DOI: 10.5061/dryad.z612jm6j8
title: Data from: Research on potential disruptive technology identification based on technology network

description: Three evident and meaningful characteristics of disruptive technology are
 the zeroing effect that causes sustaining technology useless for its
 remarkable and unprecedented progress, reshaping the landscape of
 technology and economy, and leading the future mainstream of technology
 system, all of which have profound impacts and positive influences. The
 identification of disruptive technology is a universally difficult task.
 Therefore, the paper aims to enhance the technical relevance of potential
 disruptive technology identification results and improve the granularity
 and effectiveness of potential disruptive technology identification
 topics. According to the life cycle theory, dividing the time stage, then
 constructing and analyzing the dynamic of technology networks to identify
 potential disruptive technology. Thereby, using the LDA topic model
 further to clarify the topic content of potential disruptive technologies.
 This paper takes the large civil UAVs as an example to prove the
 feasibility and effectiveness of the model. The results show that the
 potential disruptive technology in this field is the main equipment, data
 acquisition, and information transmission.
description: Through the analysis of the technology life cycle, the division
 of the patents, the construction of the technology network, the
 identification of nodes leaping, the clustering of technical topics, we
 aim to identify potential disruptive technology.
   
 <strong>Procedures</strong>: 
 Knowledge flow: being familiar with the technical background
 knowledge in the field of large civil UAVs, and accomplishing the
 technical decomposition. Invention patents: analyzing
 the technology life cycle by the loget lab to separate the invention
 patents into four parts. According to each part, constructing the IPC
 technical network and identifying the leapfrogging and diffusible
 nodes. Technical topics: making use of the LDA model
 to cluster and explain the broad and various content of the
 inventions.   
 <strong>Testing</strong>: Dividing the
 inventions of the embryonic stage into two groups and examining them by
 means of the Mann-Whitney test. Finally, the result shows the huge
 differences in the patent value, sustaining influence, and creativity for
 the superiority of disruptive technology.
   
 <strong>Software</strong>： loget lab ==
 4.0 gephi == 0.10.1 python
 == 3.11.4 spss == 26
description: This README file was generated on 2023-11-25 by Mingli Ding. GENERAL
 INFORMATION 1. Title of Dataset: **technical network in the field of large
 civilian UAVs** 2. Author Information Investigators Contact Information
 Name: Mingli Ding; Wangke Yu; Ran Li; Zhenzhen Wang; Jianing Li
 Institution: Jingdezhen Ceramic University Address: Jingdezhen, Jiangxi,
 China Email: 1. Date of data collection:2005-2018
 ## DATA & FILE OVERVIEW 1. File List: A)patent (2005-2008).csv
 B)patents (2009-2012).csv C)patents (2013-2015).csv D)patents
 (2016-2018).csv E)technical network (2005-2008).csv F)technical network
 (2009-2012).csv G)technical networks (2013-2015).csv H)technical network
 (2016-2018).csv ### DATA-SPECIFIC INFORMATION FOR: patent (2005-2008).csv
 1. Number of variables: 2 2. Number of cases/rows: 234 3. Variable List: *
 source: the source of the edges in the technical network * target: the
 target of the edges in the technical network 4\. Specialized formats or
 other abbreviations used: None ### DATA-SPECIFIC INFORMATION FOR: patents
 (2009-2012).csv 1. Number of variables: 2 2. Number of cases/rows: 681 3.
 Variable List: * source: the source of the edges in the technical network
 * target: the target of the edges in the technical network 4\. Specialized
 formats or other abbreviations used: None
 \################################################################# ###
 DATA-SPECIFIC INFORMATION FOR: patents (2013-2015).csv 1. Number of
 variables: 2 2. Number of cases/rows: 4395 3. Variable List: * source: the
 source of the edges in the technical network * target: the target of the
 edges in the technical network 4\. Specialized formats or other
 abbreviations used: None
 \################################################################# ###
 DATA-SPECIFIC INFORMATION FOR: patents (2016-2018).csv 1. Number of
 variables: 2 2. Number of cases/rows: 4772 3. Variable List: * source: the
 source of the edges in the technical network * target: the target of the
 edges in the technical network 4\. Specialized formats or other
 abbreviations used: None
 \################################################################# ###
 DATA-SPECIFIC INFORMATION FOR: technical network (2005-2008).csv 1. Number
 of variables: 4 2. Number of cases/rows: 78 3. Variable List: * Degree
 Centrality: means that the node is more important in the network and the
 cohesion is stronger, the larger the degree of a node, the higher the
 degree centrality of the node. * Eccentricity: represents the distance
 from a given starting node to the farthest node from it. * Closeness
 Centrality: checks whether the node is at the core of the technology
 network, intending to the closeness of the node to all other nodes in the
 network. * Betweenness centrality: depends on the degree to which a node
 is at the center of multiple nodes, which can be measured according to the
 mediating role of nodes in the network. 4\. Specialized formats or other
 abbreviations used: None
 \################################################################# ###
 DATA-SPECIFIC INFORMATION FOR: technical network (2009-2012).csv 1. Number
 of variables: 4 2. Number of cases/rows: 167 3. Variable List: * Degree
 Centrality: means that the node is more important in the network and the
 cohesion is stronger, the larger the degree of a node, the higher the
 degree centrality of the node. * Eccentricity: represents the distance
 from a given starting node to the farthest node from it. * Closeness
 Centrality: checks whether the node is at the core of the technology
 network, intending to the closeness of the node to all other nodes in the
 network. * Betweenness centrality: depends on the degree to which a node
 is at the center of multiple nodes, which can be measured according to the
 mediating role of nodes in the network. 4\. Specialized formats or other
 abbreviations used: None
 \################################################################# ###
 DATA-SPECIFIC INFORMATION FOR: technical network (2013-2015).csv 1. Number
 of variables: 4 2. Number of cases/rows: 481 3. Variable List: * Degree
 Centrality: means that the node is more important in the network and the
 cohesion is stronger, the larger the degree of a node, the higher the
 degree centrality of the node. * Eccentricity: represents the distance
 from a given starting node to the farthest node from it. * Closeness
 Centrality: checks whether the node is at the core of the technology
 network, intending to the closeness of the node to all other nodes in the
 network. * Betweenness centrality: depends on the degree to which a node
 is at the center of multiple nodes, which can be measured according to the
 mediating role of nodes in the network. 4\. Specialized formats or other
 abbreviations used: None
 \################################################################# ###
 DATA-SPECIFIC INFORMATION FOR: technical network (2016-2018).csv 1. Number
 of variables: 4 2. Number of cases/rows: 583 3. Variable List: * Degree
 Centrality: means that the node is more important in the network and the
 cohesion is stronger, the larger the degree of a node, the higher the
 degree centrality of the node. * Eccentricity: represents the distance
 from a given starting node to the farthest node from it. * Closeness
 Centrality: checks whether the node is at the core of the technology
 network, intending to the closeness of the node to all other nodes in the
 network. * Betweenness centrality: depends on the degree to which a node
 is at the center of multiple nodes, which can be measured according to the
 mediating role of nodes in the network. 4\. Specialized formats or other
 abbreviations used: None ## SOFTWARES loget lab == 4.0 gephi == 0.10.1
 python == 3.11.4 spss == 26