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BIOMD0000000407
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SciGym: Measuring Scientific Capabilities of Language Models with a Systems Biology Dry Lab

Dataset Description

SciGym is a benchmark dataset for evaluating the scientific discovery capabilities of language models through iterative experiment design in a simulated biological environment. It challenges models to uncover biological mechanisms by designing and interpreting simulated experiments.

Dataset Structure

The dataset is organized into two splits:

  • small: The tasks used in the paper evaluation
  • large: Additional instances with more complex biological systems

Each instance contains the following files:

  • truth.xml: The reference SBML model that the agent is tasked with discovering
  • partial.xml: The starting SBML model provided to the agent (truth.xml with reactions removed)
  • truth.sedml: The SED-ML file containing simulation configuration for the experiment

Dataset Creation

This dataset was created using expert-curated biological models from the BioModels repository. Models were selected and modified to create appropriate scientific discovery challenges, with reactions removed from the truth models to create the partial models that agents begin with.

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