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  • 1
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    Editorial (2010)
    Springer
    Details
    Publication Date: 2010-08-05
    Print ISSN: 1618-2162
    Electronic ISSN: 1610-1995
    Topics: Computer Science
    Published by Springer
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    PAPER CURRENT
  • 2
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    Editorial (2013)
    Springer
    Details
    Publication Date: 2013-05-24
    Print ISSN: 1618-2162
    Electronic ISSN: 1610-1995
    Topics: Computer Science
    Published by Springer
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  • 3
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    Editorial (2016)
    Springer
    Details
    Publication Date: 2016-06-08
    Print ISSN: 1618-2162
    Electronic ISSN: 1610-1995
    Topics: Computer Science
    Published by Springer
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  • 4
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    Editorial to the Special Issue: “Database Systems” (2009)
    Springer
    Details
    Publication Date: 2009-06-26
    Print ISSN: 1865-2034
    Electronic ISSN: 1865-2042
    Topics: Computer Science
    Published by Springer
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  • 5
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    Grundlagen und Visionen großer Forschungsfragen im Bereich Big Data (2014)
    Springer
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    Publication Date: 2014-02-15
    Print ISSN: 0170-6012
    Electronic ISSN: 1432-122X
    Topics: Computer Science
    Published by Springer
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  • 6
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    Harmonizing heterogeneous multi-proxy data from lake systems (2021)
    Elsevier
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    Publication Date: 2021-08-01
    Print ISSN: 0098-3004
    Electronic ISSN: 1873-7803
    Topics: Geosciences , Computer Science
    Published by Elsevier
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  • 7
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    A Quantitative Graph-Based Approach to Monitoring Ice-Wedge Trough Dynamics in Polygonal Permafrost Landscapes (2021)
    Molecular Diversity Preservation International
    Details
    Publication Date: 2021-08-05
    Description: In response to increasing Arctic temperatures, ice-rich permafrost landscapes are undergoing rapid changes. In permafrost lowlands, polygonal ice wedges are especially prone to degradation. Melting of ice wedges results in deepening troughs and the transition from low-centered to high-centered ice-wedge polygons. This process has important implications for surface hydrology, as the connectivity of such troughs determines the rate of drainage for these lowland landscapes. In this study, we present a comprehensive, modular, and highly automated workflow to extract, to represent, and to analyze remotely sensed ice-wedge polygonal trough networks as a graph (i.e., network structure). With computer vision methods, we efficiently extract the trough locations as well as their geomorphometric information on trough depth and width from high-resolution digital elevation models and link these data within the graph. Further, we present and discuss the benefits of graph analysis algorithms for characterizing the erosional development of such thaw-affected landscapes. Based on our graph analysis, we show how thaw subsidence has progressed between 2009 and 2019 following burning at the Anaktuvuk River fire scar in northern Alaska, USA. We observed a considerable increase in the number of discernible troughs within the study area, while simultaneously the number of disconnected networks decreased from 54 small networks in 2009 to only six considerably larger disconnected networks in 2019. On average, the width of the troughs has increased by 13.86%, while the average depth has slightly decreased by 10.31%. Overall, our new automated approach allows for monitoring ice-wedge dynamics in unprecedented spatial detail, while simultaneously reducing the data to quantifiable geometric measures and spatial relationships.
    Electronic ISSN: 2072-4292
    Topics: Architecture, Civil Engineering, Surveying , Geography
    Published by Molecular Diversity Preservation International
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  • 8
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    Data collection of age determination data for LANDO multi-core study (2022)
    PANGAEA
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    Publication Date: 2023-06-27
    Description: This data collection of age determination data from high latitude lake systems (50° N to 90° N, 55 sediment cores, and a total of 602 dating points) was part of the multi-core study of the LANDO approach. The term “LANDO” refers to the implementation by Pfalz et al. (2022), which stands for “Linked age and depth modeling”. We collected the data between 2018 and 2021 from either the Pangaea database, PaleoLake database, or tables within the main body or supplementary material of publications. The uploaded data collection contains links to the main data source and paper reference for the corresponding age determination dataset. The data harmonization followed the syntax and semantics proposed by Pfalz et al. (2021) (https://doi.org/10.1016/j.cageo.2021.104791) on harmonizing heterogeneous multi-proxy data. This data collection is based on drilling campaigns from 1993 to 2020.
