CASRAI Dictionary

Tag: field weighted citation impact

  • Dimensions Altmetrics, Scopus & Web of Science: A DORA-Aligned Comparison

    Dimensions altmetrics, Scopus CiteScore, and Web of Science’s Impact Factor answer different questions about the same paper: how much online attention it attracted, how its journal’s four-year citation average compares, and how its two-year citation count compares against a curated index. No single number from any one database satisfies the San Francisco Declaration on Research Assessment (DORA)’s call for multi-indicator, qualitative-plus-quantitative evaluation — which is why research offices increasingly triangulate across all three.

    A citation database is a structured index of scholarly publications and their citation links, used to measure research coverage, impact, and attention across disciplines. Dimensions, Scopus, and Web of Science each build that index differently, and the differences matter directly for institutions trying to run dimensions altmetrics-aware, DORA-compliant assessment rather than single-metric ranking.

    How does coverage differ across Dimensions, Scopus and Web of Science?

    Coverage breadth is the single biggest structural difference between the three databases, and it is measurable rather than a matter of opinion. A 2021 Scientometrics study by Singh, Singh, Karmakar, Leta and Mayr found that Dimensions indexes 82.22% more journals than Web of Science and 48.17% more journals than Scopus, largely because Dimensions ingests preprints, grants, patents, clinical trials, and policy documents alongside conventional journal articles.

    A separate large-scale comparison published in Quantitative Science Studies (Visser, van Eck and Waltman, 2021, MIT Press) benchmarked Scopus, Web of Science, Dimensions, Crossref and Microsoft Academic together and found that Dimensions and Crossref offer the broadest raw coverage, while Scopus and Web of Science retain more curated, higher-quality affiliation and subject metadata. Web of Science’s Core Collection remains the most selective of the three, with editorial evaluation criteria dating to Eugene Garfield’s 1960 Science Citation Index; Scopus, launched by Elsevier in 2004, applies a comparatively more inclusive Content Selection and Advisory Board process.

    The practical implication: a citation count pulled from only one database will systematically undercount or overcount depending on discipline, document type, and region. A 2020 comparison from the German Kompetenznetzwerk Bibliometrie (Stahlschmidt and Hinze) reached the same conclusion — the three sources are not interchangeable, and cross-checking is a foundational bibliometric hygiene step, not an optional extra.

    What metrics does each database produce?

    Each platform has developed its own headline indicator, and none of the three is a like-for-like substitute for the others.

    Database Owner Headline metric Citation window Altmetrics integration
    Dimensions Digital Science Citation counts + linked Altmetric Attention Score No fixed window; article-level Native — shares parent company with Altmetric
    Scopus Elsevier CiteScore; Field-Weighted Citation Impact (FWCI) via SciVal 4-year rolling window PlumX Metrics
    Web of Science Clarivate Journal Impact Factor (JCR) 2-year window (5-year variant available) Article-level usage counts; expanding via Research Intelligence tools

    CiteScore, introduced by Elsevier in 2016, divides all citations a journal receives in a given year by all documents (not only “citable items”) published in the preceding four years, and is published free of charge — a deliberate contrast with the subscription-gated Journal Impact Factor. Field-Weighted Citation Impact normalises a paper’s citations against the world average for its subject, publication year, and document type, where a score of 1.0 represents parity with the global average; this makes FWCI more field-comparable than a raw citation count. The Altmetric Attention Score, meanwhile, is not a citation metric at all — it is a weighted count of online attention (news coverage, policy documents, X/social posts, Wikipedia references, blogs) that Dimensions surfaces natively because Dimensions and Altmetric are both Digital Science products.

    Which database best supports DORA-compliant, multi-indicator assessment?

    DORA, published in 2012 and now signed by thousands of organisations worldwide, asks institutions to stop using journal-based metrics such as the Impact Factor as a proxy for the quality of an individual researcher’s contributions, and instead to consider the value and impact of all research outputs alongside qualitative peer judgement. The 2015 Leiden Manifesto (Hicks, Wouters, de Rijcke and Rafols, published in Nature) added ten operating principles for responsible metrics use, including that quantitative evaluation should support, not replace, qualitative expert assessment.

    All three database vendors now publicly reference these frameworks, but their practical alignment differs. Digital Science, Dimensions’ parent company, is listed on DORA’s public signatory register, and Dimensions’ native pairing with Altmetric gives assessors an attention-based indicator alongside citations without needing a separate subscription. Elsevier has endorsed the Leiden Manifesto and built CiteScore’s open methodology partly in response to its principles. Clarivate likewise cites the Leiden Manifesto in its own responsible-metrics guidance and has begun layering a “Societal Impact Framework” onto Web of Science Research Intelligence to capture impact beyond citation counts.

    None of the three databases is independently DORA-compliant by design — compliance is a property of how an institution uses the data, not of the database itself. A single Impact Factor, CiteScore, or Altmetric Attention Score used alone to rank individuals contradicts DORA regardless of source. Multi-indicator assessment requires combining citation-based indicators from at least one curated database with attention-based indicators and qualitative peer review — which is precisely why UK funders and the Research Excellence Framework have explicitly excluded journal impact factors from submission guidance since 2014, requiring panel-level qualitative judgement instead.

    Where does OpenAlex fit as an open alternative?

    OpenAlex, launched in 2022 by the non-profit OurResearch as a fully open successor to the discontinued Microsoft Academic Graph, has emerged as the fourth reference point in this comparison. Unlike Dimensions, Scopus, and Web of Science, OpenAlex publishes its entire dataset and API without subscription cost, drawing on Crossref, ORCID, and ROR identifiers for disambiguation rather than proprietary matching.

    OpenAlex does not yet match the curated metadata quality or the established institutional trust of Scopus or Web of Science, and it carries no equivalent to the Altmetric Attention Score. But for institutions constrained by licensing budgets, or for bibliometrics tools built on reproducible, auditable pipelines, OpenAlex is increasingly used as a free cross-check against the commercial databases rather than a replacement for them.

    Answer-first questions

    What is Altmetric a measure of?

    Altmetric measures online attention, not citation impact. It tracks mentions of a research output across news media, policy documents, social platforms, blogs, and Wikipedia, then produces a weighted Attention Score. Because it captures engagement that predates or bypasses formal citation, it is treated as complementary to citation-based indicators, not a replacement for them.

    What counts as a good Altmetric score?

    There is no universal threshold, because Attention Scores vary enormously by field, output type, and publication date. As a rough benchmark, Altmetric itself notes that a score above roughly 20 typically outperforms most tracked outputs, but comparisons are only meaningful against similar papers in the same journal and timeframe, never as an absolute cutoff.

    Is Scopus or Web of Science better for research assessment?

    Neither is unconditionally “better” — Scopus offers broader, more geographically diverse journal coverage with a transparent four-year CiteScore, while Web of Science offers deeper historical coverage back to 1900 and the still-widely-recognised Impact Factor. DORA-aligned assessment favours using both alongside non-citation indicators rather than choosing one as authoritative.

    Implications for research offices

    Research administrators selecting or combining these tools should treat the choice as an assessment-design decision, not a procurement afterthought. Three practical consequences follow directly from the coverage and metric differences above:

    • A researcher’s citation count and h-index will differ meaningfully between Dimensions, Scopus and Web of Science — institutions must specify and disclose which source underlies any reported figure.
    • Attention-based data (Altmetric, PlumX) captures policy and public engagement that citation-only databases miss entirely, which matters for funders assessing societal impact pathways.
    • Free, open sources such as OpenAlex are viable supplementary cross-checks, particularly where licensing cost restricts access to all three commercial platforms.

    Conclusion

    The three databases are converging on responsible-metrics language while remaining structurally distinct in coverage, indicator design, and cost. Institutions that want genuinely DORA-compliant, multi-indicator assessment should treat Dimensions, Scopus and Web of Science as complementary evidence sources — pairing at least one citation database with an attention-based indicator and qualitative peer review — rather than defaulting to whichever single number is easiest to pull from a subscription dashboard.

  • Limitations of Bibliometrics: DORA and CoARA

    Bibliometrics — the statistical analysis of publication and citation data — cannot reliably stand in for research quality on its own: field-specific citation practices, author self-citation, and outright metric gaming all distort single-number scores such as the h-index or Journal Impact Factor. This is the documented evidentiary basis for DORA and CoARA’s push to replace single-score evaluation with qualitative, multi-indicator assessment.

    Bibliometrics is the quantitative study of academic literature — citation counts, publication volume, and derived indices — used as a proxy for scholarly influence. The proxy breaks down whenever a single number is asked to carry the full weight of a quality judgement, which is precisely what large-scale hiring, promotion, tenure, and funding panels have done for decades.

    What is bibliometrics, and why does one score fall short?

    Bibliometric indicators — citation counts, the h-index, the Journal Impact Factor (JIF), and derived composite scores — were built for large-scale, aggregate comparisons, not for judging an individual scholar’s contribution. Bergstrom, West and Wiseman’s 2008 analysis in the Journal of Neuroscience put it plainly: quantitative metrics are poor choices for assessing an individual’s research output compared with the “gold standard” of reading the work and consulting domain experts.

    A single score compresses conflicting dimensions of scholarly value — novelty, rigour, reproducibility, societal reach — into one figure. That compression, not citation data itself, is the structural weakness reform movements target.

