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The GEOMAR Helmhotz Centre for Ocean Research Kiel is operating a Media Asset Management System (MAMS) which manages large amounts image- and video data. Similar systems are operated by the Helmholtz Centre HEREON and by the Alfred Wegener Institute (AWI). Although the MAMS system provides access to data and metadata using an API, it is not possible to directly request image data without prior knowledge of the internal MAMS data structure. The image broker is a web service which brokers between a client (e.g. web-browser) and the MAMS. It allows users to request images by metadata values (e.g. image uuid). The broker uses the [IIIF](https://iiif.io/) standard which allows users to request the images in different formats, scaled copies or only specific parts of the image.

Image Quality control / quality assurance and curation Tools (IQT) conceptualised and developed by the MareHub working group on Videos/Images (part of the DataHub, a research data manangement initiative by the Helmholtz association). The MarIQT core is a python package which is helpful on its own but is key to the more user-friendly jupyter notebooks that make extensive use of the python package.

With the growing demand of assessing the ecological status, there is the need to fully understand the relationship between the planktic diversity and the environmental factors. Species richness and Shannon index have been widely used to describe the biodiversity of a community. Besides, we introduced the first ordination value from non-metric multidimensional scaling (NMDS) as a new index to represent the community similarity variance. In this study, we hypothesized that the variation of diatom community in rivers in an agricultural area was influenced by hydro-chemical variables. We collected daily mixed water samples using ISCO auto water samplers for diatoms and for water-chemistry analysis at the outlet of a lowland river for a consecutive year. An integrated modeling was adopted including random forest (RF) to decide the importance of the environmental factors influencing diatoms, generalized linear models (GLMs) combined with 10-folder cross validation to analyze and predict the diatom variation. The hierarchical analysis highlighted antecedent precipitation index (API) as the controlling hydrological variable while water temperature, Si2+ and PO4-P as the main chemical controlling factors in our study area. The generalized linear models performed better prediction for Shannon index (R2 = 0.44) and NMDS (R2 = 0.51) than diatom abundance (R2 = 0.25) and species richness (R2 = 0.25). Our findings confirmed that Shannon index and the NMDS as an index showed good performance in explaining the relationship between stream biota and its environmental factors and in predicting the diatom community development based on the hydro-chemical predictors. Our study showed and highlighted the important hydro-chemical factors in the agricultural rivers, which could contribute to the further understanding of predicting diatom community development and could be implemented in the future water management protocol.

This presentation will detail the design, implementation, and operation of ERDA, which is a collection of external version-controlled research datasets, of multiple synchronized deployments of the data, of a growing set of minimal examples using the datasets from various deployments, of stand-alone tools to create, maintain, and deploy new datasets, and of documentation targeting different audiences (users, maintainers, developers).ERDA was designed with the following principles in mind: Provide clear data provenance and ensure long-term availability, minimize effort for adding data and make all contents available to all users immediately, ensure unambiguous referencing and develop transparent versioning conventions, embrace mobility of scientists and target independence from the infrastructure of specific institutions.The talk will show how the data management is done with Git-LFS, demonstrate how data repositories are rendered from human-readable data, and give an overview of the versioning scheme that is applied.

Diatoms are widely used as bio-indicators in water quality monitoring and ecological status assessment. Compare to chemical variables, the effects of hydrological variables on diatom dynamics have been rarely reported. In this study, we aimed to examine dynamics of diatoms based on the classification of a morphological trait (cell size) and to investigate the relationship between the diatom community and selected chemical and hydrological variables. We collected 339 daily-mixed samples by using an auto-sampler, from which we recorded 113 diatom taxa from 45 genera. Analysis of morphological trait showed variations among cell size classes throughout the year with big cells in winter and spring while small cells dominated in summer and autumn. Redundancy analysis (RDA) and partial RDA indicated that chemical and hydrological variables could jointly explain 50.2% of the diatom community variation, in which chemical variables explain 36.5% and hydrological variables explain 12.9%. The most important chemical variables were Orthophosphate-phosphorus (PO 4-P) and Silicon (Si 2+) concentrations, while the most significant hydrological variables were antecedent precipitation index (API) and base flow. We conclude that besides chemical variables (nutrients and metal ions) hydrological variables showed also remarkable effects on the diatom community variation, especially during wet conditions. A key implication of our findings for freshwater management is that future bio-monitoring protocols should take hydrological variables into consideration.

Autonomous underwater vehicles (AUVs) equipped with multibeam echosounders (MBES) are essential for collecting high-resolution bathymetric data in the deep sea. Navigation of AUVs and accuracy of acquired MBES data is challenging, especially in deep water or rough terrain. Here, we present the AUV Abyss operational workflow that uses mission planning together with a long baseline (LBL) positioning network, and systematic post-processing of the MBES data using feature matching. The workflow enables autonomous exploration even in difficult terrain, makes ultrashort baseline navigation during the AUV survey obsolete and with this, increases the efficiency of ship time. It provides an efficient workflow for multi-survey mapping campaigns to produce high-resolution, large-coverage seafloor maps. Automated documentation of post-processing steps enhances the archiving of produced results, facilitates knowledge transfer, adaptation to other systems and management of large datasets. Comprehensive documentation allows developing routines that provide a first step towards automatization of AUV operations and MBES data processing.

There has been increasing interest in diatom-based bio-assessment but we still lack a comprehensive understanding of how to capture diatoms’ temporal dynamics with an appropriate sampling frequency (ASF). To cover this research gap, we collected and analyzed daily riverine diatom samples over a 1-year period (25 April 2013–30 April 2014) at the outlet of a German lowland river. The samples were classified into five clusters (1–5) by a Kohonen Self-Organizing Map (SOM) method based on similarity between species compositions over time. ASFs were determined to be 25 days at Cluster 2 (June-July 2013) and 13 days at Cluster 5 (February-April 2014), whereas no specific ASFs were found at Cluster 1 (April-May 2013), 3 (August-November 2013) (>30 days) and Cluster 4 (December 2013 - January 2014) (