET (https://www.cesnet.cz/?lang=en) in the frame of EGI-ACE project (https://im.egi.eu/). Several deployments have been made to serve the geoscience community, both for teaching and for research work. To date, more than 100 researchers have been trained on Pangeo@EOSC deployments and more are expected to join, in particular with easy access to large amounts of Copernicus data through a recent collaboration established with the C-SCALE project. In this presentation, we will provide details on the different deployments, how to get access to JupyterHub deployments and more generally how to contribute to Pangeo@EOSC.

• Castellan, Giorgio; Foglini, Federica; Giovanna, Fabio Di; Grande, Valentina; Iaquinta, Jean & Trasatti, Elisa [Vis alle 7 forfattere av denne artikkelen] (2023). How to adopt FAIR and Open Science principles in EOSC: supporting the research lifecycle management within Earth Science communities, with the RELIANCE project services. Fulltekst i vitenarkiv Vis sammendrag

  The RELIANCE Project (Research Lifecycle Management for Earth Science Communities and Copernicus users in EOSC) aims to realize the vision of FAIR research in EOSC and enable the adoption of Open Science practices in EOSC by implementing an holistic research management approach based on three key and complementary technologies: i) Research objects (RO) as the overarching mechanism to manage scientific research activities, which relies upon ROHub platform as the reference service; ii) data cubes as the mechanisms enabling an efficient and scalable Earth Observation data access and discovery, which relies upon the Advanced geospatial Data Management (ADAM) platform as the reference service; iii) text mining and semantic enrichment services allowing to extract machine-readable metadata from RO resources, enabling researchers to discover scientific information at scale and to structure their own research, and which rely on the AI-based platform COGITO as base system. As part of the integration in EOSC, RELIANCE services leverage and integrate with some of the EOSC core-cross cutting and added value services, playing a complementary role to what is already available and bridging between various EOSC services. RELIANCE validated the project services through multidisciplinary and thematic real life use cases led by three different Earth Science communities: Geohazards, Sea Monitoring and Climate Change communities. In our presentation, we will showcase different types of scenarios for the three Earth Science communities represented in Reliance to highlight how the scientists in our respective disciplines fostered their work towards Open Science.

• Iaquinta, Jean; Fouilloux, Anne Claire Mireille; Odaka, Tina; Coca-Castro, Alejandro; Eynard-Bontemps, Guillaume & Luna-Valero, Sebastian [Vis alle 10 forfattere av denne artikkelen] (2023). Open big-data geoscience with PANGEO@EOSC. Fulltekst i vitenarkiv Vis sammendrag

  The benefits of Open Science (OS) and FAIR foundational principles - Findable, Accessible, Interoperable and Reusable - are increasingly valued by academia, although what OS and FAIR entail in practice is largely misunderstood. Once researchers manage to grasp OS and FAIR principles, they are often hit by practical difficulties. The European Open Science Cloud (EOSC) is the main initiative in Europe for providing a federated and open multi-disciplinary environment where European researchers, innovators, companies and citizens can share, publish, find and re-use data, tools and services for research, innovation and educational purposes. One of the goals of the EOSC is to co-design with communities the tools and services that are useful for their day to day research work, to facilitate collaboration and to foster wider adoption of Open Science practices. The Pangeo (https://pangeo.io/) community is a world-wide community of scientists and developers, who thrives to facilitate the deployment of ready-to-use and community-driven platforms for big data geoscience. While a number of services based on Jupyter Notebooks were already available, no public Pangeo deployments providing fast access to large amounts of data and compute resources were accessible on EOSC. Most existing cloud-based Pangeo deployments are USA-based, and members of the Pangeo community in Europe did not have a shared platform where scientists or technologists could exchange know-how and experiences. Pangeo teamed up with two EOSC projects, namely EGI-ACE (https://www.egi.eu/project/egi-ace/) and C-SCALE (https://c-scale.eu/) to demonstrate how to deploy and use Pangeo on EOSC and emphasise the benefits for the European community. The Pangeo Europe Community together with EGI deployed a DaskHub, composed of a Dask Gateway (https://gateway.dask.org/) and JupyterHub (https://jupyter.org/hub), with a Kubernetes cluster backend on EOSC using the infrastructure of the EGI Federation (https://www.egi.eu/egi-federation/). The Pangeo EOSC JupyterHub deployment makes use of 1) the EGI Check-In to enable user registration (and thereby authenticated and authorised access to the Pangeo JupyterHub portal and to the underlying distributed compute infrastructure); and 2) the EGI Cloud Compute and the cloud-based EGI Online Storage (to distribute the computational tasks to a scalable compute platform and to store intermediate results produced by the user jobs). To facilitate future Pangeo deployments on top of a wide range of cloud providers (AWS, Google Cloud, Microsoft Azure, EGI Cloud Computing, OpenNebula, OpenStack, and many more), the Pangeo EOSC JupyterHub deployment is now possible through a so-called Infrastructure Manager (IM) Dashboard (https://im.egi.eu/). All the computing and storage resources are currently supplied by CESNET (https://www.cesnet.cz/?lang=en) in the frame of EGI-ACE project (https://www.egi.eu/project/egi-ace/). Several deployments have been made to serve the geoscience community, both for teaching and for research work. One major advantage of these deployments for teaching and on-boarding researchers is the possibility to train them with realistic, large and complex data analysis problems similar to or directly part of their research work. Participants are taught the usage of Xarray, Dask and more generally how to efficiently access and analyse large online datasets. With this approach, attendees have the opportunity to ask questions, collaborate with other researchers as well as Research Software Engineers, and apply Open Science practices without the burden of trying and (sometimes) failing alone and without having to build their own infrastructure. To date, more than 100 researchers have been trained on Pangeo@EOSC deployments. With a growing community, discussions arose about the need to define clear practices for writing and publishing FAIR Jupyter Notebooks that can be reused and built upon for new research. This is where the community of

