A Web Application for Exploring Distribution in Academic Publications Across Geography and Institutions in India

The paper introduces Indiapub, an open-access web application that leverages OpenAlex data to visualize and analyze the geographic and temporal distribution of academic publications across Indian states and institutions, thereby providing actionable insights for policymakers and researchers to address regional disparities and foster a more inclusive research ecosystem.

The Gist

Imagine India as a massive, bustling library with thousands of different rooms (states and territories). Inside these rooms, scholars are writing millions of books (research papers) every year. For a long time, if you wanted to know which rooms were the busiest, you'd have to ask the librarian for a giant, confusing spreadsheet that just said, "India wrote 4 million books." It didn't tell you where the magic was happening or which rooms were empty.

This paper introduces Indiapub, a new, magical "smart map" that turns that confusing spreadsheet into a colorful, interactive dashboard. Think of it as a GPS for academic research that lets you zoom in on any room in the library to see exactly who is writing, what they are writing about, and how it compares to the size of the room itself.

Here is a simple breakdown of what the paper is about:

1. The Problem: The "Blurred Map"

Before this tool, looking at India's research output was like looking at a foggy map. You knew the country was producing a lot of science, but you couldn't see the details.

• The Analogy: Imagine a school where the principal only knows the total number of essays written by the whole school. They don't know that the 10th-grade class is writing 80% of them while the 1st-grade class is barely writing anything.
• The Reality: India has huge variations. Some states are research powerhouses, while others are struggling to get their voices heard. Previous tools were either too complicated for regular people or didn't break the data down by state.

2. The Solution: The "Indiapub" Dashboard

The authors built a free website (Indiapub) that acts like a high-tech telescope. You can point it at any topic (like Artificial Intelligence, COVID-19, or Economics) and any time period (2014–2025).

• How it works: It grabs data from a giant, open database called OpenAlex (which is like a global library catalog) and automatically cleans it up.
• The Magic Features:
  • The Heat Map: A map of India where states glow brighter the more research they produce.
  • The "Fairness" Check: This is the coolest part. It compares a state's research output to its population size.
    • The Analogy: If a state has 10% of India's people but only writes 1% of the papers, the map turns orange (warning: underperforming). If a small state writes way more papers than its population size suggests, it turns green (great job: overperforming!).

3. What They Discovered: The "Research Giants" and the "Hidden Gems"

When the authors used their new telescope, they saw some clear patterns:

• The Heavy Hitters: A few big states—like Tamil Nadu, Maharashtra, Delhi, and Uttar Pradesh—are doing the heavy lifting. They are the "Supermarkets" of research, producing the most papers.
• The Overachievers: Tamil Nadu and Delhi are writing way more papers than their population size would predict. They are like a small bakery that somehow feeds the whole city.
• The Underdogs: Bihar, a very populous state, is writing far fewer papers than its size suggests. It's like a giant stadium that is mostly empty.
• The Specialists: In specific fields, different cities shine. For example, Delhi dominates "Development Economics" (like a capital of policy), while Tamil Nadu leads in "Artificial Intelligence" and "Electronic Health Records."

4. Why This Matters: Building a Better Library

The authors aren't just counting books; they are trying to fix the library.

• For Students: It helps them find the "hot spots" where the best research is happening so they can apply to the right universities.
• For Policymakers: It's a wake-up call. If a state is underperforming, leaders can see it clearly and decide to build more labs or hire more professors there.
• For Everyone: It proves that research isn't evenly spread. To make India a true global science leader, they need to help the quiet rooms in the library start writing too.

The Bottom Line

This paper is about making the invisible visible. By turning dry numbers into a colorful, interactive game, the authors have given India a tool to see its own research strengths and weaknesses. It's like giving the country a mirror so it can finally see exactly where to focus its energy to build a more balanced and brilliant future.

Technical Summary

1. Problem Statement

India's national research output has grown significantly, contributing to global scientific expansion. However, aggregated national data masks substantial heterogeneity in research capacity and infrastructure across India's diverse states and union territories (UTs). Existing bibliometric tools (e.g., CiteSpace, VOSviewer) are often designed for technical users, lack seamless integration with external demographic data (such as population size or Human Development Index), and do not offer granular, subnational visualizations of publication trends over time. Furthermore, existing India-specific dashboards focus on health or economic indicators but lack dedicated tools to analyze research publication patterns at the state/UT level. This gap hinders evidence-based policy planning, resource allocation, and the identification of regional research strengths and weaknesses.

2. Methodology

The authors developed Indiapub, an open-access web application designed to quantify and visualize academic publication patterns in India.

