📚 Scholare — Automated Literature Review Pipeline

An end-to-end, config-driven Python tool that searches academic literature, downloads papers, and generates structured research notes — ready to plug into any research topic.

🚀 Try it instantly — no installation required!

Click the badge above to run Scholare directly in your browser via Google Colab.

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✨ What It Does

1. Searches Free APIs via OpenAlex natively, along with preprint servers like arXiv and bioRxiv/medRxiv.
2. Enriches every result through Semantic Scholar — abstracts, TLDRs, DOIs, code/data hints. (Falls back to DOI lookups for highest accuracy).
3. Discovers Open-Access Links dynamically using the Unpaywall API.
4. Categorizes papers using configurable keyword rules.
5. Downloads open-access PDFs into a local folder (with a --no-download CLI override).
6. Generates visualizations — category distribution, open-access status, citation histogram, year timeline.
7. Produces structured Markdown research notes — executive summary, taxonomy, top-cited, per-category breakdown with TLDRs, embedded charts, full paper index.
8. Compares runs — pass a previous CSV to isolate newly discovered papers.
9. Semantic Relevance Scoring (Optional) — ranks papers using keyword heuristics or deep-learning embeddings via sentence-transformers.

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🛠️ Setup

1. Prerequisites

• Python 3.10+
• (Optional) A Semantic Scholar API key — semanticscholar.org/product/api (Highly recommended to avoid rate limits).

2. Install

From source (recommended for now):

git clone https://github.com/OWNER/scholare.git
cd scholare
python -m venv venv

# Activate
# Windows PowerShell:
.\venv\Scripts\Activate.ps1
# macOS / Linux:
source venv/bin/activate

pip install -e .

Eventually via PyPI:

pip install scholare

3. Configure API Keys

cp .env.example .env

Edit .env:

S2_API_KEY=your_actual_semantic_scholar_key
UNPAYWALL_EMAIL=your_email@example.com

4. Create Your Config

cp config_example.json my_config.json

Edit my_config.json:

{
  "query": "your search query here",
  "limit": 30,
  "output_dir": "./my_output",
  "categories": {
    "Category A": ["keyword1", "keyword2"],
    "Other": []
  },
  "default_category": "Other",
  "download_pdfs": true,
  "sources": ["openalex", "arxiv", "biorxiv"],
  "search_intent": "your natural language description of what you are looking for",
  "use_embeddings": true,
  "compare_methods": false
}

Field	Description
query	Search string (mapped appropriately across OpenAlex and preprints)
limit	Max number of papers to retrieve per API source
output_dir	Base output directory (subfolders auto-named by date + terms)
categories	Category name → keyword list for paper classification
default_category	Fallback when no keywords match
download_pdfs	Set false to skip PDF downloading by default
sources	(Optional) List of sources to query. Available: openalex, arxiv, biorxiv
search_intent	(Optional) Natural language phrase for semantic relevance scoring
use_embeddings	(Optional) Set to true to use sentence-transformers for ML-based relevance ranking (requires pip install scholare[ml])
compare_methods	(Optional) Set to true to output both keyword and ML embedding scores for comparison in the CSV

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🚀 Usage

CLI

# Run the pipeline
scholare --config my_config.json

# Skip downloading PDFs (overrides config)
scholare --config my_config.json --no-download

# Compare with a previous run
scholare --config my_config.json --previous-csv ./old_output/results.csv

Programmatic & Cloud Notebooks (Colab / Kaggle)

⚡ Zero-install quick start — Run Scholare directly in your browser!

No Python setup, no terminal, no installation. Just click and run.

You can also install the package manually in any cloud notebook: Install the package directly from GitHub:

!pip install git+https://github.com/zikt/scholare.git

Then, you can define your configuration natively in Python and pass it to the pipeline:

import os
from scholare.config import load_config
from scholare.pipeline import run_pipeline

# Setting API Keys:
# Method A: Direct Injection
# os.environ["S2_API_KEY"] = "your_key_here"
# os.environ["UNPAYWALL_EMAIL"] = "your_email@example.com"

# Method B: Secure Colab Secrets (Recommended)
# from google.colab import userdata
# os.environ["S2_API_KEY"] = userdata.get('S2_API_KEY')

# Define config as a dictionary mapping
my_config = {
    "query": "federated learning",
    "limit": 10,
    "output_dir": "./output",
    "categories": {"Privacy": ["dp"]},
    "default_category": "Other",
    "download_pdfs": False
}

config_obj = load_config(my_config)
df = run_pipeline(config_obj)

print(f"Found {len(df)} papers")

Tip

See the full interactive Cloud Notebook Template (examples/cloud_notebook_template.ipynb) to get started immediately!

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📁 Output Structure

Each run creates a descriptive subfolder:

output/
└── 2026-02-25_EEG_BCI_melanin_bias/
    ├── papers/                        # Downloaded open-access PDFs
    ├── visualizations/                # PNG charts
    │   ├── category_distribution.png
    │   ├── open_access_status.png
    │   ├── citation_distribution.png
    │   └── year_distribution.png
    ├── research_notes.md              # Structured Markdown summary
    ├── results.csv                    # Raw data
    └── new_discoveries.csv            # (if --previous-csv was used)

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📝 Example Configs

See the examples/ directory for ready-to-use configs:

• eeg_bias_config.json — EEG/BCI bias research
• federated_learning_config.json — Federated learning in healthcare

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🗺️ Roadmap

Scholare is actively growing. See ROADMAP.md for planned features including:

• 📡 More APIs — OpenAlex, Unpaywall, arXiv, Crossref, PubMed, CORE
• 📤 Export — BibTeX/RIS for Zotero & Mendeley
• 🔗 Integration — Zotero/Mendeley library sync
• 🌐 Chrome extension — one-click literature searches
• 📊 Analysis — citation networks, clustering, PRISMA diagrams
• 🎨 UX — rich CLI, interactive HTML dashboards
• 🤖 RAG Chat — interactive CLI chat across downloaded PDFs using local or cloud LLMs

Contributions welcome! Pick any item from the roadmap.

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🤝 Contributing

We welcome contributions! See CONTRIBUTING.md for setup and guidelines.

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⚠️ Limitations

• Only open-access PDFs can be downloaded. Paywalled papers are noted with links.
• Semantic Scholar without a key is limited, which might slow down enrichment on large searches. Adding an S2_API_KEY solves this.
• Categorization is keyword-based (heuristic, not an AI classifier).
• bioRxiv searching uses the Crossref endpoint simulating a search, leading to slightly different handling.

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📦 Package Structure

scholare/
├── __init__.py          # Public API 
├── __main__.py          # CLI entry point
├── config.py            # Config loader
├── api.py               # OpenAlex, preprint, Unpaywall, Semantic Scholar clients
├── pipeline.py          # Main orchestration
├── downloader.py        # PDF downloading
├── notes.py             # Markdown research notes generator
├── visualizations.py    # Chart generation
└── utils.py             # Categorization & comparison helpers

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📄 License

MIT — use it, fork it, build on it.