BEGIN:VCALENDAR
VERSION:2.0
PRODID:-// - ECPv6.15.20//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-ORIGINAL-URL:https://eosc-austria.at
X-WR-CALDESC:Events for 
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20240331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20241027T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20250330T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20251026T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20261025T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250918T050000
DTEND;TZID=Europe/Paris:20250918T170000
DTSTAMP:20260422T183204
CREATED:20250605T085850Z
LAST-MODIFIED:20250605T085850Z
UID:20655-1758171600-1758214800@eosc-austria.at
SUMMARY:EOSC Nodes with federating capabilities for the EOSC Federation
DESCRIPTION:Expected Outcome:Project results are expected to contribute to the following outcomes: \n\nThe EOSC federation will be established as a distributed system of systems\, comprising of independently managed EOSC nodes that collaborate to augment their contribution to EOSC users whilst ensuring resilience of the interconnected systems by supporting the common federating capabilities.\nResearchers will benefit from unified support aimed at integrating their research environments with the EOSC federation and this coordinated assistance will streamline the alignment of research practices with the EOSC ecosystem.\n\nScope:The call aims to further develop the cross-domain EOSC system of systems\, building upon the results of the previous INFRAEOSC calls[1]. The focus is on developing and expanding the EOSC federation through a network of nodes as baseline elements of the federation model. These EOSC nodes will establish a set of essential federating capabilities\, compatible with the EOSC EU Node reference architecture\, following the EOSC Federation Handbook and EOSC interoperability framework. They should have a clearly described identity and offer unique value to EOSC users\, for example representing a specific thematic domain (e.g. data or computing) or geographical area amongst the Horizon Europe participating and associated countries. \nTo become an EOSC node\, the proposals are expected to demonstrate the ability to assess and address the existing gaps regarding the federating capabilities at technical\, legal and organisational level. The proposals are expected to indicate adherence to the EOSC governance structure\, federation policies and capabilities. They should also address business models\, service management procedures\, and technical and semantic interoperability. Furthermore\, the proposals should propose a credible plan to ensure the sustainability of the proposed solutions beyond the project lifespan\, including how to take over or replicate the federating role currently ensured by the EOSC EU Node[2]. \nMore specifically\, the proposals should focus on all of the following aspects: \n\nSatisfy the minimal node requirements as defined by the EOSC tripartite governance[3]\, and go beyond them by further development and refinement of the harmonized participation model\, taking into account the variety of thematic and national dimensions in the EOSC federation. These requirements include:\n\nCompliance with the requirements on the legal status of the organisation;\nCompliance with the requirements aiming at large-scale\, quality service provisioning;\nCapacity to onboard third-party services[4];\nCapacity to contribute to and/or take-over the EOSC federating capabilities;\nAdoption and integration of the EOSC federation rules and policies\, and further refinement and practical adaptation of best practices;\nSupport to effectively monitor and report the activities of the services provided.\n\n\nIntegrate and offer EOSC core federating capabilities pioneered by the EOSC EU Node\, such as Authentication\, Authorisation and Accounting\, Research Catalogues and Knowledge Graph\, Application Workflows\, Monitoring and Helpdesk\nContribute to a robust and coordinated strategy for evolving and sustaining the federated governance model for EOSC by fostering effective collaboration and coordination among the other node operators offering federating capabilities. This can be done for example by distributing the work and taking a share in facilitating the identification\, selection and integration/enrolment of other organisations interested in becoming responsible for operating an EOSC node and representing various countries\, regions and scientific disciplines.\nEnrich the existing guidelines and best practices for the onboarding and enrolment processes.\nEvolve and refine the EOSC federation specifications to drive the evolution across governance\, operations\, sustainability and technical interoperability.\nDesignate and train the EOSC node operators\, especially those offering federating capabilities\, for high level of responsibilities within the EOSC federation.\nDevelop and run community engagement and support programmes around the EOSC nodes and the surrounding ecosystem.\n\n[1] https://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/wp-call/2021-2022/wp-3-research-infrastructures_horizon-2021-2022_en.pdf; \nhttps://ec.europa.eu/info/funding-tenders/opportunities/docs/2021-2027/horizon/wp-call/2023-2024/wp-3-research-infrastructures_horizon-2023-2024_en.pdf \n[2] https://open-science-cloud.ec.europa.eu/. Financial support for third-parties (FSTP)\, that will make value-added data\, tools and services ready to be onboarded and available via the node\, can be included in the proposals. The FSTP budget will only cover the cost of onboarding. \n[3] https://eosc.eu/wp-content/uploads/2024/05/EOSC-A_GA8_20240527-28_Paper-G_Update_EOSC_Nodes_requirements-DRAFT-v240524.pdf \n[4] Financial support for third-parties (FSTP)\, that will make value-added data\, tools and services ready to be onboarded and available via the node\, can be included in the proposals. The FSTP budget will only cover the cost of onboarding.
