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metadata 
dataset_info:
  - config_name: diffusion_path_multimodal_grid10
    features:
      - name: row_uuid
        dtype: string
      - name: dataset_uuid
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      - name: image
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  - config_name: diffusion_path_multimodal_grid25
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  - config_name: diffusion_path_multimodal_grid5
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  - config_name: diffusion_path_multimodal_grid50
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  - config_name: fsm_traversal_multimodal_len10
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  - config_name: fsm_traversal_multimodal_len25
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  - config_name: fsm_traversal_multimodal_len5
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  - config_name: fsm_traversal_multimodal_len50
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  - config_name: particle_energy_2d
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  - config_name: particle_energy_3d
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  - config_name: particle_energy_multimodal_2d_n10
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  - config_name: particle_energy_multimodal_2d_n25
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  - config_name: particle_energy_multimodal_2d_n5
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  - config_name: particle_energy_multimodal_2d_n50
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  - config_name: particle_energy_multimodal_3d_n10
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  - config_name: particle_energy_multimodal_3d_n25
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  - config_name: particle_energy_multimodal_3d_n5
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  - config_name: particle_energy_multimodal_3d_n50
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  - config_name: peak_sorting_multimodal_n10
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  - config_name: peak_sorting_multimodal_n100
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  - config_name: peak_sorting_multimodal_n25
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  - config_name: peak_sorting_multimodal_n5
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  - config_name: peak_sorting_multimodal_n50
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  - config_name: tree_traversal_multimodal_n10
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  - config_name: tree_traversal_multimodal_n25
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  - config_name: tree_traversal_multimodal_n5
    features:
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  - config_name: tree_traversal_multimodal_n50
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        dtype: string
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configs:
  - config_name: diffusion_path_multimodal_grid10
    data_files:
      - split: train
        path: diffusion_path_multimodal_grid10/train-*
  - config_name: diffusion_path_multimodal_grid25
    data_files:
      - split: train
        path: diffusion_path_multimodal_grid25/train-*
  - config_name: diffusion_path_multimodal_grid5
    data_files:
      - split: train
        path: diffusion_path_multimodal_grid5/train-*
  - config_name: diffusion_path_multimodal_grid50
    data_files:
      - split: train
        path: diffusion_path_multimodal_grid50/train-*
  - config_name: fsm_traversal_multimodal_len10
    data_files:
      - split: train
        path: fsm_traversal_multimodal_len10/train-*
  - config_name: fsm_traversal_multimodal_len25
    data_files:
      - split: train
        path: fsm_traversal_multimodal_len25/train-*
  - config_name: fsm_traversal_multimodal_len5
    data_files:
      - split: train
        path: fsm_traversal_multimodal_len5/train-*
  - config_name: fsm_traversal_multimodal_len50
    data_files:
      - split: train
        path: fsm_traversal_multimodal_len50/train-*
  - config_name: particle_energy_2d
    data_files:
      - split: train
        path: particle_energy_2d/train-*
  - config_name: particle_energy_3d
    data_files:
      - split: train
        path: particle_energy_3d/train-*
  - config_name: particle_energy_multimodal_2d_n10
    data_files:
      - split: train
        path: particle_energy_multimodal_2d_n10/train-*
  - config_name: particle_energy_multimodal_2d_n25
    data_files:
      - split: train
        path: particle_energy_multimodal_2d_n25/train-*
  - config_name: particle_energy_multimodal_2d_n5
    data_files:
      - split: train
        path: particle_energy_multimodal_2d_n5/train-*
  - config_name: particle_energy_multimodal_2d_n50
    data_files:
      - split: train
        path: particle_energy_multimodal_2d_n50/train-*
  - config_name: particle_energy_multimodal_3d_n10
    data_files:
      - split: train
        path: particle_energy_multimodal_3d_n10/train-*
  - config_name: particle_energy_multimodal_3d_n25
    data_files:
      - split: train
        path: particle_energy_multimodal_3d_n25/train-*
  - config_name: particle_energy_multimodal_3d_n5
    data_files:
      - split: train
        path: particle_energy_multimodal_3d_n5/train-*
  - config_name: particle_energy_multimodal_3d_n50
    data_files:
      - split: train
        path: particle_energy_multimodal_3d_n50/train-*
  - config_name: peak_sorting_multimodal_n10
    data_files:
      - split: train
        path: peak_sorting_multimodal_n10/train-*
  - config_name: peak_sorting_multimodal_n100
    data_files:
      - split: train
        path: peak_sorting_multimodal_n100/train-*
  - config_name: peak_sorting_multimodal_n25
    data_files:
      - split: train
        path: peak_sorting_multimodal_n25/train-*
  - config_name: peak_sorting_multimodal_n5
    data_files:
      - split: train
        path: peak_sorting_multimodal_n5/train-*
  - config_name: peak_sorting_multimodal_n50
    data_files:
      - split: train
        path: peak_sorting_multimodal_n50/train-*
  - config_name: tree_traversal_multimodal_n10
    data_files:
      - split: train
        path: tree_traversal_multimodal_n10/train-*
  - config_name: tree_traversal_multimodal_n25
    data_files:
      - split: train
        path: tree_traversal_multimodal_n25/train-*
  - config_name: tree_traversal_multimodal_n5
    data_files:
      - split: train
        path: tree_traversal_multimodal_n5/train-*
  - config_name: tree_traversal_multimodal_n50
    data_files:
      - split: train
        path: tree_traversal_multimodal_n50/train-*

