This tool provides comprehensive journal metrics and categorization for academic papers using their DOI.

1. Get Journal Metrics from DOI - Extract comprehensive metrics for any journal
2. Automatic Categorization - Categorize journals into 7 research areas
3. Top Journals Database - Generate lists of top 25 journals in each category

1. Biotechnology and Biomedical Engineering
2. Electrochemical Engineering
3. Nanotechnology for Advanced Materials
4. Process Systems Engineering
5. Soft Matter & Polymer Engineering
6. Sustainable Reaction Engineering
7. Multidisciplinary & Core Engineering

from script import get_journal_metrics

doi = "10.1016/j.electacta.2025.147532"
metrics = get_journal_metrics(doi)

print(f"Journal: {metrics['journal_name']}")
print(f"Category: {metrics['category']}")
print(f"Impact Factor: {metrics['impact_factor']}")
print(f"h-index: {metrics['h_index']}")

Run the script to generate a comprehensive list:

python generate_top_journals.py

This creates top_journals_by_category.txt with the top 25 journals in each category.

The get_journal_metrics() function returns:

• journal_name: Name of the journal
• category: Research area classification
• issn: International Standard Serial Number
• impact_factor: 2-year impact factor
• h_index: h-index (cumulative impact measure)
• i10_index: Number of papers with ≥10 citations
• mean_citedness_2y: Average citations per paper (2-year window)
• citations_per_paper: Overall citation rate
• papers_count: Total papers published
• cited_by_count: Total citations received
• publication_growth_rate: Growth trend (5-year comparison)
• publisher: Publisher name
• country: Country code
• oa_works_count: Open access paper count
• apc_usd: Article processing charge (USD)
• counts_by_year: Yearly publication/citation data

• CrossRef API: DOI metadata and journal information
• OpenAlex: Comprehensive journal metrics, topics, and classifications

• Impact Factor and mean_citedness_2y use the same metric (2-year citation average)
• h-index serves as an open alternative to Eigenfactor for measuring journal impact
• Categorization uses weighted scoring: journal name (10x) > article topics (2x) > article keywords (1.5x) > journal topics (1x)