biorxiv-database

What this skill does

# bioRxiv Database

## Overview

This skill provides efficient Python-based tools for searching and retrieving preprints from the bioRxiv database. It enables comprehensive searches by keywords, authors, date ranges, and categories, returning structured JSON metadata that includes titles, abstracts, DOIs, and citation information. The skill also supports PDF downloads for full-text analysis.

## When to Use This Skill

Use this skill when:
- Searching for recent preprints in specific research areas
- Tracking publications by particular authors
- Conducting systematic literature reviews
- Analyzing research trends over time periods
- Retrieving metadata for citation management
- Downloading preprint PDFs for analysis
- Filtering papers by bioRxiv subject categories

## Core Search Capabilities

### 1. Keyword Search

Search for preprints containing specific keywords in titles, abstracts, or author lists.

**Basic Usage:**
```python
python scripts/biorxiv_search.py \
  --keywords "CRISPR" "gene editing" \
  --start-date 2024-01-01 \
  --end-date 2024-12-31 \
  --output results.json
```

**With Category Filter:**
```python
python scripts/biorxiv_search.py \
  --keywords "neural networks" "deep learning" \
  --days-back 180 \
  --category neuroscience \
  --output recent_neuroscience.json
```

**Search Fields:**
By default, keywords are searched in both title and abstract. Customize with `--search-fields`:
```python
python scripts/biorxiv_search.py \
  --keywords "AlphaFold" \
  --search-fields title \
  --days-back 365
```

### 2. Author Search

Find all papers by a specific author within a date range.

**Basic Usage:**
```python
python scripts/biorxiv_search.py \
  --author "Smith" \
  --start-date 2023-01-01 \
  --end-date 2024-12-31 \
  --output smith_papers.json
```

**Recent Publications:**
```python
# Last year by default if no dates specified
python scripts/biorxiv_search.py \
  --author "Johnson" \
  --output johnson_recent.json
```

### 3. Date Range Search

Retrieve all preprints posted within a specific date range.

**Basic Usage:**
```python
python scripts/biorxiv_search.py \
  --start-date 2024-01-01 \
  --end-date 2024-01-31 \
  --output january_2024.json
```

**With Category Filter:**
```python
python scripts/biorxiv_search.py \
  --start-date 2024-06-01 \
  --end-date 2024-06-30 \
  --category genomics \
  --output genomics_june.json
```

**Days Back Shortcut:**
```python
# Last 30 days
python scripts/biorxiv_search.py \
  --days-back 30 \
  --output last_month.json
```

### 4. Paper Details by DOI

Retrieve detailed metadata for a specific preprint.

**Basic Usage:**
```python
python scripts/biorxiv_search.py \
  --doi "10.1101/2024.01.15.123456" \
  --output paper_details.json
```

**Full DOI URLs Accepted:**
```python
python scripts/biorxiv_search.py \
  --doi "https://doi.org/10.1101/2024.01.15.123456"
```

### 5. PDF Downloads

Download the full-text PDF of any preprint.

**Basic Usage:**
```python
python scripts/biorxiv_search.py \
  --doi "10.1101/2024.01.15.123456" \
  --download-pdf paper.pdf
```

**Batch Processing:**
For multiple PDFs, extract DOIs from a search result JSON and download each paper:
```python
import json
from biorxiv_search import BioRxivSearcher

# Load search results
with open('results.json') as f:
    data = json.load(f)

searcher = BioRxivSearcher(verbose=True)

# Download each paper
for i, paper in enumerate(data['results'][:10]):  # First 10 papers
    doi = paper['doi']
    searcher.download_pdf(doi, f"papers/paper_{i+1}.pdf")
```

## Valid Categories

Filter searches by bioRxiv subject categories:

- `animal-behavior-and-cognition`
- `biochemistry`
- `bioengineering`
- `bioinformatics`
- `biophysics`
- `cancer-biology`
- `cell-biology`
- `clinical-trials`
- `developmental-biology`
- `ecology`
- `epidemiology`
- `evolutionary-biology`
- `genetics`
- `genomics`
- `immunology`
- `microbiology`
- `molecular-biology`
- `neuroscience`
- `paleontology`
- `pathology`
- `pharmacology-and-toxicology`
- `physiology`
- `plant-biology`
- `scientific-communication-and-education`
- `synthetic-biology`
- `systems-biology`
- `zoology`

