---
license: apache-2.0
dataset_info:
  features:
  - name: id
    dtype: string
  - name: prompt
    dtype: string
  - name: figure
    dtype: image
  - name: text
    dtype: string
  - name: gt
    dtype: string
  - name: difficulty_level
    dtype: string
  splits:
  - name: train
    num_bytes: 128377449
    num_examples: 138
  download_size: 128251166
  dataset_size: 128377449
configs:
- config_name: biogr
  data_files:
  - split: train
    path: data/biogr-*
- config_name: dft
  data_files:
  - split: train
    path: data/dft-*
- config_name: geo
  data_files:
  - split: train
    path: data/geo-*
- config_name: hfd
  data_files:
  - split: train
    path: data/hfd-*
- config_name: hfe
  data_files:
  - split: train
    path: data/hfe-*
- config_name: mpve
  data_files:
  - split: train
    path: data/mpve-*
- config_name: pdb
  data_files:
  - split: train
    path: data/pdb-*
- config_name: qecc
  data_files:
  - split: train
    path: data/qecc-*
task_categories:
- question-answering
language:
- en
tags:
- biology
- physics
- science
- reasoning
size_categories:
- n<1K
---

# Curie Dataset

HF version of the dataset:

[CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning](https://arxiv.org/pdf/2503.13517).

Also available via [GitHub](https://github.com/google/curie/tree/main) (Apache-2.0 license).

##  Dataset Structure 

CURIE consists of 10 tasks that are mapped to 8 datasets. The datasets are: 

| Dataset ID     | Task Name                          | Domain                  | Description                                                                 |
|-------------|------------------------------------|----------------------------|-----------------------------------------------------------------------------|
| biogr     | Biodiversity Georeferencing        | Biodiversity               | Determine the latitude, longitude bounding box encompassing the region in the map image.      |
| dft       | Density Functional Theory Analysis | Condensed Matter Physics   | 3 Tasks related to DFT.   |
| pdb       | Protein Sequence Reconstruction    | Protein Sequencing         | Reconstruct a protein’s amino acid sequence form the 3D structure.           |
| geo       | Geospatial Dataset Extraction      | Geospatial Analysis        | Extract information for all geospatial datasets used along with the spatial and temporal extents.            |
| mpve      | Materials Property Value Extraction| Materials Science          | Identify all instances of materials,their properties, and descriptors          |
| qecc      | Quantum Error Correction Codes      | Quantum Computing         | Create a YAML file with the Error Correction Code’s properties.    |
| hfd       | Hartree-Fock Tasks Derivation      |  Condensed Matter Physics  | Derive the Hartree-Fock mean-field Hamiltonian for a quantum many-body system       |
| hfe       | Hartree-Fock Tasks Extraction       | Condensed Matter Physics  | Extract the most general mean-field Hamiltonian. |     

Each dataset contains the fields: 

`id (str)`: The sample id. \
`prompt (str)`: The prompt containing the task description for the LLM. \
`text (str)`: The sample specific information needed to solve the task. \
`gt (str)`: The groundtruth answer as a json-string. To obtain the structured representation load the string with json5 (see Example). \
`difficulty_level (str)`: Difficulty level of the task.

Special fields for some datasets:

| Dataset       | Additional Fields       | Content                        |
|---------------|--------------------------|--------------------------------|
| biogr         | figure (PIL Image)       | Figure containing geographical map |
| dft           | prompt_metadata (str), prompt_structure_data (str) | Prompts for the subtasks dft-structure & dft-metadata |
| mpve          | prompt_exclude_trivia (str) <br> prompt_bandgap_refractive (str) | Ablation prompts | 




## Example Usage 

Example: Query `gpt-4o` for a response on the hfd dataset using a LangChain chat model:

```python

import json5

from datasets import load_dataset
from langchain.chat_models.base import init_chat_model

dataset = load_dataset('nhop/curie','hfd')
llm = init_chat_model('gpt-4o')

for sample in dataset["train"]:
  print(sample["prompt"])
  prompt = sample["prompt"].replace("{{text}}",sample["text"])
  response = llm.invoke(prompt)
  print(response.content)
  groundtruth = json5.loads(sample["gt"])
  print(groundtruth)
  break

```



## Citation

```
@inproceedings{cui2025curie,
  title={CURIE: Evaluating LLMs on Multitask Scientific Long-Context Understanding and Reasoning},
  author={Cui, Hao and Shamsi, Zahra and Cheon, Gowoon and Ma, Xuejian and Li, Shutong and Tikhanovskaya, Maria and Norgaard, Peter Christian and Mudur, Nayantara and Plomecka, Martyna Beata and Raccuglia, Paul and others},
  booktitle={The Thirteenth International Conference on Learning Representations}
  year={2025}
}
```