Deliverables
D1.1 Description of use cases
Published Feb 2023, Public
This deliverable clarifies the problem to be solved in AIDAVA, i.e. (1) develop a "data cleaning" machine with maximum automation in curation and publishing of personal healthcare data and (2) maximise engagement with the patients in the curation process of these data when automation is not possible.
The deliverable then describes the 2 use cases for testing the prototype: (1) the first use case is hospital centric and will demonstrate the feasibility of a federated "EU" breast cancer registry composed by interoperable extracts issued form multiple different sources from each of the evaluation sites; (2) the second use case is patient-centric and will demonstrate how patient data curated into an individual longitudinal health record can be reused for visualising of the patient record and for computing a cardiac risk score supporting physicians in monitoring risk for their patients. We will also explore the possibility of presenting the risk score to the patient. Finally, the deliverable provides more information on the metrics that will support assessment of the prototype and identify key elements to be taken into account when deploying the prototype across sites.D1.2 Report from user survey with personas canvas
Published May 2023, Public
The AIDAVA project's Task 1.2 (T1.2) aimed to better understand the different user groups of the AIDAVA "AI-powered data curation and publishing virtual assistant" by involving two patient organisations, hospitals and health data intermediaries (HDIs). Also, T1.2 assessed patients' and citizens' interest and willingness to control and curate their personal health data. To achieve this goal, the project team developed 8 personas based on 39 in-depth interviews, consisting of 2 patient personas, 2 data user personas, 2 data curator personas, and 2 third-party app developer personas. Foundation documents and persona canvases were created for each persona. Additionally, a survey was conducted with 250 participants to determine the general willingness of citizens to use a virtual assistant and what functionalities they would like the AIDAVA virtual assistant to have.
This information will be used to support the user-centred development of an AI-based data curation and publishing assistant. The personas will help developers empathise with different user groups, leading to more user-centric decisions. It will also serve as the foundation of the explainability and feedback layer for the user interface for patients - based on user profiles gathered when the user starts using the system for the first time. The personas will complement the business requirements specified in deliverable D1.3, and the default user profiles can be based on the main characteristics of the personas.
D1.3 Business requirements for G1
Published Jun 2023, Public
This document provides a detailed description of the business requirements – functional and non-functional requirements - of the AI-powered data curation and publishing assistant, aimed at supporting patients (and expert curators) in managing and cleaning their personal health data – in compliance with ethical and regulatory requirements.
The first objective of the AIDAVA project is to demonstrate that the prototype works in a realistic - though strictly controlled through an assessment protocol approved by the local ethical committees as described in Deliverable D1.4 - environment, considering data privacy and regulatory constraints. The second objective is to develop a solution that can be transformed into a full-fledged product, including MDR certification; to meet this second objective we decided to keep all requirements that were captured, and to indicate if they were in scope of the prototype or only in scope of the future product. While the product related requirements will not be developed during the AIDAVA project, it is expected that the technology architects will take these into account when defining the architecture of the system and ensure the prototype can smoothly evolve toward a marketable product. The requirements were gathered through a structured approach.
First, the user journey was defined. It includes the following steps: registration and logging, upload and ingestion of patient personal data from different sources, integration & curation of data from these sources, use of the resulting curated data, deletion of account (and data). Second, the requirements were gathered along the steps of this user journey for the different users - and potential customers - identified for the prototype: patient, expert curator, data users but also administrator and third-party app developers. Capture of requirements took place mainly through structured workshops and on-line meetings across the different sites.
- The requirements across the users were then consolidated and clustered in epics, defined in Deliverable D2.3 Solution Design; duplicate requirements across user groups were deleted.
- Each consolidated requirement was then provided with a level of severity (blocking/crucial, major, minor, out of scope) indicating the importance of having the requirement successfully developed. While assessing the severity, specific attention was given on data privacy, security, and regulatory process as well as on patient needs and acceptability.
- Finally, the team identified the need to have the requirement in the prototype or only in the future product.
A total of 596 requirements were gathered with the different users; after consolidation across users and prioritisation, 277 requirements were considered as needed for the prototype (46 blocking, 178 major, 53 minor) and 99 additional ones were considered in scope for a product. As the need for information and documentation came regularly while gathering requirements, the content of four different documents was drafted.