• Data Source: The application utilizes the OpenAlex API (an open-access scholarly metadata database) to retrieve publication data from 2014 to 2025.
• Data Retrieval & Filtering:
  • Affiliation Matching: Institutional affiliations are extracted using Research Organization Registry (ROR) identifiers. An author is considered "India-affiliated" if they have at least one affiliation with an Indian institution.
  • Topic Selection: Users can filter by predefined topics (e.g., EHR, GWAS, AI, COVID-19) via keyword searches in titles/abstracts, or select "Any Topics" using a precomputed dataset. Custom keyword searches are also supported.
  • Attribution Rules: The system offers four author-position counting rules to attribute publications to specific states/UTs:
    1. First author (default for specific topics).
    2. Last author.
    3. First or last author.
    4. Any author (default for "Any Topics").
  • Population Stratification: States/UTs are categorized into three groups based on population share: High (>5.0%), Medium (1.0–5.0%), and Low (<1.0%).
• Visualization Modules: The application features five interactive modules:
  1. Geographic Map: Displays publication counts (total or per capita) with color gradients.
  2. Time Trend Plot: Line graphs showing publication volume over time for the nation or specific states.
  3. Publication vs. Population Share: A scatter plot comparing a state's share of publications against its share of the population to identify over/underrepresentation.
  4. Stacked Bar Plot: Visualizes the relative contribution of states/UTs to total publications over time, benchmarked against population shares.
  5. Bubble Plot: Correlates publication volume with the Human Development Index (HDI) over time.
• Technical Architecture: The app supports two data loading modes: "Snapshot Only" (instant pre-aggregated data) and "Snapshot + Live API Loading" (progressive loading of full records for advanced filtering).

3. Key Contributions

• Indiapub Web Application: A novel, user-friendly tool that democratizes access to subnational bibliometric data for India, bridging the gap between raw publication data and policy-relevant insights.
• Granular Subnational Analysis: Unlike previous tools, Indiapub specifically targets the Indian context, allowing for the comparison of research output against demographic and development metrics (HDI) at the state/UT level.
• Flexible Attribution Logic: The inclusion of multiple author-position counting rules allows users to analyze research leadership (first/last author) versus broad collaboration footprints (any author).
• Case Study Framework: The paper validates the tool through six diverse case studies (EHR, GWAS, AI, Development Economics, Environmental Science, and COVID-19), demonstrating its utility across mature and emerging research domains.

4. Key Results

Analysis of 2014–2025 data (approx. 4 million publications) revealed several critical patterns:

• Geographic Concentration: Research output is heavily concentrated in a few states. The top five contributors are Tamil Nadu, Maharashtra, Delhi, Uttar Pradesh, and Karnataka.
• Over/Underrepresentation:
  • Tamil Nadu and Delhi are consistently overrepresented (publication share exceeds population share by >10 percentage points) across most domains.
  • Bihar is consistently underrepresented across all domains in the high-population group.
  • Chandigarh and Uttarakhand show significant overrepresentation in the low-population group, suggesting high institutional efficiency in smaller regions.
• Domain-Specific Findings:
  • EHR & AI: Dominated by institutions in Tamil Nadu (e.g., Vellore Institute of Technology) and Punjab (Chandigarh University).
  • Development Economics: Highly concentrated in Delhi (University of Delhi, JNU, IIT Delhi).
  • COVID-19: Showed a massive surge in 2021 followed by a decline, with Delhi leading the trend.
• HDI Correlation: Generally, higher HDI correlates with higher publication volume. However, Uttar Pradesh is a notable outlier, showing high publication output despite a relatively low HDI, indicating that capacity and collaboration can sometimes outweigh development levels.
• Impact Analysis (≥50 Citations): When filtering for high-impact papers, the geographic distribution shifted slightly. For example, in Development Economics, the top institution shifted from Delhi-based entities to Malaviya National Institute of Technology Jaipur (Rajasthan), highlighting that high-impact research is not always confined to the most populous or high-HDI states.

5. Significance

• Policy and Planning: Indiapub provides policymakers, funders, and institutional leaders with evidence to identify underrepresented regions and target investments to bridge research capacity gaps.
• Educational Utility: It serves as a navigable resource for students and early-career researchers to identify active research hubs and potential collaboration partners.
• Scalability: The framework is adaptable to other countries, offering a reusable template for quantitative enumeration of publication trends linked to demographic and development indicators.
• Transparency: By making the data and code (via GitHub) publicly available, the tool promotes transparency in research capacity assessment, moving beyond simple national aggregates to a more nuanced understanding of India's research ecosystem.

Limitations: The study relies on OpenAlex indexing and ROR matching, potentially missing unindexed publications or institutions with poor metadata. The analysis is descriptive and does not include formal statistical uncertainty quantification. Additionally, publication counts reflect institutional affiliation rather than the geographic location of the research subjects.