README.md

metadata

license: cc-by-4.0
task_categories:
  - question-answering
language:
  - en
tags:
  - biology
  - plant
  - molecular
  - gene function
  - gene regulation

Dataset Card for MoBiPlant

Table of Contents

1. Dataset Summary
2. Dataset Details
3. Supported Tasks and Leaderboards
4. Languages
5. Dataset Structure
6. Data Fields
7. Usage
8. Citation

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Dataset Summary

MoBiPlant is a multiple-choice question-answering dataset curated by plant molecular biologists worldwide. It comprises two merged versions:

• Expert MoBiPlant: 565 expert-level questions authored by leading researchers.
• Synthetic MoBiPlant: 1,075 questions generated by large language models from papers in top plant science journals.

Each example consists of a question about plant molecular biology, a set of answer options, and the index of the correct answer. This dataset benchmarks MCQ-based knowledge in models within the plant molecular biology domain.

Dataset Details

• Name: MoBiPlant
• Version: v1.0
• GitHub: https://github.com/manoloFer10/mobiplant
• License: Creative Commons Attribution 4.0 International (CC BY 4.0)
• Release Date: 2025-06-09

Supported Tasks and Leaderboards

The primary task is:

• Multiple-Choice Question Answering: Given a question and a list of answer choices, predict the index of the correct option.

Leaderboard

Benchmark on Expert MoBiPlant (565 questions):

Model	CoT Answer Accuracy (%)
LLaMA 3.1 405B	77.6
GPT-4o	81.2
o1-mini	81.1
deepseek v3	84.3
deepseek-r1	86.4
Claude 3.5 Sonnet	88.1
Gemini 1.5 Pro	76.8

For full results on both versions, see the associated paper.

Languages

• Language: English

Dataset Structure

• Versions:

  • Expert: 565 expert-authored questions.
  • Synthetic: 1,075 LLM-generated questions.

• Splits:

  • The train split contains all examples (1,640 total). To access each version, see Usage.

• Number of Examples:

  • 1,640 total examples across expert and synthetic sets.

Data Fields

Each entry in the train split contains:

Field	Type	Description
question	string	The MCQ question text.
options	list[string]	A list of possible answer strings.
answer	int	Index of the correct option in options (0-based).
area	string	General research area (e.g., GENE REGULATION - TRANSLATION).
normalized_area	string	Normalized research area category (e.g., GENE REGULATION).
plant_species	list[string]	Original plant species labels (e.g., ["Arabidopsis thaliana", "Zea mays"]).
normalized_plant_species	string	Normalized plant species label (e.g., Non-specific).
doi	string	DOI of the primary source publication.
source	string	URL or citation of the source article.
source_journal	string	Journal of publication of the source article.
Year	int	Publication year of the source.
Citations	int	Number of citations the source article has received.
is_expert	bool	True if the example belongs to the Expert MoBiPlant subset; False otherwise.

Usage

from datasets import load_dataset

# Load from HF
mobiplant = load_dataset("manufernandezbur/MoBiPlant")['train']

# Filter out expert and synthetic versions (optional)
expert_mobiplant = mobiplant.filter(lambda question: question['is_expert'])
synth_mobiplant = mobiplant.filter(lambda question: not question['is_expert'])

# Example iteration
for example in expert_mobiplant:
    question = example["question"]
    options = example["options"]
    label = example["answer"]
    print(f'Question: {question}')
    print('Options: ','\n'.join([ chr(65+i) + opt for i,opt in enumerate(options)]))
    print('Correct Answer: ', options[label])

Citation