README.md

---
license: cc-by-4.0
task_categories:
- token-classification
- text-classification
- zero-shot-classification
language:
- en
tags:
- biomedical
- single-cell
- cell-type-annotation
- normalization
- ontology
- bond
size_categories:
- 10k<n<100k
---

# BOND: Biomedical Ontology Normalization and Disambiguation

**BOND** is a context-aware benchmark for biomedical entity normalization, designed to evaluate how AI models interpret ambiguous biological terms (e.g., "macrophage", "RB", "Oligs") when provided with rich metadata context.

Unlike traditional string-matching benchmarks, BOND requires **reasoning over biological context**—including tissue, disease, organism, and development stage information—to accurately map author-provided annotations to standardized ontology labels.

## Key Features

- **Context-Aware**: Each normalization pair includes tissue, disease, organism, and development stage metadata
- **Multi-Entity**: Covers cell types, tissues, diseases, assays, sex, development stage, and ethnicity
- **Cross-Modal**: Includes both single-cell RNA-seq and spatial transcriptomics datasets
- **High Quality**: Semantically validated with embedding similarity filtering and expert review

## Data Distribution

![BOND Data Distribution](figures/bond_data_distribution.png)

**Figure 1: BOND Benchmark Data Distribution.** Panel A shows entity type distribution across 25,416 pairs from 85 datasets. Panel B displays dataset composition by organism and modality (72 single-cell, 13 spatial). Panel C shows tissue diversity (186 unique tissues). Panel D lists top contributing studies.

## Dataset Statistics

| Metric | Value |
|:-------|------:|
| **Total Pairs** | 25,416 |
| **Datasets** | 85 |
| **Tissues** | 186 unique types |
| **Organisms** | *Homo sapiens*, *Mus musculus* |
| **Modalities** | Single-Cell (72), Spatial (13) |

### Data Splits

Stratified **80/10/10** split supporting both zero-shot evaluation and supervised fine-tuning:

| Split | Count | Percentage |
|:------|------:|-----------:|
| Train | 20,332 | 80% |
| Validation | 2,542 | 10% |
| Test | 2,542 | 10% |

## Data Fields

| Field | Description |
|:------|:------------|
| `author_term` | Raw annotation from source data (e.g., "RB", "Oligs", "10x") |
| `ontology_label` | Ground-truth standardized label (e.g., "rod bipolar cell") |
| `ontology_id` | Unique ontology identifier (e.g., `CL:0000782`) |
| `field_type` | Entity category: `cell_type`, `tissue`, `disease`, `assay`, `sex`, `development_stage`, `self_reported_ethnicity` |
| `tissue` | Biological tissue context (e.g., "retina", "lung") |
| `organism` | Source organism (*Homo sapiens* or *Mus musculus*) |
| `disease` | Disease state (e.g., "small cell lung carcinoma", "normal") |
| `assay` | Sequencing technology (e.g., "10x 3' v3", "Smart-seq2") |
| `dataset_id` | Source dataset identifier from CELLxGENE |

## Dataset Provenance

BOND aggregates data from **85 high-quality studies** via CELLxGENE Census:

| Modality | Human | Mouse | Total |
|:---------|------:|------:|------:|
| Single-Cell | 61 | 13 | 74 |
| Spatial | 9 | 2 | 11 |
| **Total** | **70** | **15** | **85** |

### Representative Studies

| Study | Pairs |
|:------|------:|
| Tabula Sapiens | 6,842 |
| Human Lung Cell Atlas | 3,114 |
| Human Retina Atlas | 1,830 |
| Mouse Embryonic Dev Atlas | 1,663 |
| Human Fetal Gene Atlas | 1,326 |
| MSK Ovarian Cancer Atlas | 771 |
| HCA Integration Atlas | 681 |
| Human Embryonic Limb Atlas | 622 |

## Creation Pipeline

![BOND Pipeline Workflow](figures/bond_pipeline_workflow.png)

**Figure 2: Benchmark Creation Pipeline.** Context-aware tuples extracted from 99 CELLxGENE datasets undergo embedding-based filtering (sim ≥ 0.40), tiered verification (auto-accept for high confidence, LLM judge for edge cases), and human review. Final benchmark contains 25,416 verified pairs across 85 datasets.

## Usage

```python
from datasets import load_dataset

dataset = load_dataset("pankajrajdeo/bond-benchmark-v1")

# Access splits
train = dataset["train"]
val = dataset["validation"]
test = dataset["test"]

# Example
sample = train[0]
print(f"Input: {sample['author_term']}")
print(f"Context: {sample['tissue']}, {sample['disease']}")
print(f"Target: {sample['ontology_label']} ({sample['ontology_id']})")
```

## Citation

```bibtex
@dataset{bond_benchmark_2026,
  author = {Rajdeo, Pankaj},
  title = {BOND: Biomedical Ontology Normalization and Disambiguation},
  year = {2026},
  publisher = {Hugging Face},
  url = {https://huggingface.co/datasets/pankajrajdeo/bond-benchmark-v1}
}
```

## License

This dataset is released under [CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/).