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2026
Conference article
Open Access
A conversational assistant for geoscientists in virtual research environments
Peccerillo Biagio, Oliviero Alfredo, Procaccini Marco, Candela Leonardo, Frosini Luca, Mangiacrapa Francesco, Panichi Giancarlo, Assante Massimiliano, Pagano Pasquale
D4Science provides web-based Virtual Research Environments (VREs) that support FAIR, open, and reproducible science across multiple research domains, including Earth science. These environments integrate data access, computation, and collaboration services, offering powerful capabilities to researchers and enabling complex, data-intensive scientific activities within a shared digital infrastructure. This contribution introduces a conversational intelligent assistant integrated into D4Science VREs, designed to support Earth scientists in their research activity. The assistant provides a natural language interface that helps users interact with D4Science VREs' services, locate relevant datasets and research items, obtain guidance on common tasks, and support exploratory and operational activities within the VRE. The assistant is designed with a modular approach. The user interacts with a coordinator agent that orchestrates a multi-agent system, where specialized AI agents collaborate to perform a variety of tasks. This architecture allows the assistant to handle heterogeneous requests and to support users across different phases of their research activities, while also facilitating maintenance and extensibility. The conversational agent adopts a Retrieval-Augmented Generation (RAG) approach that leverages the knowledge already captured by the VRE through its regular use by research communities. In fact, as VREs naturally accumulate updated knowledge created and curated by researchers over time, the assistant's knowledge base evolves incorporating new information. This way, the assistant can ground its responses in domain-specific and up-to-date information, effectively acting as a domain-aware expert embedded within the research environment. By serving as an accessible entry point to the VRE, the assistant complements existing interfaces without altering established workflows. The presentation discusses the motivation, design choices, and integration strategy. It also presents various concrete use cases relevant to Earth scientists, demonstrating how the conversational assistant can be effectively employed to support their research activity.DOI: 10.5194/egusphere-egu26-13138
Metrics:
Peccerillo Biagio, Oliviero Alfredo, Procaccini Marco, Candela Leonardo, Frosini Luca, Mangiacrapa Francesco, Panichi Giancarlo, Assante Massimiliano, Pagano Pasquale
D4Science provides web-based Virtual Research Environments (VREs) that support FAIR, open, and reproducible science across multiple research domains, including Earth science. These environments integrate data access, computation, and collaboration services, offering powerful capabilities to researchers and enabling complex, data-intensive scientific activities within a shared digital infrastructure. This contribution introduces a conversational intelligent assistant integrated into D4Science VREs, designed to support Earth scientists in their research activity. The assistant provides a natural language interface that helps users interact with D4Science VREs' services, locate relevant datasets and research items, obtain guidance on common tasks, and support exploratory and operational activities within the VRE. The assistant is designed with a modular approach. The user interacts with a coordinator agent that orchestrates a multi-agent system, where specialized AI agents collaborate to perform a variety of tasks. This architecture allows the assistant to handle heterogeneous requests and to support users across different phases of their research activities, while also facilitating maintenance and extensibility. The conversational agent adopts a Retrieval-Augmented Generation (RAG) approach that leverages the knowledge already captured by the VRE through its regular use by research communities. In fact, as VREs naturally accumulate updated knowledge created and curated by researchers over time, the assistant's knowledge base evolves incorporating new information. This way, the assistant can ground its responses in domain-specific and up-to-date information, effectively acting as a domain-aware expert embedded within the research environment. By serving as an accessible entry point to the VRE, the assistant complements existing interfaces without altering established workflows. The presentation discusses the motivation, design choices, and integration strategy. It also presents various concrete use cases relevant to Earth scientists, demonstrating how the conversational assistant can be effectively employed to support their research activity.DOI: 10.5194/egusphere-egu26-13138
Metrics:
See at:
CNR IRIS 
| www.egu26.eu | CNR IRIS 
2026
Conference article
Metadata Only Access
Publishing as a First-Class Service in Virtual Research Environments: The D4Science Catalogue
Massimiliano Assante, Leonardo Candela, Andrea Dell’amico, Luca Frosini, Francesco Mangiacrapa, Alfredo Oliviero, Pasquale Pagano, Giancarlo Panichi, Biagio Peccerillo, Tommaso Piccioli, Marco Procaccini
Virtual Research Environments (VREs) and Science Gateways support collaborative and data-intensive research by integrating data access, computing, and analytical services. However, publishing research outputs often remains external to these environments, requiring researchers to transfer datasets, workflows, software, and other artifacts to external repositories. This separation introduces friction and limits timely sharing and reuse. This paper presents the D4Science Catalogue, a configurable service designed to make publishing a native capability of Virtual Research Environments. The Catalogue enables communities to publish diverse research objects together with community-defined metadata and to support discovery through both human and machine access mechanisms. Experiences from multiple deployments illustrate how integrated catalogue-based services facilitate early sharing, improve discoverability, and support FAIR research practices within science gateways.DOI: 10.5281/zenodo.22096211
Project(s): A federated European FAIR and Open Research Ecosystem for oceans, seas, coastal and inland waters
Metrics:
Massimiliano Assante, Leonardo Candela, Andrea Dell’amico, Luca Frosini, Francesco Mangiacrapa, Alfredo Oliviero, Pasquale Pagano, Giancarlo Panichi, Biagio Peccerillo, Tommaso Piccioli, Marco Procaccini
Virtual Research Environments (VREs) and Science Gateways support collaborative and data-intensive research by integrating data access, computing, and analytical services. However, publishing research outputs often remains external to these environments, requiring researchers to transfer datasets, workflows, software, and other artifacts to external repositories. This separation introduces friction and limits timely sharing and reuse. This paper presents the D4Science Catalogue, a configurable service designed to make publishing a native capability of Virtual Research Environments. The Catalogue enables communities to publish diverse research objects together with community-defined metadata and to support discovery through both human and machine access mechanisms. Experiences from multiple deployments illustrate how integrated catalogue-based services facilitate early sharing, improve discoverability, and support FAIR research practices within science gateways.DOI: 10.5281/zenodo.22096211
Project(s): A federated European FAIR and Open Research Ecosystem for oceans, seas, coastal and inland waters
Metrics:
See at:
CNR IRIS | zenodo.org
2026
Journal article
Open Access
Deploying conversational agents in virtual research environments: approaches and lessons learned
Assante Massimiliano, Candela Leonardo, Dell'Amico Andrea, Frosini Luca, Mangiacrapa Francesco, Oliviero Alfredo, Pagano Pasquale, Panichi Giancarlo, Peccerillo Biagio, Piccioli Tommaso, Procaccini Marco
