Structure

T2.1: Ecosystem initiation and ECCCH common planning (Lead: CERTH; Contributors: All; [M01-M06]) – This task includes the initiation and the inclusion of the project in the ECCCH ecosystem. As first step, CERTH will lead in actions for designing a costly affordable plan for the joint events with the ECCCH ecosystem. In one hand, the planning of technical meetings with the ECCCH-2023 and ECCCH-2024 will try to ensure the alignment of the project meetings with sister and mother projects. On the other hand, collaboration activities will also be planned with the ECCCH ecosystem for maximising the impact and awareness of the project. These meetings will be scheduled on time for better preparation and allocation of the budget. This initial plan will be continuously updated, through T2.2 and T2.3. (Output: D2.1). 

T2.2: Technical collaboration for alignment with ECCCH infrastructure (Lead: CTL; Contributors: All; [M04-M42]) – This task will run until the end of the project. Through T2.2 all technical partners of the consortium will interact with the ECCCH ecosystem and mainly with the ECHOES project to ensure that the proposed and developed solutions will be compatible with the ECCCH infrastructure. Moreover, any potential expansion of the libraries and procedures of the ECCCH will be discussed further, to ensure that all the suggested toolkits and methods that aims to bring innovation and lead beyond SotA will be taken into account. Moreover, through this task the early knowledge exchange with different with the sister and mother projects could lead in even more enhanced solutions, through the adoption of open-source assets. (Output: D2.2).

T2.3: Clustering and common activities with the ECCCH ecosystem (Lead: CERTH; Contributors: All; [M04-M42]) – This task aims at close collaboration and communication through joint events with the ECCCH ecosystem. This task will assist our project to increase its visibility. The use of ECCCH ecosystem will be an enhanced tool to raise the awareness of the project during the open call, the test pilots, as well as for the training tasks of on the innovative toolkits and methods. (Output: D2.2). 

T3.1: Keys for digital transformation of CH (Lead: IHU; Contributors: All; [M01-M09]) – This task will review and analyse the sector of digitisation of acoustics and musical CH through different perspectives. Extended research in the scientific, research, and technological achievements will take place in the context of T3.1. The collaboration among the interdisciplinary consortium of the MusicSphere project will deliver the results to the technical partners, as well as to the scientific community and CH experts. (Output: D3.1).

T3.2: Interdisciplinary user requirements and analysis (Lead: REIGER; Contributors: All; [M04-M12]) – This task will identify the expectations and the requirements of the users with respect to the different tools that the MusicSphere project intends to develop. Focus groups, surveys and in-depth interviews with representatives of the target beneficiaries will be organised, with the objective to identify the perceived needs and stimulate a creative thinking of the new potential benefits coming from the use of the MusicSphere technological tools. The involvement of CH experts and professionals through WP2 is the key and for that reason they will define the needs and requirements in digitisation and analysis of dynamic processes, objects and complex combined data of acoustics and musical CH. (Output: D3.1).

T3.3: Technological challenges and restrictions (Lead: FH; Contributors: All; [M04-M12]) – This task will address the technological challenges and restrictions associated with digitisation and analysis of dynamic processes, objects and complex combined data in acoustics and musical cultural heritage, by exploring innovative solutions that will overwhelm the technical barriers that can prevent the process. As an innovative project, MusicSphere will integrate several new technologies that will go beyond state-of-the-art. However, this is linked with a set of challenges that the project has to deal with. The aim of this task is to identify these challenges and define a strategy that will be collected in the holistic framework defined in T3.4. On the other hand, this task requires inputs for the user requirements identified in T3.2, as well as in WP2 through the interaction with the ECCCH ecosystem. The methodology proposed consists in the study of the connection of the different components that will be in alignment with the ECCCH infrastructure. (Output: D3.1).

