Multidimensional Data Analysis for Enhancing In-Depth Knowledge on the Characteristics of Science and Technology Parks
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Título: | Multidimensional Data Analysis for Enhancing In-Depth Knowledge on the Characteristics of Science and Technology Parks |
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Autor/es: | Francés Hernández, Olga | Abreu Salas, José Ignacio | Fernández Martínez, Javier | Gutiérrez, Yoan | Palomar, Manuel |
Grupo/s de investigación o GITE: | Procesamiento del Lenguaje y Sistemas de Información (GPLSI) |
Centro, Departamento o Servicio: | Universidad de Alicante. Departamento de Lenguajes y Sistemas Informáticos |
Palabras clave: | Science and technology parks | Multidimensional data analysis | Core features | Dataset | Classification | Decision making | Machine learning |
Fecha de publicación: | 22-nov-2023 |
Editor: | MDPI |
Cita bibliográfica: | Francés O, Abreu-Salas J, Fernández J, Gutiérrez Y, Palomar M. Multidimensional Data Analysis for Enhancing In-Depth Knowledge on the Characteristics of Science and Technology Parks. Applied Sciences. 2023; 13(23):12595. https://doi.org/10.3390/app132312595 |
Resumen: | The role played by science and technology parks (STPs) in technology transfer, industrial innovation, and economic growth is examined in this paper. The accurate monitoring of their evolution and impact is hindered by the lack of uniformity in STP models or goals, and the scarcity of high-quality datasets. This work uses existing terminologies, definitions, and core features of STPs to conduct a multidimensional data analysis that explores and evaluates the 21 core features which describe the key internal factors of an STP. The core features are gathered from a reliable and updatable dataset of Spanish STPs. The methodological framework can be replicated for other STP contexts and is based on descriptive techniques and machine-learning tools. The results of the study provide an overview of the general situation of STPs in Spain, validate the existence and characteristics of three types of STPs, and identify the typical features of STPs. Moreover, the prototype STP can be used as a benchmark so that other STPs can identify the features that need to be improved. Finally, this work makes it possible to carry out classifications of STPs, in addition to prediction and decision making for innovation ecosystems. |
Patrocinador/es: | This research work has been partially funded by the Generalitat Valenciana through the project NL4DISMIS: Natural Language Technologies for dealing with dis- and misinformation with grant reference (CIPROM/2021/21); the Ministry of Science and Innovation, PID2021-123956OB-I00, CORTEX; PID2021-122263OB-C22 COOLANG; and the R&D project CLEARTEXT TED2021-130707B-I00. |
URI: | http://hdl.handle.net/10045/138984 |
ISSN: | 2076-3417 |
DOI: | 10.3390/app132312595 |
Idioma: | eng |
Tipo: | info:eu-repo/semantics/article |
Derechos: | © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
Revisión científica: | si |
Versión del editor: | https://doi.org/10.3390/app132312595 |
Aparece en las colecciones: | INV - GPLSI - Artículos de Revistas |
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Frances_etal_2023_ApplSci.pdf | 2,87 MB | Adobe PDF | Abrir Vista previa | |
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