    How does field bias distort bibliometric comparisons?

    Citation practices vary sharply by discipline, so raw citation counts cannot be compared across fields. Mathematics and the humanities publish and cite far less frequently than biomedicine, and books and conference proceedings — the dominant outputs in many humanities and computing sub-fields — are tracked inconsistently, or not at all, by Web of Science and Scopus.

    Coverage gaps compound the bias. Indexing databases differ in subject breadth, subject depth, geographic coverage, language coverage, and how far back citation histories extend, so researchers publishing outside the Anglophone, journal-dominant core of a database are systematically under-counted. Belter’s 2015 review in PMC also notes that citation-based indicators require roughly two to three years after publication before they stabilise enough to be considered reliable — a lag that penalises early-career researchers and recent work by design.

    Why does self-citation inflate bibliometric scores?

    Self-citation — an author citing their own prior work — is a normal and often legitimate part of building on a research programme. It becomes a distortion when it is used strategically to inflate an individual’s citation count or a journal’s Impact Factor beyond what independent uptake of the work would justify.

    Clarivate’s Journal Citation Reports has, in past cycles, suppressed the calculated Impact Factor of titles found to display anomalous citation behaviour, including excessive journal self-citation and coordinated “citation stacking” arrangements between journals — a documented, database-level enforcement action against exactly this failure mode. At author level, unusually concentrated self-citation rates are one of the diagnostic flags bibliometricians use when auditing whether a headline citation figure reflects genuine external uptake or engineered inflation.

    Does field-weighted citation impact solve the problem?

    Field-weighted citation impact (FWCI) is a normalised metric — used in tools such as Scopus/SciVal — that adjusts a publication’s citation count against the average for its subject field, publication year, and document type, so that a score of 1.0 represents “as expected” performance for that context. It is a genuine improvement on raw citation counts because it corrects for the field-bias problem described above.

    FWCI does not, however, correct for self-citation gaming or database coverage gaps, and it remains a single number: it shows how a paper performed against a benchmark, not whether the research was rigorous or original. Reform frameworks treat field normalisation as a refinement of bibliometrics, not a licence to keep using any single indicator as a proxy for quality.

    What evidence underlies DORA and CoARA’s reform case?

    The San Francisco Declaration on Research Assessment (DORA), launched in 2012, explicitly recommends against using the Journal Impact Factor as a surrogate measure of the quality of individual research articles, and calls on institutions to assess research on its own merits using a range of qualitative and quantitative indicators. The Coalition for Advancing Research Assessment (CoARA), formed in 2022, builds on DORA’s diagnosis: its signatories commit to basing assessment primarily on qualitative, peer-reviewed judgement, supported by responsible — not exclusive — use of quantitative indicators, and to abandoning inappropriate use of journal- and publication-based metrics such as the JIF and h-index.

    Both build directly on the failure modes above: field bias, self-citation gaming, database coverage gaps, and the two-to-three-year reliability lag are the documented evidence, not abstract principle, behind the push for reform.

    Initiative Launched Core commitment
    DORA (San Francisco Declaration on Research Assessment) 2012 Stop using the Journal Impact Factor as a proxy for individual article or researcher quality
    Leiden Manifesto 2015 (Hicks et al., Nature 520, 429–431) Ten principles for the responsible, transparent use of quantitative indicators alongside expert judgement
    CoARA (Coalition for Advancing Research Assessment) 2022 Base assessment primarily on qualitative peer review; abandon inappropriate JIF/h-index use in hiring, promotion and funding decisions

    Answer-first questions on bibliometric limitations

    What are the main limitations of bibliometrics in research assessment?

    The main limitations are field bias (citation norms differ by discipline), database coverage gaps (books, non-English and non-journal outputs are under-tracked), self-citation inflation, and a two-to-three-year lag before citation counts stabilise. Together these mean a single score cannot substitute for expert, qualitative judgement of research quality.

    Why is the h-index considered a poor measure of individual research quality?

    The h-index rewards volume and career length over insight, cannot distinguish a highly cited author from a member of a large collaborative team, and does not account for field-specific citation norms. Bergstrom, West and Wiseman (2008) concluded that reading the work and consulting experts remains the more reliable standard for individual evaluation.

    What is the difference between DORA and CoARA?

    DORA (2012) is a signable declaration focused primarily on eliminating Journal Impact Factor misuse. CoARA (2022) is a membership coalition of funders, universities and academies that goes further, committing signatories to a broader, peer-review-centred reform agenda across hiring, promotion, and institutional evaluation, with periodic reporting on progress.

    What is a self-citation rate and why does it matter?

