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Institute: search.filters.UBSInstitut.Höchstleistungsrechenzentrum Stuttgart (HLRS)Subject: search.filters.subject.020

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    ItemOpen Access
    A unified research data infrastructure for catalysis research : challenges and concepts
    (2021) Wulf, Christoph; Beller, Matthias; Boenisch, Thomas; Deutschmann, Olaf; Hanf, Schirin; Kockmann, Norbert; Kraehnert, Ralph; Oezaslan, Mehtap; Palkovits, Stefan; Schimmler, Sonja; Schunk, Stephan A.; Wagemann, Kurt; Linke, David
    Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high‐quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level.
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    ItemOpen Access
    An innovative technological infrastructure for managing SARS-CoV-2 data across different cohorts in compliance with General Data Protection Regulation
    (2024) Dellacasa, Chiara; Ortali, Maurizio; Rossi, Elisa; Abu Attieh, Hammam; Osmo, Thomas; Puskaric, Miroslav; Rinaldi, Eugenia; Prasser, Fabian; Stellmach, Caroline; Cataudella, Salvatore; Agarwal, Bhaskar; Mata Naranjo, Juan; Scipione, Gabriella
    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.
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    ItemOpen Access
    Forschungsdatenmanagement im Kontext dunkler Daten in den Simulationswissenschaften
    (2019) Schembera, Björn; Resch, Michael M. (Prof. Dr.-Ing. Dr. h.c. Dr. h.c. Prof. E.h.)
    In der Dissertation wird das Konzept von dunklen Daten auf das Höchstleistungsrechnen erweitert. Dunkle Daten entstehen durch fehlende Metadaten oder inaktive Nutzerinnen und Nutzer. Die Dissertation stellt Konzepte zur Minimierung solcher Daten vor. Sie umfassen ein Metadaten-Modell (EngMeta) und eine automatisierte Metadaten-Extraktionsmethode, die entworfen und implementiert wurde. Da solche technischen Lösungsansätze ohne entsprechende organisatorische Prozesse nutzlos sind, werden sie in der Dissertation um einen spezifischen Datenkurator sowie Entscheidungskriterien ergänzt.