Rezultaty
This deliverable will provide the reference on how to collect, structure, and prepare the data to inputs the models and maps developed in WP4
MOOD System ArchitectureA report on the system architecture of the MOOD platform It will describe the conceptual design of all the platforms components and its subcomponents
Risk registerRegister with all the risks noted by each WP, at project submission and updated during implementation of the project. It will also consist of a detailed description of the risks, their impact, and suitable preventive and response measures. This document will be updated at M24, M36 and M48.
Project management planReport with procedures for providing day-to-day administrative guidance, coordination & troubleshooting for the MOOD consortium partners
Preliminary report on impact and innovation pathwayThis report will describe the innovation process and set up the methodology to monitor the impact of the MOOD project through the outcome assessment and the theory of change that is at work Some preliminary results about the KeyPerformance Indicators concerning the first outputs will be given
Scientific report on prototype pathogens and disease profilesFor each selected pathogen and disease, a detailed profile based on the state-of-the-art of the scientific knowledge and knowledge gaps will be produced.
Report on outreach of outcomesThe analysis of the outreach of the outcomes will be based on two crossed sources : a multicriteria evaluation method (OASIS-like survey) to compare end-user practices in the case countries at start and end of the project and a participatory assessment based on narratives of the change and participatory indicators.
Guidelines for consideration of local stakeholders' engagementThe guidelines will be built during and following the workshops with the local stakeholders of the study case in Corsica and Occitanie.
Report on users’ needsThe synthesis of operational needs will be based on the results of the crossed socio-technical analysis of individual interviews, and also the conclusions of the 2 first workshops about the operational problems to solve.
Report on learning loops fostering the utility of social and technical innovationsThe lessons learnt from the collective learning of co-conception of tools and services (3 loops) will be summarised.
The dataset used within the project and processed according to DL3.1. These data will be stored in a traditional structured database system and made available through this database.
Integration chain (processing algorithms and protocols) for disease datasetsThis deliverable will describe the data integration toolchain, including the data processing algorithms and the data exchange protocols that are focused on disease data, associated with Task 3.3 of WP3 and also with implications regarding other tasks (e.g., Tasks 3.1 and 3.4 of WP3). The envisioned toolchain will focus on data standardization and linking between disease and covariate data, according to spatial and temporal dimensions, and combining text-mining, spatial analysis, and semantic approaches. Specifically, the toolchain will ingest and standardize the different types of data, geo-reference the disease data in order for it to be linked to particular covariates, and make the results available for subsequent tasks. Deliverable 3.2 will specifically focus on the tools regarding data integration (e.g., the geo-referencing component), tailored for the processing of disease data.
Linking chain (processing algorithms and protocols) for disease and covariate datasetsThe linking task is based on algorithms and processing that will be proposed, implemented and evaluated. These linking algorithms/process need specific features (and set of features) to extract in the disease and covariate data.
Project website (implemented via a Content Management System) and social media pages and channels including Twitter, , Linkedin, TIB (https://av.tib.eu/), Zenodo.org, and similar. Publication steps, house-style-sheet, and roles and responsibilities will be described in the project manual.
Data processing tutorials, good practice documents and technical reports will be released via github (source code) with a copy on zenodo.org using the Github-zenodo webhooks (see e.g. https://doi.org/10.25815/3YZR-1564).
The DMP will detail what data MOOD will generate and how, how it will be curated and preserved, what datasets will be open, whether and how datasets will be exploited or made accessible for verification and re-use. First version delivered at month 6 and updated version at month 18 and 36.
Publikacje
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