Session Contribution Rest List
A contribution rest export view for sessions.
GET /programme/25/export/?format=api
[
{
"title": "The Helmholtz Model Zoo: Enabling AI Model Sharing and Inference in the Helmholtz Cloud",
"url": "/submissions/88",
"abstract": "<p>\n The\n <strong>\n Helmholtz Model Zoo (HMZ)\n </strong>\n is a cloud-based platform that provides remote access to deep learning models within the Helmholtz Association. It enables seamless inference execution via both a web interface and a REST API, lowering the barrier for scientists to integrate state-of-the-art AI models into their research.\n</p>\n<p>\n Scientists from all 18 Helmholtz centers can contribute their models to HMZ through a streamlined, well-documented submission process on GitLab. This process minimizes effort for model providers while ensuring flexibility for diverse scientific use cases. Based on the information provided about the model, HMZ automatically generates the web interface and API, tests the model, and deploys it. The REST API further allows for easy integration of HMZ models into other computational pipelines.\n</p>\n<p>\n With the launch of HMZ, researchers can now run AI models within the\n <strong>\n Helmholtz Cloud\n </strong>\n while keeping their data within the association. The platform imposes no strict limits on the number of inferences or the volume of uploaded data, and it supports both open-access and restricted-access model sharing. Data uploaded for inference is stored within\n <strong>\n HIFIS dCache InfiniteSpace\n </strong>\n and remains under the ownership of the uploading user.\n</p>\n<p>\n HMZ is powered by GPU nodes equipped with\n <strong>\n four NVIDIA L40 GPUs per node\n </strong>\n , hosted as part of the Maxwell cluster at\n <strong>\n DESY Hamburg\n </strong>\n . Model inference is managed through the\n <strong>\n NVIDIA Triton Inference Server\n </strong>\n , ensuring efficient GPU utilization. The development and maintenance of HMZ are led by the\n <strong>\n Helmholtz Imaging Support Team at DESY\n </strong>\n , with support from\n <strong>\n HIFIS\n </strong>\n and\n <strong>\n Helmholtz AI\n </strong>\n . Hardware and implementation have been supported by funds from the\n <strong>\n Haicore\n </strong>\n initiative.\n</p>\n<p>\n We invite you to join us in shaping the future of the\n <strong>\n Helmholtz Model Zoo\n </strong>\n —upload your models, explore its capabilities, and share your ideas for new features. Your feedback and contributions will help make HMZ a powerful resource for AI-driven research within the Helmholtz community.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T16:30:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Hans William",
"last_name": "Werners",
"orcid": null
},
"affiliation": [
"Helmholtz Imaging, Deutsche Elektronen-Synchrotron DESY"
],
"is_presenter": true
},
{
"author": {
"first_name": "Engin",
"last_name": "Eren",
"orcid": "0000-0002-6371-5252"
},
"affiliation": [
"Helmholtz Imaging, Deutsche Elektronen-Synchrotron DESY"
],
"is_presenter": false
},
{
"author": {
"first_name": "Jennifer",
"last_name": "Ahrens",
"orcid": "0000-0001-8128-8787"
},
"affiliation": [
"Helmholtz Imaging, Deutsche Elektronen-Synchrotron DESY"
],
"is_presenter": false
},
{
"author": {
"first_name": "Philipp",
"last_name": "Heuser",
"orcid": "0000-0001-6316-9311"
},
"affiliation": [
"Helmholtz Imaging, Deutsche Elektronen-Synchrotron DESY"
],
"is_presenter": false
}
],
"submitter": "Hans William Werners",
"event": "Data Science Symposium 2025",
"activity": "AK Portal & Viewer Technologies (Viewer)",
"accepted": true,
"license": "Creative Commons Attribution Non Commercial No Derivatives 4.0 International (CC-BY-NC-ND-4.0)",
"affiliations": [
"Helmholtz Imaging, Deutsche Elektronen-Synchrotron DESY"
]
},
{
"title": "The Data Science Unit at GEOMAR",
"url": "/submissions/98",
"abstract": "<p>\n In 2022, GEOMAR created the Data Science Unit as its internal start-up to centralize Data Science support and activities. With up to eight data scientists as support personnel for GEOMAR, various projects and services were addressed in the following years. Now, three years since the foundation, we present lessons-learned such as the importance of on-site training programs, the challenges in balancing generalisation and customization or the varied success in achieving science-based key performance indicators.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T16:45:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Timm",
"last_name": "Schoening",
"orcid": null
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": true
}
],
"submitter": "Timm Schoening",
"event": "Data Science Symposium 2025",
"activity": null,
"accepted": true,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
]
},
{
"title": "Advancing FAIR Seismic Data Publications in PANGAEA and Supplementary Visualization with the Viewer Technology of the German Marine Data Portal",
