Evidence map›Paper›PMID 42318070›Full record

ArticleMethodsX2026

A reproducible bibliometric-content analysis workflow for mapping research fields in computer science.

Thanh-Cong Truong

Abstract read
In one paragraph

Article in MethodsX, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Thanh-Cong TruongFaculty of Data Science, University of Finance-Marketing, Ho Chi Minh City, Viet Nam.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Computer science literature indexed in Scopus and Web of Science has grown at double-digit annual rates, making comprehensive manual synthesis infeasible for individual researchers. Bibliometric workflows partially address this problem but rarely yield the interpretive depth needed to characterize a field's accomplishments or gaps. This paper introduces a two-phase reproducible workflow integrating bibliometric science mapping (Phase 1) with structured thematic content analysis (Phase 2), implemented in R using the bibliometrix package. Phase 1 clusters publications by keyword co-occurrence; these clusters serve as the sampling frame for purposive selection of representative papers, which undergo deductive-inductive thematic coding in Phase 2. Thematic coding of this type typically requires dual-coder reliability checks; a test-retest procedure replaces that requirement, maintaining

Indexed as

Bibliometric analysisBibliometrixComputer scienceContent analysisResearch methodologyScience mapping

Identifiers

PMID42318070
PMCPMC13273891

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.