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  • 1
    Publication Date: 2022-08-18
    Description: 43rd IAMSLIC Annual Conference: Honolulu, Hawaii, USA, October 22-26, 2017
    Type: Proceedings , NonPeerReviewed
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  • 2
    Publication Date: 2022-08-18
    Description: 46th IAMSLIC Annual Conference: 1st IAMSLIC Virtual Conference Online, 13-14 October 2020
    Type: Proceedings , NonPeerReviewed
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  • 3
    Publication Date: 2022-08-18
    Type: Proceedings , NonPeerReviewed
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  • 4
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    In:  ISPRS International Journal of Geo-Information, 5 (4). p. 40.
    Publication Date: 2019-02-01
    Description: Sharing and secondary analysis of data have become increasingly important for research. Especially in geography, the collection of digital data has grown due to technological changes. Responsible handling and proper documentation of research data have therefore become essential for funders, publishers and higher education institutions. To achieve this goal, universities offer support and training in research data management. This article presents the experiences of a pilot workshop in research data management, especially for geographers. A discipline-specific approach to research data management training is recommended. The focus of this approach increases researchers’ interest and allows for more specific guidance. The instructors identified problems and challenges of research data management for geographers. In regards to training, the communication of benefits and reaching the target groups seem to be the biggest challenges. Consequently, better incentive structures as well as communication channels have to be established.
    Type: Article , PeerReviewed
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  • 5
    Publication Date: 2021-02-16
    Description: Arctic and subarctic regions are sensitive to climate change and, reversely, provide dramatic feedbacks to the global climate. With a focus on discovering paleoclimate and paleoceanographic evolution in the Arctic and Northwest Pacific Oceans during the last 20,000 years, we proposed this German–Sino cooperation program according to the announcement “Federal Ministry of Education and Research (BMBF) of the Federal Republic of Germany for a German–Sino cooperation program in the marine and polar research”. Our proposed program integrates the advantages of the Arctic and Subarctic marine sediment studies in AWI (Alfred Wegener Institute) and FIO (First Institute of Oceanography). For the first time, the collection of sediment cores can cover all climatological key regions in the Arctic and Northwest Pacific Oceans. Furthermore, the climate modeling work at AWI enables a “Data-Model Syntheses”, which are crucial for exploring the underlying mechanisms of observed changes in proxy records.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , peerRev
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  • 6
    Publication Date: 2019-02-01
    Description: Lake Urmia, the second largest saline Lake on earth and a highly endangered ecosystem, is on the brink of a serious environmental disaster similar to the catastrophic death of the Aral Sea. Progressive drying has been observed during the last decade, causing dramatic changes to Lake Urmia’s surface and its regional water supplies. The present study aims to improve monitoring of spatiotemporal changes of Lake Urmia in the period 1975–2015 using the multi-temporal satellite altimetry and Landsat (5-TM, 7-ETM+ and 8-OLI) images. In order to demonstrate the impacts of climate change and human pressure on the variations in surface extent and water level, Lake Sevan and Van Lake with different characteristics were studied along with the Urmia Lake. Normalized Difference Water Index-Principal Components Index (NDWI-PCs), Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), Normalized Difference Moisture Index (NDMI), Water Ratio Index (WRI), Normalized Difference Vegetation Index (NDVI), Automated Water Extraction Index (AWEI), and MultiLayer Perceptron Neural Networks (MLP NNs) classifier were investigated for the extraction of surface water from Landsat data. The presented results revealed that MLP NNs has a better performance in the cases where the other models generate poor accuracy. The results show that the area of Lake Sevan and Van Lake have increased while the area of Lake Urmia has decreased by ~65.23% in the past decades, far more than previously reported (~25% to 50%). Urmia Lake’s shoreline has been receding severely between 2010 and 2015 with no sign of recovery, which has been partly blamed on prolonged droughts, aggressive regional water resources development plans, intensive agricultural activities, and anthropogenic changes to the system. The results also indicated that (among the proposed factors) changes in inflows due to overuse of surface water resources and constructing dams (mostly during 1995–2005) are the main reasons for Urmia Lake’s shoreline receding. The model presented in this manuscript can be used by managers as a decision support system to find the effects of building new dams or other infrastructures
    Type: Article , PeerReviewed
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  • 7
    Publication Date: 2021-01-08
    Description: Arctic and subarctic regions are sensitive to climate change and, reversely, provide dramatic feedbacks to the global climate. With a focus on discovering paleoclimate and paleoceanographic evolution in the Arctic and Northwest Pacific Oceans during the last 20,000 years, we proposed this German–Sino cooperation program according to the announcement “Federal Ministry of Education and Research (BMBF) of the Federal Republic of Germany for a German–Sino cooperation program in the marine and polar research”. Our proposed program integrates the advantages of the Arctic and Subarctic marine sediment studies in AWI (Alfred Wegener Institute) and FIO (First Institute of Oceanography). For the first time, the collection of sediment cores can cover all climatological key regions in the Arctic and Northwest Pacific Oceans. Furthermore, the climate modeling work at AWI enables a “Data-Model Syntheses”, which are crucial for exploring the underlying mechanisms of observed changes in proxy records.
