Session Contribution Rest List
A contribution rest export view for sessions.
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[ { "title": "Towards the detection of ocean carbon regimes", "url": "/events/data-science-symposium-7/submissions/55", "abstract": "<p dir=\"ltr\">\n In the context of global climate change and environmental challenges, one research question is how different ocean regions take up carbon dioxide and which bio-physical drivers are responsible for these patterns. The carbon uptake at the sea surface is different in different areas. It depends on several drivers (sea surface temperature, the salinity of the water, alkalinity, dissolved inorganic carbon, phytoplankton, etc.), which enormously vary on both a spatial and seasonal time scale. We name a carbon regime a region having common relationships (on a seasonal and spatial scale) between carbon uptake and its drivers (sea surface temperature, etc.).\n</p>\n<p>\n <br/>\n We are using the output of a global ocean biogeochemistry model providing surface fields of carbon uptake and its drivers on a monthly time scale. We aim to use spatial and seasonal correlations to detect the regimes. We take advantage of both supervised and unsupervised machine learning methodologies to find different carbon states. The aim is to determine individual local correlations in each carbon state. We build a top-down grid-based algorithm that incorporates both regression and clustering algorithms. The technique divides the entire ocean surface into smaller grids. The regression model detects a linear relationship between carbon uptake and other ocean drivers in each grid box and over each of the twelve months in a year. The correlation clustering model provides clusters of carbon states that have a distinct connection between carbon uptake and different ocean drivers. While the detection of clusters that exhibit correlations relies on static data, here, the aim is to include both the spatial and temporal dimensions, which will reveal temporal trajectories of changes in correlations.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Sweety", "last_name": "Mohanty", "orcid": null }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel", "Christian-Albrechts Universität zu Kiel" ], "is_presenter": true }, { "author": { "first_name": "Dr. Daniyal", "last_name": "Kazempour", "orcid": null }, "affiliation": [ "Christian-Albrechts Universität zu Kiel" ], "is_presenter": false }, { "author": { "first_name": "Dr. Lavinia", "last_name": "Patara", "orcid": null }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ], "is_presenter": false }, { "author": { "first_name": "Prof. Dr. Peer", "last_name": "Kröger", "orcid": null }, "affiliation": [ "Christian-Albrechts Universität zu Kiel" ], "is_presenter": false } ], "submitter": "Sweety Mohanty", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel", "Christian-Albrechts Universität zu Kiel" ] }, { "title": "Layerwise Relevance Propagation for Echo State Networks applied to Earth System Variability.", "url": "/events/data-science-symposium-7/submissions/15", "abstract": "<p>\n Artificial neural networks (ANNs) are known to be powerful methods for many hard problems (e.g. image classification or timeseries prediction). However, these models tend to produce black-box results and are often difficult to interpret. Here we present Echo State Networks (ESNs) as a certain type of recurrent ANNs, also known as reservoir computing. ESNs are easy to train and only require a small number of trainable parameters. They can be used not only for timeseries prediction but also for image classification, as shown here: Our ESN model serves as a detector for El Nino Southern Oscillation (ENSO) from sea-surface temperature anomalies. ENSO is actually a well-known problem and has been widely discussed before. But here we use this simple problem to open the black-box and apply layerwise relevance propagation to Echo State Networks.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Marco", "last_name": "Landt-Hayen", "orcid": "0000-0003-3606-7760" }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ], "is_presenter": true } ], "submitter": "Marco Landt-Hayen", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ] }, { "title": "HELMI – The Hereon Layer For Managing Incoming Data", "url": "/events/data-science-symposium-7/submissions/61", "abstract": "<p>\n The Hereon operates a larger number of continuously measuring sensors on mobile and stationary platforms outside the Hereon Campus. Transferring the data from the sensors to the internal network is a critical step , as data is often required to be accessible for researchers in near-real-time (NRT) and needs be retrieved from the outside in a secure way that does not pose a threat to the internal infrastructure.\n</p>\n<p>\n Therefore, we developed the\n <u>\n He\n </u>\n reon\n <u>\n L\n </u>\n ayer for\n <u>\n M\n </u>\n anaging\n <u>\n I\n </u>\n ncoming data (HELMI). Using HELMI, data from external sensor systems is moved securely via a Virtual Private Network solution ( Wireguard ) to the Hereon internal infrastructure. The Wireguard client can be either installed directly on the sensor system or on a piece of hardware dedicated for data transfer that is connected to the sensor. Data is transferred as files via the RSYNC or as NRT data via the Message Queuing Telemetry Transport (MQTT) protocol, respectively. After transfer researchers can retrieve their files and access telemetry from an internal endpoint. NRT-Data can be automatically visualized using web applications and parameters at the client can be remotely controlled .\n</p>\n<p>\n The automatic data transfer via HELMI minimizes the risk of data loss, reduces memory requirements on the measuring system and allows to provide data in near real-time and thus to speed up the publication process of data.\n</p>", "presentation_type": "Live demo of a tool", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Max", "last_name": "Böcke", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Joost", "last_name": "Hemmen", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Christian", "last_name": "Jacobsen", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Tim", "last_name": "Leefmann", "orcid": "0000-0002-5784-8657" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Oliver", "last_name": "Listing", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Jörn", "last_name": "Plewka", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false } ], "submitter": "Tim Leefmann", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon" ] }, { "title": "HARMONise – Enhancing the interoperability of marine biomolecular (meta)data across Helmholtz Centres", "url": "/events/data-science-symposium-7/submissions/43", "abstract": "<p>\n <span>\n <span>\n <span>\n Biomolecules, such as DNA and RNA, make up all ocean life, and biomolecular research in the marine realm is pursued across several Helmholtz Centres. Biomolecular (meta)data (i.e. DNA and RNA sequences and all steps involved in their creation) provide a wealth of information about the distribution and function of marine organisms. However, high-quality (meta)data management of biomolecular data is not yet well developed in environmentally focused Helmholtz Centres. This impedes every aspect of FAIR data exchange internally and externally, and the pursuit of scientific objectives that depend on this data. In this Helmholtz Metadata Collaboration project between the Alfred-Wegener-Institut Helmholtz Zentrum für Polar- und Meeresforschung and the GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel (with scientific PIs rooted in POFIV Topic 6), we will develop sustainable solutions and digital cultures to enable high-quality, standards-compliant curation and management of marine biomolecular metadata, to better embed biomolecular science in broader