T3.4: MusicSphere holistic framework for advanced digitisation and analysis in CH (Lead: CERTH; Contributors: All; [M10-M18]) – This task focuses on the development of a holistic framework for digitisation and analysis of dynamic processes, objects and complex combined data of acoustics and musical CH objects and knowledge, that incorporates the findings and solutions of the WP3 previous tasks. It will provide a solid, flexible framework that enables enhanced digitisation and analysis of dynamic processes, objects and complex combined data, efficiently by combining cutting-edge technical solutions while abiding by industry standards and best practices. Through this task, all the proposed methods and tools for digitisation and analysis of dynamic processes, objects and complex combined data of acoustics and musical CH objects will be included. The framework will be the blueprint for the development of the innovative tools and solutions of WP5 and WP8, empowered by AI and the algorithms of WP4 and WP7. The MusicSphere holistic framework will be transformed into the human-based and centralised platform of WP6 and WP9 that will be migrated to the ECCCH. (Output: D3.2).

T4.1: Mapping of AI algorithms in CH, initial digitisation, and preparation of datasets (Lead: CERTH; Contributors: All; [M04-M12]) – This task will be responsible for the mapping of existed AI algorithms that are applied in the sector of acoustics and musical CH, as well as potential adaptation from other fields that could offer empowered solutions in digitisation and analysis of dynamic processes, objects and complex combined data. In addition, partners will recruit and prepare existing datasets from the CH partners of the consortium and deliver it to the tech partners for early experiments and training of the systems. 3D and 2D digital representations will be some of them. In addition, this task will be the starting point for the following toolkits (TKs): TK4, TK5 and TK9. (Output: D4.1).

T4.2: AI for dynamic digitisation in acoustics and musical CH (Lead: TUIL; Contributors: IHU, UMA, FH, BEN#7, SCHUM, LUTHI; [M07-M18]) – This task focuses on the development of the first versions for TK3, TK4, TK5 and TK9. The methodologies that will be applied are M1.2.1.1, M1.2.1.3, M1.2.1.6 and M1.2.1.7. More specific, the task will involve the leveraging of advanced methodologies and tools for accurate and real-time digitization of acoustic profiles and musical artifacts. It will integrate acoustic profiling, monitoring and predictive modelling and machine learning-driven processes supported by AI algorithms. (Output: D4.2).

T4.3: AI for monitoring the evolution of musical CH (Lead: FH; Contributors: IHU, UMA, TUIL, BEN#7, SCHUM, LUTHI; [M07-M18]) – This task focuses on the development of the first versions for TK4, TK5, TK7 and TK9. The methodologies that will be applied are M1.2.1.2, M1.2.1.3, M1.2.1.4 and M1.2.1.5. More specific, the task will focus on applying artificial intelligence to track and analyse changes in the condition, acoustics, and material properties of musical artifacts over time. It will involve predictive modelling to forecast material degradation and sound quality shifts, cross-modal data integration for combining diverse datasets, and AI-based synthesis for generating realistic acoustic profiles. (Output: D4.2).

T4.4: AI for improved interaction in acoustics and musical CH (Lead: FH; Contributors: All; [M07-M18]) – This task focuses on the development of the first versions for TK6, TK7 and TK9. The methodologies that will be applied are M1.2.1.4 and M1.2.1.8. More specific, the task will focus on enhancing user engagement and interaction with musical heritage using advanced AI techniques. It will include immersive 3D visual and acoustic reconstruction to create engaging virtual experiences, cross-modal integration for seamless interaction between visual, acoustic, and tactile elements, and real-time AI-based synthesis to enable dynamic and responsive musical performances. (Output: D4.2).

T5.1: Definition and design of advanced MusicSphere tools architecture (Lead: CERTH; Contributors: All; [M04-M12]) – This task will define the specifications of the separate tools and toolkits that will be developed in the context of MusicSphere. More specific, this task will focus on the design and on architecture of the following toolkits (TKs): TK1, TK2, TK3, TK6, TK7, TK8, and TK9, taking into account the input by the end-users, the challenges, and the regulations from WP2, WP3, and the available libraries from the ECCCH infrastructure. The methodologies that will be applied are M1.2.1.1, M1.2.1.2, M1.2.1.3 and M1.2.1.5 Moreover, early or mature knowledge and best practices from relevant and ongoing projects will be considered for adoption. (Output: D5.1).

