📊 Main Information: Overview of Your Bibliometric Collection

The Main Information page provides a comprehensive, at-a-glance summary of the key bibliometric indicators for your collection. This dashboard-style interface displays 12 core metrics organized into visual cards, allowing you to quickly assess the scope, composition, and characteristics of your dataset.

This section is the ideal starting point for understanding your collection before diving into more detailed analyses. It answers fundamental questions such as: How large is my dataset? What is the temporal coverage? How collaborative is the research? How impactful are the documents?

📈 Core Metrics Explained

The Main Information dashboard displays the following indicators:

1. Timespan

• Definition: The temporal range covered by the collection, from the earliest to the most recent publication year.
• Example: 1985-2020 indicates documents published between 1985 and 2020.
• Interpretation: A wider timespan enables longitudinal trend analysis and historical perspectives. Collections spanning decades are suitable for studying research evolution and paradigm shifts.

2. Sources

• Definition: The total number of distinct publication venues (journals, conferences, books) represented in the collection.
• Interpretation: A higher number of sources suggests a multidisciplinary or dispersed research field, while a lower number indicates concentration in a few core journals. This metric is useful for identifying dominant publication venues via Bradford's Law analysis.

3. Documents

• Definition: The total number of bibliographic records (articles, reviews, proceedings, etc.) in the collection.
• Interpretation: This is the fundamental sample size for all subsequent analyses. Larger collections (>1,000 documents) provide more robust insights, especially for network and clustering analyses.

4. Annual Growth Rate

• Definition: The average percentage increase in the number of publications per year over the collection's timespan.
• Formula: Compound Annual Growth Rate (CAGR) calculated as: [(N_final / N_initial)^(1/years) - 1] × 100
• Interpretation: A positive growth rate indicates an expanding research field, while negative or near-zero values suggest maturity or decline. High growth rates (>10%) often signal emerging topics attracting increasing scholarly attention.

5. Authors

• Definition: The total number of unique authors who contributed to the documents in the collection.
• Interpretation: This metric reflects the size of the research community. A high author-to-document ratio suggests collaborative research, while a low ratio may indicate a field dominated by a few prolific researchers.

6. Authors of Single-Authored Docs

• Definition: The number of authors who published at least one single-authored document in the collection.
• Interpretation: Single-authored papers are more common in humanities and theoretical disciplines. A low proportion suggests high collaboration intensity, typical of experimental sciences and interdisciplinary fields.

7. International Co-Authorship

• Definition: The percentage of documents authored by researchers from multiple countries.
• Interpretation: High international collaboration (>30%) indicates global research networks and is often associated with higher citation impact. This metric is a proxy for research globalization and cross-border knowledge exchange.

8. Co-Authors per Document

• Definition: The average number of authors per document in the collection.
• Interpretation: Values typically range from 2 (social sciences, humanities) to 5+ (biomedical sciences, physics). Increasing values over time reflect the trend toward team science and large-scale collaborative projects.

9. Author's Keywords (DE)

• Definition: The total number of unique keywords provided by authors (DE = Descriptors) across all documents.
• Interpretation: A rich keyword set (>1,000 unique terms) enables robust thematic analysis and topic modeling. The diversity of keywords reflects the conceptual breadth of the research field.

10. References

• Definition: The total number of cited references listed in the bibliographies of all documents in the collection.
• Interpretation: This metric is essential for citation-based analyses (co-citation, bibliographic coupling, reference publication year spectroscopy). Larger reference pools enable more comprehensive intellectual structure mapping.

11. Document Average Age

• Definition: The average number of years elapsed since publication, calculated relative to the current year.
• Formula: Current Year - Mean(Publication Years)
• Interpretation: Lower values (<5 years) indicate a focus on recent research, while higher values suggest inclusion of foundational or historical literature. This metric helps assess whether the collection is contemporary or retrospective.

12. Average Citations per Document

• Definition: The mean number of citations received by documents in the collection (based on database citation counts).
• Interpretation: Higher values indicate high-impact research. Average citation rates vary widely by discipline (e.g., biomedical sciences >20, social sciences ~10). This metric is influenced by document age, field norms, and database coverage.

