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--- |
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dataset_info: |
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features: |
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- name: description |
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dtype: string |
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- name: label |
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dtype: string |
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- name: completion |
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dtype: string |
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splits: |
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- name: train |
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num_bytes: 121725759 |
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num_examples: 242509 |
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download_size: 35893211 |
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dataset_size: 121725759 |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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--- |
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# Generic Resource Type Classification Dataset |
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This dataset contains training data for classifying academic resources into 32 generic resource types as defined by DataCite metadata standards. The dataset is designed for fine-tuning large language models to improve classification accuracy for the ~25 million works currently classified only with the generic type "Text" in DataCite metadata. |
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## Dataset Description |
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The dataset is part of the COMET enrichment and curation workflow pilot project focused on improving generic resource type classification. It contains structured metadata descriptions of academic resources along with their corresponding resource type labels. |
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### Dataset Structure |
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The dataset contains three columns: |
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- **description**: Text description of the resource containing structured metadata fields from DataCite records |
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- **label**: The generic resource type label (e.g., "JournalArticle", "Dataset", "Software") |
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- **completion**: Numeric representation of the resource type (0-31 mapping to the 32 categories) |
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### Resource Type Categories |
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The dataset classifies resources into 32 categories: |
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1. Audiovisual - Visual representations with motion (films, videos) |
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2. Award - Funding, grants, scholarships, recognition |
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3. Book - Bound collection of pages with text/images |
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4. BookChapter - Division of a book |
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5. Collection - Aggregation of multiple resources |
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6. ComputationalNotebook - Virtual notebook for literate programming |
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7. ConferencePaper - Paper intended for conference acceptance |
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8. ConferenceProceeding - Collection of conference papers |
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9. DataPaper - Publication describing specific datasets |
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10. Dataset - Structured data files |
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11. Dissertation - Academic thesis (especially PhD) |
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12. Event - Time-based occurrences (webcasts, conventions) |
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13. Image - Visual representations (photos, drawings) |
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14. Instrument - Physical devices for data collection |
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15. InteractiveResource - Resources requiring user interaction |
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16. Journal - Scholarly periodical publication |
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17. JournalArticle - Individual article within a journal |
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18. Model - Abstract/mathematical representations |
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19. OutputManagementPlan - Research output handling plans |
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20. PeerReview - Evaluation by field experts |
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21. PhysicalObject - Physical specimens or artifacts |
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22. Preprint - Pre-peer-review scholarly papers |
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23. Project - Planned collaborative endeavors |
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24. Report - Organized information documents |
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25. Service - Organized systems for end users |
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26. Software - Computer programs and applications |
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27. Sound - Audio recordings |
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28. Standard - Established reference models |
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29. StudyRegistration - Research plan descriptions |
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30. Text - General textual resources |
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31. Workflow - Structured process sequences |
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32. Other - Resources not fitting other categories |
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### Data Source and Processing |
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The training data is created by: |
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1. Sampling from DataCite metadata records |
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2. Filtering to exclude generic "Text" and "Other" categories for training |
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3. Balancing samples across categories (up to 10,000 examples per category) |
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4. Formatting metadata into structured text descriptions |
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### Intended Use |
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This dataset is designed for: |
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- Fine-tuning language models for resource type classification |
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- Training models to distinguish between similar resource types (e.g., Software vs Dataset) |
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- Improving automated metadata curation for academic repositories |
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- Supporting the COMET project's enrichment workflows |
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### Model Training |
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The dataset is used with: |
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- Models: Qwen2.5-7B-Instruct and similar instruction-tuned LLMs |
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- Training: LoRA fine-tuning with completion-only loss |
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- Evaluation: Accuracy metrics and confusion matrices |
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- Inference: Zero-temperature sampling with probability tracking |
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### Limitations |
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- Limited to resources with existing DataCite metadata |
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- Class imbalance despite sampling efforts (because certain classes are under-represented in the base distribution) |
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- Mostly English-language academic resources |
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### Citation |
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If you use this dataset, please cite the COMET project and DataCite metadata standards. |
