Data from: Research on potential disruptive technology identification based on technology network
Data files
Jan 11, 2024 version files 181.75 KB
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A)patents(2005-2008).csv
3.30 KB
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B)patents(2009-2012).csv
9.70 KB
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C)patents(2013-2015).csv
62.25 KB
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D)patents(2016-2018).csv
67.82 KB
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E)technical_network(2005-2008).csv
1.95 KB
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F)technical_network(2009-2012).csv
4.18 KB
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G)technical_network(2013-2015).csv
12.02 KB
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H)technical_network(2016-2018).csv
14.58 KB
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README.md
5.95 KB
Abstract
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.
README
This README file was generated on 2023-11-25 by Mingli Ding.
GENERAL INFORMATION
Title of Dataset: technical network in the field of large civilian UAVs
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: mlding1@163.com
- Date of data collection:2005-2018
DATA & FILE OVERVIEW
- 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
Number of variables: 2
Number of cases/rows: 234
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
Number of variables: 2
Number of cases/rows: 681
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
Number of variables: 2
Number of cases/rows: 4395
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
Number of variables: 2
Number of cases/rows: 4772
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
Number of variables: 4
Number of cases/rows: 78
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
Number of variables: 4
Number of cases/rows: 167
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
Number of variables: 4
Number of cases/rows: 481
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
Number of variables: 4
Number of cases/rows: 583
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
Methods
- 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.