documentation/README.md

GotPsi Parquet Dataset Documentation

This directory contains comprehensive documentation for all 8 parquet datasets generated by the GotPsi data cleaning pipeline.

Overview

The GotPsi project processed 20+ years (2000-2022) of online psi (parapsychology) experiment data, cleaning and standardizing it into analysis-ready parquet files. These datasets represent millions of trials from thousands of participants across multiple experiment types.

Quick Navigation

Core Datasets

Dataset	Type	Description	Complexity
users	Demographics	User surveys with psi beliefs & hemispheric dominance	⭐ Simple
card	ESP Test	Basic 1-in-5 card guessing test	⭐ Simple
cardd	ESP Test	Card drawing with Markov chain RNG	⭐⭐⭐ Complex
cardS	ESP Test	Sequential card finding (mixed row types)	⭐⭐ Moderate
rv	Remote Viewing	Full RV with 16 dimensional attributes	⭐⭐⭐ Complex
rvq	Remote Viewing	Quick RV with 1-in-5 image selection	⭐⭐ Moderate
location	Remote Viewing	Coordinate guessing on 300×300 grid	⭐⭐ Moderate
lottery	Precognition	Lottery number prediction (mixed row types)	⭐⭐ Moderate

Documentation Status Legend

Each documentation file includes status indicators:

Completion Status

• 🔄 STATUS: Complete - Dataset fully processed and documented
• 🔄 STATUS: In Progress - Processing ongoing
• 🔄 STATUS: Pending - Not yet started

Confidence Scores

Indicates how confident we are in the documentation accuracy:

• 🎯 CONFIDENCE: 95-100% - Fully verified from source code and testing
• 🎯 CONFIDENCE: 80-94% - Well-documented with minor gaps
• 🎯 CONFIDENCE: 60-79% - Core structure clear, some details need clarification
• 🎯 CONFIDENCE: <60% - Significant documentation gaps remain

Outstanding Items

• 🚧 OUTSTANDING: Marks specific items that need further investigation
• ⚠️ IMPORTANT: Critical information or warnings

Getting Started

1. Choose Your Dataset

Start with the dataset matching your research question:

Demographic Analysis:

• Use users.md - contains survey responses, beliefs, location data

Basic ESP Performance:

• Use card.md - simplest test, largest sample size

Advanced ESP Analysis:

• Use cardd.md - explores RNG influence
• Use cardS.md - explores sequential decision-making

Remote Viewing Research:

• Use rv.md - dimensional attribute analysis
• Use rvq.md - high-volume forced-choice RV
• Use location.md - spatial coordinate perception

Precognition Studies:

• Use lottery.md - future event prediction

2. Read the Documentation

Each dataset documentation includes:

• What This Dataset Contains - Plain-language overview
• Real-World Context - Experimental design and purpose
• Data Schema - Complete column reference
• Data Processing Notes - Cleaning rules and validation
• Statistical Analysis Examples - Ready-to-use code snippets
• Known Limitations - Data quality concerns and gaps
• Related Datasets - How to join with other data

3. Load and Analyze

import pandas as pd

# Load a dataset
card = pd.read_parquet('outputs/parquet/card.parquet')

# Explore structure
print(card.info())
print(card.head())

# Run basic analysis
hit_rate = card['is_hit'].mean()
print(f"Hit rate: {hit_rate:.2%} (chance = 20%)")

4. Join with Demographics

Most experiment datasets can be joined with user demographics:

users = pd.read_parquet('outputs/parquet/users.parquet')
card = pd.read_parquet('outputs/parquet/card.parquet')

# Join on username_hash
combined = card.merge(users, on='username_hash', how='left')

# Analyze performance by psi belief
combined.groupby('psi_01')['is_hit'].mean()

Common Analysis Patterns

Calculating Hit Rates

# Overall performance
hit_rate = df['is_hit'].mean()

# By user
user_perf = df.groupby('user_id_hash')['is_hit'].agg(['mean', 'count'])

# Filter to experienced users (>100 trials)
experienced = user_perf[user_perf['count'] >= 100]

Statistical Significance Testing

from scipy import stats

# Test against chance (e.g., 20% for card tests)
n_trials = len(df)
n_hits = df['is_hit'].sum()

result = stats.binomtest(n_hits, n_trials, p=0.2, alternative='greater')
print(f"p-value: {result.pvalue}")

Temporal Analysis

# Performance over time
df['date'] = df['timestamp'].dt.date
daily_perf = df.groupby('date')['is_hit'].mean()

daily_perf.plot(title='Performance Over Time')

Data Processing Flags

When generating parquet files, two important flags control output:

Audit Mode (--audit)

python scripts/process_all.py --audit

Adds columns:

• source_file - Original filename
• source_row_number - Row number in source file

Use when:

• You need full data lineage
• Debugging data quality issues
• Tracing anomalies to source

File size: +10-15% larger

PII Exclusion (--exclude-pii)

python scripts/process_all.py --exclude-pii

Removes columns:

• user_id (or username in users dataset)
• email (in users dataset)

Retains:

• user_id_hash / username_hash - for joining datasets

Use when:

• Preparing data for publication
• Sharing with external researchers
• Complying with privacy requirements

File size: Slightly smaller

Combining Flags

python scripts/process_all.py --audit --exclude-pii

Data Quality Notes

Test User Filtering

All datasets automatically filter out test users:

• Usernames starting with _test99 are removed
• Known cheaters removed (card dataset, 2001 only)

Validation & Cleaning

Each dataset applies specific validation rules:

• Range checks (e.g., card responses must be 1-5)
• Type coercion (strings → numbers where appropriate)
• Timestamp parsing with timezone handling
• User ID length limits (max 30 characters)

Invalid rows are:

• Logged to logs/latest/{dataset}_latest_errata.jsonl
• Excluded from final parquet files
• Counted in processing statistics

Schema Versions

Some datasets have multiple schema versions:

Dataset	Versions	Change Date	Impact
card	v1, v2	2006-01-10	seed2 → trperrun rename
cardd	v1, v2, mixed	2006-06-22	Markov bits split
cardS	Mixed rows	N/A	Step vs completion rows
lottery	Mixed rows	N/A	Lottery vs immediate rows

The processors automatically detect and unify these versions.

Converting to PDF

These markdown files can be easily converted to PDF:

Using Pandoc (Recommended)

# Install pandoc
brew install pandoc  # macOS
apt-get install pandoc  # Linux

# Convert single file
pandoc users.md -o users.pdf

# Convert all files
for file in *.md; do
    pandoc "$file" -o "${file%.md}.pdf"
done

Using Python

pip install markdown-pdf

md2pdf users.md

Using Online Tools

• Markdown to PDF
• CloudConvert

Dataset Size Reference

Approximate file sizes (uncompressed, without audit mode):

Dataset	Rows	Size	Join Key
users	~50K	5-10 MB	username_hash
card	~15M	500 MB - 1 GB	user_id_hash
cardd	~5M	200-400 MB	user_id_hash
cardS	~10M	300-500 MB	user_id_hash
rv	~100K	20-50 MB	user_id_hash
rvq	~2M	100-200 MB	user_id_hash
location	~500K	30-60 MB	user_id_hash
lottery	~200K	10-30 MB	user_id_hash

Note: Actual sizes depend on raw data availability.

Citation & Usage

When using these datasets in research, please cite:

GotPsi Dataset (2000-2022). Cleaned and processed by [Your Lab/Name]. Original data collected by GotPsi online psi experiment platform.

Recommended Attribution

Data Source: GotPsi online psi experiments (2000-2022)
Processing: GotPsi Data Cleaning Pipeline v1.0
Access Date: [Your access date]

Support & Questions

Documentation Issues

If you find errors or gaps in the documentation:

1. Check the 🚧 OUTSTANDING sections - known gaps are marked
2. Review the source processor code in src/processors/
3. Examine processing logs in logs/latest/
4. Open an issue with specific questions

Data Quality Concerns

If you notice data anomalies:

1. Check if audit mode is enabled (has source_file column?)
2. Review errata logs: logs/latest/{dataset}_latest_errata.jsonl
3. Verify against raw source files
4. Report with specific examples (file, row number, issue)

Roadmap

Future documentation improvements:

High Priority

• Document RV attribute dimensions (attr_00 through attr_15)
• Clarify RV scoring algorithms (accuracy, relevance, form)
• Verify location dataset count offset removal
• Document card/cardd x, y, bias parameters

Medium Priority

• Add complete statistical analysis cookbook
• Create data quality report per dataset
• Add visualization examples (plots, charts)
• Document temporal trends and patterns

Low Priority

• Add cross-dataset analysis examples
• Create dataset comparison matrix
• Add machine learning examples
• Generate automated data profiles

Contributing

To improve this documentation:

1. Add details - Fill in 🚧 OUTSTANDING items
2. Verify accuracy - Test code examples and formulas
3. Add examples - Contribute useful analysis patterns
4. Report issues - Note discrepancies or errors

Version History

• 2025-01-09 - Initial documentation created
• Status: All 8 datasets documented with status/confidence indicators
• Coverage: Core structure complete, experimental details need investigation

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Last Updated: 2025-01-09 Documentation Format: Markdown (PDF-ready) Target Audience: Researchers and scientists analyzing psi experiment data