T5.2: Basic toolkits for dynamic digitisation in acoustics and musical CH (Lead: IHU; Contributors: UMA, FH, TUIL, BEN#7, SCHUM, LUTHI; [M07-M18]) – This task focuses on the development of the first versions for TK1, TK2, and TK3. The methodologies that will be applied are M1.2.1.1, M1.2.1.2 and M1.2.1.3. More specific, the task involves the development of foundational tools and frameworks to capture and process acoustic data and material properties of musical artifacts in real-time or near real-time. These toolkits aim to ensure high fidelity in the digitization of acoustic profiles and physical characteristics, enabling dynamic updates to reflect changes over time. The task integrates acoustic profiling techniques, monitoring tools, and cross-modal data integration to combine diverse datasets, such as visual, acoustic, and structural data. (Output: D5.2).

T5.3: Basic toolkits for monitoring the evolution of musical CH (Lead: FH; Contributors: IHU, UMA, TUIL, BEN#7, SCHUM, LUTHI; [M07-M18]) – This task focuses on the development of the first versions for TK4 and TK5. The methodologies that will be applied are M1.2.1.4 and M1.2.1.5. More specific, the task will create essential tools for tracking and analysing the physical and acoustic changes that occur in musical artifacts over time. These toolkits are designed to monitor the condition, material degradation, and acoustic performance of cultural heritage objects, ensuring their preservation. Key elements include the development of monitoring systems that assess wear and tear, environmental effects, and deterioration, and predictive modelling techniques that anticipate future changes in the artifacts’ conditions and acoustics. (Output: D5.2).

T5.4: Basic toolkits for improved interaction in acoustics and musical CH (Lead: CERTH; Contributors: All; [M07-M18]) – This task focuses on the development of the first versions for TK6, TK7, TK8 and TK9. The methodologies that will be applied are M1.2.1.7 and M1.2.1.8. More specific, the task will develop foundational tools to enhance user interaction with musical heritage in both physical and virtual spaces. These tools aim to facilitate intuitive engagement through immersive experiences, integrating acoustic and visual elements to bring historical instruments and environments to life. Key components include the development of interactive systems that dynamically respond to user input, leveraging augmented and virtual reality (XR) technologies for exploration of virtual reconstructions. Cross-modal data integration ensures seamless interaction across sensory modalities, enriching the experience. These basic toolkits will form the foundation of more advanced systems, where AI-driven processes enhance the usability and accessibility of musical cultural heritage, enabling more engaging, educational, and immersive interactions for both the public and researchers. (Output: D5.2).

T6.1: Design platform architecture and elicitation of technical requirements, and ECCCH (Lead: CTL; Contributors: All; [M07-M12]) – T6.1 will define MusicSphere’s architecture and high-level representation of all subsystems to be integrated into an interoperable platform. The architecture will form the essential theoretical scheme based on which, MusicSphere will build the appropriate interfaces with databases, define common data formats for subsystem interfaces, and exchange information between software and hardware components. The architecture design will offer recommendations on the high-level design of the subsystems. Novel concepts of operations will be elaborated to narrate the process to be followed in implementing the MusicSphere platform. The architectural layout of the MusicSphere subsystem interactions will be further validated by the feedback obtained through participatory approach and co-design methodologies. This approach will facilitate the adoption of the technologies and will ensure the conformation of a system addressing the real needs of the end-users (i.e. site managers, public authorities, etc.). The architecture will comply with relevant industrial standards for software and hardware integration supported by ISO/TC 184/SC 5 (Interoperability, integration, and architectures for enterprise systems and automation applications). The architecture will be in full alignment with the guidelines of the ECCCH infrastructure and recommendations (see T2.2). The architecture will incorporate a series of security measures ensuring data protection (see T1.5) and ethical guidelines (see T1.4). (Output: D6.1).

T6.2: Backend infrastructure, security, and basic integration (Lead: CTL; Contributors: -; [M09-M18]) – T6.2 will deploy a 2-Tier data workflow model infrastructure that will include: a Big Data capability and microservices set up with Docker, Jenkins and Kubernetes orchestration as needed, and Data Ops for data exploitation. The Data Ops will entail: i) connectors via REST APIs for MusicSphere interfaces for raw data gathering; ii) development of data wrangling/data munging algorithms for transforming and structuring data from raw data form into a unified format (e.g., XML, JSON); iii) development of data cleaning algorithms for filtering of unwanted characters; iv) data enrichment for merging third-party data from external authoritative sources; v) Data validation routines for ensuring data quality and peculiar unexpected data-value violations; vi) Publication of Data process to a data middleware stack via APIs or Message Brokers (e.g. Kafka) that will be consumed from other MusicSphere tools. The entire data deriving from the projects’ gathering activities will be pre-processed and stored to the Data Middleware Stack (DMS) with security on-transfer and on-storage. The MusicSphere Open API will be developed, with data anonymization methods that will guarantee privacy via k-anonymity and l-diversity algorithms. A layered architecture will be implemented where its layers will be determined by the tier onto which the software operates. (Output: D6.2).

