Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Platform for wildlife monitoring integrating Copernicus and ARGOS data (EO4wildlife)

Framework: Horizon 2020 — Industrial Leadership
Programme: Space
Action type: Research and Innovation actions
Countries involved: Spain,France,France,France,United Kingdom,United Kingdom,United Kingdom
Call: Stimulating wider research use of Copernicus Sentinel Data (H2020-EO-2015, 2015)
Start date: 2016-01-01
End date: 2018-12-31
Duration (months): 36.0
Total project cost (EUR): 2665325.0
EU funding (EUR): 2665325.0
Participants: 9
Project status: CLOSED

EO4wildlife main objective is to bring large number of multidisciplinary scientists such as biologists, ecologists and ornithologists around the world to collaborate closely together while using European Sentinel Copernicus Earth Observation more heavily and efficiently. In order to reach such important objective, an open service platform and interoperable toolbox will be designed and developed. It will offer high level services that can be accessed by scientists to perform their respective research. The platform front end will be easy-to-use, access and offer dedicated services that will enable them process their geospatial environmental stimulations using Sentinel Earth Observation data that are intelligently combined with other observation sources. Specifically, the EO4wildlife platform will enable the integration of Sentinel data, ARGOS archive databases and real time thematic databank portals, including Wildlifetracking.org, Seabirdtracking.org, and other Earth Observation and MetOcean databases; locally or remotely, and simultaneously. EO4wildlife research specialises in the intelligent management big data, processing, advanced analytics and a Knowledge Base for wildlife migratory behaviour and trends forecast. The research will lead to the development of web-enabled open services using OGC standards for sensor observation and measurements and data processing of heterogeneous geospatial observation data and uncertainties. EO4wildlife will design, implement and validate various scenarios based on real operational use case requirements in the field of wildlife migrations, habitats and behaviour. These include: (1) Management tools for regulatory authorities to achieve real-time advanced decision-making on the protection of protect seabird species; (2) Enhancing scientific knowledge of pelagic fish migrations routes, reproduction and feeding behaviours for better species management; and (3) Setting up tools to assist marine protected areas and management.

Keywords: sentinel data,argos,wildlife monitoring,fast and intelligent big data exploration,high level data fusion,reasoning and forecast
Piloting RRI in Industry: a roadmap for tranSforMAtive technologies (PRISMA)

Framework: Horizon 2020 — Science with and for Society
Programme: Develop the governance for the advancement of responsible research and innovation by all stakeholders, which is sensitive to society needs and demands and promote an ethics framework for research and innovation
Action type: Coordination and support actions
Countries involved: Italy,Germany,Netherlands,Netherlands,United Kingdom
Call: Responsible Research and Innovation in industrial context (H2020-GARRI-2015-1, 2015)
Start date: 2016-08-01
End date: 2019-07-31
Duration (months): 36.0
Total project cost (EUR): 1708096.25
EU funding (EUR): 1708095.0
Participants: 5
Project status: CLOSED

There is now only limited experience with RRI in industry and there is also limited evidence of the added value of opening up the innovation process in industry for social engagement and gender considerations. We will overcome these current limitations by carrying out eight RRI pilot projects in a real-world industry context. To establish the added value of the RRI approach and the gender dimension in and for industry, we will assess the pilot projects on a number of product and process RRI dimensions and compare the score of the pilots on the relevant RRI dimensions with the score of similar projects in the same companies in which the RRI approach has not been followed. We focus on implementing RRI for some of the major technological challenges in the EU including nanotechnology, synthetic biology, Internet of Things and self-driving or automated cars. These are all transformative technologies that have the potential to transform existing modes of production and to change the relation of the company with users, suppliers or other stakeholders. The pilots aim at integrating RRI in the CSR (Corporate Social Responsibility) policies of the participating companies. Some pilots will take place in private companies and some in public-private partnerships (PPPs). The project will be supported by extensive stakeholder consultations and dialogues. These will feed into the set-up and the carrying out of the pilots. The project will result in a RRI-CSR roadmap for transformative technologies. The roadmap will be widely disseminated through the partner’s extensive industry network, and through industry branch and CSR organizations. An important means of dissemination will also be the MOOC (Massive Open Online Course) on RRI in industry. The dissemination is expected to lead to a better uptake of RRI approaches by industry. This will also contribute to the competiveness of the European industry.

