Scientometrics最新文献_第9页

Towards a new approach to analyzing the geographical scope of national research. An exploratory analysis at the country level 采用新方法分析国家研究的地理范围。国家层面的探索性分析

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-06-03 DOI: 10.1007/s11192-024-05045-9

Sandra Miguel, Claudia M. González, Zaida Chinchilla-Rodríguez

引用次数: 0

Altmetric data quality analysis using Benford’s law 使用本福德定律分析 Altmetric 数据质量

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-06-03 DOI: 10.1007/s11192-024-05061-9

Solanki Gupta, Vivek Kumar Singh, Sumit Kumar Banshal

{"title":"Altmetric data quality analysis using Benford’s law","authors":"Solanki Gupta, Vivek Kumar Singh, Sumit Kumar Banshal","doi":"10.1007/s11192-024-05061-9","DOIUrl":"https://doi.org/10.1007/s11192-024-05061-9","url":null,"abstract":"Altmetrics, or alternative metrics, refer to the newer kind of events around scholarly articles, such as the number of times the article is read, tweeted, mentioned in blog posts etc. These metrics have gained a lot of popularity during last few years and are now being collected and used in several ways, ranging from early measure of article impact to a potential indicator of societal relevance of research. However, there are several studies which have cautioned about use of altmetrics on account of quality and reliability of altmetric data, as they may be more prone to manipulations and artificial inflations. This study proposes a framework based on application of Benford’s Law to evaluate the quality of altmetric data. A large sized altmetric data sample is considered and the fits with Benford’s Law are computed. The analysis is performed by doing plots of the empirical data distributions and the theoretical Benford's, and by employing relevant statistical measures and tests. Results for fit on first and second leading digit of altmetric data show conformity to Benford's distribution. To further explore the usefulness of the framework, the altmetric data is subjected to artificial manipulations through a systematic process and the fits to Benford’s law are reassessed to see if there are distortions. The results and analysis suggest that Benford’s Law based framework can be used to test the quality of altmetric data. Relevant implications of the research are discussed.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"4 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach 利用主题建模方法识别开放获取的 LIS 期刊中的潜在主题和研究趋势的十年期研究

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-06-03 DOI: 10.1007/s11192-024-05058-4

Abhijit Thakuria, Dipen Deka

{"title":"A decadal study on identifying latent topics and research trends in open access LIS journals using topic modeling approach","authors":"Abhijit Thakuria, Dipen Deka","doi":"10.1007/s11192-024-05058-4","DOIUrl":"https://doi.org/10.1007/s11192-024-05058-4","url":null,"abstract":"The study utilized Latent Dirichlet Allocation (LDA) Topic modeling to identify prevalent latent topics within Open Access (OA) Library and Information Science (LIS) journals from 2013 to 2022. Eight core OA Scopus indexed journals were selected based on their SJR scores and DOAJ listing. Titles, Abstracts and keywords of 2589 articles were extracted from the Scopus database. R software packages were used to perform data analysis and LDA topic modeling. The optimal value of k was determined to be 9. The analysis revealed that 53.89% of documents comprise over 50% of a certain topic (θ > 0.50). Notably, ‘Scholarly Communication’ and ‘Information Systems, Models and Frameworks’ emerged as dominant topics with the highest proportions of research literature in the corpus. The topic ‘Scholarly Communication’ experienced significant growth with an average annual growth rate (AAGR) of 4.37%, while ‘Collection development and E-resources’ exhibited the lowest research proportion and a negative AAGR of − 4.22%. Additionally, topics such as ‘Information Seeking Behaviour and User Studies’, ‘User Education and Information Literacy’, and ‘Information Retrieval and Systematic Review’ remained stable and persistent topics. Conversely, research on traditional topics like ‘Librarianship and Profession’, ‘Bibliometrics’ and ‘Medical Library and Health Information’ showed a gradual decline. The LDA topic modeling approach unveiled previously unknown or unexplored topics in open access LIS research literature, enhancing our understanding of emerging trends.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"126 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141253093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

On weighted two-mode network projections 关于加权双模式网络投影

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-28 DOI: 10.1007/s11192-024-05041-z

Vladimir Batagelj

引用次数: 0

Research funding in the Middle East and North Africa: analyses of acknowledgments in scientific publications indexed in the Web of Science (2008–2021) 中东和北非的研究经费：分析科学网收录的科学出版物中的致谢（2008-2021 年）

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-28 DOI: 10.1007/s11192-024-04983-8

