What is Smart Bibliometrics?
Smart Bibliometrics is a topic-led bibliometric research workspace. It transforms a research query into a structured analytical environment where you can explore publication patterns, identify influential authors, assess journal quality, map geographic distribution, and export curated datasets — all from a single search.
The platform was developed at the Universidade Federal do Espírito Santo (UFES), Brazil, under the direction of Professor Vilker Zucolotto Pessin, as a modern replacement for legacy dashboard-based bibliometric tools. It is designed specifically for graduate students, researchers, and faculty conducting systematic literature reviews, bibliometric analyses, and scientometric studies.
Smart Bibliometrics is built on open scholarly data and aims to make rigorous bibliometric analysis accessible without requiring institutional subscriptions to proprietary databases.
Data Source — OpenAlex
Where your data comes from
All bibliographic data in Smart Bibliometrics is sourced from OpenAlex, the world’s largest open catalog of scholarly metadata. OpenAlex is maintained by OurResearch, a nonprofit organization dedicated to making research more accessible.
How it compares to Scopus and Web of Science
OpenAlex provides approximately twice the coverage of Scopus or Web of Science, with significantly better representation of non-English publications and research from the Global South. Independent studies have confirmed that, for shared corpora, OpenAlex achieves reference coverage rates comparable to both proprietary databases (Culbert et al., 2025, Scientometrics).
Unlike Scopus and Web of Science, OpenAlex data is freely and openly available, which means that bibliometric analyses conducted with Smart Bibliometrics can be fully reproduced by peer reviewers without requiring paid database subscriptions — a significant methodological advantage.
Transparency about limitations
No bibliographic database is perfect. Being transparent about what OpenAlex does well and where it has known limitations helps you make informed decisions:
- Language metadata accuracy: Approximately 98% of works in OpenAlex contain language information, though studies have found that roughly 14.7% may be inaccurately labeled (Céspedes et al., 2025). Smart Bibliometrics displays language data as provided by OpenAlex.
- Institutional affiliations: Some works lack complete institutional affiliation data, particularly in the social sciences and humanities. Country-level analysis may undercount contributions from institutions with incomplete metadata.
- Document type classification: OpenAlex’s document type categorization is less precise than Scopus or Web of Science in some cases. A small percentage of works may be misclassified (e.g., a review labeled as an article).
- Citation count differences: Citation counts in OpenAlex may differ slightly from Scopus or Web of Science due to differences in deduplication algorithms and coverage scope. This is normal and expected — no two databases produce identical citation counts.
We recommend that researchers conducting formal bibliometric studies for publication acknowledge OpenAlex as the data source and note these characteristics in their methodology sections. We provide a suggested methodology statement below.
How Smart Bibliometrics Works
From query to workspace — how your data is collected and analyzed
Search
Your query (title keywords, abstract terms, author names, institutions, date ranges, document types, and boolean operators) is translated into an OpenAlex API request. The system searches across the full OpenAlex corpus.
Scope preview
Before collecting data, the system shows you a live preview: how many total records exist on OpenAlex for your query. This helps you refine your search terms before committing to data collection.
Data collection (ETL)
The system fetches up to 1,500 records from OpenAlex, ordered by relevance to your query. For each record, it retrieves: title, authors, abstract, publication year, journal/source, citations, DOI, keywords, document type, language, institutional affiliations, and country data. This data is stored in a private working dataset linked to your session.
Analytics
Once your dataset is built, all analytics pages (World Research, Authors, Journals, Language, Smart Graphs, Dataset) compute their visualizations from your stored dataset. Filters you apply (year range, citation thresholds, keyword filters) refine all analytics simultaneously.
Export
When your analysis is complete, you can export the full dataset as CSV or Excel for use in other tools (VOSviewer, Bibliometrix R package, etc.).
Why 1,500 records?
The 1,500-record working set is a deliberate design choice. Bibliometric analysis is most useful when applied to a focused, well-defined corpus rather than an uncurated mass of results. The cap ensures that analytics render quickly and remain interactive, while the relevance-ordered collection ensures the most pertinent works are included. If your OpenAlex query matches more than 1,500 works, the system collects the 1,500 most relevant records. You can see the total available count in the “Live OpenAlex preview” indicator in the session header.
Search result caching
If you run the same query (identical filters and parameters) within 24 hours, Smart Bibliometrics reuses the previously collected dataset rather than re-fetching from OpenAlex. This ensures consistency: if you return to refine your analysis, you’re working with the exact same data. The cache indicator in the session header shows when your data was last updated.