"url": "/submissions/93",
"abstract": "<p>\n Compliant with the FAIR data principles, the long-term archiving of marine seismic data acquired from active-source surveys remains a critical yet complex task within the geophysical data life cycle. Data infrastructures such as PANGAEA – Data Publisher for Earth & Environmental Science and affiliated repositories must address the increasing volume, heterogeneity, and complexity of these datasets, which are produced using a variety of acquisition systems. To support this, the German marine seismic community is actively developing metadata standards tailored to different seismic data types, enabling their proper integration and archiving in PANGAEA. In parallel, new semi-automated workflows and standard operating procedures (SOPs) are being established and implemented to ensure consistent data publication and sustainable long-term stewardship.\n</p>\n<p>\n These advancements are being driven by the “Underway” Research Data project, a cross-institutional initiative of the German Marine Research Alliance (Deutsche Allianz Meeresforschung e.V., DAM). Initiated in mid-2019, the project aims to standardize and streamline the continuous data flow from German research vessels to open-access repositories, in alignment with FAIR data management practices. Marine seismic data curation, in particular, stands out as a successful use case for integrating expedition-based data workflows. By leveraging the tools, infrastructure, and expertise provided by the “Underway” Research Data project, newly acquired seismic datasets can be efficiently archived and visualized via the Marine Seismic Compilation Viewer - promoting transparency, facilitating access, and fostering collaboration across institutional and disciplinary boundaries.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T17:00:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Daniel",
"last_name": "Damaske",
"orcid": "0000-0003-0658-912X"
},
"affiliation": [
"MARUM - Center for Marine Environmental Sciences, University Bremen"
],
"is_presenter": true
},
{
"author": {
"first_name": "Janine",
"last_name": "Berndt",
"orcid": "0000-0003-3720-3568"
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": false
},
{
"author": {
"first_name": "Niklas",
"last_name": "Selke",
"orcid": "0000-0002-9954-2250"
},
"affiliation": [
"MARUM - Center for Marine Environmental Sciences, University Bremen"
],
"is_presenter": false
},
{
"author": {
"first_name": "Marianne",
"last_name": "Rehage",
"orcid": "0000-0002-9351-8014"
},
"affiliation": [
"MARUM - Center for Marine Environmental Sciences, University Bremen"
],
"is_presenter": false
},
{
"author": {
"first_name": "Gauvain",
"last_name": "Wiemer",
"orcid": null
},
"affiliation": [
"Deutsche Allianz Meeresforschung e.V. (DAM)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Janine",
"last_name": "Felden",
"orcid": null
},
"affiliation": [
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
],
"is_presenter": false
},
{
"author": {
"first_name": "Christian",
"last_name": "Berndt",
"orcid": "0000-0001-5055-0180"
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": false
}
],
"submitter": "Philipp Sebastian Sommer",
"event": "Data Science Symposium 2025",
"activity": "MareHub",
"accepted": true,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"MARUM - Center for Marine Environmental Sciences, University Bremen",
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel",
"Deutsche Allianz Meeresforschung e.V. (DAM)",
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
]
},
{
"title": "Increasing the availability and visibility of data deriving from AUVs and ROVs in Marine Research",
"url": "/submissions/80",
"abstract": "<p>\n Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs) are essential tools for investigating marine environments. These large-scale platforms are equipped with a variety of sensors and systems, including CTD, fluorometers, multibeam echosounders, side-scan sonar, and camera systems. ROVs also have the capability to collect water, biological, and geological samples. As a result, the datasets acquired from these missions are highly heterogeneous, combining diverse data types that require careful handling, standardization of metadata information, and publication.\n <br/>\n At GEOMAR, we develop and implement within the context of the Helmholtz DataHub a comprehensive workflow that spans the entire data lifecycle for large scale facilities.\n <br/>\n It combines using the infrastructures of O2A Registry for device management, Ocean Science Information System (OSIS) for cruise information, PANGAEA for data publication and the portal earth-data.de for future visualization of AUV and ROV missions.\n <br/>\n The presented workflow is currently deployed for GEOMAR’s REMUS6000 AUV \"Abyss\", and is being designed with scalability in mind, enabling its future application to other AUVs and ROVs.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T17:15:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Ulrike",
