Jacob Thomas Redmond (Not Messer)

Script for Monitoring Image and Data Integrity in Scientific Publications

This script is designed to monitor all images and data, supplements, and their associated papers published in various scientific journals. It detects image manipulation, reuse, and a lack of repeatability of findings across research platforms and publications.

Outline

• Setup
  • Import necessary libraries
  • Configure access to multiple journal databases
  • Setup internal databases for authors, reviewers, co-authors, sponsors, conflict of interest disclosures, journals, citations, retractions, and notifications
• Image and Data Collection
  • Retrieve all images and data associated with each publication
  • Store images and data in a structured format for analysis
• Image Manipulation Detection
  • Check for image duplications within and across publications
  • Detect image alterations using forensic analysis tools
• Data Integrity Check
  • Verify data consistency within individual papers
  • Compare data sets across multiple publications for anomalies
• Repeatability Analysis
  • Identify studies that have been replicated
  • Analyze the consistency of results across replications
• Database Management
  • Maintain database of authors, reviewers, co-authors, and sponsors
  • Track conflict of interest disclosures
  • Log all citations of the research
  • Record retraction status and notifications to journals and authors
• Reporting
  • Generate reports on detected issues
  • Notify editors and authors of potential problems

Pseudocode

# Import necessary libraries
import os
import requests
import imageio
import numpy as np
from skimage import io, img_as_float
from skimage.metrics import structural_similarity as ssim
from deepdiff import DeepDiff
import sqlite3

# Configure access to multiple journal databases
JOURNAL_API_URLS = {
    "Nature": "https://api.nature.com/content",
    "Science": "https://api.sciencemag.org/content",
    "Cell": "https://api.cell.com/content"
}
API_KEYS = {
    "Nature": "nature_api_key",
    "Science": "science_api_key",
    "Cell": "cell_api_key"
}

# Setup internal databases
def setup_databases():
    conn = sqlite3.connect('research_integrity.db')
    cursor = conn.cursor()
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Authors (
            id INTEGER PRIMARY KEY,
            name TEXT,
            affiliation TEXT
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Reviewers (
            id INTEGER PRIMARY KEY,
            name TEXT,
            affiliation TEXT
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS CoAuthors (
            id INTEGER PRIMARY KEY,
            name TEXT,
            affiliation TEXT
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Sponsors (
            id INTEGER PRIMARY KEY,
            name TEXT,
            type TEXT
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS ConflictsOfInterest (
            id INTEGER PRIMARY KEY,
            author_id INTEGER,
            conflict TEXT,
            FOREIGN KEY (author_id) REFERENCES Authors (id)
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Journals (
            id INTEGER PRIMARY KEY,
            name TEXT,
            api_url TEXT,
            api_key TEXT
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Citations (
            id INTEGER PRIMARY KEY,
            publication_id INTEGER,
            citation_count INTEGER
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Retractions (
            id INTEGER PRIMARY KEY,
            publication_id INTEGER,
            retracted BOOLEAN
        )
    ''')
    cursor.execute('''
        CREATE TABLE IF NOT EXISTS Notifications (
            id INTEGER PRIMARY KEY,
            publication_id INTEGER,
            notified BOOLEAN
        )
    ''')
    conn.commit()
    conn.close()

# Retrieve all images and data associated with each publication
def get_publication_data(journal, publication_id):
    api_url = JOURNAL_API_URLS[journal]
    api_key = API_KEYS[journal]
    response = requests.get(f"{api_url}/{publication_id}", headers={"Authorization": f"Bearer {api_key}"})
    if response.status_code == 200:
        return response.json()
    return None

def save_image_data(publication_data):
    for image_info in publication_data['images']:
        image_url = image_info['url']
        image = io.imread(image_url)
        io.imsave(os.path.join('images', image_info['filename']), image)

# Check for image duplications within and across publications
def detect_image_duplications(image_folder):
    image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))]
    for i, image_file1 in enumerate(image_files):
        image1 = img_as_float(io.imread(os.path.join(image_folder, image_file1)))
        for j, image_file2 in enumerate(image_files):
            if i != j:
                image2 = img_as_float(io.imread(os.path.join(image_folder, image_file2)))
                ssim_index, _ = ssim(image1, image2, full=True)
                if ssim_index > 0.95:
                    print(f"Duplicate images found: {image_file1} and {image_file2}")

# Detect image alterations using forensic analysis tools
def detect_image_alterations(image_folder):
    # Placeholder for image forensic analysis implementation
    pass

# Verify data consistency within individual papers
def verify_data_consistency(publication_data):
    # Placeholder for data consistency checks implementation
    pass

# Compare data sets across multiple publications for anomalies
def compare_data_sets(publications_data):
    differences = DeepDiff(publications_data[0]['data'], publications_data[1]['data'])
    if differences:
        print("Data anomalies detected:", differences)

# Identify studies that have been replicated
def identify_replications(publications_data):
    # Placeholder for replication identification implementation
    pass

# Analyze the consistency of results across replications
def analyze_replication_consistency(replication_data):
    # Placeholder for replication consistency analysis implementation
    pass

# Generate reports on detected issues
def generate_report(issues):
    # Placeholder for report generation implementation
    pass

# Notify editors and authors of potential problems
def notify_issues(issues):
    # Placeholder for notification implementation
    pass

# Main function
def main():
    setup_databases()
    # Placeholder for main script logic
    pass

if __name__ == "__main__":
    main()
    

Conclusion

This script provides a framework for monitoring the integrity of images and data in scientific publications. By implementing and expanding the pseudocode provided, scientific journals can proactively detect and address issues related to image manipulation, data consistency, and repeatability, thereby enhancing the reliability of published research.

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