A5079102922- Scientific Computing and Data Management
- Research Data Management Practices
- Distributed and Parallel Computing Systems
- Meteorological Phenomena and Simulations
- Atmospheric and Environmental Gas Dynamics
- Data Quality and Management
- Soil Moisture and Remote Sensing
- Advanced Data Storage Technologies
- Computational Physics and Python Applications
- Geophysics and Gravity Measurements
- GNSS positioning and interference
- Climate variability and models
- Library Science and Information Systems
- Precipitation Measurement and Analysis
- Air Quality Monitoring and Forecasting
- earthquake and tectonic studies
- Seismology and Earthquake Studies
- Advanced Database Systems and Queries
- Maritime and Coastal Archaeology
- Earthquake Detection and Analysis
- Big Data and Business Intelligence
- Methane Hydrates and Related Phenomena
- Geochemistry and Geologic Mapping
- Libraries and Information Services
- Big Data Technologies and Applications
German Climate Computing Centre
2011-2024
Helmholtz-Zentrum Hereon
2023-2024
Helmholtz Institute Mainz
2023-2024
Computing Center
2017-2023
Institute of Bioinformatics and Systems Biology
2023
Universität Hamburg
2013-2019
The FAIR principles have been widely cited, endorsed and adopted by a broad range of stakeholders since their publication in 2016. By intention, the 15 guiding do not dictate specific technological implementations, but provide guidance for improving Findability, Accessibility, Interoperability Reusability digital resources. This has likely contributed to adoption principles, because individual stakeholder communities can implement own solutions. However, it also resulted inconsistent...
Abstract. The World Climate Research Programme (WCRP)'s Working Group on Modelling (WGCM) Infrastructure Panel (WIP) was formed in 2014 response to the explosive growth size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005–2006) CMIP5 (2011–2012). This article presents WIP recommendations for global data infrastructure needed support CMIP design, future growth, evolution. Developed close coordination with those who build run existing (the Earth System Grid...
Recently, Machine learning (ML) has been widely utilized for laboratory earthquake (labquake) prediction using various types of data. This study pioneers in time to failure (TTF) based on ML acoustic emission (AE) records from three stick-slip experiments performed Westerly granite samples with naturally fractured rough faults, more similar the heterogeneous fault structures nature. 47 catalog-driven seismo-mechanical and statistical features are extracted introducing some new focal...
Sinkholes can cause significant damage to infrastructures, agriculture, and endanger lives in active karst regions like the Dead Sea’s eastern shore at Ghor Al-Haditha. The common sinkhole mapping methods often require costly high-resolution data manual, time-consuming expert analysis. This study introduces an efficient deep learning model designed improve using accessible satellite imagery, which could enhance management practices related sinkholes other geohazards evaporite regions....
Research data currently face a huge increase of objects with an increasing variety types (data types, formats) and workflows by which need to be managed across their lifecycle infrastructures. Researchers desire shorten the from generation analysis publication, full workflow needs become transparent multiple stakeholders, including research administrators funders. This poses challenges for infrastructures user-oriented services in terms not only making findable, accessible, interoperable...
The CODATA Data Science Journal is a peer-reviewed, open access, electronic journal, publishing papers on the management, dissemination, use and reuse of research data databases across all domains, including science, technology, humanities arts. scope journal includes descriptions systems, their implementations publication, applications, infrastructures, software, legal, reproducibility transparency issues, availability usability complex datasets, with particular focus principles, policies...
Several scientific communities relying on e-science infrastructures are in need of persistent identifiers for data and contextual information. In this article, we present a framework identification that fundamentally supports context It is installed as number low-level requirements abstract type descriptions, flexible enough to envelope information while remaining compatible with existing definitions infrastructures. The draw from the exemplary use cases can act an evaluation tool...
Abstract. Machine learning (ML) algorithms can be used in Earth System models (ESMs) to emulate sub-grid-scale processes. Due the statistical nature of ML and high complexity ESMs, these hybrid ML-ESMs require careful validation. Simulation stability needs monitored fully coupled simulations, plausibility results evaluated suitable experiments. We present coupling SuperdropNet, a machine model for emulating warm rain processes cloud microphysics, into ICON~2.6.5. SuperdropNet is trained on...
Abstract. Machine learning (ML) algorithms can be used in Earth system models (ESMs) to emulate sub-grid-scale processes. Due the statistical nature of ML and high complexity ESMs, these hybrid ESMs require careful validation. Simulation stability needs monitored fully coupled simulations, plausibility results evaluated suitable experiments. We present coupling SuperdropNet, a machine model for emulating warm-rain processes cloud microphysics, with ICON (Icosahedral Nonhydrostatic) v2.6.5....
Abstract The current handling of data in earth observation, modelling and prediction measures gives cause for critical consideration, since we all too often carelessly ignore uncertainty. We think that Earth scientists are generally aware the importance linking to quantitative uncertainty measures. But also quantification observation fails at very early stages. claim acquisition without is not sustainable machine learning computational cannot unfold their potential when analysing complex...
Abstract. The World Climate Research Programme (WCRP)'s Working Group on Modeling (WGCM) Infrastructure Panel (WIP) was formed in 2014 response to the explosive growth size and complexity of Coupled Model Intercomparison Projects (CMIPs) between CMIP3 (2005-06) CMIP5 (2011-12). This article presents WIP recommendations for global data infrastructure needed support CMIP design, future evolution. Developed close coordination with those who build run existing (the Earth System Grid Federation),...
Artificial intelligence (AI) is proliferating and developing faster than any domain scientist can adapt. To support the scientific enterprise in Helmholtz association, a network of AI specialists has been set up to disseminate expertise as an infrastructure among scientists. As this effort exposes evolutionary step science organization Germany, article aspires describe our setup, goals, motivations. We comment on past experiences, current developments, future ideas we bring closer scientists...
The European Network for Earth System Modelling (ENES) Climate Analytics Service (ECAS) is a new service from the EOSC-hub project. It offers Virtual Research Environment (VRE) to scientific users, combining Python (Jupyter) work environment with support services data access, computing and sharing. ECAS motivated by providing users remote access extensive storage resources beyond what they may have locally, reducing need conduct costly transfer, helping realize vision of FAIR management....
<p>In coupled global circulation models, chemical interaction between atmospheric trace gases is modelled through dedicated chemistry submodels. As these components tend to be computationally expensive, one often faced with the situation either run models in relatively coarse resolution, or ignore altogether. Here an alternative approach presented order overcome high computational costs while attaining comparable quality of results. A fully connected neural network used make...