Bibliometric analysis of neuroscience publications quantifies the impact of data sharing.
Bibliometric analysis of neuroscience publications quantifies the impact of data sharing.
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对神经科学出版物的文献计量分析量化了数据共享的影响。
DOI：
10.1101/2023.09.12.557386
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发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Ascoli,GiorgioA
中科院分区：
文献类型：
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作者：
Emissah,Herve;Ljungquist,Bengt;Ascoli,GiorgioA
概述神经形态学，即脑细胞的分支几何形状，是神经系统功能和病理学的重要细胞基质。尽管神经形态的数字重建正在加速生产，但数据的公共可访问性仍然是神经科学的核心问题。现有数据的可用性方面的缺陷造成研究工作的冗余，并限制了协同作用。我们对神经形态学出版物进行了全面的文献计量分析，以量化神经科学界数据共享的影响。我们的研究结果表明，通过NeuroMorpho.org共享神经形态的数字重建会导致对原始文章的引用显着增加，从而直接使作者受益。数据重复使用率在共享后至少16年内保持不变（整个分析期），这几乎是该领域同行评审发现的两倍。此外，最近可获得的更大和更多的数据集促进了综合应用，这平均增加了对单个数据集重新分析的引用的两倍。我们还发布了一个开源的引文跟踪网络服务，允许研究人员在独立的同行评审报告中监控其数据集的重复使用。这些结果和工具可以促进对共享数据的认可，并将其用于绩效评估和供资决策。http://cng-nmo-dev3.orc.gmu.edu:8181/源代码位于https://github.com/HerveEmissah/nmo-authors-app和https://github.com/HerveEmissah/nmo-bibliometric-analysis。
SummaryNeural morphology, the branching geometry of brain cells, is an essential cellular substrate of nervous system function and pathology. Despite the accelerating production of digital reconstructions of neural morphology, the public accessibility of data remains a core issue in neuroscience. Deficiencies in the availability of existing data create redundancy of research efforts and limit synergy. 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 NeuroMorpho.Org leads to a significant increase of citations to the original article, thus directly benefiting authors. 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 applications, which accrue on average twice the citations of re-analyses of individual datasets. We also released an open-source citation tracking web-service allowing researchers to monitor reusage of their datasets in independent peer-reviewed reports. These results and tools can facilitate the recognition of shared data reuse for merit evaluations and funding decisions.Availability and implementationThe application is available at: http://cng-nmo-dev3.orc.gmu.edu:8181/. The source code at https://github.com/HerveEmissah/nmo-authors-app and https://github.com/HerveEmissah/nmo-bibliometric-analysis.