README.md

---
pretty_name: DataSpace
license: mit
language:
- en
- zh
task_categories:
- table-question-answering
size_categories:
- n<1K
tags:
- data-agent
- benchmark
- heterogeneous-data
- multimodal
- tabular
- document
- video
configs:
- config_name: default
  data_files:
  - split: test
    path: data/tasks.jsonl
---

# DataSpace

DataSpace is a benchmark for data agents that perform verifiable analytics over
heterogeneous, task-local workspaces. Each task provides a natural-language
question and a workspace containing a combination of CSV, JSON, SQLite,
Markdown, PDF, and video artifacts. The required output is a complete tabular
result.

DataSpace is also the official benchmark of the
[KDD Cup 2026 Data Agent Track](https://dataagent.top).

## Release policy

This release makes all 410 task inputs public. It includes `gold.csv` and the
frozen evaluation configuration for a representative set of 60 tasks, allowing
end-to-end local development and validation. References for the other 350 tasks
are withheld for official full-benchmark evaluation.

The public-reference set was selected by deterministic constrained coverage
optimization over source domains, question--workspace language combinations,
evidence-carrier combinations, required modalities, analytical operations,
answer shapes, numeric comparison modes, and ordering behavior. It covers all
six modalities, all annotated analytical-operation families, and all
evidence-carrier groups without repeated source samples.

## Benchmark summary

| Property | Value |
| --- | ---: |
| Task inputs | 410 |
| Public reference packages | 60 |
| Context files | 7,439 |
| Uncompressed context size | 15.0 GB |
| CSV files | 2,809 |
| JSON files | 1,927 |
| SQLite databases | 402 |
| Markdown documents | 1,024 |
| PDF documents | 1,088 |
| Videos | 189 |

Chinese and English may occur across both the question and workspace artifacts.
Tasks may require evidence discovery, structured extraction, joins across
representations, filtering, aggregation, ranking, temporal reasoning, and
document or video understanding.

## Repository layout

```text
.
├── README.md
├── CITATION.cff
├── LICENSE
├── data/
│   ├── tasks.jsonl
│   └── public_reference_tasks.json
└── release/
    ├── DataSpace-Benchmark.zip
    └── DataSpace-Benchmark.zip.sha256
```

`data/tasks.jsonl` is a lightweight index for the Hugging Face Dataset Viewer.
It contains questions, workspace paths, available modality composition, file
inventories, and a public-reference availability flag. It does not expose
answer or solution-path metadata for withheld tasks.

## Download

Install the Hugging Face Hub client and download the complete release:

```bash
pip install -U huggingface_hub
hf download HKUSTDial/DataSpace \
  release/DataSpace-Benchmark.zip \
  release/DataSpace-Benchmark.zip.sha256 \
  --repo-type dataset \
  --local-dir .
```

Verify and extract it:

```bash
cd release
sha256sum --check DataSpace-Benchmark.zip.sha256
unzip DataSpace-Benchmark.zip
```

For task `task_N`, the agent receives:

```text
DataSpace-Benchmark/input/task_N/task.json
DataSpace-Benchmark/input/task_N/context/
```

and should write:

```text
predictions/task_N/prediction.csv
```

## Loading the task index

```python
from datasets import load_dataset

tasks = load_dataset("HKUSTDial/DataSpace", split="test")
task = tasks[0]
print(task["question"])
print(task["workspace_path"])
```

Resolve `workspace_path` relative to the extracted
`DataSpace-Benchmark/` directory.

## Evaluation

The official evaluator and baseline implementations are maintained in the
[DataSpace GitHub repository](https://github.com/BugMaker-Boyan/DataSpace).
Local evaluation is available for the 60 public-reference tasks. Official
full-benchmark submissions are scored once over all 410 tasks, and missing
predictions count as incorrect. Submission and leaderboard instructions will
be published on the benchmark website.

## Construction and quality control

DataSpace is constructed from EHRSQL and BULL Text-to-SQL source instances.
The construction pipeline applies cross-language transformation,
constraint-aware data sampling, modality routing and artifact rendering, and
human review and data repair. Eleven domain experts cross-reviewed task
solvability, question clarity, workspace evidence, reference answers, and
evaluation configurations.

## License

DataSpace is released under the MIT License. See [LICENSE](LICENSE).

## Citation

```bibtex
@misc{dataspace2027,
  title        = {DataSpace: Benchmarking Data Agents for Verifiable Analytics
                  over Heterogeneous Workspaces},
  author       = {Boyan Li and Zhuowen Liang and Yupeng Xie and Xiaotian Lin and
                  Tianqi Luo and Xinyu Liu and Yizhang Zhu and Zhangyang Peng and
                  Yuan Li and Zhengxuan Zhang and Jiayi Zhang and Nan Tang and
                  Guoliang Li and Yuyu Luo},
  year         = {2027},
  howpublished = {Hugging Face dataset},
  url          = {https://huggingface.co/datasets/HKUSTDial/DataSpace}
}
```