What is insightSci?
insightSci (Insights for Science) is a Scientific Intelligence Platform designed to transform scholarly data into actionable knowledge that supports research, discovery, and innovation.
The platform enables researchers, graduate students, faculty members, universities, research centers, and innovation organizations to explore scientific landscapes, identify influential authors and institutions, analyze research trends, evaluate scientific impact, discover emerging opportunities, and uncover knowledge gaps across any field of study.
By integrating bibliometrics, scientometrics, network analysis, data science, and artificial intelligence, insightSci transforms large volumes of scientific information into structured insights that support evidence-based decision-making throughout the research process.
More than a search tool, insightSci provides a comprehensive environment for scientific mapping, literature exploration, research evaluation, publication strategy, institutional benchmarking, and knowledge discovery. Its mission is to help researchers better understand how knowledge evolves, where opportunities for contribution exist, and how scientific evidence can be transformed into meaningful impact.
Developed at the Federal University of Espírito Santo (UFES), Brazil, insightSci was created to democratize access to scientific intelligence and strengthen the capacity of researchers and institutions to generate high-quality scientific knowledge in an increasingly complex information landscape.
Artificial Intelligence with Method. Researchers at the Center of Knowledge Creation.
At insightSci, we believe that artificial intelligence should enhance scientific thinking, not replace it.
While many AI-powered research tools focus primarily on generating answers through prompts, insightSci was built around a different principle: Artificial Intelligence with Method. Our approach combines scientific evidence, structured analytical processes, and responsible AI to support rigorous knowledge creation.
The researcher remains at the center of the process. Artificial intelligence acts as an intelligent partner that helps organize information, identify patterns, reveal opportunities, and accelerate analytical tasks, while preserving the critical thinking, creativity, and scientific judgment that drive meaningful discoveries.
insightSci is designed not only to improve productivity, but also to contribute to the development of researchers. Through scientific mapping, evidence-based literature analysis, knowledge-gap identification, and guided exploration of scientific domains, users strengthen their understanding of how knowledge is produced, validated, and advanced.
This educational and methodological perspective distinguishes insightSci from generic AI solutions. Rather than simply generating content, the platform seeks to cultivate scientific competence, intellectual autonomy, and evidence-based reasoning. The result is a research environment where technology amplifies human intelligence, supports ethical scientific practices, and helps transform data into knowledge, knowledge into discovery, and discovery into innovation.
Data Source — OpenAlex
Advancing Scientific Discovery Through Open Knowledge
insightSci is powered by OpenAlex, one of the world's largest and most comprehensive open catalogs of scholarly metadata. Developed and maintained by OurResearch, a nonprofit organization dedicated to increasing access to research, OpenAlex represents a transformative movement toward transparency, accessibility, and reproducibility in science.
We are deeply grateful to the OpenAlex team and the broader open-science community for their commitment to making high-quality scholarly data freely available to researchers worldwide. Their work contributes directly to a more inclusive scientific ecosystem, expanding the visibility of research outputs that have historically been underrepresented in traditional commercial databases.
Global Scientific Coverage
OpenAlex currently indexes:
This broad coverage enables researchers to explore scientific production across disciplines, regions, languages, and institutions at an unprecedented scale.
Why insightSci Uses OpenAlex
Scientific knowledge advances when research can be discovered, examined, validated, and built upon by others. OpenAlex supports this vision by providing open access to scholarly metadata, helping remove barriers that have traditionally limited participation in scientific discovery.
Compared to proprietary databases, OpenAlex offers significantly broader representation of non-English publications, regional journals, and research produced in emerging and developing scientific ecosystems. This expanded coverage helps reveal valuable contributions that may otherwise remain invisible in global scientific analyses.
By leveraging OpenAlex, insightSci seeks to promote a more comprehensive and equitable understanding of scientific activity, allowing researchers to identify knowledge, expertise, collaborations, and emerging opportunities regardless of geographic origin or institutional affiliation.
Supporting Transparency and Reproducible Research
One of the most important advantages of OpenAlex is its commitment to openness and reproducibility.
Because OpenAlex data is openly available, scientific analyses performed within insightSci can be independently verified, replicated, and scrutinized by other researchers without requiring access to expensive commercial databases. This aligns with the growing international movement toward Open Science and strengthens methodological transparency in bibliometric and scientometric research.
At insightSci, we believe that transparency is fundamental to scientific progress. Open scholarly infrastructure helps ensure that knowledge creation remains accessible, verifiable, and collaborative.
Commitment to Responsible Scientific Analysis
No bibliographic database is perfect, and responsible research requires awareness of data limitations. While OpenAlex provides exceptional coverage and accessibility, users should be aware that certain metadata fields may contain inconsistencies or gaps.
Examples include language classification, institutional affiliations, document-type categorization, and citation counts, which may occasionally differ from those reported by other databases due to differences in indexing policies, coverage scope, metadata availability, and processing methodologies.
These limitations are not unique to OpenAlex; they are inherent challenges in large-scale scholarly databases. For this reason, insightSci encourages researchers to interpret results critically and to document their data sources and methodological choices when conducting formal scientific studies.
Building the Future of Scientific Intelligence
The emergence of open scholarly infrastructures such as OpenAlex represents an important milestone in the evolution of science itself. By making scientific metadata openly available, initiatives like OpenAlex empower researchers, institutions, educators, innovators, and society to participate more fully in the creation and dissemination of knowledge.
insightSci is proud to build upon this foundation, transforming open scientific data into actionable insights that support discovery, innovation, collaboration, and evidence-based decision-making.
Together, open data, scientific methodology, and artificial intelligence create new opportunities to accelerate knowledge generation and expand humanity's collective capacity for discovery.
How insightSci 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 a Cloudflare-backed index request. The system searches across the local scholarly corpus stored for insightSci.
Scope preview
Before the stored dataset is ready, the system opens a fast preview for your query. This lets the workspace become useful quickly while the capped result set is prepared.
Data collection (ETL)
The system materializes up to 1,000 publications selected by OpenAlex relevance and article importance, then ranks them inside the workspace by Index/performance metrics. The broader OpenAlex match count remains available as context.
Analytics
Once your dataset is built, the analytics pages (Global Landscape, Authors, Journals, Graphs, Dataset) compute their visualizations from your stored dataset, and Library lets you organize selected references for literature review work. Filters you apply (year range, citation thresholds, language, keyword filters) refine the working view.
Export
When your analysis is complete, you can export the stored 1,000-publication dataset as CSV or Excel for use in other tools (VOSviewer, Bibliometrix R package, etc.).
Why a ranked 1,000-publication working set?
The 1,000-publication working set is deliberate for OpenAlex topic searches. Bibliometric analysis needs a useful corpus quickly, so insightSci selects publications by article relevance and importance, opens the workspace, and ranks the set by Index/performance metrics. If your query matches more than 1,000 works, the broader match count remains visible as context while analytics/export stay scoped to the curated ranked set.
Search result caching
If you run the same query (identical filters and parameters) within 30 days, insightSci reuses the previously collected result data rather than re-fetching the same first page from OpenAlex. This ensures consistency: if you return to refine your analysis, you’re working with the same stored 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, insightSci doesn’t just show you a list of links — it builds a complete analytical workspace tailored to your query. The first publications should appear quickly, and broader datasets continue growing in the background.
You won’t be staring at a blank screen during this time. As soon as you search, the system shows you a live preview of matching results from OpenAlex within the fast path. While you browse this preview, additional publications are fetched, deduplicated, and stored without blocking the dashboards. You can watch the session header to see whether the dataset is ready or still growing.
Here’s what’s happening behind the scenes:
Returning to your data
If you search the same topic again within 30 days, your workspace loads quickly — 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 when the stored data is available.
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 insightSci examines your working dataset from a different angle. Here is what each page measures and why it matters for your research.
Global Landscape
The geographic view. Contributor and institution rankings sit beside the global production map so you can identify which countries and organizations lead research in your topic and where geographic gaps may exist.
Authors
The people view. A citation-ranked bar chart shows the most-cited first 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, SJR, and SJR Best Quartile 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.
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 Index relevance score, citation counts, SJR, keywords, language filters, and direct links to source documents. Use this for final inspection before export, and to select papers for your Library.
Library
The reference workspace. Save selected Dataset papers into collections, organize them with tags and notes, export references, generate citations, and choose the documents that will feed the AI literature review workspace.
Cross-filtering
All analytics pages share the same global filter bar: YEAR, CITATIONS, TYPE, SJR, and QUALIS. When you set a filter on any page, it applies across all pages — so if you filter to “Last 4 years”, a custom citation range, and a journal-quality slice on the Authors page, the same filter persists when you navigate to Journals or Global Landscape. This ensures a consistent analytical frame throughout your session.
Metrics & Methodology
How the platform’s performance scores are computed
insightSci derives a small set of performance indicators from your working dataset to help you compare papers and authors within the current research slice. All scores are computed in relation to the dataset assembled by your current search, so values cannot be compared directly across different sessions or queries.
Normalization
Each indicator is rescaled to the 0–1 interval using a logarithmic normalization, which prevents a small number of unusually high values from dominating the ranking:
norm(x) = log(1 + x) / log(1 + max(x))
Where x is the value for a given paper or author, and max(x) is the largest value observed in the current dataset slice.
Index — paper performance
The Index combines a paper’s citation rate per year with the SJR impact of its publication venue. Citation rate captures sustained scholarly attention, while SJR reflects journal prestige using SCImago Journal Rank data matched to OpenAlex venue records by ISSN.
Index = citation weight * norm(citations / year)
+ SJR weight * norm(SJR)The result lies between 0 and 1 and is relative to the active dataset. Two papers ranked by Index are only meaningfully comparable when they belong to the same search session.
SJR — journal impact
SJR means SCImago Journal Rank. It weights citations according to the prestige of the citing journals and is distributed by SCImago Journal & Country Rank.
The connection key is the journal ISSN. OpenAlex provides the journal ISSN for each work when available, and insightSci uses that ISSN to match the journal to the SJR file downloaded from SCImago. The same match provides SJR Best Quartile values.
SJR Best Quartile is divided into Q1, Q2, Q3, and Q4. Q1 represents the top quartile for the journal’s best-ranked SCImago subject category, followed by Q2, Q3, and Q4.
Open SCImago Journal RankCit./Yr. — citation rate per year
Cit./Yr. measures the average citation rate of papers where the author is listed first in the current slice, expressed as citations per year per paper. Because it is an average rather than a sum, it captures the typical impact of first-author work without rewarding sheer publication volume.
Cit./Yr.(author) =
mean over the author's first-author papers of (citations / years since publication)Network — collaborative reach
Network counts the number of distinct coauthors an author has across the current dataset. It is a structural indicator of collaborative reach: an author who appears alongside many different colleagues will score higher than one who consistently publishes with the same small group.
Network(author) = | { coauthors of author in this dataset } |SJR Best Quartile
What is SJR?
SJR (SCImago Journal Rank) is a journal prestige metric based on Scopus-indexed citation relationships. It weights citations according to the prestige of the citing journals, so a citation from a highly ranked journal contributes more than a citation from a lower-ranked source.
insightSci uses SJR as the journal impact component of the Index and exposes it under the product label SJR. The value is connected to OpenAlex data through the journal ISSN, which is visible in the downloaded SCImago ranking file.
SJR Best Quartile in insightSci
SJR Best Quartile groups journals into Q1, Q2, Q3, and Q4. Q1 is the strongest quartile, followed by Q2, Q3, and Q4. When a journal appears in multiple SCImago subject categories, insightSci stores the best available quartile for the ISSN.
Matching by ISSN
OpenAlex provides journal metadata for each work, including ISSN when available. insightSci uses that ISSN to connect each journal to the downloaded SCImago Journal Rank table. If either side lacks a usable ISSN, SJR and quartile fields may remain empty for that venue.
How to Cite insightSci
If you use insightSci 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). insightSci: Insights for Science. Universidade Federal do Espírito Santo. Available at: https://insightsci.com
Methodology statement template
Bibliometric data were collected using insightSci (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] publications from a total of [TOTAL] available works on OpenAlex. The dataset was analyzed for [geographic distribution / authorial influence / keyword clustering / publication venue assessment / etc.]. Journal SJR values and SJR Best Quartile classifications were matched by ISSN from SCImago Journal Rank to assess venue impact internationally.
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
For broad OpenAlex topic searches, insightSci displays up to 1,000 publications selected by article relevance and importance, then ranks them with the active Index/performance settings. The full match count remains available as context, so the working set is curated for responsive analysis rather than incomplete.
When you search a new topic, insightSci is not only showing links. It is building a research workspace with complete metadata for papers, authors, affiliations, abstracts, citations, keywords, journal details, languages, countries, and document types.
The analytical preview is designed to appear quickly. For broad topics with thousands or millions of matches, insightSci keeps the workspace bounded to the 1,000 most relevant OpenAlex results so the analysis remains responsive and consistent.
If you or another researcher has recently searched the same topic, the workspace can reuse stored Cloudflare data and the 30-day Supabase result cache, so repeated searches load much faster.
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 insightSci, you receive the most current data available from OpenAlex at that moment. Cached results reused within 30 days of an identical query reflect the data from the original search.
insightSci 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.
SJR data is matched to OpenAlex journals by ISSN. If a journal has no ISSN in OpenAlex, or if the ISSN is not present in the SCImago Journal Rank file used by insightSci, the SJR value and SJR Best Quartile may be unavailable. Missing SJR data does not mean the article is invalid; it means the venue could not be matched to the SJR table.
Yes. Each search creates a private session linked to your account. Your queries, datasets, and analysis results are not visible to other users.
Index is a paper-performance score normalized inside the current dataset. By default it combines 50% citation rate per year with 50% journal SJR, and the Weighting control can adjust that split. It uses log normalization so unusually large values do not dominate the ranking. Because the score is relative to the current session's dataset, Index values cannot be compared across different searches. See the Metrics & Methodology section for the full formula.
The Advanced Search panel does not currently offer a language pre-filter, but the Dataset and Library filters let you focus the working set by language after data collection.
insightSci 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
insightSci 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 insightSci and value user feedback. Feature suggestions from the research community directly shape our development roadmap.