digital ecosystems and research domains. The approach will build on locally administered relational databases and establish a web-based hub to exchange metadata compliant with domain-specific standards, such as the MIxS (Minimum Information about any (x) Sequence). To interface with and enhance the Helmholtz digital ecosystem, we aim to link the operations and archiving workflows of our local databases with existing Helmholtz repositories (e.g. the PANGAEA World Data Center) and with systems currently under development (e.g. Sample Management, Marine Data Portal). This will be done through stable, synchronised, and persistent solutions for the export and exchange of (meta)data. By enabling sustainable data stewardship, as well as export and publishing routines, this will support biomolecular researchers in delivering Helmholtz biomolecular data to national European and global repositories in alignment with community standards. Throughout the project, we will establish and cultivate human communication channels, to ensure implementations do not drift apart. Furthermore, we will provide use cases to connect our data holdings with other global interoperability frameworks, such as UNESCO’s Ocean Data and Information System. Here we will present a conceptual outline and first steps taken on our road to practically enabling FAIR management of biomolecular (meta)data. The project HARMONise (ZT-I-PF-3-027) is funded by the Initiative and Networking Fund as part of the Helmholtz Metadata Collaboration Project cohort 2021.\n </span>\n </span>\n </span>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Christina", "last_name": "Bienhold", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": true }, { "author": { "first_name": "Lars", "last_name": "Harms", "orcid": "0000-0001-7620-0613" }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": true }, { "author": { "first_name": "Stefan", "last_name": "Neuhaus", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": true }, { "author": { "first_name": "Till", "last_name": "Bayer", "orcid": "0000-0002-4704-2449" }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ], "is_presenter": false }, { "author": { "first_name": "Roland", "last_name": "Koppe", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false } ], "submitter": "Christina Bienhold", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung", "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ] }, { "title": "Fast and Accurate Physics-constrained Learning with Symmetry Constraints for the Shallow Water Equations", "url": "/events/data-science-symposium-7/submissions/27", "abstract": "<p>\n The shallow water equations (SWEs) are widely employed for governing a large-scale fluid flow system, for example, in the coastal regions, oceans, estuaries, and rivers. These partial differential equations (PDEs) are often solved using semiimplicit schemes that solve a linear system iterativelyat each time step, resulting in high computational costs. Here we use physics constrained deep learning to train a convolutoinal network to solve the SWEs, while training on the discretized PDE directly without any need for numerical simulations as training data data. To improve accuracy and stability over longer integration times, we utilise group equivariant convolutional networks, so that the the learned model respects rotational and translational symmetries in the PDEs as hard constraints at every point in the training process. After training, our networks accurately predict the evolution of SWEs for freely chosen initial conditions and multiple time steps. Overall, we find that symmetry constraints signficantly improve performance compared to standard convolution networks.\n</p>\n<p>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Yunfei", "last_name": "Huang", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "David Solomon", "last_name": "Greenberg", "orcid": "0000-0002-8515-0459" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false } ], "submitter": "Yunfei Huang", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon" ] }, { "title": "Data conversion for the MOSAiC webODV", "url": "/events/data-science-symposium-7/submissions/29", "abstract": "<p>\n This contribution is an introduction to the data management process in the MOSAiC - Virtual Research Environment (M-VRE) project. The M-VRE project aims to make the unique and interdisciplinary data set of MOSAiC easily accessible to the field of scientists from different research areas [ADD22]. In addition, a virtual environment is available to analyze and visualize them directly online. This supports the research in improving transparency, traceability, reproducibility and visibility.\n <br/>\n One tool that is incorporated within M-VRE is webODV, the online version of Ocean Data View (ODV) [Sch22]. ODV is a software for visualization of oceanographic data in oceanography since almost 30 years. Given its software structure, it is equally suitable for data of the atmosphere, on land, on ice. Yet, there are requirements of ODV regarding the format of the data set which is why a conversion of the data is required.\n <br/>\n In the following, the workflow of data from archive to webODV is described.\n <br/>\n First of all, the data source needed to be defined. As part of the MOSAiC project, an agreement was reached through the MOSAiC Data Policy to upload the data to the long-term archive PANGAEA [Imm+19]. For this reason, PANGAEA is used as the data source for the webODV implementation in M-VRE.\n <br/>\n Secondly, the automated query and download of the MOSAiC data is applied. The search of entries with tag ”mosaic20192020” is automated. The PANGAEA Request Results service [PAN22b] is used to access the metadata.\n <br/>\n The third step is the conversion of the data format. It is based on the code pangaea2odv written by R. Koppe (AWI Bremerhaven). It is a Python script to convert the PANGAEA .tab format to an ASCII format executable by ODV. The target format is a .txt file consisting in header and data in tab-separated columns. The following meta variables are defined: Basis, Cruise, Event, Station, Project, URL, RIS and BibTeX citation, Version, Last modified, Scientists, main scientist, Contact, Method, Bot. Depth [m], Original file URL, Longitude and Latitude. The data variables include all the data variables defined in PANGAEA. Depending on the data types of the collection, the primary variable is selected.\n <br/>\n Furthermore, the collections are supposed to resemble the PANGAEA data sets as closely as possible. However, it is necessary that similar measurements are combined in the same. For instance, 89 data sets were uploaded by [Aka+21]. Each record is an event and the variables and many metadata are identical. A python routine generates collection names based on the titles of the PANGAEA entries. Among other things, dates, leg numbers, etc. are removed. Finally, to build collections readable by webODV the spreadsheet files first have to be imported into ODV and then saved as a collection (consisting of .odv file and .data folder). This is automated using the terminal.\n <br/>\n The deployment of the M-VRE webODV is accessible through the M-VRE [ADD22] project website or directly through the URL https://mvre.webodv.cloud.awi.de/ [AWI22]. However, the MOSAiC data policy [Imm+19] established that the public release will be on 01/01/2023. Until then, the login requires an AWI account and membership in the MOSAiC consortium. The data structure in which the collections are embedded is based on the structure of the science teams during the Expedition.\n <br/>\n <br/>\n [Imm+19] Immerz et al. MOSAiC Data Policy. 2019\n <br/>\n [Aka+21] Akansu et al. Tethered balloon-borne measurements of turbulence during the MOSAiC expedition from December 2019 to May 2020. 2021\n <br/>\n [AWI22] AWI. MOSAiC webODV. 