Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Knowledge in the making in the European society (KNOWMAK)

Framework: Horizon 2020 — Societal Challenges
Programme: Europe in a changing world - inclusive, innovative and reflective societies
Theme: Innovative societies
Action type: Coordination and support actions
Countries involved: Italy,Austria,France,France,Netherlands,United Kingdom,United Kingdom,United Kingdom
Call: A European map of knowledge production and co-creation in support of research and innovation for societal challenges (H2020-SC6-CO-CREATION-2016-1, 2016)
Start date: 2017-01-01
End date: 2019-12-31
Duration (months): 36.0
Total project cost (EUR): 1998138.75
EU funding (EUR): 1998138.75
Participants: 11
Project status: CLOSED

KNOWMAK project aims at developing a web-based tool, which provides interactive visualisations and state-of-the-art indicators on knowledge co-creation in the European Research Area (ERA). It is structured around three integrative elements: • Research topics, by developing ontologies around Societal Grand Challenges and Key Enabling Technologies. • Actors, with a focus on the quadruple helix and the involvement of societal actors in knowledge co-creation. • Geographical spaces, with a focus on multiple level metropolitan, regional, national and European spaces and their interconnectedness. The tool combines three main data sources: established indicators of scientific and technological knowledge production based on scientific publications and patents; information on knowledge in the making derived from research projects’ descriptions; information on social innovation projects and user attention to knowledge production derived from the Internet and from social media. The integrative elements (topics, actors, space) allow for the interlinking of data items, to produce a characterisation of different dimensions of knowledge in the making. KNOWMAK will be tailored to the needs of specific user groups with a focus on four groups: policy-makers, regional actors and representatives of the civil society, business sector, and managers of public research organisations and universities. User groups will be involved in the design of the system, the specification of the indicators and of the visualisations to be provided. This user-centred approach will ensure responsiveness of the tool to (changing) needs of relevant stakeholders in the ERA. Moving beyond the existing approaches to S&T indicators, the project will design and implement a consistent infrastructure where different types of data sources are interlinked and mobilized to produce a rich set of indicators and visualisations responding to the needs of specific user groups, thanks to experienced consortium.

Keywords: knowledge dynamics,knowledge cooperation,european mapping of knowledge
Urban Nature Labs (UNALAB)

Framework: Horizon 2020 — Societal Challenges
Programme: Climate action, environment,  resource efficiency and raw materials
Theme: Fighting and adapting to climate change
Action type: Innovation actions
Countries involved: Spain,Italy,Germany,Italy,Italy,Germany,Italy,Argentina,Belgium,Sweden,China,Finland,Norway,Türkiye,Finland,France,Czechia,Netherlands,Netherlands,Netherlands,Netherlands,Netherlands,Portugal
Call: Demonstrating innovative nature-based solutions in cities (H2020-SCC-NBS-2stage-2016, 2016)
Start date: 2017-06-01
End date: 2022-11-30
Duration (months): 66.0
Total project cost (EUR): 14278699.25
EU funding (EUR): 12768931.75
Participants: 36
Project status: SIGNED

UNaLab will develop, via co-creation with stakeholders and implementation of ‘living lab’ demonstration areas, a robust evidence base and European framework of innovative, replicable, and locally-attuned nature-based solutions to enhance the climate and water resilience of cities. UNaLab focuses on urban ecological water management, accompanied with greening measures and innovative and inclusive urban design. The UNaLab partners aim to develop smarter, more inclusive, more resilient and more sustainable local societies through nature based innovation jointly created with and for stakeholders and citizens. UNaLab’s 3 front runner cities: Tampere, Eindhoven and Genova, have a track record in smart and citizen driven solutions for sustainable development. They support 7 follower cities: Stavanger, Prague, Castellon, Cannes, Basaksehir, Hong Kong and Buenos Aires plus share experiences with observers as City of Guangzhou and the Brazilian network of Smart Cities. Therefore UNaLab results will impact on different urban socio-economic realities, with diversity in size, challenges and climate conditions. In order to create an EU reference demonstration and go-to-market environment for NBS, UNaLab will use and further develop the ENoLL Urban Living Lab model, and the European Awareness Scenario Workshop method for the co-creation of solutions, and the roadmap approach, in this way achieving an innovative NBS toolbox. Roadmaps will be used in all 10 cities, but in particular serve the follower cities. VTT, with a track record in the field of urban sustainability and Smart Cities, leads UNaLab. The UNaLab consortium is comprised of 29 partners across 12 different European countries and three non-EU countries. The consortium is well-balanced, representing key stakeholders within the value chain of urban challenges and smart, sustainable cities (public bodies, research institutions, large industries, small and medium enterprises.

