{"title":"How does the Research Capacity Gap among Institutions Change over Time?","authors":"Tianyuan Huang, Ting Yue","doi":"10.2478/jdis-2022-0022","DOIUrl":"https://doi.org/10.2478/jdis-2022-0022","url":null,"abstract":"","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"3 - 4"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45801508","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose We aim to extend our investigations related to the Relative Intensity of Collaboration (RIC) indicator, by constructing a confidence interval for the obtained values. Design/methodology/approach We use Mantel-Haenszel statistics as applied recently by Smolinsky, Klingenberg, and Marx. Findings We obtain confidence intervals for the RIC indicator Research limitations It is not obvious that data obtained from the Web of Science (or any other database) can be considered a random sample. Practical implications We explain how to calculate confidence intervals. Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement. Our approach presents a suggestion to solve this problem. Originality/value Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.
摘要目的通过为所得值构建置信区间，扩展我们对相对协作强度(RIC)指标的研究。设计/方法/方法我们使用了最近由Smolinsky、Klingenberg和Marx应用的Mantel-Haenszel统计。我们获得了RIC指标的置信区间研究局限性从Web of Science(或任何其他数据库)获得的数据不明显可以被视为随机样本。我们解释如何计算置信区间。文献计量指标往往以精确值而不是根据数据库和测量时间的近似值表示。我们的方法提出了解决这个问题的建议。我们的方法结合了二元分类数据的统计和文献计量学研究的合作。
{"title":"Confidence Intervals for Relative Intensity of Collaboration (RIC) Indicators","authors":"J. E. Fuchs, Lawrence J. Smolinsky, R. Rousseau","doi":"10.2478/jdis-2022-0021","DOIUrl":"https://doi.org/10.2478/jdis-2022-0021","url":null,"abstract":"Abstract Purpose We aim to extend our investigations related to the Relative Intensity of Collaboration (RIC) indicator, by constructing a confidence interval for the obtained values. Design/methodology/approach We use Mantel-Haenszel statistics as applied recently by Smolinsky, Klingenberg, and Marx. Findings We obtain confidence intervals for the RIC indicator Research limitations It is not obvious that data obtained from the Web of Science (or any other database) can be considered a random sample. Practical implications We explain how to calculate confidence intervals. Bibliometric indicators are more often than not presented as precise values instead of an approximation depending on the database and the time of measurement. Our approach presents a suggestion to solve this problem. Originality/value Our approach combines the statistics of binary categorical data and bibliometric studies of collaboration.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"5 - 15"},"PeriodicalIF":0.0,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48220604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuqi Wang, Yue Chen, Zhiqi Wang, Kang Wang, Kai Song
Abstract Purpose The 5th Plenary Session of the 19th Communist Party of China (CPC) Central Committee clearly states that developing science and technology through self-reliance and self-strengthening provides the strategic underpinning for China's development. Based on this background, this paper explores a metric model for assessing national scientific research strength through collaboration on research papers. Design/methodology/approach We propose a novel metric model for assessing national scientific research strength, which sets two indicators, national scientific self-reliance (SR) and national academic contribution (CT), to reflect “self-reliance” and “self-strengthening” respectively. Taking the research papers in quantum technology as an example, this study analyzes the scientific research strength of various countries around the world, especially China in quantum technology. Findings The results show that the research of quantum technology in China has always been relatively independent with fewer international collaboration papers and located in a more marginal position in cooperation networks. China's academic contribution (CT) to global quantum technology research is increasing and has been greater than that of the United States in 2020. Combining the two indicators, CT and SR, China's research strength in the quantum field closely follows the United States, and the United States is the most powerful with high research autonomy. Research limitations This paper only reflects China's scientific research strength in quantum technology from collaboration on research papers and doesn’t consider the segmentation of quantum technology and the industrial upstream and downstream aspects, which need further study. Practical implications The model is helpful to better understand the national scientific research strength in a certain field from “self-reliance” and “self-strengthening”. Originality/value We propose a novel metric model to measure the national scientific research strength from the perspective of “self-reliance” and “self-strengthening”, which provides a solid basis for the assessment of the strength level of scientific research in countries/regions and institutions.