2022\n <br/>\n [ADD22] AWI, DKRZ, and DLR. MOSAiC –Virtual Research Environment.2022\n <br/>\n [PAN22a] PANGAEA. Data Publisher for Earth & Environmental Science. 2022\n <br/>\n [PAN22b] PANGAEA. OAI 2.0 Request Results. 2022\n <br/>\n [Sch22] Schlitzer. ODV 5.6.2. 2022\n</p>\n<p>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Julia", "last_name": "Freier", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": true }, { "author": { "first_name": "Sebastian", "last_name": "Mieruch-Schnülle", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false }, { "author": { "first_name": "Reiner", "last_name": "Schlitzer", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false } ], "submitter": "Julia Freier", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ] }, { "title": "The Coastal Pollution Toolbox – data services and products in support of knowledge for action", "url": "/events/data-science-symposium-7/submissions/59", "abstract": "<p>\n <span>\n <span>\n <span>\n Knowledge transfer requires, first, meaningful approaches and products to transfer knowledge amongst different users and, second, appropriate measures for the creation of knowledge across scientific disciplines. The\n <i>\n Coastal Pollution Toolbox\n </i>\n (\n <a href=\"https://www.coastalpollutiontoolbox.org/index.php.en\">\n https://www.coastalpollutiontoolbox.org/index.php.en\n </a>\n ), a central product of the program-oriented funding topic on “Coastal Transition Zones under Natural and Human Pressures”, serves as a digital working environment for scientists and knowledge hub and information platform for decision-makers. It supports action and optimisation of scientific concepts to investigate pollution in the land-to-sea continuum.\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n In order to address demands of various users the toolbox comprises of three compartments:\n <i>\n Science Tools\n </i>\n provide expert users with information on new methods, approaches or indicators for baseline assessments or for the re-evaluation of complex problems.\n <i>\n Synthesis Tools\n </i>\n address challenges of global environmental change. They are information-rich products based on consolidated data of different types and origin and provide expert users with knowledge.\n <i>\n Management Tools\n </i>\n provide usable information and options for action. Ready-to-use tools grounded on evidence-based science are available to those involved in planning and management of coastal and marine challenges.\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n As part of the development process coastal pollution information services will be created and co-developed with stakeholders and end-users. This will ensure optimal interest and use by a range of actors involved in the direct and indirect impact of coastal and marine pollution. The contribution will highlight the basic approach of the toolbox and some of the products already and planned to be developed.\n </span>\n </span>\n </span>\n</p>", "presentation_type": "Live demo of a tool", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Marcus", "last_name": "Lange", "orcid": "0000-0002-0486-8769" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Ralf", "last_name": "Ebinghaus", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Ulrike", "last_name": "Kleeberg", "orcid": "0000-0002-9170-1739" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Linda", "last_name": "Baldewein", "orcid": "0000-0002-9477-516X" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false } ], "submitter": "Marcus Lange", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon" ] }, { "title": "New approaches for distributed data analysis with the DASF Messaging Framework", "url": "/events/data-science-symposium-7/submissions/52", "abstract": "<p>\n The Data Analytics Software Framework (DASF,\n <a href=\"https://doi.org/10.5880/GFZ.1.4.2021.004\">\n https://doi.org/10.5880/GFZ.1.4.2021.004\n </a>\n ) supports scientists to conduct data analysis in distributed IT infrastructures by sharing data analysis tools and data. For this purpose, DASF defines a remote procedure call (RPC) messaging protocol that uses a central message broker instance. Scientists can augment their tools and data with this protocol to share them with others or re-use them in different contexts.\n</p>\n<p>\n Our framework takes standard python code developed by a scientist, and automatically transforms the functions and classes of the scientists code into an abstract layer. This abstraction, the server stub as it is called in RPC, is connected to the message broker and can be accessed by submitting JSON-formatted data through a websocket in the so-called client stub. Therefore the DASF RPC messaging protocol in general is language independent, so all languages with Websocket support can be utilized. As a start DASF provides two ready-to-use language bindings for the messaging protocol, one for Python and one for the Typescript programming language.\n</p>\n<p>\n DASF is developed at the GFZ German Research Centre for Geosciences and was funded by the Initiative and Networking Fund of the Helmholtz Association through the Digital Earth project (\n <a href=\"https://www.digitalearth-hgf.de/\">\n https://www.digitalearth-hgf.de/\n </a>\n ). In this talk, we want to present the framework with some simple examples, and present two new approaches for the framework. One is an alternative light-weight message broker based on the python web-framework Django, that supports containerization, user-management and token authentification. The other one is an approach for easily applicable end-to-end-encryption in the messaging framework and user-authentification in the backend module for secure federation of data analysis between research centers.\n</p>", "presentation_type": "Live demo of a tool", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Philipp Sebastian", "last_name": "Sommer", "orcid": "0000-0001-6171-7716" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Daniel", "last_name": "Eggert", "orcid": "0000-0003-0251-4390" }, "affiliation": [ "GFZ German Research Centre for Geosciences" ], "is_presenter": false }, { "author": { "first_name": "Viktoria", "last_name": "Wichert", "orcid": "0000-0002-3402-6562" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Linda", "last_name": "Baldewein", "orcid": "0000-0002-9477-516X" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Tilman", "last_name": "Dinter", "orcid": "0000-0002-1505-8833" }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false }, { "author": { "first_name": "Christian", "last_name": "Werner", "orcid": "0000-0001-7032-8683" }, "affiliation": [ "Karlsruhe Institute of Technology" ], "is_presenter": false } ], "submitter": "Philipp Sebastian Sommer", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon", "GFZ German Research Centre for Geosciences", "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung", "Karlsruhe Institute of Technology" ] }, { "title": "Robust Detection of Marine Life with Label-free Image Feature Learning and Probability Calibration", "url": "/events/data-science-symposium-7/submissions/41", "abstract": "<p>\n Advances in imaging technology for in situ observation of marine life has significantly increased the size and quality of available datasets, but methods for automatic image analysis have not kept pace with these advances. On the other hand, knowing about distributions of different species of plankton for example would help us to better understand their lifecycles, interactions with each other or the influence of environmental changes on different species. While machine learning methods have proven useful in solving and automating many image processing tasks, three major challenges currently limit their effectiveness in practice. First, expert-labeled training data is difficult to obtain in practice, requiring high time investment whenever the marine species, imaging technology or environmental conditions change. Second, overconfidence in learned models often prevents efficient allocation of human time. Third, human experts can exhibit considerable disagreement in categorizing images, resulting in noisy labels for training. To overcome these obstacles, we combine recent developments in self-supervised feature learning based with temperature scaling and divergence-based loss functions. We show how these techniques can reduce the required amount of labeled data by ~100-fold, reduce overconfidence, cope with disagreement among experts and improve the efficiency of human-machine interactions. Compared to existing methods, these techniques result in an overall 2 % to 5 % accuracy increase, or a more than 100-fold decrease in the human-hours required to guarantee semiautomated outputs at the same accuracy level as fully supervised approaches. We demonstrate our results by using two different plankton image datasets collected from underwater imaging systems at the coast of Helgoland and from a research vessel cruise in front of Kap Verde.