"last_name": "Schroller-Lomnitz",
"orcid": null
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": true
}
],
"submitter": "Ulrike Schroller-Lomnitz",
"event": "Data Science Symposium 2025",
"activity": "MareHub",
"accepted": true,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
]
},
{
"title": "A novel system to integrate DSHIP data from marine research vessels",
"url": "/submissions/97",
"abstract": "<p>\n The German research vessels Alkor, Elisabeth Mann Borgese, Heincke, Maria S. Merian, Meteor, Polarstern and Sonne steadily provide oceanographic, meteorological and other data to the scientific community. However, accessing and integrating time series raw data from these platforms has traditionally been fragmented and technically challenging. The newly deployed DSHIP Land System addresses this issue by consolidating time series data from marine research vessels into a unified and scalable data warehouse.\n</p>\n<p>\n At its core, the new system stores raw measurement data in the efficient and open Apache Parquet format. These columnar storage files allow for rapid querying and filtering of large datasets. To ensure flexible and high-performance access, the system uses a Trino SQL query engine running on a Kubernetes cluster composed of three virtual machines. This setup can be elastically scaled to meet variable demand, enabling efficient data access even under high load.\n</p>\n<p>\n This talk will briefly introduce the technical foundations of the DSHIP Land System, highlight the choice of storage format, the architecture of the Trino engine, and its deployment in a containerized Kubernetes environment. The focus will then shift to a demonstration how users can interactively query the datasets using standard SQL, enabling cross-vessel data exploration, filtering by time ranges and geospatial boundaries, and joining with external datasets. Finally, a brief outlook on the current status and future data integration is given.\n</p>\n<p>\n By making time series data easily accessible and queryable, the DSHIP Land System opens new opportunities for data-driven interdisciplinary environmental research. It enables reproducible AI-ready workflows and long-term data integration across missions and platforms.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T17:30:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Maximilian",
"last_name": "Betz",
"orcid": "0000-0002-2944-2537"
},
"affiliation": [
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
],
"is_presenter": true
},
{
"author": {
"first_name": "Norbert",
"last_name": "Anselm",
"orcid": "0000-0003-0367-6850"
},
"affiliation": [
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
],
"is_presenter": false
},
{
"author": {
"first_name": "Frank Oliver",
"last_name": "Glöckner",
"orcid": null
},
"affiliation": [
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
],
"is_presenter": false
},
{
"author": {
"first_name": "Sebastian",
"last_name": "Immoor",
"orcid": "0000-0002-7563-4521"
},
"affiliation": [
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
],
"is_presenter": false
}
],
"submitter": "Maximilian Betz",
"event": "Data Science Symposium 2025",
"activity": "MareHub",
"accepted": true,
"license": "Creative Commons Attribution Non Commercial Share Alike 4.0 International (CC-BY-NC-SA-4.0)",
"affiliations": [
"Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung"
]
},
{
"title": "Application of SMOS SSS L4 data to improve the understanding of the salinity dynamics and circulation of the Baltic Sea",
"url": "/submissions/100",
"abstract": "<p>\n The Baltic Sea is a semi-enclosed shelf sea and characterized by its distinct geographical and oceanographic features. One of the Baltic’s most remarkable features is its surface salinity gradient that is horizontally decreasing from the saline North Sea to the near fresh Bothnian Sea in the north, and Gulf of Finland in the east. Additionally, a vertical gradient and strong stratification separate between less saline surface water and deep saline water. These salinity features are mainly driven by a combination of river runoff, net precipitation, wind conditions, and geographic features that lead to restricted and irregular inflow of saltwater into the Baltic and limited mixing. The overall positive freshwater balance causes the Baltic to be much fresher compared to fully marine ocean waters with a mean salinity of only about 7 g/kg. The Baltic Sea is particularly sensitive to climate change and global warming due to its shallowness, small volume and limited exchange with the world oceans. Consequently, it is changing more rapidly than other regions. Recent changes in salinity are less clear due to a high variability but overall surface salinity seems to decrease with a simultaneous increase in the deeper water layers. Furthermore. the overall salinity distribution is indirectly linked to the general circulation of the Baltic Sea which consists mainly of cyclonic circulation cells comprising the main sub-basins of the Baltic Sea. Thus, improving the understanding of the salinity dynamics ultimately leads to a better understanding of the circulation in the Baltic Sea.