The content of the business requirements will be consolidated with the automation requirements from Task 2.1 and from quality management requirements from Task 4.2 and transformed by the development team into features-centric user stories to be further used as the basis of the development.
The content of the deliverable will be re-assessed – and potentially adapted – after the evaluation of Generation 1 of the prototype to deliver Generation 2.
D1.4 Definition of assessment study including test scenarios & metrics, and study initiation package
Published Nov 2023, Public
The AIDAVA prototype will be delivered in 2 generations: Generation 1 in Q3 2024 and Generation 2 in Q2 2026. It will be tested in 4 hospitals and 2 Health Data Intermediaries, with 45 patients respectively per therapeutic area across all sites (90 patients for the 2 therapeutic areas in scope). This deliverable includes the description of the 4 documents developed to support the execution of this assessment study of the two generations of the AIDAVA prototype in an ELSI compliant way, with a minimum burden for the patients and the sites.
The first document - and the most important one - is the study protocol (Annexe 1); it starts with a synopsis of the study and includes a description of the objectives of the study, the specification of the primary and secondary endpoints, the study schedule with the different activities to take place during the evaluation of the prototype across the 2 generations (including the washout period between the 2 generations), the study population with eligibility criteria, the data points to be collected with associated data collection forms (in RedCap) and the statistical analysis.
Another important document, related to the protocol is the English version of the Study Information Package and Informed Consent Form (Annexe 2) to be translated by each site and provided to patients during the recruitment process.
The third document includes a training plan (Annexe 3) for the patients participating in the evaluation and for the study team. It includes a specification of the different modules and a training program for the participants of the study, based on their role.
The final document is a template Data Sharing agreement (Annexe 4), to be adapted and finalised by each site, including guidance for technical and legal provisions.
The deliverable also includes description of work that was conducted with the help of Health Data Intermediaries (HDI) who helped to identify vendors who would provide a patient app application (to collect Quality of Life information) and a blood pressure medical device to be used during the study; the collected data will be managed by the HDI and provided to AIDAVA for integration in the patient record.
We also provide an overview on the feedback provided by the patients´ consultants for the different materials mentioned above, and specify the study design with the schedule of activities as well as the Study Information Package and the Informed Consent Form.
D2.1 Global Data Sharing Standard
Published May 2023, Public
Ontologies are increasingly used to support harmonisation of population data from heterogeneous data sources in support of clinical research, with a specific research question requiring a well defined dataset. AIDAVA is exploring the possibility of using an ontology to harmonise all patient data, extracted from heterogeneous data sources, into an individual personal health knowledge graph (PHKG) that can then be reused for multiple purposes, in clinical care and clinical research.
The decision to take an ontology approach in AIDAVA, rather than to follow a structural standard such as an information model, was made already at proposal time as ontologies are semantic rich and agnostic of structural and syntactical formats, increasing potentially of interoperability and reuse in compliance to the FAIR principles. Moreover, new knowledge can be added smoothly by extending the ontology concepts with RDF triples and data quality constraints through SHACL rules.
Development of the AIDAVA Reference Ontology followed a structured approach including ideation, requirement analysis, design and development. The requirements took into account the use cases developed in WP1, the requirements extracted from the automation phases described in Task 2.1 and the annotation process described in Task 4.3. The data quality constraints were built in alignment with Task 4.2. We identified 4 Ontology Strategic Requirements and 6 Ontology Requirement Specifications that provided directions for the design and the developement of the ontology.
A critical aspect of an ontology like the AIDAVA Reference Ontology to comply with FAIR principles as effectively as possible is to maximise alignment with emerging and existing standards. While reviewing the work on semantic interoperability of related initiatives, including TEHDAS and the European Electronic Health Record exchange format (EEHRxf), we came to the conclusion that SNOMED CT and LOINC were priority standards to be included. However they need to be completed by other standards to cover additional relationships and other domains. Several candidates were considered and it was decided to include the semantics subsumed in the HL7 FHIR General Purpose Data Types, and relevant HL7 FHIR profiles through the governance process, as second priority. We expect that other semantic standards will be required to achieve the long term objective of the AIDAVA Reference Ontology to cover a majority of medical concepts contained in personal health medical records.
