Kubernetes is becoming relevant as a scaling infrastructure technology for more and more services. The Kubernetes evaluation project (Kube 3) focuses on the further development of cloud infrastructures in the state of Baden-Württemberg for the provision of Kubernetes. This also supports existing approaches in current bw projects, including bwGitLab, bwJupyter and bwCloud. The aim of the project is to develop and implement the necessary concepts and methods as well as forms of cross-university collaboration and to test them with specific use cases.
The project aims to reconstruct spatial patterns of timescale-dependent climate variability. For that a Bayesian Hierarchical Model will be developed that incorporates a variety of proxy data while considering proxy processes and noise. It aims to quantify limitations and uncertainties of derived climate variability reconstructions related to the covariance structure used and the sparseness, spatial heterogeneity and noisiness of the observational data through Bayesian posterior distributions. We will use the climate variability map to investigate regional patterns of low-frequency variability and the corresponding implications e.g. for the range of possible future climate trends in natural variability and of the frequency of extreme events. The project is supported by Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) and co-supervised by Prof. Dr. Thomas Laepple from Alfred Wegener Institute (AWI) and Prof. Dr. Tobias Krüger from the Humboldt University.
Explaining and understanding the underlying interactions of genomic regions are crucial for proper pathogen phenotype characterization such as predicting the virulence of an organism or the resistance to drugs. Existing methods for classifying the underlying large-scale data of genome sequences face challenges with regard to explainability due to the high dimensionality of data, making it difficult to visualize, access and justify classification decisions. This is particularly the case in the presence of interactions, such as of genomic regions. To address these challenges, we will develop methods for variable selection and structured explainability that capture the interactions of important input variables: More specifically, we address these challenges (i) within a deep mixed models framework for binary outcomes fusing generalized linear mixed models and a deep variant of structured predictors. We thereby combine statistical logistic regression models with deep learning for disentangling complex interactions in genomic data. We particularly enable estimation when no explicitly formulated inputs are available for the models, as for instance relevant with genomics data. Further, (ii), we will extend methods for explainability of classification decisions such as layerwise relevance propagation to explain these interactions. Investigating these two complementary approaches on both the model and explainability levels, it is our main objective to formulate and postulate structured explanations that not only give first-order, single variable explanations of classification decisions, but also regard their interactions. While our methods are motivated by our genomic data, they can be useful and extended to other application areas in which interactions are of interest.
This project proposes informed, data-driven methods to reveal pathological trajectories based on high-dimensional medical data obtained from magnetic resonance imaging (MRI), which are relevant as both inputs and outputs in regression equations to adequately perform early diagnosis and to model, understand, and predict actual and future disease progression. For this, we will fuse deep learning (DL) methods with Bayesian statistics to (1) accurately predict the complete outcome distributions of individual patients based on MRI data and further confounders and covariates (such as clinical or demographical variables) to adequately quantify uncertainty in predictions in contrast to point predictions not delivering any measures of confidence (2) model temporal dynamics in biomedical patient data. Regarding (1), we will develop deep distributional regression models for image inputs to accurately predict the entire distributions of the different disease scores (e.g. symptom severity), which can be multivariate and are typically highly non-normally distributed. Regarding (2), we will model the complex temporal evolution in neurological diseases by developing DL-based state-space models. Neither model is tailored to a specific disease, but both will be exemplary developed and tested for two neurological diseases, namely Alzheimer’s disease (AD) and multiple sclerosis (MS), chosen for their different disease progression profiles.
The Joint Lab HiRSE and HiRSE concept see the establishment of central activities in RSE and the targeted sustainable funding of strategically important codes by so-called Community Software Infrastructure (CSI) groups as mutually supportive aspects of a single entity. In a first “preparatory study”, HiRSE_PS evaluated the core elements of the HiRSE concept and their interactions in practice over the funding period of three years (2022 to 2024). The two work packages in HiRSE_PS for one dealt with the operation of CSI groups, in particular also for young codes, and with consulting and networking. The goal of the preparatory study was the further refinement of the concept, which can then be rolled out to the entire Helmholtz Information, or, if desired, to the entire Helmholtz Association with high prospects of success and high efficiency. The Joint Lab HiRSE follows on the preparatory study, continuing the incorporation of successful elements and extending it with the evaluatioon of the integration of additional CSI groups.
In order to implement a sustainable and professional approach to the development and management of energy-optimized research software, suitable support structures and services operated by highly specialized research software engineers are required. This is where the new state service bwRSE4HPC comes in.
Existing machine learning (ML) models typically on correlation, but not causation. This can lead to errors, bias, and eventually suboptimal performance. To address this, we aim to develop novel ways to integrate causality into ML models. In the project CausalNet, we advance causal ML toward flexibility, efficiency, and robustness: (1) Flexibility: We develop a general-purpose causal ML model for high-dimensional, timeseries, and multi-modal data. (2) Efficiency: We develop techniques for efficient learning algorithms (e.g., synthetic pre-training, transfer learning, and few-shot learning) that are carefully tailored to causal ML. (3) Robustness: We create new environments/datasets for benchmarking. We also develop new techniques for verifying and improving the robustness of causal ML. (4) Open-source: We fill white spots in the causal ML toolchain to improve industry uptake. (5) Real-world applications: We demonstrate performance gains through causal ML in business, public policy, and bioinformatics for scientific discovery.
Purchase and operation of experiment-specific Tier-2 online storage for ATLAS and CMS at GridKa at KIT.
Distributional copula regression for space-time data develops novel models for multivariate spatio-temporal data using distributional copula regression. Of particular interest are tests for the significance of predictors and automatic variable selection using Bayesian selection priors. In the long run, the project will consider computationally efficient modeling of non-stationary dependencies using stochastic partial differential equations.
The goal of this project is to use deep neural networks as building blocks in a numerical method to solve the Boltzmann equation. This is a particularly challenging problem since the equation is a high-dimensional integro-differential equation, which at the same time possesses an intricate structure that a numerical method needs to preserve. Thus, artificial neural networks might be beneficial, but cannot be used out-of-the-box. We follow two main strategies to develop structure-preserving neural network-enhanced numerical methods for the Boltzmann equation. First, we target the moment approach, where a structure-preserving neural network will be employed to model the minimal entropy closure of the moment system. By enforcing convexity of the neural network, one can show, that the intrinsic structure of the moment system, such as hyperbolicity, entropy dissipation and positivity is preserved. Second, we develop a neural network approach to solve the Boltzmann equation directly at discrete particle velocity level. Here, a neural network is employed to model the difference between the full non-linear collision operator of the Boltzmann equation and the BGK model, which preserves the entropy dissipation principle. Furthermore, we will develop strategies to generate training data which fully sample the input space of the respective neural networks to ensure proper functioning models.