    A self-citation rate is the proportion of an author’s or journal’s total citations that come from their own prior work rather than independent external uptake. Bibliometricians and citation-database auditors (including Clarivate’s Journal Citation Reports process) use unusually high self-citation rates as a flag for possible metric gaming rather than genuine scholarly influence.

    What should research administrators do differently?

    For research administrators and institutional leaders, the practical implication is not to discard citation data but to stop letting any single figure carry a hiring, promotion, or funding decision unsupervised. That means:

    • Pairing field-normalised indicators such as FWCI with narrative, qualitative peer assessment, as CoARA commitments require.
    • Auditing self-citation and journal self-citation patterns before citing a headline figure in a case file.
    • Recognising a fuller range of outputs — datasets, software, policy influence — rather than journal articles alone.
    • Crediting individual contributions on multi-author papers explicitly, rather than inferring credit from author position or aggregate citation share.

    On that last point, standardised contributor-role taxonomies address a related gap directly. CASRAI originated the CRediT contributor role taxonomy in 2014; the standard is now stewarded by NISO as ANSI/NISO Z39.104-2022, and it lets institutions record which named contributor performed which specific role on a paper — conceptualisation, data curation, writing — rather than relying on citation share or author-list position as a proxy for who did what.

    Where bibliometric reform goes next

    The evidentiary case against single-number bibliometric scores is now well established: field bias, database coverage gaps, self-citation gaming, and a multi-year reliability lag are documented, auditable failure modes, not theoretical objections. DORA and CoARA translate that evidence into institutional commitments, and field-normalised metrics such as FWCI narrow — without eliminating — the field-bias problem.

    The direction of travel for funders, universities and academies is toward layered assessment: responsibly used quantitative indicators, transparent contributor-role attribution, and peer judgement at the centre, rather than any one score standing alone.

  • SciVal Bibliometrics vs the Leiden Ranking: Benchmarking Under DORA

    SciVal is Elsevier’s Scopus-based platform for benchmarking research output; the CWTS Leiden Ranking is Leiden University’s field-normalised ranking that deliberately avoids one composite score. Institutions increasingly run both together, but DORA warns that any league-table framing can reduce research quality to a single misleading number.

    SciVal bibliometrics refers to the citation and output metrics — including Field-Weighted Citation Impact (FWCI) — that Elsevier’s SciVal platform generates from Scopus data to support institutional research evaluation. Research offices now routinely pair this proprietary layer with the CWTS Leiden Ranking’s open, transparent indicators, creating a benchmarking workflow that sits in direct tension with the San Francisco Declaration on Research Assessment (DORA).

    What is SciVal and what does it measure?

    SciVal is Elsevier’s research-analytics platform, built on Scopus abstract-and-citation data, that lets subscriber institutions benchmark output, impact, and collaboration against named peer groups. It does not produce publicly indexed rankings; access is by institutional subscription, and outputs are configured per user for internal decision-making rather than public comparison.

    Core SciVal modules include:

    • Overview — publication and citation summaries for an entity over time
    • Benchmarking — side-by-side comparison against selected competitor or aspirational institutions
    • Collaboration — network maps of co-authorship at institutional, national, and international level
    • Trends — topic-level growth signals used for strategic investment decisions

    Its signature indicator is Field-Weighted Citation Impact (FWCI), the ratio of citations a set of publications actually received to the citations expected for publications of the same type, year, and subject field. A FWCI of 1.0 represents the world average for that field; values above 1.0 indicate above-average citation impact.

    How does the CWTS Leiden Ranking differ from SciVal?

    The CWTS Leiden Ranking, produced annually since 2007 by the Centre for Science and Technology Studies at Leiden University, is a free, publicly available ranking that explicitly refuses to combine indicators into one overall score. Instead it publishes separate, field-normalised tables — including MNCS (mean normalised citation score) and PP(top 10%), the proportion of an institution’s output among the world’s most-cited 10% of papers in its field.

    Where SciVal is a private diagnostic tool tuned to whatever comparator group an institution chooses, the Leiden Ranking is a public, methodologically documented instrument built for cross-institutional transparency. The distinction matters for governance: SciVal data informs internal strategy conversations, while Leiden Ranking data is citable externally by journalists, funders, and prospective students.

    Dimension SciVal CWTS Leiden Ranking
    Underlying data source Scopus Web of Science (Classic edition) or OpenAlex (Open Edition)
    Access model Institutional subscription Free and publicly browsable
    Composite score Configurable dashboards, no single mandated score Explicitly none — indicators kept separate by design
    Level of analysis Author, department, institution, custom groups Institution-level only
    Signature indicator Field-Weighted Citation Impact (FWCI) MNCS and PP(top 10%)
    Governing body Elsevier (commercial) CWTS, Leiden University (academic)

    Why does DORA caution against benchmarking with league tables?