T7.1: Advanced digitisation, datasets acquisition, and reuse (Lead: IHU; Contributors: All; [M19-M28]) – T7.1 will focus on the development of TK1, TK2, and TK3. The methodologies that will be applied are M1.2.1.1, M1.2.1.2 and M1.2.1.3. More specific, the task will develop cutting-edge techniques for capturing and processing high-resolution digital representations of musical cultural heritage (CH) artifacts. This will involve the use of advanced technologies, such as 3D scanning, acoustic profiling, and material analysis, to create detailed, accurate digital models of historical instruments and acoustic spaces. These digital representations encompass both the physical and acoustic characteristics of the artifacts, providing a comprehensive record for preservation and restoration purposes. (Output: D7.1). 

T7.2: Advanced AI for dynamic digitisation in acoustics and musical CH (Lead: UMA; Contributors: IHU, FH, TUIL, BEN#7, SCHUM, LUTHI; [M19-M32]) – This task focuses on the development of the first versions for TK3, TK4, TK5 and TK9. The methodologies that will be applied are M1.2.1.1, M1.2.1.3, M1.2.1.6 and M1.2.1.7. More specific, the task will focus on applying cutting-edge artificial intelligence (AI) technologies to enhance the real-time digitization and analysis of musical artifacts. By integrating machine learning and deep learning techniques, this approach enables the creation of highly accurate, adaptive digital representations that capture both the acoustic and material properties of musical heritage, detecting subtle changes in the artifacts’ condition over time, offering predictive insights into their evolution and degradation. Furthermore, AI will optimize the digitization process, making it more efficient and scalable, while supporting interactive, immersive experiences for users. (Output: D7.2).

T7.3: Advanced AI for monitoring the evolution of musical CH (Lead: TUIL; Contributors: IHU, UMA, BEN#7, SCHUM, LUTHI; [M19-M32]) – This task focuses on the development of TK4 and TK5. The methodologies that will be applied are M1.2.1.4 and M1.2.1.5. More specific, the task will apply sophisticated artificial intelligence techniques to track and predict the ongoing changes in the physical, material, and acoustic properties of musical heritage artifacts over time. By utilizing predictive modelling and machine learning algorithms, this approach will enable continuous monitoring of artifacts, identifying subtle shifts in their condition that may not be immediately apparent. AI models will analyse data from different factors to forecast future degradation or changes, allowing for proactive conservation strategies.(Output: D7.2).

T7.4: Advanced AI for improved interaction in acoustics and musical CH (Lead: FH; Contributors: All; [M19-M32]) – This task focuses on the development of the first versions for TK6, TK7, TK8 and TK9. The methodologies that will be applied are M1.2.1.7 and M1.2.1.8. More specific, the task will enhance user engagement with musical heritage through personalized, immersive experiences. AI-driven real-time acoustic analysis adapts interactions with historical instruments and acoustic spaces based on user preferences. By integrating cross-modal elements (sound, touch, and visuals), AI enables dynamic virtual environments for interaction with recreated instruments and soundscapes. Additionally, adaptive learning systems provide personalized feedback, guiding users through historical contexts and techniques. This AI application transforms how users experience, learn, and engage with musical cultural heritage, making it more accessible and impactful. (Output: D7.2).

T8.1: Advanced toolkits for dynamic digitisation in acoustics and musical CH (Lead: IHU; Contributors: All; [M19-M32]) – This task focuses on the development of the first versions for TK1, TK2, TK3 and TK7. The methodologies that will be applied are M1.2.1.1, M1.2.1.2 and M1.2.1.3 More specific, the task will focus on developing innovative technologies to capture and preserve the acoustic and material properties of musical artifacts in real-time. These toolkits integrate high-resolution 3D scanning, acoustic profiling, and environmental sensing to dynamically monitor and update digital representations of historical instruments and spaces. (Output: D8.1).