Based on the GitLab software, a state service for the administration, versioning and publication of software repositories for universities in Baden-Württemberg is being created as part of the IT alliance. GitLab also offers numerous possibilities for collaborative work and has extensive functionalities for software development and automation. The service enables or simplifies cross-location development projects between universities and with external partners, new possibilities in the field of research data management and can be used profitably in teaching. It also creates an alternative to cloud services such as GitHub and makes it easier to keep data from research and teaching in Baden-Württemberg.
The ongoing warming of the earth's climate due to man-made climate change is fundamentally changing our weather. Traditionally, weather forecasts have been made based on numerical models, so-called Numerical Weather Predictions (NWP). Data-driven machine learning models and in particular deep neural networks offer the potential as surrogate models for fast and (energy) efficient emulation of NWP models. As part of the SmartWeather21 project, we want to investigate which DL architecture for NWP is best suited for weather forecasting in a warmer climate, based on the high-resolution climate projections generated with ICON as part of WarmWorld. To incorporate the high resolutions of the WarmWorld climate projections, we will develop data- and model-parallel approaches and architectures for weather forecasting in a warmer climate. Furthermore, we will investigate which (learnable combinations of) variables from the ICON climate projections provide the best, physically plausible forecast accuracy for weather prediction in a warmer climate. With this in mind, we develop dimension reduction techniques for the various input variables that learn a latent, lower-dimensional representation based on the accuracy of the downstream weather forecast as an upstream task. The increased spatial resolution of the ICON simulations also allows conclusions to be drawn about the uncertainties of individual input and output variables at lower resolutions. As part of SmartWeather21, we will develop methods that parameterise these uncertainties using probability distributions and use them as input variables with lower spatial resolution in DL-based weather models. These can be propagated through the model as part of probabilistic forecasts.
EOSC Beyond overall objective is to advance Open Science and innovation in research in the context of the European Open Science Cloud (EOSC) by providing new EOSC Core capabilities allowing scientific applications to find, compose and access multiple Open Science resources and offer them as integrated capabilities to researchers
Resilient, powerful and flexible network operation and the provision of individualized and secure network services are at the core of bwNET2.0. The aim is to contribute to fundamental research in the field of autonomous networks on the one hand and to design and implement concrete building blocks and mechanisms that can be used to further develop BelWü and campus networks on the other.
The aim of this project is to strengthen research-oriented teaching, especially in the areas of AI, machine learning, simulation and modeling, by providing a state-wide service bwJupyter.
Solar tower power plants play a key role in facilitating the ongoing energy transition as they deliver dispatchable climate neutral electricity and direct heat for chemical processes. In this work we develop a heliostat-specific differentiable ray tracer capable of modeling the energy transport at the solar tower in a data-driven manner. This enables heliostat surface reconstruction and thus drastically improved the irradiance prediction. Additionally, such a ray tracer also drastically reduces the required data amount for the alignment calibration. In principle, this makes learning for a fully AI-operated solar tower feasible. The desired goal is to develop a holistic AI-enhanced digital twin of the solar power plant for design, control, prediction, and diagnosis, based on the physical differentiable ray tracer. Any operational parameter in the solar field influencing the energy transport may be, optimized with it. For the first time gradient-based, e.g., field design, aim point control, and current state diagnosis are possible. By extending it with AI-based optimization techniques and reinforcement learning algorithms, it should be possible to map real, dynamic environmental conditions with low-latency to the twin. Finally, due to the full differentiability, visual explanations for the operational action predictions are possible. The proposed AI-enhanced digital twin environment will be verified at a real power plant in Jülich. Its inception marks a significant step towards a fully automatic solar tower power plant.
Collaboration and sharing of resources is critical for research. Authentication and Authorisation Infrastructures (AAIs) play a key role in enabling federated interoperable access to resources. The AARC Technical Revision to Enhance Effectiveness (AARC TREE) project takes the successful and globally recognised “Authentication and Authorisation for Research Collaboration” (AARC) model and its flagship outcome, the AARC Blueprint Architecture (BPA), as the basis to drive the next phase of integration for research infrastructures: expand federated access management to integrate user-centring technologies, expand access to federated data and services (authorisation), consolidating existing capacities and avoiding fragmentation and unnecessary duplication. SCCs participates in AARC-TREE to continue developing the Blueprint Architectures. Here we contribute to technical recommendations, as well as to policy development. Since SCC is also a core member of the IAM project of the german NFDI, we can raise the awareness of NFDI requirements in AARC, as well as feed new developments back to NFDI, in a very timely manner.
The overall goal of this project is to improve the radiological diagnosis of human prostate cancer in clinical MRI by AI-based exploitation of information from higher resolution modalities. We will use the brilliance of HiP-CT imaging at beamline 18 and an extended histopathology of the entire prostate to optimise the interpretation of MRI images in the context of a research prototype. In parallel, the correlation of the image data with the molecular properties of the tumours is planned for a better understanding of invasive tumour structures. An interactive multi-scale visualisation across all modalities forms the basis for vividly conveying the immense amounts of data. As a result, at the end of the three-year project phase, the conventional radiological application of magnetic resonance imaging (MRI) is to be transferred into a diagnostic standard that also reliably recognises patients with invasive prostate tumours that have often been incorrectly diagnosed to date, taking into account innovative AI algorithms. In the medium term, a substantial improvement in the care of patients with advanced prostate carcinoma can therefore be expected. In addition, we will make the unique multimodal data set created in the project, including visualisation tools, available as open data to enable further studies to better understand prostate cancer, which could potentially lead to novel diagnostic and therapeutic approaches.
