Creating scientific publications is a complex process, typically composed of a number of different activities, such as designing the experiments, data preparation, programming software and writing and editing the manuscript. The information about the contributions of individual authors of a paper is important in the context of assessing authors' scientific achievements. Some publications in biomedical disciplines contain a description of authors' roles in the form of a short section written in natural language, typically entitled "Authors' contributions". In this paper, we present an analysis of roles commonly appearing in the content of these sections, and propose an algorithm for automatic extraction of authors' roles from natural language text in scientific publications. During the first part of the study, we used clustering techniques, as well as Open Information Extraction (OpenIE), to semi-automatically discover the most popular roles within a corpus of 2,000 contributions sections obtained from PubMed Central resources. The roles discovered by our approach include: experimenting (1,743 instances, 17% of the entire role set within the corpus), analysis (1,343, 16%), study design (1,132, 13%), interpretation (879, 10%), conceptualization (865, 10%), paper reading (823, 10%), paper writing (724, 8%), paper review (501, 6%), paper drafting (351, 4%), coordination (319, 4%), data collection (76, 1%), paper review (41, 0.5%) and literature review (41, 0.5%). Discovered roles were then used to automatically build a training set for the supervised role extractor, based on Naive Bayes algorithm. According to the evaluation we performed, the proposed role extraction algorithm is able to extract the roles from the text with precision 0.71, recall 0.49 and F1 0.58.
SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships
Nguyen Tat Thanh University（阮爱国大学）；Yeungnam University（岭南大学）；Information Technology College（信息技术学院）；University of Information Technology（信息技术大学）；Chung-Ang University（中央大学）
F1000 recommendations as a new data source for research evaluation: A comparison with citations
F1000是生物和医学研究的出版后同行评议服务。F1000旨在推荐生物医学文献中的重要出版物，从这个角度来看，F1000可能是一个有趣的研究评估工具。通过将完整的F1000建议数据库与Web of Science书目数据库相连接，我们能够对F1000建议和引文进行全面的比较。我们发现，生物医学文献中约2%的出版物至少获得一项F1000推荐。推荐出版物平均收到1.30条推荐，90%以上的推荐是在出版物出版后半年内给出的。事实证明，F1000推荐和引用之间存在明显的相关性。然而，这种相关性相对较弱，至少弱于期刊影响力和引用之间的相关性。需要进行更多的研究，以确定在评估出版物的影响方面，建议和引用之间存在差异的主要原因。
Delving into LLM-assisted writing in biomedical publications through excess vocabulary
Hertie Institute for AI in Brain Health, University of Tübingen, Germany（人工智能与脑健康赫尔蒂研究所、图宾根大学，德国）；Northwestern University, Evanston, Illinois, USA（西北大学，埃文斯顿，伊利诺伊州，美国）
Deep forecasting of translational impact in medical research
UCL Queen Square Institute of Neurology, University College London（伦敦大学学院皇后广场神经病学研究所）；NIHR University College London Hospitals Biomedical Research Centre（英国国家健康研究所伦敦大学学院医院生物医学研究中心）；Wellcome Data Labs, Wellcome Trust（惠康基金会数据实验室）；UCL Institute of Cardiovascular Sciences, University College London（伦敦大学学院心血管科学研究所）；Faculty of Life Sciences, University College London（伦敦大学学院生命科学学院）
Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains
Graz University of Technology（格拉茨工业大学）；GESIS - Leibniz Institute for the Social Sciences（GESIS——莱布尼茨社会科学研究所）；University of Koblenz-Landau（科布伦茨-兰道大学）；Stanford University（斯坦福大学）