T8.2: Advanced toolkits for monitoring the evolution of musical CH (Lead: FH; Contributors: All; [M19-M32]) – This task focuses on the development of the first versions for TK4, TK5 and TK7. The methodologies that will be applied are M1.2.1.4 and M1.2.1.5. More specific, the task will focus on developing innovative systems to track and analyse changes in the physical, material, and acoustic properties of musical artifacts over time. By integrating environmental sensors, acoustic measurements, and material analysis, these toolkits will provide continuous, real-time monitoring along with AI-driven algorithms to detect subtle shifts and predict future degradation, enabling proactive conservation. Cross-modal data integration offers a comprehensive view of the artifacts’ evolution, supporting informed decision-making for preservation. (Output: D8.1).

T8.3: Advanced toolkits for improved interaction in acoustics and musical CH (Lead: UMA; Contributors: All; [M19-M32]) – This task focuses on the development of the first versions for TK6, TK7, TK8 and TK9. The methodologies that will be applied are M1.2.1.7 and M1.2.1.8. More specific, the task will focus on developing systems that enhance user engagement with musical artifacts through immersive and interactive experiences. By integrating real-time acoustic analysis, augmented and virtual reality (XR) technologies, and AI, these toolkits will enable dynamic interaction with historical instruments and soundscapes, supporting cross-modal engagement, combining sound, touch, and visual feedback, and personalize experiences based on user preferences. (Output: D8.1).

T9.1: Continuous integration (Lead: CTL; Contributors: All; [M19-M32]) – This task describes the action that will be taken for the continuous integration of the toolkits, in the interoperable human-centric platform. All the versions of the toolkits and methods, as well as assets and CH digital twins will be integrated immediately when ready. This task will focus to keep the timeline and milestones, regarding the second phase of pilots, as well as the availability of the toolkits for the training purposes and to assist open call funded projects. In close collaboration with T2.2, the project will ensure the alignment of the solutions with the ECCCH guidelines. (Output: D9.1).

T9.2: System validation and continuous improvement based on users’ feedback (Lead: CTL; Contributors: All; [M25-M36]) – MusicSphere platform will be an open web-based hub for the provision of MusicSphere’s AI tools, XR applications and web UIs in the relevant, artistic, professional, scientific, and research community. In this second phase of integration, outcomes from WP7-WP8 will be immediately tuned towards services provided by the MusicSphere platform and accompanied by detailed documentation, aiming to sustain the system development. The consequent prototype developments on the basic edition will be driven by regular development cycles and the feedback generated during the pilot implementation and validation. The platform will continue to materialise all AI, XR models of WP7 and WP8 and will make them readily available to the engaged target stakeholders, including the widest international research and CH community, through the designated demonstration workshops and open days (T2.3 and T13.2) with the ECCCH. (Output: D9.2).

T9.3: Migration of the holistic MusicSphere framework to ECCCH (Lead: CTL; Contributors: -; [M33-M39]) – The primary objective of this task is to migrate the entire MusicSphere framework to the ECCCH platform while ensuring seamless integration and functionality. Additionally, comprehensive documentation of the migration process and the adapted framework will be created to facilitate future reference and maintenance. The task should initially analyse the existing MusicSphere framework and its components, assess compatibility and dependencies to determine the feasibility of migration to the ECCCH platform and develop a migration plan outlining the step-by-step process for each component, considering any necessary adaptations or enhancements. Then, it should proceed with integrating the migrated MusicSphere framework seamlessly into the ECCCH platform and ensure that all functionalities of the original framework remain intact and fully operational within the ECCCH environment. Then, it will transfer all relevant data, including CH assets, metadata, ontologies, and interconnections, to the ECCCH platform while maintaining data integrity and consistency and establish a data migration strategy that will ensure that the ECCCH platform can effectively handle and utilise the migrated data. Last but not least, T9.3 will create detailed documentation for the migration process, including step-by-step instructions, configurations, and any necessary code modifications, providing clear guidelines for platform users, administrators, and developers on how to utilise the migrated framework within the ECCCH ecosystem. (Output: D9.2).

