---
name: bio-dataset-search-2dc34861cd4c
description: "**Step 3: Dataset search and task matching (\u6570\u636e\u96c6\u641c\u7d22\u4e0e\u5339\u914d)**"
---

# bio-dataset-search

**Step 3: Dataset search and task matching (数据集搜索与匹配)**

Find suitable datasets for each task and map datasets to the task system defined earlier in the manuscript pipeline.

## Purpose

1. Extract datasets from related papers when possible
2. Search public repositories directly when needed
3. Normalize dataset metadata into a common structure
4. Match datasets to tasks in a defendable way

## Input Format

```text
topic: [research topic]
task_system: [task system from Step 2]
paper_count: [number of related papers]
existing_papers: [optional list of related papers]
```

## Workflow

### Step 3.1: Extract datasets from existing work

If `paper_count >= 5`, start from the strongest existing papers.

Read Methods / Data Availability sections and extract:

- dataset name
- data source
- platform
- modality
- sample scale
- download path
- annotation availability

### Step 3.2: Search datasets directly

If there is not enough prior work, search repositories such as:

- GEO
- ArrayExpress
- project-specific public portals

Use keyword sets built from:

- topic
- modality
- tissue / disease
- benchmark intent

### Step 3.3: Normalize dataset metadata

For each dataset, record:

- source
- platform
- species
- tissue / disease
- sample size
- feature count
- modalities
- annotation quality
- histology / region metadata
- format
- preprocessing needs
- recommended task fit

### Step 3.4: Match datasets to tasks

A good match should satisfy:

1. Every major task has at least one viable dataset
2. Dataset structure matches the task's technical assumptions
3. Download remains feasible
4. Metadata quality is sufficient for evaluation
5. Prefer at least one backup dataset per important task

## Output Format

```markdown
# Dataset Catalog

## Data Sources
- Extracted from related papers:
- Direct repository search:
- Borrowed from adjacent domains:

## Dataset Entries

### Dataset 1: [name]
- Source:
- Platform:
- Species:
- Tissue / disease:
- Modalities:
- Sample scale:
- Annotation quality:
- Download URL:
- Format:
- Recommended tasks:
- Why it fits:

## Dataset-Task Mapping
| Task | Recommended dataset | Why it fits | Notes |
|------|---------------------|-------------|-------|
| ... | ... | ... | ... |

## Acquisition Notes
- GEO download hints
- Public portal download hints

## Preprocessing Recommendations
| Dataset | Preprocessing needs | Suggested skill / tool |
|---------|---------------------|------------------------|
| ... | ... | ... |

## Next Step
- Build the metric system in Step 4
```

## Usage

```bash
/bio-dataset-search "spatial multi-omics integration | paper_count: 5 | task_system: [task system from Step 2]"
```

## Notes

1. Prefer datasets already used in related work when possible.
2. Verify links before committing them to the benchmark plan.
3. Capture QC and annotation metadata whenever available.
4. Match datasets to tasks based on actual experimental needs, not just popularity.


---
Source attribution: Runchuan-BU
Original project: https://github.com/Runchuan-BU/BioClaw
License: MIT
License information: https://opensource.org/license/mit
Original upstream instructions; downloaded from Dave AI.

## Upstream license notice

MIT License

Copyright (c) 2026 Gabriel Moreira

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.



# Dave AI resource metadata

Creator / publisher: Runchuan-BU
Original project: https://github.com/Runchuan-BU/BioClaw
Attribution basis: Upstream repository explicitly recorded in package mirror path

Fields of work: Data & Analytics, Education & Research
Roles: Data Scientist, Market Research Analyst, Researcher
Specialties: API
Tools: Geo
Country applicability: Country not specified
Classification basis: Source catalog labels
Instruction SHA-256: 19fc9ee4ce7a437916c3a61b9342310a2d1a18975e63d246a28f14fbde195a91