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A dataset of publication records for Nobel laureates
A central question in the science of science concerns how to develop a quantitative understanding of the evolution and impact of individual careers. Over the course of history, a relatively small fraction of individuals have made disproportionate, profound, and lasting impacts on science and society...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472427/ https://www.ncbi.nlm.nih.gov/pubmed/31000709 http://dx.doi.org/10.1038/s41597-019-0033-6 |
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author | Li, Jichao Yin, Yian Fortunato, Santo Wang, Dashun |
author_facet | Li, Jichao Yin, Yian Fortunato, Santo Wang, Dashun |
author_sort | Li, Jichao |
collection | PubMed |
description | A central question in the science of science concerns how to develop a quantitative understanding of the evolution and impact of individual careers. Over the course of history, a relatively small fraction of individuals have made disproportionate, profound, and lasting impacts on science and society. Despite a long-standing interest in the careers of scientific elites across diverse disciplines, it remains difficult to collect large-scale career histories that could serve as training sets for systematic empirical and theoretical studies. Here, by combining unstructured data collected from CVs, university websites, and Wikipedia, together with the publication and citation database from Microsoft Academic Graph (MAG), we reconstructed publication histories of nearly all Nobel prize winners from the past century, through both manual curation and algorithmic disambiguation procedures. Data validation shows that the collected dataset presents among the most comprehensive collection of publication records for Nobel laureates currently available. As our quantitative understanding of science deepens, this dataset is expected to have increasing value. It will not only allow us to quantitatively probe novel patterns of productivity, collaboration, and impact governing successful scientific careers, it may also help us unearth the fundamental principles underlying creativity and the genesis of scientific breakthroughs. |
format | Online Article Text |
id | pubmed-6472427 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-64724272019-04-19 A dataset of publication records for Nobel laureates Li, Jichao Yin, Yian Fortunato, Santo Wang, Dashun Sci Data Data Descriptor A central question in the science of science concerns how to develop a quantitative understanding of the evolution and impact of individual careers. Over the course of history, a relatively small fraction of individuals have made disproportionate, profound, and lasting impacts on science and society. Despite a long-standing interest in the careers of scientific elites across diverse disciplines, it remains difficult to collect large-scale career histories that could serve as training sets for systematic empirical and theoretical studies. Here, by combining unstructured data collected from CVs, university websites, and Wikipedia, together with the publication and citation database from Microsoft Academic Graph (MAG), we reconstructed publication histories of nearly all Nobel prize winners from the past century, through both manual curation and algorithmic disambiguation procedures. Data validation shows that the collected dataset presents among the most comprehensive collection of publication records for Nobel laureates currently available. As our quantitative understanding of science deepens, this dataset is expected to have increasing value. It will not only allow us to quantitatively probe novel patterns of productivity, collaboration, and impact governing successful scientific careers, it may also help us unearth the fundamental principles underlying creativity and the genesis of scientific breakthroughs. Nature Publishing Group UK 2019-04-18 /pmc/articles/PMC6472427/ /pubmed/31000709 http://dx.doi.org/10.1038/s41597-019-0033-6 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files associated with this article. |
spellingShingle | Data Descriptor Li, Jichao Yin, Yian Fortunato, Santo Wang, Dashun A dataset of publication records for Nobel laureates |
title | A dataset of publication records for Nobel laureates |
title_full | A dataset of publication records for Nobel laureates |
title_fullStr | A dataset of publication records for Nobel laureates |
title_full_unstemmed | A dataset of publication records for Nobel laureates |
title_short | A dataset of publication records for Nobel laureates |
title_sort | dataset of publication records for nobel laureates |
topic | Data Descriptor |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6472427/ https://www.ncbi.nlm.nih.gov/pubmed/31000709 http://dx.doi.org/10.1038/s41597-019-0033-6 |
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