• Iaquinta, Jean & Fouilloux, Anne Claire Mireille (2023). Using FAIR and Open Science practices to better understand vegetation browning in Troms and Finnmark (Norway). Fulltekst i vitenarkiv Vis sammendrag

  In most places on the planet vegetation thrives: it is known as “greening Earth”. However in certain regions, especially in the Arctic, there are areas exhibiting a browning trend. This phenomenon is well known but not fully understood yet, and grasping its impact on local ecosystems requires involvement of scientists from different disciplines, including social sciences and humanities, as well as local populations. Here we focus on the Troms and Finnmark counties in northern Norway to assess the extent of the problem and any link with local environmental conditions as well as potential impacts. We have chosen to adopt an open and collaborative process and take advantage of the services offered by RELIANCE on the European Open Science Cloud (EOSC). RELIANCE delivers a suite of innovative and interconnected services that extend the capabilities of the European Open Science Cloud (EOSC) to support the management of the research lifecycle within Earth Science Communities and Copernicus Users. The RELIANCE project has delivered 3 complementary technologies: Research Objects (ROs), Data Cubes and AI-based Text Mining. RoHub is a Research Object management platform that implements these 3 technologies and enables researchers to collaboratively manage, share and preserve their research work. We will show how we are using these technologies along with EGI notebooks to work open and share an executable Jupyter Notebook that is fully reproducible and reusable. We use a number of Python libraries from the Pangeo software stack such as Xarray, Dask and Zarr. Our Jupyter Notebook is bundled with its computational environment, datacubes and related bibliographic resources in an executable Research Object. We believe that this approach can significantly speed up the research process and can drive it to more exploitable results. Up to now, we have used indices derived from satellite data (in particular Sentinel-2) to assess how the vegetation cover in Troms and Finnmark counties has changed. To go a bit further we are investigating how to relate such information to relevant local parameters obtained from meteorological reanalysis data (ERA5 and ERA5-land from ECMWF). That should give a good basis for training an Artificial Intelligence algorithm and testing it, with the objective of getting an idea about the possibility of “predicting” what is likely to happen in the near future with certain types of vegetation like mosses and lichens which are essential for local populations and animals.