Keywords: co design,co creation,innovative financing models,roadmapping,scenario thinking
Gene therapy of inherited retinal diseases (EYEGET)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Prevention, Diagnosis and Treatment of Human Diseases
Panel description: Medical technologies and tools for prevention, diagnosis and treatment of human diseases, therapeutic approaches and interventions, pharmacology, preventative medicine, epidemiology and public health, digital medicine
Countries involved: Italy,Italy
Call: ERC Advanced Grant (ERC-2015-AdG, 2015)
Start date: 2017-01-01
End date: 2022-12-31
Duration (months): 72.0
Total project cost (EUR): 2499564.0
EU funding (EUR): 2364314.5
Participants: 3
Project status: CLOSED

Inherited retinal degenerations (IRDs) are a major cause of blindness worldwide. IRD patients witness inexorable progressive vision loss as no therapy is currently available. In the last decade my group has significantly contributed to a change of this scenario by developing efficient adeno-associated viral (AAV) vectors for retinal gene therapy that are safe and effective in humans. The objective of EYEGET (EYE GEne Therapy) is to overcome some of the current major limitations in the field of retinal gene therapy to expand initial therapeutic successes to a larger number of IRDs. To achieve this, we propose to use four parallel, highly innovative and complementary approaches: i. expansion of the limited AAV cargo capacity by a novel methodology based on co-administration of multiple AAVs that reassemble in target retinal cells and reconstitute large genes; ii. targeting of frequent dominant gain-of-function mutations that cause RP using state-of-the-art AAV-mediated genome editing technologies; iii. induction of retinal cells clearance of toxic IRD products by AAV-mediated activation of autophagy and lysosomal function; iv. development of methodologies to directly convert fibroblasts to photoreceptors that can be transplanted in retinas from IRD patients with advanced PR loss and for whom in vivo gene therapy is no longer an option. We will use a combination of in vitro and in vivo state-of-the-art technologies including novel AAV vector design, high content screening of drugs that enhance AAV transduction, genome editing, and advanced in vivo retinal phenotyping to obtain proof-of-concept for each of these therapeutic strategies. The results from this study may impact the quality of life of millions of people worldwide by providing a cure based on gene and/or cell therapy for a large group of IRDs.

Keywords: retina,inherited retinal degenerations
A Unified Framework for the Assessment and Application of Cognitive Models (UNIFY)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: The Human Mind and Its Complexity
Panel description: Cognitive science, psychology, linguistics, theoretical philosophy
Countries involved: Netherlands
Call: ERC Advanced Grant (ERC-2016-ADG, 2016)
Start date: 2018-01-01
End date: 2022-12-31
Duration (months): 60.0
Total project cost (EUR): 2493318.0
EU funding (EUR): 2493318.0
Participants: 1
Project status: CLOSED

