Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
High-speed data-processing software framework that speeds up and streamlines processes in the space, aerospace, robotics and IoT industries (Klepsydra)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Switzerland
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-12-01
End date: 2020-03-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

Klepsydra is a friendly and accessible software framework aimed at building high performance and quality space, robotics, aerospace, autonomous vehicles and IoT embedded applications. Klepsydra reduces development times by 50% and speeds up data processing by 40%. Our primary market is the space industry, which is segmented globally. Space is a massive international market, with a market value of greater than €40B worldwide. The USA and EU budgets together represent a market of > €25B. By 2030 over 13,000 satellites will be launched , meaning it is an exceptionally fast-growing market. We have very close relations with ESA (as we are part of ESA Business Incubator Center) and with NASA. With this project we will upgrade our current software framework and deploy our business model internationally. As a result of it, we expect a ROI of 6.3 after four years of commercialization.

Keywords: software framework environment,high frequency,event loop
Development of retrieval techniques for aerosol microphysical characterization from multi-wavelength space-borne lidar and radiometric measurements (ACE_GFAT)

Framework: Horizon 2020 — Excellent Science
Programme: Marie-Sklodowska-Curie Actions
Theme: Marie Skłodowska-Curie
Destination: Individual Fellowships (IF)-Reintegration panel
Action type: Marie Skłodowska-Curie actions
Panel: Environment and Geosciences
Countries involved: Spain
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): 158121.6
EU funding (EUR): 158121.6
Participants: 1
Project status: CLOSED

This work focuses in establishing a collaboration between NASA and the Atmospheric Physics Group of the University of Granada (GFAT) in the framework of the upcoming Aerosol-Cloud-Ecosystems (ACE) mission. Such collaboration will create a good environment for the progress in the atmospheric sciences and advance our knowledge of the Earth-Atmosphere radiative system. The research activity focuses on performing lidar ACE simulations studies that includes defining the optimum capabilities of such system in order to obtain aerosol microphysical properties under the accuracy and errors claimed in ACE white paper. The techniques to be used are regularization and Linear Estimation (LE), and special emphasis will be paid to the retrieval of coarse particles predominance. In this context GFAT offers a unique environment thanks to the possibility of evaluating these techniques by MW ground-based lidar and to its large set of in-situ and radiometric instrumentation. The advances in LE will be used to perform high temporal-resolution of fine and coarse mode of columnar aerosol properties using measurements of aerosol optical depth (AOD) by AERONET sun-photometers. This will be done combined with the Spectral Deconvolution Algorithm (SDA+). Finally, as GFAT operates one of the few star-photometers in the world, we will evaluate the potentiality of improving lidar retrievals using AOD from star-photometry. In this context, a secondment with the Laboratory of Atmospheric Optics at the University of Lille (LOA) is planned to extend night-time retrievals of the GARRLiC algorithm, and thus obtain vertical profiles of particle volume through an easy deployment of backscatter elastic lidar and star-photometry.

Keywords: simulations of lidar signals for the system to be deployed at ace,evaluation of retrieval techniques for aerosol microphysics aeronet and lidar
Revolutionary Artificial Intelligence Platform for Managing and Optimising Event Planning (EventAI)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: Denmark
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2018)
Start date: 2018-12-01
End date: 2019-05-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

EventAI is a revolutionary AI platform for managing and optimising event planning. It enables seamless organisation of events intelligently providing data analytics, professional network access, native processing tools (i.e. text processor, email server) and integrating numerous external tools via APIs. Using EventAI Event Managers and professionals can access and manage all their information and processes from one place. EventAI uniqueness lies in its underlying system architecture, the first of its kind, that includes technology such as Graph Model Databases, Microservices Architecture that runs on Kubernetes Clusters, API-first approach built on the new GraphQL Language and 256-bit SSL certificate data encryption, among others. EventAI is game changing in that it creates a network of event professionals and vendors thus enabling the first global online market for supply and demand in the Event Management industry that is expected to spend up to €11 billion on software growing at CAGR of 11.6% - showcasing clear market need for an innovative product like ours. Our research has shown that EventAI platform increases planning efficiency by 30-50%, reduces time spent on repetitive tasks by 25%; provides Event Managers with the ability to compare supplier prices in real time; increases competition by having all the stakeholders in one place; and reduces complexity and inefficiency by seamlessly integrating external APIs. Our solution comes at 1/10 of the price of a high-level event management software while it increases job growth by 2-3% through improving competitiveness and transparency in the entire event industry. EventAI project is situated at level 6 on the TRL scale. We therefore propose a Feasibility Study to verify the technical, commercial and financial viability of the EventAI platform with a view to continue to a Phase 2 application of the SME Instrument.

