Performance analysis: growth, sources, authors and impact
Describe the collection, measure growth, identify core sources with Bradford’s law, productive authors with Lotka’s law, and influential documents and countries.
Performance analysis answers the “who, where and how much” questions. It is the descriptive backbone of every bibliometric paper and usually fills the first half of the results section.
Run the performance analysis
- Open Main information and Annual scientific production.
- Open Most relevant sources (J Digit Health Res leads with 44 documents) and Sources’ local impact.
- Open Most relevant authors, Countries’ scientific production and Most globally cited documents.
Main information
Report this table first. For the demo collection: a timespan of 2008 to 2025, 418 documents from 30 sources, an annual growth rate of 12.6%, an average document age of 7.4 years, 28.3 citations per document, 228 authors with 955 author appearances, 188 single-authored documents, 2.28 co-authors per document and 28.7% international co-authorship. The annual growth rate is the compound rate between the first and last year: ((documents in the last year / documents in the first year)1/(last year − first year) − 1) × 100. A rate of 12.6% means output doubles roughly every six years.
Annual scientific production and citations
The annual production chart shows phases: a slow start, take-off and maturity. Relate the turning points to events in the field (a key paper, a technology, a policy, a pandemic). The average citations per year chart corrects for document age; older papers have had more time to collect citations, so compare citations per year rather than totals.
Sources and Bradford’s law
The most relevant source in the demo collection is J Digit Health Res with 44 documents, followed by Int J Med Inform (28), Expert Syst Appl and J Biomed Inform (20 each). Bradford’s law describes how a literature is concentrated: when sources are ranked by productivity and divided into three zones with equal numbers of documents, the first zone (the core) contains few sources, and each later zone needs many more. The core journals are where a researcher should publish and search first. The Sources’ local impact page adds the h-index, g-index and m-index of each source within your collection.
Authors and Lotka’s law
The most productive author is HANSEN W with 28 documents, followed by KOWALSKI N (26) and FAROUK HE (21). Look at the “articles fractionalized” column too: it gives each co-author 1/n of a paper with n authors, which corrects for large teams. HANSEN W has 14.85 fractionalised articles, KOWALSKI N 10.15 and ZHANG J 8.13, so ZHANG J ranks higher on fractional counting than his raw count suggests. Lotka’s law states that the number of authors with n publications is roughly proportional to 1/n²: most authors publish only once on a topic, and a few are very productive. The authors’ production over time chart shows when each top author was active.
Citations: global and local
Global citations count all citations in the database, from any field. Local citations count citations from other documents in your collection. A document with many global but few local citations is influential elsewhere; one with many local citations is central to this specific community. The most globally cited document in the demo data is Zhang (2016, Front Public Health) with 640 citations; normalised by the average for its publication year, the strongest papers are recent ones, such as Osei (2024) and Wang (2025), with normalised scores above 26. Report both raw and normalised citations, because raw counts favour old papers.
Impact indicators
- h-index: the largest h such that h documents have at least h citations each. It balances productivity and impact.
- g-index: the largest g such that the top g documents together have at least g² citations; it gives more weight to highly cited papers.
- m-index: the h-index divided by the number of years since the first publication, which allows comparison between early-career and senior authors.
Countries and affiliations
Country production counts author affiliations: a paper with three authors from Italy adds three to Italy. In the demo data, China (226), the United States (136) and the United Kingdom (122) lead. The corresponding author’s countries table separates single-country publications (SCP) from multiple-country publications (MCP); a high MCP ratio means a country collaborates internationally. Most cited countries complete the picture: a country can publish a lot with moderate impact, or little with high impact.
Reporting performance results
The collection comprised 418 documents published in 30 sources between 2008 and 2025, with an annual growth rate of 12.6% and an average of 28.3 citations per document. J Digit Health Res was the most productive source (44 documents). The most productive author was Hansen (28 documents; 14.85 fractionalised), and China, the United States and the United Kingdom contributed the most author affiliations. International co-authorship accounted for 28.7% of documents.
Key takeaways
- Start with the Main information table and the annual production curve.
- Use Bradford’s law to identify core sources and Lotka’s law to describe author productivity.
- Distinguish global from local citations, and normalise citations by publication year.
- Report h-, g- and m-indices and fractionalised counts to avoid bias toward old papers and large teams.
Check your understanding
Answer each question to see the explanation. Finishing the quiz marks this lesson as complete.