Zusammen mit den gewonnenen Erkenntnissen, Bewertungen und Empfehlungen sollen die aktuellen Herausforderungen und definierten Handlungsfelder des Rahmenkonzepts der Universitäten des Landes Baden-Württemberg für das HPC und DIC im Zeitraum 2025 bis 2032 durch folgende Maßnahmen im Projekt konkretisiert werden: • Weiterentwicklung der Wissenschaftsunterstützung bzgl. Kompetenzen zur Unterstützung neuartiger System- und Methodekonzepte (KI, ML oder Quantencomputing), Vernetzung mit Methodenfor- schung, ganzheitliche Bedarfsanalysen und Unterstützungsstrategien (z.B. Onboarding) • Steigerung der Energieeffizienz durch Sensibilisierung sowie Untersuchung und Einsatz neuer Be- triebsmodelle und Workflows inkl. optimierter Software • Erprobung und flexible Integration neuer Systemkomponenten und -architekturen, Ressourcen (z.B. Cloud) sowie Virtualisierung- und Containerisierungslösungen • Umsetzung neue Software-Strategien (z.B. Nachhaltigkeit und Entwicklungsprozesse) • Ausbau der Funktionalitäten der baden-württembergischen Datenföderation (z.B. Daten-Transfer- Service) • Umsetzung von Konzepten beim Umgang mit sensiblen Daten und zur Ausprägung einer digitalen Souveränität • Vernetzung und Kooperation mit anderen Forschungsinfrastrukturen
The bwCloud has been an established and well-received state service since 2018, which meets the demand for interactive, high-performance, resource-intensive infrastructure as a service services. The bwCloud3 project aims to add explicit service components to this existing infrastructure as a service service and thus create a range of value-added services, such as a connection to campus management and learning management systems. In addition, the aim is to improve the climate compatibility of the bwCloud through various measures, to ensure consistent IT security at a high level and to establish a business model. These goals are to be achieved over a project duration of four years, during which time the bwCloud is to be released into independent and long-term financing.
“ICON-SmART” addresses the role of aerosols and atmospheric chemistry for the simulation of seasonal to decadal climate variability and change. To this end, the project will enhance the capabilities of the coupled composition, weather and climate modelling system ICON-ART (ICON, icosahedral nonhydrostatic model – developed by DWD, MPI-M and DKRZ with the atmospheric composition module ART, aerosols and reactive trace gases – developed by KIT) for seasonal to decadal predictions and climate projections in seamless global to regional model configurations with ICON-Seamless-ART (ICON-SmART). Based on previous work, chemistry is a promising candidate for speed-up by machine learning. In addition, the project will explore machine learning approaches for other processes. The ICON-SmART model system will provide scientists, forecasters and policy-makers with a novel tool to investigate atmospheric composition in a changing climate and allows us to answer questions that have been previously out of reach.
This project uses artificial neural networks in an inverse design problem of finding nano-structured materials with optical properties on demand. Achieving this goal requires generating large amounts of data from 3D simulations of Maxwell's equations, which makes this a data-intensive computing problem. Tailored algorithms are being developed that address both the learning process and the efficient inversion. The project complements research in the SDL Materials Science on AI methods, large data sets generated by simulations, and workflows.
The ASSAS project aims at developing a proof-of-concept SA (severe accident) simulator based on ASTEC (Accident Source Term Evaluation Code). The prototype basic-principle simulator will model a simplified generic Western-type pressurized light water reactor (PWR). It will have a graphical user interface to control the simulation and visualize the results. It will run in real-time and even much faster for some phases of the accident. The prototype will be able to show the main phenomena occurring during a SA, including in-vessel and ex-vessel phases. It is meant to train students, nuclear energy professionals and non-specialists. In addition to its direct use, the prototype will demonstrate the feasibility of developing different types of fast-running SA simulators, while keeping the accuracy of the underlying physical models. Thus, different computational solutions will be explored in parallel. Code optimisation and parallelisation will be implemented. Beside these reliable techniques, different machine-learning methods will be tested to develop fast surrogate models. This alternate path is riskier, but it could drastically enhance the performances of the code. A comprehensive review of ASTEC's structure and available algorithms will be performed to define the most relevant modelling strategies, which may include the replacement of specific calculations steps, entire modules of ASTEC or more global surrogate models. Solutions will be explored to extend the models developed for the PWR simulator to other reactor types and SA codes. The training data-base of SA sequences used for machine-learning will be made openly available. Developing an enhanced version of ASTEC and interfacing it with a commercial simulation environment will make it possible for the industry to develop engineering and full-scale simulators in the future. These can be used to design SA management guidelines, to develop new safety systems and to train operators to use them.
Identity and Access Management (IAM) is concerned with the processes, policies and technologies for managing digital identities and their access rights to specific resources. Providing IAM as a basic serivce, IAM4NFDI is funded by Base4NFDI. The task of the Basic Service IAM is to establish and provide a state-of-the-art AAI, that fosters cross-consortial and international collaboration. A central goal of IAM4NFDI is therefore enable unified access to data, software, and compute resources, as well as sovereign data exchange and collaborative work for all NFDI consortia, as well as compatible initiatives. In order to achieve this, an extended version of the AARC Blueprint Architecture will be implemented an provided as a service to interested NFDI consortia. This will enable researchers from different domains and institutions to access digital resources within and beyond NFDI. Users from approx. 400 German research and higher education institutions plus approx. 4800 home organisations worldwide will be able to access services and resources provided by the NFDI Community AAI. Also google, github, or ORCID accounts are enabled to access specific parts of the provided services. All tools and solutions developed within IAM4NFDI are compatible with standards and the AARC Recommendations.
The AquaINFRA project aims to develop a virtual environment equipped with FAIR multi-disciplinary data and services to support marine and freshwater scientists and stakeholders restoring healthy oceans, seas, coastal and inland waters. The AquaINFRA virtual environment will enable the target stakeholders to store, share, access, analyse and process research data and other research digital objects from their own discipline, across research infrastructures, disciplines and national borders leveraging on EOSC and the other existing operational dataspaces. Besides supporting the ongoing development of the EOSC as an overarching research infrastructure, AquaINFRA is addressing the specific need for enabling researchers from the marine and freshwater communities to work and collaborate across those two domains.
The amount and diversity of digitally available environmental data is continuously increasing. However, they are often hardly accessible or scientifically usable. The datasets frequently lack sufficient metadata description, are stored in a variety of data formats, and are still saved on local storage devices instead of data portals or repositories. Based on the virtual research environment V-FOR-WaTer, which was developed in a previous project, ISABEL aims at making this data abundance available in an easy-to-use web portal. Environmental scientists get access to data from different sources, e.g. state offices or university projects, and can share their own data through the portal. Integrated tools help to easily pre-process and scale the data and make them available in a consistent format. Further tools for more complex scientific analyses will be included. These are both implemented by the developers of the portal according to the requirements of the scientific community and contributed directly by the portal’s users. The possibility to store workflows together with the tools and respective data ensures reproducible data analysis. Additionally, interfaces with existing data repositories enable easy publication of the scientists’ data directly from the portal. ISABEL addresses the needs of researchers of hydrology and environmental science to not only find and access datasets but also conduct efficient data-based learning with standardised tools and reproducible workflows.
