Conférences  >  Informatique  >  Calcul scientifique et science des données  >  Allemagne

Mathematisches Forschungsinstitut Oberwolfach (MFO, Oberwolfach Research Institute for Mathematics)

Réseaux de neurones et intelligence artificielle, apprentissage automatique,    Gestion des connaissances, Big Data Computing

Mit dem Ziel, den Herausforderungen der Digitalisierung in der naturwissenschaftlichen Grundlagenforschung schnell und zukunftsorientiert zu begegnen und exzellente Forschung in und aus Deutschland zu fördern, legt das Bundesministerium für Bildung und Forschung (BMBF) als Teil des 2017 veröffentlichten Rahmenprogramms „Erforschung von Universum und Materie – ErUM“ den Aktionsplan ErUM-Data fest. Der Aktionsplan ErUM-Data schafft Voraussetzungen für die naturwissenschaftliche Grundlagenforschung von morgen und fördert eine koordinierte Herangehensweise und breite Verständigung. Langfristiges Ziel ist es, den Weg zu übergeordneten Lösungen zu ebnen und optimale Bedingungen für eine effiziente und finanzierbare Datenverarbeitung und -verwertung zu schaffen. Der einfache Zugang zu Daten und Werkzeugen ist eine zentrale Säule des Aktionsplans. 

Carrière et management,    Réseaux de neurones et intelligence artificielle, apprentissage automatique

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Many big data sets in various application domains have complex relationships, which can be modelled as graphs, consisting of entities and relationships between them. Consequently, graphs are extensively studied in both Mathematics and Computer science. In particular, planar graphs, which can be drawn without edge crossings in the plane, form a distinguished role in Graph Theory and Graph Algorithms. Many structural properties of planar graphs are investigated, in terms of excluded minors, low density, and small separators, which lead to efficient algorithms for planar graphs. Consequently, fundamental algorithms for planar graphs have been discovered. However, most real-world graphs, such as social networks and biological networks, are nonplanar. For example, the scale-free networks, which are used to model web graphs, social networks and biological networks, are globally sparse nonplanar graphs, with locally dense clusters and low diameters. To understand such real-world networks, we need to solve fundamental mathematical and algorithmic research questions on beyond-planar graphs, which generalize the notion of planar graphs, in terms of topological constraints or forbidden edge crossing patterns.

Théorie des graphes et combinatoire,    Gestion des connaissances, Big Data Computing

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The global fight against the COVID-19 pandemic has presented massive challenges and real opportunities for data science and visualization research and technology. Epidemiologists, virologists, infection control experts and public health officials are required to make sense of information from a huge variety of data sources. Physicians and medical researchers have access to rapidly changing medical data as the pandemic evolves. Political stakeholders (supported by economists, public health officials, and other experts analyzing the data) make decisions on which action to take, balancing the impact on public health with social and economic impacts. The general public is regularly presented with visualization, through news, conventional and social media, and government publications, and these inform their actions for the next days and weeks.

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

There is currently an insufficient transfer of state-of-the-art research knowledge from computer science to research software engineering, and vice versa, in part leading to an incomplete understanding in computer science of the domain-specific and general challenges in research software engineering. This interactive seminar therefore brings the computer science and software engineering research community and the research software engineering community together to define a common language, and apply it to improve reciprocal knowledge transfer.

Génie logiciel,    Physique numérique

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

Metabolomic data, usually from mass spectrometry or nuclear magnetic resonance spectroscopy, is highly complex and information rich. With continuing advances in metabolite detection technologies, the volume and complexity of data is ever increasing. Computational analysis of metabolomic data, from raw data processing to biological interpretation, is therefore fundamental to realizing the impact of metabolomics on a diverse array of application fields, e.g., environmental toxicity, industrial biotechnology, and biomedicine. This Dagstuhl Seminar extends the Computational Metabolomics series to examine how we can enhance the utility and interpretation of metabolomics data.

Logiciels,    Algorithmes et structures de données

Schloss Dagstuhl - Leibniz-Zentrum für Informatik

The vision of NFDI4DS is to support all steps of the complex and interdisciplinary research data lifecycle in Data Science and Artificial Intelligence. The overarching objective of NFDI4DS is the development, establishment, and sustainment of a national research data infrastructure for the Data Science and Artificial Intelligence community. The key idea is to work towards increasing the transparency, reproducibility and fairness of Data Science and Artificial Intelligence projects, by making all digital artifacts available, interlinking them, and offering innovative tools and services. Within this Dagstuhl Research Meeting, the NFDI4DS partners will reflect on their progress so far, and will work on their long-term strategy.

Gestion des connaissances, Big Data Computing,    Réseaux de neurones et intelligence artificielle, apprentissage automatique