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Measuring the impact of a researcher's body of work has long been one of the most contested problems in bibliometrics. Simple counts of publications or citations fail to capture the full picture, while complex composite indices are frequently opaque and difficult to reproduce. ERIS is Expertini's answer: a transparent, formula-driven impact score — normalised to a 0–100 scale — built from three well-established scholarly indicators and designed to apply fairly across academic, student, corporate, and independent researchers in every scientific discipline.
The Expertini Researcher Impact Score (ERIS) is a proprietary composite metric developed by Expertini Research to provide a single, interpretable figure representing a researcher's scholarly impact. It is calculated from three input variables — total publications, total citations, and h-index — each of which captures a distinct dimension of research performance. The three components are independently normalised, weighted according to their bibliometric significance, and summed to produce a final score bounded between 0 and 100.
ERIS is not intended to replace the nuanced professional judgement that governs peer review, promotion, or grant allocation. It is designed instead as a transparent, reproducible, and field-agnostic snapshot of research output — one that allows researchers to contextualise their own productivity, enables fair comparisons across institution types, and signals to collaborators and readers the depth of a researcher's recorded scholarly contribution. ERIS should be read alongside other evidence of a researcher's work — the scope and originality of their publications, the communities they serve, the problems they address — not instead of it.
The ERIS methodology is grounded in decades of bibliometric research. The h-index, introduced by Hirsch in 2005 [1], was among the first widely adopted metrics to combine productivity and citation impact into a single number. Subsequent work by Bornmann and Daniel [2], and later by Costas and Bordons [3], demonstrated that composite metrics consistently outperform single-variable measures when ranking researchers across disciplines. ERIS applies these insights in a maximally transparent way: every component, weighting, and normalisation step is published openly, and the score can be independently reproduced from a researcher's public record.
ERIS combines publication productivity, citation influence, and h-index into one normalised score — capped at 100 to prevent any single component from dominating the result and to ensure fair comparison across disciplines, institution types, and career stages.
Each component reflects a fundamentally different dimension of scholarly contribution. Together they form a more complete picture than any single metric alone. The choice of exactly three — and no others — reflects a deliberate design principle: additional variables risk introducing discipline-specific bias or data unavailability, while these three are universally recorded, consistently defined, and empirically validated as meaningful proxies for research output.
The weighting scheme — 40% publications, 30% citations, 30% h-index — reflects the relative importance of raw productivity versus demonstrable impact. Publications carry the highest weight because they are the most direct and most controllable measure of output. Citations and h-index share secondary weight because, while they capture impact more directly, they are partly determined by factors external to the researcher: field norms, publication venue, time elapsed, and the volume of active researchers in the area.
The ERIS formula is fully reproducible from public data. Each component is normalised independently using a clipped ratio against its ceiling, then multiplied by its assigned weight. The three weighted components are summed and bounded at 100. Formally:
This formulation has three important properties. Normalisation ensures no component exceeds its assigned maximum — 200 publications scores the same 40 points as exactly 50, preventing outliers from distorting the result. The ceiling at 100 means ERIS functions like a percentile-anchored scale: it indicates where a researcher stands relative to defined output thresholds, not relative to other platform users. The formula is also continuous and monotone — any genuine improvement in publications, citations, or h-index always produces a proportional improvement in ERIS.
An ERIS of 44.00 reflects moderate productivity and meaningful but not exceptional citation impact — consistent with a researcher who has established a credible body of work but is still some distance from the ceilings the metric is calibrated against. A researcher with 50 papers, 1,000 citations, and an h-index of 50 achieves the maximum ERIS of 100.00.
Enter your current bibliometric data below. All values are self-reported — see the verification and data policy section for what Expertini does and does not independently confirm.
Many research evaluation systems encode structural bias: they favour researchers at elite institutions, in high-citation-volume disciplines, with long careers at research-intensive universities. ERIS is explicitly designed to resist these biases. The formula does not know where you work, how old you are, what language you publish in, or which institution granted your degree. It responds only to what you have published and how it has been received — everywhere, not just in venues a particular database happens to index.
Expertini takes the integrity of ERIS seriously and makes a clear, honest distinction between what it can confirm directly and what it accepts in good faith from the researcher. A metric built on unverified self-report is only as trustworthy as the people reporting it. We are transparent about both sides of this line.
All bibliometric inputs used to calculate ERIS — external publications, citations, and h-index — are self-reported. Expertini takes its best interest to cross-reference profile data against observable signals where possible and may periodically audit suspicious profiles, but it cannot independently verify third-party citation databases in real time. Submitting inflated, fabricated, or misleading metrics violates the Expertini Research Terms of Service and may result in profile suspension or permanent removal. The integrity of ERIS as a platform-wide signal depends entirely on researchers reporting honestly.
Expertini does not claim that ERIS is a complete picture of a researcher's value, contribution, or potential. The score is one data point — useful, transparent, and reproducible, but inherently limited. The following variables are all genuinely significant indicators of research impact that ERIS does not and cannot measure:
The selection of indicators involves design trade-offs. Additional variables — journal impact factor, field-normalised citation rate, coauthorship network centrality, altmetric scores — introduce complexity and field-specificity that undermines comparability. ERIS restricts itself to three indicators that are universally available, consistently defined across databases, and validated by decades of bibliometric research [4, 5, 6].
Publication count is the most direct measure of research productivity. A researcher who publishes consistently has demonstrated the capacity to formulate questions, conduct enquiries, and communicate findings at a level sufficient for public dissemination. Critics rightly note [4] that volume alone does not distinguish high-impact from low-impact work — which is precisely why ERIS weights publications at 40% rather than 100%, with the citation and h-index components providing quality modulation.
When a researcher's work is cited, another scholar has made an explicit judgement that it contributed to their own inquiry. Aggregated citation counts are the most widely accepted proxy for scholarly influence [5]. The Leiden Manifesto [6] recommends that citation indicators be normalised — ERIS implements this by measuring citations as a proportion of the 1,000-citation ceiling, ensuring a researcher in a low-citation-volume discipline is not penalised relative to one in a high-volume field.
Hirsch's h-index [1] was proposed specifically to address the weaknesses of both raw publication counts and raw citation totals. A researcher with h-index h has produced at least h papers each cited at least h times — making it robust against a single highly-cited outlier and against high publication count with mostly uncited outputs. In ERIS, the h-index serves as the primary quality control mechanism.
ERIS was designed to apply meaningfully to every researcher category on the platform — not only those at research-intensive universities with long publication histories.
ERIS is field-agnostic. Normalisation against fixed ceilings means that a researcher who has reached the top of their field in publications and citations approaches a score of 100 regardless of whether that field is low-citation-volume (Mathematics, Philosophy, History) or high-citation-volume (Medicine, Biology, AI). ERIS applies across all nineteen research categories on the platform:
Publications contributing to ERIS may take any of the fourteen recognised output types on Expertini Research. ERIS does not distinguish between types when counting publications — a preprint, a peer-reviewed article, a doctoral thesis, and a technical report each contribute one unit. The citation component provides the implicit quality filter: outputs that attract citations contribute to all three components, while uncited outputs contribute only to publications. ERIS is applicable across all output types:
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Your ERIS score grows with every paper you publish and every citation you receive.