🧠 Biblio AI Integration

If Biblio AI is enabled, you can click the Biblio AI tab to receive an automated narrative summary of these indicators. The AI-generated text provides contextualized interpretations, highlights notable patterns, and offers insights suitable for inclusion in research reports or presentations.

Example AI-generated insights:

• 'The collection exhibits a strong annual growth rate of 14.05%, suggesting an emerging and rapidly expanding research domain.'
• 'With 36.41% international co-authorship, the field demonstrates moderate global collaboration, indicating opportunities for further cross-border partnerships.'
• 'The average of 37.12 citations per document reflects high scholarly impact, placing this collection above typical citation rates for the social sciences.'

📋 Viewing Options

The Main Information page offers three viewing modes via tabs at the top:

• Plot: Visual card-based dashboard (default view) with color-coded metrics
• Table: Tabular representation of all indicators for easy export to reports
• Biblio AI: AI-generated narrative summary and interpretation (requires Gemini API key)

💡 How to Use Main Information

This section is designed for multiple purposes:

• Initial Data Assessment: Quickly validate that your collection has been imported correctly and contains the expected number of documents and metadata fields.
• Research Reporting: Extract summary statistics for the 'Methods' or 'Data' section of a systematic review or bibliometric study.
• Comparative Analysis: Compare indicators across different datasets (e.g., two time periods, competing research streams) to identify differences in growth, collaboration, or impact.
• Presentation Material: Export the dashboard or AI-generated text for use in slides, posters, or grant proposals.

📌 Best Practices

• Always review Main Information first before proceeding to advanced analyses—it helps identify potential data quality issues (e.g., missing years, incomplete author data).
• Compare with field benchmarks: Contextualize your indicators by comparing them with known norms for your discipline (e.g., citation rates, collaboration patterns).
• Document your collection: Use the 'Brief Description' text box (visible in the Import/Load section) to record search queries, inclusion criteria, and data sources for reproducibility.
• Export summary statistics: Save the table view as a reference for your research documentation or supplementary materials.

⚠️ Important Considerations

• Database Bias: Indicators reflect the coverage and indexing policies of the source database(s). Web of Science and Scopus have different journal lists, which affects metrics like citation counts and international co-authorship.
• Citation Lag: Recent documents (<2 years old) typically have lower citation counts due to insufficient time for accumulation. Average citations per document may be biased downward if your collection includes many recent papers.
• Incomplete Metadata: Some databases (e.g., PubMed, Dimensions) provide limited metadata, which may result in missing or incomplete values for certain indicators (e.g., author affiliations for international co-authorship calculation).
• Growth Rate Sensitivity: Annual growth rate calculations are sensitive to the start and end years of the collection. Unusual spikes or drops in specific years can distort the overall trend.

🔍 Next Steps

After reviewing the Main Information dashboard, proceed to more detailed analyses:

• Filters: Refine your collection by applying metadata filters (e.g., document type, time range, subject category)
• Sources: Identify the most productive journals and analyze publication patterns
• Authors: Examine author productivity, collaboration networks, and impact metrics
• Conceptual Structure: Explore thematic evolution and topic clustering via keyword co-occurrence and thematic maps
• Intellectual Structure: Investigate citation networks through co-citation analysis and historiography

📚 References

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Zupic, I., & Čater, T. (2015). Bibliometric methods in management and organization. Organizational Research Methods, 18(3), 429–472. https://doi.org/10.1177/1094428114562629

📈 Life Cycle of Scientific Production: Modeling Research Topic Evolution

The Life Cycle of Scientific Production module implements a logistic growth model to analyze the temporal dynamics of research topics. This approach, grounded in the theory of scientific paradigms and innovation diffusion, allows researchers to identify the current developmental stage of a field, predict future trends, and estimate when a topic will reach maturity or saturation.

By fitting a logistic curve to the annual publication counts in your collection, this analysis reveals whether a research area is in its emergence phase, rapid growth phase, maturity phase, or decline phase.