Nano-optics deals with the optical properties of structures that are comparable to or smaller than the wavelength. All optical properties of a scatterer are determined by its T-matrix. Currently, these T-matrices are recalculated over and over again and are not used systematically. This wastes computing resources and does not allow novel questions to be addressed. DAPHONA remedies this deficiency. The project provides technologies with which the geometric and material properties of an object and its optical properties are brought together in a data structure. This data is systematically used to extract the T-matrix for a given object. It should also be possible to identify objects with predefined optical properties. Using these approaches, the DAPHONA project will answer novel questions that can only be addressed using this data-driven approach. The aim of the project is also to train young scientists at various qualification levels and to anchor the described approach in teaching. In addition, the data structure is to be coordinated within the specialist community. The data will be discussed in workshops and available methods for its use will be disseminated. The DAPHONA concept is open, based on the FAIR principles and will bring sustainable benefits to the entire community.
Skills4EOSC brings together leading experts from national, regional, institutional and thematic open science and data competence centers from 18 European countries with the aim of unifying the current training and education landscape into a common cross-European ecosystem to train researchers and data specialists from Europe at an accelerated pace in the fields of FAIR open data, data-intensive science and scientific data management.
The EQUIPE project deals with the quantification of uncertainties in large transformer models for time series prediction. Although the transformer architecture is able to achieve astonishingly high prediction accuracy, it requires immense amounts of computational resources. Common approaches to error estimation in neural networks are equally computationally intensive, which currently makes their use in transformers considerably more difficult. The research work within EQUIPE aims to solve these problems and to develop scalable algorithms for quantifying uncertainties in large neural networks, which will enable the methods to be used in real-time systems in the future.
iMagine is an EU-funded project that provides a portfolio of ‘free at the point of use’ image datasets, high-performance image analysis tools empowered with Artificial Intelligence (AI), and Best Practice documents for scientific image analysis. These services and materials enable better and more efficient processing and analysis of imaging data in marine and freshwater research, relevant to the overarching theme of ‘Healthy oceans, seas, coastal and inland waters’.
The AI4EOSC (Artificial Intelligence for the European Open Science Cloud) is an EU-funded project that delivers an enhanced set of advanced services for the development of AI/ML/DL models and applications in the European Open Science Cloud (EOSC). These services are bundled together into a comprehensive platform providing advanced features such as distributed, federated and split learning; novel provenance metadata for AI/ML/DL models; event-driven data processing services. The project builds on top of the DEEP-Hybrid-DataCloud outcomes and the EOSC compute platform.
The main goal of the present project is the further development and validation of a new computational fluid dynamics (CFD) method using a combination of grid-free (particles) and grid-based techniques. A fundamental assumption of this novel approach is the decomposition of any physical quantity into the grid based (large scale) and the fine scale parts, whereas large scales are resolved on the grid and fine scales are represented by particles. Dynamics of large and fine scales is calculated from two coupled transport equations one of which is solved on the grid whereas the second one utilizes the Lagrangian grid free Vortex Particle Method (VPM).
InterTwin co-designs and implements the prototype of an interdisciplinary Digital Twin Engine (DTE), an open source platform that provides generic and tailored software components for modelling and simulation to integrate application- specific Digital Twins (DTs). Its specifications and implementation are based on a co-designed conceptual model - the DTE blueprint architecture - guided by the principles of open standards and interoperability. The ambition is to develop a common approach to the implementation of DTs that is applicable across the whole spectrum of scientific disciplines and beyond to facilitate developments and collaboration.
In the project "Materialized Holiness" Torah scrolls are studied as an extraordinary codicological, theological, and social phenomenon. Unlike, for example, copies of the Bible, the copying of sacred scrolls has been governed by strict regulations since antiquity and is complemented by a rich commentary literature. Together with experts in Jewish studies, materials research, and the social sciences, we would like to build a digital repository of knowledge that does justice to the complexity of this research subject. Jewish scribal literature with English translations, material analyses, paleographic studies of medieval Torah scrolls, as well as interview and film material on scribes of the present day are to be brought together in a unique collection and examined in an interdisciplinary manner for the first time. In addition, a 'virtual Torah scroll' to be developed will reveal minute paleographic details of the script and its significance in cultural memory.
The SFB 1475, located at the Ruhr-Universität Bochum (RUB), aims to understand and methodically record the religious use of metaphors across times and cultures. To this end, the subprojects examine a variety of scriptures from Christianity, Islam, Judaism, Zoroastrianism, Jainism, Buddhism, and Daoism, originating from Europe, the Near and Middle East, as well as South, Central, and East Asia, and spanning the period from 3000 BC to the present. For the first time, comparative studies on a unique scale are made possible through this collaborative effort. Within the Collaborative Research Center, the SCC, together with colleagues from the Center for the Study of Religions (CERES) and the RUB, is leading the information infrastructure project "Metaphor Base Camp", in which the digital data infrastructure for all subprojects is being developed. The central component will be a research data repository with state-of-the-art annotation, analysis and visualization tools for the humanities data. Translated with www.DeepL.com/Translator (free version)
Weeds are one of the major contributors to crop yield loss. As a result, farmers deploy various approaches to manage and control weed growth in their agricultural fields, most common being chemical herbicides. However, the herbicides are often applied uniformly to the entire field, which has negative environmental and financial impacts. Site-specific weed management (SSWM) considers the variability in the field and localizes the treatment. Accurate localization of weeds is the first step for SSWM. Moreover, information on the prediction confidence is crucial to deploy methods in real-world applications. This project aims to develop methods for weed identification in croplands from low-altitude UAV remote sensing imagery and uncertainty quantification using Bayesian machine learning, in order to develop a holistic approach for SSWM. The project is supported by Helmholtz Einstein International Berlin Research School in Data Science (HEIBRiDS) and co-supervised by Prof. Dr. Martin Herold from GFZ German Research Centre for Geosciences.
In the bwCampusnetz project, several universities in Baden-Württemberg are working together to shed light on university campus networks away from the constraints of day-to-day business, but still very close to the realities of operation. Future-proof concepts are to be found and their practical feasibility is to be investigated and demonstrated through prototypical implementations.
PUNCH4NFDI is the NFDI consortium of particle, astro-, astroparticle, hadron and nuclear physics, representing about 9.000 scientists with a Ph.D. in Germany, from universities, the Max Planck society, the Leibniz Association, and the Helmholtz Association. PUNCH physics addresses the fundamental constituents of matter and their interactions, as well as their role for the development of the largest structures in the universe - stars and galaxies. The achievements of PUNCH science range from the discovery of the Higgs boson over the installation of a 1 cubic kilometer particle detector for neutrino detection in the antarctic ice to the detection of the quark-gluon plasma in heavy-ion collisions and the first picture ever of the black hole at the heart of the Milky Way. The prime goal of PUNCH4NFDI is the setup of a federated and "FAIR" science data platform, offering the infrastructures and interfaces necessary for the access to and use of data and computing resources of the involved communities and beyond. The SCC plays a leading role in the development of the highly distributed Compute4PUNCH infrastructure and is involved in the activities around Storage4PUNCH a distributed storage infrastructure for the PUNCH communities.