URL:https://eosc-austria.at/events/eosc-nodes-with-federating-capabilities-for-the-eosc-federation/
CATEGORIES:Funding Calls
ATTACH;FMTTYPE=image/png:https://eosc-austria.at/wp-content/uploads/2025/06/EC2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250918T050000
DTEND;TZID=Europe/Paris:20250918T170000
DTSTAMP:20260422T183204
CREATED:20250605T090019Z
LAST-MODIFIED:20250605T090019Z
UID:20657-1758171600-1758214800@eosc-austria.at
SUMMARY:FAIR Integration for Enhanced Research Data in the EOSC ecosystem and beyond
DESCRIPTION:Expected Outcome:Projects are expected to contribute to all of the following expected outcomes: \n\nimproved findability\, accessibility\, interoperability and re-usability (“FAIRness”) of research data and other digital research outputs;\nwider uptake of and compliance with FAIR data principles and practices by national and European research data and metadata providers\, repositories and databases;\noperationalisation of the concept of FAIR digital objects throughout the entire research data lifecycle;\nenhanced and mainstreamed technical specifications for FAIR digital objects to facilitate the creation of digital objects that are FAIR-by-design.\n\nScope:The scope of this call topic is centred on advancing the interoperability and integration of research data within the European Open Science Cloud (EOSC)\, in alignment with the broader context of the Common European Data Spaces and cross-sector collaboration. FAIR digital objects provide a conceptual and implementation framework to develop scalable cross-disciplinary capabilities\, deal with the increasing data volumes and their inherent complexity\, build tools that help to increase trust in data\, create mechanisms to efficiently operate in the domain of scientific assertions\, and promote data interoperability. Proposals should cover all of the following areas and activities: \n\nFAIR-by-Design digital objects creation: the development of tools that enable the creation of digital objects adhering to the FAIR (Findable\, Accessible\, Interoperable\, Reusable) principles both from the “source” or as a result of an analysis.\nAutomated standardisation and data quality assessment: development and provision of automated tools and procedures for standardisation and data quality assessment. This will ensure that data across different domains adhere to common standards\, fostering greater compatibility and enhancing overall data quality.\nOperational data services specification: delivering technical specifications for operational data services that support the transformation of digital objects into FAIR entities. Such services may include the integration of AI-based tools capable of autonomously operating on data repositories\, contributing to the automatic establishment of FAIR practices throughout the research ecosystem.\nInteroperability and training to promote the uptake of open standards: the action should make substantial contributions to the development\, upkeep and widespread adoption of open standards for metadata\, formats\, vocabularies\, semantics and APIs. Activities should foster compatibility among digital objects across different domains\, facilitating seamless data exchange and integration. Training and dissemination activities will facilitate the uptake of these standards\, fostering collaboration and compatibility.\nCollaboration and alignment with Common European Data Spaces: harmonisation of EOSC technical specifications with those of other Common European Data Spaces.\nInteroperability demonstration and content integration: demonstrating the tangible outcomes of applying FAIR tools\, standards\, and specifications will showcase the achieved interoperability and integration\, strengthening the case for data sharing and reuse across disciplines and sectors.\nCross-Sector data utilization: by enhancing content integration from data spaces\, industry\, and beyond\, the reuse of science data in various sectors is to be encouraged. This will ease access to real-life data from other data spaces\, fostering its utilization in research and expanding its impact.\n\nThese activities will have to be developed in accordance with standards and guidelines defined or adopted by EOSC\, promoting data quality and open access practices. To ensure complementarity of outcomes\, proposals are expected to cooperate and align with activities of the EOSC Partnership and to coordinate with relevant initiatives and projects contributing to the development of EOSC\, including projects funded by the call topics HORIZON-INFRA-2023-EOSC-01-04 – ‘Next generation services for operational and sustainable EOSC Core Infrastructure’ and HORIZON-INFRA-2024-EOSC-01-05 – ‘Innovative and customizable services for EOSC Exchange’. In addition\, cooperation is expected with project(s) funded by the call topic HORIZON-INFRA-2025-01-EOSC-01. Finally\, proposals should build on the work delivered by the projects FAIR-IMPACT and FAIRCORE4EOSC\, especially in the areas of interoperability across disciplines and sectors\, as well as in the mainstreaming of creating FAIR-by-Design digital objects. This topic implements the co-programmed European Partnership for the European Open Science Cloud.