T10.1: Pilots Preparation, Deployment & Validation Plan, Definition of Pilot KPIs (Lead: IHU; Contributors: All; [M13-M18]) – A pivotal facet of our role entails collaborating closely with the project team to define the Key Performance Indicators (KPIs) for the pilot phase. These KPIs serve as the cornerstone for objectively measuring the project’s success, ensuring alignment with its objectives, and furnishing a structured framework for comprehensive evaluation. (Output: D10.1). 

T10.2: Open call design and preparation (Lead: VLABS; Contributors: CERTH; [M13-M18]) – This task aims at the design of the open call, as well as content development and the creation of an Application Handbook. This resource will serve as a comprehensive guide for applicants, offering clear instructions on submission processes, evaluation criteria, and proposal formats. The toolkit will be designed to be user-friendly and accessible, often including templates, examples, and a FAQ section to address common concerns. Multilingual options may also be provided to foster inclusivity and reach a diverse applicant pool. (Output: D10.1).

T10.3: Training design and preparation (Lead: HEMO; Contributors: All; [M13-M18]) – This task describes the activities that will take place during the preparation of the training. The scopes, the target groups, the toolkits that will be used from the project, as well the structure of the training programme will take place here. Moreover, the mapping of training platforms and the possibility of in person or hybrid training will also take place. Under the directions of lifelong learning, the whole educational programme will be designed. (Output: D10.1).

T11.1: Small-scale pilots (Lead: LUTHI; Contributors: All; [M19-M24]) – This task aims to demonstrate the initial editions at a smaller scale the work towards the initial versions of the MusicSphere’s toolkits from WP4-WP6. Operational prototypes will be tested by the members of the MusicSphere consortium, both CH and technical partners, as well as by the advisory board and members from the ECCCH consortia. Through this testing process, the primary functionalities of the toolkits will be evaluated by the experts and professionals in acoustics, and musical CH. The tests of this task will be executed according to the plan of T10.1. In addition, the results from the first tests will provide feedback to the technical partners for further improvement. (Output: D11.1). 

T11.2: Open call submission, evaluation, and selection (Lead: VLABS; Contributors: All; [M19-M24]) – This task involves the active engagement of stakeholders— cultural institutions, researchers, and community groups—that will co-define eligibility criteria, evaluation metrics, and expected outcomes. The process ensures that the open call reflects the needs of the cultural heritage sector of acoustics and musical organs, while maintaining transparency and fairness. This task includes the proposal submission, evaluation, and selection through a secure and efficient submission platform, complemented by accessible support channels for applicants. Proposals are evaluated by a multidisciplinary panel using predefined, transparent criteria. Selected projects are chosen based on their potential to address the objectives of the call and deliver meaningful impact in the cultural heritage domain. Feedback is provided to all applicants to enhance future participation and capacity building. (Output: D11.1).

T11.3: Trainees recruitment and training content creation (Lead: HEMO; Contributors: All; [M19-M24]) – This task focuses on the actions that will be taken for maximisation of the awareness regarding the training activities. All the communication channels of the consortium will be activated, as well as the clustering and collaboration in the ECCCH ecosystem. Moreover, the content creation that will based on the development of the toolkits and methodologies, will be shaped into multimedia education material. (Output: D11.1).

T12.1: Training sessions (Lead: HEMO; Contributors: All; [M25-M38]) – This task includes the execution of the training in the innovative toolkits and methods of the project, according to the plan of T10.3, and using the educational material of T11.3. The whole description and the evaluation of the training will be included in the common D12.1. (Output: D12.1).

T12.2: Large-scale pilots and open call execution (Lead: VLABS; Contributors: All; [M27-M38]) – This task aims to demonstrate the second editions at a larger scale the work towards latest versions of the MusicSphere’s toolkits from WP7-WP9. Cultural heritage partners will be assisted by the technical partners of the consortium. The tests of this task will be executed according to the plan of T10.1. During this task, the selected projects from the open call will also be included in the large-scale pilots. (Output: D12.1).

T12.3: Evaluation results and validation (Lead: UMA; Contributors: All; [M37-M40]) – This task will analyse the results of the large-scale tests training sessions, and open call completion. The findings will be communicated through the common D12.1. (Output: D12.1).