Keywords: transformative industries,corporate social responsibility,stakeholder dialogues
Coherent Optomechanical Nonlinear Systems for Evolving Quantum and Electronic Nano- Technologies (CONSEQUENT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Physics
Countries involved: Austria,United States
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2019-02-01
End date: 2022-01-31
Duration (months): 36.0
Total project cost (EUR): 261208.8
EU funding (EUR): 261208.8
Participants: 2
Project status: CLOSED

This proposal aims at the first investigation and utilization of nonlinear interactions between mechanical vibrational modes, i.e phonons. These phonons will be excited, controlled and measured through their interaction with light in optomechanical crystals, which are designed and made by means for nano-fabrication techniques. Optomechanical crystals have shown powerful applications in electronics, as they are resonant at GHz frequencies, commonly used in electronic signal processing. Furthermore, mechanical oscillators, when cooled to few phonons, exhibit quantum mechanical properties, which can be exploited for quantum information processing. Limiting factors for both classical and quantum applications, however, are the high optical absorption and low thermal conductivities of common materials used for optomechanical crystals, such as silicon nitride. Diamond on the other hand has a two-orders of magnitude higher thermal conductivity and better mechanical properties than silicon nitride. These unique features enable the excitation of extremely high phonon intensities and coherent laser-like mechanical oscillations, as recently demonstrated by the outgoing host. While lasers gave birth to the field of nonlinear optics, one of the largest research fields in physics, the high intensity and coherent phonon oscillations in diamond enable for the first time the investigation of nonlinear phonon interactions. The aim of this project is to conduct the first characterization of nonlinear phonon interactions, thereby opening-up the new research-field of Nonlinear Optomechanics. These nonlinear interactions will then be used for novel classical and quantum functionalities. In particular, nonlinear phonon gain and energy transfer between different frequency modes will be investigated with applications in electronics, while nonlinear coupling between frequency modes will enable controllable superposition in phonon quantum states.

Keywords: nonlinear physics,optomechanics,nonlinear mechanics,nonlienar optics,nanophotonics,quantum optics,quantum information
Entrepreneurial Multidisciplinary scientists forging Pathways Onto clean Water, sustainable Energy and Resources (EMPOWER)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Co-funding of regional, national and international programmes (COFUND)-Doctoral programmes
Action type: Marie Skłodowska-Curie actions
Countries involved: Netherlands
Call: Co-funding of regional, national and international programmes (H2020-MSCA-COFUND-2020, 2020)
Start date: 2022-01-01
End date: 2026-12-31
Duration (months): 60.0
Total project cost (EUR): 3417120.0
EU funding (EUR): 1708560.0
Participants: 1
Project status: SIGNED

EMPOWER is an innovative entrepreneurial-based doctoral programme aiming to train scientific entrepreneurs with the skillset to operate between academia, companies, start-ups, public authorities and the general public. This professional figure will have high impact in a post-COVID future where the efforts of research are required to transform the European Union into a modern, green, inclusive and resilient economy, where growth is de-coupled from resource use. EMPOWER will build-up on the experience of Wetsus, a multidisciplinary research institute where intersectoral cooperation between companies, universities and public authorities takes place. In EMPOWER the partners will be 3 universities and 14 companies. EMPOWER will recruit 14 ESRs in one widely advertised call and a transparent 3-round selection procedure. ESRs are employed by the academic partners, according to the Collective Labour Agreement of the Dutch Universities, to earn their PhD degree together with an integrated business-oriented training. Each ESR will shape her/his own project around the idea of achieving clean water and energy, resource recovery and smart water management. Two supervisors (one from Wetsus and one from academia) and an industry mentor will constitute a multidisciplinary “knowledge triangle” to guide each ESR. Together with this, the training of each ESR will focus on research, multidisciplinary expertise, entrepreneurship and personal development.

Keywords: scientific entrepreneurs,clean water,clean energy,resource recovery,smart water management,key enabling technologies
Artificial Intelligence-driven, Decentralized Production for Advanced Therapies in the Hospital (AIDPATH)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Innovation actions
Countries involved: Germany,Spain,Germany,Spain,Germany,Greece,Hungary,France,Netherlands,Netherlands,Netherlands,United Kingdom,United Kingdom
Call: AI for the smart hospital of the future (H2020-DT-2020-1, 2020)
Start date: 2021-01-01
End date: 2024-12-31
Duration (months): 48.0
Total project cost (EUR): 10964007.5
EU funding (EUR): 9860991.51
Participants: 15
Project status: SIGNED