URL:https://eosc-austria.at/events/fair-integration-for-enhanced-research-data-in-the-eosc-ecosystem-and-beyond/
CATEGORIES:Funding Calls
ATTACH;FMTTYPE=image/png:https://eosc-austria.at/wp-content/uploads/2025/06/EC2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250918T050000
DTEND;TZID=Europe/Paris:20250918T170000
DTSTAMP:20260422T183204
CREATED:20250605T090153Z
LAST-MODIFIED:20250605T090153Z
UID:20659-1758171600-1758214800@eosc-austria.at
SUMMARY:Advancing AI-readiness and Machine-Actionability in the EOSC Ecosystem
DESCRIPTION:Expected Outcome:Project results are expected to contribute to the following outcomes: \n\nEOSC will be advancing AI-readiness and Machine-Actionability (MA) in the ecosystem by offering AI-ready federated infrastructure and easy-to-use platform services for EOSC\, in order to respond to one of the main challenges of research infrastructures for AI in science\, namely the lack of interoperability between AI/ML solutions.\nEOSC will focus on integrating machine-actionable repositories within the ecosystem and demonstrating their reliability and effectiveness by collaborating with repository owners and service providers to implement MA tools and protocols\, ensuring seamless integration with the EOSC EU Node.\n\nScope:Today\, the sustainable FAIRification of data can be a bottleneck towards the goal of a European web of FAIR data and services. The use of AI/ML can significantly help in the process of FAIRification\, data curation and data quality assurance\, close to the source of the data. EOSC shall promote actions that give incentives to further advance AI-readiness and Machine-Actionability in the EOSC federation for FAIRification and to support their application. \nEuropean researchers need access to compute and repository services to develop\, train and validate AI/ML models\, in line with the GenAI4EU initiative and other key initiatives\, like the Apply AI strategy\, based on AI-ready research data from research infrastructures and third-party repositories. The proposed infrastructures should complement the EOSC EU Node capacity and be able to scale to a large number of users within transnational access to high-value datasets from national and European Research Infrastructure and e-Infrastructure ecosystems. \nAI-based assistance tools shall be customised and trained for the discovery and composition of open science resources into custom workflows allowing researchers to discover and interact with open science infrastructures\, combining relevant data\, software and application assets. \nOpen Data and Open Research Software are essential for reliable\, trustworthy\, and transparent AI/ML. They ensure that datasets and algorithms are well documented\, accessible\, and reproducible\, enabling others to validate and understand AI/ML algorithms. This transparency fosters trust\, supports ethical standards\, and ensures compliance with regulations\, particularly important in the field of AI/ML. \nThe proposals should focus on all following aspects: \n\nDevelop and prototype tools to drive machine-actionability in repositories\, data\, and services\, establishing a network of trusted repositories linked to the EOSC EU Node;\n\nformulate open protocols and policies to facilitate effortless data access\, transfer\, processing\, and provenance updates within EOSC’s repository and service network;\ndeliver AI-based capabilities to make interoperable AI/ML solutions and facilitate the setup of custom workflows for research data processing;\noffer tools/services for automatic quality measures of inputs and outputs of the AI/ML models.\n\n\nProvide federated infrastructure services for serving AI models integrating horizontal and thematic EOSC nodes:\n\nensure capacities for AI model retraining and inference;\ntake into account the whole research data life cycle\, including raw data retention before AI modelling.\n\n\nProvide access to an easy-to-use technology platform offering reference implementations and recipes to quickly get started working with AI/ML with limited engineering overhead:\n\npromote and apply state-of-the-art AI/ML operational best practices;\nvalidate reference implementations and share commonly used recipes within EOSC.\n\n\nEstablish and/or provide access to existing AI/ML model repositories and services to serve models for retraining of generic models for specific needs for future predictions and reproducibility:\n\ncreate AI/ML model repository and enhance FAIRness of existing AI/ML models;\noffer services for utilisation of these models\, including fine-tuning and inference\, thus providing the foundational building blocks for the development of AI applications in EOSC.