Indra is a suite of large-volume cosmological N-body simulations with the goal of providing excellent statistics of the large-scale features of the distribution of dark matter. Each of the 384 simulations is computed with the same cosmological parameters and different initial phases, with 10243 dark matter particles in a box of length 1 h−1 Gpc, 64 snapshots of particle data and halo catalogs, and 505 time steps of the Fourier modes of the density field, amounting to almost a petabyte of data. All of the Indra data are immediately available for analysis via the SciServer science platform, which provides interactive and batch computing modes, personal data storage, and other hosted data sets such as the Millennium simulations and many astronomical surveys. We present the Indra simulations, describe the data products and how to access them, and measure ensemble averages, variances, and covariances of the matter power spectrum, the matter correlation function, and the halo mass function to demonstrate the types of computations that Indra enables. We hope that Indra will be both a resource for large-scale structure research and a demonstration of how to make very large datasets public and computationally-accessible.
SPIDR (Space Physics Interactive Data Resource) is a standard data source for solar-terrestrial physics, functioning within the framework of the ICSU World Data Centers. It is a distributed database and application server network, built to select, visualize and model historical space weather data distributed across the Internet. SPIDR can work as a fully-functional webapplication (portal) or as a grid of web-services, providing functions for other applications to access its data holdings.
SciServer Compute uses Jupyter notebooks running within server-side Docker containers attached to large relational databases and file storage to bring advanced analysis capabilities close to the data. SciServer Compute is a component of SciServer, a big-data infrastructure project developed at Johns Hopkins University that will provide a common environment for computational research. SciServer Compute integrates with large existing databases in the fields of astronomy, cosmology, turbulence, genomics, oceanography and materials science. These are accessible through the CasJobs service for direct SQL queries. SciServer Compute adds interactive server-side computational capabilities through notebooks in Python, R and MATLAB, an API for running asynchronous tasks, and a very large (hundreds of terabytes) scratch space for storing intermediate results. Science-ready results can be stored on a Dropbox-like service, SciDrive, for sharing with collaborators and dissemination to the public. Notebooks and batch jobs run inside Docker containers owned by the users. This provides security and isolation and allows flexible configuration of computational contexts through domain specific images and mounting of domain specific data sets. We present a demo that illustrates the capabilities of SciServer Compute: using Jupyter notebooks, performing analyses on data selections from diverse scientific fields, and running asynchronous jobs in a Docker container. The demo will highlight the data flow between file storage, database, and compute components.
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