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HIDSS-0002
DASHH: Data Science in Hamburg - Helmholtz Graduate School for the Structure of Matter
| Coordinator | Rohringer, Nina ; Rarey, Matthias ; Le Borne, Sabine |
| Grant period | 2019 - 2025 |
| Funding body | Helmholtz Gemeinschaft Deutscher Forschungszentren |
| HGF | |
| Further information: | Description |
| Identifier | G:(DE-HGF)2019_IVF-HIDSS-0002 |
⇧ Impuls- und Vernetzungsfonds ⇧
Note: ‘Data Science’ is the science of extracting knowledge from typically large amounts of data. The increasing level of automatization and the increasing number and resolution of sensors in scientific experiments result in large, heterogeneous and highly complex data collections. Therefore, Data Science is seen as a key technology in modern and future natural sciences. Data intensive research in experimental and theoretical science at the Forschungscampus Hamburg-Bahrenfeld requires beyond off-the-shelf software solutions for data management, processing and analysis. Tailored or even completely new computational data science methods are indispensable. At the same time, the challenges ahead will foster new innovative ideas in computer science and applied mathematics. To develop these methods, scientists have to be highly skilled in the corresponding physics domain and computer science alike. The Data Science in Hamburg – Helmholtz Graduate School for the Structure of Matter (DASHH) will be established to address this highly interdisciplinary need. DASHH bundles competences of scientists with high international reputation in basic research for the structure of matter, computer science and applied mathematics in Hamburg in a new and unique fashion. The graduate school covers data challenges from application fields such as structural biology, particle physics, material science and ultrafast X-ray science. These data challenges have in common that they cannot be addressed with standard computational methods, but require modern data science techniques instead. Several areas from computer science and mathematics play important roles, especially data management and engineering, machine learning and data analytics, signal and image processing, algorithm design, optimization and simulation, software engineering and automation and control systems.All known publications ...
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Preprint
A Review of Techniques for Ageing Detection and Monitoring on Embedded Systems
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Contribution to a book
Data-Based Condition Monitoring and Disturbance Classification in Actively Controlled Laser Oscillators
Information Modelling and Knowledge Bases XXXV
Amsterdam : IOS Press, Frontiers in Artificial Intelligence and Applications 380, 94 - 114
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Journal Article
Strategy and performance of the CMS long-lived particle trigger program in proton–proton collisions at √s=13.6 TeV
Physics reports 1205, 1 - 83 (2027) [10.1016/j.physrep.2026.07.007]
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Preprint
Search for pair-produced vector-like top quarks decaying into Lorentz-boosted top quarks and scalar bosons in proton-proton collisions at $\sqrt{s}$ = 13 TeV
(2026) [10.3204/PUBDB-2026-03402]
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Search for dark matter produced in association with a low-multiplicity jet in proton-proton collisions at $\sqrt{s}$ = 13 TeV
(2026)
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Measurement of the triple-differential cross section of Z+jet production in proton-proton collisions at $\sqrt{s}$ = 13 TeV
(2026)
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Search for resonant and nonresonant production of pairs of dijet resonances with b jets in the final state in proton-proton collisions at $\sqrt{s}$ = 13 TeV
(2026)
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Evidence for the dead-cone effect in bottom quark initiated jets produced in proton-proton collisions at $\sqrt{s}$ = 13 TeV
(2026)
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Preprint
Observation of an excess at the top quark pair production threshold in the single-lepton channel
(2026)
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Journal Article
Search for single vector-like quark production in opposite-sign dilepton final states in proton-proton collisions at $ \sqrt{s}=13 $ TeV
Journal of high energy physics 09(9), 230 (2026) [10.1007/JHEP09(2026)230]
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All known publications ...
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