---
dataset_info:
- config_name: aspects
  features:
  - name: filename
    dtype: large_string
  - name: abstract
    dtype: large_string
  - name: aspect_annotation
    dtype: large_string
  - name: aspects
    dtype: large_string
  splits:
  - name: train_ru
    num_bytes: 1628735
    num_examples: 186
  - name: train_kz
    num_bytes: 1607806
    num_examples: 186
  - name: test_ru
    num_bytes: 167148
    num_examples: 20
  - name: test_kz
    num_bytes: 165328
    num_examples: 20
  download_size: 1639452
  dataset_size: 3569017
- config_name: ner_re
  features:
  - name: filename
    dtype: large_string
  - name: abstract
    dtype: large_string
  - name: annotation
    dtype: large_string
  - name: entities
    dtype: large_string
  - name: relations
    dtype: large_string
  splits:
  - name: train_ru
    num_bytes: 1620694
    num_examples: 186
  - name: train_kz
    num_bytes: 1593191
    num_examples: 186
  - name: test_ru
    num_bytes: 163368
    num_examples: 20
  - name: test_kz
    num_bytes: 163073
    num_examples: 20
  download_size: 1564526
  dataset_size: 3540326
configs:
- config_name: aspects
  data_files:
  - split: train_ru
    path: aspects/train_ru-*
  - split: train_kz
    path: aspects/train_kz-*
  - split: test_ru
    path: aspects/test_ru-*
  - split: test_kz
    path: aspects/test_kz-*
- config_name: ner_re
  data_files:
  - split: train_ru
    path: ner_re/train_ru-*
  - split: train_kz
    path: ner_re/train_kz-*
  - split: test_ru
    path: ner_re/test_ru-*
  - split: test_kz
    path: ner_re/test_kz-*
---
# SciMDIX Dataset

## Dataset Description
SciMDIX is a bilingual dataset containing scientific abstracts in Russian and Kazakh across four domains: **IT, Linguistics, Medicine, and Psychology**. It is designed for advanced Information Extraction tasks and is divided into two main configurations:

1. **ner_re**: Contains annotations for Named Entity Recognition (NER) and Relation Extraction (RE).
2. **aspects**: Contains aspect-level markup (AIM, MATERIAL, METHOD, RESULT, TASK, TOOL, USAGE) for the same texts.

## Data Structure

Each configuration contains four splits: `train_ru`, `train_kz`, `test_ru`, and `test_kz`. 
*Note: You can identify the domain of a specific text by looking at the prefix in the `filename` column (`it-`, `ling-`, `med-`, `psy-`).*

### Configuration: `ner_re`
- `filename`: Original text file name (includes domain prefix).
- `abstract`: The raw text of the scientific abstract.
- `annotation`: Text with inline BRAT-style markup `[Entity|ID|TYPE]`.
- `entities`: Extracted entities in BRAT format.
- `relations`: Extracted relations between entities.

### Configuration: `aspects`
- `filename`: Original text file name (includes domain prefix).
- `abstract`: The raw text of the scientific abstract.
- `aspect_annotation`: Text with inline aspect markup `[Span|ID|TYPE]`.
- `aspects`: Extracted aspects in BRAT format.

## How to use

You can load the dataset using the `datasets` library. Specify the configuration (`ner_re` or `aspects`) and the split you want to use:

```python
from datasets import load_dataset

# Load the Russian Train split for NER and Relation Extraction
ds_ner_ru_train = load_dataset("tvbat/SciMDIX", "ner_re", split="train_ru")
print(ds_ner_ru_train[0])

# Load the Kazakh Test split for Aspects
ds_asp_kz_test = load_dataset("tvbat/SciMDIX", "aspects", split="test_kz")
```

## Repository

The code, models, and additional resources related to this dataset can be found in our [GitHub repository](https://github.com/tvbat/sci-text-miner-scimdix/tree/main)

## Citation

If you use the **SciMDIX** dataset in your research, please cite our papers:

1. Batura T., Yerimbetova A., Mukazhanov N., Shvarts N., Sakenov B., Turdalyuly M. [Information Extraction from Multi-Domain Scientific Documents: Methods and Insights](https://doi.org/10.3390/app15169086). Applied Sciences. MDPI. 2025. V.15, 9086.

```bibtex
@article{scimdix2025,
  author = {Batura, Tatiana and Yerimbetova, Aigerim and Mukazhanov, Nurzhan and Shvarts, Nikita and Sakenov, Bakzhan and Turdalyuly, Mussa},
  title = {Information Extraction from Multi-Domain Scientific Documents: Methods and Insights},
  journal = {Applied Sciences},
  volume = {15},
  year = {2025},
  number = {16},
  article-number = {9086},
  publisher = {MDPI},
  doi = {https://doi.org/10.3390/app15169086}
}
```

2. Shvarts N., Batura T., Mukazhanov N., Yerimbetova A., Turdalyuly M., Sakenov B. [SciMDIX: A dataset for aspect extraction from multi-domain scientific documents in Kazakh and Russian](https://doi.org/10.1016/j.procs.2026.01.056). *Procedia Computer Science*. 2026. V. 275, pp.474-483.

```bibtex
@article{scimdix2026,
  title={SciMDIX: A dataset for aspect extraction from multi-domain scientific documents in Kazakh and Russian},
  author={Shvarts, Nikita and Batura, Tatiana and Mukazhanov, Nurzhan and Yerimbetova, Aigerim and Turdalyuly, Mussa and Sakenov, Bakzhan},
  journal={Procedia Computer Science},
  volume={275},
  pages={474--483},
  year={2026},
  publisher={Elsevier},
  doi = {https://doi.org/10.1016/j.procs.2026.01.056}
}
```