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The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV

Patent bibliometrics data are the most reliable business performance metric for applied research and development activities when investigating the knowledge domains or the technological evolution of vehicle powertrain technologies in the automotive industry. Our paper describes a global patents data...

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Detalles Bibliográficos
Autores principales: Mirzadeh Phirouzabadi, Amir, Savage, David, Blackmore, Karen, Juniper, James
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7394751/
https://www.ncbi.nlm.nih.gov/pubmed/32775562
http://dx.doi.org/10.1016/j.dib.2020.106042
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author Mirzadeh Phirouzabadi, Amir
Savage, David
Blackmore, Karen
Juniper, James
author_facet Mirzadeh Phirouzabadi, Amir
Savage, David
Blackmore, Karen
Juniper, James
author_sort Mirzadeh Phirouzabadi, Amir
collection PubMed
description Patent bibliometrics data are the most reliable business performance metric for applied research and development activities when investigating the knowledge domains or the technological evolution of vehicle powertrain technologies in the automotive industry. Our paper describes a global patents dataset for the internal combustion engine vehicles (ICEV), hybrid electric vehicles (HEV) and battery electric vehicles (BEV) over 1985–2016. We extracted the patents granted in each powertrain field from Thomson Reuters' Derwent Innovations Index (DII). We applied a combined search strategy of international patent classifications (IPCs) and keywords as well as ‘patent families’ and ‘priority dates’ to construct our global patents dataset. This strategy returned a total of 78,732 patents, within which we identified 49,154 ICEV patents; 10,888 HEV patents; and 18,690 BEV patents. Our database includes numerous descriptive features of each patent such as title, abstract, claim, priority, application and publication dates, IPCs, assignees/applicants, inventors, and cited references. These data are associated with the research article ‘The evolution of dynamic interactions between the knowledge development of powertrain systems’ [1]. The full dataset, which is attached to this article, may be useful to both researchers and practitioners interested in investigating, modelling or forecasting the complexity and evolution of the technical and knowledge domains of the vehicle powertrains, across a variety of instruments such as social network analysis and regression models.
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spelling pubmed-73947512020-08-06 The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV Mirzadeh Phirouzabadi, Amir Savage, David Blackmore, Karen Juniper, James Data Brief Business, Management and Accounting Patent bibliometrics data are the most reliable business performance metric for applied research and development activities when investigating the knowledge domains or the technological evolution of vehicle powertrain technologies in the automotive industry. Our paper describes a global patents dataset for the internal combustion engine vehicles (ICEV), hybrid electric vehicles (HEV) and battery electric vehicles (BEV) over 1985–2016. We extracted the patents granted in each powertrain field from Thomson Reuters' Derwent Innovations Index (DII). We applied a combined search strategy of international patent classifications (IPCs) and keywords as well as ‘patent families’ and ‘priority dates’ to construct our global patents dataset. This strategy returned a total of 78,732 patents, within which we identified 49,154 ICEV patents; 10,888 HEV patents; and 18,690 BEV patents. Our database includes numerous descriptive features of each patent such as title, abstract, claim, priority, application and publication dates, IPCs, assignees/applicants, inventors, and cited references. These data are associated with the research article ‘The evolution of dynamic interactions between the knowledge development of powertrain systems’ [1]. The full dataset, which is attached to this article, may be useful to both researchers and practitioners interested in investigating, modelling or forecasting the complexity and evolution of the technical and knowledge domains of the vehicle powertrains, across a variety of instruments such as social network analysis and regression models. Elsevier 2020-07-20 /pmc/articles/PMC7394751/ /pubmed/32775562 http://dx.doi.org/10.1016/j.dib.2020.106042 Text en © 2020 The Authors http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Business, Management and Accounting
Mirzadeh Phirouzabadi, Amir
Savage, David
Blackmore, Karen
Juniper, James
The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV
title The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV
title_full The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV
title_fullStr The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV
title_full_unstemmed The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV
title_short The global patents dataset on the vehicle powertrains of ICEV, HEV, and BEV
title_sort global patents dataset on the vehicle powertrains of icev, hev, and bev
topic Business, Management and Accounting
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7394751/
https://www.ncbi.nlm.nih.gov/pubmed/32775562
http://dx.doi.org/10.1016/j.dib.2020.106042
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