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General laws of funding for scientific citations:how citations change in funded and unfunded research between basic and applied sciences

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Journal of Data and Information science 2024年第4期9卷 71-89页

作者： Mario Coccia Saeed Roshani CNR--National Research Council of Italy IRCRES-CNR-Turin Research Area CNRStrada delle Cacce73-10135-TurinItaly Arizona State University School of Complex Adaptive SystemsTempeAZ 85281-2701USA Allameh Tabataba’i University Department of Technology and Entrepreneurship ManagementDehkadeh-ye-OTehran***Iran

Purpose:The goal of this study is to analyze the relationship between funded and unfunded papers and their citations in both basic and applied ***/methodology/approach:A power law model analyzes the relationship between research funding and citations of papers using 831,337 documents recorded in the Web of science ***:The original results reveal general characteristics of the diffusion of science in research fields:a)Funded articles receive higher citations compared to unfunded papers in journals;b)Funded articles exhibit a super-linear growth in citations,surpassing the increase seen in unfunded *** finding reveals a higher diffusion of scientific knowledge in funded ***,c)funded articles in both basic and applied sciences demonstrate a similar expected change in citations,equivalent to about 1.23%,when the number of funded papers increases by 1%in *** result suggests,for the first time,that funding effect of scientific research is an invariant driver,irrespective of the nature of the basic or applied ***/value:This evidence suggests empirical laws of funding for scientific citations that explain the importance of robust funding mechanisms for achieving impactful research outcomes in science and *** findings here also highlight that funding for scientific research is a critical driving force in supporting citations and the dissemination of scientific knowledge in recorded documents in both basic and applied *** implications:This comprehensive result provides a holistic view of the relationship between funding and citation performance in science to guide policymakers and R&D managers with science policies by directing funding to research in promoting the scientific development and higher diffusion of results for the progress of human society.

关键词： Research funding Citations Scientific development science diffusion Scientific laws Power law model Matthew effect science policy Research policy science of science dynamics of science Evolution of science

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Embedding-based Detection and Extraction of Research Topics from Academic Documents Using Deep Clustering

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Journal of Data and Information science 2021年第3期6卷 99-122页

作者： Sahand Vahidnia Alireza Abbasi Hussein A.Abbass University of New South Wales Canberra2612ACTAustralia

Purpose:Detection of research fields or topics and understanding the dynamics help the scientific community in their decisions regarding the establishment of scientific *** also helps in having a better collaboration with governments and *** study aims to investigate the development of research fields over time,translating it into a topic detection ***/methodology/approach:To achieve the objectives,we propose a modified deep clustering method to detect research trends from the abstracts and titles of academic *** embedding approaches are utilized to transform documents into vector-based *** proposed method is evaluated by comparing it with a combination of different embedding and clustering approaches and the classical topic modeling algorithms(***)against a benchmark dataset.A case study is also conducted exploring the evolution of Artificial Intelligence(AI)detecting the research topics or sub-fields in related AI ***:Evaluating the performance of the proposed method using clustering performance indicators reflects that our proposed method outperforms similar approaches against the benchmark *** the proposed method,we also show how the topics have evolved in the period of the recent 30 years,taking advantage of a keyword extraction method for cluster tagging and labeling,demonstrating the context of the *** limitations:We noticed that it is not possible to generalize one solution for all downstream ***,it is required to fine-tune or optimize the solutions for each task and even *** addition,interpretation of cluster labels can be subjective and vary based on the readers’*** is also very difficult to evaluate the labeling techniques,rendering the explanation of the clusters further *** implications:As demonstrated in the case study,we show that in a real-world example,how the proposed method would enable the researchers and

关键词： dynamics of science science mapping Document clustering Artificial intelligence Deep learning

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