Machine learning (ML) and in particular artificial neural networks (ANNs) push state-of-the-art solutions for many hard problems e.g., image classification, speech recognition or time series forecasting. In the domain of climate science, ANNs have good prospects to identify causally linked modes of climate variability as key to understand the climate system and to improve the predictive skills...
Air quality in urban areas is an important topic not only for science but also for the concerned citizen as the air quality affects personal wellbeing and health. At Hereon in the project SMURBS (SMART URBAN SOLUTIONS) a model to forecast urban air quality at high spatial and timely resolution was developed (CityChem) with the aim to carry out exposure studies and future scenario calculations...
Advances in computer vision are applicable across aquatic ecology to detect objects from images, ranging from plankton and marine snow to whales. Analyzing digital images enables quantification of fundamental properties (e.g., species identity, abundance, size, and traits) for richer ecological interpretation. Yet less attention has been given to imaging fauna from coastal sediments, despite...
Polar ice sheets Greenland and Antarctica play a crucial role in the Earth's climate system. Accurately determining their past accumulation rates and understanding their dynamics is essential for predicting future sea level changes. Ice englacial stratigraphy, which assigns ages to radar reflections based on ice core samples, is one of the primary methods used to investigate these...
The detection of earthquakes in seismological time series is central to observational seismology. Generally, seismic sensors passively record data and transmit it to the cloud or edge for integration, storage, and processing. However, transmitting raw data through the network is not an option for sensors deployed in harsh environments like underwater, underground, or in rural areas with...
As Helmholtz AI consultants we support researchers in their machine learning and data science projects. Since our group was founded in 2020, we have completed over 40 projects in various subfields of Earth and Environmental sciences, and we have established best practices and standard workflows. We cover the full data science cycle from data handling to model development, tuning, and roll-out....
The NFDI4Earth Academy is a network of early career – doctoral and postdoctoral - scientists interested in bridging Earth System and Data Sciences beyond institutional borders. The research networks Geo.X, Geoverbund ABC/J, and DAM offer an open science and learning environment covering specialized training courses and collaborations within the NFDI4Earth consortium with access to all...
Coordinated by the German Marine Research Alliance (Deutsche Allianz Meeresforschung (DAM)) the project “Underway”–Data is supported by the marine science centers AWI, GEOMAR and Hereon of the Helmholtz Association research field “Earth and Environment”. AWI, GEOMAR and Hereon develop the marine data hub (Marehub). This MareHub initiative is a contribution to the DAM. It builds a decentralized...
The MareHub is a cooperation to bundle the data infrastructure activities of the marine Helmholtz centers AWI, GEOMAR and Hereon as a contribution to the German Marine Research Alliance and its complementary project “Underway”-Data.
The MareHub is part of the DataHub, a common initiative of all centers of the Helmholtz Association in the research field Earth and Environment. The overarching...
Recording the workflow of numerical simulations and their postprocessing is an important component of quality management as it, e.g., makes the CFD result files reproducible. It has been implemented in an existing workflow in coastal engineering and has proven its worth in practical application.
The data history recording is achieved by saving the following information as character variables...
Remote sensing data is generally distributed as individual scenes, often with several variations based on the level of preprocessing applied. While there is generally good support for filtering these scenes by metadata, such as intersecting areas of interest or acquisition time, users are responsible for any additional filtering, processing, and analytics. This requires users to be experienced...
Despite the vast growth in accessible data from environmental sciences over the last decades, it remains difficult to make this data openly available according to the FAIR principles. A crucial requirement for this is the provision of metadata through standard catalog interfaces or data portals for indexing, searching, and exploring the stored data.
With the release of the community-driven...
The processes of search, access and evaluation of geospatial datasets is often a challenging task for researches, managers, administrations and interested users due to highly distributed data sources.
To provide a single access point to governmentally provided datasets for marine applications, the project “Marine Spatial Data Infrastructure Germany” (MDI-DE) was launched in 2013. Its...
The Ocean Science Information System (OSIS) was first established in 2008 at GEOMAR as a metadata-centric platform for ongoing marine research collaborations. It has become a vital platform for collecting, storing, managing and distributing ocean science (meta)data. We will discuss how OSIS has enabled multidisciplinary ocean research and facilitated collaborations among scientists from...