This deliverable also describes the technical specification of the AIDAVA Reference Ontology, which defines the structure, components, and relationships within the scope of the two targeted use cases (Breast cancer registry and Cardiovascular score) and in a broader context (ensuring semantic interoperability across systems). It includes a formal representation of the concepts, entities and their attributes, which are specified in the AIDAVA Dataset.
While developing the ontology, we realised that additional concepts and relationships as well data quality constraints will need to be added when data sources to be curated will be onboarded across sites, and when more narrative texts will be annotated. This requires a governance process to be executed during the project lifetime, as described in Section 3.4. In addition, and assuming the project will be successful, governance will also be needed beyond the project to maximise sustainability and reuse of the results. While is not in scope of this deliverable, the proposed approach is introduced here; it will be discussed extensively during the planned meetings with the Sustainability Advisory Board.
D2.2 Details on data curation & publishing process
Published June 2023, Sensitive
The document provides a comprehensive outline of AIDAVA's approach to the onboarding and (automatic) curation of patient health data sources into a Personal Health Knowledge Graph (PHKG). This document is the outcome of extensive literature reviews, consultation between project partners, and proof of concept build and testing; with a specific focus on automation, data interoperability, data quality and privacy compliance.
The document details the current limitations and interoperability challenges with respect to health data and health data systems, highlighting the need and potential applications for the AIDAVA approach towards curating such data and publishing an accurate and complete PHKG without compromising data privacy or accuracy.
Furthermore, the specific limitations and interoperability challenges are classified and detailed alongside the assumptions made and (potential) open-source tools which can be integrated into the AIDAVA platform, thus minimising the manual overhead for patients and data stewards involved in the onboarding, curation and publishing processes. The bulk of the document describes the proposed workflows supporting maximum automation of these processes. During the development phase - as part of Task 5.2 - these workflows will probably require adaptations.
D2.3 Solution Design Document for G1
Published Jun 2023, Public
Availability of integrated, high-quality personal health data (PHD) remains limited, with impact on quality & cost of care and limiting possibilities for research and analytics. Indeed, PHD is currently distributed, heterogeneous, captured through different modalities, with variable quality. Findability and Accessibility of this data – following the FAIR principles – is addressed in numerous projects; Interoperability and Reuse remains a challenge due to several factors that are addressed by the intelligent virtual assistant being prototyped in the AIDAVA project. Concretely, the objective of the project is to maximise automation in data curation & publishing3 of heterogeneous PHDs while empowering individuals – patients or their deputies and data curators – when automation is not possible due to lack of contextual information. Through the data curation workflows (Deliverable 2.2. Data curation and publishing process), the AIDAVA virtual assistant prototype is expected to transform each patient's PHD into a Personal Health Knowledge Graph (PHKG). All PHKGs will be generated in compliance with the AIDAVA Reference Ontology (Deliverable 2.1. AIDAVA Reference Ontology as a Global Data Sharing Standard). From the PHKGs and the mapping information contained in the Reference Ontology, the publishing module will generate different target outputs as required by different use cases (see Deliverable 1.1. Description of Use Cases). This deliverable focuses on the solution design of the AIDAVA prototype virtual assistant. The solution includes a backend and a frontend. The backend includes foundational components such as the user directory, a master data reference repository, the catalogue of data sources with metadata supporting automation, a library of curation tools used in the automation workflows, the reference ontology which is the standard of reference for each PHKG, the repository of patient data – from raw format to PHKG and published format in the target standard – and the overarching orchestration module that supports automation and interaction with the end users. The frontend is the module that interacts with the end users. In Generation 1 of the AIDAVA prototype, user interaction will be minimum; in Generation 2, the user interface will build on advanced technologies from human-computer interaction, with explainability to facilitate understanding of the questions raised by the virtual assistant during the curation process. Explanations will be tailored to users categorised through different user profiles as identified in Deliverable D1.2. Report from user survey with personas canvas.
