netneurotools: a trainee-oriented approach to network neuroscience.

IF 18.4 1区 生物学 Q1 BIOCHEMICAL RESEARCH METHODS
Zhen-Qi Liu, Vincent Bazinet, Justine Y Hansen, Filip Milisav, Andrea I Luppi, Eric G Ceballos, Asa Farahani, Laura E Suarez, Golia Shafiei, Ross D Markello, Bratislav Misic
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引用次数: 0

Abstract

Brain imaging is an increasingly interdisciplinary field, encompassing multiple data types and multiple analytic traditions. Projects typically involve many moving parts, such as building customized preprocessing pipelines, transforming between data formats, preparing datasets for analysis and, ultimately, displaying results. The field is conventionally built on highly specialized software packages that solve these individual challenges well but are not necessarily designed to be interoperable. Trainees new to the field are therefore often left to come up with isolated heuristics and workarounds to complete a project. Here we present a set of tools to navigate the increasingly complex informatics ecosystem of brain imaging. netneurotools is a Python toolkit that has been continuously developed and maintained by this laboratory's trainees. The philosophy of the toolkit is that it should be the Swiss army knife of the laboratory: functions and routines that we often use but that are not part of any established pipeline or package. Since its inception, the toolkit has been open and welcomes contributions from neuroscientists across the globe. netneurotools presents a necessary counterweight to out-of-the-box software packages and highlights the importance of smaller, ad hoc functions for implementing projects. By opening a window into the inner workings of a laboratory, netneurotools also presents an opportunity to begin a new type of discourse among groups and establish tangible links within the community.

网络神经工具:面向学员的网络神经科学方法。
脑成像是一个日益跨学科的领域,包括多种数据类型和多种分析传统。项目通常涉及许多活动部分,例如构建定制的预处理管道,在数据格式之间进行转换,准备用于分析的数据集,并最终显示结果。该领域通常建立在高度专业化的软件包之上,这些软件包可以很好地解决这些单独的挑战,但不一定设计成可互操作的。因此,新进入该领域的受训者经常需要想出孤立的启发式方法和变通方法来完成一个项目。在这里,我们提出了一套工具来导航日益复杂的脑成像信息学生态系统。netneurotools是一个Python工具包,由该实验室的学员不断开发和维护。工具包的理念是,它应该是实验室的瑞士军刀:我们经常使用的功能和例程,但不是任何已建立的管道或包的一部分。自成立以来,该工具包一直是开放的,欢迎来自全球神经科学家的贡献。Netneurotools为开箱即用的软件包提供了必要的平衡,并强调了实现项目的更小、特别功能的重要性。通过打开一扇进入实验室内部工作的窗口,netneurotools也提供了一个机会,在群体之间开始一种新型的话语,并在社区内建立切实的联系。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Nature Protocols
Nature Protocols 生物-生化研究方法
CiteScore
29.10
自引率
0.70%
发文量
128
审稿时长
4 months
期刊介绍: Nature Protocols focuses on publishing protocols used to address significant biological and biomedical science research questions, including methods grounded in physics and chemistry with practical applications to biological problems. The journal caters to a primary audience of research scientists and, as such, exclusively publishes protocols with research applications. Protocols primarily aimed at influencing patient management and treatment decisions are not featured. The specific techniques covered encompass a wide range, including but not limited to: Biochemistry, Cell biology, Cell culture, Chemical modification, Computational biology, Developmental biology, Epigenomics, Genetic analysis, Genetic modification, Genomics, Imaging, Immunology, Isolation, purification, and separation, Lipidomics, Metabolomics, Microbiology, Model organisms, Nanotechnology, Neuroscience, Nucleic-acid-based molecular biology, Pharmacology, Plant biology, Protein analysis, Proteomics, Spectroscopy, Structural biology, Synthetic chemistry, Tissue culture, Toxicology, and Virology.
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