paperscraper is a python package that ships via pypi and facilitates scraping
publication metadata as well as full PDF files from PubMed or from preprint servers such as arXiv,
medRxiv, bioRxiv and chemRxiv. It provides a streamlined interface to scrape metadata and comes
with simple postprocessing functions and plotting routines for meta-analysis.
Since v0.2.4 paperscraper also supports scraping PDF files directly! Thanks to @daenuprobst for suggestions!
pip install paperscraperThis is enough to query PubMed, arXiv or Google Scholar.
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).
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 fileNOTE: Once the dumps are stored, please make sure to restart the python interpreter so that the changes take effect.
Since v0.2.5 paperscraper also allows to scrape {med/bio/chem}rxiv for specific dates! Thanks to @achouhan93 for contributions!
medrxiv(begin_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.
paperscraper is build on top of the packages pymed,
arxiv and scholarly.
Consider you want to perform a publication keyword search with the query:
COVID-19 AND Artificial Intelligence AND Medical Imaging.
- Scrape papers from PubMed:
from paperscraper.pubmed import get_and_dump_pubmed_papers
covid19 = ['COVID-19', 'SARS-CoV-2']
ai = ['Artificial intelligence', 'Deep learning', 'Machine learning']
mi = ['Medical imaging']
query = [covid19, ai, mi]
get_and_dump_pubmed_papers(query, output_filepath='covid19_ai_imaging.jsonl')- Scrape papers from arXiv:
from paperscraper.arxiv import get_and_dump_arxiv_papers
get_and_dump_arxiv_papers(query, output_filepath='covid19_ai_imaging.jsonl')- Scrape papers from bioRiv, medRxiv or chemRxiv:
from paperscraper.xrxiv.xrxiv_query import XRXivQuery
querier = XRXivQuery('server_dumps/chemrxiv_2020-11-10.jsonl')
querier.search_keywords(query, output_filepath='covid19_ai_imaging.jsonl')You can also use dump_queries to iterate over a bunch of queries for all available databases.
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:
from paperscraper.load_dumps import QUERY_FN_DICT
print(QUERY_FN_DICT.keys())
QUERY_FN_DICT['biorxiv'](query, output_filepath='biorxiv_covid_ai_imaging.jsonl')
QUERY_FN_DICT['medrxiv'](query, output_filepath='medrxiv_covid_ai_imaging.jsonl')- 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.
from paperscraper.scholar import get_and_dump_scholar_papers
topic = 'Machine Learning'
get_and_dump_scholar_papers(topic)paperscraper also allows you to download the PDF files.
from paperscraper.pdf import save_pdf
paper_data = {'doi': "10.48550/arXiv.2207.03928"}
save_pdf(paper_data, filepath='gt4sd_paper.pdf')If you want to batch download all PDFs for your previous metadata search, use the wrapper. Here we scrape the PDFs for the metadata obtained in the previous example.
from paperscraper.pdf import save_pdf_from_dump
# Save PDFs in current folder and name the files by their DOI
save_pdf_from_dump('medrxiv_covid_ai_imaging.jsonl', pdf_path='.', key_to_save='doi')NOTE: This works robustly for preprint servers, but if you use it on a PubMed dump, dont expect to obtain all PDFs. Many publishers detect and block scraping and many publications are simply behind paywalls.
A plus of the Scholar endpoint is that the number of citations of a paper can be fetched:
from paperscraper.scholar import get_citations_from_title
title = 'Über formal unentscheidbare Sätze der Principia Mathematica und verwandter Systeme I.'
get_citations_from_title(title)NOTE: The scholar endpoint does not require authentification but since it regularly prompts with captchas, it's difficult to apply large scale.
You can also retrieve the impact factor for all journals:
>>>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 or NLM ID.
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, min_impact=1.5, max_impact=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', 'journal_abbr': 'npj Quantum Inf', 'eissn': '2056-6387', 'journal': 'npj Quantum Information', 'nlm_id': '101722857', 'issn': '', 'score': 92},
# {'factor': 1.577, 'jcr': 'Q3', 'journal_abbr': 'Nation', 'eissn': '0027-8378', 'journal': 'NATION', 'nlm_id': '9877123', 'issn': '0027-8378', 'score': 91}
# ]When multiple query searches are performed, two types of plots can be generated automatically: Venn diagrams and bar plots.
Compare the temporal evolution of different queries across different servers.
from paperscraper import QUERY_FN_DICT
from paperscraper.postprocessing import aggregate_paper
from paperscraper.utils import get_filename_from_query, 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'
data_dict = dict()
for query in queries:
filename = get_filename_from_query(query)
data_dict[filename] = dict()
for db,_ in QUERY_FN_DICT.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
data_keys = [
'deeplearning_molecule_fingerprint.jsonl',
'deeplearning_molecule_smiles.jsonl',
'deeplearning_molecule_gcn.jsonl'
]
plot_comparison(
data_dict,
data_keys,
title_text="'Deep Learning' AND 'Molecule' AND X",
keyword_text=['Fingerprint', 'SMILES', 'Graph'],
figpath='mol_representation'
)
from paperscraper.plotting import (
plot_venn_two, plot_venn_three, plot_multiple_venn
)
sizes_2020 = (30842, 14474, 2292, 35476, 1904, 1408, 376)
sizes_2019 = (55402, 11899, 2563)
labels_2020 = ('Medical\nImaging', 'Artificial\nIntelligence', 'COVID-19')
labels_2019 = ['Medical Imaging', 'Artificial\nIntelligence']
plot_venn_two(sizes_2019, labels_2019, title='2019', figname='ai_imaging')
plot_venn_three(
sizes_2020, labels_2020, title='2020', figname='ai_imaging_covid'
)
)
Or plot both together:
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'
)
If you use paperscraper, please cite the papers that motivated our development of this tool.
@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}
}
@article{born2021on,
title = {On the role of artificial intelligence in medical imaging of COVID-19},
journal = {Patterns},
volume = {2},
number = {6},
pages = {100269},
year = {2021},
issn = {2666-3899},
url = {https://doi.org/10.1016/j.patter.2021.100269},
author = {Jannis Born and David Beymer and Deepta Rajan and Adam Coy and Vandana V. Mukherjee and Matteo Manica and Prasanth Prasanna and Deddeh Ballah and Michal Guindy and Dorith Shaham and Pallav L. Shah and Emmanouil Karteris and Jan L. Robertus and Maria Gabrani and Michal Rosen-Zvi}
}
![dependabot[bot] avatar](https://avatars.githubusercontent.com/in/29110?v=4 "dependabot[bot]")
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent