| --- |
| 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} |
| } |
| ``` |
| |
