forked from susheel/papers
/
paper-extractor.py
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paper-extractor.py
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#!/usr/bin/env python
# usage: paper-extractor.py
__author__ = "Susheel Varma"
__copyright__ = "Copyright (c) 2019-2020 Susheel Varma All Rights Reserved."
__email__ = "susheel.varma@hdruk.ac.uk"
__license__ = "Apache 2"
import csv
import json
import urllib
import requests
from pprint import pprint
EPMC_BASE_URL = "https://www.ebi.ac.uk/europepmc/webservices/rest/search?resultType=core&pageSize=1000&format=json&"
PMCID_LOOKUP_URL = "https://www.ncbi.nlm.nih.gov/pmc/utils/idconv/v1.0/?ids={}&format=json&tool=my_tool&email=my_email@example.com"
PREPRINT_QUERY = '(SRC:PPR)'
PAPER_QUERY = 'NOT ' + PREPRINT_QUERY
HDRUK_GRANTS='((GRANT_ID:"HDRUK2020.138" OR GRANT_ID:"MC_PC_20029" OR GRANT_ID:"MC_PC_20058") OR ("HDRUK2020.138" OR "MC_PC_20029" OR "MC_PC_20058") OR ("Data and Connectivity" OR "National Core Studies"))'
HDRUK_ACK_AFF_QUERY=HDRUK_GRANTS + ' OR ' + '((ACK_FUND:"HDRUK" OR ACK_FUND:"HDR UK" OR ACK_FUND:"HDR-UK" OR ACK_FUND:"Health Data Research UK") OR (AFF:"HDRUK" OR AFF:"HDR UK" OR AFF:"HDR-UK" OR AFF:"Health Data Research UK"))'
HDRUK_PAPERS_QUERY = HDRUK_ACK_AFF_QUERY + ' AND ' + PAPER_QUERY
COVID_QUERY = '("2019-nCoV" OR "2019nCoV" OR "COVID-19" OR "SARS-CoV-2" OR "COVID19" OR "COVID" OR "SARS-nCoV" OR ("wuhan" AND "coronavirus") OR "Coronavirus" OR "Corona virus" OR "corona-virus" OR "corona viruses" OR "coronaviruses" OR "SARS-CoV" OR "Orthocoronavirinae" OR "MERS-CoV" OR "Severe Acute Respiratory Syndrome" OR "Middle East Respiratory Syndrome" OR ("SARS" AND "virus") OR "soluble ACE2" OR ("ACE2" AND "virus") OR ("ARDS" AND "virus") or ("angiotensin-converting enzyme 2" AND "virus"))'
COVID_PAPERS_QUERY = COVID_QUERY + ' AND ' + HDRUK_ACK_AFF_QUERY + ' AND ' + PAPER_QUERY
COVID_PREPRINTS_QUERY = COVID_QUERY + ' AND ' + HDRUK_ACK_AFF_QUERY + ' AND ' + PREPRINT_QUERY
# HDR UK Custom tags
NATIONAL_PRIORITIES_CSV = "data/national-priorities.csv"
LAY_SUMMARIES_CSV = "data/lay-summaries.csv"
def request_url(URL):
"""HTTP GET request and load into json"""
r = requests.get(URL)
if r.status_code != requests.codes.ok:
r.raise_for_status()
return json.loads(r.text)
def retrieve_papers(query="", data=None, cursorMark="*"):
if data is None:
DATA = []
else:
DATA = data
query_url_encoded = urllib.parse.quote_plus(query)
URL = EPMC_BASE_URL + "&".join(["query=%s" % query_url_encoded, "cursorMark=%s" % cursorMark])
print("Retrieving papers from", URL)
d = request_url(URL)
numResults = d['hitCount']
DATA.extend(d['resultList']['result'])
if len(DATA) < numResults:
retrieve_papers(query, DATA, cursorMark=d['nextCursorMark'])
return DATA
def get_dois_from_pmcids(data):
pmcids = ",".join([ p['id'] for p in data if p['doi'] == 'https://doi.org/' and p['id'].startswith('PMC')])
ret = request_url(PMCID_LOOKUP_URL.format(pmcids))
for r in ret['records']:
for d in data:
if d['id'] == r['pmcid']:
d['doi'] = "https://doi.org/" + r['doi']
return data
def export_json(data, filename, indent=2):
with open(filename, 'w') as jsonfile:
json.dump(data, jsonfile, indent=indent)
def export_csv(data, header, outputFilename):
with open(outputFilename, 'w') as csvfile:
writer = csv.DictWriter(csvfile, fieldnames=header)
writer.writeheader()
writer.writerows(data)
def read_csv(filename):
header = []
data = []
with open(filename, mode='r', encoding='utf-8-sig', newline='') as csvfile:
reader = csv.DictReader(csvfile)
header = reader.fieldnames
