Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Safe Hydrogen-On-Demand Fuel for E-Vehicles (Safe Hydrogen Fuel)

Framework: Horizon 2020 — Societal Challenges
Programme: Smart, green and integrated transport
Action type: SME instrument
Countries involved: Israel
Call: Small business innovation research for Transport and Smart Cities Mobility (H2020-SMEINST-1-2016-2017, 2016)
Start date: 2016-07-01
End date: 2016-09-30
Duration (months): 3.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Hydrogen fuel-cells are increasingly used as a clean and silent power generator for applications in different markets including transport. However, a prime barrier for mass fuel-cell adoption is the notorious and hazard quality of Hydrogen, which makes it unsafe, difficult to handle and store, and as a result expensive. Terragenic makes safe Hydrogen available for consumer markets thanks to its ground breaking technology. Our T-Fuel™ is rich in Hydrogen while being safe, green and cost competitive. Our Hydrogen-on-demand solution accelerates the adoption of Hydrogen fuel-cell powered vehicles and e-bikes, and thus the replacement of polluting fossil fuel based vehicles with clean and silent Fuel-Cell Electric Vehicles ("FCEV"). The growing use of FCEV supports the European effort to reduce carbon emission and ambient noise, for better urban environment. Our innovation project's objective is to promote the adoption of Terragenic's novel solution through the collaboration with public and private fuel-cell electric vehicles initiatives, and their active members. The proposed feasibility project objectives are to (1) identify the relevant European partners/initiatives, and (2) work with them to fine-tune our solution's technology value proposition and economic feasibility, and to (3) form partnerships for the integration of our technology in FCEV. The proposed project is directly related to the “SMEInst-10-2016-2017 - Small business innovation research for Transport and Smart Cities Mobility”. The project contributes in a sustainable way to decarbonise and increase the efficiency of the automotive energy systems with the development of a sustainable, clean, resource-efficient, cost-effective and affordable technology solution that will support significant reduction in the carbon footprint and impact on the urban environmental.

Keywords: hydrogen,fuel cell,e bus,fcev,e bike
Learning, Applying, Multiplying Big Data Analytics (LAMBDA)

Framework: Horizon 2020 — Spreading excellence and widening participation
Programme: Widening participation and spreading excellence
Theme: Improved access to Excellence
Action type: Coordination and support actions
Countries involved: Germany,Germany,Serbia,United Kingdom
Call: Twinning (H2020-WIDESPREAD-05-2017-Twinning, 2017)
Start date: 2018-07-01
End date: 2021-06-30
Duration (months): 36.0
Total project cost (EUR): 984952.5
EU funding (EUR): 984952.5
Participants: 4
Project status: CLOSED

In the last ten years, Open Data and Big Data technologies have attracted the attention of the research community, software providers and policy decision makers as they are expected to create huge economic value for years to come. Considering the current and future social-economic challenges in the EU, especially in the South-East Europe, as well as scientific and industry gaps and deficiencies between Serbia and EU, the project shall define a scientific strategy for stepping up and stimulating scientific excellence and innovation capacity, increasing research capacities and unlocking the research potential of the biggest and the oldest R&D Institute in the ICT area in the whole West Balkan region, turning the Institute Mihajlo Pupin into a regional point of reference when it comes to multidisciplinary ICT competence related to Big Data analytics. The project shall implement a thoroughly prepared plan of actions for knowledge exchange and transfer between the linked institutions and carry out a wide range of dissemination and outreach activities in the West Balkan countries and across Europe. The emphasis shall be on the sustainability of activities of existing Center of Excellence for Semantic Web technologies and their upgrade and extension in one of the fastest developing fields, the Big Data research. Different knowledge transfer instruments (mentorships, brainstorming sessions, school type activities) and different types of twinning relationships (institution to institution, institution to network) shall be used to further strengthen and extend the collaboration with internationally-leading counterparts and produce a Strategy for long-term collaboration for mutual benefits to partners and other stakeholders. The partner organizations shall use the available infrastructure of the PUPIN TTO and organize numerous community building and outreach events aimed at strengthening links to policy makers, industry, standardization bodies, other TTOs and society.

