SoniMet - A tool for sonifying and visualizing the performance of single researchers
本文介绍了一种名为SoniMet的工具，通过将单个研究者的出版和引用数据转化为声音和视觉形式来辅助分析，使用直接参数映射方法解决科研评价中的信息处理问题。
0 citationsRead paper
Selected work
Representative Papers
D3D-GEN: Robot-Aware Domain-Grounded Interactive 3D World Generation for Social Robotics
Existing simulation environments for social robots struggle to balance realism with simulatability and lack efficient methods for generating semantically plausible, diverse, and fully interactive 3D scenes. This work proposes D3D-GEN, a novel system that achieves, for the first time, fully automated generation of simulation environments driven solely by domain knowledge without relying on predefined 3D model libraries. By integrating domain-specific agents with a retrieval-augmented generation (RAG) pipeline, D3D-GEN automatically constructs a persistent semantic database and dynamically queries it to produce coherent floorplans and object arrangements. The system supports multiple domains—including residential, hospital, and office settings—and generates dozens of structurally varied, semantically consistent, and interactable 3D worlds per domain, seamlessly integrating with mainstream robotic simulators such as Isaac Sim and Gazebo.
0 citationsRead paper
Female participation in science in the past 125 years: An analysis of the Matilda effect over time
This study systematically investigates the Matilda effect—the systematic undervaluation of women’s contributions in science—tracing its global evolution from 1900 to the present. Leveraging a corpus of over 220 million academic publications spanning 1900–2025, and integrating gender inference algorithms with corresponding author identification, this work provides the first long-term, global-scale dynamic assessment of the phenomenon. Findings reveal that women’s participation in research has risen from approximately 10% in the 1910s to over 40% in recent years. While the Matilda effect was pervasive throughout the twentieth century, it has markedly attenuated over the past two decades: health sciences and Latin America now approach gender parity, whereas physical sciences and regions in Asia continue to exhibit pronounced disparities. The study thus elucidates historical trends and cross-regional, cross-disciplinary heterogeneity in the gender gap in scholarly leadership.
0 citationsRead paper
Judges matter more than papers in post-publication research assessment
This study addresses the substantial impact of reviewer subjectivity on the reliability of scholarly evaluations. Leveraging 239,521 peer review records from the H1 Connect platform, the authors employ multilevel linear modeling and variance decomposition to systematically quantify the dominant contribution of reviewer-related variability for the first time. The results reveal that reviewer-level effects account for 61% of the total variance in scores—far exceeding the combined influence of manuscript and journal factors (7%) and demographic or institutional biases such as author gender or affiliation (<1%). These findings demonstrate that “reviewer noise” constitutes the primary source of evaluation bias, prompting the authors to advocate for the implementation of “noise audits” in high-stakes academic assessments to enhance fairness and scientific rigor.
0 citationsRead paper
Paper self-citation: An unexplored phenomenon
Citation analysis implicitly assumes all citations are external; however, “paper-level self-citations”—where a paper cites itself—remain uncharacterized despite potential impacts on bibliometric indicators. Method: Leveraging Web of Science data, we systematically identify and quantify this phenomenon via metadata analysis, clustering, and distributional modeling. Contribution/Results: We detect 44,857 papers exhibiting paper-level self-citation. Three primary causes are identified: author-initiated self-citation in conclusions or appendices, publisher-driven promotional citations, and bibliographic indexing errors. Self-citations are non-random, exhibiting strong temporal accumulation and multidimensional heterogeneity across countries, journals, disciplines, and document types. This challenges the foundational assumption of exogenous citation in bibliometrics, demonstrates that paper-level self-citation is both prevalent and structurally patterned, and reveals systematic biases it introduces into metrics such as the h-index. Our findings provide empirical grounding for refining citation quality assessment and adjusting bibliometric indicators accordingly.
0 citationsRead paper
Recent publications
Latest Papers
SoniMet - A tool for sonifying and visualizing the performance of single researchers
本文介绍了一种名为SoniMet的工具，通过将单个研究者的出版和引用数据转化为声音和视觉形式来辅助分析，使用直接参数映射方法解决科研评价中的信息处理问题。
0 citationsRead paper
D3D-GEN: Robot-Aware Domain-Grounded Interactive 3D World Generation for Social Robotics
Existing simulation environments for social robots struggle to balance realism with simulatability and lack efficient methods for generating semantically plausible, diverse, and fully interactive 3D scenes. This work proposes D3D-GEN, a novel system that achieves, for the first time, fully automated generation of simulation environments driven solely by domain knowledge without relying on predefined 3D model libraries. By integrating domain-specific agents with a retrieval-augmented generation (RAG) pipeline, D3D-GEN automatically constructs a persistent semantic database and dynamically queries it to produce coherent floorplans and object arrangements. The system supports multiple domains—including residential, hospital, and office settings—and generates dozens of structurally varied, semantically consistent, and interactable 3D worlds per domain, seamlessly integrating with mainstream robotic simulators such as Isaac Sim and Gazebo.
0 citationsRead paper
Female participation in science in the past 125 years: An analysis of the Matilda effect over time
This study systematically investigates the Matilda effect—the systematic undervaluation of women’s contributions in science—tracing its global evolution from 1900 to the present. Leveraging a corpus of over 220 million academic publications spanning 1900–2025, and integrating gender inference algorithms with corresponding author identification, this work provides the first long-term, global-scale dynamic assessment of the phenomenon. Findings reveal that women’s participation in research has risen from approximately 10% in the 1910s to over 40% in recent years. While the Matilda effect was pervasive throughout the twentieth century, it has markedly attenuated over the past two decades: health sciences and Latin America now approach gender parity, whereas physical sciences and regions in Asia continue to exhibit pronounced disparities. The study thus elucidates historical trends and cross-regional, cross-disciplinary heterogeneity in the gender gap in scholarly leadership.
0 citationsRead paper
Judges matter more than papers in post-publication research assessment
This study addresses the substantial impact of reviewer subjectivity on the reliability of scholarly evaluations. Leveraging 239,521 peer review records from the H1 Connect platform, the authors employ multilevel linear modeling and variance decomposition to systematically quantify the dominant contribution of reviewer-related variability for the first time. The results reveal that reviewer-level effects account for 61% of the total variance in scores—far exceeding the combined influence of manuscript and journal factors (7%) and demographic or institutional biases such as author gender or affiliation (<1%). These findings demonstrate that “reviewer noise” constitutes the primary source of evaluation bias, prompting the authors to advocate for the implementation of “noise audits” in high-stakes academic assessments to enhance fairness and scientific rigor.
0 citationsRead paper
Paper self-citation: An unexplored phenomenon
Citation analysis implicitly assumes all citations are external; however, “paper-level self-citations”—where a paper cites itself—remain uncharacterized despite potential impacts on bibliometric indicators. Method: Leveraging Web of Science data, we systematically identify and quantify this phenomenon via metadata analysis, clustering, and distributional modeling. Contribution/Results: We detect 44,857 papers exhibiting paper-level self-citation. Three primary causes are identified: author-initiated self-citation in conclusions or appendices, publisher-driven promotional citations, and bibliographic indexing errors. Self-citations are non-random, exhibiting strong temporal accumulation and multidimensional heterogeneity across countries, journals, disciplines, and document types. This challenges the foundational assumption of exogenous citation in bibliometrics, demonstrates that paper-level self-citation is both prevalent and structurally patterned, and reveals systematic biases it introduces into metrics such as the h-index. Our findings provide empirical grounding for refining citation quality assessment and adjusting bibliometric indicators accordingly.
0 citationsRead paper