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  • 1
    Keywords: Earth sciences. ; Artificial intelligence Data processing. ; Statistics . ; Mathematics. ; Earth Sciences. ; Data Science. ; Statistics. ; Mathematics.
    Description / Table of Contents: Data Science and Earth System Science -- The Digital Earth project: focus and agenda -- Data analysis and exploration with visual approaches -- Data analysis and exploration with computational approaches -- Data analysis and exploration with scientific workflows -- The Digital Earth SMART monitoring concept and tools -- Interdisciplinary collaboration -- Evaluating the success of the Digital Earth project -- Lessons learned in the Digital Earth project.
    Abstract: This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
    Type of Medium: Online Resource
    Pages: XIV, 148 p. 47 illus. in color. , online resource.
    Edition: 1st ed. 2022.
    ISBN: 9783030995461
    Series Statement: SpringerBriefs in Earth System Sciences,
    DDC: 550
    Language: English
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  • 2
    Call number: 978-3-030-99546-1 (e-book)
    Description / Table of Contents: Data Science and Earth System Science -- The Digital Earth project: focus and agenda -- Data analysis and exploration with visual approaches -- Data analysis and exploration with computational approaches -- Data analysis and exploration with scientific workflows -- The Digital Earth SMART monitoring concept and tools -- Interdisciplinary collaboration -- Evaluating the success of the Digital Earth project -- Lessons learned in the Digital Earth project.
    Description / Table of Contents: This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
    Type of Medium: 12
    Pages: 1 Online-Ressource (XIV, 148 Seiten) , Illustrationen
    Edition: 1st ed. 2022.
    ISBN: 9783030995461
    Series Statement: SpringerBriefs in Earth System Sciences
    Language: English
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  • 3
    Call number: M 23.95066 1. Ex. ; M 23.95066 2.Ex.
    Description / Table of Contents: Data Science and Earth System Science -- The Digital Earth project: focus and agenda -- Data analysis and exploration with visual approaches -- Data analysis and exploration with computational approaches -- Data analysis and exploration with scientific workflows -- The Digital Earth SMART monitoring concept and tools -- Interdisciplinary collaboration -- Evaluating the success of the Digital Earth project -- Lessons learned in the Digital Earth project.
    Description / Table of Contents: This open access book presents the results of three years collaboration between earth scientists and data scientist, in developing and applying data science methods for scientific discovery. The book will be highly beneficial for other researchers at senior and graduate level, interested in applying visual data exploration, computational approaches and scientifc workflows.
    Type of Medium: Monograph available for loan
    Pages: XIV, 148 Seiten , Illustrationen
    ISBN: 9783030995454
    Series Statement: SpringerBriefs in Earth System Sciences
    Language: English
    Location: Upper compact magazine
    Location: Upper compact magazine
    Branch Library: GFZ Library
    Branch Library: GFZ Library
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  • 4
    Publication Date: 2023-02-08
    Description: Enrichment of the oceans with CO2 may be beneficial for some marine phytoplankton, including harmful algae. Numerous laboratory experiments provided valuable insights into the effects of elevated pCO(2) on the growth and physiology of harmful algal species, including the production of phycotoxins. Experiments close to natural conditions are the next step to improve predictions, as they consider the complex interplay between biotic and abiotic factors that can confound the direct effects of ocean acidification. We therefore investigated the effect of ocean acidification on the occurrence and abundance of phycotoxins in bulk plankton samples during a long-term mesocosm experiment in the Gullmar Fjord, Sweden, an area frequently experiencing harmful algal blooms. During the experimental period, a total of seven phycotoxin-producing harmful algal genera were identified in the fjord, and in accordance, six toxin classes were detected. However, within the mesocosms, only domoic acid and the corresponding producer Pseudo-nitzschia spp. was observed. Despite high variation within treatments, significantly higher particulate domoic acid contents were measured in the mesocosms with elevated pCO(2). Higher particulate domoic acid contents were additionally associated with macronutrient limitation. The risks associated with potentially higher phycotoxin levels in the future ocean warrants attention and should be considered in prospective monitoring strategies for coastal marine waters.