    ### Configuration: `particel_energy_2d`

    This configuration contains text-only problems for calculating the total energy of a 2D particle system.

    **Task Description:**

    Calculate the total energy of a system with 10 particles in a 10x10 Angstrom box. The energy calculation involves:
    1.  **Particle Energy**: The individual energy of each particle.
    2.  **Pairwise Interaction Energy**: For particles within a 5 Angstrom cutoff distance, the interaction energy is `distance * (energy_A + energy_B)`.

    **Data Fields:**
    - `uuid`: A unique identifier for the example.
    - `input`: The question text describing the particle system's coordinates and energies.
    - `target`: The numerical answer for the total energy in eV.
    - ... (and other relevant metadata fields).

    **Usage:**

    ```python
    from datasets import load_dataset

    dataset = load_dataset("n0w0f/scirex-text", name="particel_energy_2d", split="train")
    print(dataset[0])

Configuration: particle_energy_2d

Type: Multimodal Examples: 20 Task: Energy computation for 2D particle systems

Description: Calculate the total energy of a system with 10 particles in a 10×10 Angstrom box. The energy calculation involves:

  1. Particle Energy: Individual energy contribution from each particle
  2. Pairwise Interaction Energy: For particles within 5 Angstrom cutoff, interaction energy = distance × (energy_A + energy_B)

Usage:

from datasets import load_dataset

dataset = load_dataset("particle_energy_computation_multimodal", name="particle_energy_2d")
example = dataset['train'][0]
print(example)

Data Fields:

  • uuid: Unique identifier

  • examples: Problem data (JSON string)

  • description: Task description

  • image: Visualization of particle system

  • Other metadata fields

          ---
      ## Configuration: `particle_energy_multimodal_3d_n25`

      **Description**: Calculate total energy of 25 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n5`

      **Description**: Calculate total energy of 5 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n5`

      **Description**: Calculate total energy of 5 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n25`

      **Description**: Calculate total energy of 25 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n10`

      **Description**: Calculate total energy of 10 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n50`

      **Description**: Calculate total energy of 50 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n10`

      **Description**: Calculate total energy of 10 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n50`

      **Description**: Calculate total energy of 50 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n25`

      **Description**: Calculate total energy of 25 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n5`

      **Description**: Calculate total energy of 5 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n5`

      **Description**: Calculate total energy of 5 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n25`

      **Description**: Calculate total energy of 25 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n10`

      **Description**: Calculate total energy of 10 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_2d_n50`

      **Description**: Calculate total energy of 50 2D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `2d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_2d_n50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n10`

      **Description**: Calculate total energy of 10 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `particle_energy_multimodal_3d_n50`

      **Description**: Calculate total energy of 50 3D particles (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `physics`, `computational chemistry`, `energy`, `particles`, `pairwise-interactions`, `3d-systems`, `numerical-reasoning`, `multimodal`, `visualization`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="particle_energy_multimodal_3d_n50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `peak_sorting_multimodal_n100`

      **Description**: Sort a list of 100 spectral peaks by position or intensity (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `data processing`, `sorting`, `spectroscopy`, `analytical chemistry`, `list manipulation`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="peak_sorting_multimodal_n100")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `peak_sorting_multimodal_n25`

      **Description**: Sort a list of 25 spectral peaks by position or intensity (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `data processing`, `sorting`, `spectroscopy`, `analytical chemistry`, `list manipulation`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="peak_sorting_multimodal_n25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `peak_sorting_multimodal_n10`