Conversational agents have the potential to streamline tasks, provide support, and enhance user experience across various domains including Virtual Research Environments (VREs). The recent progress in conversational artificial intelligence and Large Language Models (LLMs) offers novel strategies for the development of these agents. This paper reports on the potential benefits, the challenges and the approaches resulting from concrete experiences in developing and equipping D4Science-based VREs with suitable conversational agents. The paper presents three successive implementation approaches and the resulting agent solution, each designed to address the limitations identified in the preceding iteration and to leverage the advantages offered by newer implementation and development options. The proposed approaches led to the progressive refinement of the agent design and functionality, resulting in DAVE, a conversational agent capable of securely interacting with multiple D4Science services and supporting a wide range of user workflows. The iterative process highlighted critical requirements—including authentication handling, usability, and extensibility—that can inform the design of conversational agents in similar research infrastructures. The study shows that conversational agents can effectively lower the barrier to accessing VRE functionalities and enhance user engagement. The resulting design principles and lessons learned provide a foundation for future work aimed at extending DAVE with an enhanced feedback mechanism and locally hosted LLM integration, and conducting systematic usability evaluations within active research communities.Source: SN COMPUTER SCIENCE, vol. 7
DOI: 10.1007/s42979-026-04863-3
Project(s): A federated European FAIR and Open Research Ecosystem for oceans, seas, coastal and inland waters, FOSSR—Fostering Open Science in Social Science Research
Metrics:
Assante Massimiliano, Candela Leonardo, Dell'Amico Andrea, Frosini Luca, Mangiacrapa Francesco, Oliviero Alfredo, Pagano Pasquale, Panichi Giancarlo, Peccerillo Biagio, Piccioli Tommaso, Procaccini Marco
Conversational agents have the potential to streamline tasks, provide support, and enhance user experience across various domains including Virtual Research Environments (VREs). The recent progress in conversational artificial intelligence and Large Language Models (LLMs) offers novel strategies for the development of these agents. This paper reports on the potential benefits, the challenges and the approaches resulting from concrete experiences in developing and equipping D4Science-based VREs with suitable conversational agents. The paper presents three successive implementation approaches and the resulting agent solution, each designed to address the limitations identified in the preceding iteration and to leverage the advantages offered by newer implementation and development options. The proposed approaches led to the progressive refinement of the agent design and functionality, resulting in DAVE, a conversational agent capable of securely interacting with multiple D4Science services and supporting a wide range of user workflows. The iterative process highlighted critical requirements—including authentication handling, usability, and extensibility—that can inform the design of conversational agents in similar research infrastructures. The study shows that conversational agents can effectively lower the barrier to accessing VRE functionalities and enhance user engagement. The resulting design principles and lessons learned provide a foundation for future work aimed at extending DAVE with an enhanced feedback mechanism and locally hosted LLM integration, and conducting systematic usability evaluations within active research communities.Source: SN COMPUTER SCIENCE, vol. 7
DOI: 10.1007/s42979-026-04863-3
Project(s): A federated European FAIR and Open Research Ecosystem for oceans, seas, coastal and inland waters, FOSSR—Fostering Open Science in Social Science Research
Metrics:
See at:
CNR IRIS | link.springer.com | CNR IRIS
2026
Journal article
Open Access
Supporting open science in virtual research environments: the DAVE experience
Dell’amico Andrea, Oliviero Alfredo, Panichi Giancarlo, Peccerillo Biagio, Procaccini Marco
DAVE, a conversational assistant integrated into D4Science Virtual Research Environments, simplifies access to services and supports Open Science workflows through natural language interaction. Virtual Research Environments (VREs) provide integrated access to data, services, and collaboration tools for open and data-intensive research. Building on the D4Science Virtual Research Environments, which support over 230 VREs and around 28,000 users worldwide, our work on conversational agents has led to the development of DAVE (D4Science Assistant for Virtual Research Environments), a system designed to assist researchers directly within their workflows.Source: ERCIM NEWS, vol. 144, pp. 39-40
Dell’amico Andrea, Oliviero Alfredo, Panichi Giancarlo, Peccerillo Biagio, Procaccini Marco
DAVE, a conversational assistant integrated into D4Science Virtual Research Environments, simplifies access to services and supports Open Science workflows through natural language interaction. Virtual Research Environments (VREs) provide integrated access to data, services, and collaboration tools for open and data-intensive research. Building on the D4Science Virtual Research Environments, which support over 230 VREs and around 28,000 users worldwide, our work on conversational agents has led to the development of DAVE (D4Science Assistant for Virtual Research Environments), a system designed to assist researchers directly within their workflows.Source: ERCIM NEWS, vol. 144, pp. 39-40
See at:
ercim-news.ercim.eu | CNR IRIS | CNR IRIS
2025
Journal article
Open Access
Empowering collaborative and reproducible large-scale data analytics with D4Science
Assante M., Lettere M., Oliviero A., Pagano P.
The D4Science platform is advancing reproducible research by providing scientists with robust, cloud-based tools for large-scale data analysis such as the Cloud Computing Platform (CCP). CCP enhances collaboration, allowing researchers to share, reuse, and build on each other’s work across diverse scientific disciplines.Source: ERCIM NEWS, vol. 140, pp. 11-12
Assante M., Lettere M., Oliviero A., Pagano P.
The D4Science platform is advancing reproducible research by providing scientists with robust, cloud-based tools for large-scale data analysis such as the Cloud Computing Platform (CCP). CCP enhances collaboration, allowing researchers to share, reuse, and build on each other’s work across diverse scientific disciplines.Source: ERCIM NEWS, vol. 140, pp. 11-12
See at:
ercim-news.ercim.eu | CNR IRIS | CNR IRIS
2025
Journal article
Restricted
NAVIGATOR: a regional multimodal imaging biobank initiative powered by AI tools for precision medicine in oncology
Aghakhanyan G., Barucci A., Pascali M. A., Assante M., Bagnacci G., Bertelli E., Caputo F. P., Cuibari M. E., Carlini E., Carpi R., Caudai C., Cioni D., Colantonio S., Colcelli V., Dell'Amico A., Vecchio V. D., Gangi D. D., Faggioni L., Formica V., Francischello R., Frosini L., Kotsa C., Lipari G., Manghi P., Martino V. D., Marzi C., Mazzei M. A., Mangiacrapa F., Meglio N. D., Miele V., Molinaro E., Paiar F., Pagano P., Panichi G., Pasquinelli F., Peccerillo B., Perrella A., Piccioli T., Oliviero A., Olivoni M., Rucci D., Tampucci M., Tumminello L., Volpini F., Zanuzzi A., Fanni S. C., Neri E.
The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.Source: EUROPEAN JOURNAL OF RADIOLOGY, vol. 191 (issue 112327)
DOI: 10.1016/j.ejrad.2025.112327
Project(s): An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Metrics:
Aghakhanyan G., Barucci A., Pascali M. A., Assante M., Bagnacci G., Bertelli E., Caputo F. P., Cuibari M. E., Carlini E., Carpi R., Caudai C., Cioni D., Colantonio S., Colcelli V., Dell'Amico A., Vecchio V. D., Gangi D. D., Faggioni L., Formica V., Francischello R., Frosini L., Kotsa C., Lipari G., Manghi P., Martino V. D., Marzi C., Mazzei M. A., Mangiacrapa F., Meglio N. D., Miele V., Molinaro E., Paiar F., Pagano P., Panichi G., Pasquinelli F., Peccerillo B., Perrella A., Piccioli T., Oliviero A., Olivoni M., Rucci D., Tampucci M., Tumminello L., Volpini F., Zanuzzi A., Fanni S. C., Neri E.
