Tutorials#

This page includes description of the Citizen Science tutorials. The overall purpose of these tutorials is to guide Rubin scientists through the process of creating a Zooniverse project and retrieving the classifications. There are three notebooks:

  1. Introductory notebook - 01_Introduction_to_Citsci_Pipeline.ipynb

  2. Creating an image flipbook on Zooniverse - 02_Send_Flipbook_Variable_Stars_Imaging.ipynb

  3. How to download and aggregate Zooniverse user classifications - 03_Aggregate_Classifications.ipynb

Link to Github repo

Here we briefly describe the purpose of each notebook.

Introduction to Rubin citizen science#

Notebook: 01_Introduction_to_Citsci_Pipeline.ipynb

This is our recommended starting notebook. It guides a PI through the process of creating a Zooniverse project, sending data (specifically images) from the Rubin Science Platform (RSP) to the Zooniverse, and retrieving raw classifications from Zooniverse.

Create an image flipbook on Zooniverse#

Notebook: 02_Send_Flipbook_Variable_Stars_Imaging.ipynb

This notebook demonstrates how to query and send a flipbook of variable star images from the RSP to Zooniverse. A flipbook is a collection of multiple images, which could be useful for tracking and identifying sources at the same location that change with time. For example, this notebook examines variable stars, which change brightness with time.

Download and aggregate Zooniverse user classifications#

Notebook: 03_Aggregate_Classifications.ipynb

This notebook guides a PI through the process of retrieving and aggregating classification data from Zooniverse. Aggregation is the process of combining classifications. In this case, application means grouping classifications by task and user. We select the most recent classification from each user and use the Zooniverse question_consesus_reducer function to determine the consensus for each subject ID amongst all users. It builds upon the example of retrieval in the first tutorial notebook, this time demonstrating how to aggregate the raw user classifications.