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REVIEW 4 major objections 8 minor 24 references

MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis

T0 review · 4 major / 8 minor · reviewed 2026-08-07 · deepseek-v4-flash

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Pith's one-line read MetaInfoSci claims to be the first web tool that combines bibliometric, network, and AI-summary analysis in one place.

desk verdict A genuine-looking web tool paper whose central 'nothing else does this' claim is contradicted by its own description of pyBibX; send to review only with a demand for a fair comparison. read the letter →

arxiv 2506.09056 v1 pith:PD5KR4O7 submitted 2025-06-04 cs.DL physics.data-an

Kiran Sharmaa , Parul Khurana , Ziya Uddina This is my paper
classification cs.DLphysics.data-an
keywords BibliometricsScientometricsNetworkanalysisCustomvisualizationAI-enabledsummaryScholarlydataCross-databasemergingWebtool
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper tries to establish that a new web platform, MetaInfoSci, closes a practical gap in scholarly literature analysis: researchers today must switch among separate tools to do bibliometrics, network analysis, custom visualization, and AI summarization. The authors argue that no single existing platform offers comprehensive features from both bibliometric and network domains, and that most tools lack customizable visualizations, cross-database merging, and AI-assisted interpretation. If the claim holds, MetaInfoSci would reduce the need for programming skills and multi-software workflows, making advanced scientometric analysis accessible to researchers, administrators, and policy analysts. The paper demonstrates the platform on 890 Scopus records from one university and describes its core modules, customization options, and AI-generated summaries.

What carries the argument

The platform's architecture is a modular analysis pipeline: four named modules—BibTrail for bibliometrics, SciTrace for scientometrics, ColabriX for collaboration and network analysis, and ThemantiX for thematic analysis—operate on merged, deduplicated bibliographic data. The load-bearing design element is the integration layer: a common data-mapping step that accepts files from databases like Scopus and Web of Science, plus a chart-control panel that lets users change plot type, colors, scale, and labels and then generate an AI textual summary of each visualization. That coupling of data integration, user-controlled visualization, and AI explanation is what the paper claims no existing tool provides.

What would settle it

Run a feature-matrix check on the current releases of pyBibX, Biblioshiny, VOSviewer, CitNetExplorer, and SciMAT to see whether any already lets a non-programmer upload Scopus and Web of Science files, build a collaboration network, customize a chart, and receive an AI summary without leaving the tool; if such a workflow exists, the paper's central gap claim is falsified.

Watch

Extended reading notes

Core claim

The paper's central claim is that MetaInfoSci is a web-based platform that, for the first time, integrates bibliometric analysis, scientometric analysis, collaboration-network analysis, thematic analysis, customizable visualizations, cross-database data merging, and AI-generated summaries into a single user interface. It presents this integration as filling a gap left by tools such as Biblioshiny, VOSviewer, pyBibX, CiteSpace, and others, which each cover only part of the workflow. The authors demonstrate the platform on 890 Scopus records from BML Munjal University and describe the pipeline: upload, field mapping, cleaning, module-wise analysis, customizable plotting, and AI summary generation.

Load-bearing premise

The whole motivation rests on the comparison table in Section 2, which surveys ten tools and asserts none combines AI summaries with network analysis; that assertion is not backed by a systematic, checked evaluation of every tool's current features.

Editorial extensions

If this is right

  • Researchers can upload files from Scopus or Web of Science, merge and deduplicate them automatically, and run bibliometric, collaboration-network, and thematic analyses in a single session without writing code.
  • Every result plot can be restyled through a control panel—chart type, colors, labels, scale, orientation, year window, and legend—and exported as PNG, JPEG, or CSV.
  • Journal quartile analysis is performed by matching ISSNs against a Scimago master file, and author gender is estimated by AI models from names and country information.
  • Collaboration networks support degree, betweenness, closeness, and eigenvector centrality, giant-component extraction, and community detection by Girvan-Newman or modularity-based methods.
  • Each visualization comes with an AI-generated textual summary, so users can interpret trends without deep expertise in bibliometrics.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • An implication the authors leave implicit: if the upload-map-analyze-summarize pipeline proves reliable, it could be adapted to other structured corpora—patent filings, clinical trial records, or preprint repositories—where the same need for merging, network views, and plain-language summaries arises.
  • A natural extension not developed in the paper is conversational querying of the underlying dataset, letting users ask follow-up questions in natural language rather than only receiving static AI summaries of each chart.
  • The single-university demonstration of 890 records leaves scalability as an open question; a test on a multi-million-record corpus would show whether the integrated pipeline and network algorithms hold up outside the demo setting.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 8 minor

Summary. The paper presents MetaInfoSci, a web-based platform that aims to unify bibliometric, scientometric, and network analyses with customizable visualizations, cross-database data merging, tailored queries, and AI-generated summaries of results. The authors motivate the tool by claiming that no existing single platform combines these capabilities, and they support this claim with a comparison of ten tools in Table 1. The methodology section restates standard centrality measures (degree, betweenness, closeness, eigenvector) and describes four analytic modules (BibTrail, SciTrace, ColabriX, ThemantiX). A demonstration on 890 Scopus records from one university is provided as an illustration. The paper also enumerates potential user groups and benefits.

Significance. If the novelty and completeness claims were established, MetaInfoSci would address a genuine practical gap: researchers currently switch among VOSviewer, Biblioshiny, CiteSpace, and similar tools, often without AI-assisted interpretation. The paper has real strengths: it reports a concrete, accessible web tool with a public URL; it gives a clear end-to-end workflow (upload, map, merge, analyze, visualize, summarize); the centrality definitions in Section 3 are textbook-correct; and the demonstration dataset is concrete and reproducible. The central falsifiable claim ('currently no single platform that integrates the full range of features from both domains', Abstract and Section 2) is checkable, which is a virtue. However, as submitted, that claim is not established: Table 1 is selective and internally inconsistent, no systematic feature matrix is provided, and the demonstration section is not a validation of correctness or usefulness. The contribution could still stand as a no-code integration layer, but only if the claims are narrowed and the evidence is strengthened.

major comments (4)
  1. [Section 2 / Table 1] The central novelty claim is internally contradicted by the manuscript's own description of pyBibX. The Abstract and Section 2 assert that existing bibliometric software lacks 'AI summary of results' and that no single platform combines bibliometric and network analysis. Yet Section 2.1 credits pyBibX with 'topic modeling and text summarization made possible by BERT and ChatGPT', the ability to 'study entire networks such as those for citations and collaboration', and custom queries — exactly the differentiators claimed for MetaInfoSci. Table 1 nonetheless categorizes pyBibX under 'Bibliometric analysis only' and lists only 'Machine learning integration' as a feature. This is a load-bearing inconsistency: it directly undermines the stated gap that motivates the paper. The authors must either correct Table 1 to reflect pyBibX's documented capabilities and revise the gap claim, or substantiate the claim with a systematic feature-matrix survey of a defined tool set with explicit inclusion criteria.
  2. [Section 2 / Table 1] The comparative survey is not systematic enough to support the universal claim that 'the tools that currently exist ... usually don't link together and rely on AI'. Ten tools are selected without stated inclusion or exclusion criteria, and the table does not contain a structured feature matrix (e.g., columns for AI summarization, network analysis, cross-database merging, GUI availability, and customization). Several widely used tools in the bibliometric ecosystem (e.g., Bibliometrix itself, Litmaps, Connected Papers, OpenAlex-based interfaces, and newer AI-assisted tools) are absent. Because the novelty claim is the paper's central motivation and contribution, the comparison must be made complete and transparent, or the claim must be restricted to what the evidence supports (for instance, a narrower claim about free web-based GUIs).
  3. [Section 5] The 'Results Demonstration' is not a validation. It reports a single dataset of 890 Scopus records from one institution and shows screenshots of outputs, but there is no evaluation of whether the bibliometric indicators, centrality values, or AI-generated summaries are correct. To support the abstract's promise of 'automated, AI-driven summaries of analytical results', the paper should at minimum specify the AI model and prompting strategy behind the summaries (Section 4.4 gives no such detail) and provide evidence of summary accuracy or usefulness. A comparison of the computed centrality and bibliometric outputs against a reference implementation (e.g., Bibliometrix or NetworkX applied to the same data) would also strengthen confidence in the tool's correctness.
  4. [Sections 3 and 4] The manuscript gives insufficient technical detail to assess reproducibility or feasibility. The data-merging and deduplication rules are described only as 'automatically merges, deduplicates, and maps fields'; the gender analysis (Section 3.2) is attributed to unspecified 'AI-based prediction models'; and the AI-summary feature (Section 4.4) is described only as an 'AI-generated explanatory summary'. Without details on the algorithms, the underlying AI services or models, and the system architecture, the evidence for the tool's behavior is entirely self-reported through screenshots. Adding a section on implementation architecture and the specific algorithms/models used would be necessary for a tool paper.
minor comments (8)
  1. [Figure captions (Figures 7, 8, 9, 10)] Figures 7, 8, 9, and 10 all carry the caption 'Upload file page', which is clearly incorrect for Figures 8, 9, and 10; each figure should have a descriptive caption that matches its content.
  2. [Equation (3)] Equation (3) is typeset as 1∑ over d(v,t); the division should be written as C_C(v) = 1 / Σ_t d(v,t) for readability.
  3. [Table 4] Table 4 contains misspellings: 'Girven-Newman' should be 'Girvan-Newman' and 'Liden' should be 'Leiden' (or the intended algorithm should be named explicitly).
  4. [Table 1] The section header 'Citations Analysis + Bibliometric Networks' should be 'Citation Analysis + Bibliometric Networks' for grammatical consistency.
  5. [Section 2.1] The prose in Section 2.1 is informal and inconsistent with journal style ('you can't merge your databases with AI-derived information using it', 'you have to do the work manually', 'it doesn't allow for many custom queries'); this should be rewritten in a neutral, technical register.
  6. [Table 6] Table 6 uses unclear notation: 'BG-W' and 'BG-T' are not defined, 'Color pallette' is a typo for 'palette', and 'Count Slider 1 to max In bar body, display top count' is grammatically incomplete.
  7. [Section 3.2] The gender-analysis subsection reports prediction of 'the likely gender' of authors via AI models without discussing accuracy, bias, or ethical limitations; given the societal sensitivity of gender inference, at least a caveat and a reference to the underlying model would be expected.
  8. [Conflict of interest statement] The conflict-of-interest statement reads 'The author declares no conflict of interest' while the manuscript has three authors; this should be pluralized or corrected.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the paper is a tool description whose only load-bearing claims are market-gap assertions, not results derived from their own inputs.

full rationale

MetaInfoSci is a software/tool presentation, not a derivation paper. Equations (1)-(4) are textbook definitions of degree, betweenness, closeness, and eigenvector centrality, and they are not used to predict anything from fitted parameters. The central novelty claim — 'there is currently no single platform that offers comprehensive features from both domains in one place' (Abstract and Section 2) — is supported by Table 1, a comparative survey, and by a citation to the authors' prior work for the limitation on merging databases (Khurana et al., 2022). That is an evidentiary claim about the software landscape, not a conclusion that is equivalent to its own premise by construction. Even if Table 1 is internally inconsistent — pyBibX is categorized under 'Bibliometric analysis only' while the text credits it with citation/collaboration networks, custom queries, and AI summaries — that is a correctness or completeness problem, not circular reasoning. The self-citations in the introduction (Sharma and Khurana, 2021; Khurana and Sharma, 2024) provide background context and are not invoked as a uniqueness theorem or as the proof of the tool's capabilities. The 890-record demonstration is illustrative rather than a fitted prediction. No step in the paper reduces to its own input, so the circularity score is 0.

Assumptions & free parameters 0 free parameters · 5 assumptions · 0 invented entities

The paper introduces no new free parameters or theoretical entities. Its analytical claims rest on standard math and on unvalidated domain assumptions about external data sources and AI components. The novelty lies in software integration, not in modeling.

assumptions (5)
  • standard math Standard definitions of degree, betweenness, closeness, and eigenvector centrality
    Equations 1-4 in Section 3 use textbook definitions; no derivation is given and none is needed.
  • domain assumption Scimago journal quartile data reliably maps journals to quartiles via ISSN
    Section 3.1 assumes the Scimago master file provides correct quartile assignments used in journal analysis.
  • domain assumption AI-based gender prediction from author names and country information is accurate enough for gender analysis
    Section 3.2 uses AI-based prediction models without reporting accuracy or validation, yet presents gender proportions as analytical results.
  • domain assumption LDA topic modeling and co-word analysis on titles and abstracts reveal meaningful thematic structure
    Table 5 lists LDA and clustering as thematic analysis features without validating model choice or output quality.
  • domain assumption The web platform itself remains operational and implements the described features as of the publication
    The paper relies on screenshots and a live URL rather than a versioned release to support the existence of the tool.

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Cite this review

Pith. "Pith review of MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis." pith.science (2026). https://pith.science/paper/PD5KR4O7

@misc{pith2026250609056,
  author       = {Pith},
  title        = {Pith review of: MetaInfoSci: An Integrated Web Tool for Scholarly Data Analysis},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PD5KR4O7}},
  note         = {Machine review of arXiv:2506.09056}
}
read the original abstract

The exponential increase in academic publications has made it increasingly difficult for researchers to remain up to date and systematically synthesize knowledge scattered across vast and fragmented research domains. Literature reviews, particularly those supported by bibliometric methods, have become essential in organizing prior findings and guiding future research directions. While numerous tools exist for bibliometric analysis and network science, there is currently no single platform that integrates the full range of features from both domains. Researchers are often required to navigate multiple software environments, many of which lack customizable visualizations, cross-database integration, and AI-assisted result summarization. Addressing these limitations, this study introduces MetaInfoSci at www.metainfosci.com, a comprehensive, web-based platform designed to unify bibliometric, scientometric, and network analytical capabilities. The platform supports tailored query design, merges data from diverse sources, enables rich and adaptable visual outputs, and provides automated, AI-driven summaries of analytical results. This integrated approach aims to enhance the accessibility, efficiency, and depth of scientific literature analysis for scholars across disciplines.

Figures

Figures reproduced from arXiv: 2506.09056 by the authors.

Figure 1
Figure 1. MetaInfoSci home page. 2. Literature Review [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. MetaInfoSci work flow. 3.3.2. Betweenness centrality This quantifies the number of times a node acted as a bridge in the shortest path between two nodes. It is crucial for understanding which nodes are responsible for controlling the information flow between nodes and can be calculated as (Eq 2) 𝐶𝐵(𝑣) = ∑ 𝑠≠𝑣≠𝑡 𝜎𝑠𝑡(𝑣) 𝜎𝑠𝑡 (2) where 𝜎𝑠𝑡 is the total number of shortest paths from node 𝑠 to node 𝑡 and 𝜎𝑠𝑡(𝑣) is the num… view at source ↗
Figure 3
Figure 3. Upload file page [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (16 more)
Figure 4
Figure 4. Figure 4: Mapping data fields to required format. 4.2. Data overview & custom filtering • Displays the uploaded dataset with a summary of key statistics (e.g., number of records, authors, institutions, countries). • Offers filtering tools to refine datasets based on user-defined…
Figure 5
Figure 5. Figure 5: After mapping top 10 data entries are displayed [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 6
Figure 6. Figure 6: After mapping top 10 data entries are displayed. 4.3. Comprehensive analytical capabilities Supports a wide range of analyses including: • Bibliometric analysis (e.g., citations, publications, journal impact) • Scientometric analysis (e.g., research trends, gender anal…
Figure 7
Figure 7. Figure 7: Upload file page [PITH_FULL_IMAGE:figures/full_fig_p008_7.png]
Figure 8
Figure 8. Figure 8: Upload file page [PITH_FULL_IMAGE:figures/full_fig_p008_8.png]
Figure 9
Figure 9. Figure 9: Upload file page. Page 8 of 17 [PITH_FULL_IMAGE:figures/full_fig_p008_9.png]
Figure 10
Figure 10. Figure 10: Upload data file page [PITH_FULL_IMAGE:figures/full_fig_p011_10.png]
Figure 11
Figure 11. Figure 11: Plot customization includes (i) chart control panel, (ii) customization panel, and (iii) download options. (iv) generate AI summary. 5. Results Demonstration To demonstrate the results, we used a sample dataset from Scopus for a university named BML Munjal University.…
Figure 12
Figure 12. Figure 12: Growth and impact analysis: (left) Total number of papers published per year. (right) Total citations received per year [PITH_FULL_IMAGE:figures/full_fig_p012_12.png]
Figure 13
Figure 13. Figure 13: Growth and impact analysis: (left) Total number of papers published in two years cycle. (right) Total citations received in two years cycle [PITH_FULL_IMAGE:figures/full_fig_p012_13.png]
Figure 14
Figure 14. Figure 14: Journal and quartile analysis: (left) Top 10 journals as per number of publications. (right) Quartile wise paper distribution. Page 12 of 17 [PITH_FULL_IMAGE:figures/full_fig_p012_14.png]
Figure 15
Figure 15. Figure 15: Journal and quartile analysis: (left) Year wise paper distribution in each quartile. (right) Top 10 journals as per number of publications in quartile one (Q1) [PITH_FULL_IMAGE:figures/full_fig_p013_15.png]
Figure 16
Figure 16. Figure 16: Author’s analysis: (left) Top 10 authors as per number of publications. (right) Team wise paper distribution [PITH_FULL_IMAGE:figures/full_fig_p013_16.png]
Figure 17
Figure 17. Figure 17: Degree centrality of author’s collaboration network: (left) Giant connected component of authors collaboration network with degree centrality. (right) Probability distribution of degree centrality of each authors in giant connected component. Page 13 of 17 [PITH_FULL…
Figure 18
Figure 18. Figure 18: Country analysis: (left) Top 10 countries as per number of publications. (right) Country collaboration network with degree centrality [PITH_FULL_IMAGE:figures/full_fig_p016_18.png]
Figure 19
Figure 19. Figure 19: Keywords analysis: Word cloud of keywords. References Altay, E., Balım, A.G., 2023. Vosviewer application within the scope of bibliometric analysis: Literature review on the use of virtual laboratories in education. Education Mind 3, 9. Aria, M., Cuccurullo, C., 2017.…

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Works this paper leans on

24 extracted references · 23 canonical work pages

  1. [1]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 global.max substring 't := if while FUNCTION word.in bbl.in ":" * " " * FUNCTION f...

  2. [2]

    , author Balım, A.G

    author Altay, E. , author Balım, A.G. , year 2023 . title Vosviewer application within the scope of bibliometric analysis: Literature review on the use of virtual laboratories in education . journal Education Mind volume 3 , pages 9

    work page 2023

  3. [3]

    , author Cuccurullo, C

    author Aria, M. , author Cuccurullo, C. , year 2017 . title bibliometrix: An r-tool for comprehensive science mapping analysis . journal Journal of informetrics volume 11 , pages 959--975

    work page 2017

  4. [4]

    , year 2011

    author B \"o rner, K. , year 2011 . title Science of science studies: Sci2 tool . journal Communications of the ACM volume 54 , pages 60--69

    work page 2011

  5. [5]

    , author Denyer, D

    author Briner, R.B. , author Denyer, D. , year 2012 . title Systematic review and evidence synthesis as a practice and scholarship tool

    work page 2012

  6. [6]

    , year 2004

    author Chen, C. , year 2004 . title Searching for intellectual turning points: progressive knowledge domain visualization . journal Proceedings of the National Academy of Sciences volume 101 , pages 5303--5310

    work page 2004

  7. [7]

    , et al., year 2016

    author Chen, C. , et al., year 2016 . title CiteSpace: a practical guide for mapping scientific literature . publisher Nova Science Publishers Hauppauge, NY, USA

    work page 2016

  8. [8]

    , author L \'o pez-Herrera, A.G

    author Cobo, M.J. , author L \'o pez-Herrera, A.G. , author Herrera-Viedma, E. , author Herrera, F. , year 2012 . title Scimat: A new science mapping analysis software tool . journal Journal of the American Society for information Science and Technology volume 63 , pages 1609--1630

    work page 2012

Show all 24 references
  1. [9]

    , author Waaijer, C.J

    author Guler, A.T. , author Waaijer, C.J. , author Palmblad, M. , year 2016 . title Scientific workflows for bibliometrics . journal Scientometrics volume 107 , pages 385--398

    2016

  2. [10]

    , year 2010

    author Harzing, A.W. , year 2010 . title The publish or perish book . publisher Tarma Software Research Pty Limited Melbourne

    2010

  3. [11]

    , author Ganesan, G

    author Khurana, P. , author Ganesan, G. , author Kumar, G. , author Sharma, K. , year 2022 . title A comparative analysis of unified informetrics with scopus and web of science . journal Journal of Scientometric Research volume 11 , pages 146--154

    2022

  4. [12]

    , author Sharma, K

    author Khurana, P. , author Sharma, K. , year 2024 . title Growth and impact of blockchain scientific collaboration network: A bibliometric analysis . journal Multimedia Tools and Applications volume 83 , pages 44979--44999

    2024

  5. [13]

    , author Herrera-Viedma, E

    author Moral-Muñoz, J.A. , author Herrera-Viedma, E. , author Santisteban-Espejo, A. , author Cobo, M.J. , year 2020 . title Software tools for conducting bibliometric analysis in science: An up-to-date review . journal El Profesional de la Información volume 29

    2020

  6. [14]

    , author Basilio, M.P

    author Pereira, V. , author Basilio, M.P. , author Santos, C.H.T. , year 2023 . title pybibx—a python library for bibliometric and scientometric analysis powered with artificial intelligence tools . journal arXiv preprint arXiv:2304.14516

    2023 arXiv

  7. [15]

    , author Basilio, M.P

    author Pereira, V. , author Basilio, M.P. , author Santos, C.H.T. , year 2025 . title Pybibx--a python library for bibliometric and scientometric analysis powered with artificial intelligence tools . journal Data Technologies and Applications

    2025

  8. [16]

    , author Danell, R

    author Persson, O. , author Danell, R. , author Schneider, J.W. , year 2009 . title How to use bibexcel for various types of bibliometric analysis . journal Celebrating scholarly communication studies: A Festschrift for Olle Persson at his 60th Birthday volume 5 , pages 9--24

    2009

  9. [17]

    , author Barab \'a si, A.L

    author P \'o sfai, M. , author Barab \'a si, A.L. , year 2016 . title Network science . volume volume 3 . publisher Citeseer

    2016

  10. [18]

    , year 2012

    author Rousseau, D.M. , year 2012 . title The Oxford handbook of evidence-based management . publisher Oxford University Press

    2012

  11. [19]

    , author Ram \' rez-Gonz \'a lez, G

    author Ruiz-Rosero, J. , author Ram \' rez-Gonz \'a lez, G. , author Viveros-Delgado, J. , year 2019 . title Software survey: Scientopy, a scientometric tool for topics trend analysis in scientific publications . journal Scientometrics volume 121 , pages 1165--1188

    2019

  12. [20]

    , author Khurana, P

    author Sharma, K. , author Khurana, P. , year 2021 . title Growth and dynamics of econophysics: a bibliometric and network analysis . journal Scientometrics volume 126 , pages 4417--4436

    2021

  13. [21]

    , author Waltman, L

    author Van Eck, N. , author Waltman, L. , year 2009 . title Software survey: Vosviewer, a computer program for bibliometric mapping . journal scientometrics volume 84 , pages 523--538

    2009

  14. [22]

    , author Waltman, L

    author Van Eck, N.J. , author Waltman, L. , year 2014 . title Citnetexplorer: A new software tool for analyzing and visualizing citation networks . journal Journal of informetrics volume 8 , pages 802--823

    2014

  15. [23]

    , author Waltman, L

    author Van Eck, N.J. , author Waltman, L. , year 2017 . title Citation-based clustering of publications using citnetexplorer and vosviewer . journal Scientometrics volume 111 , pages 1053--1070

    2017

  16. [24]

    , author Gao, J

    author Yang, F. , author Gao, J. , author Kan, X. , year 2024 . title Biblioshiny-r application on bioinformatics education research (2004--2023) . journal Curriculum and Teaching Methodology volume 7 , pages 192--199

    2024

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Reviewed August 7, 2026 · model on record in the stance chip above.