📐 The Logistic Growth Model

The life cycle analysis is based on the logistic growth function, which models how the cumulative number of publications evolves over time:

Formula:

Where:

• P(t): Cumulative number of publications at time t
• K: Saturation level (maximum total publications the topic will produce)
• b: Growth rate parameter (determines the steepness of the curve)
• t₀: Inflection point (time when growth rate is highest)

The annual publication rate is derived as the first derivative of P(t), producing a bell-shaped curve that peaks at the inflection point and gradually declines as the topic approaches saturation.

🔬 Model Overview: Key Parameters

The Model Overview section displays four fundamental indicators derived from the fitted logistic model:

1. Saturation (K)

• Definition: The estimated maximum total number of publications that will ever be produced on this research topic.
• Interpretation:
  • High K values (>5,000) indicate a broad, impactful research domain with sustained long-term interest.
  • Low K values (<1,000) suggest a niche topic with limited scope or a specialized subtopic within a larger field.
  • The current cumulative total as a percentage of K reveals how close the topic is to exhaustion.
• Example: K = 8,980 publications suggests the topic will produce approximately 8,980 total documents before reaching saturation.

2. Peak Year (T_m)

• Definition: The year when annual publication output is predicted to reach its maximum.
• Interpretation:
  • If the peak year is in the future, the topic is still in a growth phase and attracting increasing attention.
  • If the peak year is in the past, the topic has entered a maturity or decline phase, with decreasing annual output.
  • If the peak year is near the present, the topic is at the zenith of its popularity.
• Example: Peak Year = 2029 indicates the topic will reach maximum annual productivity in 2029, suggesting it is currently in an accelerating growth phase.

3. Peak Annual

• Definition: The maximum number of publications per year predicted to occur at the Peak Year.
• Interpretation: This metric reflects the intensity of research activity at the topic's peak. Higher values indicate greater scholarly attention and resource allocation.
• Example: Peak Annual = 592 pubs/year means the topic will generate approximately 592 publications annually at its zenith.

4. Growth Duration (Δ_t)

• Definition: The estimated time span (in years) from the topic's emergence (10% of K) to near-saturation (90% of K).
• Interpretation:
  • Short duration (<10 years): Rapid maturation, typical of hot topics, technological innovations, or crisis-driven research (e.g., COVID-19 studies).
  • Medium duration (10-20 years): Typical of mainstream research domains with sustained but gradual growth.
  • Long duration (>20 years): Slow-developing fields, foundational topics, or interdisciplinary areas requiring extensive infrastructure.
• Example: Growth Duration = 16.7 years suggests the topic will take approximately 17 years to mature from its early stage to near-saturation.

✅ Model Fit Quality

The Model Fit Quality section assesses how well the logistic curve fits the observed publication data using four statistical metrics:

1. R² (Coefficient of Determination)

• Range: 0 to 1 (higher is better)
• Interpretation: Proportion of variance in publication counts explained by the model.
  • R² > 0.90: Excellent fit—the logistic model accurately captures the publication trend.
  • 0.70 < R² < 0.90: Good fit—the model is reasonable but may not capture all nuances (e.g., fluctuations due to external events).
  • R² < 0.70: Poor fit—the logistic model may not be appropriate for this dataset (non-logistic growth pattern, data quality issues).
• Example: R² = 0.953 indicates an excellent fit, with 95.3% of publication variance explained by the model.

2. RMSE (Root Mean Squared Error)

• Definition: Average deviation between observed and predicted annual publications.
• Interpretation: Lower values indicate better fit. RMSE should be interpreted relative to the scale of annual publications (e.g., RMSE = 10 is negligible for topics with 500+ annual pubs, but significant for topics with <50 pubs/year).

3. AIC (Akaike Information Criterion)

• Purpose: Balances model fit against complexity (penalizes overfitting).
• Interpretation: Lower AIC values indicate a better model. AIC is most useful for comparing alternative models rather than assessing absolute fit quality.

4. BIC (Bayesian Information Criterion)

• Purpose: Similar to AIC but applies a stronger penalty for model complexity.
• Interpretation: Lower BIC values indicate better models. BIC is more conservative than AIC and favors simpler models.

