Chun Ly is the Research Data Developer at the Princeton University Plasma Physics Laboratory and an Affiliate Assistant Astronomer at Steward Observatory at the University of Arizona (UofA). He is responsible for developing an ORCiD integration for the management of ORCiD profiles for PPPL researchers and for automating the dissemination of open-access publications at a top-tier Department of Energy laboratory.
In his previous role at the UofA, he was the Research Data Systems Integration Specialist where he built and maintained the UofA research data repository service (ReDATA) that enables open and reproducible science at the UofA.
He enjoys developing open-source software with Python and spends his time thinking about “DevOps” and how to implement it in reproducible research.
Chun earned his Bachelor's degrees in Astronomy and Physics from the UofA, and received his Master's and Ph.D. from the University of California−Los Angeles. After completing his Ph.D., Chun held a Giacconi Postdoctoral Fellowship position at the Space Telescope Science Institute (Baltimore, MD) and a NASA Postdoctoral Fellowship at the NASA Goddard Space Flight Center (Greenbelt, MD). He was a Queue Observer for the MMT Observatory (Mt. Hopkins, AZ) between 2016 and 2019.
Ph.D. in Astronomy, 2010
University of California, Los Angeles
M.S. in Astronomy, 2006
University of California, Los Angeles
B.S. in Astronomy and Physics, 2004
University of Arizona
I am leading the technical deployment of the UofA institutional data repository called ReDATA.
My roles include:
I am leading the technical deployment of the UofA institutional data repository called ReDATA.
My roles include:
An institutional data repository to enable open and reproducible science at the UofA
Tutorials, talks, and blog posts on coding, research tools, and building software
Extending narrowband surveys toward higher redshifts with wide-field imaging in the near-infrared
A Python/PyRAF data reduction pipeline for longslit near-infared spectra from Gemini/GNIRS instrument
A deep extragalactic survey of star-forming galaxies with the 8.4-m Subaru Telescope
A Python tool to enable automation of spectral data reduction
Using public data to understand chemical enrichment in galaxies
Spectroscopic survey of emission-line galaxies to understand galaxy evolution