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Tobias", "last_name": "Schanz", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Klas Ove", "last_name": "Möller", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Saskia", "last_name": "Rühl", "orcid": "0000-0002-4650-6045" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "David Solomon", "last_name": "Greenberg", "orcid": "0000-0002-8515-0459" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false } ], "submitter": "Tobias Machnitzki", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon" ] }, { "title": "MOSAiC webODV – An online service for the exploration, analysis and visualization of MOSAiC data", "url": "/events/data-science-symposium-7/submissions/34", "abstract": "<p>\n Introduction\n</p>\n<p>\n MOSAiC (https://mosaic-expedition.org/) has been the largest polar expedition in history. The German icebreaker Polarstern was trapped in the ice from October 2019 to October 2020, and rich data have been collected during the polar year. The M-VRE project (The MOSAiC Virtual Research Environment, https://mosaic-vre.org/) has the aim to support the analysis and exploitation of the MOSAiC data by providing online software tools for the easy, interdisciplinary and efficient exploration and visualization of the data. One service provided by M-VRE is webODV, the online version of the Ocean Data View Software (ODV, https://odv.awi.de/).\n</p>\n<p>\n Setup\n</p>\n<p>\n The MOSAiC webODV is available via https://mosaic-vre.org/services/ or dirctly at https://mvre.webodv.cloud.awi.de/. Due to a moratorium until the end of 2022, the data can only be accessed by the MOSAiC consortium. From 2023 on, MOSAiC data and thus webODV will be available for the science community and the general public. In the webODV configuration, datasets as well as the ODV software reside and run on a server machine, not on the client computer. The browser client communicates with the server over the Internet using secure websockets. Up to now we provide two webODV services, which are described in the following.\n</p>\n<p>\n Data Extraction\n</p>\n<p>\n The Data Extraction service is based on intuitive web elements like buttons, dropdowns, date widgets and a drag & drop zoom function for the map. The aim is to provide data sub-setting as easy and fast as possible. A pager on the top of the site guides the user through the pages, which includes mainly the selection of stations, variables and finally the download function. Data can be finely granulated selected e.g. by zooming into the map, defining time windows and restrictions to specific variables. Finally the selected data can be downloaded as text files, ODV collections or netCDF files for later use with the standalone ODV or other tools.\n</p>\n<p>\n Data Exploration\n</p>\n<p>\n The Data Exploration service or ODV-online aims to provide the look-and-feel and functionality of the desktop ODV in the browser window for creating maps, surface plots, section plots, scatter plots, filtering data etc. Here the browser window resembles the ODV desktop application window consisting of canvas with station map and data windows and metadata, sample data and isosurface value lists on the right side. As in the desktop ODV, left mouse clicks on stations or data points select these items. Right-clicks bring up context menus providing the familiar ODV functionality. Users can download image files of the entire canvas or individual windows and can export the data of the current station set or of individual data windows.\n</p>\n<p>\n Data provider traceability\n</p>\n<p>\n A “cite” button has been implemented, which provides all dataset DOIs or citations (.txt, .bib or .ris) involved in the current visualization.\n</p>\n<p>\n Reproducibility\n</p>\n<p>\n Analyses and visualizations which have been created with ODV-online can be saved as so-called xviews, which are essentially XML files, which include instructions for ODV to create visualizations. Via our xview manager users can download, upload and delete personal xviews. These files can be used to fully reproduce the analyses and visualizations and can be shared e.g. with colleagues. Additionally, the xview files can be added e.g. as supplementary material to publications, and together with the link to the MOSAiC data collection in webODV, provide full reproducibility.\n</p>\n<p>\n Sharing\n</p>\n<p>\n A quick share functionality has been implemented, i.e. a link to a visualization, which can be retrieved by a single click and send to a colleague and is valid for 72h. If a colleague opens the link in the browser he/she is immediately send to the just created visualization to quickly discuss e.g. important findings or to continue own research.\n</p>", "presentation_type": "Live demo of a tool", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Sebastian", "last_name": "Mieruch-Schnülle", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": true }, { "author": { "first_name": "Julia", "last_name": "Freier", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false }, { "author": { "first_name": "Reiner", "last_name": "Schlitzer", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false } ], "submitter": "Sebastian Mieruch-Schnülle", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ] }, { "title": "Assessing the Feasibility of Self-Supervised Video Frame Interpolation and Forecasting on the Cloudcast Dataset", "url": "/events/data-science-symposium-7/submissions/64", "abstract": "<p>\n <span>\n <span>\n Cloud dynamics are integral to forecasting and monitoring weather and climate processes. Due to a scarcity of high-quality datasets, limited research has been done to realistically model clouds. This proposal applies state-of-the art machine-learning techniques to address this shortage,using a real-life dataset,CloudCast.\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n Potential techniques, such as RNNs and CNNs paired with data augmentations are explored. Preliminary results show promise for the task of supervised video frame interpolation and video prediction. High performance is achieved with a supervised approach.\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n These video techniques demonstrate a potential to lower the cost for satellite capture, restoration, and calibration of errors in remote sensing data. Future work is proposed to develop more robust video predictions on this and other similar datasets. With these additions, climate scientists and other practitioners could successfully work at a higher frequency.