Jamal El-Ouahi

{"title":"Research funding in the Middle East and North Africa: analyses of acknowledgments in scientific publications indexed in the Web of Science (2008–2021)","authors":"Jamal El-Ouahi","doi":"10.1007/s11192-024-04983-8","DOIUrl":"https://doi.org/10.1007/s11192-024-04983-8","url":null,"abstract":"Funding acknowledgments are important objects of study in the context of science funding. This study uses a mixed-methods approach to analyze the funding acknowledgments found in 2.3 million scientific publications published between 2008 and 2021 by authors affiliated with research institutions in the Middle East and North Africa (MENA). The aim is to identify the major funders, assess their contribution to national scientific publications, and gain insights into the funding mechanism in relation to collaboration and publication. Publication data from the Web of Science is examined to provide key insights about funding activities. Saudi Arabia and Qatar lead the region, as about half of their publications include acknowledgments to funding sources. Most MENA countries exhibit strong linkages with foreign agencies, mainly due to a high level of international collaboration. The distinction between domestic and international publications reveals some differences in terms of funding structures. For instance, Turkey and Iran are dominated by one or two major funders whereas a few other countries like Saudi Arabia showcase multiple funders. Iran and Kuwait are examples of countries where research is mainly funded by domestic funders. The government and academic sectors mainly fund scientific research in MENA whereas the industry sector plays little or no role in terms of research funding. Lastly, the qualitative analyses provide more context into the complex funding mechanism. The findings of this study contribute to a better understanding of the funding structure in MENA countries and provide insights to funders and research managers to evaluate the funding landscape.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"43 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The scientometrics and reciprocality underlying co-authorship panels in Google Scholar profiles 谷歌学术档案中共同作者面板所蕴含的科学计量学和互惠性

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-27 DOI: 10.1007/s11192-024-05026-y

Ariel Alexi, Teddy Lazebnik, Ariel Rosenfeld

引用次数: 0

Extracting problem and method sentence from scientific papers: a context-enhanced transformer using formulaic expression desensitization 从科学论文中提取问题句和方法句：使用公式化表达脱敏的语境增强转换器

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-27 DOI: 10.1007/s11192-024-05048-6

Yingyi Zhang, Chengzhi Zhang

{"title":"Extracting problem and method sentence from scientific papers: a context-enhanced transformer using formulaic expression desensitization","authors":"Yingyi Zhang, Chengzhi Zhang","doi":"10.1007/s11192-024-05048-6","DOIUrl":"https://doi.org/10.1007/s11192-024-05048-6","url":null,"abstract":"Billions of scientific papers lead to the need to identify essential parts from the massive text. Scientific research is an activity from putting forward problems to using methods. To learn the main idea from scientific papers, we focus on extracting problem and method sentences. Annotating sentences within scientific papers is labor-intensive, resulting in small-scale datasets that limit the amount of information models can learn. This limited information leads models to rely heavily on specific forms, which in turn reduces their generalization capabilities. This paper addresses the problems caused by small-scale datasets from three perspectives: increasing dataset scale, reducing dependence on specific forms, and enriching the information within sentences. To implement the first two ideas, we introduce the concept of formulaic expression (FE) desensitization and propose FE desensitization-based data augmenters to generate synthetic data and reduce models’ reliance on FEs. For the third idea, we propose a context-enhanced transformer that utilizes context to measure the importance of words in target sentences and to reduce noise in the context. Furthermore, this paper conducts experiments using large language model (LLM) based in-context learning (ICL) methods. Quantitative and qualitative experiments demonstrate that our proposed models achieve a higher macro F1 score compared to the baseline models on two scientific paper datasets, with improvements of 3.71% and 2.67%, respectively. The LLM based ICL methods are found to be not suitable for the task of problem and method extraction.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"65 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Can document characteristics affect motivations for literature usage? 文献特征会影响文献使用动机吗？

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-27 DOI: 10.1007/s11192-024-05044-w

