Data supplement: Search strings, screening log, and coding tables for a narrative review and bibliometric mapping of AI governance in agriculture (2023-2026)

This dataset supports the article "Governing Algorithms in the Field: A Narrative Review and Bibliometric Mapping of Inclusive Data Governance, Algorithmic Accountability, and Explainability in Agricultural AI Policy" (Muhamad Ridwan, Universitas Hasanuddin, Indonesia). The study combines a bibliometric mapping of 2,620 Scopus-indexed articles and reviews (2023–2026) with a narrative synthesis of 40 articles on inclusive data governance, algorithmic accountability, and explainability in agricultural AI. Searches were run in Scopus on 21 October 2026. Contents: - 00_README.txt: overview of files, settings, and licence. - 01_Scopus_Search_Strings.txt: the three Scopus Advanced Search strings (String 1: 2,641 documents; String 2: 2,620 documents, bibliometric corpus; String 3: 667 articles, narrative synthesis). - 02_Screening_Log_667_records.csv: title and abstract screening of 667 records, with decision, exclusion reason, and relevance score. - 03_Fulltext_Assessment_48_articles.csv: full-text assessment of 48 articles, with eligibility decision (40 included) and the research questions addressed. - 04_Coding_Tables_RQ2-RQ4.xlsx: cross-study comparison tables used in the narrative synthesis. - 05_Selection_Criteria_Coding_Scheme.xlsx: selection-flow counts, inclusion and exclusion criteria, and the coding scheme. - Figure1_bibliometric_workflow.png and Figure2_selection_flow.png: the bibliometric workflow and the article selection flow. The full Scopus export is not redistributed because of the database licence. It can be regenerated with the search strings by users with Scopus access; results may differ slightly because Scopus is updated continuously. Bibliometric maps were produced with VOSviewer 1.6.21 and Tableau Desktop. Elicit was used to assist full-text data extraction; all records were screened by the author.