The Document Foundation

The Document Foundation (TDF) is a non-profit organization that promotes open-source document handling software. It was created by members of the OpenOffice.org community to manage and develop LibreOffice, a free and open-source office suite, and is legally registered in Germany as a ''Stiftung''. Its goal is to produce a vendor-independent office suite with ODF support in a development environment free from company control.

The Document Foundation was created partially over fears that Oracle Corporation, after acquiring Sun Microsystems, would discontinue developing OpenOffice.org as it had done with OpenSolaris. Provided by Wikipedia
Showing 1 - 4 results of 4 for search 'Thorsten Behrens', query time: 0.03s Refine Results
  1. 1
    3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning

    3D mapping of soil organic carbon content and soil moisture with multiple geophysical sensors and machine learning by Tobias Rentschler, Ulrike Werban, Mario Ahner, Thorsten Behrens, Philipp Gries, Thomas Scholten, Sandra Teuber, Karsten Schmidt

    Published 2020-01-01
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  2. 2
    Contextual spatial modelling in the horizontal and vertical domains

    Contextual spatial modelling in the horizontal and vertical domains by Tobias Rentschler, Martin Bartelheim, Thorsten Behrens, Marta Díaz-Zorita Bonilla, Sandra Teuber, Thomas Scholten, Karsten Schmidt

    Published 2022-06-01
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  3. 3
    Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China.

    Comparison of catchment scale 3D and 2.5D modelling of soil organic carbon stocks in Jiangxi Province, PR China. by Tobias Rentschler, Philipp Gries, Thorsten Behrens, Helge Bruelheide, Peter Kühn, Steffen Seitz, Xuezheng Shi, Stefan Trogisch, Thomas Scholten, Karsten Schmidt

    Published 2019-01-01
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  4. 4
    Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space

    Improving the Spatial Prediction of Soil Organic Carbon Content in Two Contrasting Climatic Regions by Stacking Machine Learning Models and Rescanning Covariate Space by Ruhollah Taghizadeh-Mehrjardi, Karsten Schmidt, Alireza Amirian-Chakan, Tobias Rentschler, Mojtaba Zeraatpisheh, Fereydoon Sarmadian, Roozbeh Valavi, Naser Davatgar, Thorsten Behrens, Thomas Scholten

    Published 2020-03-01
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