Data


Found 8 datasets

Datasets Project Group Resources

Supplementary data of Hensel et al. 2019, E&G Quaternary Sci. J. SFB806 doi Spatial

This data corresponds to the article and shall be quoted as such using the provided DOI: Hensel, E. A., Bödeker, O., Bubenzer, O., and Vogelsang, R.: Combining geomorphological–hydrological analyses and the location of settlement and raw material sites – a case study on un-derstanding prehistoric human settlement activity in the southwestern Ethiopian Highlands, E&GQuaternary Sci. J., 68, 201–213, https://doi.org/10.5194/egqsj-68-201-2019, 2019.
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Geodata of 'Loess distribution and related Quaternary sediments in the Carpathian Basin' SFB806 doi Spatial

This set of data depicts the distribution of loess and related Quaternary sediments in the Carpathian Basin. Using a Geographic Information System (GIS), we compiled a map showing the distribution of loess and related Quaternary sediments in the Carpathian Basin, with unprecedented detail. We vectorized and analyzed existing data (mainly from geological maps) and combined and transferred these into a common (loess) sediment classification system. This cross-border map shows the distribution of eolian...
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Geodata of "Loess and other Quaternary sediments in Germany" SFB806 doi Spatial Temporal

Geo- and palaeoecological studies focusing on the late Pleistocene require a detailed knowledge of the spatial distribution of aeolian sediments. In Germany existing maps are either on large scales, have a regional focus or show significant inaccuracies such as artificial boundaries within different geological units. To obtain a more detailed, seamless map of the distribution of aeolian sediments and their potential source areas, we combined and reanalysed available geo-data, using a Geographical...
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GIS-basierte Distanzanalysen für die Modellierung von Einzugsgebieten prähistorischer Fundstätten SFB806 doi Spatial

Die Bewohner einer archäologischen Fundstätte müssen sich regelmäßig mit Ressourcen versorgen, dabei geht man davon aus, dass die Fläche die er dazu nutzen kann, durch seine fußläufige Mobilität begrenzt ist. Die Geschwindigkeit mit der der Mensch sich durch seine Umwelt bewegen kann, ist dabei, neben seiner physiologie, maßgeblich durch topographische Faktoren beeinflusst. Die Arbeit untersucht die Möglichkeit mit Hilfe von Berechnung der hangneigungsabhängigen Gehgeschwindigkeit Einzugsgebiete (Site...
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Köppen-Geiger classification of MPI-ESM-P Pre-Industrial simulation SFB806 doi Spatial PaleoMaps

This geospatial dataset, in raster and vector format, is a Kppen-Geiger climate classification of the MPI-ESM-P PreIndustrial r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
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Köppen-Geiger classification of MPI-ESM-P Mid-Holocene simulation SFB806 doi Spatial PaleoMaps

This geospatial dataset, in raster and vector format, is a Kppen-Geiger climate classification of the MPI-ESM-P Mid-Holocene (6k yBP) r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
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Köppen-Geiger classification of MPI-ESM-P LGM simulation SFB806 doi Spatial PaleoMaps

This geospatial dataset, in raster and vector format, is a Köppen-Geiger climate classification of the MPI-ESM-P Last Glacial Maximum (21k yBP) r1i1p1 model simulations according to the PMIP III 21k experiment. The classifications were computed using the Python pyGRASS library and GRASS GIS.
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pyGRASS implementation of Köppen-Geiger classifications from CMIP5 simulations SFB806 doi Spatial

Python based pyGRASS scripts implementing Köppen-Geiger climate classifications from CMIP5 and PMIP3 climate model simulation datasets. And a script for computation of area statistics of the classification data, as well as documentation for using the scripts and accessing the source climate simulation data.
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