for row in reader:
data.append(row)
return data, header
NATIONAL_PRIORITIES, NP_HEADER = read_csv(NATIONAL_PRIORITIES_CSV)
def get_national_priorities(d):
for np in NATIONAL_PRIORITIES:
if d.get('id') == np['id']:
return {
'nationalPriorities': np['nationalPriorities'],
'healthCategories': np['healthCategories']
}
return {
'nationalPriorities': "",
'healthCategories': ""
}
LAY_SUMMARIES, LS_HEADER = read_csv(LAY_SUMMARIES_CSV)
def get_lay_summary(d):
doi = "https://doi.org/" + d.get('doi','')
for ls in LAY_SUMMARIES:
if ls['doi'] == doi:
return ls['lay summary']
return ""
def format_data(data):
HEADER = ['id', 'doi', 'title', 'authorString', 'authorAffiliations', 'journalTitle', 'pubYear', 'date', 'isOpenAccess', 'keywords', 'nationalPriorities', 'healthCategories', 'abstract', 'laySummary', 'urls']
DATA = []
for d in data:
print(d['id'])
# Get National Priorities & Health Categories
np = get_national_priorities(d)
# Get lay Summary
lay_summary = get_lay_summary(d)
# Extracting Author affiliations
authorAffiliations = []
if 'authorList' in d.keys():
for author in d['authorList']['author']:
if 'authorAffiliationsList' in author.keys():
if 'authorAffiliation' in author['authorAffiliationsList'].keys():
if None not in author['authorAffiliationsList']['authorAffiliation']:
affiliation = "; ".join(author['authorAffiliationsList']['authorAffiliation'])
authorAffiliations.append(affiliation)
# Extracting URLS
URLS = []
if d.get('fullTextUrlList', None) is not None:
for url in d.get('fullTextUrlList')['fullTextUrl']:
URLS.append("{}:{}".format(url['documentStyle'], url['url']))
# Extracting Keywords
keywords = ""
if 'keywordList' in d.keys():
keywords = keywords + "; ".join(d['keywordList']['keyword'])
if d.get('journalInfo', None) is None:
journalTitle = "No Journal Info"
else:
journalTitle = d.get('journalInfo')['journal']['title']
row = {
'id': d.get('id', ''),
'doi': "https://doi.org/" + d.get('doi',''),
'title': d.get('title'),
'authorString': d.get('authorString'),
'authorAffiliations': "; ".join(authorAffiliations),
'journalTitle': journalTitle,
'pubYear': d.get('pubYear'),
'date': d.get('firstPublicationDate', None),
'isOpenAccess': d.get('isOpenAccess'),
'keywords': keywords,
'nationalPriorities': np['nationalPriorities'],
'healthCategories': np['healthCategories'],
'abstract': d.get('abstractText', ''),
'laySummary': lay_summary
}
if len(URLS):
row['urls'] = "; ".join(URLS)
else:
row['urls'] = ""
DATA.append(row)
return DATA, HEADER
def merge(key, *lists):
import itertools
from collections import defaultdict
result = defaultdict(dict)
for dictionary in itertools.chain.from_iterable(lists):
result[dictionary[str(key)]].update(dictionary)
return list(result.values())
def main():
# retrieve papers with author affiliation or funding acknowledgement to HDR-UK
papers = retrieve_papers(query=HDRUK_PAPERS_QUERY, data=[])
data, header = format_data(papers)
# data = get_dois_from_pmcids(data)
export_csv(data, header, 'data/papers.csv')
export_json(data, 'data/papers.json')
# retrieve COVID-19 papers with author affiliation or funding acknowledgement to HDR-UK
covid_papers = retrieve_papers(query=COVID_PAPERS_QUERY, data=[])
data, header = format_data(covid_papers)
export_csv(data, header, 'data/covid/papers.csv')
# retrieve COVID-19 preprints with author affiliation or funding acknowledgement to HDR-UK
covid_preprints = retrieve_papers(query=COVID_PREPRINTS_QUERY, data=[])
data, header = format_data(covid_preprints)
export_csv(data, header, 'data/covid/ack-preprints.csv')
if __name__ == "__main__":
main()