↓Xia Peng, Zequan Xiong, Li Yang

{"title":"Can document characteristics affect motivations for literature usage?","authors":"↓Xia Peng, Zequan Xiong, Li Yang","doi":"10.1007/s11192-024-05044-w","DOIUrl":"https://doi.org/10.1007/s11192-024-05044-w","url":null,"abstract":"Beyond citations, the impact of scientific publications is often measured by usage metrics, such as downloads, save counts and sharing counts. However, the motivations behind the utilization of these publications and their influencing factors have not yet been well studied. Therefore, it remains questionable whether and to what extent usage metrics can reflect the impact of publications. Based on expectancy-value theory, the aim of the present study was to examine the differences in behavioral characteristics and driving factors between article downloading, sharing, and saving, especially document characteristics. For the present study, survey data from 480 respondents across Chinese universities were collected and investigated in terms of the frequency and purpose of three literature usage behaviors, namely, downloading, sharing, and saving. Additionally, 11 document characteristics were used to construct three variables in the research models: intrinsic interest value, attainment value, and utility value. Their effects on three usage behaviors were examined based on path analysis via SmartPLS. The results showed that the overall frequency of article downloading and saving was greater than that of article sharing. The primary purposes of downloading and saving were closely related to scientific research, such as for review and citing. The sharing of articles on social media was mainly for agreeing with their opinions. Both intrinsic interest value and utility value exhibited a significant positive influence on article-downloading, whereas attainment value and intrinsic interest value showed a significant relationship with sharing and saving, respectively. In conclusion, different literature usage behaviors can be triggered and driven by the distinct values of research articles. The results obtained in this study could help to clarify the determinants of different usage behaviors; additionally, they might promote the reasonable application of usage metrics or altmetrics in scientific evaluation.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"38 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141170225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Visualizing hidden communities of interest: A case-study analysis of topic-based social networks in astrobiology 将隐藏的兴趣社区可视化：天体生物学中基于主题的社交网络案例分析

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-27 DOI: 10.1007/s11192-024-05047-7

Christophe Malaterre, Francis Lareau

{"title":"Visualizing hidden communities of interest: A case-study analysis of topic-based social networks in astrobiology","authors":"Christophe Malaterre, Francis Lareau","doi":"10.1007/s11192-024-05047-7","DOIUrl":"https://doi.org/10.1007/s11192-024-05047-7","url":null,"abstract":"Author networks in science often rely on citation analyses. In such cases, as in others, network interpretation usually depends on supplementary data, notably about authors’ research domains when disciplinary interpretations are sought. More general social networks also face similar interpretation challenges as to the semantic content specificities of their members. In this research-in-progress, we propose to infer author networks not from citation analyses but from topic similarity analyses based on a topic-model of published documents. Such author networks reveal, as we call them, “hidden communities of interest” (HCoIs) whose semantic content can easily be interpreted by means of their associated topics in the model. We use an astrobiology corpus of full-text articles (N = 3,698) to illustrate the approach. Having conducted an LDA topic-model on all publications, we identify the underlying communities of authors by measuring author correlations in terms of topic distributions. Adding publication dates makes it possible to examine HCoI evolution over time. This approach to social networks supplements traditional methods in contexts where textual data are available.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"4 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141173168","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A deep learning method for recommending university patents to industrial clusters by common technological needs mining 一种通过挖掘共性技术需求向产业集群推荐大学专利的深度学习方法

IF 3.9 3区管理学

Scientometrics Pub Date : 2024-05-27 DOI: 10.1007/s11192-024-05052-w

Zhaobin Liu, Yongxiang Zhang, Weiwei Deng, Jian Ma, Xia Fan

{"title":"A deep learning method for recommending university patents to industrial clusters by common technological needs mining","authors":"Zhaobin Liu, Yongxiang Zhang, Weiwei Deng, Jian Ma, Xia Fan","doi":"10.1007/s11192-024-05052-w","DOIUrl":"https://doi.org/10.1007/s11192-024-05052-w","url":null,"abstract":"Industrial clusters, geographical concentrations of interconnected companies, aim to achieve technological innovation by acquiring common technology, which is the technology shared by all companies in an industrial cluster. Obtaining patents from universities is a primary way to gain common technology. However, existing patent recommendation methods have primarily focused on meeting the technological needs of individual companies, thus falling short in addressing the common technological requirements of all companies within an industrial cluster. To address the problem, we propose a deep learning (DL) method that recommends patents to industrial clusters based on common technological needs mining (DL_CTNM). The proposed method mines the common needs from patents owned by the companies and domain knowledge about potential technologies common to industries. Specifically, we mine the technological needs of the companies from their patents using long short-term memory networks and obtain their patent-based common needs by designing a candidate patent-aware attention mechanism. Then, we extract implicit technology directions from the domain knowledge using a capsule network and obtain domain knowledge-based common needs by designing an industrial cluster-aware attention mechanism. We evaluate the proposed method through offline and online experiments, comparing it to various benchmark methods. The experimental results demonstrate that our method outperforms these benchmarks in terms of recall and normalized discounted cumulative gain.","PeriodicalId":21755,"journal":{"name":"Scientometrics","volume":"11 1","pages":""},"PeriodicalIF":3.9,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141169975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0