These different components are described in the solution architecture, with the related Epics, in turn consolidated in Initiatives for the development team. In addition, the first 2 levels of a formal description of the system - based on the C4 model - is provided; an in-depth description of lower levels of the C4 models will be developed in Deliverable D3.1. VA Architecture.
This deliverable also describes the proposed support model to be implemented when evaluating the prototype across the different sites. Finally, this deliverable introduces potential target customers. A full market analysis, with in-depth analysis of customers, market size and market potential for a product that could be developed on the result of the AIDAVA project will be provided in Deliverable 2.4, as an updated version of this deliverable, after evaluation of the first generation of the prototype.
D3.1 VA architecture (Application and Technical)
Published Sep 2023, Public
The objective of the AIDAVA project is to prototype an intelligent virtual assistant that will maximise automation in data curation & publishing of heterogeneous personal health data while empowering individual patients when automation is not possible due to lack of contextual information. The solution includes a backend and a frontend described in the solution design (see Deliverable D2.3. Solution Design). This deliverable focuses on the technical and data architecture of the AIDAVA prototype and on its deployment, integration and testing with the different evaluation sites. As the consortium intends to develop a reusable prototype, we first clarify the difference between product and prototype and confirm the importance of taking into account product constraints in the technical architecture to ensure reuse. We also define a set of architecture principles that guided the elaboration of the deliverable. The technical framework relies on a microservices-based structure, encompassing numerous satellite applications and curation tools. These components will be seamlessly integrated to facilitate the automation process using predefined workflows that incorporate workflow orchestration tools. Additionally, the architecture encompasses connectivity with various medical partners. In this context, the system will acquire input data from file shares or databases and establish connections with health data intermediaries, receiving data either through API endpoints or by utilising SDKs. The data architecture expands on the components identified in the solution design deliverable, from an implementation perspective. As the AIDAVA project aims to test the solution in real life with real patients consenting to manage and curate their data, an important part of this deliverable relates to integration in the different evaluation sites, the needed hardware as well as deployment and testing.
This technical architecture is the consolidation of 1 year of efforts across different teams. It provides the consortium with a solid description of the solution that needs to be implemented to successfully meet the objectives of the project. While there are challenges ahead, there is confidence that the first generation (G1) of the prototype can be successfully developed and deployed. The technical architecture - and this document - will be updated for Generation 2 (G2) of the prototype, taking into account the results of the evaluation by patients and clinical sites, the need to integrate more powerful NLP curation tools and an improved human computer interaction front end developed in other work packages of the project.
D3.2 Epics and user stories for G1
Published Nov 2023, Sensitive
The project has made significant strides in understanding and addressing the needs of the targeted end-user groups, including patients, expert curators, and data users, among others. Through extensive interviews and collaboration, in “Deliverable No. 1.3 – Business requirements for G1”, a comprehensive list of Business Requirements (BR) and corresponding Acceptance Criteria (AC) emerged. This process has allowed us to gain valuable insights into the specific expectations and functional as well as non-functional requirements of each user group.
To enhance the clarity and manageability of these BRs, we have categorized them into well-defined action initiatives3 identified in D2.3 Solution Design for G1, among which the most important are the ingestion and curation. This categorization enables the prototype developers to understand better the desired functions and functionalities for each aspect of the application.
The AC associated with each BR has undergone thorough refinement and validation by the prototype owner group members and the prototype development representatives. This process ensures that the solution aligns precisely with the expectations and standards set by the end users.
To gain a holistic view of the distribution of BR across different actors and epics, we have transferred the BRs from an Excel spreadsheet to a visualization app, called FigJam. This allows us to identify areas with the most BR and make informed decisions about resource allocation and prioritization.
In terms of tracking our work progress, we have established links between the BRs and ACs first to the user interface or architecture diagrams, then to user stories (US), and finally to user screens. This ensures that the development efforts are aligned with the overarching project goals and user expectations. The Work Breakdown Structure (WBS) is designed to provide a clear hierarchy of tasks. Epics define high-level requirements, US break down each Epic into specific user screens or technical components, and Tasks outline the specific development tasks required for completion. This structured approach streamlines the development process and project management.
