The dataset is calculated from Scopus author profiles as they exist on a fixed snapshot date — for version 9, the August 1, 2026 snapshot. A new version is produced annually, each based on a fresh Scopus snapshot.

What the c-score is, and how it is calculated

The c-score — the composite citation score — is the single indicator used to rank scientists in this database. It is not a citation count. It is built from six separate measures of impact and authorship position, and it exists to stop any one of them from deciding the result on its own.

Each of the six indicators is converted into a normalised score using a logarithmic formula, and the six normalised scores are then added together. Normalisation puts indicators of very different size on a comparable scale; the logarithm dampens the influence of extremely large values so that one enormous number cannot overwhelm the rest. Every indicator contributes to the final figure.

What each symbol means

The six terms map directly onto columns in the dataset: nc9625, h25, hm25, ncs, ncsf and ncsfl — each of which is also given in a self-citation-excluded form. The denominator in every term is the maximum value of that same indicator across the reference population, which is why the c-score is a relative measure and only means something against the population it is compared with.

The development, validation and use of the composite citation indicator were set out in Ioannidis, Boyack and Baas (2020), Updated science-wide author databases of standardized citation indicators, PLOS Biology 18(10): e3000918, DOI 10.1371/journal.pbio.3000918. For further detail, see the Elsevier Top-Cited Scientists Database FAQs.

What the database actually contains

Metrics are shown both with and without self-citations, and separate figures are given for career-long impact and for a single recent calendar year. Scientists are classified into 20 scientific fields and 174 sub-fields using the Science-Metrix classification.

The archive is widely used and widely reused: the repository records millions of views and over a million downloads, alongside news mentions, blog mentions, and tens of thousands of social media shares. That reach is precisely why its own authors keep repeating what it is not.

Column definitions in detail

The following table lists the columns in the dataset and what they represent.

Why rankings move

Each version reflects a new Scopus snapshot and an additional year of citation data, so as the report explains movement in rank is expected. Typical causes include new citations accrued by the scientist and by others in the field, changes to the scientist’s own Scopus profile (papers added, merged, or reassigned), shifts in field and sub-field classification, and updates to self-citation calculations.

Because rankings are relative to all other scientists in the database, even an increase in one’s own citation counts can coincide with a lower or higher rank, depending on how the wider field has moved.

What does not count

Number of publications does not count at all in the composite citation indicator and the respective ranking. Impact may be achieved with very few or many publications. A scientist with only one or two papers who achieves very high impact is not excluded simply for publishing little.

Conversely, some scientists publish extremely high numbers of papers, which may reflect either genuine productivity or manipulative practices. To make that question researchable, a separate database includes all scientists who have published more than sixty full papers — full articles, reviews, or conference papers, excluding all other types of published items — in any single calendar year.

Self-citations, counted from everyone

Self-citations are counted from all authors of a given paper. If a paper has 10 authors, all the papers that cite it and have one of those 10 original authors in their author list are counted as self-citations. Percentiles of citations for each scientific discipline are provided so that numbers can be placed in context.

Retractions are part of the record

Since 2024, the annual editions include information for each top-cited scientist on the number of retractions, the total number of citations received by their retracted papers, and the total number of citations they have received from any retracted papers.

Hints, not proof

The presented data may offer some hints of citation orchestration and similar manipulations, but judgement needs to be cautious and linked with additional evidence. Metrics can be used against gamed metrics; they are not themselves a finding of misconduct.

This is not Google Scholar

Each citation database has its own inclusion and eligibility criteria, and therefore different citations are counted. These databases use Scopus data. In most disciplines, Google Scholar tends to count more citations than Scopus, as it is far less selective regarding the citing sources it considers eligible.

The data are not limited to citations from original papers. They come from all published items included in Scopus — not only traditional articles, but also reviews, conference papers, editorials, notes, letters, news items, and more. Classification of article type may have substantial error in Scopus, and the boundary of what counts as “original” can be contested. If a published work can be cited, citations should be counted.

What the list does not say

Being absent means the composite indicator value was not high enough to meet the inclusion threshold for that version, either the top 100,000 scientists overall or the top 2% within a sub-field. It does not mean the author’s work is not of good quality.

