Yingjie Yang | Data Science | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Yingjie Yang – De Montfort University

Yingjie Yang
Affiliation De Montfort University
Country United Kingdom
Scopus ID 7409384730
Documents 236
Citations 5720
h-index 38
Subject Area Data Science
Event International Research Data Analysis Excellence & Awards
ORCID 0000-0003-4525-5624

Yingjie Yang is a researcher affiliated with De Montfort University whose scholarly profile is associated with Data Science and interdisciplinary research involving data-driven methods. The available Scopus indicators record 236 documents, 5720 citations and an h-index of 38, providing a quantitative basis for assessing publication activity and scholarly influence in relation to the Best Researcher Award. [1]

Abstract

This article presents an academic recognition profile of Yingjie Yang, affiliated with De Montfort University, for consideration under the Best Researcher Award. Available bibliometric indicators, including 236 documents, 5720 citations and an h-index of 38, are considered alongside research activity in Data Science and related scholarly contributions. [1]

Keywords

Best Researcher Award; Yingjie Yang; De Montfort University; Data Science; Research Data Analysis; Bibliometric Assessment; Scholarly Publications; Citation Impact; Research Excellence; Academic Recognition. [2]

Introduction

Academic recognition commonly considers research productivity, citation performance, publication continuity and contribution to a specialist field. Yingjie Yang’s available scholarly indicators provide a measurable profile for examining research achievement within Data Science. Such evaluation supports transparent comparison of documented academic output and influence using established bibliometric information. [3]

Research Profile

Yingjie Yang is affiliated with De Montfort University in the United Kingdom and is associated with research in Data Science. The available Scopus profile records 236 documents, 5720 citations and an h-index of 38. These indicators collectively describe sustained scholarly activity and a documented record of academic visibility. [1]

Research Contributions

The research profile demonstrates contributions represented through a substantial body of indexed scholarly documents. Within the Data Science context, such work contributes to the continuing development, application and evaluation of data-driven knowledge. The accumulated publication record also indicates engagement with research communication and dissemination across relevant academic channels. [1]

Publications

The available Scopus record lists 236 documents associated with the researcher profile, indicating sustained publication activity. Indexed publications provide an important basis for evaluating scholarly productivity because they document research dissemination and enable subsequent citation analysis. Specific publication details should be interpreted through the linked author profile and corresponding publisher records.[2]

Research Impact

The profile records 5720 citations and an h-index of 38, indicating that the published work has received measurable scholarly attention. Citation indicators are not complete measures of research quality, but they provide useful evidence of academic visibility and uptake. Their interpretation is strengthened when considered with disciplinary context and documented research outputs. [3]

Award Suitability

Based on the available publication and citation indicators, Yingjie Yang presents a documented academic profile relevant to consideration for the Best Researcher Award. The combination of publication volume, citation performance and an h-index of 38 provides objective evidence for assessment. Final recognition should remain subject to the event’s eligibility criteria and review process. [1]

Conclusion

The available research profile of Yingjie Yang reflects sustained scholarly publication activity and measurable citation impact in association with Data Science. With 236 documents, 5720 citations and an h-index of 38, the profile provides evidence suitable for structured academic evaluation. These documented indicators support informed consideration for research recognition. [1]

 References

  1. Predicting the number of care beds for older people by a novel grey Verhulst cosine self-memory model: two case studies of Jiangsu and Shanghai, China.
    https://link.springer.com/article/10.1186/s12877-026-07337-6
  2. Interpretable Temporal Graph Attention Network and Cross-Modal Fusion for Early Rumor Detection.
    https://www.researchgate.net/publication/405011816_Interpretable_Temporal_Graph_Attention_Network_and_Cross-Modal_Fusion_for_Early_Rumor_Detection
  3. A novel time-varying Wiener process for adaptive RUL prediction under multiple uncertainties
    https://www.researchgate.net/publication/401712852_A_novel_time-varying_Wiener_process_for_adaptive_RUL_prediction_under_multiple_uncertainties

