name: findpapers-guide description: "Search multiple academic databases simultaneously with Findpapers" metadata: openclaw: emoji: "🔍" category: "literature" subcategory: "search" keywords: ["Findpapers", "multi-database search", "systematic review", "arXiv", "PubMed", "Scopus", "IEEE"] source: "https://github.com/jonatasgrosman/findpapers"

Findpapers Guide

Overview

Findpapers is a Python tool for searching multiple academic databases simultaneously — arXiv, bioRxiv, IEEE, medRxiv, PubMed, and Scopus — using a single query. It automates the tedious process of running the same search across multiple platforms, deduplicates results, and exports to structured formats for systematic reviews.

Installation

pip install findpapers

Basic Usage

Search Multiple Databases

import findpapers
import datetime

# Define search
query = '([deep learning] AND [medical imaging]) AND NOT [survey]'
since = datetime.date(2022, 1, 1)
until = datetime.date(2026, 12, 31)

# Run search across all databases
findpapers.search(
    outputpath="search_results.json",
    query=query,
    since=since,
    until=until,
    databases=["arxiv", "pubmed", "ieee", "scopus"],
    limit_per_database=200,
)

Query Syntax

# Boolean operators: AND, OR, NOT
# Brackets for grouping
# Terms in square brackets

# Example: find NLP papers about healthcare
query = '[natural language processing] AND ([healthcare] OR [clinical])'

# Example: exclude surveys
query = '[transformer] AND [attention] AND NOT [survey]'

# Example: specific domain
query = '[reinforcement learning] AND [robotics] AND [simulation]'

Refine and Filter Results

# Load previous search
search = findpapers.load("search_results.json")

# Interactive refinement (in Jupyter/terminal)
findpapers.refine(
    inputpath="search_results.json",
    categories=["relevant", "maybe", "irrelevant"],
)

# Programmatic filtering
for paper in search.papers:
    if paper.citations and paper.citations > 50:
        paper.selected = True

Export Results

# Export to BibTeX
findpapers.generate_bibtex(
    inputpath="search_results.json",
    outputpath="references.bib",
    only_selected=True,
)

# Export to CSV
findpapers.generate_csv(
    inputpath="search_results.json",
    outputpath="papers.csv",
)

Database Configuration

API Keys (Optional)

# Scopus requires an Elsevier API key
# IEEE requires an IEEE Xplore API key
# arXiv and PubMed are free

import os
os.environ["SCOPUS_API_TOKEN"] = "your-scopus-key"
os.environ["IEEE_API_TOKEN"] = "your-ieee-key"

Database Support

Systematic Review Workflow

import findpapers
import datetime

# Step 1: Define protocol
query = '[machine learning] AND [drug discovery]'
since = datetime.date(2020, 1, 1)

# Step 2: Search
findpapers.search(
    outputpath="slr_search.json",
    query=query,
    since=since,
    limit_per_database=500,
)

# Step 3: Remove duplicates (automatic)
search = findpapers.load("slr_search.json")
print(f"Found {len(search.papers)} unique papers")

# Step 4: Screen titles/abstracts
findpapers.refine("slr_search.json",
                   categories=["include", "exclude", "uncertain"])

# Step 5: Export included papers
findpapers.generate_bibtex("slr_search.json", "included.bib",
                            only_selected=True)

CLI Usage

# Search from command line
findpapers search "search.json" \
  --query "[climate change] AND [adaptation]" \
  --since 2022-01-01 \
  --databases arxiv pubmed

# Refine results interactively
findpapers refine "search.json"

# Export to BibTeX
findpapers bibtex "search.json" "refs.bib" --only-selected

References

• Findpapers GitHub
• Findpapers PyPI
• Grosman, J. & Ciferri, R. (2020). "Findpapers: A tool for systematic literature searching."