paperscraper

paperscraper is a python package for scraping publication metadata or full text files (PDF or XML) from PubMed or preprint servers such as arXiv, medRxiv, bioRxiv and chemRxiv. It provides a streamlined interface to scrape metadata, allows to retrieve citation counts from Google Scholar, impact factors from journals and comes with simple postprocessing functions and plotting routines for meta-analysis.

Table of Contents

1. Getting Started
   • Download X-rxiv Dumps
   • Arxiv Local Dump
2. Examples
   • Publication Keyword Search
   • Full-Text Retrieval (PDFs & XMLs)
   • Citation Search
   • Journal Impact Factor
3. Plotting
   • Barplots
   • Venn Diagrams
4. Citation
5. Contributions

Getting started

console pip install paperscraper

This is enough to query PubMed, arXiv or Google Scholar.

Download X-rxiv Dumps

However, to scrape publication data from the preprint servers biorxiv, medrxiv and chemrxiv, the setup is different. The entire dump is downloaded and stored in the server_dumps folder in a .jsonl format (one paper per line).

py from paperscraper.get_dumps import biorxiv, medrxiv, chemrxiv medrxiv() # Takes ~30min and should result in ~35 MB file biorxiv() # Takes ~1h and should result in ~350 MB file chemrxiv() # Takes ~45min and should result in ~20 MB file NOTE: Once the dumps are stored, please make sure to restart the python interpreter so that the changes take effect. NOTE: If you experience API connection issues (ConnectionError), since v0.2.12 there are automatic retries which you can even control and raise from the default of 10, as in biorxiv(max_retries=20).

Since v0.2.5 paperscraper also allows to scrape {med/bio/chem}rxiv for specific dates. py medrxiv(start_date="2023-04-01", end_date="2023-04-08") But watch out. The resulting .jsonl file will be labelled according to the current date and all your subsequent searches will be based on this file only. If you use this option you might want to keep an eye on the source files (paperscraper/server_dumps/*jsonl) to ensure they contain the paper metadata for all papers you're interested in.

Arxiv local dump

If you prefer local search rather than using the arxiv API:

py from paperscraper.get_dumps import arxiv arxiv(start_date='2024-01-01', end_date=None) # scrapes all metadata from 2024 until today.

Afterwards you can search the local arxiv dump just like the other x-rxiv dumps. The direct endpoint is paperscraper.arxiv.get_arxiv_papers_local. You can also specify the backend directly in the get_and_dump_arxiv_papers function: py from paperscraper.arxiv import get_and_dump_arxiv_papers get_and_dump_arxiv_papers(..., backend='local')

Examples

paperscraper is build on top of the packages arxiv, pymed, and scholarly.

Publication keyword search

Consider you want to perform a publication keyword search with the query: COVID-19 AND Artificial Intelligence AND Medical Imaging.

• Scrape papers from PubMed:

```py from paperscraper.pubmed import getanddumppubmedpapers covid19 = ['COVID-19', 'SARS-CoV-2'] ai = ['Artificial intelligence', 'Deep learning', 'Machine learning'] mi = ['Medical imaging'] query = [covid19, ai, mi]

getanddumppubmedpapers(query, outputfilepath='covid19ai_imaging.jsonl') ```

• Scrape papers from arXiv:

```py from paperscraper.arxiv import getanddumparxivpapers

getanddumparxivpapers(query, outputfilepath='covid19ai_imaging.jsonl') ```

• Scrape papers from bioRiv, medRxiv or chemRxiv:

```py from paperscraper.xrxiv.xrxiv_query import XRXivQuery

querier = XRXivQuery('serverdumps/chemrxiv2020-11-10.jsonl') querier.searchkeywords(query, outputfilepath='covid19aiimaging.jsonl') ```

You can also use dump_queries to iterate over a bunch of queries for all available databases.

```py from paperscraper import dump_queries

queries = [[covid19, ai, mi], [covid19, ai], [ai]] dump_queries(queries, '.') ```

Or use the harmonized interface of QUERY_FN_DICT to query multiple databases of your choice: ```py from paperscraper.loaddumps import QUERYFNDICT print(QUERYFN_DICT.keys())

QUERYFNDICT'biorxiv' QUERYFNDICT'medrxiv' ```

• Scrape papers from Google Scholar:

Thanks to scholarly, there is an endpoint for Google Scholar too. It does not understand Boolean expressions like the others, but should be used just like the Google Scholar search fields.

py from paperscraper.scholar import get_and_dump_scholar_papers topic = 'Machine Learning' get_and_dump_scholar_papers(topic) NOTE: The scholar endpoint does not require authentication but since it regularly prompts with captchas, it's difficult to apply large scale.