{"title":"A Novel Metric for Assessing National Strength in Scientific Research: Understanding China's Research Output in Quantum Technology through Collaboration","authors":"Yuqi Wang, Yue Chen, Zhiqi Wang, Kang Wang, Kai Song","doi":"10.2478/jdis-2022-0019","DOIUrl":"https://doi.org/10.2478/jdis-2022-0019","url":null,"abstract":"Abstract Purpose The 5th Plenary Session of the 19th Communist Party of China (CPC) Central Committee clearly states that developing science and technology through self-reliance and self-strengthening provides the strategic underpinning for China's development. Based on this background, this paper explores a metric model for assessing national scientific research strength through collaboration on research papers. Design/methodology/approach We propose a novel metric model for assessing national scientific research strength, which sets two indicators, national scientific self-reliance (SR) and national academic contribution (CT), to reflect “self-reliance” and “self-strengthening” respectively. Taking the research papers in quantum technology as an example, this study analyzes the scientific research strength of various countries around the world, especially China in quantum technology. Findings The results show that the research of quantum technology in China has always been relatively independent with fewer international collaboration papers and located in a more marginal position in cooperation networks. China's academic contribution (CT) to global quantum technology research is increasing and has been greater than that of the United States in 2020. Combining the two indicators, CT and SR, China's research strength in the quantum field closely follows the United States, and the United States is the most powerful with high research autonomy. Research limitations This paper only reflects China's scientific research strength in quantum technology from collaboration on research papers and doesn’t consider the segmentation of quantum technology and the industrial upstream and downstream aspects, which need further study. Practical implications The model is helpful to better understand the national scientific research strength in a certain field from “self-reliance” and “self-strengthening”. Originality/value We propose a novel metric model to measure the national scientific research strength from the perspective of “self-reliance” and “self-strengthening”, which provides a solid basis for the assessment of the strength level of scientific research in countries/regions and institutions.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"39 - 60"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48119715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Covid-related Papers Contribute 50% on the JIF of High Impact Medicine Journals","authors":"Jiandong Zhang, Yahui Liu, Zhesi Shen","doi":"10.2478/jdis-2022-0020","DOIUrl":"https://doi.org/10.2478/jdis-2022-0020","url":null,"abstract":"","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"1 - 2"},"PeriodicalIF":0.0,"publicationDate":"2022-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45017425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.
{"title":"What Does Information Science Offer for Data Science Research?: A Review of Data and Information Ethics Literature","authors":"Brady D. Lund, Ting Wang","doi":"10.2478/jdis-2022-0018","DOIUrl":"https://doi.org/10.2478/jdis-2022-0018","url":null,"abstract":"Abstract This paper reviews literature pertaining to the development of data science as a discipline, current issues with data bias and ethics, and the role that the discipline of information science may play in addressing these concerns. Information science research and researchers have much to offer for data science, owing to their background as transdisciplinary scholars who apply human-centered and social-behavioral perspectives to issues within natural science disciplines. Information science researchers have already contributed to a humanistic approach to data ethics within the literature and an emphasis on data science within information schools all but ensures that this literature will continue to grow in coming decades. This review article serves as a reference for the history, current progress, and potential future directions of data ethics research within the corpus of information science literature.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"16 - 38"},"PeriodicalIF":0.0,"publicationDate":"2022-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46391765","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Patent classification is one of the areas in Intellectual Property Analytics (IPA), and a growing use case since the number of patent applications has been increasing worldwide. We propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this task. Design/methodology/approach We applied three different approaches in this paper. First, we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble methods. After preprocessing data by applying TF-IDF, FastText and Doc2Vec, the models were evaluated by cross-validation in 5 folds. In a second approach, we used two different Neural Networks architectures, a Convolutional Neural Network (CNN) and a bi-directional Long Short-Term Memory (BiLSTM). Finally, we used pre-trained BERT, DistilBERT, and ULMFiT models in the third approach. Findings BERTTimbau, a BERT architecture model pre-trained on a large Portuguese corpus, presented the best results for the task, even though with a performance of only 4% superior to a LinearSVC model using TF-IDF feature engineering. Research limitations The dataset was highly imbalanced, as usual in patent applications, so the classes with the lowest samples were expected to present the worst performance. That result happened in some cases, especially in classes with less than 60 training samples. Practical implications Patent classification is challenging because of the hierarchical classification system, the context overlap, and the underrepresentation of the classes. However, the final model presented an acceptable performance given the size of the dataset and the task complexity. This model can support the decision and improve the time by proposing a category in the second level of ICP, which is one of the critical phases of the grant patent process. Originality/value To our knowledge, the proposed models were never implemented for Portuguese patent classification.