\n\n\nEstablish an EOSC AI/ML competency centre for the pooling of expertise and coordinated support on AI/ML use of data\, compute infrastructure and AI/ML models for the upskilling and technical support of EOSC users and research operators\, as a strategic asset that will enable a new paradigm for science production.\n\nThe proposers should take into account and leverage on the results of relevant projects in the field\, including EOSC Data Commons[1]\, and the other initiatives and projects contributing to the development of EOSC\, especially in the area of machine-actionability and data FAIRification. \nThis topic implements the co-programmed European Partnership for the European Open Science Cloud. \n[1] EOSC Data Commons\, Grant agreement ID: 101188179 https://cordis.europa.eu/project/id/101188179
URL:https://eosc-austria.at/events/advancing-ai-readiness-and-machine-actionability-in-the-eosc-ecosystem/
CATEGORIES:Funding Calls
ATTACH;FMTTYPE=image/png:https://eosc-austria.at/wp-content/uploads/2025/06/EC2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250918T170000
DTEND;TZID=Europe/Paris:20250918T170000
DTSTAMP:20260422T183204
CREATED:20250605T083007Z
LAST-MODIFIED:20250605T083740Z
UID:20650-1758214800-1758214800@eosc-austria.at
SUMMARY:Data stewards\, skills and training for Open Science and FAIR practices
DESCRIPTION:Expected Outcome:Projects are expected to contribute to all of the following expected outcomes: \n\nDefinition of consistent core curricula for data stewards throughout Europe\, fostering the adoption of Open Science and FAIR principles.\nEnhanced data steward skills\, enhancing their ability to manage and interpret complex data.\nAdvancement of Open Science education throughout all research career stages. Creation and standardisation of open science curricula tailored to researchers at all career stages\, promoting consistency and collaboration in Open Science practices.\nExpansion and strengthening of existing competence networks\, broadening their scope across countries and disciplines and improving their readiness to support the uptake of Open Science and of EOSC. Development of a sustainable coordination network model to support synergies and continued growth.\nMainstreaming transparent\, aligned\, and interoperable Open Science practices and promoting efficiency and trustworthiness in the management of FAIR digital objects.\n\nScope:The uptake of Open Science practices and of the European Open Science Cloud (EOSC) requires dedicated\, professional profiles for data curation and data management\, as well as equipping researchers with adequate skills and supporting them for the sharing and re-use of FAIR research digital objects. However\, at present\, data stewards and related profiles lack well-defined career paths\, and data sharing and other open science practices are not fully mainstreamed within the research community and are often not recognised in research assessment practices. \nThe objective of this topic is to foster a stronger culture of Open Science and to address gaps related to the professionalisation of data stewards and to the acquisition and recognition of open science and data management skills at all career levels. This requires the identification of consistent core curricula for data stewards\, together with the further development and coordination of competence centres at the European level. \nProposals are expected to cover the following activities: \n\nCoordinating European-level actions to make data steward curricula management consistent and to propose mechanisms to monitor their suitability and possible evolution.\nEnhancing data steward and researcher curricula with Open Science and FAIR practices\, ensuring adaptability at the different contexts\, levels and scientific domains of applicability.\nAddressing diverse data steward levels\, including support staff and researchers.\nCollaborating with existing competence centres to foster Open Science and FAIR networks.\nLeveraging national networks and related institutional initiatives for European-level coordination.\nLaunching outreach programs targeting early-career researchers and less-structured communities.\nOffering support to countries and institutions that are underrepresented and bolstering national competence centre networks.