• Liborio, Leandro; Chadwick, Elizabeth A; Austin, Patrick; Gonzalez-Beltran, Alejandra; Savchenko, Volodymyr & Neronov, Andrii [Vis alle 8 forfattere av denne artikkelen] (2023). Galaxy and Scientific Communities: How to Incorporate New Ones, Uplift Engaged Ones and Maintain Mature Ones. Fulltekst i vitenarkiv Vis sammendrag

  The Galaxy platform has been designed as science domain-neutral and, recently, it has been expanding into new scientific communities. In this talk I will present examples from our work in the EuroScienceGateway project, which aims to leverage European computing infrastructures for data-intensive research guided by FAIR principles. This work comprises the incorporation of the new communities of muon science, astrophysics, biodiversity and climate science into the Galaxy platform. As regards the muon community, I will introduce MuonGalaxy: a new Galaxy instance that allows members of the muon community to have better access to the software tools developed by the MSCP. Furthermore, I will also discuss how the biodiversity, climate science and astrophysics communities are integrating their state-of-the-art tools and workflows into Galaxy. The biodiversity community is using Galaxy to tackle the problem of annotating new genomes in the context of a tremendous increase of new genomic data. As regards the climate science community, it is using Galaxy to address computationally intensive use cases such as forecasting changes of vegetation and sea-ice in the Arctic. Finally, Astrophysics has a long tradition of FAIR data and workflow stewardship, and they are using Galaxy to harmonize these practices with those of other communities.

• Fouilloux, Anne Claire Mireille; Coca-Castro, Alejandro & Iaquinta, Jean (2023). Research Object Hub (RoHub) for FAIR research data management and Open Science in practice. Fulltekst i vitenarkiv Vis sammendrag

  RoHub is a Research Object (RO) management platform that enables researchers to collaboratively manage, share and preserve their research work. RoHub implements the full RO model and paradigm: resources associated to a particular research work are aggregated into a single FAIR digital object, and metadata relevant for understanding and interpreting the content is represented as semantic metadata that are user and machine readable. In this mini-workshop, you will learn how to use RoHub to search, create and reuse different types of ROs: Bibliography-centric, data-centric, executable, software-centric, workflow-centric ROs. A new RO can either be created from scratch (empty Live RO) or from another existing RO (fork RO) and aggregate new objects (or modify existing objects) through its whole life cycle. This means, RO is filled incrementally by aggregating relevant resources such as software, workflows, datasets, documents according to its typology that are being created, reused or repurposed. These resources can be modified at any point in time. We can copy and keep ROs in time through snapshots which reflect their status at a given point in time. Snapshots can have their own identifiers (DOIs) which facilitates tracking the evolution of a research. At some point in time, a RO can be published and archived (so called Archived RO) with a permanent identifier (DOI). New Live ROs can be derived based on an existing Archived RO, for instance by forking it. In this mini-workshop, we will fork an existing executable RO containing a Jupyter Notebook as a main resource. Then we will check its reproducibility (through a Binder service such as MyBinder and/or EGI Binder) and modify the Jupyter Notebook. Finally, the updated Jupyter Notebook will be saved in the forked RO to update it. We will also discuss the reusability of Jupyter Notebook by introducing the need for defining and adopting Best practices. As an example, we will use the Environmental Data Science Book (EDS) where the EDS community has defined clear guidelines for submission and reviewing, templates for creating and scheduling notebooks using GitHub Actions CI/CD tools, FAIR practices through RoHub and Binder to facilitate fully documented, shareable and reproducible notebooks.

• Stoeckl, Vanessa; Fouilloux, Anne Claire Mireille; Coca-Castro, Alejandro; Iaquinta, Jean & Gruening, Bjoern (2023). Implementation of reproducible pipeline for forecasting sea ice. Fulltekst i vitenarkiv Vis sammendrag

  The Environmental Data Science book (EDS book, https://edsbook.org/) is a pan-european community-driven resource that leverages Pangeo software stack (https://pangeo.io/), Jupyter notebooks (http://jupyter.org) and Research Objects (https://www.researchobject.org/) for FAIR environmental data science. EDS book allows researchers to co-design, collaboratively and openly review, and curate interactive, shareable and reproducible executable notebooks. Clear guidelines for writing modular and reusable Jupyter notebooks are provided but the need for understanding a specific programming language (Python, Julia, R) remains. This is clearly a barrier for inter-disciplinary research and to overcome this limitation, individual and modular Galaxy Tools can be created out of the Jupyter notebook, for each section (for instance, download and preparation of input data, data analysis, visualisation). The different Galaxy tools can then be reused by anyone from Galaxy portals to derive new innovative and fully annotated reproducible workflows. In this talk, we show this approach with a EDS book Jupyter notebook written in Python reproducing the scientific results of the Nature Communications paper titled "Seasonal Arctic sea ice forecasting with probabilistic deep learning (https://www.nature.com/articles/s41467-021-25257-4)". The resulting workflow uses existing Galaxy Tools and the two newly developed Galaxy Tools for forecasting seasonal arctic sea ice with pre-trained probabilistic deep learning models.