{"title":"A Use Case of Patent Classification Using Deep Learning with Transfer Learning","authors":"R. Henriques, Adria Ferreira, M. Castelli","doi":"10.2478/jdis-2022-0015","DOIUrl":"https://doi.org/10.2478/jdis-2022-0015","url":null,"abstract":"Abstract Purpose Patent classification is one of the areas in Intellectual Property Analytics (IPA), and a growing use case since the number of patent applications has been increasing worldwide. We propose using machine learning algorithms to classify Portuguese patents and evaluate the performance of transfer learning methodologies to solve this task. Design/methodology/approach We applied three different approaches in this paper. First, we used a dataset available by INPI to explore traditional machine learning algorithms and ensemble methods. After preprocessing data by applying TF-IDF, FastText and Doc2Vec, the models were evaluated by cross-validation in 5 folds. In a second approach, we used two different Neural Networks architectures, a Convolutional Neural Network (CNN) and a bi-directional Long Short-Term Memory (BiLSTM). Finally, we used pre-trained BERT, DistilBERT, and ULMFiT models in the third approach. Findings BERTTimbau, a BERT architecture model pre-trained on a large Portuguese corpus, presented the best results for the task, even though with a performance of only 4% superior to a LinearSVC model using TF-IDF feature engineering. Research limitations The dataset was highly imbalanced, as usual in patent applications, so the classes with the lowest samples were expected to present the worst performance. That result happened in some cases, especially in classes with less than 60 training samples. Practical implications Patent classification is challenging because of the hierarchical classification system, the context overlap, and the underrepresentation of the classes. However, the final model presented an acceptable performance given the size of the dataset and the task complexity. This model can support the decision and improve the time by proposing a category in the second level of ICP, which is one of the critical phases of the grant patent process. Originality/value To our knowledge, the proposed models were never implemented for Portuguese patent classification.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"49 - 70"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49541715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose This article explores the implications of publication requirements for the research output of Ukrainian academics in Scopus in 1999–2019. As such it contributes to the existing body of knowledge on quantitative and qualitative effects of research evaluation policies. Design/methodology/approach Three metrics were chosen to analyse the implications of publication requirements for the quality of research output: publications in predatory journals, publications in local journals and publications per SNIP quartile from the disciplinary perspective. Findings Study results highlight, that, firstly, publications of Ukrainian authors in predatory journals rose to 1% in 2019. Secondly, the share of publications in local journals reached the peak of 47.3% in 2015. In 2019 it fell to 31.8%. Thirdly, though the total number of publications has risen dramatically since 2011, but the share of Q3+Q4 has exceeded the share of Q1+Q2. To summarise, the study findings highligh, that research evaluation policies are required to contain not only quantitative but also qualitative criteria. Research limitation The study does not explore in detail the effects of a particular type of publication requirements. Practical implications The findings of the study have practical implications for policymakers and university managers aimed to develop research evaluation policies. Originality/value This paper gains insights into the effects of publication requirements on the research output of Ukrainian academics in Scopus.
{"title":"Implications of Publication Requirements for the Research Output of Ukrainian Academics in Scopus in 1999–2019","authors":"Myroslava Hladchenko","doi":"10.2478/jdis-2022-0016","DOIUrl":"https://doi.org/10.2478/jdis-2022-0016","url":null,"abstract":"Abstract Purpose This article explores the implications of publication requirements for the research output of Ukrainian academics in Scopus in 1999–2019. As such it contributes to the existing body of knowledge on quantitative and qualitative effects of research evaluation policies. Design/methodology/approach Three metrics were chosen to analyse the implications of publication requirements for the quality of research output: publications in predatory journals, publications in local journals and publications per SNIP quartile from the disciplinary perspective. Findings Study results highlight, that, firstly, publications of Ukrainian authors in predatory journals rose to 1% in 2019. Secondly, the share of publications in local journals reached the peak of 47.3% in 2015. In 2019 it fell to 31.8%. Thirdly, though the total number of publications has risen dramatically since 2011, but the share of Q3+Q4 has exceeded the share of Q1+Q2. To summarise, the study findings highligh, that research evaluation policies are required to contain not only quantitative but also qualitative criteria. Research limitation The study does not explore in detail the effects of a particular type of publication requirements. Practical implications The findings of the study have practical implications for policymakers and university managers aimed to develop research evaluation policies. Originality/value This paper gains insights into the effects of publication requirements on the research output of Ukrainian academics in Scopus.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"71 - 93"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48285511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose Measuring the exact technology complementarity between different institutions is necessary to obtain complementary technology resources for R&D cooperation. Design/methodology/approach This study constructs a morphology-driven method for measuring technology complementarity, taking medical field as an example. First, we calculate semantic similarities between subjects (S and S) and action-objects (AO and AO) based on the Metathesaurus, forming clusters of S and AO based on a semantic similarity matrix. Second, we identify key technology issues and methods based on clusters of S and AO. Third, a technology morphology matrix of several dimensions is constructed using morphology analysis, and the matrix is filled with subjects -action-objects (SAO) structures according to corresponding key technology issues and methods for different institutions. Finally, the technology morphology matrix is used to measure the technology complementarity between different institutions based on SAO. Findings The improved technology complementarity method based on SAO is more of a supplementary and refined framework for the traditional IPC method. Research limitations In future studies we will reprocess and identify the SAO structures which were not in the technology morphology matrix, and find other methods to characterize key technical issues and methods. Furthermore, we will add the comparison between proposed method and traditional and mostly used complementarity measurement method based on industry chain and industry code. Practical implications This study takes medical field as an example. The morphology-driven method for measuring technology complementarity can be migrated and applied for any given field. Originality/value From the perspective of complementary technology resources, this study develops and tests a more accurate morphology-driven method for technology complementarity measurement.