Throughout this process, the user stories have been fine-tuned based on feedback gained through iterative meetings with end-user representatives. This feedback-driven approach has been instrumental in ensuring that the application not only meets but exceeds user expectations.
We have to refer to the user stories list, in Annex 1, as a living requirement list, meaning that as the development of the prototype will progress, potentially missing use cases will be identified. Those new use cases will have to be recorded, defined, estimated from complexity and maturity point of view, and finally scheduled for implementation based on the development roadmap.
D4.1 Annotation guidelines, tools & training
Published Dec 2023, Public
Manual annotations of clinical narratives are crucial for the adoption and evaluation of NLP tools, which support an overall AI assisted data curation approach within the AIDAVA project. In the preparation phase - in scope of this deliverable - for the Task “T4.3 Manual Annotation of text documents in 3 languages”, and based on the data elements identified for the use cases cross border breast cancer patient registries, and longitudinal individual health records for patients at risk of sudden cardiac arrest, requirements for the manual annotation tool have been formulated. Grounded on the requirement analysis, INCEpTION was chosen to support the manual annotation task. A first manual annotation schema was developed and tested, with a focus on the use of SNOMED CT and FHIR for the normalized form of the entity types of interest. A first version of the annotation guidelines is drafted in this document and will be revised in close cooperation with the manual annotators at the three different clinical sides (Med Uni Graz with MUG, Northern Estonian Medical Center with NEMC, Maastricht Medical University Center with UM), AVER and ONTO during the piloting phase until Q1 2023.
D4.2 Data Management Plan
Published Feb 2023, Public
This Deliverable provides the Data Management Plan (DMP) for AIDAVA. It is based on the European Commission Template for Horizon 2020 projects available at https://ec.europa.eu/research/participants/data/ref/h2020/gm/reporting/h2020-tpl-oa-data-mgt-plan_en.docx.
AIDAVA has populated this Data Management Plan in line with recommended EC guidelines. It will be updated as the project proceeds.
A DMP is an important component of any data intensive programme because it imposes a need for balance between protection of data, success of the programme and the potential for reuse of data. AIDAVA is unique as a project because the primary data handling is focused on data ingestion and curation as a tool to assist citizens in managing their own health data.
The approach to developing the data management plan has included workshop discussions with partners at the October 2022 Kick Off Meeting in Maastricht and a dedicated data flow workshop held in Tallinn in December 2023.
The details gathered were compared with the proposal and obligations on the partners as described in the consortium agreement. They were also compared with the developing Research Protocols for both the Breast Cancer and Cardiovascular Disease (CVD) use cases developed in Task 1.4
The results of the details gathered are presented as the Data Management Plan in Section 3 of this Deliverable. It concludes with the next steps and specification of updates in time for M40’s second version of the Data Management Plan.
D4.3 Update to Annotation guidelines, tools & training
Published May 2023, Public
Manual annotations of clinical narratives are crucial for the adoption and evaluation of Natural Language Processing (NLP) tools, which support an overall AI-assisted data curation approach within AIDAVA. For a symbolic representation of clinical entities of interest and the way how they are related, normalisations that use international standards like SNOMED CT, FHIR or LOINC are crucial. For this deliverable, we updated the first version of the manual annotation guideline (see AIDAVA Deliverable D4.1), where requirements for annotation tooling were formulated with respect to the AIDAVA use cases, together with some initial annotation instructions. Grounded on this requirement analysis, INCEpTION was chosen as an annotation tool after a rigorous investigation of available annotation software. A first manual annotation schema was developed and tested, with a focus on the use of SNOMED CT and FHIR for the normalisation of the types of clinical entities (as annotating them with terminology codes) referred to by clinical narratives. Within this preparation phase, INCEpTION was deployed on all three clinical sites (MUG, NEMC, MUMC), with a first version of a consolidated INCEpTION layer definition. A bi-weekly “train the trainers” session was started at the end of 2022, supporting a continuous transition into the piloting phase of the developed guideline, analysing example narratives and how they should be annotated according to the first version of the guideline. Within the piloting phase lasting from January 2023 to May 2023, in communication with the responsible clinicians, relevant attributes were identified, and a selection of them was used for updating, testing and refinement of the annotation guideline. Annotators were recruited at all three different sites and their feedback was taken into account for the customization and technical set up of INCEpTION. Alignment with Deliverable D2.1 "Reference Ontology as a Global Data Sharing Standard" defining the AIDAVA Reference Ontology was identified as crucial, therefore this deliverable was postponed for one month from April to May 2023.Building on the first version of the guideline delivered early January 2023, this updated descriptive guideline provides a comprehensive framework and detailed instructions to ensure accurate annotation of clinical narratives. It covers crucial aspects like data standardisation and best practices in annotation (including annotation tool, general principles, specific instructions, concrete examples, and quality control items), ensuring consistent, interoperable, and high-quality annotations. This is invaluable for effective knowledge graph construction, data analysis, and knowledge extraction as central requirements in AIDAVA.