AIDPATH (Artificial Intelligence-driven, Decentralized Production for Advanced Thera-pies in the Hospital) is a high-energy EU consortium, dedicated to enable and to augment the next-generation of personalized medicine at EU hospitals through the use of AI tech-nology. The exemplary embodiment of gene-engineered immune cells in AIDPATH will be T cells expressing a synthetic chimeric antigen receptor (CAR-T). These cells are already a revolutionary novel treatment in hematology and oncology, and will also be useful for treating infections and autoimmune diseases. Conventional CAR-T therapy is complicated by complex logistics from centralized manufacturing facilities, inflexible manufacturing and clinical use schemes that disregard patient and cell characteristics, thus limiting patient access and therapeutic outcome. AIDPATH will apply top-notch AI technology to integrate patient-specific data and biomarkers in CAR-T therapy, and apply flexible manufacturing schemes to obtain CAR-T cell products with optimal fitness and anti-tumor potency. AI technology will be applied in pre- and in-process controls to reduce cost and hospital resource utilization and to augment patient access. AIDPATH will establish a role model that integrates the hospital in a smart manner, incorporating aspects of logistics, capacity planning, data management and cybersecurity by design. The project is designed to enable smart ‘bedside’ provision of personalized treatments and to accomplish end-to-end automation of hospital-based CAR-T manufacture. A key deliverable in AIDPATH will be an integrated cyberphysical infrastructure for rapid dissemination of the ‘smart CAR-T manufacturing and clinical use’ concept to hospitals throughout the EU. AIDPATH brings together a world-class consortium to address these challenges, consisting of key opinion leaders in science and translational medicine, innovative SMEs and commercial partners, patient advocates, policy makers and regulatory experts.

Keywords: car t,personalized medicine,advanced therapies,automation,pilot,smart bioprocess,smart hospital,digital twin,internet of things,cyberphysical systems,data science,automated machine learning
Venomics of Prey-Specialised Spiders in the Evolutionary and Ecological Context (SpecSpiderVenom)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Life Sciences
Countries involved: Australia,Czechia
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-04-01
End date: 2025-03-31
Duration (months): 36.0
Total project cost (EUR): 261963.6
EU funding (EUR): 261963.6
Participants: 2
Project status: SIGNED

Venoms of a wide range of animals are currently under scrutiny in the search for toxins that can be utilized as new bioactive compounds with potential medical and industrial applications. Venomous predators produce complex mixtures of bioactive components in their venoms, making them ideal candidates for a thorough investigation. Predatory toxins are usually very effective against certain prey as the result of evolutionary arms races. Many venomous animals are still understudied, including spiders, one of the most diversified groups of venomous predators. Prey-specialised spiders are particularly intriguing in this respect, as they preferentially feed on one prey type only, often represented by dangerous prey, such as ants, termites or other spiders. The current evidence suggests they immobilise prey with a potent venom. The aim of this project is to explore the venomic adaptations of those spiders from organismal to a molecular level, employing diverse methods that include behavioural experiments, venom potency bioassays, and proteo-transcriptomics. I will elucidate the venom composition of selected prey-specialised species and identify toxins with high selective efficiency. I want to analyse whether 1) prey-specialised spiders rely on their effective venoms to subdue their focal prey; 2) venoms of the specialised spiders are more efficient towards focal than to alternative prey; and 3) venom specificity is due to the presence of prey-specific highly potent venom compounds. Information on the venom properties will be put into ecological context to shed more light on the evolution of venomic adaptations in specialised predators. Furthermore, toxins of prey-specialised predators may be potentially useful as novel active ingredients of bio-pesticides due to their high specificity. My project will help to understand the mechanisms by which specialised venoms work, and could help in the development of environmentally safe bio-pesticides specifically targeting pests.

Keywords: adaptation,evolution,predator,prey,specialization,spiders,venom
Multimodal spectrAl control of laSer processing with cognitivE abilities (MAShES)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Italy,Germany,Germany,Spain,Spain,Greece,Slovenia,Sweden,France,France
Call: Process optimisation of manufacturing assets (H2020-FoF-2014, 2014)
Start date: 2014-12-01
End date: 2017-11-30
Duration (months): 36.0
Total project cost (EUR): 3673157.0
EU funding (EUR): 3673157.0
Participants: 12
Project status: CLOSED

MAShES proposes a breakthrough approach to image-based laser processing closed-loop control. Firstly, a compact, snapshot, and multispectral imaging system in the VIS/MWIR spectral range will be developed. This approach will enable a multimodal process observation that combines different imaging modalities. Moreover, it will enable an accurate estimation of temperature spatially resolved and independent on emissivity values, even for non-grey bodies and dissimilar materials. Secondly, a fully embedded approach to real time (RT) control will be adopted for efficient processing of acquired data and high speed -multiple inputs/ multiple outputs- closed-loop control. Thirdly, a cognitive control system based on the use of machine learning techniques applied to process quality diagnosis and self-adjustment of the RT control will be developed. As a result, a unified and compact embedded solution for RT-control and high speed monitoring will be developed that brings into play: - The accurate measurement of temperature distribution, - The 3D seam profile and 2D melt pool geometry, - The surface texture dynamics, and process speed. MAShES control will act simultaneously on multiple process variables, including laser power and modulation, process speed, powder and gas flow, and spot size. MAShES will deal with usability and interoperability issues for compliance with cyber-physical operation of the system in a networked and cognitive factory. Moreover, standardisation issues will be addressed regarding the processes and the control system and contributions in this regard are envisaged. MAShES will be designed under a modular approach, easily customizable for different laser processing applications in highly dynamic manufacturing scenarios. Validation and demonstration of prototypes of MAShES system will be done for laser welding and laser metal deposition (LMD) in operational scenarios at representative end-user facilities.