Cognitive models formalize substantive theory about how people reason, learn, decide, and act. Cognitive models also serve as measurement tools that explain observed behavior in terms of constituent psychological processes. Because of their unique ability to estimate latent processes, cognitive models are increasingly applied throughout cognitive neuroscience and clinical psychology. Despite their theoretical appeal and growing popularity, however, the field of cognitive modeling presents an often bewildering proliferation of ideas and techniques. Current applications appear idiosyncratic, and the state-of-the-art remains unclear. This lack of systematicity makes it difficult for researchers and practitioners to develop, understand, and apply important cognitive models. This proposal outlines a unified program for the assessment and application of cognitive models. Based on the foundations of Bayesian inference, a Quantitative Development Team develops new generic methods to assess absolute and relative goodness-of-fit, explores efficient algorithms to estimate model parameters, and examines how the models can be applied to data from popular experimental designs. A Core Application Team focuses on three classes of cognitive models of particular impact: the drift decision models, the stop-signal race models, and the reinforcement learning models. These model classes are enriched by the construction of plausible parameter priors, the development of diagnostic experiments, the assessment of Bayes factors for hierarchical designs, and the model-averaged assessment of changes in parameters. The proposed work aims to set a new standard for cognitive modeling. Practical relevance is enhanced by incorporating the techniques in JASP, a user-friendly statistical software package developed in my lab (jasp-stats.org). By adding the new techniques to JASP, the cognitive models and associated new methodology become available for students, researchers, and practitioners.

Keywords: bayesian inference,drift decision model,stop signal race models,reinforcement learning models
HyBRid photocatalyitic air filtEr for rEmoving pollutants and odours from aircraft cabin ZonE (BREEZE)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Theme: Public-private partnerships
Destination: Clean Sky 2
Action type: Innovation actions
Partnership: CS2 (Art-187)
Countries involved: Spain
Call: Alternative recirculation filter for better cabin air quality (H2020-CS2-CFP04-2016-02, 2016)
Start date: 2017-06-01
End date: 2020-11-30
Duration (months): 42.0
Total project cost (EUR): 708347.5
EU funding (EUR): 708347.5
Participants: 1
Project status: CLOSED

An aircraft cabin is a unique environment due to the extremely high density of occupation and limited available air volume. This favours the dispersion of germs and the accumulation of pollutants such as Volatile Organic Compounds (VOCs), polybrominated diphenyl ethers (PBDEs) and ozone, which lead to poor air quality affecting travellers and employees. On the majority of aircrafts, air treatment consists of High Efficiency Particulate Air (HEPA) filters, which can trap particles but do not remove other pollutants (i.e. VOCs, ozone) and bio-contaminants (bacteria, viruses or fungi) are not inactivated. There is a need of efficient technologies capable of removing pollutants and bio-contaminants under aircraft environmental conditions while fulfilling constraints imposed by the size, weight and energy consumption. BREEZE project will develop an alternative air purification filter based on combined adsorption and photocatalytic oxidation using UVA-LEDs as irradiation source. The efficiency of BREEZE technology will be validated for the elimination of VOCs (>85% removal), ozone (up to 80% removal) and bio-contaminants such as bacteria and viruses (>95% inactivation). Ageing tests will be carried out in order to validate under real pressure and humidity conditions the air filter durability, which will be of at least 3,000 h. The optimised BREEZE device (including also a particulate filter) will be finally integrated in an Environment Control System demonstrator in topic manager’s facilities for its final validation in a real environment. The results obtained will be employed for the evaluation of BREEZE impacts i) focusing on safety and security (Risk Assessment); ii) from an environmental point of view (Life Cycle Assessment and Environmental Technical Verification) and iii) on the economy (Benefit Cost Analysis). Market uptake will be boosted by means of Dissemination activities aimed at spreading BREEZE results and impacts.

Keywords: air filter,aircraft cabin,activated carbon,titainum oxide,nanoparticles,ozone
Impacts of the North Atlantic Decadal variability on European Climate: mechanisms and predictability (INADEC)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Standard European Fellowships
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Spain
Call: Individual Fellowships (H2020-MSCA-IF-2017, 2017)
Start date: 2018-08-01
End date: 2020-07-31
Duration (months): 24.0
Total project cost (EUR): 170121.6
EU funding (EUR): 170121.6
Participants: 1
Project status: CLOSED

During the last century, the North Atlantic sea surface temperature exhibited superimposed long-term warming trend and multidecadal fluctuations. This multidecadal variability is referred to as the Atlantic Multidecadal Variability (AMV). The AMV has been pointed as the source of marked climate anomalies and associated human impacts over many areas of the globe. This includes droughts in Africa and North America, decline in sea-ice, changes of tropical cyclone activity in the Atlantic, as well as temperature and precipitation impacts over Europe. Decadal climate forecast systems are able to capture the historical evolution of the AMV. This is encouraging for the prospect of getting skillful decadal predictions over continents through the impacts of the AMV. However, to date decadal prediction systems show only limited prediction skill over Europe in terms of temperature and precipitation. A possible explanation for this paradoxical missing skill is that current decadal forecast systems are missing key mechanisms to correctly simulate the AMV-Europe teleconnections and their associated predictability. The main goal of INADEC is to better estimate and understand the teleconnections between the AMV and European climate in order to precisely determine their predictability.