The NFDI-MatWerk consortium receives five years of funding as part of the National Research Data Infrastructure (NFDI) for the development of a standardized platform for research data in materials science and engineering. In this specialist area, the physical mechanisms of materials are characterized in order to develop resource-saving materials with ideal properties for the respective applications. Using a knowledge graph-based infrastructure, it should be possible to access research data distributed throughout Germany in such a way that even search queries and evaluations on complex topics can be made quickly and easily accessible. At KIT, the Scientific Computing Center (SCC) and the Institute for Applied Materials (IAM) are involved. At the SCC, we are building the digital research environment with the NFDI-MatWerk partners Translated with DeepL.com (free version)
NEP stands for “Nanoscience Foundries & Fine Analysis Europe Pilot” and is a European infrastructure project. It provides important resources for nanoscience research and develops new cooperative working methods. Comprehensive data management technologies and an open access data archive will make the research data FAIR and ensure interoperability with the European Open Science Cloud. In this project, the SCC is developing innovative tools for research data and metadata management, which are essential components of the NEP infrastructure. The SCC is also responsible for the “Virtual Access” work package. Translated with DeepL.com (free version)
Within the Joint Lab VMD, the SDL Materials Science develops methods, tools and architectural concepts for supercomputing and big data infrastructures, which are tailored to tackle the specific application challenges and to facilitate the digitalization in materials research and the creation of digital twins. In particular, the Joint Lab develops a virtual research environment (VRE) that integrates computing and data storage resources in existing workflow managements systems and interactive environments for simulation and data analyses.
As part of the Joint Lab “Integrated Model and Data Driven Materials Characterization” (MDMC), the Simulation Data Laboratory (SDL) for Materials Science is developing a concept for a data and information platform. This platform is intended to make data on materials available in a knowledge-oriented manner as an experimental basis for digital twins on the one hand and for the development of simulation-based methods for predicting material structure and properties on the other. The platform defines a metadata model for the description of samples and data sets from experimental measurements. In addition, data models for material simulation and correlative characterization are harmonized using materials science vocabularies and ontologies. Translated with DeepL.com (free version)
NFDI4ING is an engineering consortium that promotes the management of technical research data. NFDI4ING was founded in 2017 and is in close exchange with researchers from all engineering disciplines. The consortium offers a unique method-oriented and user-centered approach to make research data FAIR - findable, accessible, interoperable and reusable. An important challenge here is the large number of engineering sub-disciplines and their subject-specific characteristics. KIT is involved with a co-spokesperson, Britta Nestler from the Institute for Applied Materials (IAM) and a co-spokesperson, Achim Streit from the Scientific Computing Center (SCC). As part of NFDI4Ing, the SCC develops and implements the concepts for federated research data infrastructures, data management processes, repositories and metadata management in close cooperation with the partners. The NFDI4Ing application (https://doi.org/10.5281/zenodo.4015200) describes the planned research data infrastructure in detail. Translated with www.DeepL.com/Translator (free version)
The vision of NFDI4Chem is the digitization of all work processes in chemical research. To this end, infrastructure is to be established and expanded to support researchers in collecting, storing and archiving, processing and analyzing research data, as well as publishing the data in repositories together with descriptive metadata and DOIs, thus making them referencable and reusable. As a professional consortium, NFDI4Chem represents all disciplines of chemistry and works closely with the major professional societies to this end. Translated with www.DeepL.com/Translator (free version)
Recent progress in computer science has led to data structures of increasing size, detail and complexity in many scientific studies. In particular nowadays, where such big data applications do not only allow but also require more flexibility to overcome modelling restrictions that may result in model misspecification and biased inference, further insight in more accurate models and appropriate inferential methods is of enormous importance. This research group will therefore develop statistical tools for both univariate and multivariate regression models that are interpretable and that can be estimated extremely fast and accurate. Specifically, we aim to develop probabilistic approaches to recent innovations in machine learning in order to estimate models for huge data sets. To obtain more accurate regression models for the entire distribution we construct new distributional models that can be used for both univariate and multivariate responses. In all models we will address the issues of shrinkage and automatic variable selection to cope with a huge number of predictors, and the possibility to capture any type of covariate effect. This proposal also includes software development as well as applications in natural and social sciences (such as income distributions, marketing, weather forecasting, chronic diseases and others), highlighting its potential to successfully contribute to important facets in modern statistics and data science.
The overarching goal of the Helmholtz Metadata Collaboration Platform is to promote the qualitative enrichment of research data through metadata in the long term, to support researchers - and to implement this in the Helmholtz Association and beyond. With the FAIR Data Commons Technologies work package, the SCC is developing technologies and processes to make research data from all research areas of the Helmholtz Association available and to provide researchers with easy access in accordance with the FAIR principles. This is achieved on a technical level through standardized interfaces that are based on recommendations and standards developed within globally networked research data initiatives, e.g. the Research Data Alliance (RDA, https://www.rd-alliance.org/). For researchers, these interfaces are made usable through easy-to-use tools, generally applicable processes and recommendations for handling research data in everyday scientific work. Translated with www.DeepL.com/Translator (free version)
The Helmholtz AI Platform is a research project of the Helmholtz Incubator "Information & Data Science". The overall mission of the platform is the "democratization of AI for a data-driven future" and aims at making AI algorithms and approaches available to a broad user group in an easy-to-use and resource-efficient way. (Translated with DeepL.com)
In the Research Training Group (RTG) "Tailored Scale-Bridging Approaches to Computational Nanoscience" we investigate problems, that are not tractable by computational chemistry standard tools. The research is organized in seven projects. Five projects address scientific challenges such as friction, materials aging, material design and biological function. In two further projects, new methods and tools in mathematics and computer science are developed and provided for the special requirements of these applications. The SCC is involved in projects P4. P5 and P6.
Helmholtz Federated IT Services (HIFIS) establishes a secure and easy-to-use collaborative environment with ICT services that are efficient and accessible from anywhere. HIFIS also supports the development of research software with a high level of quality, visibility and sustainability.
CAMMP stands for Computational and Mathematical Modeling Program. It is an extracurricular offer of KIT for students of different ages. We want to make the public aware of the social importance of mathematics and simulation sciences. For this purpose, students actively engage in problem solving with the help of mathematical modeling and computer use in various event formats together with teachers. In doing so, they explore real problems from everyday life, industry or research.
