Data Collection

Data Collection

This guide covers integrating with Prolific and retrieving experimental data.

Prolific Integration

Setup

1. Create study on Prolific
2. Configure URL parameters in Prolific:
   • PROLIFIC_PID
   • STUDY_ID
   • SESSION_ID

Configuration

1. Add completion code in creds.ts:

const prolificCompletionCode = "YOUR-CODE";

2. Verify URL parameter handling in your code:

const urlParams = new URLSearchParams(window.location.search);
const prolificPID = urlParams.get('PROLIFIC_PID');
const studyID = urlParams.get('STUDY_ID');
const sessionID = urlParams.get('SESSION_ID');

Data Storage

Data Structure

The template uses three Firestore collections:

• exptData: Main experimental data

  {
    uid: string,
    trials: Array<{
      currentTrial: number,
      response: string|number,
      // ... other trial data
    }>,
    // ... metadata
  }

• userData: User-specific data
• sharedData: Shared experiment configuration

Security Rules

Default Firestore rules:

match /expData/{uid} {
    allow read: if true;
    allow write: if request.auth.uid == uid;
}

Data Retrieval

Using the Retrieval Script

1. Generate Firebase Admin credentials:

   • Go to Firebase Console
   • Project Settings > Service Accounts
   • Generate New Private Key
   • Save JSON file securely

2. Run retrieval script:

python retrieve_data.py \
    --cred "path/to/firebase-adminsdk.json" \
    --out "path/to/output" \
    --collection 'exptData' 'sharedData'

Data Format

Retrieved data structure:

{
  "participant_id": {
    "trials": [
      {
        "currentTrial": 0,
        "response": "value",
        "timestamp": "2024-01-01T12:00:00Z"
      }
      // ... more trials
    ],
    "metadata": {
      "prolificPID": "...",
      "studyID": "...",
      "sessionID": "...",
      "version": "1.0.0",
      "commitHash": "abc123"
    }
  }
  // ... more participants
}

Data Security

Best Practices

1. Secure credential storage:

   • Never commit credentials to git
   • Use encrypted storage for admin SDK key
   • Limit access to production data

2. Data backup:

   • Regular exports
   • Version control for analysis scripts
   • Secure backup storage

3. Data cleanup:

   • Remove debug data regularly
   • Archive completed studies
   • Maintain audit trail

Analysis Pipeline

Example Analysis Script

import pandas as pd
import firebase_admin
from firebase_admin import credentials, firestore

def load_experiment_data(cred_path):
    cred = credentials.Certificate(cred_path)
    firebase_admin.initialize_app(cred)
    db = firestore.client()
    
    # Get all documents from exptData collection
    docs = db.collection('exptData').stream()
    
    # Convert to pandas DataFrame
    data = []
    for doc in docs:
        participant_data = doc.to_dict()
        # Flatten trial data
        for trial in participant_data['trials']:
            trial_data = {
                'participant_id': doc.id,
                **participant_data['metadata'],
                **trial
            }
            data.append(trial_data)
    
    return pd.DataFrame(data)

Next Steps

• Return to Development Workflow
• Review Deployment Guide