How to reduce the carbon footprint of your research computing

The storage and processing of data uses energy, and therefore has a carbon footprint. This footprint is likely to increase in coming years given increased adoption of machine learning techniques for analysis. In this session, I'll discuss how computing leads to carbon emissions, and what can be done to reduce your personal research computing footprint. Specifically, I’ll provide context on:
- The scale of impact of the ICT sector on the climate
- The impact of accumulating unused ‘dark data’
- The importance of carbon intensity and task scheduling
- How to cut back on unnecessary computing
- Current methods for tracking computing energy usage and carbon emissions
This will include specific examples from neuroimaging research, but the messages and approaches discussed here will apply to any discipline requiring the processing of large amounts of data. The session will end with live demonstrations of green computing tools, including an online carbon-tracking calculator, and a climate-aware task scheduler.