\n</p>\n<p>\n Within the project 4DBALTDYN highly spatially resolved SMOS SSS (Sea Surface Salinity) satellite data will be combined with\n <i>\n in situ\n </i>\n observational data and numerical modeling to improve our understanding of the salinity dynamics of the Baltic Sea. SMOS SSS data (2011-2019) provide a continuous monitoring of the evolution of the surface salinity of the entire area of the Baltic Sea.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T17:45:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Andreas",
"last_name": "Lehmann",
"orcid": null
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": false
},
{
"author": {
"first_name": "Hela",
"last_name": "Mehrtens",
"orcid": "0000-0002-4526-2472"
},
"affiliation": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
],
"is_presenter": true
}
],
"submitter": "Andreas Lehmann",
"event": "Data Science Symposium 2025",
"activity": "MareHub",
"accepted": true,
"license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)",
"affiliations": [
"GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel"
]
},
{
"title": "Science Meets Data Spaces: FAIR Digital Objects as a Gateway to Interdisciplinary Science",
"url": "/submissions/87",
"abstract": "<p style=\"\">\n The growing complexity of digital research environments and the explosive increase in data volume demand robust, interoperable infrastructures to support sustainable Research Data Management (RDM). In this context, data spaces have emerged—especially in industry—as a powerful conceptual framework for organizing and sharing data across ecosystems, institutional boundaries, and disciplines. Although the term is not yet fully established in the research community, it maps naturally onto scientific practice, where the integration of heterogeneous datasets and cross-disciplinary collaboration are increasingly central.\n</p>\n<p style=\"\">\n Aligned with the principles of open science, FAIR Digital Objects (FDOs) provide a promising infrastructure for structuring these emerging data spaces. FDOs are standardized, autonomous, and machine-actionable digital entities that encapsulate data, metadata, software, and semantic assertions. They enable both humans and machines to Find, Access, Interoperate, and Reuse (FAIR) digital resources efficiently. By abstracting from underlying technologies and embedding persistent, typed relations, FDOs allow for seamless data integration, provenance tracking, and rights management across domains. This structure promotes reproducibility, trust, and long-term sustainability in data sharing.\n</p>\n<p style=\"\">\n Using an example from climate research, we demonstrate how data from from different data spaces can be combined. By employing STACs (Spatio Temporal Asset Catalogs) defined as FAIR Digital Objects facilitating the European Open Science Cloud (EOSC) Data Type Registry, we address a specific interdisciplinary research question. This approach not only illustrates the practical application of FDOs but also highlights how they can provide a robust framework for tackling larger and more complex scientific challenges by streamlining workflows and enabling collaboration across disciplinary and institutional boundaries.\n</p>\n",
"presentation_type": "Talk",
"session": "Data Management Workflows",
"start": "2025-09-03T18:00:00+02:00",
"duration": "00:15:00",
"authors": [
{
"author": {
"first_name": "Ivonne",
"last_name": "Anders",
"orcid": "0000-0001-7337-3009"
},
"affiliation": [
"German Climate Computing Center (DKRZ)"
],
"is_presenter": true
},
{
"author": {
"first_name": "Beate",
"last_name": "Krüss",
"orcid": null
},
"affiliation": [
"German Climate Computing Center (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Marco",
"last_name": "Kulüke",
"orcid": "0000-0003-0611-2567"
},
"affiliation": [
"German Climate Computing Center (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Karsten",
"last_name": "Peters-von Gehlen",
"orcid": "0000-0003-0158-2957"
},
"affiliation": [
"German Climate Computing Center (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Hannes",
"last_name": "Thiemann",
"orcid": "0000-0002-2329-8511"
},
"affiliation": [
"German Climate Computing Center (DKRZ)"
],
"is_presenter": false
},
{
"author": {
"first_name": "Heinrich",
"last_name": "Widmann",
"orcid": "0000-0001-9871-2687"
},
"affiliation": [
"German Climate Computing Center (DKRZ)"
],
"is_presenter": false
}
],
"submitter": "Ivonne Anders",
"event": "Data Science Symposium 2025",
"activity": null,
"accepted": true,
"license": "Creative Commons Attribution Share Alike 4.0 International (CC-BY-SA-4.0)",
"affiliations": [
"German Climate Computing Center (DKRZ)"
]
}
]