\n\nProposals are expected to build on and align with the European Competence Framework for Researchers (ResearchComp)[1] and with the revised Charter for Researchers[2]\, which underline the importance of Open Science competences and practices in research careers. Proposals should also seek for synergies with the activities of the Coalition for Advancing Research Assessment (CoARA) in order to reach a better recognition of open\, collaborative practices in research assessment. \nTo ensure complementarity of outcomes\, proposals are expected to cooperate and align with activities of the EOSC Partnership and to coordinate with relevant initiatives and projects contributing to the development of EOSC. In particular\, proposals should take account of the results of the Skills4EOSC and FAIR-IMPACT projects and interact with related initiatives aimed at developing competence centres and at improving FAIR data practices in different contexts\, like in research infrastructures\, research performing organisation and higher education institutions. In particular\, cooperation is expected with project(s) funded by call topic HORIZON-INFRA-2025-01-EOSC-02. Proposals are expected to propose adequate measures and tailor its support to different levels and contexts of data stewardship. \nProposals are expected to establish interactions with the operators of the EOSC Federation\, in order to ensure alignment with the policies and practices of the EOSC Federation\, notably on the area of data interoperability standards\, persistent identifiers and others to identify useful tools and resources for the broad EOSC community. This topic implements the co-programmed European Partnership for the European Open Science Cloud. \n[1] https://research-and-innovation.ec.europa.eu/jobs-research/researchcomp-european-competence-framework-researchers_en. \n[2] https://euraxess.ec.europa.eu/hrexcellenceaward/european-charter-researchers.
URL:https://eosc-austria.at/events/data-stewards-skills-and-training-for-open-science-and-fair-practices/
CATEGORIES:Funding Calls
ATTACH;FMTTYPE=image/png:https://eosc-austria.at/wp-content/uploads/2025/06/EC2.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20250918T170000
DTEND;TZID=Europe/Paris:20250918T170000
DTSTAMP:20260422T183204
CREATED:20250605T090317Z
LAST-MODIFIED:20250605T090317Z
UID:20661-1758214800-1758214800@eosc-austria.at
SUMMARY:Using Generative AI (GenAI4EU ) for Scientific Research via EOSC
DESCRIPTION:Expected Outcome: \nProject results are expected to contribute to the following outcomes: \n\nEOSC will make available the high-quality machine-readable scientific datasets to be consumed by machine-driven Generative AI applications at the service of science in line with the GenAI4EU[1] initiative and other key EU initiatives\, like the Apply AI strategy.\nEOSC will facilitate the pooling and sharing of high-value data sets originated from EOSC and other data spaces identified as priorities (including\, but not limited to\, public sector\, health\, climate\, environmental\, manufacturing\, agriculture\, energy\, financial and mobility data). The large-scale actions supported by EOSC will include the creation of common data platforms enabling secure and compliant sharing and reuse of sensitive\, confidential\, proprietary and personal data\, as well as large-scale experimentation based on Generative AI\, in line with the GenAI4EU initiative and other key EU initiatives\, like the Apply AI strategy.\n\nScope: \nThe scope of this call is to demonstrate and foster the use of Generative AI for Scientific Research\, in line with the GenAI4EU initiative and other key EU initiatives\, like the Apply AI strategy\, throughout the research data lifecycle supported by EOSC. Generative AI can be used for activities such as writing\, data generation and analysis\, reporting and many others\, for improving productivity. This enables lifting science beyond the human scale by facilitating the deployment and use of smart algorithms\, machine learning and AI services onto the Web of FAIR Data. The awareness and readiness of using Generative AI for scientific research must be raised by training activities. \nAI-powered natural language interfaces can transform the way researchers interact with open science infrastructures\, how they discover and combine relevant data\, software and application assets. EOSC should evolve towards offering such capabilities in ways that ensure unbiased and trustworthy responses. This includes adopting FAIR practices\, for AI-trained models as well\, to address challenges ranging from reproducibility to trustworthiness. \nOpen Data and Open Research Software are essential for reliable\, trustworthy\, and transparent GenAI. They ensure that datasets and algorithms are well-documented\, accessible\, and reproducible\, enabling others to validate and understand GenAI algorithms. This transparency fosters trust\, supports ethical standards\, and ensures compliance with regulations\, particularly important in the field of GenAI. \nThe proposals shall focus on all following aspects: \n\nEnrich the EOSC federation with Generative AI tools for evaluating research data quality\, ensuring trustworthiness across the European network of trusted repositories\, accessible by humans\, machines\, and Generative AI services: formulate protocols and policies to facilitate effortless data access\, processing\, and provenance updates within EOSC’s repository and service network.