With the rise of artificial intelligence and the accompanying demand in compute resources, the energy efficiency of large scale deep learning (DL) becomes increasingly important. The goal of EPAIS is to evaluate and correlate computational performance and energy consumption of state-of-the-art DL models at scale, and to improve the latter by optimising the former In this project, we measure and analyze energy consumption and computational performance of scientific DL workloads at scale intending to uncover the correlation between these two. Along these lines, we develop easy-to-use, low overhead tools for measuring energy consumption and performance. These tools can be incorporated by AI developers into their code for basic assessment of these metrics, fostering awareness for GreenAI and GreenHPC. Based on these insights, we develop new approaches to increase the energy efficiency of DL workloads through means of performance optimization.
The Gaia-X is an European initiative that aims to provide a secure and trustworthy platform for data sharing and collaboration across various industries and sectors in Europe. One of the main goals of the Gaia-X4ICM research initiative for the Innovations Campus Future Mobility (ICM) is to create the basic infrastructure with all necessary hardware and software components that play a significant role in connecting various sectors involved in an industrial production process. The SCC builds and runs such a cloud infrastructure that operates on our own hardware retaining control over the digital infrastructure and data – ensuring data sovereignty, privacy and security.
The HiRSE concept sees the establishment of central activities in RSE and the targeted sustainable funding of strategically important codes by so-called Community Software Infrastructure (CSI) groups as mutually supportive aspects of a single entity.
The Simulated Worlds project aims to provide students in Baden-Württemberg with a deeper critical understanding of the possibilities and limitations of computer simulations. The project is jointly supported by the Scientific Computing Center (SCC), the High Performance Computing Center Stuttgart (HLRS) and the University of Ulm and is already working with several schools in Baden-Württemberg.
Despite steady improvements in numerical weather prediction models, they still exhibit systematic errors caused by simplified representations of physical processes, assumptions about linear behavior, and the challenges of integrating all available observational data. Weather services around the world now recognize that addressing these shortcomings through the use of artificial intelligence (AI) could revolutionize the discipline in the coming decades. This will require a fundamental shift in thinking that integrates meteorology much more closely with mathematics and computer science. TEEMLEAP will foster this cultural change through a collaboration of scientists from the KIT Climate and Environment and MathSEE centers by establishing an idealized testbed to explore machine learning in weather forecasting. In contrast to weather services, which naturally focus on improvements of numerical forecast models in their full complexity, TEEMLEAP intends to evaluate the application possibilities and benefits of AI in this testbed along the entire process chain of weather forecasting.
Despite significant overlap and synergy, machine learning and statistical science have developed largely in parallel. Deep Gaussian mixture models, a recently introduced model class in machine learning, are concerned with the unsupervised tasks of density estimation and high-dimensional clustering used for pattern recognition in many applied areas. In order to avoid over-parameterized solutions, dimension reduction by factor models can be applied at each layer of the architecture. However, the choice of architectures can be interpreted as a Bayesian model choice problem, meaning that every possible model satisfying the constraints is then fitted. The authors propose a much simpler approach: Only one large model needs to be trained and unnecessary components will empty out. The idea that parameters can be assigned prior distributions is highly unorthodox but extremely simple bringing together two sciences, namely machine learning and Bayesian statistics.
The outlined project bwIDM2 is dedicated to the increased demands on IT security and takes into account current technical developments. It creates the prerequisites for the integration of services across higher education institutions and establishes a group/role administration for supraregional and national communities with delegation mechanisms. In addition, specialist concepts for the integration of a long-term person identifier in bwIDM, as required for use in research data management, are being developed.
Cardiovascular diseases are among the most common causes of death worldwide: Every year, more than 300,000 people die in Germany as a result. Around half of these deaths are caused by cardiac arrhythmias. In the European MICROCARD project, in which the Karlsruhe Institute of Technology (KIT) is involved, researchers are now developing a simulation platform that can digitally map the electrophysical signal transmissions in the heart. The computer simulations are to contribute in particular to improved diagnosis and therapy. KIT will receive about 1.3 million euros for its contributions within the framework of the "European High-Performance Computing Joint Undertaking".
EOSC Future responds to INFRAEOSC-03-2020 call in order to integrate, consolidate, and connect e-infrastructures, research communities, and initiatives in Open Science to further develop the EOSC Portal, EOSC-Core and EOSCExchange of the European Open Science Cloud (EOSC).
The primary objective of the project is to establish an integrated nationwide computing and data infrastructure and to increase efficiency and effectiveness by providing first-class support to scientists and users.
EGI-ACE empowers researchers from all disciplines to collaborate in data- and compute-intensive research across borders through free at point of use services. Building on the distributed computing integration in EOSChub, it delivers the EOSC Compute Platform and contributes to the EOSC Data Commons through a federation of Cloud compute and storage facilities, PaaS services and data spaces with analytics tools and federated access services. The Platform is built on the EGI Federation, the largest distributed computing infrastructure for research. The EGI Federation delivers over 1 Exabyte of research data and 1 Million CPU cores which supported the discovery of the Higgs Boson and the first observation of gravitational waves, while remaining open to new members. The Platform pools the capacity of some of Europe’s largest research data centres, leveraging ISO compliant federated service management. Over 30 months, it will provide more than 82 M CPU hours and 250 K GPU hours for data processing and analytics, and 45 PB/month to host and exploit research data. Its services address the needs of major research infrastructures and communities of practice engaged through the EOSC-hub project. The Platform advances beyond the state of the art through a data-centric approach, where data, tools and compute and storage facilities form a fully integrated environment accessible across borders thanks to Virtual Access. The Platform offers heterogeneous systems to meet different needs, including state of the art GPGPUs and accelerators supporting AI and ML, making the Platform an ideal innovation space for AI applications. The data spaces and analytics tools are delivered in collaboration with tens of research infrastructures and projects, to support use cases for Health, the Green Deal, and fundamental sciences. The consortium builds on the expertise and assets of the EGI federation members, key research communities and data providers, and collaborating initiatives.
The Data Infrastructure Capacities for EOSC (DICE) consortium brings together a network of computing and data centres, research infrastructures, and data repositories for the purpose to enable a European storage and data management infrastructure for EOSC, providing generic services and building blocks to store, find, access and process data in a consistent and persistent way. Specifically, DICE partners will offer 14 state-of-the-art data management services together with more than 50 PB of storage capacity. The service and resource provisioning will be accompanied by enhancing the current service offering in order to fill the gaps still present to the support of the entire research data lifecycle; solutions will be provided for increasing the quality of data and their re-usability, supporting long term preservation, managing sensitive data, and bridging between data and computing resources. All services provided via DICE will be offered through the EOSC Portal and interoperable with EOSC Core via a lean interoperability layer to allow efficient resource provisioning from the very beginning of the project. The partners will closely monitor the evolution of the EOSC interoperability framework and guidelines to comply with a) the rules of participation to onboard services into EOSC, and b) the interoperability guidelines to integrate with the EOSC Core functions. The data services offered via DICE through EOSC are designed to be agnostic to the scientific domains in order to be multidisciplinary and to fulfil the needs of different communities. The consortium aims to demonstrate their effectiveness of the service offering by integrating services with community platforms as part of the project and by engaging with new communities coming through EOSC.
