Purpose of the data collection generation and its relation to the objectives of the project. Coping with breast cancer more and more becomes a major socio-economic challenge not least due to its constantly increasing incidence in the developing world. There is a growing need for novel strategies to improve understanding and capacity to predict resilience of women to the variety of stressful experiences and practical challenges related to breast cancer. This is a necessary step toward efficient recovery through personalized interventions. BOUNCE will bring together modelling, medical, and social sciences experts to advance current knowledge on the dynamic nature of resilience as it relates to efficient recovery from breast cancer. BOUNCE will take into consideration clinical, cancer-related biological, lifestyle, and psychosocial parameters from relevant data, in order to predict individual resilience trajectories throughout the cancer continuum with the aim to eventually increase resilience in breast cancer survivors, help them remain in the workforce and enjoy a better quality of life. The overreaching goal of BOUNCE is to incorporate elements of a dynamic, predictive model of patient outcomes in building a decision-support system used in routine clinical practice to provide physicians and other health professionals with concrete, personalized recommendations regarding optimal psychosocial support strategies. As such collecting, integrating and processing various types of data is essential for completing the aforementioned project goals.
Purpose of the data collection generation The NextFOOD Project's aim is to generate an innovative European science and education roadmap for sustainable agriculture along the value chain from research via fabrication into the application. In order to achieve this goal, the project team will engage in several actions to ensure a meaningful result. These actions are organized in 8 work packages as follows • WP1 Inventory of the skills needed for a transition to more sustainable agriculture, forestry and associated bio-value chains. • WP2 Action research facilitation • WP3 Future curriculum, education and training system • WP4 Policy assessment and recommendations • WP5 Quality assured knowledge transfer • WP6 Communication, dissemination and exploitation • WP7 Management • WP8 Ethics requirements For all the above Work packages and especially for the Work packages 1 to 5 data will be collected and produced during the NextFOOD project for • research purposes • development of new educational modules • development of new educational material • communication purposes within project partners and other interested bodies
Purpose of the data collection. GENERATION POLYPHEM will produce several datasets during the lifetime of the project. The nature of the data will be both quantitative and qualitative and will be analysed from a range of perspectives for project development and scientific purposes. The created datasets will have the same structure, in accordance with the guidelines of Horizon H2020 for the Data Management Plan. The completion of the work plans associated to the 8 technical Work-Packages (WP) of POLYPHEM will generate new and original scientific and technical data. Some of these data will be created by a group of participants as a result of collaborative work, while others will be created by one specific partner in individual work. Data will also be collected in previous scientific publications or patents and will serve as reference cases, results or knowledge for new research developments. The data collection, selection, classification and preservation is a critical action which will be maintained and carefully monitored all along the execution of the project. It will enable to exchange relevant technical information among the beneficiaries and therefore increase the efficiency of the collaborative research work for the achievement of the objectives of the project. The preservation of the data after the completion of the project will permit to continue some research by providing useful and re-usable information to the partners engaged in the long-term development of similar technologies. Technical specifications of instruments, components or processes, design of new components, lessons learned from observations and experimental operation will serve for conceptual improvements and future testing procedures without repeating the same work. Finally, the data management aims at sharing public results with communities of professors, students, researchers, engineers, managers and policy makers, during and after the end of the project. This will contribute to increase the impact of the project in the short, mid and long-term.