Keywords: event management,event managers,event management tools,event management software,social media,network sharing,machine learning,artificial intelligence,communications,saas
A holistic approach to understand Earth formation (HolyEarth)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Advanced grants
Action type: ERC actions
Panel: Earth system science
Panel description: Physical geography, geology, geophysics, atmospheric sciences, oceanography, climatology, cryology, ecology, global environmental change, biogeochemical cycles, natural resources management
Countries involved: Germany,France
Call: ERC ADVANCED GRANT (ERC-2020-ADG, 2020)
Start date: 2022-01-01
End date: 2026-12-31
Duration (months): 60.0
Total project cost (EUR): 2500000.0
EU funding (EUR): 1854875.0
Participants: 2
Project status: SIGNED

How did Earth form with the properties that made it the only inhabited planet of the Solar System? Answering this question is the Holy Grail of planetary science and is key for (i) placing the formation of Earth into the context of general planet formation (ii) unveiling the fundamental processes that led to the formation of a planet able to sustain life and (iii) assessing the likelihood that similar processes occurred in other, extrasolar systems. However, despite decades of research, it is still unknown how Earth formed. Although several competing models exist, the reason we do not yet have a consensus on how Earth formed is that all proposed models have only been tested against a limited number of constraints (and sometimes different constraints for different models) with no common intersection. HolyEarth will overcome these limitations by developing a holistic approach to understand Earth’s formation that is based on the largest set of observational constraints: orbits, masses, formation timescales, chemical and isotopic properties of terrestrial planets and meteorites, and the structure of the asteroid belt (the relict of planet formation). The overarching goal of this project is to identify a unique scenario which, for the first time, simultaneously satisfies all these constraints in a self-consistent way. This project will break the boundaries between Earth science and astronomy by placing the formation of Earth in the context of a cosmic evolution towards stars and planets. Its impact will reach other scientific domains, such as biology and exobiology, and resonate with the public’s keen interest in the question of our origins.

Keywords: earth formation,meteorites,asteroids and planetesimals,protoplanetary disk
Targeting the brain sigma-1 receptor: A paradigm shift for the treatment of neuro-degenerative disorders (NeuroPa)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: France
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-12-01
End date: 2020-05-31
Duration (months): 6.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

An increasingly aging population suffering from neurodegenerative diseases represents one of the biggest challenges for Western societies. Alzheimer’s disease (AD) is the most prevalent neurodegenerative disease with over 50 million patients worldwide and an estimated cost of 1 trillion euros in 2018. In addition, orphan neurodegenerative diseases such as Amyotrophic Lateral Sclerosis (ALS) have a comparably urgent need for treatment, because of the severity and life changing effect of the motor symptoms (muscle spasms, speaking and swallowing problems, full paralysis and lethality). As current treatments are only symptomatic and fail to slow down the disease progression, there is a pressing need for novel effective disease-modifying treatments. SigmaThera is a French company that tries to address the need for effective treatments for neurodegenerative diseases by focusing on the repositioning of drug candidates: applying the proven qualities of previously developed drugs for new diseases. For our lead compound SIT-161 with an excellent safety profile in humans (the candidate was abandoned after original Phase 2 trials for major depression) we have obtained impressive proof-of-concept data and patent protection for the treatment of neurodegenerative diseases, including AD & ALS. The drug targets a brain sigma-1 receptor, a critical regulator of multiple processes in neurodegeneration and provides unique efficacy and safety compared to other competing candidates in the pipeline. The high commercial potential of the drug is reflected in the €6.2B total addressable market and projected peak sales of €2B. In the NeuroPa project, SigmaThera will investigate market positioning, clinical and regulatory feasibility, future patent strategy and investor strategy to bring this promising asset close to market implementation.
Next generation single molecule protein fluorescence (SMPFv2.0)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator grants
Action type: ERC actions
Panel: Physical and analytical chemical sciences
Panel description: Analytical chemistry, chemical theory, physical chemistry/chemical physics
Countries involved: Germany,Germany
Call: ERC Consolidator Grant (ERC-2014-CoG, 2014)
Start date: 2015-09-01
End date: 2021-04-30
Duration (months): 68.0
Total project cost (EUR): 1996990.0
EU funding (EUR): 542673.46
Participants: 2
Project status: CLOSED