Keywords: big data,analytics,applications,knowledge transfer,capacity building,cooperation,teaching,innovations
Determining in vivo protein structures and understanding genetic interactions using deep mutagenesis (MUTANOMICS)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Integrative Biology: from Genes and Genomes to Systems
Panel description: Genetics, epigenetics, genomics and other ‘omics studies, bioinformatics, systems biology, genetic diseases, gene editing, innovative methods and modelling, ‘omics for personalised medicine
Countries involved: Spain
Call: ERC Advanced Grant (ERC-2019-ADG, 2019)
Start date: 2021-01-01
End date: 2025-12-31
Duration (months): 60.0
Total project cost (EUR): 2333279.0
EU funding (EUR): 2333279.0
Participants: 1
Project status: SIGNED

The goal of my research is to understand mutations and how they interact to alter phenotypes and disease. We recently initiated a new research direction that uses systematic (‘deep’) mutagenesis to quantify, understand, and predict how diverse mutations interact non-additively within and between molecules to affect phenotypes at multiple scales. Very excitingly, we have also shown that quantifying genetic interactions by deep mutagenesis provides sufficient information to determine the 3D structures of proteins. In this project we will leverage this experience in deep mutagenesis and computational modelling to address three specific aims: 1. To develop simple generic experimental and computational methods to determine the in vivo structures of proteins using deep mutagenesis and to apply these methods to solve the structures of domains of unknown structure. 2. To use deep mutagenesis and computational modelling to understand how mutations globally interact within and between molecules, when these interactions can – and cannot – be predicted from phenotypic measurements alone, and how these interactions alter in response to changes in gene expression. 3. To use deep mutagenesis to understand the cellular toxicity of pathological prion-like domains and to reveal the in vivo structures of these ‘unstructured’ regions, as well as those of disordered proteins that function as agents of protein-based epigenetic inheritance. Taken together, this will provide rich insights into how mutations combine to alter phenotypes, a question of central importance to evolution, engineering, and human disease. It will also develop methods that use deep mutagenesis to determine protein structures, including of intrinsically disordered proteins relevant to disease and epigenetic inheritance. Our goal is to develop techniques that will allow labs across the world to use deep mutagenesis to solve protein structures, including potentially in large-scale systematic projects.

Keywords: genetic interactions,deep mutagenesis,epistasis,genotype phenotype maps,intrinsically disordered proteins,structure determination,epigenetic inheritance
Advanced Sports Data Measurement and Analysis Solution for Athletics (SPECTO Athletics)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Finland
Call: SME instrument (H2020-SMEInst-2018-2020-2, 2019)
Start date: 2019-04-01
End date: 2021-08-31
Duration (months): 29.0
Total project cost (EUR): 3449602.5
EU funding (EUR): 2414721.75
Participants: 1
Project status: CLOSED

Sports analytics is redefining predictive sports business decision-making. It allows team managers to improve winning percentages and fans thrive on having more information about a game they are watching. Sports analytics’ market was valued at €24 billion in 2017 and is anticipated to reach €61 billion by 2024. However, most of the companies engaged in sports analytics have been mainly concentrated on football, owing to an increasing attendance and growing fan-base, media and sponsors engagement. Other sports and specifically athletics have yet to reap the benefits of advanced sports analytics. Current solutions in athletics focus on performance measurements after the game, in a laboratory. There is no solution in the market providing live and engaging data for fans and deep valuable insights to commentators/broadcasters for post-event analysis and also to athletes and coaches to improve athlete’s performance and avoid injuries. This is where SPECTO Athletics comes into force. SPECTO Athletics core technology is adapted from our successful SPECTO Bowling® product which is capable to perform both 3D and 2D motion analysis, providing a detailed vision of the actions through real time data during the game, opening a whole new possibility for its exploitation by customising the final experience to different audiences: players, coaches, fans, researchers or sports associations. Having already validated SPECTO Athletics in IAAF World U20 Championships, July 2018 in Tampere, and with several pilot agreements signed with Olympic training centres, broadcasters and Sports Federations, SPECTO Athletics aims to establish itself as a pioneering state of art system in Athletics sport. Our project will achieve a ROI of 52.11, profit of €144M and create 59 direct jobs by 2025. Also, we are fully committed to keeping our product development in Europe.

Keywords: sports technology,advanced sports analytics,fan engagement,broadcasting,data analytics,athletics performance
Diagnosis of preterm birth risk and success of induced labour (Fine Birth)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Spain
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2019-01-01
End date: 2019-04-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Context Preterm Birth (PTB) is the birth of a baby before 37 weeks of pregnancy. Problem Preterm Delivery - Leading cause of death in children under 5 years - Affects more than 15 million births a year - Preterm rates are increasing, particularly for extremely preterm birth (less than 28 weeks) - Many survivors face a lifetime of disability, including learning disabilities and visual and hearing problems. - Immediate costs of a PTB are on average 10 (31-34 weeks) to 100 (25-27 weeks) times more than a full-term one. The total cost of PTB in the US was estimated at $26 billion. There are currently no tools that accurately diagnose and predict PreTerm Birth Solution – Fine Birth Fine Birth is an in-vivo diagnostic technology that uses torsional ultrasound to study the shear mechanical properties of the cervix tissue. We can accurately identify if and when a baby will be born prematurely and also quickly and cost effectively identify if a PTB is imminent. By improving the sensitivity of current techniques from 30% to 70%, we can decrease the number of Preterm Birth cases and reduce their impact on patients and health systems. Fine Birth could avoid 369,000 Premature Births and save more than 22,000M € only in USA & Europe by accurately predicting PTB, while saving 800M€ in identifying imminent PTBs. Project We aim to use the feasibility study to test our business model. Furthermore, we aim to hold conversations with different players along the value chain and generate interest. During the phase 2 we aim to clinically validate and certify a POC device and prepare it for sublicensing. Impact & Market By 2024, we aim for Fine Birth to be integrated in 7.5% of the diagnostics ultrasound devices for Obstetrics/ Gynaecology market with sales of 3,115 units, generating sales of 9.3€M and an IRR of 65% based on a total investment of 2€M.

