2026-10-14T23:22:21Z https://www.scidb.cn/oai

10.57760/sciencedb.38347 2026-06-03T18:09:47Z

2026-06-03 A Dataset of Node Centrality and Structural Hole Indicators in China’s Chip Technology Innovation Cooperation Network (2009–2023) doi:10.57760/sciencedb.38347 en This dataset serves as supplementary data for the paper Characteristics and Evolution of China’s Chip Technology Innovation Collaboration Network, primarily supporting the content in Section 2.3 Analysis of Individual Network Nodes. Due to length constraints, the main text only presents nodes ranking high in degree centrality across different stages. This dataset further provides centrality and structural hole indicators for all network nodes within five time windows, enabling readers to verify and conduct extended analyses on the network position, bridging role and structural hole advantages of various innovation entities in China’s chip technology innovation collaboration network.This dataset does not contain raw patent database records, full texts, titles, abstracts, claims or other original patent content, nor does it allow bulk redistribution of raw database data. Instead, it consists of node-level indicators calculated by the research team using social network analysis tools based on the established innovation collaboration network.The dataset covers the period from 2009 to 2023, divided into five time windows in accordance with the research design: 2009–2011, 2012–2014, 2015–2017, 2018–2020 and 2021–2023. Geographically, it targets innovation entities within China’s chip technology innovation collaboration network. Nodes mainly include enterprises, universities, research institutes and other R&D institutions, with no specific geographic coordinates or spatial resolution involved.The data processing procedures are as follows: Based on the phased innovation collaboration networks constructed in the paper, the collaboration matrices of each phase were imported into social network analysis tools such as Ucinet to calculate node degree centrality, betweenness centrality and structural hole indicators. All results were organized into text and spreadsheet files.Degree centrality reflects the number of direct collaborative ties between a node and other entities, as well as its relative importance.Betweenness centrality measures a node’s intermediary and bridging role in the shortest paths of the network.Structural hole indicators indicate a node’s relative advantages in non-redundant connections, information acquisition and resource control.The dataset contains three categories of files:Degree centrality files for each phase: Degree Centrality 2009-2011.txt, Degree Centrality 2012-2014.txt, Degree Centrality 2015-2017.txt, Degree Centrality 2018-2020.txt, Degree Centrality 2021-2023.txt. Core fields include Node Name, Degree, NrmDegree and Share. Degree refers to raw degree centrality; NrmDegree stands for normalized degree centrality; Share represents the proportion of the node’s degree in total network connections.Betweenness centrality files for each phase: Betweenness Centrality 2009-2011.txt, Betweenness Centrality 2012-2014.txt, Betweenness Centrality 2015-2017.txt, Betweenness Centrality 2018-2020.txt, Betweenness Centrality 2021-2023.txt. Core fields include Node Name, Betweenness and nBetweenness. Betweenness denotes raw betweenness centrality, and nBetweenness denotes normalized betweenness centrality.Structural hole indicator files for each phase: Structural Holes 2009-2011.xlsx, Structural Holes 2012-2014.xlsx, Structural Holes 2015-2017.xlsx, Structural Holes 2018-2020.xlsx, Structural Holes 2021-2023.xlsx. These files present indicators of nodes at structural hole positions, including effective size, efficiency and network constraint.All indicators in this dataset are computational results of network analysis with no units of measurement. The number of nodes varies across files for different time windows, as the number of participating innovation entities differs in each collaboration network. Some nodes show a value of 0 for centrality or structural hole indicators, which is not missing data, but indicates that the corresponding node exerts no obvious intermediary, bridging or structural hole function in the network. Potential data errors may stem from entity name standardization during network construction, collation of collaboration matrices, and differences in calculation rules across network analysis software. Chip technology; Jointly authorized patents; Innovation cooperation network; Social network analysis; Network evolution Wei Qifeng Yang Ding PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank