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Flexible metadata harvesting for ecology using large language models

Created by
  • Haebom

Author

Zehao Lu, Thijs L van der Plas, Parinaz Rashidi, W Daniel Kissling, Ioannis N Athanasiadis

Outline

Large-scale open datasets can accelerate ecological research. In this paper, we developed an LLM-based metadata collector that flexibly extracts metadata from various data providers and transforms it into a user-defined format using existing metadata standards. This tool extracts both structured and unstructured metadata with equal accuracy, further enhancing accuracy through an LLM post-processing protocol. Furthermore, it identifies links between datasets by calculating embedding similarity and unifying the extracted metadata format. The developed tool can be used for ontology creation or graph-based queries, and can be utilized to discover relevant ecological and environmental datasets in virtual research environments.

Takeaways, Limitations

Takeaways:
LLM-based metadata collectors can integrate metadata from various datasets and identify relationships between datasets, thereby improving research efficiency.
It helps researchers find the datasets they want more easily through ontology creation and graph-based queries.
Limitations:
Additional information is needed on the performance evaluation results and accuracy of specific tools.
Due to the LLM-based nature, the possibility of model bias or misinformation must be considered.
There is a lack of information on the specific implementation and performance of dataset link identification methods.
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