Astro Data Lab Tutorial

In this era of data-intensive research, the astronomy community needs to acquire skills to handle increasingly larger data sets, and to gain access to high-performance computing and analysis tools. The Astro Data Lab is developing a platform to host premier datasets and science tools. In advance of the Data Lab public release in June, we will be hosting a half-day tutorial open to astronomy researchers of all levels from graduate students to staff/faculty.

This in-person tutorial will include an overview of Data Lab services, live demonstrations, and hands-on hacking during a half-day (3.5 hour) session. Seating will be limited to ensure adequate support to tutorial participants. We will welcome your impressions and feedback as the first Data Lab users!

Registration for 2017 tutorial closed

Learn How To:

  • Explore the astronomical catalogs available at Data Lab
  • Use the Data Lab Jupyter Notebook server
  • Submit a database query to the Data Lab
  • Use the Data Lab virtual storage system
  • Get image cutouts for a set of objects
  • Automate workflows

  • Example science cases

  • Visualize the SMASH Survey
  • Star/galaxy/QSO classification
  • Discover faint Milky Way dwarf galaxies
  • Identify RR Lyrae candidates
  • Probe large-scale structure in DECaLS and SDSS/BOSS surveys

  • What you will need:

  • Your laptop with an internet browser
  • Power cable if your laptop’s battery life is shorter than 3.5h
  • That’s all (no need to install any software)!

  • No prior experience required, everyone is welcome!

    The live demos and hacking will be partially based on Jupyter notebooks. While no prior experience is necessary, we recommend that participants read a short overview with optional more in-depth reading (Useful Links).

    Mark your calendars!

    Registration deadline: April 24, 2017
    Tutorial date: May 8, 2017
    Location: NOIRLab Main Conference Room

    Questions? Contact us at:

    Useful Links

  • Jupyter Notebook Basics
  • Project Jupyter
  • Python Introduction
  • ADQL Tutorial
  • SQL Tutorial (exercise 0-8)
  • Visual Representation of SQL Joins