Overall Assessment: Biblioshiny automatically classifies model fit as Excellent, Good, or Poor based primarily on R² values. An 'Excellent' fit (R² > 0.90) validates the use of logistic growth assumptions for forecasting.

📍 Current Status

This section provides a snapshot of the topic's present state relative to its life cycle trajectory:

• Last Observed Year: The most recent year with publication data in your collection.
• Annual Publications: The number of publications in the last observed year.
• Cumulative Total: The total number of publications from the collection's start to the last observed year.
• Progress to Saturation: The percentage of K (saturation level) already reached.
  • 0-30%: Emergence or early growth phase.
  • 30-70%: Rapid growth phase (the topic is 'hot').
  • 70-90%: Late growth phase, approaching maturity.
  • >90%: Maturity or decline phase, nearing exhaustion.

Example Interpretation: If Progress to Saturation = 10.0%, the topic is in the rapid growth phase, with 90% of its publication potential still ahead. This signals a promising emerging field attracting increasing scholarly attention.

🏁 Milestone Years

The Milestone Years section predicts when the topic will reach specific saturation thresholds:

• 10% of K: Emergence milestone—marks the topic's transition from niche to recognized research area.
• 50% of K (Midpoint): The inflection point where growth rate is highest. This coincides with the Peak Year (T_m).
• 90% of K: Maturity milestone—indicates the topic is approaching saturation, with declining annual growth.
• 99% of K: Near-complete saturation—the topic has exhausted most of its research potential.

Example:

This indicates the topic emerged around 2021, will peak in 2029, and approach saturation by 2038, with a full life cycle spanning approximately 25 years.

The system also classifies the topic's current phase (e.g., 'rapid growth phase' if between 10-50% of K) to aid interpretation.

🚀 Forecast

The Forecast section projects future publication output based on the fitted logistic model:

• Forecast Period: The time range for predictions (typically 5-50 years into the future).
• Projection for 2025: Estimated cumulative total publications by 2025 (includes annual projection in parentheses).
• Projection for 2030: Estimated cumulative total publications by 2030 (includes annual projection in parentheses).

Example:

This suggests the topic will grow from ~900 publications (current) to over 4,800 by 2030, with annual output peaking around 587 publications per year.

Important: Forecasts assume the logistic model remains valid (no disruptive events, paradigm shifts, or external shocks). Long-term forecasts (>10 years) should be interpreted with caution.

📊 Visualizations

The Plot tab provides two complementary graphs:

1. Life Cycle - Annual Publications

• Blue solid line: Logistic fit to observed data
• Blue dashed line: Forecasted annual publications
• Blue dots: Observed annual publications from your collection
• Red dashed vertical line: Peak Year (T_m)

Interpretation: This bell-shaped curve shows how publication activity rises, peaks, and eventually declines. The shape reveals the topic's maturity:

• Steep ascent, pre-peak: Emerging or rapidly growing topic.
• Near or at peak: Mature topic at maximum attention.
• Descending curve, post-peak: Declining topic losing relevance.

2. Cumulative Growth Curve

• Green solid line: Logistic fit to observed cumulative data
• Green dashed line: Forecasted cumulative publications
• Green dots: Observed cumulative publications
• Horizontal dashed lines: Saturation thresholds (50%, 90%)

Interpretation: This S-shaped curve illustrates the topic's total knowledge accumulation over time. The curve's position and steepness reveal:

• Lower left (shallow slope): Emergence phase with slow initial growth.
• Middle (steep slope): Rapid growth phase with exponential accumulation.
• Upper right (flattening): Maturity phase approaching saturation asymptote (K).

🧠 Biblio AI Integration

The Biblio AI tab allows you to generate AI-powered narrative interpretations of the life cycle analysis. Key features include:

• Customizable Prompts: Edit the default prompt to add context-specific details (e.g., research domain, database source, filter criteria).
• Graph-Based Analysis: Biblio AI analyzes the visualizations to identify trends, anomalies, and key transition points.
• Automatic Interpretation: Generates text suitable for research reports, explaining model parameters, growth phases, and forecasts in natural language.