Fluorescence techniques provide powerful means to study single protein machineries. Single molecule observation of Fluorescence Resonance Energy Transfer (FRET) has become an important tool for probing structure, distances, dynamics, physicochemical properties and size of purified (in vitro) protein complexes. Super-resolution techniques provide complementary spatial information about proteins in live (in vivo) specimen. Although these techniques have become very popular because of their high sensitivity, their throughput is limited by large statistical sampling requirements, complex sample preparation procedures and/or laborious data acquisition workflows. Here, I propose to design a unified single molecule approach by engineering ultrafast, semi-synthetic protein modification techniques and fluorescent readouts into nano/microfluidic hybrid devices. The development of an automated all-in-one Lab-on-a-Chip platform will dramatically improve throughput of single molecule fluorescence techniques. The technology will provide a multiparameter readout of molecular protein mechanisms across time, resolution and complexity scales in a generalised workflow. To demonstrate the power of the new platform, I will apply it to a mechanistic study of the multifunctionality of intrinsically disordered proteins (IDPs), which are vital to cellular function and associated with many disease mechanisms. The polymeric properties of IDPs enable them to populate a variety of states and engage with various cellular binding partners, thus exceeding the sampling limit of existing single molecule technologies. The new method will blur the boundaries between in cell superresolution microscopy and biochemical single molecule studies and unleash the true power of single molecule protein science for a range of applications in analytical sciences, drug discovery, biotechnology, systems biology and fundamental biomedical research.

Keywords: biotechnology,single molecule fluorescence,fret spectroscopy,nanofluidics,click reaction,intrinsically disordered protein,superresolution microscopy,protein multifunctionality,unnatural amino acid
Innovative AI-based Ophthalmologic Diagnosis (aiVision)

Framework: Horizon 2020 — Industrial Leadership
Programme: Innovation in SMEs
Action type: SME instrument
Countries involved: France
Call: SME instrument (H2020-SMEInst-2018-2020-1, 2019)
Start date: 2019-12-01
End date: 2020-03-31
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

AiVision is a unique telemedicine cloud platform powered by deep learning AI that supports the diagnosis of sight related diseases. It is created with the objective of optimizing the detection and treatment of a highly probable cause of blindness in diabetic patients, known as diabetic retinopathy. Diabetic Retinopathy is the leading cause of blindness affecting the population that is less than 65 years old. After 15 years of significant improvements treating diabetes, 95% of type one diabetic patients and 60% of type 2 diabetic patients suffer from at least a mild and in some cases from severe diabetic retinopathy. In 2015, 415m people suffered from diabetes and this figure will grow up to 640m in 2040. There are 4,2M diabetics in the US, and around 24,000 new cases per year of advanced retinopathy due to diabetes. AiVision intends to solve this health problem through an improved eye screening medical examination process facilitated by a telemedicine platform and automated algorithms that support diagnosis decision making. We have gathered a database of more than 107,000 categorized eye fundus screenings which we have use in our algorithms, we have a partnership with the French Orthoptists Syndicate (with 1,000 professionals, it represents 90% of the sector) and are running our first paid pilots in France on 90 hospitals and health centres and working with the WHO in 4 African pilots. There is a clear willingness to pay from the market actors: orthoptist and ophthalmologists consider that the increase in screened patients will vastly contribute to early detection of signs of retinopathy, allowing for better treatment. AiVision is the core project of AiScreenings, a start-up founded in 2017 by partners with management, research, medical sciences and IT backgrounds. The company is based at the Polytechnique Institute business incubator (France). With a team of 9, we have generated our first 100.000€ of turnover in 2018 and received a 90.000€ innovation Grant.

