• Corpus ID: 198147917

Better support for collaborations preparing for large-scale projects: the case study of the LSST Science Collaborations Astro2020 APC White Paper.

@article{Bianco2019BetterSF,
  title={Better support for collaborations preparing for large-scale projects: the case study of the LSST Science Collaborations Astro2020 APC White Paper.},
  author={Federica B. Bianco and Manda Banerji and J. J. Bochanski and W. N. Brandt and Patricia R. Burchat and John E. Gizis and Zeljko Ivezi'c and Charles Keaton and Sugata Kaviraj and Tom Loredo and Rachel Mandelbaum and Philip J. Marshall and Peregrine M. McGehee and Chad M. Schafer and Megan E. Schwamb and J. L. Sokoloski and Michael Strauss and Rachel A. Street and David E. Trilling and Aprajita Verma},
  journal={arXiv: Instrumentation and Methods for Astrophysics},
  year={2019}
}
  • F. Bianco, M. Banerji, +17 authors A. Verma
  • Published 21 July 2019
  • Engineering, Physics
  • arXiv: Instrumentation and Methods for Astrophysics
Through the lens of the LSST Science Collaborations' experience, this paper advocates for new and improved ways to fund large, complex collaborations at the interface of data science and astrophysics as they work in preparation for and on peta-scale, complex surveys, of which LSST is a prime example. We advocate for the establishment of programs to support both research and infrastructure development that enables innovative collaborative research on such scales. 

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