    DORA, the San Francisco Declaration on Research Assessment published in 2012, calls on institutions to stop using journal- and rank-based proxies as substitutes for assessing the actual content of research. Its core recommendation is definitive: evaluators must not treat a journal impact factor, or by extension a university’s league-table position, as a surrogate measure of the quality of an individual researcher’s contribution.

    The UK’s Research Excellence Framework reinforces the same principle domestically — REF guidance instructs assessment panels not to rely on journal impact factors or bibliometric rankings when judging individual outputs. A single Leiden Ranking position or SciVal FWCI score, DORA argues, compresses genuinely multidimensional research performance into one figure that is easy to misuse in hiring, promotion, and funding decisions.

    How are research offices combining SciVal and Leiden in practice?

    A DORA-conscious workflow uses SciVal for granular internal diagnostics and the Leiden Ranking for transparent, external context — never letting either stand alone as a judgement on individual quality. In practice this looks like a two-stage process rather than a single dashboard export.

    1. Research offices first use SciVal to identify departmental strengths, emerging topics, and collaboration gaps against a self-selected comparator set.
    2. They then check institutional standing against the Leiden Ranking’s published, field-normalised indicators to see how that internal picture holds up against an independently governed, public dataset.
    3. Neither output is applied directly to an individual researcher’s promotion or tenure case, consistent with DORA’s requirement that assessment be based on the substance of the work.

    This “basket of metrics” approach — pairing a proprietary analytics tool with an open, non-composite ranking — is increasingly the model that DORA-signatory universities describe in their own research-assessment policies.

    What does the OpenAlex-based Leiden Ranking Open Edition change?

    Since 2023, CWTS has published a Leiden Ranking Open Edition built entirely on OpenAlex data, run alongside the long-standing Web of Science-based Classic edition. OpenAlex, launched by OurResearch in 2022 as a free successor to the discontinued Microsoft Academic Graph, indexes a broader and more open set of scholarly outputs than either Scopus or Web of Science.

    Because the Open Edition and Classic edition draw on different underlying databases, the same institution can show a materially different position depending on which edition is consulted — a fact rarely mentioned in library guidance on SciVal or Leiden alone. This is itself a practical argument for DORA’s caution: even among ostensibly objective, field-normalised rankings, the choice of data source alone can shift an institution’s apparent standing, before any interpretive judgement is applied.

    Common questions about SciVal bibliometrics

    Is SciVal the same as Scopus?

    No. Scopus is Elsevier’s underlying abstract-and-citation database; SciVal is a separate analytics layer built on top of Scopus data. Scopus supplies the raw publication and citation records, while SciVal turns them into benchmarking dashboards, Field-Weighted Citation Impact scores, collaboration maps, and trend reports for institutions and funders.

    What is SciVal used for?

    Research offices use SciVal to benchmark departments against named peers, track Field-Weighted Citation Impact and output trends, identify emerging research strengths, map collaboration networks, and build evidence for grant applications — functions distinct from external, public rankings such as the Leiden Ranking.

    What are the limitations of SciVal?

    SciVal’s field-normalisation depends on how Scopus classifies each publication’s subject field, which can misclassify interdisciplinary work. Coverage is limited to Scopus-indexed output, under-representing books and some social-science and humanities journals — a gap DORA cites when warning against treating any single metric as definitive.

    What metrics does SciVal provide?

    Core SciVal indicators include Scholarly Output, Citation Count, Field-Weighted Citation Impact (world average equals 1.0), Outputs in Top Citation Percentiles, and Collaboration metrics. These sit alongside Leiden-style indicators such as MNCS and PP(top 10%) used for external, field-normalised comparison.

    What this means for research administrators

    For research administration teams, the practical guidance is to treat SciVal and the Leiden Ranking as complementary diagnostic inputs, not verdicts. Any institutional report that cites either should disclose the comparator group, data source (Scopus, Web of Science, or OpenAlex), and the field-normalisation method applied, so that governance committees can judge the figures in context rather than as a rank alone.

    Where SciVal or Leiden data feeds into funding, hiring, or strategic planning, DORA-aligned institutions pair the quantitative output with qualitative peer assessment — a practice increasingly documented in the research-assessment policies of DORA-signatory universities.

    Where institutional benchmarking is heading

    As open bibliographic sources such as OpenAlex mature alongside proprietary platforms, expect research offices to triangulate across multiple data sources rather than anchor decisions to one dashboard or one ranking position. The direction of travel — visible in the Leiden Ranking’s own move to publish a parallel OpenAlex edition — is toward more transparent, multi-source benchmarking, precisely the “basket of metrics” model DORA has argued for since 2012.