The NAVIGATOR project established an Italian regional imaging biobank and interactive research platform designed to support precision oncology through the integration of multimodal imaging, clinical, and omics data. The platform goes beyond a static repository, offering a secure Virtual Research Environment (VRE) where users can upload data, test AI algorithms, and execute complete analytical pipelines. The platform incorporates artificial intelligence (AI)-driven radiomics and deep learning methodologies to enable biomarker extraction, disease stratification, and predictive modeling. This manuscript presents the development and implementation of the NAVIGATOR infrastructure, including its data governance framework, ethical and legal considerations, and application to three oncological use cases: prostate, rectal, and gastric cancers. To date, the biobank has collected imaging and clinical data from over 700 patients across these cohorts. AI models were deployed within a dedicated VRE to facilitate image analysis, feature extraction, and classification tasks. The project addresses critical challenges related to data harmonization, regulatory compliance, privacy safeguards and fairness in AI systems. NAVIGATOR demonstrates the feasibility of integrating AI methodologies within imaging biobanks and provides a scalable framework to advance oncological research and support clinical decision-making.Source: EUROPEAN JOURNAL OF RADIOLOGY, vol. 191 (issue 112327)
DOI: 10.1016/j.ejrad.2025.112327
Project(s): An Imaging Biobank to Precisely Prevent and Predict cancer, and facilitate the Participation of oncologic patients to Diagnosis and Treatment
Metrics:
See at:
European Journal of Radiology | Archivio della Ricerca - Università di Pisa | CNR IRIS | CNR IRIS | Archivio della Ricerca - Università di Pisa
2025
Software
Open Access
DAVE - CheshireCAT Plugin
Oliviero A., Peccerillo B.
The software DAVE - CheshireCAT Plugin is a custom plugin designed and implemented for the Cheshire Cat framework, with the specific purpose of operating and interacting within the D4Science digital environments.
Oliviero A., Peccerillo B.
The software DAVE - CheshireCAT Plugin is a custom plugin designed and implemented for the Cheshire Cat framework, with the specific purpose of operating and interacting within the D4Science digital environments.
See at:
code-repo.d4science.org | CNR IRIS | CNR IRIS
2025
Other
Open Access
ISTI-day 2025 Proceedings
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
Del Corso G., Pedrotti A., Federico G., Gennaro C., Carrara F., Amato G., Di Benedetto M., Gabrielli E., Belli D., Matrullo Zoe, Miori V., Tolomei Gabriele, Waheed T., Marchetti E., Calabrò Antonello., Rossetti G., Stella Massimo, Cazabet Rémy, Abramski K., Cau E., Citraro S., Failla A., Mesina V., Morini V., Pansanella V., Colantonio S., Germanese D., Pascali M. A., Bianchi L., Messina N., Falchi F., Barsellotti L., Pacini G., Cassese M., Puccetti G., Esuli A., Volpi L., Moreo Alejandro, Sebastiani F., Sperduti G., Nguyen Dong, Broccia G., Ter Beek M. H., Ferrari A., Massink M., Belmonte Gina, Ciancia V., Papini O., Canapa G., Catricalà B., Manca M., Paternò F., Santoro C., Zedda E., Gallo S., Maenza S., Mattioli A., Simeoli L., Rucci D., Carlini E., Dazzi P., Kavalionak H., Mordacchini M., Rulli C., Muntean Cristina Ioana, Nardini F. M., Perego R., Rocchietti G., Lettich F., Renso C., Pugliese C., Casini G., Haldimann Jonas, Meyer Thomas, Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Peccerillo B., Procaccini M., Mannocci A., Manghi P., Lonetti F., Kang Dongjae, Di Giandomenico F., Jee Eunkyoung, Lazzini G., Conti F., Scopigno R., D'Acunto M., Moroni D., Cafiso M., Paradisi P., Callieri M., Pavoni G., Corsini M., De Falco A., Sala F., Saraceni Q., Gattiglia Gabriele
ISTI-Day is an annual information and networking event organized by the Institute of Information Science and Technologies "A. Faedo" (ISTI) of the Italian National Research Council (CNR). This event features an opening talk of the Director of the Dept. DIITET (Emilio F. Campana) as well as an overview of the Institute's activities presented by the ISTI Director (Roberto Scopigno). Those institutional segments are complemented by dedicated presentations and round tables featuring former staff members, as well as internal and external collaborators. To foster a network of knowledge and collaboration among newcomers, the 2025 ISTI Day edition also includes a large poster session that provides a comprehensive overview of current research activities. Each of the 13 laboratories contributes 1–3 posters, highlighting the most innovative work and offering early-career researchers a platform for discussion. Thus these proceedings include the posters selected for ISTI-Day 2025, reflecting the diverse and innovative nature of the Institute's research.
See at:
CNR IRIS | www.isti.cnr.it | CNR IRIS
2025
Other
Open Access
D4Science interoperability framework: developer guidance for service integration
Assante M., Dell'Amico A., Molinaro E., Oliviero A.
The D4Science Interoperability Framework provides the architectural backbone for integrating and operating heterogeneous services within the D4Science ecosystem. D4Science is a distributed digital infrastructure supporting international research communities through the operation of Virtual Research Environments (VREs) that enable collaborative access to data, analytical tools, and computing resources in line with Open Science and FAIR principles. The framework integrates core components such as the VRE Gateway, Virtual Labs, Identity and Access Management (IAM), and the Analytics Engine to deliver secure, scalable, and reproducible workflows. This document offers detailed guidance for service developers, covering containerisation, orchestration with Docker Swarm, authentication with OpenID Connect and OAuth2, and integrated monitoring and auditing tools. By combining federated access, container based deployment, and robust governance mechanisms, this solution enables developers and researchers to create reusable, interoperable, and compliant services that advance collaborative and transparent scientific research.DOI: 10.32079/isti-tr-2025/015
Metrics:
Assante M., Dell'Amico A., Molinaro E., Oliviero A.
The D4Science Interoperability Framework provides the architectural backbone for integrating and operating heterogeneous services within the D4Science ecosystem. D4Science is a distributed digital infrastructure supporting international research communities through the operation of Virtual Research Environments (VREs) that enable collaborative access to data, analytical tools, and computing resources in line with Open Science and FAIR principles. The framework integrates core components such as the VRE Gateway, Virtual Labs, Identity and Access Management (IAM), and the Analytics Engine to deliver secure, scalable, and reproducible workflows. This document offers detailed guidance for service developers, covering containerisation, orchestration with Docker Swarm, authentication with OpenID Connect and OAuth2, and integrated monitoring and auditing tools. By combining federated access, container based deployment, and robust governance mechanisms, this solution enables developers and researchers to create reusable, interoperable, and compliant services that advance collaborative and transparent scientific research.DOI: 10.32079/isti-tr-2025/015
Metrics:
2025
Other
Open Access
GreenDIGIT D5.3: Design and operation of a federated data management infrastructure for open science workflows
Assante M., Frosini L., Mangiacrapa F., Molinaro E., Oliviero A., Panichi G.
This short report accompanies the deliverable “Design and Operation of a Federated Data Management Infrastructure for Open Science Workflows”, classified as a “DEM” (Demonstrator). It provides a concise overview of the design and deployment of the key software components constituting the Federated Data Management Infrastructure (FDMI), developed to meet the current operational needs of the GreenDIGIT community in support of open science workflows.DOI: 10.5281/zenodo.17210872
DOI: https://doi.org/10.5281/zenodo.17210872
DOI: 10.5281/zenodo.17210871
Project(s): Greener Future Digital Research Infrastructures
Metrics:
Assante M., Frosini L., Mangiacrapa F., Molinaro E., Oliviero A., Panichi G.
This short report accompanies the deliverable “Design and Operation of a Federated Data Management Infrastructure for Open Science Workflows”, classified as a “DEM” (Demonstrator). It provides a concise overview of the design and deployment of the key software components constituting the Federated Data Management Infrastructure (FDMI), developed to meet the current operational needs of the GreenDIGIT community in support of open science workflows.DOI: 10.5281/zenodo.17210872
DOI: https://doi.org/10.5281/zenodo.17210872
DOI: 10.5281/zenodo.17210871
Project(s): Greener Future Digital Research Infrastructures
Metrics:
2025
Contribution to conference
Open Access
Deploying Conversational Agents in Virtual Research Environments: Approaches and Lessons Learned
Massimiliano Assante, Leonardo Candela, Andrea Dell’amico, Luca Frosini, Francesco Mangiacrapa, Alfredo Oliviero, Pasquale Pagano, Giancarlo Panichi, Biagio Peccerillo, Marco Procaccini
The rapid progress of conversational artificial intelligence and Large Language Models (LLMs) has opened new opportunities to enhance user interaction, support, and accessibility in Virtual Research Environments (VREs). This poster presents the approaches, challenges, and lessons learned from a multi-year e!ort to design, develop, and deploy conversational agents within the D4Science infrastructure. Through three successive implementation cycles—Janet, D4Science AI Agent, and DAVE—the poster traces a process of iterative refinement aimed at improving flexibility, extensibility, usability, and integration with existing VRE services. Janet, the first prototype, explored modular NLP components but revealed limitations in adaptability and feedback integration. The second approach, based on the Cheshire Cat framework, improved modularity and LLM interoperability but remained constrained by a single-agent design. The latest solution, DAVE (D4Science Assistant for Virtual research Environments), introduces a multi-agent architecture built with Google’s Agent Development Kit, enabling secure and context-aware interaction with multiple D4Science services. DAVE combines specialized agents for tasks such as document analysis, catalogue navigation, social interaction summarization, and algorithm deployment within D4Science’s computational platform. Integrated feedback mechanisms and a Retrieval-Augmented Generation (RAG) knowledge base further enhance its learning and personalization capabilities. The findings demonstrate that conversational agents can lower barriers to VRE adoption, streamline workflows, and foster user engagement by o!ering intuitive, natural language interfaces. Lessons learned from this evolution suggest key design principles for future research infrastructure agents, emphasizing modularity, interoperability, and data security. Future work will involve usability evaluations, the integration of user-driven feedback, and experimentation with locally-hosted LLMs to strengthen privacy and operational sustainability.
Massimiliano Assante, Leonardo Candela, Andrea Dell’amico, Luca Frosini, Francesco Mangiacrapa, Alfredo Oliviero, Pasquale Pagano, Giancarlo Panichi, Biagio Peccerillo, Marco Procaccini
The rapid progress of conversational artificial intelligence and Large Language Models (LLMs) has opened new opportunities to enhance user interaction, support, and accessibility in Virtual Research Environments (VREs). This poster presents the approaches, challenges, and lessons learned from a multi-year e!ort to design, develop, and deploy conversational agents within the D4Science infrastructure. Through three successive implementation cycles—Janet, D4Science AI Agent, and DAVE—the poster traces a process of iterative refinement aimed at improving flexibility, extensibility, usability, and integration with existing VRE services. Janet, the first prototype, explored modular NLP components but revealed limitations in adaptability and feedback integration. The second approach, based on the Cheshire Cat framework, improved modularity and LLM interoperability but remained constrained by a single-agent design. The latest solution, DAVE (D4Science Assistant for Virtual research Environments), introduces a multi-agent architecture built with Google’s Agent Development Kit, enabling secure and context-aware interaction with multiple D4Science services. DAVE combines specialized agents for tasks such as document analysis, catalogue navigation, social interaction summarization, and algorithm deployment within D4Science’s computational platform. Integrated feedback mechanisms and a Retrieval-Augmented Generation (RAG) knowledge base further enhance its learning and personalization capabilities. The findings demonstrate that conversational agents can lower barriers to VRE adoption, streamline workflows, and foster user engagement by o!ering intuitive, natural language interfaces. Lessons learned from this evolution suggest key design principles for future research infrastructure agents, emphasizing modularity, interoperability, and data security. Future work will involve usability evaluations, the integration of user-driven feedback, and experimentation with locally-hosted LLMs to strengthen privacy and operational sustainability.
2025
Other
Restricted
InfraScience research activity report 2024
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
Angioni S., Artini M., Assante M., Atzori C., Baglioni M., Bardi A., Bosio C., Bove P., Calanducci A., Candela L., Casini G., Castelli D., Cirillo R., Coro G., De Bonis M., Debole F., Dell'Amico A., Frosini L., Ibrahim Ahmed, La Bruzzo S., Lelii L., Manghi P., Mangiacrapa F., Mangione D., Mannocci A., Molinaro E., Oliviero A., Pagano P., Panichi G., Teresa M. T., Pavone G., Peccerillo B., Piccioli T., Procaccini M., Straccia U., Vannini G. L., Versienti L.
InfraScience is a research group within the Institute of Information Science and Technologies (ISTI) of the National Research Council of Italy (CNR), based in Pisa. This activity report outlines the group's research achievements and initiatives throughout 2024. InfraScience focused its efforts on key challenges in the areas of Data Infrastructures, e-Science, and Intelligent Systems, maintaining a strong synergy between research and development and a firm commitment to open science principles. In 2024, the group played a leading role in the development and evolution of two major Open Science infrastructures: D4Science and OpenAIRE. InfraScience researchers contributed significantly to the scientific community through the publication of peer-reviewed papers, active participation in EU-funded research projects, organization of international conferences and training activities, and engagement in various working groups and task forces. This report highlights these contributions and underscores the group's ongoing dedication to advancing open, collaborative, and impactful science.DOI: 10.32079/isti-ar-2025/001
Metrics:
2025
Conference article
Open Access
CCP: a cloud computing platform for VREs in Earth Sciences
Oliviero A., Lettere M., Dell'Amico A., Pagano P.
