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",
)