    Keywords: 09-TIK-13; 16-KP-04-L19; age depth model; Altai Mountains, Russia; Arctic; AWI_Envi; AWI Arctic Land Expedition; Basalt Sø; Changeable; Chukotka 2018; Co1309; Co1412; COMPCORE; Composite Core; CON01-6; CON01-603-5; Core; CORE; Core length; DATE/TIME; Date/Time of event; Depth, bathymetric; Elgygytgyn1998; Elgygytgyn crater lake, Sibiria, Russia; EN18208; EN18218; Event label; GC; GL-Land_1994; Gravity corer; Gravity corer Potsdam; Greenland94; Hand push corer; HSR; KAL; Kasten corer; Keperveem_2016; Ladoga Lake, Russia; Lake Baikal, Russia; Lake Billyakh, Verkhoyansk Mountains, Yakuti, Russia; Lake Bolshoe Toko, Yakutia, Russia; Lake Emanda; Lake Lyadhej-To; Lake sediment; Lama Lake; LATITUDE; Latitude of event; Lena2010; LONGITUDE; Longitude of event; Norilsk/Taymyr, Sibiria; Norilsk/Taymyr93; Norilsk97; northeastern Siberia; PC; PCUWI; PG1111; PG1205; PG1214; PG1228; PG1238; PG1341; PG1351; PG1437; PG1755; PG1756; PG1984; PG2023; PG2208; Piston corer; Piston corer, UWITEC; Polar Terrestrial Environmental Systems @ AWI; PolarUral-99; Radiocarbon chronology; Radiocarbon datings; Raffles Sø (lake); Reference/source; RU-Land_1993_Norilsk_Taymyr; RU-Land_1995_Taymyr; RU-Land_1996_Taymyr; RU-Land_1997_Norilsk; RU-Land_1998_Elgygytgyn; RU-Land_1999_PolarUral; RU-Land_2005_Verkhoyansk; RU-Land_2009_Lena-transect; RU-Land_2010_Lena; RU-Land_2013_Yakutia; RU-Land_2016_Keperveem; RU-Land_2018_Chukotka; SEDCO; Sediment corer; Site; SL_P; Taymyr; Taymyr95; Labaz_Lake_Expedition; Taymyr96; Labaz_Lake_Expedition; Tel2006; Teletskoye; Tiksi2009; Tschukotka, Sibiria, Russia; Vereshchagin; Water sampler, UWITEC; WSUWI; Yakutia2005; Yakutia2013
    Type: Dataset
    Format: text/tab-separated-values, 100 data points
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    DATA PANGAEA
  • 9
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    Metadata for LANDO multi-core study (2022)
    PANGAEA
    Details
    Publication Date: 2024-02-06
    Description: This data collection of age determination data from high latitude lake systems (50° N to 90° N, 55 sediment cores, and a total of 602 dating points) was part of the multi-core study of the LANDO approach. The term “LANDO” refers to the implementation by Pfalz et al. (2022), which stands for “Linked age and depth modeling”. We collected the data between 2018 and 2021 from either the Pangaea database, PaleoLake database, or tables within the main body or supplementary material of publications. The uploaded data collection contains links to the main data source and paper reference for the corresponding age determination dataset. The data harmonization followed the syntax and semantics proposed by Pfalz et al. (2021) on harmonizing heterogeneous multi-proxy data. This data collection is based on drilling campaigns from 1993 to 2020.
