Unlocking Ecological Data with GenAI

For Charles, a longstanding research ambition had always been hampered by the scale of the task. He wanted to test hypotheses in community ecology using data from mid-20th century publications on parasitic wasps. The information was there but locked away in unstructured text that would have taken at least two weeks of full-time manual effort to extract. 

With generative AI, that barrier disappeared. Charles fed around 500 pages of scientific papers into Google Gemini, each describing host relations of a group of parasitic wasps. The AI was asked to abstract the information and return it as a structured spreadsheet with ten specific columns. His prompt carefully explained the different conventions used in the original papers and the exact spreadsheet format required. The result was a clean dataset from which food webs could be constructed, which were then used to explore hypotheses about ecological community structuring.

AI was also used to convert a modern PDF checklist into a spreadsheet and then update and check species nomenclature against it. To test accuracy, Charles randomly selected 50 entries from the generated spreadsheet and found just two minor errors, neither of which affected the ecological analyses. 

A task Charles had considered but postponed for 20 years because of the daunting workload was achieved in minutes. What once seemed unfeasible became possible with the right application of AI. 

His advice for others at Oxford is simple: don’t be afraid of long and quite complex prompts. The more detail you provide about the data, conventions, or output format; the better the AI will perform.