Moved 2026-10-04 (import done; do not archive until source PR #3 is closed/merged). Canonical copy lives in the kk-kb monorepo at content/projects/01-vancouver-ai-community/special-features/hackathons/rival-2024-2025/round-4-music/vanai-hackathon-004/ (source commit 3cd45ed7bf74). Large media is in private Drive — see PROVENANCE.md there. This repo stays open for PR #3.

Vancouver AI Hackathon Round 4: The Soundtrack of Us

🎵 What This Is

A dataset with 1,000+ survey responses about how people discover music, what formats they use, and how they feel about AI-generated music. Perfect for creating data visualizations, apps, or insights about music and technology.

🚀 New to GitHub? Start Here

Getting the Data (3 Easy Ways)

Option 1: Download ZIP (Easiest)

1. Click the green "Code" button at the top of this page
2. Click "Download ZIP"
3. Unzip the file on your computer
4. Open data/raw/music_survey_data.csv in Excel, Google Sheets, or any data tool

Option 2: Clone with Git

git clone https://github.com/WalksWithASwagger/vanai-hackathon-004.git
cd vanai-hackathon-004

Option 3: Individual Files

• Right-click any file → "Save As" to download individual files
• Main dataset: data/raw/music_survey_data.csv

What's In The Dataset

The Data

• 1,000+ complete responses from music listeners across Canada
• 19 core questions covering music discovery, format evolution, and AI attitudes
• Rich demographics: Age, location, education, income, household composition
• 3,000+ text responses with sentiment analysis scores
• Geographic distribution: Ontario (40%+), BC (20%+), Alberta, Quebec, and more

Question Categories

• Music Discovery: How people find new music (radio, streaming, social media, etc.)
• Format Evolution: Personal experiences with vinyl, cassettes, CDs, downloads, streaming
• Listening Habits: When and how people consume music in daily life
• AI Music Attitudes: Responses to AI-generated music and voice cloning
• Social Sharing: How people share music and their guilty pleasures
• Personal Connection: Life theme songs and meaningful lyrics

💡 Quick Start Ideas

Just Want to Browse?

• Open data/raw/music_survey_data.csv in Excel or Google Sheets
• Check out data/raw/survey_questions.txt to see what was asked
• Run python scripts/explore_data.py for a quick data overview

Ready to Code?

Python Users

import pandas as pd

# Load the data
df = pd.read_csv('data/raw/music_survey_data.csv')
print(f"Total responses: {len(df)}")

# Check out some life theme songs
print(df['Q18_Life_theme_song'].dropna().head())

R Users

library(tidyverse)
music_survey <- read_csv("data/raw/music_survey_data.csv")
glimpse(music_survey)

Excel/Google Sheets Users Just open the CSV file - no coding required!

Data Structure

Key Columns

• Q1_Relationship_with_music: Music engagement level
• Q2_Discovering_music: Primary music discovery method
• Q4_Music_format_changes: Experience with format transitions
• Q7_New_music_discover_*: Multiple discovery methods
• Q8_Music_listen_time_GRID_*: Listening habits by activity
• Q10_Songs_by_AI: Attitudes toward AI-generated music
• Q11_Use_of_dead_artists_voice_feelings: AI voice cloning opinions
• Q12_Music_bingo_*: Music-related behaviors
• Q13_Share_the_music_you_love_*: Music sharing methods
• Q15_Music_guilty_pleasure: Musical guilty pleasures
• Q18_Life_theme_song: Personal theme songs
• Q19_Lyric_that_stuck_with_you: Meaningful lyrics

Demographics

• AgeGroup_Broad: 18-34, 35-54, 55+
• Province: Geographic distribution
• Education: Education level
• HH_Income_Fine_23: Household income
• Gender: Gender distribution

Sentiment Analysis

• Sentiment scores included for open-ended responses
• Polarity and subjectivity measures available
• Percentage scores for emotional content

📁 What's in This Repository

vanai-hackathon-004/
├── 📄 README.md                        # You are here!
├── 📊 data/
│   ├── raw/
│   │   ├── music_survey_data.csv      # 🎯 THE MAIN DATASET (start here!)
│   │   └── survey_questions.txt       # What questions were asked
│   ├── processed/                     # Your cleaned data goes here
│   └── analysis/                      # Your analysis results go here
├── 🔧 scripts/
│   ├── explore_data.py                # Quick data peek script
│   └── data_preprocessing.py          # Data cleaning helpers
├── 💻 src/                            # Your code goes here
├── 🎯 submissions/                    # Hackathon projects go here
└── 📚 docs/                          # Extra documentation

The Important Files:

• data/raw/music_survey_data.csv - This is your main dataset!
• data/raw/survey_questions.txt - Explains what each question means
• scripts/explore_data.py - Run this for a quick data overview

🔧 Helpful Scripts

Want a Quick Data Overview?

python scripts/explore_data.py

This shows you basic info about the dataset - how many responses, what columns exist, sample data, etc.

Need to Clean the Data?

python scripts/data_preprocessing.py

This helps clean up responses, handle missing data, and prepare data for analysis.

Hackathon Challenge

Using this dataset, create an experience that's interactive, visual, or narrative-driven. The goal is to transform raw survey data into compelling insights about music, technology, and human behavior.

Submission Requirements

• Format Freedom: Visual display, interactive demo, live performance, or unconventional approach
• Bite-Sized Impact: Engaging experience within 2-5 minutes
• Technical Details: Include all software, hardware, and instructions needed
• Data Grounding: Story must be grounded in the actual data
• AI Integration: Incorporate AI tools to enhance your narrative

Judging Criteria

• Creativity & Innovation (25%): Fresh, original approach
• Clarity & Effectiveness (25%): Clear communication of insights
• Engagement & Impact (20%): Compelling and memorable experience
• Technical Execution (20%): Well-implemented solution
• Community Value (10%): Open-source tools, shared methodologies

📅 Important Dates

• Dataset Released: September 15, 2025
• Submission Deadline: October 15, 2025
• Award Date: October 29, 2025

📦 Submission Requirements

Your final folder of deliverables should include:

• PDF Document containing:
  • Team member information (names, contact information)
  • Project title and description (max 300 words)
  • Brief explanation of your technical approach and tools used (max 300 words)
• Link to working prototype/demo, or the file itself
• 3-minute video walkthrough (strongly encouraged, especially for interactive projects)

🛠️ Toolkit

Suggested tools, not requirements:

• Cline, RooCode, Cursor, Claude Code - AI copilots to jam with
• v0 by Vercel, Lovable - interfaces at speed
• Udio, Suno, ElevenLabs - sound experiments
• Pika, Runway, Google Veo - video riffs
• p5.js, Processing, Observable - data paintings
• Seedream 4 (ByteDance), Midjourney, Stable Diffusion - visual generation

❓ Need Help?

New to Data Analysis?

• Excel/Google Sheets: Just open the CSV file and start exploring
• Python: Install pandas (pip install pandas) and use the code examples above
• R: Install tidyverse (install.packages("tidyverse")) and use the R code above

New to GitHub?

• Download: Click green "Code" button → "Download ZIP"
• Questions: Check the Issues tab or create a new issue
• Sharing: Fork this repository to make your own copy

Getting Stuck?

• Look at data/raw/survey_questions.txt to understand the questions
• Run python scripts/explore_data.py to see what's in the data
• Check the Issues tab for common questions

📋 License & Usage

This dataset is provided for hackathon use. Please respect participant privacy and use data responsibly.

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🎯 Ready to start? Download the data and explore 1,000+ music stories from across Canada!