    Keywords: 09-TIK-03; 09-TIK-05; 09-TIK-13; 16-KP-04-L19_Long_2; Altai Mountains, Russia; AWI_Envi; AWI Arctic Land Expedition; Basalt Sø; Bayan Nuur; BC2008; Big_Yarovoe_Lake_2008-3; Big Yarovoe Lake; BL02-2007; BN2016-1; Bolshaya Lozhka; Bolshoe Toko; Central Yakutia, Russia; Changeable; Chany; Chukotka 2018; Chupa-8; Co1309; Co1412; Comment; COMPCORE; Composite Core; CON01-6; CON01-603-5; Core; CORE; Core length; DATE/TIME; Depth, bathymetric; Dolgoe2012; Dolgoe Ozero; East Sayan Mountains Lake; Elgygytgyn1998; Elgygytgyn crater lake, Sibiria, Russia; EN18208; EN18218; ESM-1; Event label; GC; GL-Land_1994; Gravity corer; Gravity corer Potsdam; Greenland94; Hammer-modified gravity core, UWITEC; Hand push corer; HGCUWI; HSR; KAL; Kamchatka2007; KAS-1; Kasatka; Kasten corer; Keperveem_2016; Khaugilampi; Khienyarvi; KOL; Korzhino; Korzhino2010; Ladoga Lake, Russia; Lake Baikal, Russia; Lake Billyakh, Verkhoyansk Mountains, Yakuti, Russia; Lake Bolshoe Toko, Yakutia, Russia; Lake Emanda; Lake Lyadhej-To; Lake Temje; Lama Lake; LATITUDE; Lena2010; LENDERY180-4; LENDERY192; LENDERY200-1; LENDERY203-3; LONGITUDE; LOT83-7; LS-9; MACC; Mackereth corer; Malaya Chabyda; Maloye; Maloye-1; Malyye Chany; MC2006; Middendorf; Muan2018; Muannonyarvi; Norilsk/Taymyr, Sibiria; Norilsk/Taymyr93; Norilsk97; northeastern Siberia; Okun2018; Okunozero; OSIN; Osinovoe; PC; PCUWI; PER3; Pernatoye; PG1111; PG1205; PG1214; PG1228; PG1238; PG1341; PG1351; PG1437; PG1746; PG1755; PG1756; PG1856-3; PG1857; PG1858; PG1890; PG1972-1; PG1975-1; PG1984; PG2023; PG2133; PG2201; PG2208; Piston corer; Piston corer, UWITEC; Piston corer (Kiel type); Polar Terrestrial Environmental Systems @ AWI; PolarUral-99; Raffles Sø (lake); Reference/source; RPC; RU-Land_1993_Norilsk_Taymyr; RU-Land_1995_Taymyr; RU-Land_1996_Taymyr; RU-Land_1997_Norilsk; RU-Land_1998_Elgygytgyn; RU-Land_1999_PolarUral; RU-Land_2005_Verkhoyansk; RU-Land_2007_Kamchatka; RU-Land_2009_Lena-transect; RU-Land_2010_Lena; RU-Land_2013_Yakutia; RU-Land_2016_Keperveem; RU-Land_2018_Chukotka; Russian peat corer; SEDCO; Sediment corer; Siberia, Russia; Sigrid; Site; SL_P; Sokoch; Taymyr; Taymyr95; Labaz_Lake_Expedition; Taymyr96; Labaz_Lake_Expedition; Tel2006; Teletskoye; Teriberka17; Tiksi2009; TKT-3; TL-1-1; Tokotan; Tschukotka, Sibiria, Russia; TULOMA27; Two-Yurts; Two-Yurts Lake; UKhau2015; Uniform resource locator/link to reference; Vereshchagin; Yakutia2005; Yakutia2013
    Type: Dataset
    Format: text/tab-separated-values, 399 data points
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  • 10
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    Data collection of age determination data for LANDO multi-core study (2022)
    PANGAEA
    Details
    Publication Date: 2024-02-06
    Description: This data collection of age determination data from high latitude lake systems (50° N to 90° N, 55 sediment cores, and a total of 602 dating points) was part of the multi-core study of the LANDO approach. The term “LANDO” refers to the implementation by Pfalz et al. (2022), which stands for “Linked age and depth modeling”. We collected the data between 2018 and 2021 from either the Pangaea database, PaleoLake database, or tables within the main body or supplementary material of publications. The uploaded data collection contains links to the main data source and paper reference for the corresponding age determination dataset. The data harmonization followed the syntax and semantics proposed by Pfalz et al. (2021) on harmonizing heterogeneous multi-proxy data. This data collection is based on drilling campaigns from 1993 to 2020.
    Keywords: age depth model; Arctic; AWI_Envi; Lake sediment; Polar Terrestrial Environmental Systems @ AWI; Radiocarbon chronology; Radiocarbon datings
    Type: Dataset
    Format: application/zip, 2 datasets
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    DATA PANGAEA
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