Full-Text Retrieval (PDFs & XMLs)

paperscraper allows you to download full text of publications using DOIs. The basic functionality works reliably for preprint servers (arXiv, bioRxiv, medRxiv, chemRxiv), but retrieving papers from PubMed dumps is more challenging due to publisher restrictions and paywalls.

Standard Usage

The main download functions work for all paper types with automatic fallbacks:

py from paperscraper.pdf import save_pdf paper_data = {'doi': "10.48550/arXiv.2207.03928"} save_pdf(paper_data, filepath='gt4sd_paper.pdf')

To batch download full texts from your metadata search results:

```py from paperscraper.pdf import savepdffrom_dump

Save PDFs/XMLs in current folder and name the files by their DOI

savepdffromdump('medrxivcovidaiimaging.jsonl', pdfpath='.', keyto_save='doi') ```

Automatic Fallback Mechanisms

When the standard text retrieval fails, paperscraper automatically tries these fallbacks:

• BioC-PMC: For biomedical papers in PubMed Central (open-access repository), it retrieves open-access full-text XML from the BioC-PMC API.
• eLife Papers: For eLife journal papers, it fetches XML files from eLife's open GitHub repository.

These fallbacks are tried automatically without requiring any additional configuration.

Enhanced Retrieval with Publisher APIs

For more comprehensive access to papers from major publishers, you can provide API keys for:

• Wiley TDM API: Enables access to Wiley publications (2,000+ journals).
• Elsevier TDM API: Enables access to Elsevier publications (The Lancet, Cell, ...).
• bioRxiv TDM API Enable access to bioRxiv publications (since May 2025 bioRxiv is protected with Cloudflare)

To use publisher APIs:

1. Create a file with your API keys: WILEY_TDM_API_TOKEN=your_wiley_token_here ELSEVIER_TDM_API_KEY=your_elsevier_key_here AWS_ACCESS_KEY_ID=your_aws_access_key_here AWS_SECRET_ACCESS_KEY=your_aws_secret_key_here NOTE: The AWS keys can be created in your AWS/IAM account. When creating the key, make sure you tick the AmazonS3ReadOnlyAccess permission policy. NOTE: If you name the file .env it will be loaded automatically (if it is in the cwd or anywhere above the tree to home).

2. Pass the file path when calling retrieval functions:

```py from paperscraper.pdf import savepdffrom_dump

savepdffromdump( 'pubmedqueryresults.jsonl', pdfpath='./papers', keytosave='doi', apikeys='path/to/your/apikeys.txt' ) ```

For obtaining API keys: - Wiley TDM API: Visit Wiley Text and Data Mining (free for academic users with institutional subscription) - Elsevier TDM API: Visit Elsevier's Text and Data Mining (free for academic users with institutional subscription)

NOTE: While these fallback mechanisms improve retrieval success rates, they cannot guarantee access to all papers due to various access restrictions.

Citation search

You can fetch the number of citations of a paper from its title or DOI

```py from paperscraper.citations import getcitationsfromtitle, getcitationsbydoi title = 'Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I.' print(getcitationsfrom_title(title))

doi = '10.1021/acs.jcim.3c00132' getcitationsby_doi(doi) ```

Journal impact factor

You can also retrieve the impact factor for all journals: ```py

from paperscraper.impact import Impactor i = Impactor() i.search("Nat Comms", threshold=85, sort_by='impact') [ {'journal': 'Nature Communications', 'factor': 17.694, 'score': 94}, {'journal': 'Natural Computing', 'factor': 1.504, 'score': 88} ] This performs a fuzzy search with a threshold of 85. `threshold` defaults to 100 in which case an exact search is performed. You can also search by journal abbreviation, [E-ISSN](https://portal.issn.org) or [NLM ID](https://portal.issn.org). py i.search("Nat Rev Earth Environ") # [{'journal': 'Nature Reviews Earth & Environment', 'factor': 37.214, 'score': 100}] i.search("101771060") # [{'journal': 'Nature Reviews Earth & Environment', 'factor': 37.214, 'score': 100}] i.search('2662-138X') # [{'journal': 'Nature Reviews Earth & Environment', 'factor': 37.214, 'score': 100}]

Filter results by impact factor

i.search("Neural network", threshold=85, minimpact=1.5, maximpact=20)

[

{'journal': 'IEEE Transactions on Neural Networks and Learning Systems', 'factor': 14.255, 'score': 93},

{'journal': 'NEURAL NETWORKS', 'factor': 9.657, 'score': 91},

{'journal': 'WORK-A Journal of Prevention Assessment & Rehabilitation', 'factor': 1.803, 'score': 86},

{'journal': 'NETWORK-COMPUTATION IN NEURAL SYSTEMS', 'factor': 1.5, 'score': 92}

]

Show all fields

i.search("quantum information", threshold=90, return_all=True)

[

{'factor': 10.758, 'jcr': 'Q1', 'journalabbr': 'npj Quantum Inf', 'eissn': '2056-6387', 'journal': 'npj Quantum Information', 'nlmid': '101722857', 'issn': '', 'score': 92},

{'factor': 1.577, 'jcr': 'Q3', 'journalabbr': 'Nation', 'eissn': '0027-8378', 'journal': 'NATION', 'nlmid': '9877123', 'issn': '0027-8378', 'score': 91}

]

