Data for study "Knowledge Evolution and AI Paradigm Shifts in Pedestrian Detection: A Bibliometric and Main Path Analysis"

--------------------------Basic information---------------------------1. Journal article: Knowledge Evolution and AI Paradigm Shifts in Pedestrian Detection: A Bibliometric and Main Path Analysis 2. DOI: 10.5281/zenodo.20842363 3. Contact information Name: Vo Thanh Kiet Institution: VSB – Technical University of Ostrava E-mail: [e-mail] ORCID: https://orcid.org/0009-0002-3278-8755 4. Dataset publication date: 2026-06-25 5. Place of publication: Ostrava, Czechia ------------------------------------------------------------------6. Dataset Description================================================================================ This dataset contains the bibliographic data and the Python analysis code used toproduce the results and figures presented in the article. The study analyzes thestructural evolution of pedestrian-detection research through a bibliometric andmain path analysis of publications indexed in the Scopus database. The bibliographic records were retrieved from Scopus using a query on the Title,Abstract and Keyword fields with the search terms "pedestrian detection" and"human detection", restricted to English-language publications over the period1990–2026. A total of 14,048 initial records were retrieved and processed through amulti-step screening pipeline (duplicate removal, document-type filtering, andtopical screening based on titles and abstracts), yielding the final analytic corpusof 5,926 publications that is provided in this dataset. Keywords, author names andaffiliations were normalized to ensure consistency. All processing, cleaning andanalysis were performed in Python. The accompanying notebook reads this curated corpus and reproduces the full analyticalpipeline: descriptive publication and citation statistics with growth-curve fitting,co-citation analysis, collaboration networks, keyword co-occurrence and evolution, andmain path analysis. When using the notebook, please follow the instructions provided in its header. IMPORTANT: Do not modify the file locations within the folder structure. Folder Structure-------------------------------------------------------------------------------- bibliometric_dataset (contains 1 subfolder, 1 Python notebook and 1 readme file)│├── data│ ││ └── clean_pedestrian_detection_dataset.01.csv (curated Scopus corpus, 5,926 records)│├── Review_Bibliometric_Ver_13.ipynb (Python analysis notebook)│└── readme.txt File Descriptions-------------------------------------------------------------------------------- • clean_pedestrian_detection_dataset.01.csv The curated analytic corpus of 5,926 pedestrian-detection publications retrieved from Scopus (Title / Abstract / Keywords query, 1990–2026), obtained after the multi-step screening described in the article (14,048 initial records → duplicate removal → document-type filtering → topical screening → 5,926 final records). Each row is one publication with its full Scopus metadata fields (authors, author IDs, title, year, source title, citations, author keywords, references, affiliations, etc.). This file is the input to the analysis notebook and contains the data underlying all results and figures in the article. • Review_Bibliometric_Ver_13.ipynb Python (Jupyter) notebook that reads the curated corpus and reproduces every figure and statistic reported in the article, organized in numbered sections (data loading and cleaning, publication and citation statistics, co-citation, collaboration networks, keyword analysis, and main path analysis). Figures are written to disk through the notebook's plotting helper function.-------------------------------------------------------------------------------- 7. Funding: This work was supported in part by the European Regional Development Fund under the project Research Platform for Digital Transformation and Society 5.0 CZ.02.01.01/00/23_021/0012599 within the Jan Amos Komensky Operational Program. This work was supported in part by the Ministry of Education of the Czech Republic (Project No. SP2026/012, SP2026/075).