    Type: Article , PeerReviewed
    Format: text
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  • 5
    Publication Date: 2023-06-21
    Description: Modern digital scientific workflows - often implying Big Data challenges - require data infrastructures and innovative data science methods across disciplines and technologies. Diverse activities within and outside HGF deal with these challenges, on all levels. The series of Data Science Symposia fosters knowledge exchange and collaboration in the Earth and Environment research community. We invited contributions to the overarching topics of data management, data science and data infrastructures. The series of Data Science Symposia is a joint initiative by the three Helmholtz Centers HZG, AWI and GEOMAR Organization: Hela Mehrtens and Daniela Henkel (GEOMAR)
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 6
    Publication Date: 2023-06-21
    Description: During the MOSAiC expedition, the German research icebreaker Polarstern spends a full year drifting through the Arctic Ocean. Scientists from 20 countries participate in the largest polar expedition in history exploring the Arctic climate system. The experiment covers a large suite of in-situ and remote sensing observations of physical, ecological and biogeochemical parameters to describe the processes coupling the atmosphere, sea ice, and ocean. In addition to forefront instrumentation and observational techniques, proper data management is essential for large and complex projects and field programs. Key elements are agreements on consistent sampling strategies, the possibility to monitor the data flow, to facilitate near real-time processing, and analysis and sharing of data during and long after the expedition. Furthermore, data publication and documentation are crucial for such a collaborative effort and will build the legacy of the project and finally take climate science to the next level. We adapted our modular research data management framework O2A “Data flow from Observations to Archives” to meet the expedition requirements and ensure central data archival for generations to come. Researchers register all necessary sensor metadata beforehand. Essential metadata of scientific actions in the field are ingested immediately with the FloeNavi, a novel system enabling navigation on a drifting ice floe. O2A provides tools to automatize data ingestion, monitor the data flow and process, analyze and publish data. Integration of ship- and land-based components and a shared storage ensure seamless continuation of collaboration during and after the expedition laying the fundamentals for numerous data publications.
    Repository Name: EPIC Alfred Wegener Institut
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  • 7
    Publication Date: 2023-06-21
    Description: During the largest polar expedition in history starting in September 2019, the German research icebreaker Polarstern spends a whole year drifting with the ice through the Arctic Ocean. The MOSAiC expedition takes the closest look ever at the Arctic even throughout the polar winter to gain fundamental insights and most unique on-site data for a better understanding of global climate change. Hundreds of researchers from 20 countries are involved. Scientists will use the in situ gathered data instantaneously in near-real time modus as well as long afterwards all around the globe taking climate research to a completely new level. Hence, proper data management, sampling strategies beforehand, and monitoring actual data flow as well as processing, analysis and sharing of data during and long after the MOSAiC expedition are the most essential tools for scientific gain and progress. To prepare for that challenge we adapted and integrated the research data management framework O2A “Data flow from Observations to Archives” to the needs of the MOSAiC expedition on board Polarstern as well as on land for data storage and access at the Alfred Wegener Institute Computing and Data Center in Bremerhaven, Germany. Our O2A-framework assembles a modular research infrastructure comprising a collection of tools and services. These components allow researchers to register all necessary sensor metadata beforehand linked to automatized data ingestion and to ensure and monitor data flow as well as to process, analyze, and publish data to turn the most valuable and uniquely gained arctic data into scientific outcomes. The framework further allows for the integration of data obtained with discrete sampling devices into the data flow. These requirements have led us to adapt the generic and cost-effective framework O2A to enable, control, and access the flow of sensor observations to archives in a cloud-like infrastructure on board Polarstern and later on to land based repositories for international availability. Major roadblocks of the MOSAiC-O2A data flow framework are (i) the increasing number and complexity of research platforms, devices, and sensors, (ii) the heterogeneous interdisciplinary driven requirements towards, e. g., satellite data, sensor monitoring, in situ sample collection, quality assessment and control, processing, analysis and visualization, and (iii) the demand for near real time analyses on board as well as on land with limited satellite bandwidth. The key modules of O2A's digital research infrastructure established by AWI are implementing the FAIR principles: SENSORWeb, to register sensor applications and sampling devices and capture controlled meta data before and alongside any measurements in the field Data ingest, allowing researchers to feed data into storage systems and processing pipelines in a prepared and documented way, at best in controlled near real-time data streams Dashboards allowing researchers to find and access data and share and collaborate among partners Workspace enabling researchers to access and use data with research software utilizing a cloud-based virtualized infrastructure that allows researchers to analyze massive amounts of data on the spot Archiving and publishing data via repositories and Digital Object Identifiers (DOI)
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 8
    Publication Date: 2023-06-21