Keywords: real time closed loop control,cognitive control,embedded control,snapshot multispectral imaging,temperature imaging,real time image based monitoring
How elephants grow old (Elephant Project)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Environmental Biology, Ecology and Evolution
Panel description: Ecology, biodiversity, environmental change, evolutionary biology, behavioural ecology, microbial ecology, marine biology, ecophysiology, theoretical developments and modelling
Countries involved: Finland,United Kingdom
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2016-01-01
End date: 2021-12-31
Duration (months): 72.0
Total project cost (EUR): 1949316.0
EU funding (EUR): 1949316.0
Participants: 2
Project status: CLOSED

The ageing population structure of most European countries has major health, economic and social consequences that lead to a need to better understand both the evolutionary limitations of deferring ageing, as well as the mechanisms involved in growing old. Ageing involves reduced fertility, mobility and ability to combat disease, but some individuals cope with growing old better than others. Improving the quality of life at old age and predicting future changes in longevity patterns of societies might depend on our ability to develop indicators of how old we really are and how many healthy years we have ahead, and how those indicators depend on our health history across several decades. Yet, most model species used in biology are short-lived and provide a poor comparison to long-lived mammals such as humans. Further, they do not often inform on the mechanisms of ageing alongside its fitness consequences in natural populations of long-lived mammals. This project integrates different ageing mechanisms with unique data on lifelong disease and reproductive history in the most long-lived non-human mammal studied so far, the Asian elephant. I will examine how different mechanisms of ageing (telomere dynamics, oxidative stress and telomerase activity) interact with lifelong disease and reproductive history, and current endocrinological measures of stress and reproductive status. This will help us to better understand both the mechanisms of ageing and their consequences on senescence rates. To do so, I will combine the most comprehensive demographic data (N~10.000) on Asian elephants in the world with bi-monthly health assessments and disease records across life (N~2500) and with longitudinal markers of ageing and hormonal correlates of stress and reproductive potential (N~240). Understanding changes in health across life and its links to ageing rates, stress levels and life-history in a species as long-lived as humans will be relevant to a large range of end-users.

Keywords: aging,senescence,lifespan,asian elephant,behavioural ecology,life history theory,reproductive success,fitness,natural selection,stress level,mammal,sex difference,health
Calibrating and Improving Mechanistic models of Biodiversity (CLIMB)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Global Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Germany,New Zealand
Call: Individual Fellowships (H2020-MSCA-IF-2018, 2018)
Start date: 2019-10-01
End date: 2022-09-30
Duration (months): 36.0
Total project cost (EUR): 247020.48
EU funding (EUR): 247020.48
Participants: 2
Project status: CLOSED

Mechanistic community models have been advocated as a response to the conceptual and practical limitations of correlative approaches to modeling biodiversity. Building from ecological theory, there are multiple frameworks that could potentially act as a basis for such mechanistic models. However, these options often include a the large number of demographic rates to estimate in species-rich ecosystems, and their direct connection to empirical data has often been limited to simplified settings, something that strongly limits their use for ambitious biodiversity-modeling projects. With CLIMB, we propose an innovative statistical methodology to overcome this challenge: we will connect community data with functional trait data in an array of carefully designed mechanistic community models. More precisely, CLIMB aims to propose and test adequate transfer function(s) that allow rapid calibration of mechanistic models with available trait data and make these models suitable for reliable biodiversity predictions. The CLIMB framework will be developed and tested with simulations and two empirical study cases of temporal dynamics of grassland plant communities dynamics in two different biomes. CLIMB consists in an outgoing phase focused on (1) studying the theoretical fundations of the framework and developing appropriate mechanistic community models; and (2) collecting functional data for local grassland plant species. The return phase will focus on (3) completing the development of the modelling framework and (4) analyzing empirical data. Ultimately, CLIMB will answer pressing fundamental questions of ecology and biodiversity modelling and will offer the ground-breaking perspectives necessary to meet key environmental challenges faced by society today.