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