    Type: Article , PeerReviewed
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  • 8
    Publication Date: 2022-08-18
    Type: Proceedings , NonPeerReviewed
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  • 9
    Publication Date: 2023-05-04
    Description: The Antarctic deep-sea sponge Latrunculia (Latrunculia) biformis Kirkpatrick, 1908 (Class Demospongiae Sollas, Order Poecilosclerida Topsent, Latrunculiidae Topsent) was selected for chemical analyses due to its potent anticancer activity. Metabolomic analysis of its crude extract by HRMS/MS-based molecular networking showed the presence of several clusters of pyrroloiminoquinone alkaloids, i.e., discorhabdin and epinardin-type brominated pyridopyrroloquinolines and tsitsikammamines, the non-brominated bis-pyrroloiminoquinones. Molecular networking approach combined with a bioactivity-guided isolation led to the targeted isolation of the known pyrroloiminoquinone tsitsikammamine A (1) and its new analog 16,17-dehydrotsitsikammamine A (2). The chemical structures of the compounds 1 and 2 were elucidated by spectroscopic analysis (one-dimensional (1D) and two-dimensional (2D) NMR, HR-ESIMS). Due to minute amounts, molecular modeling and docking was used to assess potential affinities to potential targets of the isolated compounds, including DNA intercalation, topoisomerase I-II, and indoleamine 2,3-dioxygenase enzymes. Tsitsikammamines represent a small class of pyrroloiminoquinone alkaloids that have only previously been reported from the South African sponge genus Tsitsikamma Samaai & Kelly and an Australian species of the sponge genus Zyzzya de Laubenfels. This is the first report of tsitsikammamines from the genus Latrunculia du Bocage and the successful application of molecular networking in the identification of comprehensive chemical inventory of L.biformis followed by targeted isolation of new molecules. This study highlights the high productivity of secondary metabolites of Latrunculia sponges and may shed new light on their biosynthetic origin and chemotaxonomy
    Type: Article , PeerReviewed
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  • 10
    Publication Date: 2017-07-03
    Description: Lakes are a ubiquitous landscape feature in northern permafrost regions. They have a strong impact on carbon, energy and water fluxes and can be quite responsive to climate change. The monitoring of lake change in northern high latitudes, at a sufficiently accurate spatial and temporal resolution, is crucial for understanding the underlying processes driving lake change. To date, lake change studies in permafrost regions were based on a variety of different sources, image acquisition periods and single snapshots, and localized analysis, which hinders the comparison of different regions. Here, we present a methodology based on machine-learning based classification of robust trends of multi-spectral indices of Landsat data (TM, ETM+, OLI) and object-based lake detection, to analyze and compare the individual, local and regional lake dynamics of four different study sites (Alaska North Slope, Western Alaska, Central Yakutia, Kolyma Lowland) in the northern permafrost zone from 1999 to 2014. Regional patterns of lake area change on the Alaska North Slope (−0.69%), Western Alaska (−2.82%), and Kolyma Lowland (−0.51%) largely include increases due to thermokarst lake expansion, but more dominant lake area losses due to catastrophic lake drainage events. In contrast, Central Yakutia showed a remarkable increase in lake area of 48.48%, likely resulting from warmer and wetter climate conditions over the latter half of the study period. Within all study regions, variability in lake dynamics was associated with differences in permafrost characteristics, landscape position (i.e., upland vs. lowland), and surface geology. With the global availability of Landsat data and a consistent methodology for processing the input data derived from robust trends of multi-spectral indices, we demonstrate a transferability, scalability and consistency of lake change analysis within the northern permafrost region.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev , info:eu-repo/semantics/article
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