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Educational materials using the Sloan Digital Sky Survey
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SDSS Educational Activities using SciServer Notebooks

This set of Jupyter notebooks and other resources were produced by Britt Lundgren and tested in upper-level undergraduate astronomy labs at the University of North Carolina Asheville. The included activities are designed to equip university students with the basic skills to perform astronomy research with large datasets. They can be used in a sequence of 2-hour lab sessions for half of a 12-week semester, leaving the second half of the semester for guided independent research.

An example course structure using these notebooks is provided below:

  • Week 1: An introduction to the Sloan Digital Sky Survey (see the included slides)
  • Week 2: Introduction to Python
  • Week 3: Data visualization and Querying the SDSS Database
  • Week 4: Visualizing and Modeling H-R Diagrams
  • Week 5: Building a Sample for Citizen Science with Galaxy Zoo
  • Week 6: Spectroscopic Identification and Redshift Determination
  • Weeks 7-12: Reserved for guided independent research (see the example projects document)

These Jupyter notebooks are designed to be run from the SciServer Compute environment (http://www.sciserver.org/tools/compute/). SciServer provides a web-based platform for interacting with the vast database of astronomical imaging and spectroscopy from the Sloan Digital Sky Survey (SDSS; York et al. 2000). All of the computing is done in the cloud, so there's no need to download anything to a local computer. The only thing required to get started exploring the universe is a web browser!

Instructions for setting up a free account on SciServer can be found in the included document.

If you set up a SciServer account and upload these notebooks into a new “container”, they should compile without error. There are two versions of each of the python notebooks in this repository. One has all of the output cleared, and one is fully compiled with worked solutions for each provided prompt.

These activities use the programming language Python 3 to interface with the SciServer, although they could be modified to use R.

Questions or comments? Please contact Britt Lundgren: blundgre@unca.edu

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