The Cloud Computing Platform (CCP) was developed under the aegis of D4Science [1]. D4Science is an operational digital infrastructure co-funded by the European Commission, and represents a significant advancement in supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, open science, and reproducible data-intensive science. D4Science has evolved to harness the "as a Service" paradigm, offering web-accessible Virtual Research Environments (VREs) [2] that have also been instrumental in facilitating science collaborations [3] with a particular focus on Earth observation, Earth science, and marine and agricultural environments. These environments simplify access to datasets while concealing underlying complexities, and include functionalities such as a cloud-based workspace for file organisation, a platform for large-scale data analysis, a catalogue for publishing research results, and a communication system rooted in social networking practices. A key component for enabling large-scale, affordable, and reproducible computation and data analysis is CCP: a cloud computing platform specifically designed for VREs and Open Science. CCP enables researchers to import, execute, and share methods ranging from statistical analysis to image classification, from AI models to 3D reconstruction, from data format conversion to pattern searching in DNA sequences, while embodying FAIR (Findable, Accessible, Interoperable, and Reusable) principles. By leveraging container technology, an API-based design, and adherence to standards such as the OGC Processes API [4], CCP supports high interoperability, flexibility and integrability in scientific workflows. Methods can be written in any programming language (Python, Julia, R, etc) and executed either via dedicated web UIs or programmatically from virtually any development environment (command line, custom applications, Galaxy workflows, Jupyter notebooks, RStudio, etc). Code generators are provided to ease the integration into common scientific tools. CCP can be deployed on container orchestration platforms, such as Docker Swarm or Kubernetes, which can leverage specialized hardware configurations (e.g., HPC clusters or GPU-enabled nodes) depending on the policies and resources available, thereby offering flexible and scalable computational environments per the needs of each community. Automatic provenance management captures the complete history of a method's execution for reproducibility and accountability, according to common provenance models (Prov-O, RO-crate). Re-submitting executions can be as simple as clicking on a shared link. CCP has been integrated into several VREs, many related to Earth science including the Blue-Cloud [5] virtual laboratories and demonstrators and ITINERIS [6].DOI: 10.5194/egusphere-egu25-18524
Metrics:
Oliviero A., Lettere M., Dell'Amico A., Pagano P.
The Cloud Computing Platform (CCP) was developed under the aegis of D4Science [1]. D4Science is an operational digital infrastructure co-funded by the European Commission, and represents a significant advancement in supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) principles, open science, and reproducible data-intensive science. D4Science has evolved to harness the "as a Service" paradigm, offering web-accessible Virtual Research Environments (VREs) [2] that have also been instrumental in facilitating science collaborations [3] with a particular focus on Earth observation, Earth science, and marine and agricultural environments. These environments simplify access to datasets while concealing underlying complexities, and include functionalities such as a cloud-based workspace for file organisation, a platform for large-scale data analysis, a catalogue for publishing research results, and a communication system rooted in social networking practices. A key component for enabling large-scale, affordable, and reproducible computation and data analysis is CCP: a cloud computing platform specifically designed for VREs and Open Science. CCP enables researchers to import, execute, and share methods ranging from statistical analysis to image classification, from AI models to 3D reconstruction, from data format conversion to pattern searching in DNA sequences, while embodying FAIR (Findable, Accessible, Interoperable, and Reusable) principles. By leveraging container technology, an API-based design, and adherence to standards such as the OGC Processes API [4], CCP supports high interoperability, flexibility and integrability in scientific workflows. Methods can be written in any programming language (Python, Julia, R, etc) and executed either via dedicated web UIs or programmatically from virtually any development environment (command line, custom applications, Galaxy workflows, Jupyter notebooks, RStudio, etc). Code generators are provided to ease the integration into common scientific tools. CCP can be deployed on container orchestration platforms, such as Docker Swarm or Kubernetes, which can leverage specialized hardware configurations (e.g., HPC clusters or GPU-enabled nodes) depending on the policies and resources available, thereby offering flexible and scalable computational environments per the needs of each community. Automatic provenance management captures the complete history of a method's execution for reproducibility and accountability, according to common provenance models (Prov-O, RO-crate). Re-submitting executions can be as simple as clicking on a shared link. CCP has been integrated into several VREs, many related to Earth science including the Blue-Cloud [5] virtual laboratories and demonstrators and ITINERIS [6].DOI: 10.5194/egusphere-egu25-18524
Metrics:
See at:
CNR IRIS | meetingorganizer.copernicus.org | CNR IRIS
2025
Other
Open Access
IRISCC D6.4 - Report on deployed and launched IRISCC interoperability framework with guidance documentation for service developers
Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Molinaro E., Oliviero A., Pagano P., Panichi G., Peccerillo B., Piccioli T.
The Deliverable D6.4 - Report on deployed and launched IRISCC interoperability framework with guidance documentation for service developers provides a high-level overview of the IRISCC Interoperability Framework, deployed under Work Package 6 (WP6) of the IRISCC project. The interoperability framework is built on the D4Science Virtual Research Environment (VRE). It supports collaborative research by offering a unified platform for data management, computational tools, and reproducibility. At its core, the framework is accessible through the IRISCC VRE Gateway, a web portal available at https://iriscc.d4science.org. This gateway acts as a centralised access point for all services and resources, providing functionalities such as a Shared Workspace for collaborative file management, tailored Virtual Labs (VLabs) for the integration of research demonstrator tools, and user-friendly tools like Jupyter Notebooks, RStudio, Shiny Apps, and the Cloud Computing Platform (CCP) to facilitate advanced computational workflows. The framework is supported by a robust Identity and Access Management (IAM) service that ensures secure and federated access through widely adopted protocols. Integration with external systems, such as the EGI Check-in service, enables cross-institutional collaboration, while secure access controls ensure consistency and compliance across all services. The report also provides guidance for service developers, such as the IRISCC WP4 Demonstrators, on effectively leveraging the framework. Developers can utilise advanced tools to deploy customised services, containerised applications, and automated workflows. By relying on the framework’s infrastructure for critical functions such as authentication, authorisation, resource monitoring, and scalability, developers can focus on innovation and the creation of impactful solutions. Furthermore, the framework simplifies integration with external resources, enhances collaboration, and reduces operational complexity.DOI: 10.5281/zenodo.14777921
DOI: 10.5281/zenodo.14777920
Project(s): Integrated Research Infrastructure Services for Climate Change risks
Metrics:
Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Molinaro E., Oliviero A., Pagano P., Panichi G., Peccerillo B., Piccioli T.