What happens while you wait
When you search a new topic for the first time, Smart Bibliometrics doesn’t just show you a list of links — it builds a complete analytical workspace tailored to your query. This process typically takes between 1 and 3 minutes.
You won’t be staring at a blank screen during this time. As soon as you search, the system shows you a live previewof matching results from OpenAlex almost instantly — usually within a few seconds. While you browse this preview, the full dataset is being built in the background. You can watch the progress bar in the session header as your workspace takes shape.
Here’s what’s happening behind the scenes:
Returning to your data
If you search the same topic again within 24 hours, your workspace loads instantly — no waiting at all. The system recognizes that you’ve already built this dataset and reuses it directly. You’ll see the progress indicator skip ahead and your analytics appear in seconds.
This also works across the research community: if another researcher has recently searched the same topic with the same parameters, the system can build your workspace from that existing dataset in seconds instead of collecting everything from scratch. You still get your own private copy of the data — the original researcher’s workspace is never affected, and your analyses remain completely independent.
Understanding the Analytics
Each analytics page in Smart Bibliometrics examines your working dataset from a different angle. Here is what each page measures and why it matters for your research.
World Research
The geographic view. A keyword cloud shows the most frequent topics in your dataset, while the world map displays publication output by country. Use this to identify which countries are leading research in your topic and to assess geographic concentration or gaps in the literature.
Authors
The people view. A citation-ranked bar chart shows the most-cited authors in your dataset. The coauthor groups panel reveals collaboration clusters — groups of researchers who frequently publish together. The papers panel lets you drill into specific works by any selected author. Use this to identify key contributors, collaboration networks, and seminal papers.
Journals
The venue quality view. Publication counts by journal, Qualis CAPES classifications, and impact factor data help you assess which journals dominate your topic. Use this to identify target journals for your own submissions or to evaluate the quality profile of the existing literature.
Language
The linguistic view. Document type and language distribution show you the linguistic composition of your dataset. The papers panel lets you inspect works in specific languages. Use this to assess whether your topic has significant non-English literature that might be overlooked in English-only reviews.
Smart Graphs
The network view. Collaboration and co-occurrence interactive graphs. Use this to explore structural relationships between authors, journals, and research clusters.
Dataset
The evidence view. A full sortable, searchable table of every paper in your working set, with an InSmart relevance score, citation counts, impact factors, keywords, and direct links to source documents. Use this for final inspection before export, and to identify individual papers of interest.
Cross-filtering
All analytics pages share the same global filter bar (Year range and Citation thresholds). When you set a filter on any page, it applies across all pages — so if you filter to “Last 5 years, 100+ citations” on the Authors page, the same filter persists when you navigate to Journals or World Research. This ensures a consistent analytical frame throughout your session.
Qualis CAPES
What is Qualis?
Qualis Periódicos is the official journal classification system maintained by CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível Superior), the Brazilian federal agency responsible for evaluating and accrediting graduate programs. It classifies journals into quality strata — A1 (highest), A2, A3, A4, B1, B2, B3, B4, and C — based on bibliometric indicators such as CiteScore (Scopus), Journal Citation Reports impact factor (Web of Science), and h-index (Google Scholar).
Qualis is critical for Brazilian researchers because the classification of journals in which a program’s faculty and students publish directly impacts the program’s CAPES evaluation score, which determines accreditation and funding.
Qualis in Smart Bibliometrics
The Journals page includes a dedicated Qualis CAPES panel that shows how the journals in your dataset are classified across different CAPES evaluation areas (Áreas de Avaliação). A single journal may receive different strata in different areas — for example, an interdisciplinary journal might be classified as A1 in Biotechnology but A2 in Public Health. Smart Bibliometrics displays all available classifications so you can assess journal quality from the perspective most relevant to your field.
Important note on Qualis evolution
In 2024, CAPES announced that the traditional Qualis Periódicos system would be phased out starting with the 2025–2028 evaluation cycle, to be replaced by a model that evaluates individual articles rather than journals. The Qualis data displayed in Smart Bibliometrics reflects the most recent available journal-level classifications. As CAPES transitions to the new evaluation model, we will update the platform accordingly. We recommend checking the CAPES Sucupira Platform (sucupira.capes.gov.br) for the most current classifications relevant to your specific evaluation area.
How to Cite Smart Bibliometrics
If you use Smart Bibliometrics in your research, we recommend citing both the platform and the underlying data source. Below are suggested citation formats and a methodology statement template you can adapt for your publications.
Suggested citation
Pessin, V. Z. (2026). Smart Bibliometrics: A Topic-Led Bibliometric Research Workspace. Universidade Federal do Espírito Santo. Available at: https://smartbibliometrics.com
Methodology statement template
Bibliometric data were collected using Smart Bibliometrics (Pessin, 2026), a research workspace built on the OpenAlex scholarly database (Priem et al., 2022). A search for [TOPIC] using [DESCRIBE FILTERS: keywords, date range, document types] yielded [N] records from a total of [TOTAL] available works on OpenAlex. The dataset was analyzed for [geographic distribution / authorial influence / keyword clustering / publication venue assessment / etc.]. Qualis CAPES journal classifications were used to assess venue quality within the Brazilian graduate evaluation framework.
OpenAlex citation
Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. ArXiv. https://arxiv.org/abs/2205.01833
Frequently Asked Questions
Smart Bibliometrics collects up to 1,500 of the most relevant records from OpenAlex for each search query. This working set size ensures that interactive analytics remain fast and responsive while capturing the most pertinent publications. You can see the total number of available records on OpenAlex in the “Live OpenAlex preview” indicator in your session header.
When you search a new topic, Smart Bibliometrics is not performing a simple keyword lookup — it’s building a private research workspace with up to 1,500 fully detailed records. The system retrieves complete metadata for each paper (authors, affiliations, abstracts, citations, keywords, journal details, and more), organizes it, and prepares it for interactive analysis across all six analytics pages.
This process typically takes 1 to 3 minutes for a new search. You’ll see a live preview of results almost immediately, and the full dataset builds in the background — the progress bar in the session header shows you exactly how far along it is.
Once your workspace is built, all analytics are instant. And if you or another researcher has recently searched the same topic, your workspace may load in seconds — the system reuses existing data and creates a private copy for you automatically.
Think of it as the difference between browsing a library catalog and having a research assistant pull, read, and organize 1,500 papers for you. The catalog is instant; the assistant takes a few minutes — but saves you days of work.
Citation counts vary between databases because each one indexes a different set of publications and uses different algorithms to match citations. OpenAlex, Scopus, and Web of Science will rarely produce identical citation counts for the same paper. This is normal and expected in bibliometric research. If exact Scopus or WoS citation counts are required for your study, we recommend cross-referencing individual papers in those databases.
Yes. The Dataset page offers CSV and Excel export options. The exported file includes all metadata fields (title, authors, year, journal, DOI, keywords, citations, country, language, document type) and can be imported into VOSviewer, the Bibliometrix R package, or any other analysis tool that accepts tabular bibliometric data.
OpenAlex updates its database continuously, with new works being added daily. When you run a new search in Smart Bibliometrics, you receive the most current data available from OpenAlex at that moment. Cached results (reused within 24 hours of an identical query) reflect the data from the original search.
Smart Bibliometrics retrieves articles, reviews, books, book chapters, datasets, dissertations, editorials, and other document types as classified by OpenAlex. You can filter by document type in the Advanced Search panel before collecting your data.
Qualis classifications are maintained by CAPES and cover journals that are relevant to Brazilian graduate program evaluations. International journals that are not commonly used in Brazilian graduate programs may not have Qualis classifications. Additionally, newer journals may not yet have been evaluated. A missing Qualis classification does not indicate low quality — it simply means the journal has not been classified within the CAPES framework.
Yes. Each search creates a private session linked to your account. Your queries, datasets, and analysis results are not visible to other users.
InSmart is a composite relevance score calculated by Smart Bibliometrics that combines multiple factors including citation impact, journal influence, and topical relevance to help you quickly identify the most significant papers in your dataset. Higher scores indicate papers with greater combined relevance and impact.
The Advanced Search panel does not currently offer a language pre-filter, but the Language analytics page allows you to explore and filter your dataset by language after data collection. You can click on any language to see the specific papers published in that language.
Smart Bibliometrics relies on OpenAlex’s author disambiguation system, which uses machine learning to match author names across publications. OpenAlex maintains over 114 million unique author profiles and integrates ORCID identifiers where available (8M+ authors with ORCIDs). While the system is generally accurate, some ambiguity may remain for authors with common names or incomplete metadata.
Contact & Support
Smart Bibliometrics is developed and maintained at the Universidade Federal do Espírito Santo (UFES), Brazil.
For questions, feedback, bug reports, or feature requests:
If you encounter a bug or unexpected behavior, please include:
- The analytics page where the issue occurred
- A screenshot if possible
- Your browser name and version
We actively develop Smart Bibliometrics and value user feedback. Feature suggestions from the research community directly shape our development roadmap.