\nSupport European research infrastructures to improve the FAIRness of their data\, so that they are ready to be combined with data of infrastructures in scientifically neighbouring domains\, in order to provide Generative AI-ready data.\n\nconduct pilots to validate the effectiveness and accuracy of the Generative AI-driven data quality evaluation methods\, iteratively improving and refining them based on feedback and real-world use cases and removing the potential biases inherited from the training data.\n\n\nRun community engagement and support programmes for implementing Generative AI in scientific workflows via EOSC:\n\npromote a sound training programme to facilitate the uptake and the use of Generative AI as a means to facilitate the FAIRification of data and data curation;\ndemonstrate how Generative AI can facilitate quality assessment of FAIR data;\nadvance the realization of machine-actionable (MA) research data and services\, including AI-based systems;\npropose protocols and policies to govern automatic data workflows within the network of repositories and services.\n\n\n\nThe proposals are expected to deliver on one or more of the following: \n\nDevelop\, promote and support real-life use cases for Generative AI models in scientific research domains\, in line with the GenAI4EU initiative and other key EU initiatives\, like the Apply AI strategy\, such as:\n\naugment datasets in scientific fields that rely on image analysis\, such as biology\, astronomy\, and materials science: by generating synthetic images that closely resemble real data\, researchers can expand their datasets\, improve model robustness\, share anonymized version of sensitive data and generalize better to unseen scenarios;\nlearn the underlying patterns of complex time-series data\, such as sensor readings in environmental monitoring or physiological signals in healthcare: by generating data samples that match the learned distribution\, these models can detect anomalies or deviations from normal behaviour;\naccelerate materials design and discovery by predicting the properties of new materials without the need for extensive experimental testing: these models can generate novel material structures with desired properties\, such as strength\, conductivity\, or catalytic activity\, based on learned relationships between material compositions and properties;\nadvance drug design and molecular modelling by generating novel molecular structures with desired pharmacological properties: these models can explore vast chemical spaces\, predict the interactions between molecules and biological targets\, and optimize drug candidates for efficacy and safety;\nsimulate complex systems and phenomena in various scientific domains\, such as physics\, chemistry\, and ecology: by capturing the underlying dynamics and interactions of the system\, these models can generate realistic simulations that mimic observed behaviour or predict future outcomes under different conditions.\n\n\n\nThe proposers should take into account and leverage on the results of relevant projects in the field\, including AI4EOSC[2]\, iMagine[3]\, EOSC Data Commons[4]\, RI-SCALE[5]\, and other developments within the scope of the GenAI4EU initiative and other key EU initiatives\, like the Apply AI strategy. \nThis topic implements the co-programmed European Partnership for the European Open Science Cloud. \nProposals could consider the inclusion of the European Commission’s Joint Research Centre (JRC) research infrastructure in their research infrastructure portfolio for the creation and sharing or high-quality machine-readable scientific datasets to be consumed by machine-driven Generative AI applications. In this regard\, the JRC will consider collaborating with any successful proposal. \n[1] This call falls under the ‘GenAI4EU’ initiative as in the Communication from the Commission to the European Parliament\, the Council\, the European Economic And Social Committee and the Committee of the Regions on boosting startups and innovation in trustworthy artificial intelligence ((COM(2024) 28 final of 24.1.2024). \n[2] https://ai4eosc.eu/ \n[3] https://www.imagine-ai.eu/ \n[4] Grant no 101188179 from the call HORIZON-INFRA-2024-TECH-01 \n[5] Grant no 101188168 from the call HORIZON-INFRA-2024-TECH-01
URL:https://eosc-austria.at/events/using-generative-ai-genai4eu-for-scientific-research-via-eosc/
CATEGORIES:Funding Calls
ATTACH;FMTTYPE=image/png:https://eosc-austria.at/wp-content/uploads/2025/06/EC2.png
END:VEVENT
END:VCALENDAR