      **Description**: Sort a list of 10 spectral peaks by position or intensity (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `data processing`, `sorting`, `spectroscopy`, `analytical chemistry`, `list manipulation`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="peak_sorting_multimodal_n10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `peak_sorting_multimodal_n50`

      **Description**: Sort a list of 50 spectral peaks by position or intensity (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `data processing`, `sorting`, `spectroscopy`, `analytical chemistry`, `list manipulation`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="peak_sorting_multimodal_n50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `peak_sorting_multimodal_n5`

      **Description**: Sort a list of 5 spectral peaks by position or intensity (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `data processing`, `sorting`, `spectroscopy`, `analytical chemistry`, `list manipulation`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="peak_sorting_multimodal_n5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `diffusion_path_multimodal_grid50`

      **Description**: Find shortest path on a 50x50 grid (multimodal version).

      **Number of Examples**: 20

      **Keywords**: `computer science`, `pathfinding`, `BFS`, `grid`, `algorithmic reasoning`, `materials science`, `diffusion`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="diffusion_path_multimodal_grid50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `diffusion_path_multimodal_grid10`

      **Description**: Find shortest path on a 10x10 grid (multimodal version).

      **Number of Examples**: 20

      **Keywords**: `computer science`, `pathfinding`, `BFS`, `grid`, `algorithmic reasoning`, `materials science`, `diffusion`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="diffusion_path_multimodal_grid10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `diffusion_path_multimodal_grid25`

      **Description**: Find shortest path on a 25x25 grid (multimodal version).

      **Number of Examples**: 20

      **Keywords**: `computer science`, `pathfinding`, `BFS`, `grid`, `algorithmic reasoning`, `materials science`, `diffusion`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="diffusion_path_multimodal_grid25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `diffusion_path_multimodal_grid5`

      **Description**: Find shortest path on a 5x5 grid (multimodal version).

      **Number of Examples**: 20

      **Keywords**: `computer science`, `pathfinding`, `BFS`, `grid`, `algorithmic reasoning`, `materials science`, `diffusion`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="diffusion_path_multimodal_grid5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `fsm_traversal_multimodal_len5`

      **Description**: Traverse a 4-state FSM with an input string of length 5 (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `FSM`, `finite state machine`, `automata theory`, `algorithmic reasoning`, `rule-following`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="fsm_traversal_multimodal_len5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `fsm_traversal_multimodal_len25`

      **Description**: Traverse a 4-state FSM with an input string of length 25 (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `FSM`, `finite state machine`, `automata theory`, `algorithmic reasoning`, `rule-following`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="fsm_traversal_multimodal_len25")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `fsm_traversal_multimodal_len10`

      **Description**: Traverse a 4-state FSM with an input string of length 10 (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `FSM`, `finite state machine`, `automata theory`, `algorithmic reasoning`, `rule-following`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="fsm_traversal_multimodal_len10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `fsm_traversal_multimodal_len50`

      **Description**: Traverse a 4-state FSM with an input string of length 50 (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `FSM`, `finite state machine`, `automata theory`, `algorithmic reasoning`, `rule-following`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="fsm_traversal_multimodal_len50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `tree_traversal_multimodal_n5`

      **Description**: Perform a traversal on a binary tree with 5 nodes (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `data structures`, `binary tree`, `recursion`, `algorithmic reasoning`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="tree_traversal_multimodal_n5")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `tree_traversal_multimodal_n50`

      **Description**: Perform a traversal on a binary tree with 50 nodes (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `data structures`, `binary tree`, `recursion`, `algorithmic reasoning`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="tree_traversal_multimodal_n50")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `tree_traversal_multimodal_n10`

      **Description**: Perform a traversal on a binary tree with 10 nodes (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `data structures`, `binary tree`, `recursion`, `algorithmic reasoning`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="tree_traversal_multimodal_n10")

      # Access the first example
      example = dataset['train']
      print(example)
      
      ---
      ## Configuration: `tree_traversal_multimodal_n25`

      **Description**: Perform a traversal on a binary tree with 25 nodes (multimodal version).

      **Number of Examples**: 15

      **Keywords**: `computer science`, `data structures`, `binary tree`, `recursion`, `algorithmic reasoning`, `multimodal`

      **Usage:**```python
      from datasets import load_dataset

      # Load this specific configuration
      dataset = load_dataset("n0w0f/scirex-image", name="tree_traversal_multimodal_n25")

      # Access the first example
      example = dataset['train']
      print(example)