Traditional regression models often provide an overly simplistic view on complex associations and relationships to contemporary data problems in the area of biomedicine. In particular, capturing relevant associations between multiple clinical endpoints correctly is of high relevance to avoid model misspecifications, which can lead tobiased results and even wrong or misleading conclusions and treatments. As such, methodological development of statistical methods tailored for such problems in biomedicine are of considerable interest. It is the aim of this project to develop novel conditional copula regression models for high-dimensional biomedical data structures by bringing together efficient statistical learning tools for high-dimensional data and established methods from economics for multivariate data structures that allow to capture complex dependence structuresbetween variables. These methods will allow us to model the entire joint distribution of multiple endpoints simultaneously and to automatically determine the relevant influential covariates and risk factors via algorithms originally proposed in the area of statistical and machine learning. The resulting models can thenbe used both for the interpretation and analysis of complex association-structures as well as for prediction inference (simultaneous prediction intervals for multiple endpoints). Additional implementation in open software and its application in various studies highlight the potentials of this project’s methodological developments in the area of digital medicine.
Together with partners at Forschungszentrum Jülich and Fritz Haber Institute Berlin, our goal is to develop a novel intelligent management system for electric batteries that can make better decisions about battery charging cycles based on a detailed surrogate model ("digital twin") of the battery and artificial intelligence.
The Collaborative Research Centre 980 'Episteme in Motion' has been investigating processes of knowledge change in European and non-European cultures from the 3rd millennium BC to around 1750 AD since 2012. Since 2016, the SCC has been supporting the collection of digital evidence for previously unresolved questions through its expertise in modern research data management. In the subproject Information Infrastructure, SCC develops information technology procedures for data indexing for the investigation and visualization of knowledge movements in long-term traditional pre-modern knowledge stocks using the example of travels of manuscripts, prints as well as coffin and pyramid text sayings. Based on a research data repository, (1) new tools for data analysis, (2) specific vocabulary services and (3) innovative presentation layers will be developed. With the collaboration at three locations (Berlin, Karlsruhe, Darmstadt), the project has a pilot function with regard to the establishment of complex institutional collaborations in the field of research data management. translated with DeepL.com
Research data management forms the basis for applying, for example, modern artificial intelligence methods to research questions. Therefore, research data management is an important component of the KIT Climate and Environment Center. In the SmaRD-AI project (short for Smart Research Data Management to facilitate Artificial Intelligence in Climate and Environmental Sciences), the IWG, IMK, GIK, and SCC at KIT are working closely together not only to make the treasure trove of climate and environmental data available at KIT accessible, but also to be able to analyze it in a structured way using tools. Translated with DeepL
The Helmholtz AI COmpute REssources (HAICORE) infrastructure project was launched in early 2020 as part of the Helmholtz Incubator "Information & Data Science" to provide high-performance computing resources for artificial intelligence (AI) researchers in the Helmholtz Association. Technically, the AI hardware is operated as part of the high-performance computing systems JUWELS (Julich Supercomputing Centre) and HoreKa (KIT) at the two centers. The SCC primarily covers prototypical development operations in which new approaches, models and methods can be developed and tested. HAICORE is open to all members of the Helmholtz Association in the field of AI research.
The bwNET2020+ project is intended to support the expansion of the state university network and innovation within the university networks, as the consolidation of IT services at universities places higher demands on the underlying network.
The Exascale Earth System Modelling (PL-ExaESM) pilot lab explores specific concepts for applying Earth System models and their workflows to future exascale supercomputers.
The EOSC Synergy project aims to expand the European Open Science Cloud (EOSC). A team of 25 engineers and scientists will work on the expansion of the European Open Science Cloud (EOSC) by integrating National and Scientific Infrastructures.
EOSC-Pillar will coordinate national Open Science efforts across Austria, Belgium, France, Germany and Italy, and ensure their contribution and readiness for the implementation of the EOSC.
OCR-D is a coordination project of the German Research Foundation (DFG) for the further development of Optical Character Recognition techniques for German-language prints of the 16th-19th century. The main goal is the full text capture of the cultural heritage printed in German-language of this period.
Das Landesprojekt bwIPv6@Academia hat die Aufgabe, den Zustand der IPv6-Fähigkeit gemeinsam mit den teilnehmenden Einrichtungen zu analysieren, Probleme und Aufgaben zu identifizieren, sowie die Umsetzung zu begleiten.
The GÉANT Project has grown during its iterations (GN1, GN2, GN3, GN3plus, GN4-1 and GN4-2) to incorporate not just the award-winning 500Gbps pan-European network, but also a catalogue of advanced, user-focused services, and a successful programme of innovation that is pushing the boundaries of networking technology to deliver real impact to over 50 million users.
SCC is a prominent partner in the EOSC-secretariat.eu, supporting governance for EOSC while working with communities towards an all-encompassing European Open Science Cloud.
The bwCard project is carried out by the universities of the state of Baden-Württemberg. The aim is to create a federation that enables the participating institutions to reliably integrate chip cards from the other institution into their own digital processes and services.
Förderung Inkubator HMC Projektantrag durch HGF IVF
EOSC-hub möchte u. a. einen einfachen Zugang zu hochqualitativen digitalen Diensten schaffen, die von den pan-europäischen Partnern in einem offenen Service-Katalog angeboten werden.
The DHDC project investigates how to support compute-intensive applications that require high-performance computing (HPC) and graphics processors (GPUs) with the help of cloud services.
The Helmholtz Analytics Framework (HAF) pilot project will strengthen the development of data sciences in the Helmholtz Association. Together with four other Helmholtz Centres, a co-design approach between domain scientists and data analysis experts investigates challenging application problems from the respective Helmholtz Centres. Specifically, these are questions on earth system modelling, structural biology, aerospace, neurosciences and medical imaging.
