記事の特性を調べるために傾斜グラフで予想外の異常なデータパターン: 文献数学のバーストグラフに別れ
Sher-Wei Lim1,2,3, Willy Chou4,3, Julie Chi Chow5,6
1Department of Neurosurgery, Chi-Mei Medical Center, Chiali, Tainan, Taiwan.
Medicine
|September 3, 2025
まとめ
ビジュアライゼーションは改善する必要がある. この研究では,よりよい洞察を得るために,情報量少ないバーストグラフの代わりに,記事のメタデータにおける予期せぬ異常データパターン (UADP) を強調するために,傾斜グラフを導入しています.
科学分野:
- 図書館計数と科学計数
- 情報科学
- データ可視化
背景:
- CiteSpaceのような伝統的な文献測定ツールには,明確性が欠けていて,限られた洞察力があります.
- 記事のメタデータを分析し,重要なデータパターンを特定するための強化された方法が必要です.
研究 の 目的:
- 予期せぬ偏差データパターン (UADP) を強調して,図鑑データを視覚化するために傾斜グラフの使用を提案し,検証する.
- 図解学分析における伝統的なバーストグラフを,より情報的な斜率グラフに置き換える.
主な方法:
- Web of Science Core Collectionの"ヘリオン"の26,555件の記事からのメタデータ分析
- 斜面グラフ上のUADPを識別するためにRaschモデルを適用する.
- パフォーマンス分析,概要報告,ビジュアル検証モデルを使用します.
主要な成果:
- 傾斜グラフのUADPで特定された"ヘリオン"への研究貢献は中国が主導しています (アウトフィット平均平方誤差=5.28).
- Covenant University (ナイジェリア) もUADPを示した (outfit mean square error = 2.22).
- キーワード"PERFORMANCE"は典型的なデータパターンを示しています.
結論:
- UADPを使用した傾きグラフは,ビビロメトリックにおける伝統的なバーストグラフよりも価値のある洞察を提供します.
- 論文の特徴と研究動向をより深く理解するために,将来の文献測定分析はUADPを組み込むべきである.
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