T13.1: Communication and dissemination plan and activities (Lead: EUROXR; Contributors: All; [M01-M42]) – The first duty of this task will be to design a Communication and Dissemination strategic plan at M06 (D13.1). In parallel, this task will manage the KoM and annual GA of the project partners and especially the EUROXR association proposes to welcome these meetings during its annual international conferences as either as special sessions or plenary sessions, in consistency with either the internal or external communication needs of the project, or to disseminate scientific and/or technical results of the project. Moreover, besides the implementation of the project website under the ECCCH infrastructure (due M02), and social media channels, EUROXR will provide a number of services (through its Website and Social media channels) and activate its network to maximise the dissemination. To this aim regular calls and meetings (around each 2 months) will be organised with partners, as shared documents will be created, to collect information to be disseminated about the project. (Output: D13.1;D13.4).

T13.2: Clustering with external related projects and initiatives (Lead: SCHUM; Contributors: All; [M01-M42]) – This task involves identifying and establishing partnerships with relevant projects and initiatives outside of the proposal, which may share similar research interests and goals. By collaborating with external partners, the research team can leverage existing expertise, resources, and data, which can improve the quality of the project and increase the chances of success. During the periodic project meetings and through the D13.2, the progress and the results of this task will be available to the consortium and to the public. Moreover, liaison with external projects and initiatives can help to enhance the impact and dissemination of the research findings. This task involves identifying and establishing collaborations with stakeholders such as industry partners, NGOs, and policymakers, who can help to disseminate the research results and ensure that they are translated into practical solutions. By collaborating with external partners, the research team can ensure that the research is aligned with societal needs and can be applied in the real world. Moreover, CERTH will promote the open call invitation of the project through the complex network of the consortium members. Moreover, in between annual meeting that will be welcomed by the EUROXR international conferences managed (cf. T13.1), XR Open Forums on Metaverse and Cultural Heritage topics will be organised by the EuroXR association. Such forum is a concept of hybrid or remote 2-3 month periodic meetings developed by this association for networking any XR stakeholders with and without EUROXR members to brainstorm on the future of XR in cultural heritage. (Output: D13.2).

T13.3: Market analysis, exploitation and IPR management (Lead: LUTHI; Contributors: All; [M01-M42]) – This task aims at the preparation of exploitation plans for project results, that will be collected in an exploitation report included in D13.2. Identified IPR issues in WP1 and individual exploitation strategies will be discussed amongst partners and will be included in the exploitation report, exploring possible partnerships with financial and investment institutions will be investigated, and where feasible financing and follow up investment will be sought. Both individual and joint exploitation activities, market analysis and go-to-market strategy will be detailed in the exploitation report (D13.2). Furthermore, innovation management activities will ensure the effective identification of business cases that make sense in the market, deployment of the exploitation report and investigation of potential business models. The innovation potential of each result will be identified in market terms. This approach will increase the awareness of the public through social media, and focus on the communication channels, i.e. how customers are being reached, to effectively exploit the produced results and technologies. At last, the project’s exploitation and sustainability plans will follow a staged approach which aims to collect relevant market and industry data, incrementally raise awareness, mature the market, and expand targeted stakeholders and market opportunities. (Output: D13.2).

T13.4: Knowledge transfer for sustainability and long-term impact (Lead: VLABS; Contributors: All; [M01-M42]) – To enhance the dissemination activities carried out throughout the project and ensure the sustainability of MusicSphere’s findings, this task will produce a set of recommendations to be addressed to professionals and to policymakers from the national to the European levels. Policy recommendations will be published and disseminated to the targeted audiences. They will include guidelines and insights for policymakers which will outline the areas that need more attention and investment in the field of digitization and analysis of dynamic processes, objects and complex combined data in cultural heritage, etc. A recommendation booklet for stakeholders including the lessons learnt from the WP12 will be published and presented during the final symposium of the project and disseminated. These guidelines and recommendations will be widely advertised and presented at relevant European policy and professional events and made available for download on the project’s website.Moreover, EUROXR will propose to project partners to welcome them as members of this association and to create a Special Interest Group (a dedicated topic oriented structure gathering association members and outside stakeholders) for the sustainability of the project partnership and to gather and increase the community built around and during the project life. (Output: D13.3).