Understanding data: Visualisation, machine learning, and reproducibility

The ASCL has once again partnered with others on a Special Session at EWASS. This year’s Special Session (SS34) is titled Understanding data: Visualisation, machine learning, and reproducibility, and will be held on Tuesday, 25 June, in Room 3. Not at EWASS? Follow the session on Twitter at #ewass19ss34.

Full information, including abstracts for the presentations listed below, can be found in the detailed interactive program; look for the sessions in yellow and labeled SS34a, SS34b, and SS34c.

Tuesday, 25 June, 9:00 in Room 3, chaired by Rein Warmels
Reproducibility in computer-aided research by Konrad Hinsen
Publishing associated data: Challenges & opportunities by Pierre Ocvirk
FAIR data in astronomy by Mark Allen
Template for reproducible, shareable & achievable research by Mohammad Akhlaghi
These talks are followed by an open discussion moderated by David Valls-Gabaud.

Tuesday, 25 June, 14:30 in Room 3, chaired by Amruta Jaodand
High-performance machine learning in Astrophysics by Simon Portegies Zwart
Machine learning for the SKA by Anna Scaife
SuperNNova: Open-source, deep learning photometric time-series classifier by Anais Möller
Transfer learning for radio galaxy classification by Hongming Tang
Unsupervised classification of galaxy spectra and interpretability by Didier Fraix-burnet

Tuesday, 25 June, 16:30 in Room 3, chaired by John Wenskovitch
Visual Analytics of Data in Astronomy by Johanna Schmidt
Visual analytics algorithms for multidimensional astronomical data by Dany Vohl
Pulsar to Person (P2P): Data Visualization & Sonification to Experience the Universe by John Wenskovitch
Lightning talks for e-Posters
These talks are followed by an open discussion moderated by the session chair.

This Special Session was organized by:
Rachael Ainsworth (UManchester)
Mohammad Akhlaghi (Instituto De Astrofísica De Canarias)
Amruta Jaodand (ASTRON)
David Valls-Gabaud (Observatoire de Paris)
Rein Warmels (ESO)
John Wenskovitch (Virginia Tech)
Alice Allen (ASCL/UMD)

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