Daniel Condurache | Augmented Analytics | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Daniel Condurache
Affiliation Technical University Of Iasi
Country Romania
Scopus ID 15841500000
Documents 91
Citations 749
h-index 16
Subject Area Augmented Analytics
Event Research Data Analysis Awards
ORCID 0000-0001-9287-8387

Daniel Condurache
Technical University Of Iasi, Romania

Daniel Condurache is affiliated with the Technical University Of Iasi and has contributed to research in augmented analytics and related computational disciplines. His scholarly output demonstrates sustained academic engagement through peer-reviewed publications, citation impact, and interdisciplinary collaborations that support innovation in data-driven research methodologies.[1]

Abstract

This article summarizes the academic profile of Daniel Condurache, highlighting measurable research achievements, publication activity, and scholarly influence. The profile reflects recognized contributions to augmented analytics and related computational research supported by bibliometric indicators.[2]

Keywords

Augmented Analytics, Data Analysis, Machine Intelligence, Computational Methods, Artificial Intelligence, Scientific Research, Research Metrics, Bibliometrics.

Introduction

Daniel Condurache has established an active academic career through multidisciplinary investigations that integrate analytical techniques with engineering applications. His publications demonstrate continued engagement with evolving research challenges and international scientific communication.[3]

Research Profile

With 91 indexed publications, 749 citations, and an h-index of 16, his academic record reflects consistent scholarly productivity. These indicators demonstrate sustained visibility within the international research community and continued citation recognition.[1]

Research Contributions

His research contributes to augmented analytics by combining computational methodologies with practical engineering solutions. These studies encourage improved analytical performance, efficient data interpretation, and broader interdisciplinary collaboration.[4]

Publications

The publication portfolio includes peer-reviewed journal articles and conference papers addressing computational intelligence, engineering analysis, and data-centric methodologies. These works collectively demonstrate sustained scientific productivity and knowledge dissemination.[5]

Research Impact

Citation performance and publication consistency indicate meaningful academic influence within relevant research communities. The documented metrics provide quantitative evidence supporting the significance and continued visibility of his scholarly contributions.[1]

Award Suitability

The documented publication record, citation impact, and interdisciplinary research activities align with the evaluation criteria commonly associated with the Best Researcher Award. His scholarly achievements illustrate consistent academic excellence and professional dedication.

Conclusion

Daniel Condurache’s research profile reflects continuous scholarly development supported by recognized publications and measurable academic impact. His sustained contributions to augmented analytics position him as a noteworthy researcher within contemporary scientific research.

References

  1. Elsevier. (n.d.). Scopus author details: Daniel Condurache, Author ID 15841500000.
    https://www.scopus.com/authid/detail.uri?authorId=15841500000
  2. ORCID. (n.d.). Researcher Identifier Record.
    https://orcid.org/0000-0001-9287-8387
  3. Condurache, D. (2026). A unified theory of generalized Bresse properties in higher-order kinematics of rigid body and multibody systems. Mechanism and Machine Theory. Advance online publication.
  4. Condurache, D., Cojocari, M., & Popa, I. (2025). Higher-order kinematics of planar rigid motion by Euclidean tensors and complex algebra: An overview. In Higher-Order Kinematics of Planar Rigid Motion by Euclidean Tensors and Complex Algebra
    https://link.springer.com/chapter/10.1007/978-3-031-87537-3_6
  5. Condurache, D., & Cojocari, M. (2025). Hyper-state of multibody systems and trident quaternions. In Volume 5: IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA); Mechanisms and Robotics Conference (MR).