The Deliverable D6.4 - Report on deployed and launched IRISCC interoperability framework with guidance documentation for service developers provides a high-level overview of the IRISCC Interoperability Framework, deployed under Work Package 6 (WP6) of the IRISCC project. The interoperability framework is built on the D4Science Virtual Research Environment (VRE). It supports collaborative research by offering a unified platform for data management, computational tools, and reproducibility. At its core, the framework is accessible through the IRISCC VRE Gateway, a web portal available at https://iriscc.d4science.org. This gateway acts as a centralised access point for all services and resources, providing functionalities such as a Shared Workspace for collaborative file management, tailored Virtual Labs (VLabs) for the integration of research demonstrator tools, and user-friendly tools like Jupyter Notebooks, RStudio, Shiny Apps, and the Cloud Computing Platform (CCP) to facilitate advanced computational workflows. The framework is supported by a robust Identity and Access Management (IAM) service that ensures secure and federated access through widely adopted protocols. Integration with external systems, such as the EGI Check-in service, enables cross-institutional collaboration, while secure access controls ensure consistency and compliance across all services. The report also provides guidance for service developers, such as the IRISCC WP4 Demonstrators, on effectively leveraging the framework. Developers can utilise advanced tools to deploy customised services, containerised applications, and automated workflows. By relying on the framework’s infrastructure for critical functions such as authentication, authorisation, resource monitoring, and scalability, developers can focus on innovation and the creation of impactful solutions. Furthermore, the framework simplifies integration with external resources, enhances collaboration, and reduces operational complexity.DOI: 10.5281/zenodo.14777921
DOI: 10.5281/zenodo.14777920
Project(s): Integrated Research Infrastructure Services for Climate Change risks
Metrics:
See at:
CNR IRIS | zenodo.org | ZENODO | ZENODO | CNR IRIS
2025
Software
Open Access
DAVE (D4Science Assistant for Virtual research Environments)
Oliviero A., Peccerillo B.
DAVE (D4Science Assistant for Virtual research Environments) is a multi-agent Artificial Intelligence system based on a Large Language Model (LLM), strategically integrated into the D4Science infrastructure to enhance Virtual Research Environments (VREs).
Oliviero A., Peccerillo B.
DAVE (D4Science Assistant for Virtual research Environments) is a multi-agent Artificial Intelligence system based on a Large Language Model (LLM), strategically integrated into the D4Science infrastructure to enhance Virtual Research Environments (VREs).
See at:
code-repo.d4science.org | CNR IRIS | CNR IRIS
2025
Other
Open Access
Blue-Cloud2026 D5.4 - Blue Cloud VRE Common Services 2nd Release
Assante M., Candela L., Dalla Torre G., Dell'Amico A., Fernandez E., Frosini L., Lettere M., Mangiacrapa F., Molinaro E., Mugnaini M., Oliviero A., Pagano P., Panichi G., Peccerillo B., Piccioli T.
This deliverable documents the design principles and software architecture characterising the release and development of the Blue-Cloud Virtual Research Environment (VRE) common services, namely the analytics computing framework, the catalogue framework, the storage framework and the enabling framework components. This report is the second of two versions, each one describing the design associated with a specific version of the VRE. This deliverable D5.4 provides the updated and extended version of D5.1 “Blue-Cloud VRE Common Services 1st Release” [8]. The document presents the current state of the Blue-Cloud Virtual Research Environment (VRE) common services, detailing both the new components introduced in the period M13 to M36 and the enhancements applied to the existing ones to ensure compliance with interoperability, scalability, and robustness requirements. Overall, deliverable D5.4 provides a consolidated, fully up-to-date view of the Blue-Cloud VRE common services architecture, covering functional capabilities, API exposure, deployment configuration, and integration status as of the end of the second reporting period. The deliverable consists of six sections. ● Section 1 briefly introduces the role of this deliverable in the development and delivery of the Blue-Cloud VRE common services. ● Section 2 describes the Blue-Cloud VRE logical architecture of the common services and how they relate to the other services available in the VRE. ● Section 3, 4, 5 and 6 document the release of the Blue-Cloud VRE common services available at M36, reporting the design principles and reference software architecture of the released solutions. Specifically, Section 3 describes the analytics computing framework which includes the Analytics Engine, Galaxy workflows, the RStudio and the Jupyter Notebooks via JupyterHub. Section 4 presents the VRE Catalogue framework and its components, and section 5 reports on the Storage framework. ● Finally, section 6 concludes the report by illustrating the services composing the Enabling framework, which is used as a common ground for all the above-mentioned frameworks.DOI: 10.5281/zenodo.18189109
Project(s): A federated European FAIR and Open Research Ecosystem for oceans, seas, coastal and inland waters, Blue-Cloud+2026
Metrics:
Assante M., Candela L., Dalla Torre G., Dell'Amico A., Fernandez E., Frosini L., Lettere M., Mangiacrapa F., Molinaro E., Mugnaini M., Oliviero A., Pagano P., Panichi G., Peccerillo B., Piccioli T.
This deliverable documents the design principles and software architecture characterising the release and development of the Blue-Cloud Virtual Research Environment (VRE) common services, namely the analytics computing framework, the catalogue framework, the storage framework and the enabling framework components. This report is the second of two versions, each one describing the design associated with a specific version of the VRE. This deliverable D5.4 provides the updated and extended version of D5.1 “Blue-Cloud VRE Common Services 1st Release” [8]. The document presents the current state of the Blue-Cloud Virtual Research Environment (VRE) common services, detailing both the new components introduced in the period M13 to M36 and the enhancements applied to the existing ones to ensure compliance with interoperability, scalability, and robustness requirements. Overall, deliverable D5.4 provides a consolidated, fully up-to-date view of the Blue-Cloud VRE common services architecture, covering functional capabilities, API exposure, deployment configuration, and integration status as of the end of the second reporting period. The deliverable consists of six sections. ● Section 1 briefly introduces the role of this deliverable in the development and delivery of the Blue-Cloud VRE common services. ● Section 2 describes the Blue-Cloud VRE logical architecture of the common services and how they relate to the other services available in the VRE. ● Section 3, 4, 5 and 6 document the release of the Blue-Cloud VRE common services available at M36, reporting the design principles and reference software architecture of the released solutions. Specifically, Section 3 describes the analytics computing framework which includes the Analytics Engine, Galaxy workflows, the RStudio and the Jupyter Notebooks via JupyterHub. Section 4 presents the VRE Catalogue framework and its components, and section 5 reports on the Storage framework. ● Finally, section 6 concludes the report by illustrating the services composing the Enabling framework, which is used as a common ground for all the above-mentioned frameworks.DOI: 10.5281/zenodo.18189109
Project(s): A federated European FAIR and Open Research Ecosystem for oceans, seas, coastal and inland waters, Blue-Cloud+2026
Metrics:
2025
Other
Open Access
An LLM-powered agent for the D4Science digital infrastructure
Oliviero A., Peccerillo B., Procaccini M.
