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减少人类神经影像研究计算碳足迹的十条建议。

Ten recommendations for reducing the carbon footprint of research computing in human neuroimaging.

作者信息

Souter Nicholas E, Lannelongue Loïc, Samuel Gabrielle, Racey Chris, Colling Lincoln J, Bhagwat Nikhil, Selvan Raghavendra, Rae Charlotte L

机构信息

School of Psychology, University of Sussex, Brighton, United Kingdom.

Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, United Kingdom.

出版信息

Imaging Neurosci (Camb). 2024 Jan 29;1. doi: 10.1162/imag_a_00043. eCollection 2023.

Abstract

Given that scientific practices contribute to the climate crisis, scientists should reflect on the planetary impact of their work. Research computing can have a substantial carbon footprint in cases where researchers employ computationally expensive processes with large amounts of data. Analysis of human neuroimaging data, such as Magnetic Resonance Imaging brain scans, is one such case. Here, we consider ten ways in which those who conduct human neuroimaging research can reduce the carbon footprint of their research computing, by making adjustments to the ways in which studies are planned, executed, and analysed; as well as where and how data are stored.

摘要

鉴于科学实践对气候危机有影响,科学家们应该反思其工作对地球的影响。在研究人员采用计算成本高昂且涉及大量数据的过程时,研究计算可能会产生巨大的碳足迹。对人类神经影像数据的分析,比如磁共振成像脑部扫描,就是这样一个例子。在此,我们探讨了进行人类神经影像研究的人员可以通过调整研究的规划、执行和分析方式,以及数据的存储位置和方式,来减少其研究计算碳足迹的十种方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/197b/12007543/8f9741918185/imag_a_00043_fig1.jpg

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