Towards Porting Astrophysics Visual Analytics Services in the European Open Science Cloud

The European Open Science Cloud (EOSC) aims to create a federated environment for hosting and processing research data to support science in all disciplines without geographical boundaries, such that data, software, methods and publications can be shared as part of an Open Science community of practice. This work presents the ongoing activities related to the implementation of visual analytics services, integrated into EOSC, towards addressing the diverse astrophysics user communities needs. These services rely on visualisation to manage the data life cycle process under FAIR principles, integrating data processing for imaging and multidimensional map creation and mosaicing, and applying machine learning techniques for detection of structures in large scale multidimensional maps.

[1]  John T. Stasko,et al.  Toward a Deeper Understanding of the Role of Interaction in Information Visualization , 2007, IEEE Transactions on Visualization and Computer Graphics.

[2]  Daniel S. Katz,et al.  Montage: a grid-enabled engine for delivering custom science-grade mosaics on demand , 2004, SPIE Astronomical Telescopes + Instrumentation.

[3]  Sergio Molinari,et al.  Source extraction and photometry for the far-infrared and sub-millimeter continuum in the presence of complex backgrounds , 2010, 1011.3946.

[4]  Stefanie N. Lindstaedt,et al.  Realising the European Open Science Cloud , 2016 .

[5]  Dong-Han Ham.  The State of the Art of Visual Analytics , 2010 .

[6]  Christopher J. Fluke,et al.  Collaborative visual analytics of radio surveys in the Big Data era , 2016, Proceedings of the International Astronomical Union.

[7]  E. Rosolowsky,et al.  The Cube Analysis and Rendering Tool for Astronomy , 2015 .

[8]  A. Calanducci,et al.  Caesar source finder: Recent developments and testing , 2019, Publications of the Astronomical Society of Australia.

[9]  Ugo Becciani,et al.  VIALACTEA science gateway for Milky Way analysis , 2019, Future Gener. Comput. Syst..

[10]  Arthur Nishimoto,et al.  CAVE2: a hybrid reality environment for immersive simulation and information analysis , 2013, Electronic Imaging.

[11]  L. Calzoletti,et al.  unimap: a generalized least-squares map maker for Herschel data , 2015 .

[12]  Daniel A. Keim,et al.  Visual Analytics: Scope and Challenges , 2008, Visual Data Mining.

[13]  M. Molinaro,et al.  Vialactea Visual Analytics Tool for Star Formation Studies of the Galactic Plane , 2018, Publications of the Astronomical Society of the Pacific.

[14]  Erik Schultes,et al.  The FAIR Guiding Principles for scientific data management and stewardship , 2016, Scientific Data.