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Analyzing University of Virginia Health publications using open data, Python, and Streamlit

As part of a larger project to understand the publishing choices of UVA Health authors and support open access publishing, a team from the Claude Moore Health Sciences Library analyzed an open data set from Europe PMC, which includes metadata from PubMed records. We used the Europe PMC REST API to s...

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Detalles Bibliográficos
Autores principales: Parker, Anson, Heflin, Abbey, Jones, Lucy Carr
Formato: Online Artículo Texto
Lenguaje:English
Publicado: University Library System, University of Pittsburgh 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608219/
https://www.ncbi.nlm.nih.gov/pubmed/34858105
http://dx.doi.org/10.5195/jmla.2021.1360
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author Parker, Anson
Heflin, Abbey
Jones, Lucy Carr
author_facet Parker, Anson
Heflin, Abbey
Jones, Lucy Carr
author_sort Parker, Anson
collection PubMed
description As part of a larger project to understand the publishing choices of UVA Health authors and support open access publishing, a team from the Claude Moore Health Sciences Library analyzed an open data set from Europe PMC, which includes metadata from PubMed records. We used the Europe PMC REST API to search for articles published in 2017–2020 with “University of Virginia” in the author affiliation field. Subsequently, we parsed the JSON metadata in Python and used Streamlit to create a data visualization from our public GitHub repository. At present, this shows the relative proportions of open access versus subscription-only articles published by UVA Health authors. Although subscription services like Web of Science, Scopus, and Dimensions allow users to do similar analyses, we believe this is a novel approach to doing this type of bibliometric research with open data and open source tools.
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spelling pubmed-86082192021-12-01 Analyzing University of Virginia Health publications using open data, Python, and Streamlit Parker, Anson Heflin, Abbey Jones, Lucy Carr J Med Libr Assoc Virtual Project As part of a larger project to understand the publishing choices of UVA Health authors and support open access publishing, a team from the Claude Moore Health Sciences Library analyzed an open data set from Europe PMC, which includes metadata from PubMed records. We used the Europe PMC REST API to search for articles published in 2017–2020 with “University of Virginia” in the author affiliation field. Subsequently, we parsed the JSON metadata in Python and used Streamlit to create a data visualization from our public GitHub repository. At present, this shows the relative proportions of open access versus subscription-only articles published by UVA Health authors. Although subscription services like Web of Science, Scopus, and Dimensions allow users to do similar analyses, we believe this is a novel approach to doing this type of bibliometric research with open data and open source tools. University Library System, University of Pittsburgh 2021-10-01 2021-10-01 /pmc/articles/PMC8608219/ /pubmed/34858105 http://dx.doi.org/10.5195/jmla.2021.1360 Text en Copyright © 2021 Anson Parker, Abbey Heflin, Lucy Carr Jones https://creativecommons.org/licenses/by/4.0/This work is licensed under a Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Virtual Project
Parker, Anson
Heflin, Abbey
Jones, Lucy Carr
Analyzing University of Virginia Health publications using open data, Python, and Streamlit
title Analyzing University of Virginia Health publications using open data, Python, and Streamlit
title_full Analyzing University of Virginia Health publications using open data, Python, and Streamlit
title_fullStr Analyzing University of Virginia Health publications using open data, Python, and Streamlit
title_full_unstemmed Analyzing University of Virginia Health publications using open data, Python, and Streamlit
title_short Analyzing University of Virginia Health publications using open data, Python, and Streamlit
title_sort analyzing university of virginia health publications using open data, python, and streamlit
topic Virtual Project
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8608219/
https://www.ncbi.nlm.nih.gov/pubmed/34858105
http://dx.doi.org/10.5195/jmla.2021.1360
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