As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology.With the booming development of scienc...As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology.With the booming development of science and technology,the number of papers has been growing exponentially.Just like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more nodes.In this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between them.In order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout details.Our main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different levels.In addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks.Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes.The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously,but also returns some interesting observations that may drive extra attention from scientists.展开更多
Purpose:To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality,revealin...Purpose:To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality,revealing their academic and non-academic impact.Design/methodology/approach:A total of 1,092,934 RG-DOIs were collected,using the DataCite API,along with bibliographic metadata for the associated registered output(RG-DOI publications).The subsequent analysis evaluated the publication date,document type,and language.These values were crossreferenced against the full text of a random sample of 666 records to verify accuracy.Findings:RG-DOIs have served primarily to identify and make accessible scholarly gray literature,including posters,presentations,conference papers,and theses,with notable emphasis on publications in Spanish and Portuguese.Around 41,000 citations from Web of Science indexed publications to RG publications are evidence of their infrequent but perceptible use in scholarly discourse.The declining number of registrations of RG-DOIs observed may indicate a shift in researcher preferences to alternative platforms for DOI generation.Research limitations:The study uncovered substantial inconsistencies in DataCite metadata,which can be attributed to the automated DOI registration process and internal changes in the available document types on ResearchGate.Practical implications:The study encountered challenges in conducting a quantitative analysis due to inconsistencies in the metadata.These have potential implications for researchers,practitioners,and librarians relying on RG-DOIs to conduct bibliometric or bibliographic analysis.Originality/value:This study is the first comprehensive analysis of RG-DOIs and,as such,provides a unique perspective into academic gray literature.It also sheds light on the quality of ResearchGate data transmitted to DataCite when registering DOIs.展开更多
AMiner is a novel online academic search and mining system,and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks f...AMiner is a novel online academic search and mining system,and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors,papers,conferences,journals and organizations.The system is subsequently able to extract researchers’profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation.Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search.In addition,AMiner offers a set of researcher-centered functions,including social influence analysis,relationship mining,collaboration recommendation,similarity analysis and community evolution.The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.展开更多
文摘As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology.With the booming development of science and technology,the number of papers has been growing exponentially.Just like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more nodes.In this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between them.In order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout details.Our main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different levels.In addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks.Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes.The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously,but also returns some interesting observations that may drive extra attention from scientists.
基金supported by Grant PID2022-142569NA-I00,funded by MCIN/AEI/10.13039/501100011033 and by the European Union through“ERDF A way of making Europe.”。
文摘Purpose:To describe the characteristics of research outputs using persistent identifiers generated by ResearchGate to gain insight into what publications are shared and disseminated through this functionality,revealing their academic and non-academic impact.Design/methodology/approach:A total of 1,092,934 RG-DOIs were collected,using the DataCite API,along with bibliographic metadata for the associated registered output(RG-DOI publications).The subsequent analysis evaluated the publication date,document type,and language.These values were crossreferenced against the full text of a random sample of 666 records to verify accuracy.Findings:RG-DOIs have served primarily to identify and make accessible scholarly gray literature,including posters,presentations,conference papers,and theses,with notable emphasis on publications in Spanish and Portuguese.Around 41,000 citations from Web of Science indexed publications to RG publications are evidence of their infrequent but perceptible use in scholarly discourse.The declining number of registrations of RG-DOIs observed may indicate a shift in researcher preferences to alternative platforms for DOI generation.Research limitations:The study uncovered substantial inconsistencies in DataCite metadata,which can be attributed to the automated DOI registration process and internal changes in the available document types on ResearchGate.Practical implications:The study encountered challenges in conducting a quantitative analysis due to inconsistencies in the metadata.These have potential implications for researchers,practitioners,and librarians relying on RG-DOIs to conduct bibliometric or bibliographic analysis.Originality/value:This study is the first comprehensive analysis of RG-DOIs and,as such,provides a unique perspective into academic gray literature.It also sheds light on the quality of ResearchGate data transmitted to DataCite when registering DOIs.
文摘AMiner is a novel online academic search and mining system,and it aims to provide a systematic modeling approach to help researchers and scientists gain a deeper understanding of the large and heterogeneous networks formed by authors,papers,conferences,journals and organizations.The system is subsequently able to extract researchers’profiles automatically from the Web and integrates them with published papers by a way of a process that first performs name disambiguation.Then a generative probabilistic model is devised to simultaneously model the different entities while providing a topic-level expertise search.In addition,AMiner offers a set of researcher-centered functions,including social influence analysis,relationship mining,collaboration recommendation,similarity analysis and community evolution.The system has been in operation since 2006 and has been accessed from more than 8 million independent IP addresses residing in more than 200 countries and regions.
