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A data acquisition (DAQ) system is described which will be used for the next generation of prototype calorimeters using particle flow algorithms for the International Linear Collider (ILC). The design is sufficiently generic and scalable…
Climate change is a pressing global concern for governments, corporations, and citizens alike. This concern underscores the necessity for these entities to accurately assess the climate impact of manufacturing goods and providing services.…
We motivate the use of differentiable probabilistic programming techniques in order to account for the large model-space inherent to astrophysical $\gamma$-ray analyses. Targeting the longstanding Galactic Center $\gamma$-ray Excess (GCE)…
Deep Learning (DL) has the potential to optimize machine learning in both the scientific and clinical communities. However, greater expertise is required to develop DL algorithms, and the variability of implementations hinders their…
A potentially powerful method of social-scientific data collection and investigation has been created by an unexpected institution: the law. Article 15 of the EU's 2018 General Data Protection Regulation (GDPR) mandates that individuals…
Incident management for large cloud services is a complex and tedious process and requires significant amount of manual efforts from on-call engineers (OCEs). OCEs typically leverage data from different stages of the software development…
The Opacity Project (OP) and Iron Project (IP) are pioneering international collaborations which have been computing, for more than 25 years, massive atomic data sets for astrophysical applications. We review the data activities that have…
Linked data (LD) technology is hailed as a long-awaited solution in web-based information exchange. Linked Open Data (LOD) bring this to another level by enabling meaningful linking of resources and creating a global, openly accessible…
Large language models (LLMs) have become a central foundation of modern artificial intelligence, yet their lifecycle remains constrained by a rigid separation between training and deployment, after which learning effectively ceases. This…
In order to profit from the high granularity of the calorimeters proposed for the ILC that are suitable for the Particle Flow Approach, specialised clustering algorithms have to be developped. GARLIC is such an algorithm with the goal to…
Astronomical photometry is the science of measuring the flux of a celestial object. Since its introduction, the CCD has been the principle method of measuring flux to calculate the apparent magnitude of an object. Each CCD image taken must…
Lifelogging involves continuously capturing personal data through wearable cameras, providing an egocentric view of daily activities. Lifelog retrieval aims to search and retrieve relevant moments from this data, yet existing methods…
Understanding the earth's climate system and how it might be changing is a preeminent scientific challenge. Global climate models are used to simulate past, present, and future climates, and experiments are executed continuously on an array…
The integration of low earth orbit (LEO) satellites with terrestrial communication networks holds the promise of seamless global connectivity. The efficiency of this connection, however, depends on the availability of reliable channel state…
In 2002 the ATLAS experiment started a series of Data Challenges (DC) of which the goals are the validation of the Computing Model, of the complete software suite, of the data model, and to ensure the correctness of the technical choices to…
We describe the first major public data release from cosmological simulations carried out with Argonne's HACC code. This initial release covers a range of datasets from large gravity-only simulations. The data products include halo…
The International Lattice Data Grid (ILDG) is a community-wide initiative to realize the sharing of primary data from lattice QCD simulations according to the principles of FAIR data. We recall the basic concepts of ILDG as a federation of…
The Production and Distributed Analysis (PanDA) system, originally developed for the ATLAS experiment at the CERN Large Hadron Collider (LHC), has evolved into a robust platform for orchestrating large-scale workflows across distributed…
Scientific applications often contain large and computationally intensive parallel loops. Dynamic loop self scheduling (DLS) is used to achieve a balanced load execution of such applications on high performance computing (HPC) systems.…
Data-driven methods, in particular machine learning, can help to speed up the discovery of new materials by finding hidden patterns in existing data and using them to identify promising candidate materials. In the case of superconductors,…