Example Prompt Enhancement:

This contextual information helps Biblio AI produce more accurate and domain-relevant interpretations.

💡 Use Cases

• Identifying Emerging Topics: Detect rapidly growing fields in their early stages (10-30% of K) for strategic research investment.
• Timing Research Entry: Avoid entering saturated fields (>90% of K) where novelty is harder to achieve.
• Forecasting Resource Needs: Predict future publication volumes to plan journal submissions, conferences, or funding opportunities.
• Comparative Life Cycle Analysis: Run the analysis on multiple subtopics to identify which are growing vs. declining.
• Paradigm Shift Detection: Poor model fit (R² < 0.70) may signal non-logistic patterns caused by disruptive innovations or paradigm shifts.

📌 Best Practices

• Ensure sufficient data: Logistic models require at least 10-15 years of publication data for reliable fitting. Collections with <10 years may produce unstable forecasts.
• Check model fit: Always review R² and visual fit before interpreting forecasts. Poor fits (R² < 0.70) indicate the logistic model may not be appropriate.
• Consider external events: The model assumes smooth, uninterrupted growth. Real-world shocks (e.g., pandemics, funding cuts, technological breakthroughs) can invalidate long-term forecasts.
• Use relative comparisons: Life cycle parameters (K, Peak Year) are most informative when comparing multiple topics or time periods within the same field.
• Validate forecasts periodically: Re-run the analysis with updated data every 2-3 years to recalibrate predictions.

⚠️ Important Considerations

• Database Coverage: The model reflects only publications indexed in your source database(s). Incomplete coverage (e.g., missing journals, preprints) can distort saturation estimates.
• Definition Drift: Topic boundaries may shift over time (e.g., 'artificial intelligence' in 1990 vs. 2020), affecting the validity of K estimates.
• Multiple Life Cycles: Some broad topics exhibit multiple overlapping life cycles as subtopics emerge and decline independently. In such cases, aggregate logistic fits may be misleading.
• Self-Fulfilling Prophecies: Publishing forecasts may influence researcher behavior (e.g., avoiding 'saturated' topics), potentially altering actual trajectories.
• Model Limitations: The logistic model assumes a single saturation point and smooth growth. Topics experiencing resurgence (e.g., due to new technologies) may not fit this pattern.

🔍 Interpreting Fit Quality Issues

If your model shows poor fit (R² < 0.70), consider these potential causes:

• Insufficient Data: Too few years or highly irregular publication patterns.
• Non-Logistic Growth: The topic may exhibit exponential, linear, or cyclic growth rather than logistic.
• Recent Disruptions: External shocks (e.g., COVID-19 boosting health research) create anomalies that deviate from smooth curves.
• Topic Too Broad: Aggregating multiple subtopics with different life cycles can obscure individual patterns.
• Data Quality Issues: Missing years, database indexing changes, or inconsistent metadata.

Solution: Try narrowing your collection (e.g., focusing on a specific subtopic or time range) or exploring alternative growth models.

📚 References

Aria, M., Misuraca, M., & Spano, M. (2020). Mapping the evolution of social research and data science on 30 years of Social Indicators Research. Social Indicators Research, 149, 803–831. https://doi.org/10.1007/s11205-020-02281-3

Bettencourt, L. M., Kaiser, D. I., & Kaur, J. (2009). Scientific discovery and topological transitions in collaboration networks. Journal of Informetrics, 3(3), 210–221. https://doi.org/10.1016/j.joi.2009.03.001

Rogers, E. M. (2003). Diffusion of Innovations (5th ed.). New York: Free Press.

Small, H., & Upham, S. P. (2009). Citation structure of an emerging research area on the verge of application. Scientometrics, 79(2), 365–375. https://doi.org/10.1007/s11192-009-0424-0

Wang, Q. (2018). A bibliometric model for identifying emerging research topics. Journal of the Association for Information Science and Technology, 69(2), 290–304. https://doi.org/10.1002/asi.23930

🔀 Three-Field Plot

The Three-Field Plot is an advanced visualization tool that reveals the relationships among three distinct bibliographic dimensions through an interactive Sankey diagram. This plot enables researchers to explore the complex connections between different metadata fields, making it particularly useful for understanding how research topics, authors, sources, and references are interconnected within a scientific domain.