Ziel des Projekts ist die Entwicklung von neuen Werkzeugen zur Simulation von Schwerionenstrahlen in Targets. Wir möchten die Orts- und Energieverteilung aller Primär- und Sekundärteilchen charakterisieren. Dies ist von Interesse in vielen Feldern: Atomphysik (atomare Wechselwirkung, Ioneneinfang), Kernphysik (Untersuchung der Struktur von Atomkernen), Elektronik (Ablagerung von Elementen), Materialwissenschaften (Analyse von Beschädigungen z.B. eines Tokamaks), Biologie (Untersuchung der Toxikologie von Gewebe durch Ionenanalyse). Die Simulation von schweren Ionen ist schwierig aus zwei Gründen: Zum einen ist die gitterbasierte Simulation von Teilchentransport sehr herausfordernd. Zum anderen basieren die Simulationen auf Messungen der Bremsvermögen der Ionen, und müssen daher als unsicher angesehen werden. Daher entwickeln wir ein neues, Entropie-basiertes Diskretisierungsschema, welches eine Sub-Auflösung unterhalb des numerischen Gitters ermöglicht, und daher geeignet für die Simulation von Strahlen ist. Zusätzlich benutzen wir eine ähnliche Methode zur Behandlung von Unsicherheiten in der Teilchenverteilung, die durch die unsicheren Wirkungsquerschnitte bedingt werden. Unsere Methode ist rechenaufwändig, aber hochgradig parallelisierbar, was sie ideal für moderne Rechnerarchitekturen macht.
The EU-Project AARC aims to develop and pilot an integrated cross-discipline authentication and authorisation framework, built on existing AAIs and on production federated infrastructures.
The state service bwSync&Share is an online storage service for employees and students of universities and colleges in Baden-Württemberg.
The Helmholtz Data Federation (HDF) is a strategic initiative of the Helmholtz Association that addresses one of the major challenges of the next decade: Managing the flood of data in science, especially from the large research infrastructures of the Helmholtz centers. (Translated with DeepL.com)
The Baden-Württemberg Cloud provides teaching and research institutions in the state with virtual machines that can be used like corresponding offerings from commercial providers without a lengthy application process.
Development of an innovative measurement system based on P&DGNAA technology for environmental analysis including new evaluation algorithms.
The project V-FOR-WaTer - Virtuelle Forschungsumgebung für die Wasser- und terrestrische Umweltforschung im Rahmen des Netzwerks Wasserforschung Baden-Württemberg - aims to create a virtual research environment (VRE), which will combine research data gathered in universities, research centres and from continuous monitoring of state offices in Baden-Württemberg into one comprehensive system. Facilitating access to all these different data sources in one system greatly reduces pre-processing time of complex analyses and enables the study of this extensive dataset towards the development a unified environmental system theory. The project is a co-operation between the SCC and the IWG.
Future exascale HPC systems require efficient data management methods. The locality of data and the efficient access during a simulation are of great importance.
By developing a decentralized electronic laboratory book, researchers and scientists at KIT will in the future also use many advantages of digitization in laboratory documentation.
The state project bwDataInMotion (bwDIM) supports scientists at universities in Baden-Württemberg in research data management. Its goal is to simplify the flow of data between the different systems.
This project strives to design a state-wide outlined IT security concept as well as a federated IT security structure for the state of Baden-Wuerttemberg. Furthermore, a plan to implement a CERT structure for the state universities will be developed.
The Scientific Computing Center (SCC) operates the research platform Smart Data Innovation Lab (SDIL) at KIT. SDIL creates the conditions for cutting-edge research in the field of Big Data ...
The Project develops solutions for missing components in current Grid and Cloud Middleware. SCC works on components for the federated authentication and authorization as well as on the integration of archive systems in the distributed data management.
Nowadays, an ever increasing amount of data is to be seen or expected in science. There is a great potential to gain new insights in various scientific fields by using this data efficiently. The drawback is the also ever increasing complexity and amount of the data and therefore the larger effort put on scientists in their daily work. Methods for data processing, which could be used in the past efficiently, might simply become impractical by failing to process large amounts of data in a given time and new methods need to be adopted or developed. In this project a novel and generic metadata management for scientific data will be developed based on an application-oriented description via metadata. The development process is accompanied by applied scientists from various and heterogeneous domains. The metadata management not only supports the data handling, but also allows an efficient use of provided scientific infrastructures. This infrastructure is going to be realized between the computing facilities of Dresden and Karlsruhe to provide generic and distributed services for metadata-based data handling. The management includes functionalities for data description, sustainable data storage, improved information retrieval, preparation for further processing and usage of available data.
EUDAT - the collaborative Pan-European infrastructure providing research data services, training and consultancy for Researchers Research Communities Research Infrastructures & Data Centres
The SCC intensifies its Smart Data activities and starts together with the TECO research group at KIT and the SICOS-BW GmbH in Stuttgart the Smart Data Solution Center Baden-Württemberg (SDSC-BW). This research project is funded by the state of Baden-Württemberg and supports regional medium-sized companies in identifying the potential of innovative Smart Data technologies.
In the PolyEnergyNet project, resilient local grids are being researched and implemented as examples. In addition to the electricity grid as the "control grid," heat and gas grids with different types of generators, storage facilities and consumers also interact.
A reliable and sustainable access to scientific publications is a key requirement for research. Publications are normally accessed by retrieving a digital copy directly from the publisher’s or content provider’s homepages. This access can be disrupted due to several reasons such as a temporary failure of the publisher’s infrastructure, ceased operation of the publisher or simply cancellation of the subscription by the library. The project “National Hosting of Electronic Resources” develops a concept to ensure access to publications in these cases.
The CollabFuL: Secure Social Collaboration in Research and Education project aims to create an open, unified, flexible, and privacy-friendly environment for secure social academic information sharing ...
In the long term, the aim is to create added value for researchers by improving the collection, securing, analysis and general availability and searchability of data. As a positive side effect, scientists from Baden-Württemberg will then also be able to assert themselves more easily in research funding decisions by the EU and DFG, because these strongly desire and support the transfer of knowledge even beyond state borders. (Translated with www.DeepL.com)
In the scope of the project SCC will become the main archive location in Baden-Württemberg. SCC will further expand its technical infrastructure for the long-time scientific data storage from research institutions and libraries.
As part of the ASTOR project, the SCC is developing an online portal based on cloud technologies. Via this portal, users can flexibly use OpenGL-based analysis applications provided via virtual machines for their investigations, regardless of location. (Translated with DeepL.com)
In the RADAR project, a corresponding service is being set up that primarily offers researchers, institutions and publishers an infrastructure for archiving and publishing research data.
iZeus (intelligent Zero Emission Urban System) is the follow-up project of the research project MeRegioMobil. Its goal is to spur the aspects of SmartGrids and SmartTraffic in the light of the growing importance of electromobility in Germany and Europe with the help of academic research and realistic field tests
