Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and pr...Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps Design/methodology/approach： We use the combined set of 2015 Journal Citation Reports for the Science Citation Index （n of journals = 8,778） and the Social Sciences Citation Index （n = 3,212） for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map （＂wc 15.exe＂） is available at http：//www.leydesdorff.net/wc 15/index.htm. Findings： Findings appear in the form of visuals throughout the manuscript. In Figures 1 9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations： As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications： Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.Originality/value： The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.展开更多
Purpose： The authors aim at testing the performance of a set of machine learning algorithms that could improve the process of data cleaning when building datasets. Design/methodology/approach： The paper is centered ...Purpose： The authors aim at testing the performance of a set of machine learning algorithms that could improve the process of data cleaning when building datasets. Design/methodology/approach： The paper is centered on cleaning datasets gathered from publishers and online resources by the use of specific keywords. In this case, we analyzed data from the Web of Science. The accuracy of various forms of automatic classification was tested here in comparison with manual coding in order to determine their usefulness for data collection and cleaning. We assessed the performance of seven supervised classification algorithms （Support Vector Machine （SVM）, Scaled Linear Discriminant Analysis, Lasso and elastic-net regularized generalized linear models, Maximum Entropy, Regression Tree, Boosting, and Random Forest） and analyzed two properties： accuracy and recall. We assessed not only each algorithm individually, but also their combinations through a voting scheme. We also tested the performance of these algorithms with different sizes of training data. When assessing the performance of different combinations, we used an indicator of coverage to account for the agreement and disagreement on classification between algorithms. Findings： We found that the performance of the algorithms used vary with the size of the sample for training. However, for the classification exercise in this paper the best performing algorithms were SVM and Boosting. The combination of these two algorithms achieved a high agreement on coverage and was highly accurate. This combination performs well with a small training dataset （10%）, which may reduce the manual work needed for classification tasks. Research limitations： The dataset gathered has significantly more records related to the topic of interest compared to unrelated topics. This may affect the performance of some algorithms, especially in their identification of unrelated papers. Practical implications： Although the classification achieved by this means is not completely accurate, the amount of manual coding needed can be greatly reduced by using classification algorithms. This can be of great help when the dataset is big. With the help of accuracy, recall,and coverage measures, it is possible to have an estimation of the error involved in this classification, which could open the possibility of incorporating the use of these algorithms in software specifically designed for data cleaning and classification.展开更多
Energy access is vital to a nation’s economic growth and its populace’s social well-being. Still, there is a lack of adequate energy in Nigeria, negatively affecting the country’s socio-economic development. Due to...Energy access is vital to a nation’s economic growth and its populace’s social well-being. Still, there is a lack of adequate energy in Nigeria, negatively affecting the country’s socio-economic development. Due to the inadequate energy supply, some manufacturing companies shut their operations, and most Nigerians now use backup generators (BUGs) with their attendant health hazards, environmental pollution, and global warming. The need for energy access and a sustainable energy supply through renewable energy (RE) resources necessitates adopting solar photovoltaics (PV) in Nigeria. Studies on Nigeria’s energy accessibility and sustainability are generally on RE development and a few on solar PV applications. This research covers the need for an in-depth analysis of the growth of solar PV in Nigeria, and the research question is: What factors promote or limit the adoption of solar photovoltaics in Nigeria? A method of Systematic Literature Review (SLR) and Thematic Analysis (TA) is employed for the analysis. The research findings are divided into drivers, barriers, and policies. Some identified factors promoting the adoption of solar PV are energy poverty and the urgency to improve electricity supply, the ease of its operation and maintenance, and the Nigerian government’s commitment to clean electricity supply with policy initiatives and increased awareness of solar PV applications. Conversely, some noticed factors mitigating the growth of solar PV are poor tariff systems, dual subsidies of electricity and petroleum, and lack of finance and economic incentives.展开更多
文摘Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps Design/methodology/approach： We use the combined set of 2015 Journal Citation Reports for the Science Citation Index （n of journals = 8,778） and the Social Sciences Citation Index （n = 3,212） for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map （＂wc 15.exe＂） is available at http：//www.leydesdorff.net/wc 15/index.htm. Findings： Findings appear in the form of visuals throughout the manuscript. In Figures 1 9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations： As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications： Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.Originality/value： The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.
基金supported by National Natural Science Foundation of China(NSFC)(Grant No.:71173154)The National Social Science Fund of China(NSSFC)(Grant No.:08BZX076)the Fundamental Research Funds for the Central Universities
文摘Purpose： The authors aim at testing the performance of a set of machine learning algorithms that could improve the process of data cleaning when building datasets. Design/methodology/approach： The paper is centered on cleaning datasets gathered from publishers and online resources by the use of specific keywords. In this case, we analyzed data from the Web of Science. The accuracy of various forms of automatic classification was tested here in comparison with manual coding in order to determine their usefulness for data collection and cleaning. We assessed the performance of seven supervised classification algorithms （Support Vector Machine （SVM）, Scaled Linear Discriminant Analysis, Lasso and elastic-net regularized generalized linear models, Maximum Entropy, Regression Tree, Boosting, and Random Forest） and analyzed two properties： accuracy and recall. We assessed not only each algorithm individually, but also their combinations through a voting scheme. We also tested the performance of these algorithms with different sizes of training data. When assessing the performance of different combinations, we used an indicator of coverage to account for the agreement and disagreement on classification between algorithms. Findings： We found that the performance of the algorithms used vary with the size of the sample for training. However, for the classification exercise in this paper the best performing algorithms were SVM and Boosting. The combination of these two algorithms achieved a high agreement on coverage and was highly accurate. This combination performs well with a small training dataset （10%）, which may reduce the manual work needed for classification tasks. Research limitations： The dataset gathered has significantly more records related to the topic of interest compared to unrelated topics. This may affect the performance of some algorithms, especially in their identification of unrelated papers. Practical implications： Although the classification achieved by this means is not completely accurate, the amount of manual coding needed can be greatly reduced by using classification algorithms. This can be of great help when the dataset is big. With the help of accuracy, recall,and coverage measures, it is possible to have an estimation of the error involved in this classification, which could open the possibility of incorporating the use of these algorithms in software specifically designed for data cleaning and classification.
文摘Energy access is vital to a nation’s economic growth and its populace’s social well-being. Still, there is a lack of adequate energy in Nigeria, negatively affecting the country’s socio-economic development. Due to the inadequate energy supply, some manufacturing companies shut their operations, and most Nigerians now use backup generators (BUGs) with their attendant health hazards, environmental pollution, and global warming. The need for energy access and a sustainable energy supply through renewable energy (RE) resources necessitates adopting solar photovoltaics (PV) in Nigeria. Studies on Nigeria’s energy accessibility and sustainability are generally on RE development and a few on solar PV applications. This research covers the need for an in-depth analysis of the growth of solar PV in Nigeria, and the research question is: What factors promote or limit the adoption of solar photovoltaics in Nigeria? A method of Systematic Literature Review (SLR) and Thematic Analysis (TA) is employed for the analysis. The research findings are divided into drivers, barriers, and policies. Some identified factors promoting the adoption of solar PV are energy poverty and the urgency to improve electricity supply, the ease of its operation and maintenance, and the Nigerian government’s commitment to clean electricity supply with policy initiatives and increased awareness of solar PV applications. Conversely, some noticed factors mitigating the growth of solar PV are poor tariff systems, dual subsidies of electricity and petroleum, and lack of finance and economic incentives.
