Field-Weighted Citation Impact: Reading and Reporting the Number

An FWCI score doesn’t need explaining from scratch every time it shows up — it needs reading correctly. If you’re looking at a number on a SciVal dashboard, a CRIS export, or a line in a grant progress report or tenure dossier, this guide covers how to read that specific figure without misinterpreting it, and how to write it up responsibly once you do. For the underlying definition and formal limitations, see CASRAI’s Field-Weighted Citation Impact (FWCI) entry — this guide assumes that starting point and goes further into practice.

Where you’ll actually encounter the number

FWCI isn’t something you calculate by hand day to day; it’s a figure a system hands you, and where it comes from affects how to read it:

• SciVal (Elsevier’s research-analytics platform, built on Scopus data) is the primary source — the Compare Module reports FWCI at the researcher, group, or institution level. See SciVal.
• CRIS/RIM platforms that ingest Scopus or SciVal feeds (Pure and similar systems) surface FWCI on researcher and unit profile pages, often without repeating the underlying methodology on-screen.
• Grant progress reports and tenure/promotion dossiers increasingly cite an FWCI figure as supporting evidence for a body of work’s citation performance.
• Institutional benchmarking reports compare departments or research groups by average FWCI, usually alongside output counts.

The Web of Science equivalent is InCites‘s Category Normalized Citation Impact (CNCI) — same interpretation, different underlying database. If a report doesn’t say which one it’s citing, that’s the first thing to ask.

What the ratio is actually built from

FWCI takes the citations a publication received in its year of publication plus the following three years, and divides that by the expected average citation count for a comparable set of publications — matched on publication year, document type, and Scopus subject classification. The result centers on 1.00 as exactly the world average for that comparison set; a score of 1.42 means the output (or set of outputs) drew 42% more citations than expected for comparable work.

Six things to check before you trust the number

1. Self-citations, included or excluded? FWCI includes self-citations by default. SciVal’s Compare Module offers a separate “FWCI excluding self-citations” metric as a selectable option — a report doesn’t always say which version it’s showing, and the two can diverge meaningfully for a researcher who cites their own prior work heavily.
2. Which subject classification is doing the normalizing? The denominator is built from Scopus’s own subject-category assignment (ASJC). A paper that genuinely straddles two fields, or sits in a broad, heterogeneous category, is being compared against a benchmark that may not match its real audience.
3. How many outputs is this averaged across? An aggregated FWCI at the researcher or group level is a mean across every output in scope. A small output count, or a single highly-cited outlier, can swing the average substantially — the same caution documented on CASRAI’s FWCI entry: report the figure alongside the underlying citation counts and the output count it’s built from, not as a standalone number.
4. Has the citation window closed yet? FWCI uses a publication-year-plus-three-years window. A paper published within the last one to two years hasn’t finished accumulating that window, so its individual FWCI is still provisional and will keep moving.
5. Which database is it built from? FWCI is Scopus-only. A researcher or group with substantial citing activity that Scopus doesn’t index (but Web of Science does, or vice versa) will read differently under FWCI than under CNCI. Neither is “the” correct number; they’re built from different coverage.
6. Is this the mean ratio, or the percentile companion metric? The two answer different questions — see below.

The percentile companion metric: “Outputs in Top Citation Percentiles”

SciVal reports a second, related indicator that’s easy to conflate with FWCI itself: Outputs in Top Citation Percentiles. When field-weighting is applied, SciVal ranks that year’s global Scopus publications by their FWCI value, splits them into 100 percentile bands, and reports what share of an entity’s outputs fall in the top 1%, 5%, 10%, or 25% of that distribution.

The distinction matters because FWCI (a mean-based ratio) and the percentile share answer different questions. FWCI can be pulled upward by a small number of very highly-cited outputs even if most of the underlying work is closer to average — a percentile share doesn’t have that distortion, since it counts how many individual outputs actually reached the top band rather than averaging their citation performance together. When both figures are available for the same entity, reading them side by side is more informative than either alone.

A worked example

Illustrative, not a real profile: a SciVal researcher page shows an FWCI of 1.42 (self-citations included) across 18 outputs published 2021–2023, alongside an Outputs in Top 10% Citation Percentiles figure of 22%.

