SHIELD-DATA 0.2.1

A database for results collected from the SHIELD permeation rig

SHIELD-Data

A Python package providing experimental permeation data from the SHIELD hydrogen permeation rig.

Overview

SHIELD-Data bundles experimental data in an SQLite database for easy access and analysis. The package includes:

• 826,000+ measurements from 8 experimental runs
• SQLite database for efficient querying and filtering
• Simple API for loading data and metadata
• Bundled with pip install - no external data downloads needed

Installation

pip install shield-data

Quick Start

import shield_data as sd

# View all available runs
cat = sd.catalogue()
print(cat[["run_id", "date", "furnace_setpoint"]])

# Load data from a specific run
df = sd.load("25.10.06_run_1_10h41")

# Load all runs at 500K
df_500k = sd.load_filtered(furnace_setpoint=500)

# Get run metadata
metadata = sd.load_metadata("25.10.06_run_1_10h41")
print(metadata["run_info"])

API Reference

catalogue()

Load the catalogue of all experimental runs.

cat = sd.catalogue()
# Returns DataFrame with columns:
#   run_id, date, start_time, run_type, furnace_setpoint, material, coating

load(run_id)

Load pressure gauge data for a specific run.

df = sd.load("25.10.06_run_1_10h41")
# Returns DataFrame with columns:
#   timestamp, WGM701_voltage, CVM211_voltage,
#   Baratron626D_1KT_voltage, Baratron626D_1T_voltage, run_id

load_metadata(run_id)

Load metadata for a specific run.

metadata = sd.load_metadata("25.10.06_run_1_10h41")
# Returns dict with keys: version, run_info, gauges, thermocouples

load_filtered(**filters)

Load data for runs matching filter criteria.

# Filter by temperature
df = sd.load_filtered(furnace_setpoint=500)

# Filter by run type and date
df = sd.load_filtered(run_type="permeation_exp", date="2025-10-06")

# Multiple runs combined into single DataFrame

Example Analysis

import shield_data as sd
import matplotlib.pyplot as plt

# Load all 500K experiments
df = sd.load_filtered(furnace_setpoint=500)

# Plot pressure over time for each run
for run_id in df["run_id"].unique():
    run_data = df[df["run_id"] == run_id]
    plt.plot(
        run_data.index,
        run_data["Baratron626D_1T_voltage"],
        label=run_id
    )

plt.xlabel("Measurement Number")
plt.ylabel("Downstream Pressure (V)")
plt.legend()
plt.title("500K Permeation Experiments")
plt.show()

Data Structure

Database Schema

The SQLite database contains two tables:

runs table:

• run_id (TEXT, PRIMARY KEY)
• date (TEXT)
• start_time (TEXT)
• run_type (TEXT)
• furnace_setpoint (INTEGER)
• material (TEXT, nullable)
• coating (TEXT, nullable)
• metadata (TEXT, JSON)

measurements table:

• id (INTEGER, PRIMARY KEY)
• run_id (TEXT, FOREIGN KEY)
• timestamp (TEXT)
• WGM701_voltage (REAL)
• CVM211_voltage (REAL)
• Baratron626D_1KT_voltage (REAL)
• Baratron626D_1T_voltage (REAL)

Run Metadata

Each run includes detailed metadata:

• Run information (type, date, furnace setpoint)
• Gauge configurations (4 pressure gauges)
• Valve timing information
• Recording parameters

Repository Structure

SHIELD-Data/
├── run_data/                     # Raw data (not in package)
│   ├── YY.MM.DD_run_X_HHhMM/    # Individual run folders
│   │   ├── pressure_gauge_data.csv
│   │   └── run_metadata.json
│   └── ...
├── src/shield_data/
│   ├── db.py                     # Main API
│   ├── build_db.py               # Database builder
│   └── shield_data.db            # SQLite database (98 MB)
└── test/                         # Unit tests

Contributing

Adding New Data

When adding new experimental runs:

1. Add run folder to run_data/YY.MM.DD_run_X_HHhMM/
2. Rebuild database (required):

   python src/shield_data/build_db.py
   

3. Commit both new data AND updated shield_data.db
4. Submit PR (see CONTRIBUTING.md)

Note: PRs adding data without rebuilding the database will not be merged.

Development

To rebuild the database from raw data:

from shield_data.build_db import build_database

build_database("run_data")  # Creates src/shield_data/shield_data.db

Requirements

• Python >= 3.9
• pandas

License

Apache License 2.0

Citation

If you use this data in your research, please cite:

@software{shield_data_2025,
  author = {Dark, James},
  title = {SHIELD-Data: Hydrogen Permeation Experimental Data},
  year = {2025},
  doi = {10.5281/zenodo.17544506},
  url = {https://github.com/PTTEPxMIT/SHIELD-Data}
}

Contact

• Author: James Dark
• Email: [e-mail]
• Repository: https://github.com/PTTEPxMIT/SHIELD-Data