Galaxy Zoo

Galaxy Zoo is a citizen-science project hosted on the Zooniverse platform, where members of the public help classify images of galaxies (their shapes, features) to assist astronomical research.
It launched in July 2007, addressing a major bottleneck: professional astronomers had huge imaging surveys (e.g., the Sloan Digital Sky Survey) and needed morphological classification of hundreds of thousands of galaxies by eye. 

Why it matters

• Leverages human pattern recognition: While computers are strong, humans excel at recognising subtle shapes, oddities and context in images. Galaxy Zoo used this to great effect. 
• Large-scale engagement and citizen-science: The project rapidly gathered large numbers of volunteers and classifications, enabling scientific output faster than traditional manual work alone. 
• Scientific discoveries: Beyond classification, volunteers discovered unusual objects (e.g., “green pea” galaxies) and contributed to peer-reviewed research. 
• Educational & outreach value: It allows non-specialists to engage in real research, fostering interest in astronomy, big-data science, and public participation.

How it works (in broad strokes)

1. The Galaxy Zoo team presents images of galaxies on the website/portal. 
2. Volunteers are asked a series of simple questions: for example, “Is the galaxy smooth or does it have features or a disk?”, “Is there a bar?”, “Is it edge-on?” etc. 
3. Each image receives multiple classifications (many volunteers classify the same image) so that consensus can be reached and errors mitigated. 
4. The aggregated classifications feed into scientific datasets (catalogues) used by researchers to study galaxy morphology, evolution, environment effects, etc. 
5. Volunteers may also explore discussion forums, discover odd galaxies, and share findings with each other and the science team.

Key features & advantages

• Broad participation: Anyone with internet access and interest can contribute — from students to astronomy enthusiasts.
• Scalability: By distributing classification tasks across many volunteers, large datasets can be processed efficiently.
• Quality via redundancy: Multiple volunteers classify each image which allows statistical consensus and error checking.
• Open datasets for research: The datasets and derived catalogues are often made publicly available, enabling further research. For example: the “Galaxy Zoo DECaLS” dataset. 
• Baseline for AI/ML: The crowd-classified data are used to train machine-learning models for galaxy morphology classification. 

Limitations & things to watch

• Volunteer bias and variation: The quality of classifications depends on volunteer engagement, training, and attention, although redundancy mitigates this.
• Domain knowledge: While tasks are simplified, volunteers may still benefit from some background or guidance to classify well.
• Scope of imagery: The input images come from specific surveys (e.g., SDSS, Hubble) and morphological classification may miss subtle features or biases of the imaging.
• Evolving tasks: As astronomical surveys grow (bigger, deeper, multi-wavelength), classification tasks may require more sophistication (beyond the original questions) or integrate more automated approaches.

Why your lab/institution might care

Given your lab’s interests (e.g., research, data generation, integration of tools, training) here’s why Galaxy Zoo might be relevant:

• If your institution works with astronomical imaging (or plans to), Galaxy Zoo is a model of how to involve citizen-science or community participation in data processing.
• If you’re teaching students or trainees, participation (or showing the workflow) provides a hands-on example of crowd science, big data and morphology classification.
• If your lab generates imaging data (even outside astronomy), you might adopt a similar platform for classification tasks (leveraging the Galaxy Zoo model).
• In terms of outreach or science communication, offering your students or collaborators the chance to contribute to Galaxy Zoo (or analogous projects) can increase engagement and visibility.
• If your project involves machine-learning classification of images, Galaxy Zoo’s datasets and workflow (crowd-label → train ML) provide a compelling case-study to adopt/learn from.