This data set contains the individual classifications that the Gravity Spy citizen science volunteers made for glitches through 20 July 2024. Classifications made by science team members or in testing workflows have been removed as have classifications of glitches lacking a Gravity Spy identifier. See Zevin et al. (2017) for an explanation of the citizen science task and classification interface. Data about glitches with machine-learning labels are provided in an earlier data release (Glanzer et al., 2021). Final classifications combining ML and volunteer classifications are provided in Zevin et al. (2022). 

22 of the classification labels match the labels used in the earlier data release, namely 1080Lines, 1400Ripples, Air_Compressor, Blip, Chirp, Extremely_Loud, Helix, Koi_Fish, Light_Modulation, Low_Frequency_Burst, Low_Frequency_Lines, No_Glitch, None_of_the_Above, Paired_Doves, Power_Line, Repeating_Blips, Scattered_Light, Scratchy, Tomte, Violin_Mode, Wandering_Line and Whistle. One glitch class that was added to the machine-learning classification has not been added to the Zooniverse project and so does not appear in this file, namely Blip_Low_Frequency. Four classes were added to the citizen science platform but not to the machine learning model and so have only volunteer labels, namely 70HZLINE, HIGHFREQUENCYBURST, LOWFREQUENCYBLIP and PIZZICATO. The glitch class Fast_Scattering added to the machine-learning classification has an equivalent volunteer label CROWN, which is used here (Soni et al. 2021).

Glitches are presented to volunteers in a succession of workflows. Workflows include glitches classified by a machine learning classifier as being likely to be in a subset of classes and offer the option to classify only those classes plus None_of_the_Above. Each level includes the classes available in lower levels. The top level does not add new classification options but includes all glitches, including those for which the machine learning model is uncertain of the class. As the classes available to the volunteers change depending on the workflow, a glitch might be classified as None_of_the_Above in a lower workflow and subsequently as a different class in a higher workflow. Workflows and available classes are shown in the table below. 

Description of data fields

• Classification_id: a unique identifier for the classification. A volunteer may choose multiple classes for a glitch when classifying, in which case there will be multiple rows with the same classification_id.
• Subject_id: a unique identifier for the glitch being classified. This field can be used to join the classification to data about the glitch from the prior data release. 
• User_hash: an anonymized identifier for the user making the classification or for anonymous users an identifier that can be used to track the user within a session but which may not persist across sessions. 
• Anonymous_user: True if the classification was made by a non-logged in user. 
• Workflow: The Gravity Spy workflow in which the classification was made. 
• Workflow_version: The version of the workflow.
• Timestamp: Timestamp for the classification. 
• Classification: Glitch class selected by the volunteer. 

Related datasets

For machine learning classifications on all glitches in O1, O2, O3a, and O3b, please see Gravity Spy Machine Learning Classifications on Zenodo

For classifications of glitches combining machine learning and volunteer classifications, please see Gravity Spy Volunteer Classifications of LIGO Glitches from Observing Runs O1, O2, O3a, and O3b.

For the training set used in Gravity Spy machine learning algorithms, please see Gravity Spy Training Set on Zenodo.

For detailed information on the training set used for the original Gravity Spy machine learning paper, please see Machine learning for Gravity Spy: Glitch classification and dataset on Zenodo.