\n </span>\n </span>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Michelle", "last_name": "Lin", "orcid": null }, "affiliation": [ "Mila - Quebec AI Institute" ], "is_presenter": true } ], "submitter": "Viktoria Wichert", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Mila - Quebec AI Institute" ] }, { "title": "Approximation and Optimization of Environmental Simulations in High Spatio-Temporal Resolution through Machine Learning Methods", "url": "/events/data-science-symposium-7/submissions/36", "abstract": "<p>\n Environmental simulations in high spatio-temporal resolution consisting of large-scale dynamic systems are compute-intensive, thus usually demand parallelization of the simulations as well as high performance computing (HPC) resources. Furthermore, the parallelization of existing sequential simulations involves potentially a large configuration overhead and requires advanced programming expertise of domain scientists. On the other hand, despite the availability of modern powerful computing technologies, and under the perspective of saving energy, there is a need to address the issues such as complexity and scale reduction of large-scale systems’ simulations. In order to tackle these issues, we propose two approaches: 1. Approximation of simulations by model order reduction and unsupervised machine learning methods, and 2. Approximation of simulations by supervised machine learning methods.\n</p>\n<p>\n In the first method, we approximate large-scale and high-resolution environmental simulations and reduce their computational complexity by employing model order reduction techniques and unsupervised machine learning algorithms. In detail, we cluster functionally similar model units to reduce model redundancies, particularly similarities in the functionality of simulation model units and computation complexity. The underlying principle is that the simulation dynamics depend on model units’ static properties, current state, and external forcing. Based on this, we assume that similar model units’ settings lead to similar simulation dynamics. Considering this principle in the use case of a hydrological simulation named CAOS [1], we clustered the model units, ran the simulation model on a small representative subset of each cluster, and scaled the simulation output of the cluster representatives to the remaining cluster members. Experiments of this approach resulted in a balance between the simulation uncertainty and its computational effort. For evaluation of the quality of our approach, we used the proximity of the test simulation output to the original simulation, and to show the computational complexity of the approach, we measured the speedup of test simulation run time to the original simulation. Applying this approach to the CAOS use case results in a Root Mean Square Error (RMSE) of 0.0049 and a 1.8x speedup compared to the original simulation.\n</p>\n<p>\n In the second method, we approximate simulations through supervised machine learning methods focusing on deep neural networks. In this ongoing approach, we input multidimensional time series data into a Long Short-Term Memory network (LSTM). The LSTM model learns long-term dependencies and memorizes the information of previously seen data to predict the future data. In our use case simulation ICON-ART [2], the atmosphere is divided into cells with several input variables in which the concentration of trace gases is simulated. This simulation is based on coupled differential equations. The goal of this approach is to replace the compute-intensive chemistry simulation of about two million atmospheric cells with a trained neural network model to predict the concentration of trace gases at each cell and to reduce the computation complexity of the simulation.\n</p>\n<p>\n</p>\n<p>\n [1] E. Zehe, et al. 2014. HESS Opinions: From response units to functional units: a thermodynamic reinterpretation of the HRU concept to link spatial organization and functioning of intermediate scale catchments. HESS 18: 4635–4655. doi:\n <a href=\"http://doi.org/10.5194/hess-18-4635-2014\">\n 10.5194/hess-18-4635-2014\n </a>\n</p>\n<p>\n [2] D. Rieger, et al. 2015. ICON–ART 1.0 – a new online-coupled model system from the global to regional scale. GMD 8: 1659–1676. doi:\n <a href=\"http://doi.org/10.5194/gmd-8-1659-2015\">\n 10.5194/gmd-8-1659-2015\n </a>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Elnaz", "last_name": "Azmi", "orcid": "0000-0002-0073-8940" }, "affiliation": [ "Karlsruhe Institute of Technology" ], "is_presenter": true }, { "author": { "first_name": "Jörg", "last_name": "Meyer", "orcid": "0000-0003-0861-8481" }, "affiliation": [ "Karlsruhe Institute of Technology" ], "is_presenter": false }, { "author": { "first_name": "Marcus", "last_name": "Strobl", "orcid": "0000-0001-8265-227X" }, "affiliation": [ "Karlsruhe Institute of Technology" ], "is_presenter": false }, { "author": { "first_name": "Achim", "last_name": "Streit", "orcid": "0000-0002-5065-469X" }, "affiliation": [ "Karlsruhe Institute of Technology" ], "is_presenter": false } ], "submitter": "Elnaz Azmi", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Karlsruhe Institute of Technology" ] }, { "title": "Machine-Learning-Based Comparative Study to Detect Suspect Temperature Gradient Error in Ocean Data.", "url": "/events/data-science-symposium-7/submissions/39", "abstract": "<p>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n Thousands of ocean temperature and salinity measurements are collected every day around the world. Controlling the quality of this data is a human resource-intensive task because the control procedures still produce many false alarms only detected by a human expert. Indeed, quality control (QC) procedures have not yet benefited from the recent development of efficient machine learning methods to predict simple targets from complex multi-dimensional features. With increasing amounts of big data, algorithmic help is urgently needed, where artificial intelligence (AI) could play a dominant role. Developments in data mining and machine learning in automatic oceanographic data quality control need to be revolutionized. Such techniques provide a convenient framework to improve automatic QC by using supervised learning to reduce the discrepancy with the human expert evaluation.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n This scientific work proposes a comparative analysis of machine learning classification algorithms for ocean data quality control to detect the suspect temperature gradient error. The objective of this work is to obtain a very effective QC classification method from ocean data using a representative set of supervised machine learning algorithms. The work to be presented consists of the second step of our overall system, in which the first is based on a deep convolutional neural network to detect good/bad profiles, and the second is to locate bad samples. For this reason, the dataset used to train the used benchmarking models is composed only of bad profiles.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n The following algorithms are used in this study (with a hyperparameters optimisation): Multilayer Perceptron (MLP), Support Vector Machine (SVM) with different kernels, Random Forest (RF), Naive Bayes (NB), K-Nearest Neighbors (KNN), and Linear Discriminant Analysis (LDA). Optimization of the hyper-parameters using Grid-Search is required to ensure the best classification results.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n <span>\n The results obtained on the Unified Database for the Arctic and Subarctic Hydrography (UDASH) dataset are promising, especially with the MLP algorithm, in which we had an accuracy of 86.64% in the detection of good samples and 88.84% in the detection of the bad samples, where room for improvement exists. This system could have the potential to be used as a semi-automatic quality control system.