Read together: this researcher’s papers, as a set, drew 42% more citations than the world average for comparable articles matched on year, document type and subject category — and about 1 in 5 of those 18 papers individually landed in the most-cited 10% for their field and year. That combination (moderately above-average mean, meaningful top-percentile presence) is a more complete picture than either number alone. If the mean were being driven by a single outlier paper while the rest sat near 1.0, the percentile share would typically be much lower than 22% — so the two figures here are broadly consistent with genuinely above-average performance across the set, not one paper skewing an average.

Reporting it responsibly

Responsible-metrics guidance is specific on this point, and it applies directly to FWCI. The Leiden Manifesto (Hicks, Wouters, Waltman, de Rijcke & Rafols, Nature, 2015) argues quantitative evaluation should support, not replace, qualitative expert assessment, and warns specifically against false precision — treating a single ratio as more exact than the underlying citation counts justify. DORA (the San Francisco Declaration on Research Assessment) and CoARA (the Coalition for Advancing Research Assessment) both caution against reducing hiring, promotion, or funding decisions to a single citation-based number.

Concretely, in a grant report, tenure dossier, or institutional benchmarking document, that means:

• State the FWCI figure alongside the underlying citation count and the number of outputs it’s averaged across — not the ratio in isolation.
• Name the database and time window (“FWCI 1.42 across 18 Scopus-indexed outputs, 2021–2023, self-citations included”) rather than a bare number.
• Where available, report the percentile share alongside the mean ratio, since the two together are more robust than either alone (see above).
• Present it as one input among several — alongside qualitative peer assessment — rather than as a standalone verdict on quality.

For the methodology behind a closely related institutional-level percentile indicator, see CASRAI’s CWTS Leiden Ranking and the Leiden Manifesto guide.

If you’re actually asking “is this a good number”

This guide covers how to read and report a specific FWCI figure correctly. If the underlying question is closer to “is this citation performance good,” CASRAI’s How Many Citations Is Actually Good? guide compares FWCI against CNCI, MNCS and RCR side by side for that judgment call.

Frequently asked questions

Does FWCI include self-citations by default?

Yes. SciVal calculates the standard FWCI figure with self-citations included unless a report specifically selects the “FWCI excluding self-citations” option in the Compare Module. If a report doesn’t say which version it’s using, ask before comparing it to another figure that might be using the other basis.

Is an FWCI of 1.0 a bad score?

No. 1.00 is exactly the world average for that field, publication year and document type — it’s the midpoint, not a failing mark. A score meaningfully below 1.0 indicates below-average citation performance for that comparison set; a score at or near 1.0 is unremarkable, not poor.

How is FWCI different from Outputs in Top Citation Percentiles?

FWCI is a mean-based ratio against a field average, so a small number of very highly-cited outputs can pull it upward even if most outputs are closer to average. Outputs in Top Citation Percentiles instead reports what share of individual outputs land in the most-cited band (top 1%, 5%, 10% or 25%) for their field and year — it isn’t distorted by outliers the same way an average can be. Read together, not as substitutes for each other.

How many outputs do I need before an aggregated FWCI is meaningful?

There’s no fixed threshold, but the general caution in responsible-metrics guidance is proportional: the fewer outputs behind an averaged FWCI, the more a single highly- or lowly-cited paper can swing it, and the more important it is to report the output count alongside the ratio rather than the ratio alone.

Should I cite FWCI on its own in a CV or grant report?

Responsible-metrics frameworks (Leiden Manifesto, DORA, CoARA) all recommend against it. Report the figure with its underlying citation count, output count, database, and time window, and treat it as one input alongside qualitative assessment rather than a standalone verdict.

Why might my own FWCI calculation not match what SciVal reports?

Most mismatches come down to one of the checks above: a different self-citation setting, a different or updated subject-category assignment, an citation window that hasn’t closed yet for recent papers, or comparing figures pulled from Scopus against a Web of Science-based CNCI figure that isn’t built from the same underlying data.

Last verified 2026-08-31. FWCI methodology (citation window, subject-category denominator, self-citation toggle) confirmed against Elsevier’s Scopus Support Center FWCI documentation and SciVal’s Outputs in Top Citation Percentiles documentation; Leiden Manifesto citation confirmed against Hicks, Wouters, Waltman, de Rijcke & Rafols, “Bibliometrics: The Leiden Manifesto for research metrics,” Nature 520:429–431 (2015), doi:10.1038/520429a.