    Research offices that document their methodology and keep SciVal, Leiden, and open datasets in dialogue with each other will be better placed to withstand scrutiny than those relying on any single proprietary score.

  • OpenAlex API: Building a Metrics Dashboard

    The OpenAlex API is a free, fully open REST interface to a catalogue of hundreds of millions of scholarly works, authors, institutions and funders, and it is the most practical data source for building an in-house institutional research metrics dashboard without a subscription. Query the /works endpoint with an institution filter, aggregate with group_by, and you have publication counts, open-access share and citation-percentile data in a single JSON response.

    OpenAlex is an open, CC0-licensed catalogue of the global research system — works, authors, institutions, sources, funders and topics — built and maintained by the non-profit OurResearch as a successor to the discontinued Microsoft Academic Graph. Because every record and the API itself are free to query, research offices can build metrics dashboards without licensing a commercial bibliometrics platform, provided they understand the filter syntax, pagination limits and the metric gaps this guide covers.

    What is the OpenAlex API and what does it cover?

    The OpenAlex API exposes entity endpoints — Works, Authors, Institutions, Sources, Topics, Funders and Awards — each accessed at https://api.openalex.org/{entity}. Every entity supports four operations: list, get (by ID), filter, and group_by (server-side aggregation), which together are the building blocks of a dashboard.

    Each entity carries a persistent OpenAlex ID and, for institutions, a cross-walked ROR identifier — the Research Organization Registry ID also used by ORCID, Crossref and DataCite. Filtering on an institution’s ROR-linked OpenAlex ID, rather than a free-text name match, is what keeps a dashboard’s institutional attribution stable as an organisation’s name or subsidiary structure changes.

    Entity endpoint Dashboard use case Example filter
    /works Publication counts, open-access share, citation percentiles authorships.institutions.id
    /authors Researcher productivity, h-index-style summary stats affiliations.institution.id
    /institutions Peer benchmarking, collaboration networks ror
    /topics Subject-area concentration and trend detection works_count

    How do you query the Works endpoint for institutional metrics?

    Every institution-level query starts with the authorships.institutions.id filter set to the institution’s OpenAlex ID, which you resolve once via /institutions?filter=ror:https://ror.org/{your-ror-id}. From there, combine filters with commas (AND logic) and pipes (OR logic), and add group_by to turn a list query into an aggregation query in one request — no client-side loop required.

    • Publication trend: /works?filter=authorships.institutions.id:I123...,publication_year:2020-2026&group_by=publication_year
    • Open-access share: add &group_by=oa_status to the same filter to split output into gold, green, hybrid, bronze and closed counts.
    • Field distribution: &group_by=primary_topic.field.id reveals subject concentration across an institution’s output.
    • Collaboration mapping: &group_by=authorships.institutions.id returns co-publishing partner institutions ranked by shared-work count.

    Use the select parameter to strip unused fields from large responses, and switch from offset-based page/per_page pagination to cursor pagination once a query’s meta.count exceeds roughly 10,000 results — offset pagination is capped and will silently stop returning new pages beyond that depth.

    How do you approximate field-weighted citation impact with OpenAlex data?

    Field-weighted citation impact (FWCI) is a proprietary metric popularised by Elsevier’s SciVal and Scopus products, calculated by comparing a work’s citations to the average for same-year, same-subject, same-document-type publications; OpenAlex does not expose a field literally called “FWCI”, and no open API replicates the Scopus subject-classification baseline it is normalised against.

    OpenAlex’s nearest open equivalent is the cited_by_percentile_year object returned on every work record, which gives a min/max percentile rank of that work’s citation count against all works of the same publication year and type. Aggregating this field across an institution’s output — for example, the share of works in the top decile (percentile ≥ 90) per year — produces a transparent, reproducible citation-impact proxy that a dashboard can compute without a commercial licence, though it is not interchangeable with SciVal’s FWCI for benchmarking against institutions that report the Scopus figure.

    For most dashboards the honest approach is to present both: raw citation counts (context-dependent, not comparable across fields) and the percentile-year proxy (comparable within OpenAlex’s corpus), clearly labelled as distinct from any vendor-reported FWCI value cited in external reports.

    What are the authentication, rate-limit and pricing rules?

    OpenAlex’s underlying dataset, website and API are free and the data is CC0-licensed, so no purchase is required to query or redistribute results. Every request should still include a contact identifier — either a mailto query parameter with your email address or a registered api_key — to enter the “polite pool”, which OurResearch prioritises over anonymous traffic for faster, more consistent response times.

    Requests without a mailto parameter or API key are routed to a slower, lower-priority pool and are more likely to be throttled during peak load; this single parameter is the most common fix for intermittent 429 or timeout errors reported by developers building batch-harvesting scripts. Dashboard builders scheduling nightly refresh jobs should always set mailto or an API key rather than relying on the anonymous pool.

