Web of Science Overview

• Web of Science is a curated multidisciplinary bibliographic database that indexes and analyzes scientific literature with robust citation metrics and mapping capabilities.
• It employs advanced methodologies such as regression models and clustering algorithms to ensure reliable macro-level bibliometric comparisons and field mapping.
• Its selective coverage enhances data quality for high-impact journals while introducing biases in language and regional representation, affecting global research assessments.

The Web of Science (WoS) is a curated multidisciplinary bibliographic database designed for the indexing, retrieval, and analysis of scientific literature, serving as a foundational resource in scientometric research and bibliometrics. Originating from the Institute for Scientific Information (ISI) and evolving under Thomson Reuters and Clarivate, WoS functions as both an archival repository and a citation index, enabling advanced analysis of research output, citation patterns, author identities, and institutional productivity at global and regional scales.

1. Macro-level Bibliometric Comparisons and Robustness

WoS has long been the reference standard for macro-level bibliometric indicators such as publication and citation counts at the country level. The comparability of WoS with modern alternatives—principally Scopus—has been quantitatively established for the period 1996–2007. Analyses show almost perfect agreement for total papers and citations per country, with coefficients of determination $R^2$ typically exceeding 0.99 for both absolute values and country rankings. Regression models describing the cross-database correspondence, such as

$y = 1.249 x^{0.969},\quad R^2 = 0.995,$

(where $x$ and $y$ represent counts from WoS and Scopus respectively) confirm near-unity slopes and minimal intercepts, supporting the conclusion that country-level bibliometric indicators derived from WoS or Scopus are stable and mutually robust. This high correlation remains consistent even when indicators are disaggregated by natural science and engineering fields or narrowed to specialties such as nanotechnology (e.g., $R^2 = 0.991$ for papers, $R^2 = 0.967$ for citations in nanotechnology) (0903.5254).

2. Field-specific Coverage and Subject Area Mapping

The disciplinary mapping capabilities of WoS underwent a significant transition with the introduction of version 5, extending the earlier 222 ISI Subject Categories (SC) to 225 WoS Categories (WC) and the addition of 151 higher-level Subject Areas. This dual-layer classification supports both fine-grained and panoramic views of scientific research. Mapping efforts based on Journal Citation Reports data and employing clustering algorithms have demonstrated that the disciplinary structure organizes best into 19 factors, accounting for over 54% of the variance in the citation matrix. Overlay and visualization techniques using Pajek and VOSviewer support field-level analysis, enabling measures of interdisciplinarity such as the Rao-Stirling diversity index: $D = \sum_i \sum_j p_i p_j d_{ij}$ with $p_i$ the proportional weight of category $i$ and $d_{ij}$ the dissimilarity metric between categories. This framework facilitates both the quantification and visualization of field interconnection, collaboration networks, and the evolution of scientific specialties (Leydesdorff et al., 2012).

3. Database Coverage Biases and Selectivity

The selectivity and curation policies of WoS result in a data corpus distinguished by a high concentration of high-impact, peer-reviewed journals, especially in Natural Sciences, Engineering, and Biomedical Research. Comparative studies leveraging Ulrich's Periodical Directory confirm systematic overrepresentation of English-language journals and STEM fields (with, e.g., 42.7% NSE coverage in WoS vs. 27.5% in Ulrich), but underrepresentation in Social Sciences and Arts & Humanities (8–9% in WoS vs. 15.3% in Ulrich). Publisher countries with institutionalized academic traditions (e.g., USA, UK, Netherlands) are disproportionately covered, while coverage of journals from countries such as China is limited primarily to high-impact titles. This bias directly affects the output and impact profiles computed using WoS versus more inclusive databases like Scopus and especially Dimensions, which has 82% more journals than WoS and significantly enhanced coverage of Social Sciences and Arts (Mongeon et al., 2015, Singh et al., 2020).

4. Citation Network Completeness, Quality, and Data Merging

WoS is characterized by high data quality, particularly in capturing reference completeness and scientific prestige within citation networks. The Reference Coverage Rate (RCR) in WoS exceeds that of Crossref ($0.837$ vs $0.748$), and its curated references assure higher Article Scientific Prestige (ASP, akin to eigenvector centrality), thus reliably identifying influential literature. When merged with broader databases (e.g., Crossref), coverage of low-impact and niche publications is improved, which increases the completeness of the citation network (especially for underrepresented fields such as Education and Arts) but can dilute average prestige metrics by introducing more low-impact citations. Hence, while merging datasets fosters comprehensive bibliometric analyses, it also polarizes overall data quality (Rong et al., 5 Mar 2025).

5. Metadata, Document Type, and Classification System Accuracy

WoS has been consistently shown to exhibit greater accuracy and selectivity in document type assignment and journal classification:

• Document type assignment: In large-scale studies comparing WoS and Scopus to journal web sites, both databases achieved high precision (~99%) in labeling review articles, but recall was ~80%, dropping significantly for documents implicitly indicated as reviews on the publisher site (i.e., not explicitly labeled as "Review"). This under-identification affects downstream bibliometric analyses reliant on accurate document-type filters (Zhu et al., 2023).
• Journal classification: Citation-based analyses indicate that WoS is more conservative and accurate than Scopus in journal–category assignments. For example, for a relatedness threshold $a=0.1$, WoS exhibits less than half as many questionable assignments as Scopus, with only one WoS journal versus 32 Scopus journals identified as having weak citation-relatedness to the assigned category while having strong ties to unassigned categories (Wang et al., 2015).

6. Author Disambiguation and Identifier Coverage

WoS supports manually curated author name disambiguation via ResearcherID and ORCID integration, with up to 184,823 distinct ResearcherIDs and 70,043 ORCIDs identified among six million publications. While coverage for these manual identifiers is best in STEM fields, it remains under 3% per annum in recent years. Automatic author assignment (as in Scopus) ensures near 100% coverage but leads to significant homonym and transliteration errors, particularly for common names; this undermines reliability in massive-scale author-level bibliometric analyses (Krämer et al., 2017).

7. Role in Regional and Global Research Visibility

Integration of regional databases (such as SciELO Citation Index for Latin America and the Caribbean) into WoS has enhanced international visibility for regional publications, raising their citation metrics and global collaboration profiles. However, this comes with trade-offs: regional editorial independence and coverage may be subordinated to the stricter, globally oriented selection criteria of WoS, sometimes limiting the inclusivity or diversity of covered research outputs. Citation mean and co-authorship analysis reveal that while WoS-indexed articles have higher international collaboration and impact, SciELO-indexed works are more reflective of local scholarly effort—integrating both provides a more complete perspective on regional scientific contributions (Velez-Cuartas et al., 2015).

Conclusion

The Web of Science represents a rigorously curated, globally recognized bibliometric data infrastructure that prioritizes selectivity, data quality, and stability in macro-level indicators. Its architecture and curation practices support robust cross-field and cross-database comparability, albeit with acknowledged biases in field, language, and region. Methodological rigor in mapping disciplines, author disambiguation, and document type classification, combined with a persistent focus on citation network completeness and quality, establish WoS as a cornerstone for research evaluation, policy design, and the empirical paper of science. Nonetheless, as the landscape evolves with broader data integration and new bibliometric platforms, researchers must remain mindful of coverage limitations, metadata discrepancies, and the trade-offs entailed in comprehensive bibliometric practice.