Net-zero data-space

 

There are a series of inter-related challenges that are commonly experienced by data scientists working on climate challenges. These include:

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• Data discovery: where open data is dispersed throughout the internet, the discovery and aggregation of this data can take a long time.

• Data access: some data is not openly available. This might be because it is held privately, or by public bodies who aren't making it openly available.

• Data licenses: data isn't always available on licences where it can be used commercially. This limits the opportunities for it to be used.

• Data standards: data is commonly available on a range of different standards.

• Data quality: where data is openly available the quality may be poor.

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The combination of these challenges mean that data scientists end up spending a disproportionate amount of time finding and wrangling data.

The Centre for AI & Climate is working with the Global Partnership on AI, through CEIMIA, to explore these challenges further and develop a data space to help address them and so help to unlock the potential of artificial intelligence to address critical climate challenges within key sectors of the global economy - energy, transportation, agriculture and industry.