Keywords: climate decadal variability,north atlantic ocean dynamics,atmospheric teleconnection dynamics,climate predictions
A European AI On Demand Platform and Ecosystem (AI4EU)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Austria,Germany,Germany,Germany,Spain,Spain,Ireland,Ireland,Italy,Italy,Italy,Italy,Germany,Austria,Germany,Germany,Spain,Italy,Finland,Norway,Belgium,Greece,Greece,Hungary,Slovakia,Sweden,Sweden,Switzerland,Norway,Slovakia,Switzerland,Norway,Greece,Greece,Slovenia,Poland,Latvia,France,France,France,France,France,France,France,France,France,France,France,France,Czechia,United Kingdom,Portugal,United Kingdom,Luxembourg,Portugal,Denmark,United Kingdom
Call: Artificial Intelligence (H2020-ICT-2018-2, 2018)
Start date: 2019-01-01
End date: 2021-12-31
Duration (months): 36.0
Total project cost (EUR): 20667674.28
EU funding (EUR): 19999997.38
Participants: 92
Project status: CLOSED

Artificial Intelligence is a disruptive technology of our times with expected impacts rivalling those of electricity or printing. Resources for innovation are currently dominated by giant tech companies in North America and China. To ensure European independence and leadership, we must invest wisely by bundling, connecting and opening our AI resources. AI4EU will efficiently build a comprehensive European AI-on-demand platform to lower barriers to innovation, to boost technology transfer and catalyse the growth of start-ups and SMEs in all sectors through Open calls and other actions. The platform will act as a broker, developer and one-stop shop providing and showcasing services, expertise, algorithms, software frameworks, development tools, components, modules, data, computing resources, prototyping functions and access to funding. Training will enable different user communities (engineers, civic leaders, etc.) to obtain skills and certifications. The AI4EU Platform will establish a world reference, built upon and interoperable with existing AI and data components (e.g. the Acumos open-source framework, QWT search engine..) and platforms. It will mobilize the whole European AI ecosystem and already unites 80 partners in 21 countries including researchers, innovators and related talents. Eight industry-driven AI pilots will demonstrate the value of the platform as an innovation tool. In order to enhance the platform, research on five key interconnected AI scientific areas will be carried out using platform technologies and results will be implemented. The pilots and research will showcase how AI4EU can stimulate scientific discovery and technological innovation. The AI4EU Ethical Observatory will be established to ensure the respect of human centred AI values and European regulations. Sustainability will be ensured via the creation of the AI4EU Foundation. The results will feed a new and comprehensive Strategic Research Innovation Agenda for Europe.

Keywords: artificial intelligence,human centred approach,one stop shop platform,ai ecosystem
Prepolarized MRI at Earth Field to seek new contrasts linked to molecular events for very early detection of pathologies (PRIMOGAIA)

Framework: Horizon 2020 — Excellent Science
Programme: Future and Emerging Technologies (FET)
Theme: EIC Pathfinder
Destination: Novel ideas for radically new technologies
Action type: Research and Innovation actions
Countries involved: Italy,Germany,Germany,Italy,Belgium,France,France,France
Call: FET-Open Challenging Current Thinking (H2020-FETOPEN-2018-2019-2020-01, 2019)
Start date: 2019-12-01
End date: 2023-11-30
Duration (months): 48.0
Total project cost (EUR): 3387821.33
EU funding (EUR): 3387821.25
Participants: 8
Project status: SIGNED

PrimoGaia opens up a new pathway in Magnetic Resonance Imaging (MRI) by initiating the concept of in vivo “enzymatic imaging” for a better understanding of human physiology, early detection and prognosis of diseases, monitoring of therapeutic treatment. The main objective is to overcome current boundaries by making it possible to map and quantify the activity of an enzyme in a pathological tissue. It will be accomplished by building an MRI instrumentation operating at earth field in order to allow the use of 70MHz frequency for saturating the Electron Paramagnetic Resonance (EPR) transition of Nitroxyl radicals. Enzyme activity is assessed by the use of suitable probes designed to generate the radical upon action of the enzyme of interest. Upon saturation of the radical EPR resonance, polarization is transferred to the protons of the water molecules (Overhauser MRI). The enhancement factor will be high (more than two orders of magnitude). The polarized water signal thus reports on the local concentration of the radicals that reflect the enzymatic activity. 3 lines of activity will be addressed to generate the radicals of interest, namely: i) using a radical-containing molecular precursor that, upon the action of specific enzyme, yields a radical whose absorption frequency is sufficiently different to be selectively irradiated; ii) the use of paramagnetic impurities on nanodiamond surfaces to increase the OMRI effect; iii) the use of radical precursors as “prodrugs” generating a signal only after their activation. The overall methodology will be much less expensive than the current clinical scanners and will allow distribution in developping countries. PrimoGaia brings together an interdisciplinary consortium of research teams from 4 academics: Aix-Marseille University, U Mons, U Torino (reagents), CNRS Bordeaux (EPR unit, sequences, biology); Fraunhofer (Physics) and 2 companies: “Stelar” (magnetic unit) and “Pure Devices” an innovative SME (MRI instrumentation).

Keywords: dynamic polarization,prepolarization,enzyme activity,ultra low field,contrast agents,chronic obstructive pulmonary disease,overhauser,enzymatic mapping,low cost instrumentation
Dominating redox mechanisms in iron-mediated C-C bond formations: reactivity, new paradigms and applications (DoReMI)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Starting grants
Action type: ERC actions
Panel: Synthetic Chemistry and Materials
Panel description: New materials and new synthetic approaches, structure-properties relations, solid state chemistry, molecular architecture, organic chemistry
Countries involved: France
Call: ERC Starting Grant (ERC-2019-STG, 2019)
Start date: 2020-07-01
End date: 2025-06-30
Duration (months): 60.0
Total project cost (EUR): 1398620.0
EU funding (EUR): 1398620.0
Participants: 1
Project status: SIGNED

Due to the ubiquity of the C-C bond in diverse areas such as pharmaceuticals, agrochemicals or materials, development of atom-economical and efficient methods ensuring its formation is an active research field. This field witnessed incredible breakthroughs during the last decades, especially thanks to organometallic catalysis applied to cross-coupling or C-H activation methods. However, the most efficient C-C bond formation reactions rely on noble-metal catalysts, which lead to economic and toxicity issues. Iron catalysis appears as a promising alternative, since this metal is cheap and environmentally-friendly. Yet, current methods of Fe-mediated C-C bond formation suffer from strong scope limitations and lead to processes with a poor atom economy, as they often require stoichiometric organometallics or sacrificial reactants (metalation reagents, or oxidants). Dominating the reactivity of the C-Fe bond is pivotal to address those issues, since organoiron derivatives are key intermediates in Fe-mediated C-C bond formations. This reactivity is rich and complex, and is governed both by redox and spin effects. The DoReMI project aims to use this complexity as an advantage to develop new synthetic strategies, which cannot be envisioned with noble metal catalysis, aiming at overtaking the aforementioned pitfalls. In a first work package (WP1), the mechanistic features of the reaction of organoirons with electrophiles will be unveiled, shedding light on the reactivity of the C-Fe bond in a C-C bond formation context. WP2 will show for the first time that a stoichiometric C-H functionalization reaction between non-activated hydrocarbons and electrophiles can be achieved via the formation of an organoiron intermediate. Using the results gathered in WP1 and WP2, WP3 will demonstrate that this C-H functionalization methodology can be turned into an efficient catalytic method applicable to both unfunctionalized C-H substrates and to targets of synthetic interest.

Keywords: iron catalysis,reaction mechanisms,elementary steps