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Mohamed", "last_name": "Chouai", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": true }, { "author": { "first_name": "Felix", "last_name": "Reimers", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false }, { "author": { "first_name": "Myriel", "last_name": "Vredenborg", "orcid": "0000-0003-1884-2065" }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false }, { "author": { "first_name": "Stefan", "last_name": "Pinkernell", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false }, { "author": { "first_name": "Sebastian", "last_name": "Mieruch-Schnülle", "orcid": null }, "affiliation": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ], "is_presenter": false } ], "submitter": "Mohamed Chouai", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Alfred-Wegener-Institut - Helmholtz-Zentrum für Polar- und Meeresforschung" ] }, { "title": "Machine Learning Parameterization for Cloud Microphysics", "url": "/events/data-science-symposium-7/submissions/35", "abstract": "<p dir=\"ltr\">\n In weather and climate models, physical processes that can’t be explicitly resolved are often parameterized. Among them is cloud microphysics that often works in tandem with the convective parameterization to control the formation of clouds and rain.\n</p>\n<p dir=\"ltr\">\n Existing parameterization schemes available for cloud microphysics suffer from an accuracy/speed trade-off. The most accurate schemes based on Lagrangian droplet methods are computationally expensive and are only used for research and development. On the other hand, more widely used approaches such as bulk moment schemes simplify the particle size distributions into the total mass and number density of cloud and rain droplets. While these approximations are fairly realistic in many cases, they struggle to represent more complex microphysical scenarios.\n</p>\n<p dir=\"ltr\">\n We develop a machine learning based parameterization to emulate the warm rain formation process in the Super droplet scheme (a type of Lagrangian scheme) in a dimensionless control volume. We show that the ML based emulator matches the Lagrangian simulations better than the bulk moment schemes, especially in the cases of very skewed droplet distributions. Compared to previous attempts in emulating warm rain, our ML model shows a better performance. The ML model inference runs fast thereby reducing the computational time otherwise needed for Lagrangian schemes. Thus, we have developed an ML based emulator that is more accurate than the commonly used schemes with only a small computational overhead, hence, making it possible to indirectly use Lagrangian schemes in operational weather models.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Shivani", "last_name": "Sharma", "orcid": "0000-0001-6973-5660" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "David Solomon", "last_name": "Greenberg", "orcid": "0000-0002-8515-0459" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false } ], "submitter": "Shivani Sharma", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon" ] }, { "title": "MuSSeL project data management and outreach", "url": "/events/data-science-symposium-7/submissions/48", "abstract": "<p>\n <span>\n <span>\n <span>\n <span lang=\"EN-US\">\n <span>\n The collaborative project “MuSSeL” investigates various natural and anthropogenic changes, such as climate change, increase of fishing and the development of offshore wind farms, and the effect these changes have on the biodiversity and well-being of benthic communities in the North Sea. A central part of this project is to make any data gathered, easily accessible to stakeholders and the general public alike. To streamline this process it was decided to use ESRI software solutions, for data management and public outreach. The live demo will demonstrate how data can be visualised, analysied and made available on the project website, all using ESRI solutions.\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>", "presentation_type": "Live demo of a tool", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Marie", "last_name": "Ryan", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true } ], "submitter": "Marie Ryan", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon" ] }, { "title": "Investigating the coastal impacts of riverine flood events with the River Plume Workflow", "url": "/events/data-science-symposium-7/submissions/28", "abstract": "<p>\n <span>\n <span>\n <span>\n The River Plume Workflow is a part of the Digital Earth Flood event explorer (FEE), which was designed to compile different aspects of riverine flood events.\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n The focus of the River Plume Workflow is the impact of riverine flood events on the marine environment, when, at the end of a flood event chain, an unusual amount of nutrients and pollutants is washed into the coastal waters. The River Plume Workflow provides scientists with tools to detect river plumes in marine data during or after an extreme event and to investigate their spatio-temporal extent, their propagation and impact. This is achieved through the combination of in-situ data from autonomous measuring devices, drift model data produced especially for the observational data and satellite data of the observed area. In the North Sea, we use measurements from the FerryBox mounted on the Büsum-Helgoland ferry to obtain regular in-situ data and offer model trajectories from drift simulations around the time of extreme events in the Elbe River.\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n The River Plume Workflow helps scientists identify river plume candidates either manually within a visual interface or through an automatic anomaly detection algorithm, using Gaussian regression. Combining the observational data with model trajectories that show the position of a measured water body up to 10 days before and after the measurement allows to investigate the propagation of an anomaly, as well as to check its origin, e.g. the Elbe estuary. This way, scientists can identify regions of interest presumably impacted by riverine flood events. Combining model trajectories with satellite data also provides scientists with time series of parameters, e.g. Chlorophyll-A, along a model trajectory, allowing research on degradation rates and unusual behavior during or after an extreme event.\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n With the deployment of the River Plume Workflow coming up, I would like to demonstrate the functionalities of the tool and discuss its applications.\n </span>\n </span>\n </span>\n</p>", "presentation_type": "Live demo of a tool", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Viktoria", "last_name": "Wichert", "orcid": "0000-0002-3402-6562" }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": true }, { "author": { "first_name": "Holger", "last_name": "Brix", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Nicola", "last_name": "Abraham", "orcid": null }, "affiliation": [ "Helmholtz-Zentrum Hereon" ], "is_presenter": false }, { "author": { "first_name": "Daniela", "last_name": "Rabe", "orcid": null }, "affiliation": [ "GFZ German Research Centre for Geosciences" ], "is_presenter": false } ], "submitter": "Viktoria Wichert", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz-Zentrum Hereon", "GFZ German Research Centre for Geosciences" ] }, { "title": "Low-Carbon Routing using Genetic Stochastic Optimization and Global Ocean Weather", "url": "/events/data-science-symposium-7/submissions/19", "abstract": "<h1 dir=\"ltr\">\n Introduction\n</h1>\n<p dir=\"ltr\">\n Existing marine technology introduces the capability for every marine vehicle to integrate in a timely manner a large collection of global ship positioning data along with instructed higher safety and reduced emission intensity routes. New AI-assisted navigational devices could quickly integrate unsupervised routing predictions based on assimilated and forecasted global ocean and weather.