## Output Format

All searches return structured JSON with the following format:

```json
{
  "query": {
    "keywords": ["CRISPR"],
    "start_date": "2024-01-01",
    "end_date": "2024-12-31",
    "category": "genomics"
  },
  "result_count": 42,
  "results": [
    {
      "doi": "10.1101/2024.01.15.123456",
      "title": "Paper Title Here",
      "authors": "Smith J, Doe J, Johnson A",
      "author_corresponding": "Smith J",
      "author_corresponding_institution": "University Example",
      "date": "2024-01-15",
      "version": "1",
      "type": "new results",
      "license": "cc_by",
      "category": "genomics",
      "abstract": "Full abstract text...",
      "pdf_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1.full.pdf",
      "html_url": "https://www.biorxiv.org/content/10.1101/2024.01.15.123456v1",
      "jatsxml": "https://www.biorxiv.org/content/...",
      "published": ""
    }
  ]
}
```

## Common Usage Patterns

### Literature Review Workflow

1. **Broad keyword search:**
```python
python scripts/biorxiv_search.py \
  --keywords "organoids" "tissue engineering" \
  --start-date 2023-01-01 \
  --end-date 2024-12-31 \
  --category bioengineering \
  --output organoid_papers.json
```

2. **Extract and review results:**
```python
import json

with open('organoid_papers.json') as f:
    data = json.load(f)

print(f"Found {data['result_count']} papers")

for paper in data['results'][:5]:
    print(f"\nTitle: {paper['title']}")
    print(f"Authors: {paper['authors']}")
    print(f"Date: {paper['date']}")
    print(f"DOI: {paper['doi']}")
```

3. **Download selected papers:**
```python
from biorxiv_search import BioRxivSearcher

searcher = BioRxivSearcher()
selected_dois = ["10.1101/2024.01.15.123456", "10.1101/2024.02.20.789012"]

for doi in selected_dois:
    filename = doi.replace("/", "_").replace(".", "_") + ".pdf"
    searcher.download_pdf(doi, f"papers/{filename}")
```

### Trend Analysis

Track research trends by analyzing publication frequencies over time:

```python
python scripts/biorxiv_search.py \
  --keywords "machine learning" \
  --start-date 2020-01-01 \
  --end-date 2024-12-31 \
  --category bioinformatics \
  --output ml_trends.json
```

Then analyze the temporal distribution in the results.

### Author Tracking

Monitor specific researchers' preprints:

```python
# Track multiple authors
authors = ["Smith", "Johnson", "Williams"]

for author in authors:
    python scripts/biorxiv_search.py \
      --author "{author}" \
      --days-back 365 \
      --output "{author}_papers.json"
```

## Python API Usage

For more complex workflows, import and use the `BioRxivSearcher` class directly:

```python
from scripts.biorxiv_search import BioRxivSearcher

# Initialize
searcher = BioRxivSearcher(verbose=True)

# Multiple search operations
keywords_papers = searcher.search_by_keywords(
    keywords=["CRISPR", "gene editing"],
    start_date="2024-01-01",
    end_date="2024-12-31",
    category="genomics"
)

author_papers = searcher.search_by_author(
    author_name="Smith",
    start_date="2023-01-01",
    end_date="2024-12-31"
)

# Get specific paper details
paper = searcher.get_paper_details("10.1101/2024.01.15.123456")

# Download PDF
success = searcher.download_pdf(
    doi="10.1101/2024.01.15.123456",
    output_path="paper.pdf"
)

# Format results consistently
formatted = searcher.format_result(paper, include_abstract=True)
```

## Best Practices

1. **Use appropriate date ranges**: Smaller date ranges return faster. For keyword searches over long periods, consider splitting into multiple queries.

2. **Filter by category**: When possible, use `--category` to reduce data transfer and improve search precision.

3. **Respect rate limits**: The script includes automatic delays (0.5s between requests). For large-scale data collection, add add