    Description: Today's fast digital growth made data the most essential tool for scientific progress in Earth Systems Science. Hence, we strive to assemble a modular research infrastructure comprising a collection of tools and services that allow researchers to turn big data into scientific outcomes. Major roadblocks are (i) the increasing number and complexity of research platforms, devices, and sensors, (ii) the heterogeneous project-driven requirements towards, e. g., satellite data, sensor monitoring, quality assessment and control, processing, analysis and visualization, and (iii) the demand for near real time analyses. These requirements have led us to build a generic and cost-effective framework O2A (Observation to Archive) to enable, control, and access the flow of sensor observations to archives and repositories. By establishing O2A within major cooperative projects like MOSES and Digital Earth in the research field Earth and Environment of the German Helmholtz Association, we extend research data management services, computing powers, and skills to connect with the evolving software and storage services for data science. This fully supports the typical scientific workflow from its very beginning to its very end, that is, from data acquisition to final data publication. The key modules of O2A's digital research infrastructure established by AWI to enable Digital Earth Science are implementing the FAIR principles: Sensor Web, to register sensor applications and capture controlled meta data before and alongside any measurement in the field Data ingest, allowing researchers to feed data into storage systems and processing pipelines in a prepared and documented way, at best in controlled NRT data streams Dashboards, allowing researchers to find and access data and share and collaborate among partners Workspace, enabling researchers to access and use data with research software in a cloud-based virtualized infrastructure that allows researchers to analyse massive amounts of data on the spot Archiving and publishing data via repositories and Digital Object Identifiers (DOI)
    Repository Name: EPIC Alfred Wegener Institut
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  • 9
    Publication Date: 2023-06-21
    Description: Artificial Intelligence for Cold Regions (AI-CORE) is a collaborative project of the DLR, the AWI, the TU Dresden, and is funded by the Helmholtz Foundation since early 2020. The project aims at developing AI methods for addressing some of the most challenging research questions in cryosphere remote sensing, rapidly changing ice sheets and thawing permafrost. We apply data analytics approaches to discover the data variable from data set simulated with an ice sheet model, observe the migration, and time Series analysis to predict and contrast this to simulated grounding line position. For the data assimilation in simulations of the Greenland ice sheet, we engage a level set method, that allows to derive a continuous function in time and space from discrete information at satellite acquisition time steps. We use an alpha-shape method to derive a seamless product of the margin at each time step to be used in the level set method driving the simulations. We develop AI algorithms and tools that allow scaling of our analyses to very large regions. Here we focus on the detection of Retrogressive Thaw Slumps (RTS), highly dynamic erosion processes caused by rapid permafrost thaw. We apply deep-learning based object detection on dense time-series of high-resolution (3m) multi-spectral PlanetScope satellite images and auxiliary datasets such as digital elevation models. RTS detection is challenging, as they are difficult to define semantically and spatially and are highly dynamic and embedded in different landscape settings. The results will help to understand, quantify and predict RTS dynamics and their landscape-scale impacts in a rapidly warming Arctic. We upgrade the base IT-infrastructure at AWI by integrating new GPU computing hardware into the on-premise IT-infrastructure to speed up the computing, data storage capabilities, and parallel processing, supporting the analytical workflows specifically.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Conference , notRev
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  • 10
    Publication Date: 2024-05-03
    Description: Earth System Science (ESS) relies on the availability of data from varying resources and ranging over different disciplines. Hence, data sources are rich and diverse, including observatories, satellites, measuring campaigns, model simulations, case studies, laboratory experiments as well as citizen science etc. At the same time, practices of professional research data management (RDM) are differing significantly among various disciplines. There are many well-known challenges in enabling a free flow of data in the sense of the FAIR criteria. Such are data quality assurance, unique digital identifiers, access to and integration of data repositories, just to mention a few. The Helmholtz DataHub Earth&Environment is addressing digitalization in ESS by developing a federated data infrastructure. Existing RDM practices at seven centers of the Helmholtz Association working together in a joint research program within the Research Field Earth and Environment (RF E&E) are harmonized and integrated in a comprehensive way. The vision is to establish a digital research ecosystem fostering digitalization in geosciences and environmental sciences. Hereby, issues of common metadata standards, digital object identifiers for samples, instruments and datasets, defined role models for data sharing certainly play a central role. The various data generating infrastructures are registered digitally in order to collect metadata as early as possible and enrich them along the flow of the research cycle. Joint RDM bridging several institutions relies on professional practices of distributed software development. Apart from operating cross-center software development teams, the solutions rely on concepts of modular software design. For example, a generic framework has been developed to allow for quick development of tools for domain specific data exploration in a distributed manner. Other tools incorporate automated quality control in data streams. Software is being developed following guiding principles of open and reusable research software development. A suite of views is being provided, allowing for varying user perspectives, monitoring data flows from sensor to archive, or publishing data in quality assured repositories. Furthermore, high-level data products are being provided for stakeholders and knowledge transfer (for examples see https://datahub.erde-und-umwelt.de). Furthermore, tools for integrated data analysis, e.g. using AI approaches for marine litter detection can be implemented on top of the existing software stack. Of course, this initiative does not exist in isolation. It is part of a long-term strategy being embedded within national (e.g. NFDI) and international (e.g. EOSC, RDA) initiatives.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Other , notRev
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