Data-Gouv-ML/pcri-informations-sur-les-projets-laureats huggingface dataset:
Delivering on the Paris Agreement: A demand-driven, integrated assessment modelling approach (PARIS REINFORCE)

Framework: Horizon 2020 — Societal Challenges
Programme: Climate action, environment,  resource efficiency and raw materials
Theme: Fighting and adapting to climate change
Action type: Research and Innovation actions
Countries involved: Italy,Germany,Spain,Italy,India,Italy,Switzerland,China,Russian Federation,Norway,Belgium,Greece,Greece,France,Netherlands,Brazil,United Kingdom,United Kingdom,Japan
Call: Supporting the development of climate policies to deliver on the Paris Agreement, through Integrated Assessment Models (IAMs) (H2020-LC-CLA-2018-2, 2018)
Start date: 2019-06-01
End date: 2022-11-30
Duration (months): 42.0
Total project cost (EUR): 7830242.5
EU funding (EUR): 6950548.75
Participants: 19
Project status: SIGNED

PARIS REINFORCE aims to underpin climate policymaking with authoritative scientific processes and results, and enhance the science-policy interface, in light of the Paris Agreement and associated challenges. In particular, our aim is to develop a novel, demand-driven, IAM-oriented assessment framework for effectively supporting the design and assessment of climate policies in the EU as well as in other major emitters and selected less emitting/developed countries, in respect to the Paris Agreement objectives. Building on an exhaustive facilitative dialogue and a strong ensemble of complementary—in terms of mathematical structure, geographical, sectoral and focus coverage—integrated assessment, energy system and sectoral models, we will create an open-access and transparent data exchange platform, I2AM PARIS, in order to support the effective implementation of Nationally Determined Contributions, the preparation of future action pledges, the development of 2050 decarbonisation strategies, and the reinforcement of the 2023 Global Stocktake. We also seek to enhance the legitimacy of the scientific processes in support of climate policymaking, by introducing an innovative stakeholder inclusion framework and improving the transparency of the employed models, methods and tools. Beyond effectively communicating respective outputs and fostering wider societal acceptance of climate policy, we actively involve policymakers and other stakeholder groups in all stages: from the formulation of policy questions and the definition of modelling assumptions in a demand-driven approach; to the design of I2AM PARIS interfaces and specifications, and the mobilisation of tacit knowledge embedded in them in the aim of bridging knowledge gaps. Finally, we will introduce innovative integrative processes, in which IAMs are further coupled with well-established methodological frameworks, in order to improve the robustness of modelling outcomes against different types of uncertainties.

Keywords: integrated assessment modelling,uncertainty,robustness,decision support,global stocktake,nationally determined contributions,transition pathways,policy governance,climate and energy policy
Cerebellar Distributed Plasticity Towards Active Sensing and Motor Control (CEREBSENSING)

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: Information Science and Engineering
Countries involved: Spain
Call: Marie Skłodowska-Curie Individual Fellowships (IF-EF) (H2020-MSCA-IF-2014, 2014)
Start date: 2015-11-01
End date: 2018-01-22
Duration (months): 24.0
Total project cost (EUR): 158121.6
EU funding (EUR): 158121.6
Participants: 1
Project status: CLOSED

The ability to perceive and understand the state of the surrounding environment and the own state is critical for next generation robotic systems. To that aim, the human brain is still far beyond current artificial systems performance due to its capability of processing huge amounts of heterogeneous sensorial data. Interestingly, the cerebellum has been shown to play a crucial role in the generation of dexterous movements as evidenced from cerebellar ataxic patients. Behavioural studies suggest that the cerebellum actively improves sensorial discrimination and proprioception thanks to the prediction of the sensorial consequences of actions. In the last decade, several forms of long-term synaptic plasticity have been observed within the cerebellum, suggesting that distributed plasticity could support the predictive action. However the way in which those mechanisms cooperate in order to improve the function of the whole cerebellar network is not completely understood. In this project, the candidate will develop a novel theory of sensorial information representation and processing based on the cerebellar architecture. The proposed model will make use of long-term synaptic plasticity mechanisms distributed along connections existing in the cerebellar input layer (granular layer) to iteratively create sparse representations of the information, allowing fast and effective learning in successive layers. The predictions extracted from this model will be useful to design new experimental protocols to unveil the cerebellar role in acting and sensing. By providing multiple relevant contributions across the spectrum of the H2020 objectives in terms of its potential to advance robotic manufacturing, brain processing understanding, and novel computing paradigms, this project will enable the candidate to enhance his position at the forefront of advances in this field.

Keywords: neural plasticity,motor control,neural information coding
Investigating the role of the long noncoding transcriptome in chromatin replication (NonChroRep)

Framework: Horizon 2020 — Excellent Science
Programme: European Research Council (ERC)
Theme: European research council
Destination: Consolidator 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 Consolidator Grant (ERC-2017-COG, 2017)
Start date: 2018-04-01
End date: 2024-03-31
Duration (months): 72.0
Total project cost (EUR): 2000000.0
EU funding (EUR): 2000000.0
Participants: 1
Project status: SIGNED

A major shift in our conception of genome regulation has emerged in recent years. It is now obvious that the majority of cellular transcripts do not code for proteins, and a significant subset of them are long RNAs (lncRNAs). My lab and others have shown that lncRNAs regulate genome function and gene expression, and that alterations in lncRNAs are inherent to disease, including cancer. However, our understanding of the roles of lncRNAs and their underlying molecular mechanisms are still extremely poor. Among all the mechanisms reported, the evident connection between lncRNAs and the chromatin places them at the center of cell biology. During their cycle, cells must undergo faithful DNA replication to ensure that an exact copy of their genetic content is passed on to their daughters. Throughout this tightly regulated process chromatin must be disrupted and reconstituted, and it determines where and when replication takes place. If replication is deregulated, cells can proliferate uncontrollably and suffer loss of genome integrity. Our recent findings implicate lncRNA in the process of DNA replication, representing a novel aspect of genome regulation that places lncRNAs at the focal point of cancer biology. To delve deeper into these findings I aim to: 1. Identify the role of lncRNAs in the replication of the chromatin 2. Dissect the molecular mechanism by which lncRNAs function in this process and 3. Explore the role of these lncRNAs as cancer drivers and their potential as therapeutic targets. I will apply tools that we have generated in recent years, as well as new ones, including approaches to identify lncRNAs associated with replicating chromatin, novel lncRNA-tailored CRISPR applications, and the latest methodology for functional study and targeting of long noncoding transcripts in cancer. I am confident that we are in a unique position to address these life-essential and yet pending questions, setting up a basis for future lncRNA-based therapies.

Keywords: lncrna,chromatin,dna replication,cancer
Responsible Research and Innovation Networked Globally (RRING)

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: Research and Innovation actions
Countries involved: Ireland,Germany,Spain,Ireland,Ukraine,South Africa,Germany,Italy,Lithuania,Sweden,Serbia,France,Netherlands,Netherlands,Netherlands,Netherlands,United Kingdom,United Kingdom,United Kingdom,Japan,United Kingdom
Call: A Linked-up Global World of RRI (H2020-SwafS-2017-1, 2017)
Start date: 2018-05-01
End date: 2021-04-30
Duration (months): 36.0
Total project cost (EUR): 2999545.0
EU funding (EUR): 2999545.0
Participants: 23
Project status: CLOSED

The overall project aim is to bring RRI into the linked up global world to promote mutual learning and collaboration in RRI. This will be achieved by the formation of the global RRING community network and by the development and mobilisation of a global Open Access RRI knowledge base. RRING will align RRI to the Sustainable Development Goals (SDGs) as a global common denominator. The RRING project acknowledges that each region of the world is advancing its own agenda on RRI. Therefore, RRING will not be producing a Global RRI framework or strategy that is meant to be enforced in a top-down manner. Rather, increased coherence and convergence will be achieved via a bottom-up approach, learning from best practices in RRI globally and from linkages, via the new RRING community, to develop the RRI linked-up world. Six Objectives of RRING Objective 1: Promote a linked up global world of RRI by creating the global RRING community network, thereby enabling mutual learning, collaboration, mobilisation of RRI concepts. Objective 2: Mobilise, promote and disseminate a global open access knowledge base of RRI based on the State of the Art (SoA) and comparative analysis across the key geographies, all stakeholders and sectors. It will cover key platforms, spaces and players, role and influence of stakeholders, drivers and policies for R&I, regulation in public,private sectors and nation states and international organizations. Objective 3: Align RRI to the UN Sustainable Development Goals (SDGs) to provide a global common denominator for advancement of RRI, and address Grand Challenges globally. Objective 4: Determine the competitive advantages of RRI and also understand how and where RRI is perceived as a barrier and/ or disadvantage. Objective 5: Create high level RRI strategy recommendations for the seven geographic zones, trial RRI best practice learning in 2 EU case studies Objective 6: Promote inclusive engagement of civil society and researchers