Ludivine Beaud-Henry | Descriptive Analytics | Best Researcher Award

Published on by Research Data Analysis

Best Researcher Award

Ludivine Beaud-Henry
Affiliation CHU de Clermont-Ferrand
Country France
Documents 3
Citations 11
Subject Area Descriptive Analytics
Event Research Data Analysis Awards
ORCID 0009-0001-6270-1289

Ludivine Beaud-Henry
CHU de Clermont-Ferrand , France

Ludivine Beaud-Henry is affiliated with CHU de Clermont-Ferrand, France, where her scholarly activities contribute to healthcare research and descriptive analytics. Her published work reflects an evidence-based approach to clinical investigation, emphasizing systematic data interpretation and meaningful healthcare outcomes. The academic profile demonstrates an emerging research record supported by peer-reviewed publications and measurable citation impact.[1]

Abstract

This article summarizes the academic profile of Ludivine Beaud-Henry, highlighting contributions to descriptive analytics within healthcare research. The available publication record demonstrates involvement in clinical investigations supported by systematic data analysis, scientific reporting, and interdisciplinary collaboration. Citation performance and scholarly dissemination indicate a developing research portfolio aligned with evidence-based medical practice and responsible scientific communication.[2]

Keywords

Descriptive Analytics, Clinical Research, Healthcare Data, Medical Science, Evidence-Based Medicine, Scientific Publications, Data Interpretation, Hospital Research.

Introduction

Modern healthcare research increasingly relies on descriptive analytics to organize clinical observations into meaningful evidence for patient care and medical decision-making. Researchers contribute by interpreting data accurately, publishing validated findings, and supporting collaborative scientific advancement across healthcare institutions.[3]

Research Profile

The research profile reflects participation in peer-reviewed clinical studies with measurable scholarly visibility. Published articles and citation records demonstrate commitment to scientific quality, responsible research practices, and continuous academic development within descriptive healthcare analytics.[1]

Research Contributions

Research activities emphasize systematic evaluation of clinical information to improve evidence interpretation, strengthen diagnostic understanding, and support reliable healthcare decision-making through structured descriptive analytics. Collaborative investigations with healthcare professionals encourage interdisciplinary knowledge exchange, improve research quality, and promote comprehensive analysis of complex clinical datasets within hospital-based studies.

Publications

The available publication record includes three indexed research documents addressing clinical and healthcare-related topics. These publications demonstrate consistent participation in scientific dissemination while contributing measurable citation performance within the academic community.[4]

Research Impact

Current bibliometric indicators include three scholarly documents and eleven citations, reflecting emerging academic recognition. These metrics illustrate continuing visibility while supporting future opportunities for broader scientific influence and interdisciplinary collaboration.[2]

Award Suitability

The research profile aligns with evaluation principles commonly associated with research excellence awards, including scientific integrity, publication quality, collaboration, and contribution to healthcare knowledge through descriptive analytics. These characteristics support consideration for academic recognition programs.[5]

Conclusion

Ludivine Beaud-Henry has established an emerging academic profile through clinical research, peer-reviewed publications, and measurable citation performance. Continued scholarly activity and interdisciplinary collaboration are expected to strengthen future research impact and scientific contributions.

External Links

References

  1. ORCID. (n.d.). Ludivine Beaud-Henry ORCID record.
    https://orcid.org/0009-0001-6270-1289
  2. Beaud-Henry, L., Mulliez, A., Renault, A., Bouton, K., Avan, P., & Giraudet, F. (2026). Exploring electrode montages to optimize the non-invasive clinical recording of auditory evoked AP/Wave I. Clinical Neurophysiology Practice. Advance online publication.
    https://www.researchgate.net/profile/Ludivine-Beaud-Henry
  3. Giraudet, F., Beaud-Henry, L., Ismail, S., Marx, M., & Legois, Q. (2026). Electrocochleography: Techniques and practical considerations. Neurophysiologie Clinique. Advance online publication.
    https://www.researchgate.net/profile/Ludivine-Beaud-Henry
  4. Giraudet, F., Mulliez, A., de Resende, L. M., Beaud, L., Benichou, T., Brusseau, V., Tauveron, I., & Avan, P. (2022). Impaired auditory neural performance, another dimension of hearing loss in type-2 diabetes mellitus. Diabetes & Metabolism, 48(6), Article 101360.