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Users Dataset Documentation

Overview

Dataset Name: users.parquet File Pattern: users14*.dat, questions*.dat Time Period: 2000-2022 Data Type: Survey responses from psi experiment participants

🔄 STATUS: Complete 🎯 CONFIDENCE: 90% - Well-documented with clear processing logic

What This Dataset Contains

This dataset contains survey responses from users who participated in various psi (parapsychology) experiments. The survey collected demographic information, location data, and responses to two types of psychological questionnaires:

  1. Psi questionnaire - 15 questions about belief in and experiences with psi phenomena
  2. Hemispheric dominance questionnaire - 10 questions assessing left-brain vs right-brain cognitive preferences

Real-World Context

Researchers collected this data to explore potential correlations between:

  • Belief in psi phenomena and test performance
  • Hemispheric brain dominance and psi abilities
  • Geographic/demographic factors and psi performance

This dataset serves as the demographic anchor for all other experiment datasets and can be joined using username_hash.

Data Schema

Identity & Demographics

Column Type Description Possible Values
username string User-chosen username Any string (removed if --exclude-pii flag used)
username_hash string SHA-256 hash of username for joining 64-character hex string
email string User email address (2014+) Valid email or NULL (removed if --exclude-pii flag used)
timestamp datetime Survey completion timestamp Timezone-aware datetime

Location Information

Column Type Description Notes
city string User's city Free-text, may contain commas
state string State/province Free-text
coordinates string Geographic coordinates Format varies, often "lat,lon"
country string Country Free-text

⚠️ DATA QUALITY NOTE: Location fields are user-entered free text with inconsistent formatting and many missing values. Parse with caution.

Psi Questionnaire (15 questions)

Columns: psi_01 through psi_15

  • Type: Integer (1-5) or NULL
  • Scale: 1 = Strongly Disagree, 5 = Strongly Agree
  • Questions cover: Belief in telepathy, precognition, psychokinesis, personal psi experiences

Hemispheric Dominance Questionnaire (10 questions)

Columns: hemi_01 through hemi_10

  • Type: Integer (1-5) or NULL
  • Scale: 1 = Strongly Disagree, 5 = Strongly Agree
  • Purpose: Assess left-brain (analytical) vs right-brain (intuitive) cognitive preferences

Metadata

Column Type Description
how_find string How the user found the survey (free text)
na_count_psi_and_hemi integer Count of unanswered questions in psi + hemi surveys
file_type string Source file type: "users14" or "questions"
source_file string Original filename (only if --audit flag used)
source_row_number integer Row number in source file (only if --audit flag used)

Data Processing Notes

Source File Formats

The dataset combines two different file formats:

  1. users14.dat (2000-2015)

    • Format: Username, Timestamp, City, State, Coordinates, Country, Psi1-15, Hemi1-10, HowFind, na_count
    • No email field
    • Simple timestamp format

  2. questions.dat (2014-2022)

    • Format: Username, Timestamp, Email, City, State, Coordinates, Country, Psi1-15, Hemi1-10, HowFind
    • Includes email field
    • Timestamp format: "Wed Jan 1 00:33:44 2014"
    • na_count calculated during processing

Data Cleaning Challenges

🚧 OUTSTANDING: Need to document specific cleaning rules applied to location parsing

Major challenges addressed:

  1. Dirty CSV format - Username and location fields contain commas, requiring custom parsing that works backwards from the clean Psi/Hemi columns (which only contain values 1-5)

  2. Duplicate usernames - Deduplication rules:

    • Keep row with fewest NA responses in Psi and Hemi columns
    • If tied on NAs, keep oldest timestamp
    • Cross-file duplicates handled (users14 vs questions)

  3. Mixed date formats - Two different timestamp formats require format-specific parsing

  4. Test users - Rows with usernames starting with _test99 are automatically filtered out

Known Data Quality Issues

🎯 CONFIDENCE: 75% - Some edge cases in location parsing may not be fully documented

  • NA usernames: Rows with username "NA" or NULL are retained (as of June 18, 2025)
  • Location data: Highly inconsistent, user-entered free text
  • HowFind field format change: September 2002 - changed from yes/no to descriptive text

Usage Examples

Joining with Experiment Data 

import pandas as pd

# Load users and experiment data
users = pd.read_parquet('users.parquet')
card_results = pd.read_parquet('card.parquet')

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

# Analyze psi belief vs performance

Filtering Complete Surveys 

# Get users who answered all questions
complete_surveys = users[users['na_count_psi_and_hemi'] == 0]

Statistical Summary

🚧 OUTSTANDING: Add row counts and coverage statistics after processing

  • Total unique users: [To be added]
  • Duplicates removed: [Logged in processing stats]
  • Date range: 2000-2022
  • Source files: users14.dat + questions.dat

Privacy & Ethics

PII Protection

When using the --exclude-pii flag:

  • username column is removed
  • email column is removed
  • username_hash is retained for joining with other datasets

Recommended Usage

For publication or sharing:

python scripts/process_all.py --exclude-pii

This ensures user privacy while maintaining data linkage capabilities.

Related Datasets

This dataset can be joined with:

  • card.parquet - Basic ESP card test results
  • cardd.parquet - Card drawing test results
  • cardS.parquet - Sequential card test results
  • rv.parquet - Full remote viewing test results
  • rvq.parquet - Quick remote viewing test results
  • location.parquet - Coordinate remote viewing results
  • lottery.parquet - Lottery number prediction results

Join key: username_hash

References & Context

  • Original survey collected demographic and psychological profiles of psi experiment participants
  • Survey questions designed to assess belief in psi phenomena and cognitive style
  • Data cleaning pipeline handles 20+ years of format variations and dirty user input

Last Updated: 2025-01-09 Processor: src/processors/users_processor.py Schema Version: Unified (combines users14 and questions formats)