The updated annotation instructions form the core of this deliverable, enabling to start the productive phase of the manual annotation. Manual annotation of texts is iterative and dynamic. It is, therefore, crucial to recognise potential updates and improvements that may arise during the productive phase. Factors that can contribute to the modifications and enhancements of the set of annotation instructions include active feedback from the annotation team, new insights into text phenomena that lead to annotator disagreements, updates in data requirements from use cases, and evolution of project objectives as a result of dissemination and communication activities during the project. To ensure consistency and minimise inconsistencies in the annotation work, a structured feedback mechanism is established, involving documenting any challenges or updates in a shared document, and conducting meetings with the annotation team to address any emerging insights or challenges.
D4.4 Information governance framework and instruments
Published Aug 2023, Public
Deliverable 4.4 describes the Information Governance Framework for AIDAVA. Information Governance relates to regulatory compliance and risk management for information handling. It will also inform technical design and implementation, including security services such as access controls and encryption.
Partner i~HD has therefore under Task 4.1 engaged with the Consortium to conduct the requisite information gathering and risk assessments to ensure high assurance around the handling of health information in line with key Information Governance principles. It has used the Data Protection by Design and Default approach provided by GDPR to engage with the Consortium early on to ensure that it defines the data flows to achieve the goals of AIDAVA, assesses the data protection, security and ethical risks of the project and defines the key instruments that will address them.
The outcome of this is the Data Protection Impact Assessment template for the Consortium, which in turn has assisted with the production of a Data Management Plan published as D4.1. Both deliverables are based on Data Flow Diagrams initiated during a dedicated workshop held in December 2022 between WP1 and WP4, and further refined through joint meetings held throughout the project. The processes have allowed an agreement on the roles of the partners and on the contractual agreements required to govern AIDAVA with progress made on defining these contracts. The contracts themselves include a set of bilateral Data Sharing Agreements developed on a standard template defined within the consortium, including - whenever applicable - existing legal provisions and specific technical provisions; Data Processing Agreements were assessed as not needed.
Task 4.1 has also offered advisory on submissions to Research Ethics Committees - for accessing patient data for annotation purposes and for assessing the prototype - and design choices for the project. The drafting of a Code of Practice is also underway and key challenges are being collated for submission to AIDAVA’s independent Ethics Advisory Board which will meet for the first time in early October.
D6.1 Public project website
Published Feb 2023, Public
This deliverable shortly describes the initial set-up of the AIDAVA public project website with screenshots of the main pages attached in the annex.
D6.2 Plan for dissemination & exploitation of results incl. communication (PDEC), updates in official report
Published Feb 2023, Sensitive
The Plan for the Dissemination and Exploitation of results including Communication activities (PDEC) is a strategic document, helping the project consortium to establish the basis for an intellectual property strategy, as well as develop and monitor specific communication, dissemination and exploitation activities. The PDEC is part of WP6 “Innovation Management: Communication, Dissemination, Exploitation and Sustainability”. This WP focuses on creating visibility and fostering outreach of AIDAVA through external communication of AIDAVA activities, progress and achievements, as well as dedicated disseminating activities and strategic planning of exploitation routes for potential results.