    Common developer questions

    Is the OpenAlex API free?

    Yes. OpenAlex is free to query, and the underlying data is licensed under CC0, meaning it can be reused and redistributed without royalties. Registering an email via the mailto parameter or an API key gives access to the faster “polite pool” but does not change the underlying no-cost model.

    Does OpenAlex have an API for institutional data?

    Yes. The Institutions endpoint returns disambiguated organisation records cross-walked to ROR identifiers, and the Works endpoint accepts an authorships.institutions.id filter, which is the standard way to scope any query to a single institution’s publication output for a dashboard.

    What is OpenAlex used for in research administration?

    Research offices use OpenAlex to track publication trends, open-access compliance, collaboration networks and topic concentration without paying for a commercial bibliometrics subscription. Its open licence also makes it suitable for public-facing institutional reporting, since results can be republished without redistribution restrictions.

    Implications for institutional research offices

    A dashboard built directly on the OpenAlex API gives research administration teams a free, auditable alternative to proprietary bibliometrics tools for routine reporting — publication counts, open-access compliance tracking and collaboration mapping — while reserving paid platforms for tasks that genuinely require vendor-normalised metrics such as reported FWCI. The trade-off is that teams take on the engineering work themselves: handling pagination beyond 10,000 results, keeping institution ID mappings current as ROR records change, and documenting clearly that a percentile-based proxy is not the same figure a funder or ranking body may expect from Scopus.

    As OpenAlex’s topic classification and percentile fields mature, the gap between what a free, transparent API can deliver and what a paid platform delivers continues to narrow for most day-to-day institutional reporting needs, making a well-built in-house dashboard an increasingly credible default rather than a stopgap.

  • Field-Weighted Citation Impact: Where It Fails

    Field-weighted citation impact (FWCI) is a Scopus-derived metric that divides a publication’s actual citation count by the citation count expected for similar documents in the same subject field, publication year and document type — a result of 1.0 marks the global average, above 1.0 marks above-average impact. Before an institution builds review, promotion or tenure (RPT) criteria around it, the underlying normalisation assumptions need scrutiny.

    Field-weighted citation impact is defined by Elsevier as the ratio of citations actually received by an output to the citations that would be expected based on the average for the global scientific output of the same subject field, publication year and document type. It is calculated using Scopus data and surfaced through SciVal and Pure.

    What is field-weighted citation impact?

    Field-weighted citation impact is a normalised, article-level citation metric built into Elsevier’s SciVal and Scopus platforms. It expresses how a specific output, author, or institution has been cited relative to a global benchmark of comparable publications, rather than in raw citation counts that inevitably favour older papers and citation-heavy fields such as biomedicine.

    An FWCI of 1.48 means a document has been cited 48% more than expected for its field, year and type. An FWCI of 0.6 means it has been cited 40% less than expected. Because the benchmark is fixed at 1.0 by construction, roughly half of all outputs in any given field will sit below that line — a distributional fact that is frequently lost in institutional reporting.

    How is FWCI calculated?

    The field-weighted citation impact formula is simple on its face: FWCI = actual citations received ÷ expected citations for similar documents. The “expected” figure is the average citation count for all Scopus-indexed documents sharing the same Scopus subject classification (ASJC code), publication year, and document type (article, review, conference paper, and so on).

    • A microbiology article published in 2023 that has received 20 citations, against a field average of 10 for similar 2023 microbiology articles, scores an FWCI of 2.0.
    • A humanities article with 3 citations against a field average of 2 scores an FWCI of 1.5 — a superficially similar score built on a far smaller, more volatile citation base.
    • SciVal aggregates FWCI across an author’s or institution’s full output set by summing actual citations and expected citations separately, then dividing the totals — not by averaging individual FWCI scores.

    This matters: a single highly cited outlier can lift a whole portfolio’s FWCI, which is why SciVal documentation recommends reading FWCI alongside output volume and citation distribution, not as a standalone score.

    FWCI vs CiteScore and the Journal Impact Factor

    FWCI is often confused with journal-level metrics because all three numbers look similar — a decimal hovering near 1 to 10. They measure different things at different units of analysis, which is the first source of misapplication in policy documents.

    Metric Unit of analysis Field-normalised? Source and window
    Field-weighted citation impact (FWCI) Article, author, or institution Yes — field, year, document type Scopus data via SciVal; typically a rolling multi-year citation count
    CiteScore Journal No Elsevier/Scopus; launched December 2016; citations in a year to the prior 3 years of documents
    Journal Impact Factor (JIF) Journal No Clarivate Journal Citation Reports; historically a 2-year citation window

    Neither CiteScore nor the JIF adjusts for subject field, so comparing a mathematics journal’s CiteScore to an oncology journal’s compares citation cultures, not quality. FWCI’s field normalisation is what DORA-aligned reformers have asked journal metrics to do and mostly do not — which is also why FWCI is sometimes waved through review committees as the “responsible” metric without further scrutiny.

