TRILATERAL RESEARCH LIMITED

CLIMOS aims to assist mitigation of climate- and climate change-induced emergence, transmission and spread of vector-borne and zoonotic pathogens based on Eco-health and One Health approaches. This will be achieved by quantifying climate and environmental-related drivers of sand fly vector populations and the sand fly-borne diseases (SFBDs) across Europe. The project will provide an Early Warning System (EWS) and decision support frameworks for more accurate climate and health modelling, prognosis of infection risk and range expansions, and adaptation options. Socio-economic analysis and risk assessments will inform decision support providing social and cost-benefit evaluations. Towards these goals, an open access interactive CLIMOS online platform will be developed containing data on vector and SFBD species geography and up-to-date monitoring, climate, environment, and mathematical algorithms. The accompanying educational platform will enable evidence-based mitigation decision-making by social, environmental and financial stakeholders, public bodies and policy makers. CLIMOS will generate science-based predictions, actions and policy-relevant recommendations to mitigate climate change-induced emergence and spread of SFBDs, by providing: precision vector and infection surveillance methods and networks across 14 countries; creation of big data time series (on vectors, disease, micro and macroclimate, environment and health, and on infection risk) to be available also for use at the EU Climate and Health Observatory, and as European Commission ISA JoinUP open-source tool for public and healthcare; new interoperability data ontologies (Open Biological and Biomedical Ontology [OBO] and Open Geospatial Consortium [OGC] for SFBDs and climate); new innovative technical solutions for vector monitoring (new trapping solution and EWS components); and at least 30 peer-reviewed open access publications, 3 policy reports, and diversity of public engagement materials. Project CLIMOS is part of the European cluster on climate change and health.

EC contribution:

EUR 479k

Start:

2022-09-01

End:

2026-05-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-HLTH-2021-ENVHLTH-02

View on CORDIS (DOI 10.3030/101057690)

Availability of large volumes of user data combined with tailored statistical analysis present a unique opportunity for organizations across the spectrum to adapt and finetune their services according to individual needs. Having shown remarkable results in analyzing user data, machine learning models attracted global adulation and are applied in a plethora of applications including medical diagnostics, pattern recognition, and threat intelligence. However, such service improvements and personalization based on user data analysis come at the heavy cost of privacy loss. Furthermore, practice showed that systems that use such models incorporate proxies that are often inexact, biased and often unfair. In HARPOCRATES, we focus on setting the foundations of digitally blind evaluation systems that will, by design, eliminate proxies such as geography, gender, race, and others and eventually have a tangible impact on building fairer, democratic and unbiased societies. To do so, we plan to design several practical cryptographic schemes (Functional Encryption and Hybrid Homomorphic Encryption) for analyzing data in a privacy-preserving way. Besides processing statistical data in a privacy-preserving way, we also aim to enable a richer, more balanced and comprehensive approach where data analytics and cryptography go hand in hand with a shift towards increased privacy. In HARPOCRATES we will first show how to effectively combine cryptography with the principles of differential privacy to secure and privatise databases. Next, we will build privacy-preserving machine learning models able to classify encrypted data by performing high accuracy predictions directly on ciphertexts across federated data spaces. Finally, to demonstrate how these solutions respond to users’ needs, we will implement two real-world cross-border data sharing scenarios related to health data analysis for sleep medicine and threat intelligence for local authorities.

EC contribution:

EUR 314k

Start:

2022-10-01

End:

2025-09-30

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL3-2021-CS-01

View on CORDIS (DOI 10.3030/101069535)

TITAN will enrich the EOSC Interoperability Framework (IF) with a software platform solution for confidential data collaboration and secure and privacy-preserving data processing. The platform will enable access to sensitive data sets from public entities and government agencies and will be compatible by design with the EOSC IF on the technical, semantic, organisational and legal layers. To promote community adoption of TITAN’s open-source software artefacts, the solution will be practically demonstrated in several vertical cross-border scenarios - notably in the public administration and healthcare sector

EC contribution:

EUR 281k

Start:

2024-02-01

End:

2027-01-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-INFRA-2023-EOSC-01

View on CORDIS (DOI 10.3030/101129822)

Ceasefire targets the development of a highly innovative, holistic, multi-disciplinary, high-tech and versatile approach for significantly increasing/broadening the operational capabilities of EU Law Enforcement Agencies (LEAs) in their struggle to detect, analyze and track cross-border illicit firearms trafficking related activities. In particular, Ceasefire efforts will concentrate on the following major driving axes: a) The development of advanced Artificial Intelligence (ΑΙ) technologies for significantly facilitating the everyday work of the involved practitioners, and b) The establishment of fully-operational National Focal Points (NFPs), by targeting to alleviate from current organizational, operational, cooperation, legal, cross-jurisdictional, trans-border and information exchange challenges. The former corresponds to a wide set of AI-enabled tools, including solutions for cyber patrolling, Web data gathering, on-the-spot detection of firearms, advanced Big Data analytics, cryptocurrency analysis, large-scale information fusion, visual analytics and firearms-related intelligence collection. On the other hand, particular attention will be given towards defining models, protocols and procedures for making NFPs fully functional, complemented by activities for mapping the various legal frameworks (within different countries) and procedures (within different agencies) for harmonizing investigations, cooperation, lawful evidence collection and forensics analysis. The development of efficient information sharing mechanisms among LEA (police, border guard, customs), judicial and forensics authorities will further reinforce the system capabilities. Moreover, the tasks of Ceasefire will complement the objectives and activities of the EU Policy Cycle (EMPACT) – Firearms (where partner MIRPN acts as Driver and partner FMI as Co-driver), while continuous guidance from the European Firearms Experts (EFE) group (where partner PSP acts as the Chair) is foreseen.

