The European project ORCHESTRA intends to create a new pan-European cohort to rapidly advance the knowledge of the effects and treatment of COVID-19. Establishing processes that facilitate the merging of heterogeneous clusters of retrospective data was an essential challenge. In addition, data from new ORCHESTRA prospective studies have to be compatible with earlier collected information to be efficiently combined. In this article, we describe how we utilized and contributed to existing standard terminologies to create consistent semantic representation of over 2500 COVID-19-related variables taken from three ORCHESTRA studies. The goal is to enable the semantic interoperability of data within the existing project studies and to create a common basis of standardized elements available for the design of new COVID-19 studies. We also identified 743 variables that were commonly used in two of the three prospective ORCHESTRA studies and can therefore be directly combined for analysis purposes. Additionally, we actively contributed to global interoperability by submitting new concept requests to the terminology Standards Development Organizations.

The Italian territory is located at the heart of one of the global hot spots of climate change, where the implementation of climate‐smart land management practices is imperative to guarantee the present and future maintenance of ecosystem functions as well as the sustainability of human socioeconomic activities. The project HIGHLANDER (HIGH performance computing to support smart LAND sERvices) led by Cineca aims at building a comprehensive and multi‐sector framework for land‐management decision‐making in Italy. The project relies on high quality information on different components of the landscape, with a focus on climate‐driven processes, and state‐of‐the‐art computing infrastructures. The HIGHLANDER Data Portal maximizes the impact of HIGHLANDER results by providing access to data products and services. In this article, we describe the architectural features of the platform, as well as the available HIGHLANDER datasets and downstream applications.

Background: The SARS-CoV-2 pandemic has demonstrated once again that rapid collaborative research is essential for the future of biomedicine. Large research networks are needed to collect, share, and reuse data and biosamples to generate collaborative evidence. However, setting up such networks is often complex and time-consuming, as common tools and policies are needed to ensure interoperability and the required flows of data and samples, especially for handling personal data and the associated data protection issues. In biomedical research, pseudonymization detaches directly identifying details from biomedical data and biosamples and connects them using secure identifiers, the so-called pseudonyms. This protects privacy by design but allows the necessary linkage and reidentification. Objective: Although pseudonymization is used in almost every biomedical study, there are currently no pseudonymization tools that can be rapidly deployed across many institutions. Moreover, using centralized services is often not possible, for example, when data are reused and consent for this type of data processing is lacking. We present the ORCHESTRA Pseudonymization Tool (OPT), developed under the umbrella of the ORCHESTRA consortium, which faced exactly these challenges when it came to rapidly establishing a large-scale research network in the context of the rapid pandemic response in Europe. Methods: To overcome challenges caused by the heterogeneity of IT infrastructures across institutions, the OPT was developed based on programmable runtime environments available at practically every institution: office suites. The software is highly configurable and provides many features, from subject and biosample registration to record linkage and the printing of machine-readable codes for labeling biosample tubes. Special care has been taken to ensure that the algorithms implemented are efficient so that the OPT can be used to pseudonymize large data sets, which we demonstrate through a comprehensive evaluation. Results: The OPT is available for Microsoft Office and LibreOffice, so it can be deployed on Windows, Linux, and MacOS. It provides multiuser support and is configurable to meet the needs of different types of research projects. Within the ORCHESTRA research network, the OPT has been successfully deployed at 13 institutions in 11 countries in Europe and beyond. As of June 2023, the software manages data about more than 30,000 subjects and 15,000 biosamples. Over 10,000 labels have been printed. The results of our experimental evaluation show that the OPT offers practical response times for all major functionalities, pseudonymizing 100,000 subjects in 10 seconds using Microsoft Excel and in 54 seconds using LibreOffice. Conclusions: Innovative solutions are needed to make the process of establishing large research networks more efficient. The OPT, which leverages the runtime environment of common office suites, can be used to rapidly deploy pseudonymization and biosample management capabilities across research networks. The tool is highly configurable and available as open-source software.

Background: The ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR). Methods: The three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation. Results: The resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2. Conclusions: Considering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications.

: The Connecting European SARS-CoV-2 Cohorts to Increase Common and Effective Response to SARS-CoV-2 Pandemic (ORCHESTRA) consortium, led by University of Verona (Italy), brings together key European academic experts and research institutions in infectious diseases, data management and High Performance Computing (HPC) from 26 organizations (extending to 37 partners) from 15 countries. The project aims to create a new pan-European cohort built on existing and new large-scale population cohorts in European and non-European countries to significantly impact on the responsiveness to SARS-CoV-2. The integration and analysis of the very heterogeneous characteristics of SARS-CoV-2 data coming from many different sources such as EHR, retrospective and prospective patient registries, and related ‘omics’ data (incl. genomics, proteomics and transcriptomics) can benefit of data analytics enabled by HPC, where both high compute performance and fast storage capabilities are immensely important. During the first year of the project, a dedicated HPC cloud infrastructure have been designed and partially deployed to fulfill the functional requirements for data management ensuring healthcare data confidentiality/privacy, integrity and security in compliance with the European GDPR regulations. The result is an infrastructure for Data Management composed by three main layers: National Data Providers; National Hubs (one for each HPC center involved: CINECA - Italy, CINES - France and HLRS - Germany), to centralize data at national level and to support data storage, sharing and analysis on data ingested from the National Data Providers; ORCHESTRA Data Portal: the pan-European portal for sharing aggregated data and results. Currently data collection is on going; at the end of the project, clinical centers are expected to have enrolled more than 10.000 patients with about 50.000 samples for the prospective studies. Key messages: • The SARS-CoV-2 crisis made evident the need to manage and analyse very heterogeneous health data coming from many different resources across different countries. • The HPC cloud infrastructure released for the Orchestra project can act as a model to manage future public health threats.