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Integrated data access, visualization and analysis for Galactic Plane surveys: the VIALACTEA case.

, , , , , , , , , , and . Astroinformatics, volume 12 of Proceedings of the International Astronomical Union, page 291-298. Cambridge University Press, (2016)

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Machine learning based data mining for Milky Way filamentary structures reconstruction., , , , , , , , , and . CoRR, (2015)A deep learning approach for the 3D reconstruction of dust density and temperature in star-forming regions, , , , , , , , , and . (2023)cite arxiv:2308.09657Comment: 32 pages, 18 figures, submitted to Astronomy & Astrophysics, comments welcome.The signature of large scale turbulence driving on the structure of the interstellar medium, , , , , , , , , and 2 other author(s). (2022)cite arxiv:2206.00451Comment: 15 pages, 11 figures, to be published in MNRAS.C3: A Command-line Catalogue Cross-matching tool for modern astrophysical survey data., , , , , and . Astroinformatics, volume 12 of Proceedings of the International Astronomical Union, page 327-332. Cambridge University Press, (2016)Milky Way Analysis through a Science Gateway: Workflows and Resource Monitoring., , , , , , , , , and 4 other author(s). IWSG, volume 1871 of CEUR Workshop Proceedings, CEUR-WS.org, (2016)Machine Learning Based Data Mining for Milky Way Filamentary Structures Reconstruction., , , , , , , , , and . Advances in Neural Networks, volume 54 of Smart Innovation, Systems and Technologies, Springer, (2016)Integrated data access, visualization and analysis for Galactic Plane surveys: the VIALACTEA case., , , , , , , , , and 1 other author(s). Astroinformatics, volume 12 of Proceedings of the International Astronomical Union, page 291-298. Cambridge University Press, (2016)