Data for net-zero
AI systems require large amounts of good quality data. Ensuring easy access to high quality standardised data will be critical in enabling greater use of AI to support the transition to net zero.
There are a series of inter-related challenges that are commonly experienced by data scientists working on climate challenges. These include:
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.
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.
We're working with a range of climate and AI experts to iterate the design of the data space over the coming months. Are you a data scientist or engineer who struggles to find or access data related to the transition to net zero? If so we would love to hear from you. Get in touch with firstname.lastname@example.org