• Lamothe, Lucie; Kierkegaard, Mads; Black, Melissa; Eldakroury, Hager; Priya, Ankita & Machinda, Anne [Vis alle 31 forfattere av denne artikkelen] (2022). EDAM - The data analysis and management ontology (update 2022). Fulltekst i vitenarkiv Vis sammendrag

  EDAM is an ontology of data analysis and data management, within and beyond life sciences. It comprises concepts related to data analysis, modelling, optimisation, and data life cycle. Thanks to EDAM and its applications, digital research objects — such as data, tools, workflows, standards, or learning materials — can be made easier to find, understand, reuse, and combine.

• He, Yanchun; Gupta, Alok Kumar; Bentsen, Mats; Schulz, Michael; Torsvik, Tomas & Gjermundsen, Ada [Vis alle 9 forfattere av denne artikkelen] (2021). The 3rd iNES NorESM User Workshop. Fulltekst i vitenarkiv Vis sammendrag

  About 15 participants actively involved in the meeting, both physically and virtually on line. There are presentations and hands-on sessions for different topics. The agenda of the workshop is found at the workshop webpage: https://noresmhub.github.io/NorESM_Workshop_2021/

• Fouilloux, Anne Claire Mireille & Iaquinta, Jean (2021). Earth System Model as a Service (ESMaaS). Fulltekst i vitenarkiv Vis sammendrag

  We describe here how to package and distribute Earth System Models, generate automatically Docker/Singularity containers, run containerized ESMs on various platforms (laptop, cloud, HPC), distribute workflow tasks (for inputs, execution, quality control, diagnostics) and publish workflows for FAIR (Findable, Accessible, Interoperable and Reusable) and open climate research. We demonstrate that containers provide native performance on HPCs. When added to Galaxy (https://galaxyproject.org), ESMs become available to all, regardless of their technical skills.

• Fouilloux, Anne Claire & Iaquinta, Jean (2021). Galaxy and research lifecycle management technologies for earth science communities and Copernicus users in EOSC. Fulltekst i vitenarkiv Vis sammendrag

  The main objective of RELIANCE (https://www.reliance-project.eu/) is to provide Earth Scientists and Copernicus user communities with an innovative and interoperable service portfolio offering for an open-by-default, efficient, and crossdisciplinary research management environment supporting FAIR data principles and Open Science. Copernicus is the European Union's Earth observation programme, providing information services that draw from satellite Earth Observation and in-situ (non-space) data. This wealth of information is aimed at being exploited by everyone, for the benefit of all citizens but accessing Copernicus data is difficult and often requires advanced programming skills. By offering access to Copernicus data in Galaxy and deploying Galaxy tools for analyzing and creating advanced workflows, we believe it will democratize the usage of such data and facilitate cross-disciplinary research. RELIANCE service ecosystem (Research Objects, Data Cubes and AI-based Text Mining) will be "connected" to Galaxy to facilitate the re-use of generated Research Objects from Galaxy. During GCC2021, we will present the current status and the roadmap.

• Iaquinta, Jean & He, Yan-Chun (2020). 2020 INES annual meeting - WP4 - Support for effective operation and use of the infrastructure. Fulltekst i vitenarkiv
• Fouilloux, Anne Claire; Iaquinta, Jean & Krüger, Kirstin (2019). Towards an infrastructure for Earth system modelling in Norway. Fulltekst i vitenarkiv
• Fouilloux, Anne Claire; Krüger, Kirstin & Iaquinta, Jean (2019). Climate Analysis with Galaxy. Fulltekst i vitenarkiv Vis sammendrag

  Advance in the development of climate models and associated data viewers and processing tools has achieved unprecedented maturity in the environmental scientific community. This progress was accompanied by the standardization of model output formats (conventions for Climate and Forecast metadata), the availability of open databases (i.e., the Earth System Grid Federation), and of the climate model codes themselves. However, scientific communities are not fully realizing the benefits of such advances because of the complexity for both running and analysing climate models. Using Galaxy makes it possible to share tools and easily (re-)run climate model runs or analyse climate data thus opening new opportunities for multidisciplinary research for instance ecology, social and human sciences. In this presentation, we will show how climate models can be run out-of-the-box, without much effort, using Galaxy platform. We will show how climate model outputs can be visualized using the same web portal.

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