This PDEC aims to act as a guide and organising framework for all work relating to dissemination and exploitation of the project’s results, as well as the broader communication activities related to the project and its achievements. It is a central tool for the planning and documentation of communication, dissemination and exploitation activities and will therefore allow close monitoring of the progress of individual activities throughout the project lifetime. It should be noted that the current PDEC in month 6 is an initial version which builds on the plans outlined in the Description of Action (DoA). These plans will be further developed and updated as the project progresses and results become available, with updated versions of the PDEC being due in M18, M36 and M48.
D6.3 IP Manual
Published Feb 2023, Sensitive
The IP Manual offers a comprehensive guide on intellectual property (IP) within the context of collaborative innovation, detailing common IP terms, challenges, risks, and opportunities. It elaborates on IP management strategies as outlined in the Consortium Agreement, aiming to establish a unified understanding among partners on managing access to background IP, ownership, sharing, protection, and utilization of project outcomes both within and outside the project framework.
D6.4 Communication Toolkit
Published Apr 2023, Public
This communication toolkit is prepared within the scope of WP6 “Innovation Management: Communication, Dissemination, Exploitation and Sustainability” to create optimal visibility of AIDAVA and a wide project outreach to all relevant stakeholders. As a basis for all outreach activities a project-specific visual identity defining a logo and colour scheme as well as a set of Microsoft office templates has been created. The toolkit comprises the Corporate Identity (CI) and different material prepared on the basis of this CI. Emphasis with all materials to be produced in AIDAVA is on usefulness and eco-friendliness.
D6.5 Audio-visual material
Not published yet, Public
In line with the Description of the Action and to support dissemination to the interested public, audio-visual material has been produced for AIDAVA. The material is designed to effectively communicate the project's goals and significance to a wide-ranging audience. This includes the general public, the scientific community, and various related projects and initiatives. Our aim is to increase awareness and understanding of AIDAVA's objectives and the positive effects it aims to achieve.
D7.1 Project Management Platform
Published Nov 2023, Sensitive
This deliverable describes the basic functionalities of the internal project management platform set up for the AIDAVA project. Those functionalities include, inter alia, the preparation of deliverables, reportings, the organization of meetings and keeping an overview of upcoming dissemination/ communication activities and publications. The project management platform is a tool set out to support an efficient and effective project management in AIDAVA.
D7.2 Management Guide
Published Dec 2023, Sensitive
This Management Guide will lay down standard workflows and processes for the most common and recurring management activities on AIDAVA consortium level. In addition, it will give guidelines and recommendations with regard to communication within the project and dissemination of project results. The Guide will be made available to all project partners at the beginning of AIDAVA to foster active collaboration and a smooth implementation from the start.
D7.3 Risk Management & Mitigation Plan
Published Dec 2023, Sensitive
The AIDAVA project incorporates risk assessment primarily through its Work Package 7, focusing on scientific coordination, quality assurance, and risk management to ensure timely risk identification and effective decision-making. The Management Team, alongside the Steering Committee, conducts regular quality assessments and risk evaluations, facilitating immediate conflict resolution and adjustments to the work plan. Quarterly Steering Committee meetings are critical for maintaining strict risk management practices. The project's Management Team, including key project coordinators and the Project Management Office, is responsible for mitigation planning and suggesting project-level adjustments to address potential risks. This comprehensive approach ensures the project's adaptability through continuous risk monitoring, planned mitigation strategies, and necessary adjustments to the project's contracts and work plans.
D7.4 Internal progress report 1
Published Aug 2023, Sensitive
The AIDAVA deliverable, Internal Progress Report 1, covers the project's initial nine months (M1-M9), detailing the achievements and progress across various work packages and tasks. It evaluates the project's adherence to the planned timeline, identifies any challenges or delays encountered, and provides an overview of the solutions implemented to address these issues. The report also highlights key milestones achieved, summarizes the outcomes of collaboration efforts among project partners, and outlines the next steps for continued project advancement. Additionally, it includes an assessment of project risks, with a focus on mitigation strategies to ensure the project remains on track. This document serves as a comprehensive update on the AIDAVA project's development, offering insights into its management, scientific coordination, and overall progress.