The converse also holds. Not all listed scientists are excellent, and very few may even have serious, well-documented violations of research integrity in their record, as can be transparently gleaned from the retraction data provided. Citation metrics have limitations.

Several other things follow from the same logic:

• Sub-field labels are not self-descriptions. The 174 sub-field names come from the Science-Metrix classification and may not precisely correspond to what specific scientists think about themselves. The primary sub-field is the one with the largest proportion of a given scientist’s papers, and some scientists are split across many diverse sub-fields. Secondary sub-fields are also provided.
• Selection is sub-field adjusted. A selected scientist may have fewer publications, fewer total citations, or even a lower all-science ranking than a non-selected one — and still be in the top 2% of a low-citation sub-field, while a higher-ranked non-selected scientist sits in a sub-field that receives generally very high numbers of citations.
• Deceased and living scientists are both included. A recent or current latest publication year is not proof that a scientist is alive, since classic works may be republished and posthumous papers may still appear under the author’s name.
• Gender and age are not in the main databases. Publication age can be inferred from the year of starting to publish. A separate database provides gender information and stratifies top-cited scientists into four publication-age strata: first publication before 1992, 1992–2001, 2002–2011, and 2012 or later.
• Multiple countries are tracked separately. A newer set of variables captures whether an author has published a substantial portion of their career papers from affiliations in more than one country, and which countries those are. Country assignments are enriched and normalised using organisational data beyond what is listed explicitly on the paper, which can make the dataset differ from what is visible on Scopus directly. The estimated error rate for this inferential process is 4%.
• Anomalies are not filtered out upfront. Thresholds for excluding authors with high self-citation proportions, many retractions, heavy editorial output, or historical eras would be entirely arbitrary — and one of the main purposes of the database is to allow rigorous investigation of anomalous profiles and outliers. Users with specific research questions may apply filters fit for purpose.

No certificates, no endorsement

No certificates or other tokens of recognition are offered for included scientists, and no endorsement is given for any use of the data. Because the datasets are publicly available, many other scientists, non-scientists, administrators, institutions and organisations may have summarised, recast, disseminated, or interpreted them. Open public availability is worthwhile for transparency, but the dataset’s authors should not be asked to endorse the multifarious ways the data may be used or misused. If any entity sells certificates or other tokens based on the data for money, this is inappropriate, since the datasets are made available for free public use excluding commercial use.

Institutions, use with care

Many institutions pay increasing attention to these data and may disseminate how many of their scientists are included, as a signal of prestige. They often depend on re-uses of the databases by entities that place inordinate emphasis on excellence signalling and PR, and there is a real worry that these entities may have communicated the data in misleading ways.

Institutions using the databases are advised to read the FAQ carefully and understand the meaning, strengths, and limitations of the data — and to adopt more balanced and nuanced approaches that do not focus only on the number of scientists with high citation impact, but also adjust for institutional size and consider research integrity proxies. The databases can be used not only for proxies of excellence but also for indicators of integrity risk.

The methods behind the numbers

The development, validation and use of the composite citation indicator and the databases are explained in three associated PLoS Biology papers:

• Ioannidis JPA, Boyack KW, Baas J. Updated science-wide author databases of standardized citation indicators. PLoS Biol. 2020 Oct 16;18(10):e3000918.
• Ioannidis JPA, Baas J, Klavans R, Boyack KW. A standardized citation metrics author database annotated for scientific field. PLoS Biol. 2019 Aug 12;17(8):e3000384.
• Ioannidis JP, Klavans R, Boyack KW. Multiple Citation Indicators and Their Composite across Scientific Disciplines. PLoS Biol. 2016 Jul 1;14(7):e1002501.

The methods and analyses of the retraction data are covered in Ioannidis JPA, Pezzullo AM, Cristiano A, Boccia S, Baas J. Linking citation and retraction data reveals the demographics of scientific retractions among highly cited authors. PLoS Biology 2025, 23(1):e3002999. On the broader point about metric use, the dataset description points to the Leiden manifesto.

This post is based entirely on the Version 9 dataset description published by Elsevier Data Repository (DOI 10.17632/btchxktzyw.9, 7 October 2026) and on the accompanying document Frequently Asked Questions 2026. It reads that material not as a technical annex, but as a statement of limits.