---
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](#dataset-summary)
2. [Dataset Details](#dataset-details)
3. [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
4. [Languages](#languages)
5. [Dataset Structure](#dataset-structure)
6. [Data Fields](#data-fields)
7. [Usage](#usage)
8. [Citation](#citation)


---

## 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](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](#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

```python
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

```...
```