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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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Measurement of dijet angular distributions and search for beyond the standard model physics in proton-proton collisions at s=13TeV
Physics letters / B 882, 140964 - (2026) [10.1016/j.physletb.2026.140964]
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Journal Article
System-size dependence of charged-particle suppression in ultrarelativistic nucleus-nucleus collisions
Physics letters / B 880, 140679 - (2026) [10.1016/j.physletb.2026.140679]
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Search for pair production of heavy resonances in final states with a photon and large-radius jets in proton-proton collisions at s=13 TeV
Physical review / D 113(11), 112005 (2026) [10.1103/bwqs-vgqn]
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Preprint
Evidence for sequential $Υ$(nS) suppression in light ion collisions
(2026) [10.3204/PUBDB-2026-02432]
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Exploring the origin of D$^0$ meson elliptic flow in PbPb collisions at $\sqrt{s_\mathrm{NN}}$ = 5.02 TeV using event shape engineering
(2026) [10.3204/PUBDB-2026-02430]
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Search for soft unclustered energy patterns in proton-proton collisions at $\sqrt{s}$ = 13 TeV using data scouting
(2026) [10.3204/PUBDB-2026-02427]
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Precision luminosity measurement in proton-proton collisions at a center-of-mass energy of 13 TeV with the CMS detector at the Large Hadron Collider
(2026) [10.3204/PUBDB-2026-02423]
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All known publications ...
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