This report presents the design and implementation of an LLM-powered AI agent integrated into the D4Science digital infrastructure. D4Science provides Virtual Research Environments (VREs) to support collaborative and data-centric scientific workflows. By leveraging the Cheshire Cat framework, we design an AI Agent with memory, tool-use capabilities, and a well-defined identity aligned with the infrastructure's mission. Its core functionality centers on assisting researchers by retrieving, processing, and summarizing digital artifacts stored in the D4Science Workspace. The agent interacts with D4Science services via a custom plugin built atop a robust Python library, which abstracts access to RESTful APIs. To ensure consistent and context-aware behavior, we adopt a prompt engineering strategy that embeds a structured preamble defining the agent's personality, capabilities, and operational constraints. Through Retrieval-Augmented Generation (RAG), the agent maintains episodic and declarative memory using a Qdrant-based vector store, allowing it to reason over previously seen documents and interactions. We demonstrate the agent's utility via a representative use case and describe its architecture, tools, and interaction flow. This work illustrates a practical integration of state-of-the-art language models with established research infrastructures, promoting more intuitive, semantically rich access to shared scientific resources.DOI: 10.32079/isti-tr-2025/010
Metrics:
Oliviero A., Peccerillo B., Procaccini M.
This report presents the design and implementation of an LLM-powered AI agent integrated into the D4Science digital infrastructure. D4Science provides Virtual Research Environments (VREs) to support collaborative and data-centric scientific workflows. By leveraging the Cheshire Cat framework, we design an AI Agent with memory, tool-use capabilities, and a well-defined identity aligned with the infrastructure's mission. Its core functionality centers on assisting researchers by retrieving, processing, and summarizing digital artifacts stored in the D4Science Workspace. The agent interacts with D4Science services via a custom plugin built atop a robust Python library, which abstracts access to RESTful APIs. To ensure consistent and context-aware behavior, we adopt a prompt engineering strategy that embeds a structured preamble defining the agent's personality, capabilities, and operational constraints. Through Retrieval-Augmented Generation (RAG), the agent maintains episodic and declarative memory using a Qdrant-based vector store, allowing it to reason over previously seen documents and interactions. We demonstrate the agent's utility via a representative use case and describe its architecture, tools, and interaction flow. This work illustrates a practical integration of state-of-the-art language models with established research infrastructures, promoting more intuitive, semantically rich access to shared scientific resources.DOI: 10.32079/isti-tr-2025/010
Metrics:
2024
Other
Restricted
FOSSR D7.7 - Compliance testing of instruments purchased in Pisa
Piccioli T., Assante M., Oliviero A.
This document outlines the compliance testing of instruments purchased in Pisa for the FOSSR IT Pisa Node, located in the Data Center of the ISTI CNR in Pisa, and designed to enhance collaborative social science research. It begins with the project’s objectives and covers the acquisition of hardware through CONSIP agreements and competitive tenders. Details on the configuration and network setup of Dell PowerEdge R840 and Dell XE8545 servers are provided. It describes the testing protocols for the hardware, including order checks, power-on tests, and visual inspections. The conclusion summarises the infrastructure’s impact on supporting high-quality research, highlighting the strategic alignment with FOSSR's goals.Project(s): Fostering Open Science in Social Science Research
Piccioli T., Assante M., Oliviero A.
This document outlines the compliance testing of instruments purchased in Pisa for the FOSSR IT Pisa Node, located in the Data Center of the ISTI CNR in Pisa, and designed to enhance collaborative social science research. It begins with the project’s objectives and covers the acquisition of hardware through CONSIP agreements and competitive tenders. Details on the configuration and network setup of Dell PowerEdge R840 and Dell XE8545 servers are provided. It describes the testing protocols for the hardware, including order checks, power-on tests, and visual inspections. The conclusion summarises the infrastructure’s impact on supporting high-quality research, highlighting the strategic alignment with FOSSR's goals.Project(s): Fostering Open Science in Social Science Research
2024
Other
Open Access
SoBigData++ - SoBigData e-Infrastructure Operation Report 3
Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Molinaro E., Oliviero A., Pagano P., Panichi G., Piccioli T.
This Deliverable builds upon and updates the previous reports, D9.2 - “SoBigData e-Infrastructure Operation Report 2” [5] and D9.1 - “SoBigData e-Infrastructure Operation Report 1” [3]. The SoBigData e-Infrastructure has been pivotal in enabling the core services and research support required for the SoBigData++ project, including Virtual Research Environments (VREs), the Catalogue, and Analytics Services. It is accessible through the SoBigData gateway (https://sobigdata.d4science.org), which provides end-users with seamless access to tools, datasets, and services. The SoBigData e-Infrastructure is built upon the D4Science infrastructure, offering a comprehensive platform that facilitates collaborative, transparent, and interdisciplinary research. The deployment and operation of VREs followed a well-defined procedure, leveraging the consolidated process inherited from D4Science. Throughout the 60 months of the project, a total of 27 VREs were created and operated to meet project and community needs. These VREs were classified into five categories: Exploratories, Applications, Virtual Labs, Training, and Management. Notable examples include, (i) SoBigDataLab and SoBigDataLab-PlusPlus for method development and experiments, (ii) Training VREs created for events like Summer Schools and specialised workshops, and (iii) Research spaces (formerly known as Exploratories) supporting targeted domains, such as Migration Studies, Sports Data Science, and Social Impacts of AI. The SoBigData Catalogue (https://sobigdata.d4science.org/catalogue-sobigdata) emerged as a critical resource for both human users and integrated services, enabling access to datasets, services, and analytical methods. The catalogue supports customisable item profiles enriched with metadata fields, controlled vocabularies, and validation rules. By end of term, the Catalogue recorded significant growth, particularly in key item types such as Methods (192 items) and Datasets (250 items). This expansion underscores the Catalogue’s role in promoting resource discoverability and supporting research workflows. Its usage indicators demonstrate its active adoption, with 31,909 total accesses, 29,595 metadata views, and 4,171 resource views recorded. Monthly trends reveal consistent engagement, highlighting its importance in the research ecosystem. The Social Mining Analytics Engine (SMAE) transitioned through the development of a new service, namely Cloud Computing Platform (CCP), offering enhanced scalability and automation through container orchestrations. Methods hosted on the SMAE span multiple categories, such as Text Processing, Web Analytics, and Image Analysis. Over the last year, the platform executed an average of 6.4 million method invocations per month, peaking at 16 million executions in July 2024. As of mid-December ’24, the e-infrastructure serves more than 13,000 users, with an overall trend in the use of the SoBigData VREs from January 2020 to December 2024, highlighting their importance for the research community. The steady engagement through 2023 and 2024, with peaks like July 2024 (2,592 sessions), underscores the VREs continued relevance and utility.Project(s): SoBigData-PlusPlus
Assante M., Candela L., Dell'Amico A., Frosini L., Mangiacrapa F., Molinaro E., Oliviero A., Pagano P., Panichi G., Piccioli T.
