Browsing by Autor "Giorgio Triulzi"

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    Patent citation data for USPTO utility patents granted between 1976-2015 and for patents belonging to 30 technology domains
    (European Organization for Nuclear Research, 2020) Giorgio Triulzi
    These two data file contains information on patent citations for USPTO utility patents granted between 1976 and 2015 and for patents that have been classified in 30 specific technology domains. The file 'CITATION_INFO_no_neg_citlag.csv' is generated combining raw data freely dowloadable from patentsview.org from which citations where the filing year of the citing patent is younger than the filing year of the cited one have been removed. The file 'CITATIONS_DOMAINS.csv' is a sample of the previous file that only includes citations made by patents belonging to one of 30 domains defined in the paper 'Estimating technology performance improvement rates by mining patent data' by Giorgio Triulzi, Jeff Alstott and Chris Magee. These two files complement another dataset published on Mendeley Data. The two datasets can be used, together with the code published on GitHub, to replicate the main results from the paper.
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    Item type: Item ,
    Patent citation data for USPTO utility patents granted between 1976-2015 and for patents belonging to 30 technology domains
    (European Organization for Nuclear Research, 2020) Giorgio Triulzi
    These two data file contains information on patent citations for USPTO utility patents granted between 1976 and 2015 and for patents that have been classified in 30 specific technology domains. The file 'CITATION_INFO_no_neg_citlag.csv' is generated combining raw data freely dowloadable from patentsview.org from which citations where the filing year of the citing patent is younger than the filing year of the cited one have been removed. The file 'CITATIONS_DOMAINS.csv' is a sample of the previous file that only includes citations made by patents belonging to one of 30 domains defined in the paper 'Estimating technology performance improvement rates by mining patent data' by Giorgio Triulzi, Jeff Alstott and Chris Magee. These two files complement another dataset published on Mendeley Data. The two datasets can be used, together with the code published on GitHub, to replicate the main results from the paper.
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    Quantification of technological progress in greenhouse gas (GHG) capture and mitigation using patent data
    (Royal Society of Chemistry, 2019) Mahdi Sharifzadeh; Giorgio Triulzi; Christopher L. Magee
    Greenhouse gas emissions from anthropogenic sources are believed to be the main cause of global warming. We estimate performance improvement rates of various GHG capture and mitigation technologies using a method based on patent centrality.