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dr7objid
int64
587,722,982B
588,848,902B
t01_smooth_or_features_a01_smooth_debiased
float64
0
1
t01_smooth_or_features_a02_features_or_disk_debiased
float64
0
1
t01_smooth_or_features_a03_star_or_artifact_debiased
float64
0
1
t02_edgeon_a04_yes_debiased
float64
0
1
t02_edgeon_a05_no_debiased
float64
0
1
t03_bar_a06_bar_debiased
float64
0
1
t03_bar_a07_no_bar_debiased
float64
0
1
t04_spiral_a08_spiral_debiased
float64
0
1
t04_spiral_a09_no_spiral_debiased
float64
0
1
t05_bulge_prominence_a10_no_bulge_debiased
float64
0
1
t05_bulge_prominence_a11_just_noticeable_debiased
float64
0
1
t05_bulge_prominence_a12_obvious_debiased
float64
0
1
t05_bulge_prominence_a13_dominant_debiased
float64
0
1
t06_odd_a14_yes_debiased
float64
0
1
t06_odd_a15_no_debiased
float64
0
1
t07_rounded_a16_completely_round_debiased
float64
0
1
t07_rounded_a17_in_between_debiased
float64
0
1
t07_rounded_a18_cigar_shaped_debiased
float64
0
1
t08_odd_feature_a19_ring_debiased
float64
0
1
t08_odd_feature_a20_lens_or_arc_debiased
float64
0
1
t08_odd_feature_a21_disturbed_debiased
float64
0
1
t08_odd_feature_a22_irregular_debiased
float64
0
1
t08_odd_feature_a23_other_debiased
float64
0
1
t08_odd_feature_a24_merger_debiased
float64
0
1
t08_odd_feature_a38_dust_lane_debiased
float64
0
1
t09_bulge_shape_a25_rounded_debiased
float64
0
1
t09_bulge_shape_a26_boxy_debiased
float64
0
1
t09_bulge_shape_a27_no_bulge_debiased
float64
0
1
t10_arms_winding_a28_tight_debiased
float64
0
1
t10_arms_winding_a29_medium_debiased
float64
0
1
t10_arms_winding_a30_loose_debiased
float64
0
1
t11_arms_number_a31_1_debiased
float64
0
1
t11_arms_number_a32_2_debiased
float64
0
1
t11_arms_number_a33_3_debiased
float64
0
1
t11_arms_number_a34_4_debiased
float64
0
1
t11_arms_number_a36_more_than_4_debiased
float64
0
1
t11_arms_number_a37_cant_tell_debiased
float64
0
1
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End of preview. Expand in Data Studio

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Check out the documentation for more information.

Galaxy Zoo 2 Dataset

This directory contains the dataset used for training and evaluating the Hierarchical Probabilistic Vote Regression model on Galaxy Zoo 2.


Dataset Source

The dataset is derived from Galaxy Zoo 2 (GZ2), a large-scale citizen science project providing galaxy morphology annotations.

  • Total images: ~243,000
  • Labels: volunteer vote distributions across a hierarchical decision tree

The data corresponds to:

  • Willett et al. (2013)
  • Hart et al. (2016)

Directory Structure

data/
β”œβ”€β”€ raw/         # Original data (Zenodo images + metadata CSVs)
β”œβ”€β”€ processed/   # Final curated dataset
└── README.md

Raw Data

The raw dataset includes:

  • Galaxy images from Zenodo (images_gz2.zip)
  • gz2_hart16.csv β€” morphological vote distributions
  • gz2_filename_mapping.csv β€” mapping between image filenames and object IDs

Each galaxy is identified by a DR7 object ID (dr7objid), which links images to labels.


Processing Pipeline (This Work)

The raw Galaxy Zoo 2 dataset is not directly suitable for training.

In this project, the following preprocessing steps are performed:

  • Map images from asset_id β†’ dr7objid
  • Rename images for consistent indexing
  • Align images with corresponding labels
  • Remove missing or unmatched samples
  • Filter invalid or incomplete entries
  • Construct hierarchical vote distributions
  • Ensure all probability vectors are valid (sum = 1)

Pipeline implementation:

python3 scripts/data/convert_zenodo_images.py
python3 scripts/data/prepare_labels.py

Processed Data

The final dataset consists of:

  • Cleaned and aligned galaxy images
  • labels.parquet containing structured vote distributions

This dataset is directly used for training probabilistic models.


Notes

  • Final dataset size: ~3.3 GB (images + labels)
  • A small fraction of samples may be missing due to incomplete mappings
  • Missing samples are assumed to be randomly distributed

The processed dataset ensures:

  • consistent mapping between images and labels
  • valid probability distributions
  • suitability for hierarchical probabilistic modeling

Usage

This dataset is designed for:

  • probabilistic regression over Galaxy Zoo decision trees
  • predicting full vote distributions instead of single labels
  • training models that preserve uncertainty and distribution structure

Citation

If you use this dataset, please cite:


Summary

This dataset represents a cleaned and structured version of Galaxy Zoo 2, enabling reliable training of models that predict hierarchical probability distributions rather than discrete labels.

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