\n</p>\n<h1 dir=\"ltr\">\n Description of goals\n</h1>\n<p dir=\"ltr\">\n Here we design a route optimization algorithm for taking advantage of current predictions from ocean circulation models. We develop validation scenarios for a marine vehicle and show how it can propagate in a safer environment. Our optimization is designed to fulfill emission goals by achieving a lower fuel use.\n</p>\n<h1 dir=\"ltr\">\n Results and Achievements\n</h1>\n<p dir=\"ltr\">\n The weather data from the European Observational Marine Copernicus Center allows leveraging both satellite observations and high resolution wind, wave and current predictions at any position in the global ocean. We propose a low fuel consumption, carbon emission ship routing optimization that employs these real time high resolution data in a stochastic optimization algorithm.\n</p>\n<p dir=\"ltr\">\n The proposed optimization method is based on local continuous random modifications subsequently applied to an initial shortest-distance route between two points. It is parallelised using a genetic approach. The model is validated using both vessel noon reports and data from a global automated identification system records.\n</p>\n<p dir=\"ltr\">\n We show that for the performed routing scenarios across the north Atlantic the achieved fuel could save about 10% of fuel for slow-steaming scenarios and hence lead to an additional reduction of carbon emissions even for already fuel-optimized operation.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Elena", "last_name": "Shchekinova", "orcid": "0000-0001-5346-8318" }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ], "is_presenter": true }, { "author": { "first_name": "Willi", "last_name": "Rath", "orcid": "0000-0003-1951-8494" }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ], "is_presenter": false }, { "author": { "first_name": "Arne", "last_name": "Biastoch", "orcid": "0000-0003-3946-4390" }, "affiliation": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel" ], "is_presenter": false }, { "author": { "first_name": "Niko", "last_name": "Amann", "orcid": null }, "affiliation": [ "Christian-Albrechts Universität zu Kiel" ], "is_presenter": false }, { "author": { "first_name": "Frithjof", "last_name": "Hennemann", "orcid": null }, "affiliation": [ "TrueOcean GmbH" ], "is_presenter": false }, { "author": { "first_name": "Philipp", "last_name": "Kraus", "orcid": null }, "affiliation": [ "TrueOcean GmbH" ], "is_presenter": false }, { "author": { "first_name": "Anton", "last_name": "Myagotin", "orcid": null }, "affiliation": [ "TrueOcean GmbH" ], "is_presenter": false }, { "author": { "first_name": "Matthias", "last_name": "Renz", "orcid": null }, "affiliation": [ "Christian-Albrechts Universität zu Kiel" ], "is_presenter": false }, { "author": { "first_name": "Simon", "last_name": "van der Wulp", "orcid": null }, "affiliation": [ "TrueOcean GmbH" ], "is_presenter": false } ], "submitter": "Elena Shchekinova", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "GEOMAR Helmholtz-Zentrum für Ozeanforschung Kiel", "Christian-Albrechts Universität zu Kiel", "TrueOcean GmbH" ] }, { "title": "Learning deep emulators for the interpolation of satellite altimetry data", "url": "/events/data-science-symposium-7/submissions/57", "abstract": "<p>\n Over the last few years, a very active field of research has aimed at exploring new data-driven and learning-based methodologies to propose computationally efficient strategies able to benefit from the large amount of observational remote sensing and numerical simulations for the reconstruction, interpolation and prediction of high-resolution derived products of geophysical fields. These approaches now reach state-of-the performance for the reconstruction of satellite-derived geophysical fields. In this context, deep emulators emerge as new means to bridge model-driven and learning-based frameworks. Here, we focus on deep emulators for reconstruction and data assimilation issues, and more specifically on 4DVarNet schemes. These schemes bridge variational data assimilation formulation and deep learning schemes to learn 4DVar models and solvers from data. Here, we present an application of 4dVarNet schemes to the reconstruction of sea surface dynamics. More specifically, we aim at learning deep emulators for the interpolation from altimetry data. Similarly to a classic optimal interpolation, we leverage a minimization-based strategy but we benefit from the modeling flexibility of deep learning framework to embed non-linear and multi-scale priors and learn jointly a gradient-based solver for the underlying variational cost. Overall, the proposed 4dVarNet scheme defines an end-to-end neural architecture which use irregularly-sampled altimetry data as inputs and outputs a gridded and gap-free fields. We report numerical experiments within a data challenge dedicated to the benchmarking of SSH (Sea Surface Height) mapping algorithms. This data challenge relies on an observation system simulation experiments (OSSE) setting in a Gulf Stream region with nadir and wide-swath SWOT satellite altimetry data.\n <br/>\n Our numerical experiments demonstrate that we can train neural interpolation schemes with very large missing data rates (between 90% and 95%) in a supervised manner. The proposed approach outperforms state-of-the-art schemes, including model-driven ones, and significantly improves the resolved space and time scales compared to the operational optimally-interpolated SSH product. We further discuss the extensions of the proposed scheme especially towards the multi-scale reconstruction of sea surface dynamics from multi-source data.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Hugo", "last_name": "GEORGENTHUM", "orcid": null }, "affiliation": [ "IMT Atlantique" ], "is_presenter": true }, { "author": { "first_name": "ronan", "last_name": "fablet", "orcid": null }, "affiliation": [ "IMT Atlantique" ], "is_presenter": false } ], "submitter": "ronan fablet", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "IMT Atlantique" ] }, { "title": "AI4FoodSecurity: Identifying crops from space", "url": "/events/data-science-symposium-7/submissions/49", "abstract": "<p dir=\"ltr\">\n The European Space Agency (ESA) launched the AI4EO initiative to bridge the gap between the artificial intelligence (AI) and the Earth observation (EO) communities [1]. In the AI4FoodSecurity challenge [2], the goal is to identify crops in agricultural fields using time series remote sensing data from different satellites. In the first challenge track, predictions were made for a region in South Africa, including a spatial domain shift. In the second challenge track, predictions were made for a region in Germany (Brandenburg), including a spatio-temporal domain shift.\n</p>\n<p dir=\"ltr\">\n We here present our contribution to the AI4FoodSecurity challenge. As data sources we selected both radar wavelength images from the Sentinel-1 satellite, as well as visual and infrared images from the Planet Fusion Monitoring satellites. We implemented a Pixel-Set Encoder with Lightweight Temporal Attention (PseLTae) [3]. Samples are constructed by randomly selecting pixels from a given agricultural field. We train separate encoders for each data source. Attention heads are used to extract characteristic changes of the distinct crop types throughout the growing season. At the decoder stage, both sources are combined to yield a prediction for the crop type. We used data augmentation by oversampling the agricultural fields, as well as cross validation.\n</p>\n<p dir=\"ltr\">\n The quality of the predictions is evaluated by a multi-class classification score. We finished the challenge in second place in both tracks. We found the model performs better in the South African region, where only the spatial domain changed, compared to the German region that was evaluated in the next vegetation period. Our code is available on Github [4].