    Where FWCI breaks down: five assumptions to scrutinise

    FWCI’s field normalisation is a genuine improvement over raw citation counts and journal-level proxies, but it inherits several assumptions that DORA-aligned institutions should test before writing it into criteria.

    • Mean-based benchmarking, not percentile-based. FWCI compares an output to the field average, but citation distributions are heavily right-skewed: a small number of highly cited papers pull the mean upward, so most papers structurally score below 1.0 even when performing typically for their field. This is precisely why the Centre for Science and Technology Studies (CWTS) at Leiden University uses percentile-based indicators, such as the share of a unit’s output in the global top 10% most-cited, in its Leiden Ranking methodology rather than a mean-normalised ratio.
    • Subject classification is assigned to journals, not articles. Scopus’s ASJC subject codes are largely applied at the source-title level. An interdisciplinary article published in a broad-scope journal inherits that journal’s field classification, which can misrepresent the “expected” citation benchmark for a genuinely cross-disciplinary piece of work.
    • Small-sample volatility. For low-citation fields (much of the humanities, parts of engineering and mathematics) or for single articles, a difference of one or two citations can swing FWCI dramatically, because the expected-citation denominator is itself small. A score of 2.0 built on 20 citations is far more stable than one built on 2.
    • Self-citation is not excluded by default. Author, institutional, and journal self-citation inflate the numerator unless a self-citation exclusion is explicitly applied — a configurable option in SciVal, but one that is easy to omit when scores are pulled into a spreadsheet for a committee.
    • A single number cannot represent research quality, originality, or societal value. FWCI measures citation uptake within a fixed window; it says nothing about methodological rigour, reproducibility, data sharing, or the qualitative judgement DORA asks assessors to exercise in its place.

    Should FWCI drive review, promotion and tenure decisions?

    The San Francisco Declaration on Research Assessment (DORA), issued in 2012, recommends that institutions not use journal-based metrics as a surrogate for the quality of individual articles, individual researchers’ contributions, or as inputs to hiring, promotion, and funding decisions. FWCI’s article-level, field-normalised design addresses DORA’s specific objection to journal-level proxies such as the JIF — but it does not exempt FWCI from DORA’s broader principle that quantitative indicators should supplement, not replace, expert reading of the work itself.

    Institutions building RPT criteria around FWCI should require committees to read the underlying subject classification applied to a candidate’s outputs, check whether self-citations are excluded, and treat single-digit-citation scores as statistically unstable rather than definitive. A candidate’s FWCI trend across a full portfolio, read alongside narrative evidence of contribution, is a materially more defensible signal than a single score cited in isolation.

    As UK Research and Innovation and equivalent funders continue to align assessment frameworks with responsible-metrics principles, institutions that document how they weight FWCI against qualitative peer judgement — rather than adopting it as a pass/fail threshold — will be better positioned to defend their research administration processes to auditors, funders, and appeals panels alike.

    Frequently asked questions

    What is the average FWCI?

    The global average FWCI is always 1.0 by mathematical construction, because the benchmark for “expected citations” is itself the average of comparable outputs. A score above 1.0 indicates above-average citation performance for that field, year, and document type; a score below 1.0 indicates below-average performance.

    How do I get my field-weighted citation impact?

    FWCI is retrieved through a SciVal subscription, where institutional users can search an author, publication set, or institution and view the FWCI directly on the metrics dashboard. Some institutions also surface FWCI through Pure, which synchronises the metric from Scopus on a scheduled basis where the integration is enabled.

    What is field-weighted citation impact ranking?

    FWCI is not itself a ranking system — it is a ratio, not a percentile or league-table position. Institutions sometimes rank authors, departments, or outputs by their FWCI scores internally, but this practice inherits all the mean-based and small-sample limitations described above and should be treated cautiously.

    Is field-weighted citation impact the same as CiteScore?

    No. FWCI operates at the article, author, or institution level and is field-normalised; CiteScore is a journal-level average citation rate with no field normalisation. A journal’s CiteScore says nothing about how any single article within it actually performed relative to its field.

    FWCI remains one of the more defensible citation metrics precisely because it was built to correct the field-blindness of journal-level indicators. Its value depends entirely on institutions applying it the way its own documentation recommends: alongside output volume, subject classification checks, and self-citation controls — not as a solitary number standing in for expert judgement in a promotion file.