Keywords: preterm birth,ultrasound
Flexible and Mobile Economic Processing Technologies (FAME)

Framework: Horizon 2020 — Societal Challenges
Programme: Climate action, environment,  resource efficiency and raw materials
Theme: Ensuring the sustainable supply of non-energy and non-agricultural raw materials
Action type: Research and Innovation actions
Countries involved: Germany,Germany,Spain,Sweden,Finland,Finland,France,France,France,Czechia,United Kingdom,Portugal,United Kingdom,United Kingdom,Portugal,United Kingdom
Call: Flexible processing technologies (H2020-SC5-2014-one-stage, 2014)
Start date: 2015-01-01
End date: 2018-12-31
Duration (months): 48.0
Total project cost (EUR): 7458064.0
EU funding (EUR): 7458064.0
Participants: 20
Project status: CLOSED

FAME aims to increase the competitiveness of the mining of European mineral resources and to stimulate more private engagement and investment and thus business development with the potential to maintain and create high quality jobs within the EU28. The focus and a principal aim is to enhance mineral processing and mining skills within Europe. A medium to long term aim is to reduce the reliance of European Industry and consumers on raw materials that currently have to be imported from outside EU28. FAME will contribute to the more efficient exploitation of European domestic mineral resources including previously undeveloped resources that have the potential to contribute to the securing of raw material supply by optimising the extraction and processing of ores that include raw materials critical to the economic development of the EU (“critical raw materials”, CRM) and which occur in widespread deposits across the EU. This project specifically addresses primary ore deposits with skarn, pegmatite and greisen ores as they offer the most promising potential for this purpose. This proposal will consider the flexibility (and to an extent the mobility) of the processing concept, in particular, by ensuring the modularity of individual project components. FAME will consider flexible and modular processing technology demonstrated in relevant operational environments (industrially relevant environments in the case of key enabling technologies (TRL). TRL6 is envisaged feasible for processing of pegmatites, whereas TRL5 is considered more realistic for other types of ore body. The consortium has 17 partners from 7 European countries and includes industry, academia and governmental institutions. The consortium has a strong industrial background and involves strategically important reference deposits operated or/and accessible to the project partners and, additionally, associated partners within the EU28 nations and Greenland.

Keywords: skarn greisen critical raw materials
Ultra-small electrochemical aptasensors for specific dopamine real-time monitoring (eADAM)

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: Chemistry
Countries involved: Denmark
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2016-02-01
End date: 2018-01-31
Duration (months): 24.0
Total project cost (EUR): 200194.8
EU funding (EUR): 200194.8
Participants: 1
Project status: CLOSED

Nowadays, over 1.2 million people in Europe live with Parkinson’s disease (PD) considered as a complex disorder of unknown etiology, where an important number of cases are sporadic. In practice, the neurodegeneration associated with PD, also at the level of neurotransmitters (NT) misbalance, starts several years prior to the appearance of the cardinal symptoms. There are important evidences supporting that some environmental factors increase the risk of developing PD. Thus, there is the need of understanding their role and action mechanisms in order to develop therapeutic strategies and, even more important to minimize risks of PD development. The main aim of this project is to develop an ultra-small electroanalytical diagnostic platform for highly-specific real-time monitoring of NT, such as dopamine (DA). Several methods have been developed in order to address this issue, but in general they lack either specificity or high spatial/temporal resolution. In this project we propose to exploit the ability of nucleic acid aptamers to sensitively and selectively bind/catalytically transform DA and develop an ultra-small array platform for DA monitoring in small biological environments. The importance is that it will be used to measure nM/μs DA release from PC12 cells and drosophila flies, examine the molecular mechanisms of neuronal function/dysfunction, and further correlated them to particular PD risk factors. In a long-term perspective, the project aims microscopic sensors that may future be integrated with brain implants designed to restore local neuronal activity and/or NT release, such as deep brain stimulation electrodes, implanted to treat PD.

