data/README.md

GotPsi Experiment Data

Raw data files for the GotPsi psi-testing experiments. Each subdirectory contains .dat files for one experiment, plus a manifest.json that lists every file with its md5 checksum.

Quick Start

1. Download archives from Hugging Face

Use snapshot_download to pull all .tar.gz archives into data/archives/:

python3 -c "
from huggingface_hub import snapshot_download
import os

snapshot_download(
    repo_id='instNoeticSciences/gotpsi_preprocess',
    repo_type='dataset',
    token=os.environ['HUGGING_FACE_TOKEN'],
    local_dir='data/archives/',
    allow_patterns=['*.tar.gz'],
)
"

This places card.tar.gz, cardd.tar.gz, etc. in data/archives/.

2. Extract archives

# Extract all experiments
python data/unpack.py

# Extract specific experiments only
python data/unpack.py card lottery

# Verify existing data integrity (no extraction)
python data/unpack.py --verify

Archives remain in data/archives/ after extraction — rerun unpack.py any time without re-downloading.

Alternate: download and extract in one step

If you prefer to stream directly from the URL without saving archives locally:

python data/unpack.py --source https://huggingface.co/datasets/instNoeticSciences/gotpsi_preprocess/resolve/main

Experiments

Experiment	Files	Description
card	8,341	Basic ESP card test -- standard 1-in-5 Zener card guessing with forced-choice trials. Schema changed in 2006 (seed2 to trperrun).
cardd	6,934	Card Draw test -- Markov-influenced card selection where the RNG uses transition probabilities. Two format versions (pre/post 2006-06-22).
cards	7,627	Sequential Card test -- participants guess card positions in a sequence. Mixed row format: step rows (4 cols) and completion rows (11 cols).
location	7,563	Location (Remote Viewing Coordinates) -- participants guess geographic coordinates. Variable format: 13-14 columns (older files lack seed column).
lottery	2,450	Lottery number prediction -- participants predict lottery draws. Mixed row format: lottery rows (9 cols) and immediate-draw rows (16 cols).
rv	6,468	Full Remote Viewing -- dimensional attribute scoring (0-100) across 16 image attributes. 24-29 columns with multiple scoring methods.
rvq	6,248	Quick Remote Viewing -- 5-choice image selection task, similar to Card but with photographs. 14-15 columns.
users	2	User survey responses -- Psi Quotient and Hemispheric Dominance questionnaires plus demographics. Two files: users14.dat (2000-2015) and questions.dat (2014-2022).

Directory Layout

data/
├── archives/           # .tar.gz archives from HF (gitignored)
│   ├── card.tar.gz
│   ├── cardd.tar.gz
│   └── ...
├── card/               # extracted .dat files (gitignored) + manifest.json (tracked)
├── cardd/
├── cards/
├── rv/ rvq/ location/ lottery/ users/ lost_and_found/
├── pack.py             # archive experiments for distribution
├── unpack.py           # extract archives into experiment dirs
└── README.md           # this file

The .dat files and archives are gitignored. Only manifest.json files are tracked in version control.

lost_and_found/

Holds orphan files that could not be assigned to a specific experiment during data migration. Local only — not distributed or published.

Manifests

Each manifest.json contains:

• filename: The .dat file name
• md5_hash: MD5 checksum for per-file integrity verification
• archive_md5 (after packing): Checksum of the .tar.gz archive

Processors use manifests to discover files (no recursive glob needed).

For Maintainers

Pack data for distribution:

# Create archives for all experiments (output to data/archives/)
python data/pack.py --output-dir data/archives/

# Pack specific experiments
python data/pack.py card lottery --output-dir data/archives/

After packing, upload the .tar.gz files to the HF dataset repo, then update download_url in each manifest and commit.

Dataset Nuances for Parquet Users

The processing pipeline (scripts/process_all.py) produces cleaned parquet files in output/parquet/. A separate README ships with those files, but the key nuances are summarized here for reference.

card: is_hit and schema versions

The card dataset has two schema versions (schema_version column):

• v2 (post-2006, ~63.7M rows): is_hit equals target2 == response. Chance hit rate: 20% (1-in-5).
• v1 (pre-2006, ~25.8M rows): is_hit was precomputed by gotpsi using a harder criterion. Observed hit rate is ~4% (1/25). Do not recompute is_hit from target2 == response for v1 rows.

The response distribution is non-uniform (center bias toward card 3). This is participant behavior, not a data quality issue. The target2 distribution is uniform, confirming RNG integrity.

rv: Uses start_time / end_time instead of timestamp

rv: Sentinel values in score columns

The accuracy, relevance, and form columns use -1 as a sentinel for missing data (~2.1M rows for accuracy/relevance, ~24K for form). Filter these before computing score statistics. total_score is clean (0-100).

cardS: Null patterns by row type

The cardS dataset has two row types: step rows (4 columns of data) and completion rows (11 columns). Step rows have NULLs in response, steps, response_array, and target_image (~112M of 150M rows). This is structural, not missing data.

lottery: Null patterns by row type

Lottery rows (row_type = lottery) have NULLs in target/match columns (~1,575 rows). Only immediate-draw rows carry the full 16-column schema.

cardD: Null patterns by schema

image_filename is NULL for ~18.8M rows and target_image for ~41M rows, corresponding to older schema versions that did not include these fields.

users: email column sparsity

Only ~22K of ~284K users have an email value (from questions.dat, 2014-2022). The remaining rows have NULL emails (from users14.dat, 2000-2015). All non-null emails are HMAC-hashed.

All datasets: Timestamps are America/Los_Angeles

All timezone-aware timestamp columns across every dataset use America/Los_Angeles. Convert before comparing across external sources.

users: PII considerations

The username and email columns are HMAC-hashed, but city, state, coordinates, and country contain raw self-reported location data. Public releases should evaluate whether to include these columns.