EC contribution:

EUR 183k

Start:

2022-10-01

End:

2025-09-30

Status:

SIGNED

Scheme:

HORIZON-IA

Call:

HORIZON-CL3-2021-FCT-01

View on CORDIS (DOI 10.3030/101073876)

Local Energy communities (LEC) have been recognized by EC as key measure to boost EU energy transition. At the same time digitalization of EU energy system and a proper data sharing among energy players look crucial to foster best practice sharing and the creation of a knowledge community to tackle one of the most urgent global crisis of our society: climate change. DATA CELLAR aims therefore to create a public energy dataspace that will support the creation, development and management of LECs in EU. Such dataspace will be easy to be populated (also via an innovative rewarded private metering approach) and easy to interact with, also guaranteeing a smooth integration with other EU energy dataspaces and providing to LEC stakeholders services and tools for developing their activities. DATA CELLAR will implement a collaborative platform providing an interoperable, modular, and secure energy data space capable of delivering access to datasets, Decision Support Tool and Artificial Intelligence (AI) models to serve and support the spread LECs. In the framework of the project DATA CELLAR and its services will be used by representatives of nine validation cases around EU and with different level of maturity promoted by different type of actors such as public authorities, DSOs, aggregators etc.. DATA CELLAR will also used to drive transversal activities such as EU energy scenarios and market planning (EDF, NODES). DATA CELLAR will facilitate data collection/sharing from both LEC stakeholders, researchers and tenants also targeting the development of a marketplace and a tokenization rewarding approach that can stimulate such actors to both use and populate the data space. At this purpose and also considering data interaction with the other EU existing energy dataspaces/initiatives (GAIA-X, OPEN DEI…) towards the creation of a federated EU Energy Data Space, DATA CELLAR will also study ethical, regulatory, cybersecurity and governance aspects for data handling/sharing.

EC contribution:

EUR 162k

Start:

2022-06-01

End:

2025-11-30

Status:

SIGNED

Scheme:

HORIZON-IA

Call:

HORIZON-CL5-2021-D3-01

View on CORDIS (DOI 10.3030/101069694)

E-SPFdigit will bring novel onsite digital tools already at TRL5 under systemic innovation, which will be further deployed, upscaled, field-tested and demonstrated to TRL7-8, in viticulture and horticulture applications in Greece and Spain in soil-contaminated areas (located near mines, offensive industries, highways, floodwaters). E-SPFdigit brings novel developed: i) MIP-based electrochemical sensors coupled with SPME for on-site monitoring and analysis of dedicated to PFAS targeted molecules, ii) Multiplex organic Surface Plasmon Resonance optical biosensors for onsite pesticides residues monitoring and analysis, iii) IDE-based electrochemical sensors for onsite detection and quantification of heavy metals & micronutrients, and iv) UVC LED -based nutrient analysers for soil water content in-situ and real time monitoring. The AI-driven onsite digital tools will be model calibrated using machine learning algorithms to improve error distribution of a predictive model, ensuring reliability. Also, E-SPFdigit brings an edge-based remote sensing framework via a robust autonomous mobile robot self-navigating and a heavy-duty unmanned aerial vehicle for in-field detection of soil parameters regarding the aforementioned chemical and biological stressors. Finally, to predict pesticide and fertiliser and other chemical contaminants impacts on crop-soil-microbiome nexus, the project will use on-field real-time digital ground sensors combined with Earth Observation data and causal machine learning. All the onsite digital tools will be interconnected with a Decision Support Systems with blockchain and cybersecurity mechanisms enabling informed decisions and automated decision making for IPM and INM, enhanced with automated decision making for immediate soil management practices.

EC contribution:

EUR 159k

Start:

2024-10-01

End:

2027-09-30

Status:

SIGNED

Scheme:

HORIZON-IA

Call:

HORIZON-MISS-2023-SOIL-01

View on CORDIS (DOI 10.3030/101157922)

Artificial Intelligence (AI) is increasingly employed by businesses, governments, and other organizations to make decisions with far-reaching impacts on individuals and society. This offers big opportunities for automation in different sectors and daily life, but at the same time it brings risks for discrimination of minority and marginal population groups on the basis of the so-called protected attributes, like gender, race, and age. Despite the large body of research to date, the proposed methods work in limited settings, under very constrained assumptions, and do not reflect the complexity and requirements of real world applications. To this end, the MAMMOth project focuses on multi-discrimination mitigation for tabular, network and multimodal data. Through its computer science and AI experts, MAMMOth aims at addressing the associated scientific challenges by developing an innovative fairness-aware AI-data driven foundation that provides the necessary tools and techniques for the discovery and mitigation of (multi-)discrimination and ensures the accountability of AI-systems with respect to multiple protected attributes and for traditional tabular data and more complex network and visual data. The project will actively engage with numerous communities of vulnerable and/or underrepresented groups in AI research right from the start, adopting a co-creation approach, to make sure that actual user needs and pains are at the centre of the research agenda and act as guidance to the project’s activities. A social science-driven approach supported by social science and ethics experts will guide project research, and a science communication approach will increase the outreach of the outcomes. The project aims to demonstrate through pilots the developed solutions into three relevant sectors of interest: a) finance/loan applications, b) identity verification systems, and c) academic evaluation.

EC contribution:

EUR 120k

Start:

2022-11-01

End:

2025-10-31

Status:

SIGNED

Scheme:

HORIZON-RIA

Call:

HORIZON-CL4-2021-HUMAN-01

View on CORDIS (DOI 10.3030/101070285)