{"title":"A Morphology-Driven Method for Measuring Technology Complementarity: Empirical Study Involving Alzheimer's Disease","authors":"Xuefeng Wang, Rongrong Li, Yuqin Liu, Ming Lei","doi":"10.2478/jdis-2022-0017","DOIUrl":"https://doi.org/10.2478/jdis-2022-0017","url":null,"abstract":"Abstract Purpose Measuring the exact technology complementarity between different institutions is necessary to obtain complementary technology resources for R&D cooperation. Design/methodology/approach This study constructs a morphology-driven method for measuring technology complementarity, taking medical field as an example. First, we calculate semantic similarities between subjects (S and S) and action-objects (AO and AO) based on the Metathesaurus, forming clusters of S and AO based on a semantic similarity matrix. Second, we identify key technology issues and methods based on clusters of S and AO. Third, a technology morphology matrix of several dimensions is constructed using morphology analysis, and the matrix is filled with subjects -action-objects (SAO) structures according to corresponding key technology issues and methods for different institutions. Finally, the technology morphology matrix is used to measure the technology complementarity between different institutions based on SAO. Findings The improved technology complementarity method based on SAO is more of a supplementary and refined framework for the traditional IPC method. Research limitations In future studies we will reprocess and identify the SAO structures which were not in the technology morphology matrix, and find other methods to characterize key technical issues and methods. Furthermore, we will add the comparison between proposed method and traditional and mostly used complementarity measurement method based on industry chain and industry code. Practical implications This study takes medical field as an example. The morphology-driven method for measuring technology complementarity can be migrated and applied for any given field. Originality/value From the perspective of complementary technology resources, this study develops and tests a more accurate morphology-driven method for technology complementarity measurement.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"20 - 48"},"PeriodicalIF":0.0,"publicationDate":"2022-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47492432","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Purpose A new point of view in the study of impact is introduced. Design/methodology/approach Using fundamental theorems in real analysis we study the convergence of well-known impact measures. Findings We show that pointwise convergence is maintained by all well-known impact bundles (such as the h-, g-, and R-bundle) and that the μ-bundle even maintains uniform convergence. Based on these results, a classification of impact bundles is given. Research limitations As for all impact studies, it is just impossible to study all measures in depth. Practical implications It is proposed to include convergence properties in the study of impact measures. Originality/value This article is the first to present a bundle classification based on convergence properties of impact bundles.
{"title":"Convergence of Impact Measures and Impact Bundles","authors":"L. Egghe","doi":"10.2478/jdis-2022-0014","DOIUrl":"https://doi.org/10.2478/jdis-2022-0014","url":null,"abstract":"Abstract Purpose A new point of view in the study of impact is introduced. Design/methodology/approach Using fundamental theorems in real analysis we study the convergence of well-known impact measures. Findings We show that pointwise convergence is maintained by all well-known impact bundles (such as the h-, g-, and R-bundle) and that the μ-bundle even maintains uniform convergence. Based on these results, a classification of impact bundles is given. Research limitations As for all impact studies, it is just impossible to study all measures in depth. Practical implications It is proposed to include convergence properties in the study of impact measures. Originality/value This article is the first to present a bundle classification based on convergence properties of impact bundles.","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"5 - 19"},"PeriodicalIF":0.0,"publicationDate":"2022-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42650359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Bibliometrics Is Valuable Science. Why Do Some Journals Seem to Oppose It?","authors":"B. Lund","doi":"10.2478/jdis-2022-0012","DOIUrl":"https://doi.org/10.2478/jdis-2022-0012","url":null,"abstract":"","PeriodicalId":92237,"journal":{"name":"Journal of data and information science (Warsaw, Poland)","volume":"7 1","pages":"1 - 4"},"PeriodicalIF":0.0,"publicationDate":"2022-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41832717","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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