This Deliverable builds upon and updates the previous reports, D9.2 - “SoBigData e-Infrastructure Operation Report 2” [5] and D9.1 - “SoBigData e-Infrastructure Operation Report 1” [3]. The SoBigData e-Infrastructure has been pivotal in enabling the core services and research support required for the SoBigData++ project, including Virtual Research Environments (VREs), the Catalogue, and Analytics Services. It is accessible through the SoBigData gateway (https://sobigdata.d4science.org), which provides end-users with seamless access to tools, datasets, and services. The SoBigData e-Infrastructure is built upon the D4Science infrastructure, offering a comprehensive platform that facilitates collaborative, transparent, and interdisciplinary research. The deployment and operation of VREs followed a well-defined procedure, leveraging the consolidated process inherited from D4Science. Throughout the 60 months of the project, a total of 27 VREs were created and operated to meet project and community needs. These VREs were classified into five categories: Exploratories, Applications, Virtual Labs, Training, and Management. Notable examples include, (i) SoBigDataLab and SoBigDataLab-PlusPlus for method development and experiments, (ii) Training VREs created for events like Summer Schools and specialised workshops, and (iii) Research spaces (formerly known as Exploratories) supporting targeted domains, such as Migration Studies, Sports Data Science, and Social Impacts of AI. The SoBigData Catalogue (https://sobigdata.d4science.org/catalogue-sobigdata) emerged as a critical resource for both human users and integrated services, enabling access to datasets, services, and analytical methods. The catalogue supports customisable item profiles enriched with metadata fields, controlled vocabularies, and validation rules. By end of term, the Catalogue recorded significant growth, particularly in key item types such as Methods (192 items) and Datasets (250 items). This expansion underscores the Catalogue’s role in promoting resource discoverability and supporting research workflows. Its usage indicators demonstrate its active adoption, with 31,909 total accesses, 29,595 metadata views, and 4,171 resource views recorded. Monthly trends reveal consistent engagement, highlighting its importance in the research ecosystem. The Social Mining Analytics Engine (SMAE) transitioned through the development of a new service, namely Cloud Computing Platform (CCP), offering enhanced scalability and automation through container orchestrations. Methods hosted on the SMAE span multiple categories, such as Text Processing, Web Analytics, and Image Analysis. Over the last year, the platform executed an average of 6.4 million method invocations per month, peaking at 16 million executions in July 2024. As of mid-December ’24, the e-infrastructure serves more than 13,000 users, with an overall trend in the use of the SoBigData VREs from January 2020 to December 2024, highlighting their importance for the research community. The steady engagement through 2023 and 2024, with peaks like July 2024 (2,592 sessions), underscores the VREs continued relevance and utility.Project(s): SoBigData-PlusPlus
2024
Other
Open Access
Blue-Cloud VRE operation report
Assante M., Candela L., Calanducci A., Cirillo R., Dell’amico A., Frosini L., Lelii L., Molinaro E., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Piccioli T.
The Horizon Europe Blue-Cloud initiative started in 2019 with the aim of creating a European Open Science Cloud for marine data. This involves federating data and e-infrastructures to provide data products and technologies as open science resources for the wider marine research community. Since 2023, the Blue-Cloud 2026 follow-up project has sought to further evolve this pilot ecosystem into a Federated European Ecosystem, offering FAIR and open data and analytical services crucial for advancing research on oceans, EU seas, and coastal and inland waters. Building on the pilot Blue-Cloud project, the current technical framework is designed to be extensible and open, continually evolving to meet the community's needs. The Blue-Cloud platform architecture comprises two major components: (a) the Blue-Cloud Data Discovery and Access Service (DDAS) component, which facilitates federated discovery and access to 'blue data' infrastructures, and (b) the Blue-Cloud Virtual Research Environment (VRE) component, which provides a Blue-Cloud VRE as a federation of computing platforms and analytical services. The VLabs leverage both DDAS and VRE, co-created with leading marine researchers to demonstrate the power of the Blue-Cloud Open Science platform through real-life scientific cases. \ This deliverable focuses on the VRE operation, specifically on how the VRE services have been utilised and managed to support the development of the Blue-Cloud VRE gateway (https://blue-cloud.d4science.org), its underlying infrastructure, and the VLabs on top of it, during the reporting period from January 2023 (M1) to June 2024 (M18). A total of 13 VLabs were created and operated to meet the needs arising from the Blue-Cloud 2026 project. Additionally, 7 VLabs from the previous Blue-Cloud project are being maintained. These working environments serve more than 1,700 users from 34 countries. Between January 2023 and June 2024, users initiated more than 26,000 working sessions via the Blue-Cloud VRE, averaging 1,447 sessions per month. Operating the VRE and VLabs involves managing support requests, issues, and incidents. A total of 143 tickets have been created and managed in the Blue-Cloud Project Issue Trackers (23 in the project consortium tracker and 120 in the support tracker), with 85% of these tickets closed. Additionally, 24 tickets related to Blue-Cloud have been created within the D4Science overall context, with an 88% closure rate.DOI: 10.5281/zenodo.12667549
Project(s): Blue-Cloud 2026
Metrics:
Assante M., Candela L., Calanducci A., Cirillo R., Dell’amico A., Frosini L., Lelii L., Molinaro E., Mangiacrapa F., Oliviero A., Pagano P., Panichi G., Piccioli T.
The Horizon Europe Blue-Cloud initiative started in 2019 with the aim of creating a European Open Science Cloud for marine data. This involves federating data and e-infrastructures to provide data products and technologies as open science resources for the wider marine research community. Since 2023, the Blue-Cloud 2026 follow-up project has sought to further evolve this pilot ecosystem into a Federated European Ecosystem, offering FAIR and open data and analytical services crucial for advancing research on oceans, EU seas, and coastal and inland waters. Building on the pilot Blue-Cloud project, the current technical framework is designed to be extensible and open, continually evolving to meet the community's needs. The Blue-Cloud platform architecture comprises two major components: (a) the Blue-Cloud Data Discovery and Access Service (DDAS) component, which facilitates federated discovery and access to 'blue data' infrastructures, and (b) the Blue-Cloud Virtual Research Environment (VRE) component, which provides a Blue-Cloud VRE as a federation of computing platforms and analytical services. The VLabs leverage both DDAS and VRE, co-created with leading marine researchers to demonstrate the power of the Blue-Cloud Open Science platform through real-life scientific cases. \ This deliverable focuses on the VRE operation, specifically on how the VRE services have been utilised and managed to support the development of the Blue-Cloud VRE gateway (https://blue-cloud.d4science.org), its underlying infrastructure, and the VLabs on top of it, during the reporting period from January 2023 (M1) to June 2024 (M18). A total of 13 VLabs were created and operated to meet the needs arising from the Blue-Cloud 2026 project. Additionally, 7 VLabs from the previous Blue-Cloud project are being maintained. These working environments serve more than 1,700 users from 34 countries. Between January 2023 and June 2024, users initiated more than 26,000 working sessions via the Blue-Cloud VRE, averaging 1,447 sessions per month. Operating the VRE and VLabs involves managing support requests, issues, and incidents. A total of 143 tickets have been created and managed in the Blue-Cloud Project Issue Trackers (23 in the project consortium tracker and 120 in the support tracker), with 85% of these tickets closed. Additionally, 24 tickets related to Blue-Cloud have been created within the D4Science overall context, with an 88% closure rate.DOI: 10.5281/zenodo.12667549
Project(s): Blue-Cloud 2026
Metrics: