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Evidence Dataset for a Systematic Review of Data Augmentation in Scientometrics and Scholarly Knowledge Networks

Year: 2026
Type: dataset
Abstract: This dataset supports a systematic review investigating the applications, methodological foundations, opportunities, and limitations of Data Augmentation (DA) in scientometrics and scholarly knowledge networks. The dataset comprises 43 curated studies published between 2018 and 2025, organized into 31 studies providing direct scholarly or scientometric evidence and 12 studies providing adjacent methodological evidence. The studies are classified into four categories: (A) explicit data augmentation in scholarly or scientometric contexts; (B) scientometric and network-based mechanisms; (C) scientometric data quality and conceptual boundaries; and (D) adjacent data augmentation methodologies. The Excel workbook documents bibliographic metadata, study classification, evidence mapping, methodological characteristics, and quality-appraisal information. It also includes supporting materials for assessing study selection, methodological transparency, and the relationship between research questions and the available evidence. The dataset distinguishes studies explicitly investigating data augmentation from related approaches such as network representation learning, link prediction, bibliographic data integration, and synthetic data generation. This distinction is essential to avoid overstating the empirical evidence for data augmentation in scientometric applications. The dataset is intended to support transparency, reproducibility, critical appraisal, and future methodological research on data augmentation in scientometrics. It represents a curated research-evidence dataset rather than a collection of newly generated synthetic records or experimental benchmark results.
DOI: 10.17632/cc8p8nhfnv
Language: English
Open Access status: green
OpenAlex ID: W7225426475
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