Speaker
Description
SciServer is a high-impact, highly successful Science Platform with a well-developed existing code base; an established user community; and demonstrated impact on scientific discovery, research, and education. SciServer has demonstrated a transformational impact in astronomy, providing collaborative features such as groups and file sharing, and free computational resources to access large datasets in both observational (e.g. SDSS, HEASARC) and theoretical (e.g. Millennium, Indra simulations) astrophysics.
Since its inception, SciServer has grown beyond astronomy to a platform that creates significant impact in multiple science domains including – but not limited to - materials science and engineering; turbulence; oceanography; and precision medicine and genomics. SciServer has also been used in a classroom setting across a number of these disciplines. The platform has also been successfully installed in other institutes, namely Brookhaven National Laboratory to provide simple access to GPU resources for computational research needs, and at the Max Planck Institute for Extraterrestrial Physics to provide access to eRosita data.
More recently, SciServer has been selected by an NFS sustainability grant to bring the platform code and governance to the open source community. We have collected the platform code in a single github repository that is now public, updating codes for style, documentation and consolidating the automated build and release processes. SciServer can now be installed by anyone with a Kubernetes cluster using Helm, either within their data center or in a public cloud environment.
In this talk we will provide an introduction to SciServer and its impact in a variety of science disciplines, focusing on astronomy. We will talk about the underlying architecture and deployment mechanisms, about our journey to open source release and the opportunities an Open SciServer provides to the astronomy community and beyond, and finally how you can contribute!
| Affiliation of the submitter | The Johns Hopkins University Institute for Data-Intensive Engineering and Science |
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| Attendance | remote |
