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Bibliometric analysis of neuroscience publications quantifies the impact of data sharing
MOTIVATION. Neural morphology, the branching geometry of neurons and glia in the nervous system, is an essential cellular substrate of brain function and pathology. Despite the accelerating production of digital reconstructions of neural morphology in laboratories worldwide, the public accessibility...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515804/ https://www.ncbi.nlm.nih.gov/pubmed/37745378 http://dx.doi.org/10.1101/2023.09.12.557386 |
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author | Emissah, Herve Ljungquist, Bengt Ascoli, Giorgio A. |
author_facet | Emissah, Herve Ljungquist, Bengt Ascoli, Giorgio A. |
author_sort | Emissah, Herve |
collection | PubMed |
description | MOTIVATION. Neural morphology, the branching geometry of neurons and glia in the nervous system, is an essential cellular substrate of brain function and pathology. Despite the accelerating production of digital reconstructions of neural morphology in laboratories worldwide, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and prevent researchers from building on others’ work. Data sharing complements the development of computational resources and literature mining tools to accelerate scientific discovery. RESULTS. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via the NeuroMorpho.Org online repository leads to a significant increase of citations to the original article, thus directly benefiting the authors. Moreover, the rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative meta-analysis applications, which accrue on average twice the citations of re-analyses of individual datasets. We also designed and deployed an open-source citation tracking web-service that allows researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and the released tool can facilitate the recognition of shared data reuse for promotion and tenure considerations, merit evaluations, and funding decisions. |
format | Online Article Text |
id | pubmed-10515804 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Cold Spring Harbor Laboratory |
record_format | MEDLINE/PubMed |
spelling | pubmed-105158042023-09-23 Bibliometric analysis of neuroscience publications quantifies the impact of data sharing Emissah, Herve Ljungquist, Bengt Ascoli, Giorgio A. bioRxiv Article MOTIVATION. Neural morphology, the branching geometry of neurons and glia in the nervous system, is an essential cellular substrate of brain function and pathology. Despite the accelerating production of digital reconstructions of neural morphology in laboratories worldwide, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and prevent researchers from building on others’ work. Data sharing complements the development of computational resources and literature mining tools to accelerate scientific discovery. RESULTS. We carried out a comprehensive bibliometric analysis of neural morphology publications to quantify the impact of data sharing in the neuroscience community. Our findings demonstrate that sharing digital reconstructions of neural morphology via the NeuroMorpho.Org online repository leads to a significant increase of citations to the original article, thus directly benefiting the authors. Moreover, the rate of data reusage remains constant for at least 16 years after sharing (the whole period analyzed), altogether nearly doubling the peer-reviewed discoveries in the field. Furthermore, the recent availability of larger and more numerous datasets fostered integrative meta-analysis applications, which accrue on average twice the citations of re-analyses of individual datasets. We also designed and deployed an open-source citation tracking web-service that allows researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and the released tool can facilitate the recognition of shared data reuse for promotion and tenure considerations, merit evaluations, and funding decisions. Cold Spring Harbor Laboratory 2023-09-13 /pmc/articles/PMC10515804/ /pubmed/37745378 http://dx.doi.org/10.1101/2023.09.12.557386 Text en 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/) , which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use. |
spellingShingle | Article Emissah, Herve Ljungquist, Bengt Ascoli, Giorgio A. Bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
title | Bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
title_full | Bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
title_fullStr | Bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
title_full_unstemmed | Bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
title_short | Bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
title_sort | bibliometric analysis of neuroscience publications quantifies the impact of data sharing |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10515804/ https://www.ncbi.nlm.nih.gov/pubmed/37745378 http://dx.doi.org/10.1101/2023.09.12.557386 |
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