Keywords: retinopathy,diabetes,machine learning,big data,deep learning,computer vision
New Easy to Install and Manufacture PRE-Fabricated Modules Supported by a BIM based Integrated Design ProceSS (IMPRESS)

Framework: Horizon 2020 — Societal Challenges
Programme: Secure, clean and efficient energy
Theme: Reducing energy consumption and carbon foorpint by smart and sustainable use
Action type: Innovation actions
Countries involved: Ireland,Italy,Italy,Spain,Ireland,Italy,Italy,Romania,Hungary,Poland,Romania,United Kingdom,United Kingdom,United Kingdom,United Kingdom
Call: Manufacturing of prefabricated modules for renovation of building (H2020-EE-2014-1-PPP, 2014)
Start date: 2015-06-01
End date: 2019-05-31
Duration (months): 48.0
Total project cost (EUR): 6041474.28
EU funding (EUR): 4493099.74
Participants: 19
Project status: CLOSED

IMPRESS will develop three different prefabricated panels for buildings: (i) a polyurethane based insulated panel with improved thermal performance and light radiation and (ii) a thin, lightweight pre-cast concrete sandwich panel, with optimum thermal and weathering resistance, both of which are suitable for overcladding; (iii) a lightweight pre-cast concrete sandwich panel incorporating Phase Change Materials (PCM) to adapt the thermo-physical properties of the building envelope and enable optimum passive heating and cooling benefits, suitable for recladding. Innovative nano/micro particle based coatings, suitable for 3D printing, will be also developed to achieve anti-corrosion resistance, high mechanical strength, improved solar reflectance, improved ageing resistance and anti-vandalism properties. To create the panels, an innovative manufacturing process will be created that includes Reconfigurable Moulding (RM) techniques, 3D laser scanning and 3D printed technology. In addition, 3D printed microstructured formworks will be developed as permanent external layer for the polyurethane panel to match the existing building aesthetics and provide solar radiation efficiency. The overall manufacturing process will (i) allow for mass production of panels, which take into account complex architectural and aesthetic issues, (ii) allow for faster production while lowering prefabrication costs and (iii) develop new controlled and cost effective solutions. IMPRESS will also develop a new Iterative Design Methodology, which will incorporate all stages of the Design-Construct-Install-Operate process. This will be integrated with a BIM cloud based database focussing on the interoperability between software tools required for the prefabricated process. Furthermore, new penalty based business models will be investigated. The final result will be demonstrated on two existing buildings where final as-built product performance will be validated against the initial design.

Keywords: polyurethane insulated panel,phase change materials,pre cast concrete,reconfigurable moulding,3d printed formwork,iterative design methodology,building information modelling
East European Centre for Atmospheric Remote Sensing (ECARS)

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,Italy,Greece,Romania
Call: Twinning (H2020-TWINN-2015, 2015)
Start date: 2016-01-01
End date: 2018-12-31
Duration (months): 36.0
Total project cost (EUR): 1000000.0
EU funding (EUR): 1000000.0
Participants: 5
Project status: CLOSED

ECARS addresses the challenges of the Twinning programme by tackling deficiencies and networking gaps between INOE, a very dynamic research institution in Romania, and internationally-leading counterparts at EU level. The activity will strengthen the excellence of INOE Center for atmospheric remote sensing and will stimulate high-level environmental and climate research, conform to Romanian Smart Specialization Strategy. This will be achieved by consolidating INOE’s links with four highly esteemed research institutions, each one having high-level expertise in specific areas: MPI-M (ground-based remote sensing), DLR (airborne remote sensing), NOA (satellite remote sensing) and CNR-IMAA (data exploitation). The coordination and support actions address specific areas of atmospheric remote sensing where the expertise of INOE will be strengthen, increasing its innovation capacity and research profile. The focus is on cutting edge passive and active remote sensing technologies, good practices and data synergy, leading to better data exploitation for environmental and climate research. Transfer of know-how is performed in a larger context of training through research, with ECARS complementing undergoing projects in HORIZON2020 and ESA Earth Observation programs. ECARS' goal is to create a collaborative framework around INOE for sharing specialized knowledge, brainstorming for new ideas and elaborating scientific publications. Measures to maximize the impact involve short-term staff exchanges and on-site training, expert visits, virtual training, technical workshops, summer schools, webinars and outreach activities. ECARS is envisaged to increase by 50% the annual number of scientific papers published by INOE, and the impact factor by 25%. Knowledge will be further spread through national Master and PhD programs, reinforcing the atmospheric remote sensing activities in Romania and enhancing the related S&T capacities. ECARS is supported by WMO-GAW, GEO, ACTRIS and ESA/ESTEC

Keywords: atmospheric remote sensing,ground based raman lidar,airborne hsrl,satellite remote sensing,environment and climate