🎯 Purpose and Application

The Three-Field Plot serves multiple analytical purposes:

• Relationship Mapping: Visualizes how elements from three different bibliographic fields are associated with each other
• Knowledge Flow: Tracks the flow of ideas and citations across different dimensions (e.g., from cited references through authors to keywords)
• Thematic Connections: Identifies which keywords or topics are most strongly associated with specific authors or sources
• Author-Topic Associations: Shows which authors are working on which topics and citing which foundational works

📊 How It Works

The visualization consists of three vertical columns representing different bibliographic fields:

• Left Field: Typically represents sources (cited references, journals) or temporal information
• Middle Field: Usually displays authors or intermediary elements that connect the other two fields
• Right Field: Often shows keywords, topics, or other thematic elements

The width of each flow (colored band) is proportional to the frequency of co-occurrence between elements. Thicker flows indicate stronger associations, while thinner ones represent weaker connections.

⚙️ Configuration Options

The Options panel allows you to customize the plot:

• Left Field: Select from available metadata fields (e.g., Cited References, Sources, Authors' Countries)
• Middle Field: Choose the central connecting field (e.g., Authors, Sources, Keywords)
• Right Field: Define the destination field (e.g., Author's Keywords, Keywords Plus, Subject Categories)
• Number of Items: Control how many top elements to display for each field (typically 10-30 items per field)

💡 Common Field Combinations

Some particularly insightful field combinations include:

• References → Authors → Keywords: Shows which foundational works are cited by which authors working on which topics
• Sources → Authors → Countries: Maps the geographical distribution of authors publishing in specific journals
• Keywords → Authors → Cited References: Reveals the intellectual foundations of different research themes
• Authors' Countries → Authors → Keywords: Identifies national research specializations and thematic focuses
• Publication Year → Authors → Keywords: Tracks temporal evolution of author productivity and topic emergence

🔍 Interpretation Guidelines

• Flow Thickness: A thick flow between two elements indicates a strong association (high co-occurrence frequency)
• Multiple Connections: Elements with many outgoing or incoming flows are central nodes in the network
• Isolated Flows: Thin, isolated connections may represent niche specializations or emerging topics
• Color Coding: Colors help distinguish different elements in the left field, making it easier to trace specific flows
• Cross-field Patterns: Look for patterns where multiple elements from one field connect to the same element in another field, indicating convergence or interdisciplinarity

📌 Best Practices

• Start Simple: Begin with a small number of items (10-15 per field) to avoid visual clutter, then increase if needed
• Logical Sequences: Arrange fields in a logical flow (e.g., past → present, source → output, context → content)
• Interactive Exploration: Hover over flows and nodes to see exact frequencies and connections
• Export Results: Use the plot in presentations to illustrate complex relationships in an accessible way
• Combine with Networks: Use Three-Field Plots alongside network analyses for complementary perspectives on your data
• Context Matters: Always interpret the plot in the context of your research question and domain knowledge

⚠️ Limitations

• Aggregation Effects: The plot shows aggregate patterns and may obscure individual document-level details
• Top-N Selection: Only the most frequent items are displayed; rare but potentially important connections may be hidden
• Direction Ambiguity: While flows suggest relationships, they don't always imply causal or temporal direction
• Visual Complexity: With too many items, the plot can become difficult to interpret; reduce the number of items if necessary

🤖 Biblio AI Integration

When Biblio AI is enabled, you can generate automatic interpretations of the Three-Field Plot. The AI will:

• Identify the most important flows and connections
• Highlight dominant patterns and relationships
• Provide narrative explanations suitable for research reports and presentations
• Suggest potential interpretations based on the observed patterns

📚 Key References

Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. https://doi.org/10.1016/j.joi.2017.08.007

Chen, C. (2017). Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006