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