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

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 is stored.

The relevance of sustainable laboratory practices

“… 42 The location of a data centre affects the carbon impact of calculations depending on the source of energy. 138 Thus we recommend to provide access to low-carbon computing facilities and dynamically shift jobs from data centres across multiple locations with green energy mixes ( e.g. solar, wind).…”

A guidebook for sustainability in laboratories

“…Here the GREENER principles by Lannelongue and co-workers can serve as a great starting point. [68][69][70] Concrete actions: 71,72 • Identify the hardware used in the dry lab: ○ Get in contact with the IT department and include them as much as possible to determine the number of servers, the type of clusters used, and the specifications of the computational facility (location, power usage effectiveness (PUE), carbon usage effectiveness (CUE), etc.). ○ Make an inventory of all the hardware currently maintained and used in your laboratory (e.g.…”

A guidebook for sustainability in laboratories

“…Here the GREENER principles by Lannelongue and co-workers can serve as a great starting point. [65][66][67] Concrete actions: 68,69 • Identify the hardware used in the dry lab: ○ Get in contact with the IT department and include them as much as possible to determine the number of servers and type of clusters used, as well as the specifications of the computational facility (location, power usage effectiveness (PUE), carbon usage effectiveness (CUE), etc.). ○ Make an inventory of all the hardware currently maintained and used for simulations: ■ Measure the energy consumption of your hardware during both computer simulations and idle times.…”