Keywords: aptamer,aptasensor,neurotransmitters,dopamine,dendrimers,carbon fiber miroelectrodes,microarrays,thin film electrodes,neurodegenerative diseases
Breaking the Nyquist Barrier: A New Paradigm in Data Conversion and Transmission (BNYQ)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Systems and communication engineering
Panel description: Electrical, electronic, communication, optical and systems engineering
Countries involved: Israel
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2015-08-01
End date: 2021-06-30
Duration (months): 71.0
Total project cost (EUR): 2400000.0
EU funding (EUR): 1095637.14
Participants: 2
Project status: CLOSED

Digital signal processing (DSP) is a revolutionary paradigm shift enabling processing of physical data in the digital domain where design and implementation are considerably simplified. However, state-of-the-art analog-to-digital convertors (ADCs) preclude high-rate wideband sampling and processing with low cost and energy consumption, presenting a major bottleneck. This is mostly due to a traditional assumption that sampling must be performed at the Nyquist rate, that is, twice the signal bandwidth. Modern applications including communications, medical imaging, radar and more use signals with high bandwidth, resulting in prohibitively large Nyquist rates. Our ambitious goal is to introduce a paradigm shift in ADC design that will enable systems capable of low-rate, wideband sensing and low-rate DSP. While DSP has a rich history in exploiting structure to reduce dimensionality and perform efficient parameter extraction, current ADCs do not exploit such knowledge. We challenge current practice that separates the sampling stage from the processing stage and exploit structure in analog signals already in the ADC, to drastically reduce the sampling and processing rates. Our preliminary data shows that this allows substantial savings in sampling and processing rates --- we show rate reduction of 1/28 in ultrasound imaging, and 1/30 in radar detection. To achieve our overreaching goal we focus on three interconnected objectives -- developing the 1) theory 2) hardware and 3) applications of sub-Nyquist sampling. Our methodology ties together two areas on the frontier of signal processing: compressed sensing (CS), focused on finite length vectors, and analog sampling. Our research plan also inherently relies on advances in several other important areas within signal processing and combines multi-disciplinary research at the intersection of signal processing, information theory, optimization, estimation theory and hardware design.

Keywords: sub nyquist sampling,analog to digital converters,low rate processing,wideband sensing
Doctoral Training Network in Terahertz Technologies for Imaging, Radar and Communication Applications (TeraApps)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Innovative Training Networks (ITN)-European Training Networks
Action type: Marie Skłodowska-Curie actions
Panel: Information Science and Engineering
Countries involved: Austria,Germany,Germany,Spain,Germany,Spain,Italy,Belgium,France,France,United States,Portugal,Japan,United Kingdom,United Kingdom,Portugal,Portugal,United Kingdom
Call: Innovative Training Networks (H2020-MSCA-ITN-2017, 2017)
Start date: 2018-01-01
End date: 2022-06-30
Duration (months): 54.0
Total project cost (EUR): 3820636.9
EU funding (EUR): 3820636.9
Participants: 28
Project status: CLOSED

Our future world will be one of ubiquitous sensor systems - a “Real World Web” where billions of devices, machines, or “things” will connect to the internet. In 2008, more machines were connected to the internet than there are humans on the planet, and by 2020 there are predicted to be 50 billion machines connected to the internet. As a consequence, there will be an ever-increasing hunger for communications bandwidth, both at a local level within an office or apartment, and at nodes within data centres and server farms. The terahertz (THz) band is yet to be exploited, and occupies frequencies from 0.3 to 3 GHz (1mm to 100um) occupying a middle ground between microwaves and infrared light. THz technologies are also set to revolutionize a range of imaging, sensing, and quantum optics applications. Recently, tunneling devices have been shown to be the leading candidate in realizing compact, low cost, high performance THz transmitters and receivers once coupled to suitable antennas. Also, new two dimensional (2D) materials such as graphene or 1D nanowires are emerging as suitable platforms for realising highly sensitive detectors of THz radiation. These emerging research areas link e.g. semiconductor materials synthesis, high speed electronic device physics and engineering, antenna design, THz optics, and a raft of diverse applications. A new generation of academic and industry leaders in developing these devices and systems in the terahertz spectral band is now required. However, the research community is dispersed, and this highly multi-disciplinary field requires specialist training. This network addresses this future societal need by addressing this training gap, and crystallising world leading groups in a concentrated research effort. A specialist training plan, including entrepreneurship, and transferrable skills along with advanced research topic specific has been devised. Partners include a strong mix of industrial and academics, linking strongly with other lea

Keywords: terahertz,electronics,optoelectronics,terahertz sources,terahertz detectors,thz systems,resonant tunnelling diodes