\n</p>\n<p dir=\"ltr\">\n <img height=\"261\" src=\"https://lh4.googleusercontent.com/ZYt1IoJdLjI7zwbshOzHHe7ryqomaW9ikm_H2yYLpLQkrm2YwG-5v6_ES4WwGZQeWxedS8O6pb0BjBmsZFG9zRGJPNbo-4SDYY89e3NnIA2AT_r5JOkTnsTDqUtvVy3fCe13lPI\" width=\"267\"/>\n</p>\n<p dir=\"ltr\">\n Figure: Colored map of crop predictions for the region in Germany, including nine crop types.\n</p>\n<p>\n</p>\n<p dir=\"ltr\">\n References:\n</p>\n<p dir=\"ltr\">\n [1] A.-K. Debien et al, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 251-253 (2021).\n</p>\n<p dir=\"ltr\">\n [2] AI4FoodSecurity Challenge:\n <a href=\"https://ai4eo.eu/ai4eo-ai4foodsecurity-challenge\">\n https://ai4eo.eu/ai4eo-ai4foodsecurity-challenge\n </a>\n</p>\n<p dir=\"ltr\">\n [3] V. Sainte Fare Garnot et al, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12325-12334 (2020)\n</p>\n<p>\n [4]\n <a href=\"https://github.com/crlna16/ai4foodsecurity\">\n https://github.com/crlna16/ai4foodsecurity\n </a>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Frauke", "last_name": "Albrecht", "orcid": null }, "affiliation": [ "Deutsches Klimarechenzentrum (DKRZ)" ], "is_presenter": true }, { "author": { "first_name": "Caroline", "last_name": "Arnold", "orcid": null }, "affiliation": [ "Deutsches Klimarechenzentrum (DKRZ)", "Helmholtz Zentrum München" ], "is_presenter": false }, { "author": { "first_name": "Christina", "last_name": "Bukas", "orcid": null }, "affiliation": [ "Helmholtz Zentrum München" ], "is_presenter": false } ], "submitter": "Frauke Albrecht", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Deutsches Klimarechenzentrum (DKRZ)", "Helmholtz Zentrum München" ] }, { "title": "Automatic low-dimension explainable feature extraction of climatic drivers leading to forest mortality", "url": "/events/data-science-symposium-7/submissions/63", "abstract": "<p>\n Forest mortality is a complex phenomenon because of the interaction of multiple drivers over\n <br/>\n a long period. Understanding these interactions and their relevant time scale is important for\n <br/>\n forest management. Unlike climate data which are continuous (daily or hourly resolutions),\n <br/>\n forest mortality related observations are discrete (e.g. the number of trees, mortality fraction)\n <br/>\n with lower frequencies (once/twice a year). Forests also have persistent memory with a large\n <br/>\n buffering capacity. All the above-mentioned reasons make the analysis of forest mortality\n <br/>\n difficult with the conventional tools. Deep learning is well suited for modelling multivariate\n <br/>\n time series with persistent non-linear interactions. In this study, we generate 200,000 years of\n <br/>\n hourly climate data using a weather generator (AWE-GEN). We aggregate the hourly data to\n <br/>\n daily values and feed it to a process based forest model (FORMIND). The forest model gives\n <br/>\n us mortality fractions per year, in line with the forest mortality related observations. For the\n <br/>\n method development phase, we use these simulated data. First, we use a variationalautoencoder\n <br/>\n to extract climatic features and use that for the prediction of forest mortality. In\n <br/>\n the second stage, we do the prediction of forest mortality and feature extraction together and\n <br/>\n illustrate the difference between the extracted features. Further different approach to\n <br/>\n disentangle the extracted feature are tested. Finally, we present the analysis of performance\n <br/>\n versus explainability for different approaches.\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Mohit", "last_name": "Anand", "orcid": null }, "affiliation": [ "Helmholtz Centre for Environmental Research" ], "is_presenter": true }, { "author": { "first_name": "Gustau", "last_name": "Camps-Valls", "orcid": null }, "affiliation": [ "Image Processing Laboratory (IPL), Universitat de València" ], "is_presenter": false }, { "author": { "first_name": "Jakob", "last_name": "Zscheischler", "orcid": null }, "affiliation": [ "Helmholtz Centre for Environmental Research" ], "is_presenter": false } ], "submitter": "Viktoria Wichert", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz Centre for Environmental Research", "Image Processing Laboratory (IPL), Universitat de València" ] }, { "title": "Model evaluation method affects the interpretation of machine learning models for identifying compound drivers of maize variability", "url": "/events/data-science-symposium-7/submissions/65", "abstract": "<p>\n <span>\n <span>\n <span>\n <span>\n <span lang=\"EN-GB\">\n <span>\n <span>\n <span>\n Extreme impacts can be caused by the compounding effects of multiple drivers, such as weather events that might not individually be considered extreme. An example of this is the phenomenon of ‘false spring’, where a combination of a warm late winter or early spring, followed by a frost once the plants have entered a vulnerable stage of development, results in severe crop damage. The relationships between growing-season climate conditions and end-of-season crop yield are complex and nonlinear, and improving our understanding of such interactions could aid in narrowing the uncertainty in estimates of climate risk to food security. Additionally, data-driven methods that are capable of identifying such compounding effects could be useful for the study of other sectoral impacts.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n <span>\n <span lang=\"EN-GB\">\n <span>\n <span>\n <span>\n Machine learning is an option for capturing such complex and nonlinear relationships for yield prediction. In order to extract these relationships, explainable or interpretable machine learning has been identified as a potential tool. However, the usefulness of those extracted interpretations is dependent on the assumption that the model has learned the expected relationships. One prerequisite for this assumption is that the model has sufficient predictive skill. The method chosen for measuring model performance is therefore an important methodological decision, but as yet the ‘best practice’ when handling spatiotemporal climate data is not clearly defined.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>\n<p>\n <span>\n <span>\n <span>\n <span>\n <span lang=\"EN-GB\">\n <span>\n <span>\n <span>\n In this study we train machine learning models to predict maize yield variability from growing-season climate data, using global climate reanalysis data and corresponding driven process-based crop model output. We assess the impact of the cross-validation procedure used for model skill measurement on each step of the modelling process: hyperparameter tuning, feature selection, performance evaluation and model interpretation. We show that the method of evaluating model skill has significant impacts on results when using interpretable machine learning methods. Our results suggest that the design of the cross-validation procedure should reflect the purpose of the study and the qualities of the data used, which in our case are highly-correlated spatiotemporal climate and crop yield data.\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n </span>\n</p>", "presentation_type": "Poster", "session": "Posters and Live Demos", "start": null, "duration": null, "authors": [ { "author": { "first_name": "Lily-Belle", "last_name": "Sweet", "orcid": null }, "affiliation": [ "Helmholtz Centre for Environmental Research" ], "is_presenter": true }, { "author": { "first_name": "Jakob", "last_name": "Zscheischler", "orcid": null }, "affiliation": [ "Helmholtz Centre for Environmental Research" ], "is_presenter": false } ], "submitter": "Viktoria Wichert", "event": "Data Science Symposium No. 7", "activity": null, "accepted": false, "license": "Creative Commons Attribution 4.0 International (CC-BY-4.0)", "affiliations": [ "Helmholtz Centre for Environmental Research" ] } ]