Published May 5, 2025 | Version v1
Dataset Open

Wind Energy Patent Dataset

  • 1. ROR icon University of Massachusetts Amherst
  • 2. Harvard Kennedy School

Description

General Information

This dataset contains patent information related to wind energy technologies identified using the methodology described by Fung et al. (2023) and Wang et al. (2024). The methodology aims to enhance the retrieval of wind energy patents and to distinguish offshore and onshore wind energy patents. 

Data description

We provide the SQL queries used for patent retrieval, along with the resulting patent dataset in CSV format. The dataset, derived from the 2016 Spring EPO PATSTAT database, includes information such as patent application ID, patent title, patent abstract, and other relevant fields. 

  • SQL files contain the queries used for wind energy patent retrieval.
    • WEDD1.sql: query for retrieving wind patents using conventional classification codes
    • wind_keywods.sql: query for retrieving wind patents using keywords
    • WEDD3.sql: query for retrieving wind patents using enhanced codes and keywords 
    • offshore_onshore_only_domain.sql: query for distinguishing between offshore and onshore-only wind energy patents 
  • CSV files contain the resulting datasets of wind energy patents (enhanced domain), offshore wind and onshore wind energy patents.
    • appln_id: Application identification
    • appln_nr_epodoc: Application number in EPODOC format
    • appln_title: Title of application (in UTF-8 encoding) 
    • appln_abstract: Abstract of application (in UTF-8 encoding) 
    • appln_filing_year: Year of the application filing date
    • cpc_class_symbol: Cooperative Patent Classification by application
    • Note: Please refer to the Data Catalog of PATSTAT for more detailed descriptions of the fields. 

Files

offshore_only_domain.csv

Files (94.9 MB)

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md5:3f5660ec1546585b1156ea0ac2c4a52e
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Additional details

Related works

Is described by
Journal article: 10.1016/j.wpi.2023.102209 (DOI)
Journal article: 10.1088/1748-9326/ad239e (DOI)

Funding

Alfred P. Sloan Foundation
G-2020-12682

Software

Programming language
SQL