加快数字神经形态学数据的持续社区共享
Carolina Tecuatl1, Bengt Ljungquist1, Giorgio A Ascoli1,2
1Bioengineering Department and Center for Neural Informatics, Structures and Plasticity, College of Engineering and Computing George Mason University Fairfax Virginia USA.
NeuroMorpho.Org现在快速释放神经重建,加速大脑研究. 自动化大大缩短了数据共享时间,促进了开放科学,并使神经科学领域的新发现成为可能.
科学领域:
- 神经科学是一个神经科学.
- 计算生物学 计算生物学
- 数据科学数据科学数据科学
背景情况:
- 神经元和质形态对于神经系统的功能至关重要.
- 获取详细的神经解剖学数据对于分析大脑状态至关重要.
- NeuroMorpho.Org是数字神经重建的主要存储库.
研究的目的:
- 为了提高NeuroMorpho.Org.数据发布的效率和速度.
- 为了促进神经回路的定量分析和计算建模.
- 促进神经科学中开放数据共享的文化.
主要方法:
- 数据处理管道的逐步自动化.
- 从单个数据集的年度到敏捷发布的过渡.
- 实施持续改进以实现更快的数据处理.
主要成果:
- 总体而言,数据发布时间减少了63.7%.
- 现在78%的数据集在不到3个月的时间内发布.
- 每次重建的平均处理时间从3小时减少到2分钟.
结论:
- 自动化处理可以实现更快,更频繁的数据发布.
- 由NeuroMorpho.Org加速的数据共享支持开放科学.
- 有效的数据访问通过数据集的再利用推动了科学发现.
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