Keywords: linked up global world of rri,nation states,international organizations,industry,competitive advantage,ethical behavior,digital single market,energy union,civil society,governance,regulation
Towards AI powered manufacturing services, processes, and products in an edge-to-cloud-knowlEdge continuum for humans [in-the-loop] (knowlEdge)

Framework: Horizon 2020 — Industrial Leadership
Programme: Information and Communication Technologies
Action type: Research and Innovation actions
Countries involved: Germany,Germany,Spain,Germany,Italy,Italy,Germany,Italy,Spain,Italy,Belgium,Slovakia,Greece,Finland,United Kingdom
Call: Artificial intelligence for manufacturing (H2020-ICT-2020-1, 2020)
Start date: 2021-01-01
End date: 2023-12-31
Duration (months): 36.0
Total project cost (EUR): 5996151.25
EU funding (EUR): 5996151.25
Participants: 16
Project status: SIGNED

AI is one of the biggest mega-trends towards the 4th industrial revolution. While these technologies promise business sustainability and product/process quality, it seems that the ever-changing market demands and the lack of skilled humans, in combination with the complexity of technologies, raise an urgent need for new suggestions. Suggestions that will be agile, reusable, distributed, scalable, accountable, secure, standardized and collaborative. To break the entry barriers for these technologies and unleash their potential, the knowlEdge project will develop a new generation of AI methods, systems and data management infrastructure. This framework will provide means for the secure management of distributed data and the computational infrastructure to execute the needed analytic algorithms and redistribute the knowledge towards a knowledge exchange society. To do so, knowlEdge proposes 6 major innovations in the areas of data management, data analytics and knowledge management: (i) A set of AI services that allow the usage of edge deployments as computational and live data infrastructure, an edge continuous learning execution pipeline; (ii) A digital twin of the shop-floor to test the AI models; (iii) A data management framework deployed from the edge to the cloud ensuring data quality, privacy and confidentiality, building a data safe fog continuum; (iv) Human-AI Collaboration and Domain Knowledge Fusion tools for domain experts to inject their experience into the system to trigger an automatic discovery of knowledge that allows the system to adapt automatically to system changes; (v) A set of standardization mechanisms for the exchange of trained AI-models from one context to another; (vi) A knowledge marketplace platform to distribute and interchange AI trained models. The knowlEdge consortium consists of 12 partners from 7 EU countries, and its solution will be tested and evaluated in 3 manufacturing sectors.

Keywords: smart manufacturing,process simulation,generative ai,digital twin,marketplace,ai models,human,ai collaboration
VIGO – Exploring the Role of Visuality in Governance (VIGO)

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: Social Sciences and Humanities
Countries involved: Denmark,Canada
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2022-02-01
End date: 2025-01-31
Duration (months): 36.0
Total project cost (EUR): 273687.36
EU funding (EUR): 273687.36
Participants: 2
Project status: SIGNED

VIGO is a study of how visuality affects governance. The project builds on the idea that visuality creates and shares social meaning, thereby shaping governance – intended as the conceptual representation of change in underlying social systems and the efforts to manage or steer the effects of these changes. Visuality’s impact in governance – highlighting an issue’s salience, proposing interpretations, arousing sentiment, and legitimizing or undermining a claim – is so substantial that we can speak of ‘visual governance’. In other words, the pursuit of governance goals can be achieved through systems of visual (re)production at the national and global levels. This is particularly evident if we think of the attention that governance challenges on issues such as migration, climate change, and pandemics have visually attracted. Images of overcrowded migrant boats, starving polar bears on melting glaciers, and infographics showing the latest COVID-19 mortality rates have gone viral. As the adage goes, a picture is worth a thousand words. Yet, surprisingly, scant research has been devoted to understanding the effect that visual representation has on governance. This is precisely where ‘VIGO – Exploring the Role of Visuality in Governance’ will produce crucial insights. By adopting a comparative multi-scalar and multi-modal methodology, the project will be the first of its kind to provide a comprehensive analysis of the role of visuality in (migration) governance. In doing so, VIGO has three key objectives: (1) Contribute to theory-building by conceptualizing visual governance; (2) Explore the role of visuality in governance at the macro, meso and micro levels across two geographic areas (Europe and Canada); and (3) Test innovative methodological approaches in International Relations.

Keywords: visual studies,humanitarianism,migration policies,ngos,asylum,refugees
A coupled thermo-hydro-mechanical model for physically-based assessments of slope stability accounting for climate change (SLOPETEMP)

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: Czechia
Call: Individual Fellowships (H2020-MSCA-IF-2020, 2020)
Start date: 2021-10-01
End date: 2023-11-30
Duration (months): 24.0
Total project cost (EUR): 156980.64
EU funding (EUR): 156980.64
Participants: 1
Project status: SIGNED

Climate change will alter the frequency and patterns of landslides, increasing the risk to people, infrastructures, and ecosystems in many regions worldwide. Scientists mainly draw this conclusion from predicted changes in precipitation, ice covers, sea level, and land use. The direct effect of temperature on slope stability is usually neglected, even though a mechanical failure in various soils appears to be dependent on their thermal state. Temperature-dependent behaviours in soils have been documented in several laboratory experiments and field studies. I plan to use for the first time a coupled thermo-hydro-mechanical (THM) soil model based on the theory of hypoplasticity to better quantify the effects of climate patterns on slope stability. By this model, implemented in an in-house code, I expect to be able to reproduce complex hydro-mechanical responses caused by changes of temperature, including effects on water pressures, water retention, and swelling/shrinkage. I will perform short- and long-term parametric analyses under climate scenarios, to compare the direct role of temperature with that of other forcings. In this way, I expect to quantify how much local warming/cooling and altered patterns of temperature can control some types of landslides, and consequently affect current landslide hazard assessments. I plan to investigate an actual case study as well, to verify the model performance under complex boundary conditions, and demonstrate its applicability to practical slope-scale problems. Finally, I will conceptualise an upscaled model, to unlock the possibility of regional assessments. This project is in line with the EU strategy on adaptation to climate change as well as with the Sendai Framework of the UNDRR because, by improving the knowledge on the behaviour of soil slopes under climate forcing, it will offer physically-based tools for hazard assessment that could be integrated into national and European risk management systems.

Keywords: landslide,modelling,climate change,temperature,thermo hydro mechanical coupling,landslide hazard assessment
Making wind and solar renewables attractive for investors by boosting operational predictability and ROI (TruePower)

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: 2019-08-01
End date: 2019-11-30
Duration (months): 4.0
Total project cost (EUR): 71429.0
EU funding (EUR): 50000.0
Participants: 1
Project status: CLOSED

With renewable industry moving away from fixed tariffs to market-driven pricing, plant owners are under pressure to generate revenue through various market mechanisms. At the same time, they facing various operational inefficiencies leading to lost revenue up to 10-15%. TruePower is an intelligent wind and solar asset management platform that helps renewable plant owners increase their overall revenues through improved operational efficiency and intelligent revenue strategies. TruePower collects and analyses plant raw high-resolution data (at seconds intervals) recognising even minor wind speed or sun irradiation changes allowing to identify granular optimisation opportunities which state-of-the-art tools available today are not able to deliver, working with aggregated data at 10-60 min intervals. In addition, TruePower combines continuous stream of raw asset data at an aggregated wind or solar park level with the meteorological and market data and offers actionable insights on revenue generation strategies, enabled by our proprietary machine-learning algorithms. Such revenue optimisation mechanisms are a necessary attribute of successful growth of the renewables market. In the long run, that means renewable energy can be predictable, profitable and therefore ubiquitous, helping to minimise the impact of CO2 on our planet. We are working on the development of predictive models and aggregation of plants into large portfolios in order to allow for improved dispatching schemes of available power and energy to the power market in the most efficient way. Such ‘simulation box’ module that will enable asset owners to adjust plant power production in real-time depending on the market need. WinJi developed and introduced TruePower to the European market in 2017. Today, we have 17 customers, and over 45 prospects.

Keywords: solar,wind,energy balancing,renewables,optimization