In an era of rapid anthropogenically induced changes in the world’s oceans, ocean sound is considered an essential ocean variable (EOV) for understanding and monitoring long-term trends in anthropogenic sound and its effects on marine life and ecosystem health.
The International Quiet Ocean Experiment (IQOE) has identified the need to monitor the distribution of ocean sound in space and...
Interaction between pollution, climate change, the environment, and people is complex. This complex interplay is particularly relevant in coastal regions, where the land meets the sea. It challenges the scientific community to find new ways of transferring usable information for action into actionable knowledge and into used information for managing the impact of marine pollution. Therefore, a...
Digitization and the Internet in particular have created new ways to find, re-use, and process scientific research data. Many scientists and research centers want to make their data openly available, but often the data is still not easy to find because it is distributed across different infrastructures. In addition, the rights of use, citability and data access are sometimes unclear.
The...
The Earth System Grid Federation (ESGF) data nodes are usually the first address for accessing climate model datasets from WCRP-CMIP activities. It is currently hosting different datasets in several projects, e.g., CMIP6, CORDEX, Input4MIPs or Obs4MIPs. Datasets are usually hosted on different data nodes all over the world while data access is managed by any of the...
Creating data collaborations that are both effective and equitable is challenging. While there are many technical challenges to overcome, there are also significant cultural issues to deal with, for example, communication styles, privacy protection and compliance to institutional and legal norms. At ZMT Bremen, we had to address the central question of “How to build a good data collaboration...
webODV is the online version of the worldwide used Ocean Data View
Software (https://odv.awi.de). During the recent years webODV
(https://webodv.awi.de) has been evolved into a powerful and
distributed cloud system for the easy online access of large data
collections and collaborative research. Up to know more than 1000
users per months (worldwide) are visiting our webODV...
Automatic coastline classification based on machine learning is proven to be robust for sandy beaches on regional and global scales. However sandy beaches only make around one third of the world’s ice-free shoreline. The rest consists of mudflats, cliffs, different types of vegetation and human constructions. Classification of these features is
more challenging. For instance, mild foreshore...
Our research focuses on detecting and tracking ocean carbon regimes, which are useful tools for understanding the impacts of climate change on ocean carbon uptake. Geoscientific datasets in Earth System Sciences often contain local and distinct statistical distributions at a regional scale. This poses a significant challenge in applying conventional clustering algorithms for data analysis....
Compliant with the FAIR data principles ¹, long-term archiving of bathymetry data from multibeam echosounders – a highly added value for
the data life cycle - is the challenging task in the data information system PANGAEA. To cope with the increasing amount of data
(“bathymetry puzzle pieces”) acquired from research vessels and the demand for easy “map-based” means to find valuable data, new...
In an ongoing effort within the Helmholtz association to make research data FAIR, i.e. findable, accessible, interoperable and reusable, we also need to make biological samples visible and searchable. To achieve this, a first crucial step is to inventory already available samples, connect them to relevant metadata and assess the requirements for various sample types (e.g. experimental, time...
Monitoring environmental changes in terrestrial, coastal, and marine areas in remote locations, such as Greenland, can prove challenging due to harsh weather conditions and lack of infrastructure. It is therefor of great importance that any data gathered in these locations, is made freely available for viewing and reuse. To address this challenge, we developed the Greenland Drone Explorer...
Mangrove forests are threatened by multiple anthropogenic stressors, urging researchers to create improved monitoring methods for their conservation and management. Recent remote sensing efforts have found some success using high resolution imagery of mangrove forests with sparse vegetation. In this study we focus on stands of mangrove forests with dense vegetation, consisting of the endemic...
The incorporation of machine learning (ML) into wildlife monitoring has the potential to revolutionize data collection and conservation initiatives. We outline a human-in-the-loop ML approach for the identification of sharks caught along Tanzanian coasts. The image sources for this monitoring come from marketplaces or landing sites, resulting in a vast range of image quality, the state and...
In an interactive demonstration, we will showcase how already available APIs can be used to unlock the power of a federated workspace for marine research, allowing for collaboration and data sharing across multiple services and applications.
Through the use of APIs, distributed metadata-services, such as the AWI Sensor Registry and GEOMARs Ocean Science Information System, DSHIP systems...