```

Plotting

When multiple query searches are performed, two types of plots can be generated automatically: Venn diagrams and bar plots.

Barplots

Compare the temporal evolution of different queries across different servers.

```py from paperscraper import QUERYFNDICT from paperscraper.postprocessing import aggregatepaper from paperscraper.utils import getfilenamefromquery, load_jsonl

Define search terms and their synonyms

ml = ['Deep learning', 'Neural Network', 'Machine learning'] mol = ['molecule', 'molecular', 'drug', 'ligand', 'compound'] gnn = ['gcn', 'gnn', 'graph neural', 'graph convolutional', 'molecular graph'] smiles = ['SMILES', 'Simplified molecular'] fp = ['fingerprint', 'molecular fingerprint', 'fingerprints']

Define queries

queries = [[ml, mol, smiles], [ml, mol, fp], [ml, mol, gnn]]

root = '../keyword_dumps'

datadict = dict() for query in queries: filename = getfilenamefromquery(query) datadict[filename] = dict() for db, in QUERYFNDICT.items(): # Assuming the keyword search has been performed already data = load_jsonl(os.path.join(root, db, filename))

    # Unstructured matches are aggregated into 6 bins, 1 per year
    # from 2015 to 2020. Sanity check is performed by having 
    # `filtering=True`, removing papers that don't contain all of
    # the keywords in query.
    data_dict[filename][db], filtered = aggregate_paper(
        data, 2015, bins_per_year=1, filtering=True,
        filter_keys=query, return_filtered=True
    )

Plotting is now very simple

from paperscraper.plotting import plot_comparison

datakeys = [ 'deeplearningmoleculefingerprint.jsonl', 'deeplearningmoleculesmiles.jsonl', 'deeplearningmoleculegcn.jsonl' ] plotcomparison( datadict, datakeys, titletext="'Deep Learning' AND 'Molecule' AND X", keywordtext=['Fingerprint', 'SMILES', 'Graph'], figpath='mol_representation' ) ```

Venn Diagrams

```py from paperscraper.plotting import ( plotvenntwo, plotvennthree, plotmultiplevenn )

sizes2020 = (30842, 14474, 2292, 35476, 1904, 1408, 376) sizes2019 = (55402, 11899, 2563) labels2020 = ('Medical\nImaging', 'Artificial\nIntelligence', 'COVID-19') labels2019 = ['Medical Imaging', 'Artificial\nIntelligence']

plotvenntwo(sizes2019, labels2019, title='2019', figname='ai_imaging') ```

py plot_venn_three( sizes_2020, labels_2020, title='2020', figname='ai_imaging_covid' )

Or plot both together:

py plot_multiple_venn( [sizes_2019, sizes_2020], [labels_2019, labels_2020], titles=['2019', '2020'], suptitle='Keyword search comparison', gridspec_kw={'width_ratios': [1, 2]}, figsize=(10, 6), figname='both' )

Citation

If you use paperscraper, please cite a paper that motivated our development of this tool.

bibtex @article{born2021trends, title={Trends in Deep Learning for Property-driven Drug Design}, author={Born, Jannis and Manica, Matteo}, journal={Current Medicinal Chemistry}, volume={28}, number={38}, pages={7862--7886}, year={2021}, publisher={Bentham Science Publishers} }

Contributions

Thanks to the following contributors: - @mathinic: Since v0.3.0 improved PubMed full text retrieval with additional fallback mechanisms (BioC-PMC, eLife and optional Wiley/Elsevier APIs).

• @memray: Since v0.2.12 there are automatic retries when downloading the {med/bio/chem}rxiv dumps.

• @achouhan93: Since v0.2.5 {med/bio/chem}rxiv can be scraped for specific dates!

• @daenuprobst: Since v0.2.4 PDF files can be scraped directly (paperscraper.pdf.save_pdf)

• @oppih: Since v0.2.3 chemRxiv API also provides DOI and URL if available

• @lukasschwab: Enabled support for arxiv >1.4.2 in paperscraper v0.1.0.

• @juliusbierk: Bugfixes