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main.py
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main.py
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#!/usr/bin/env python3.7
# -*- coding: utf-8 -*-
###############################################################################
# This work is distributed under a BSD 3-Clause License.
# license terms see https://opensource.org/licenses/BSD-3-Clause
# developed mainly by Eva Bunge (ORCiD:0000-0002-5587-5934, github.com/ebunge)
# with support from Michaela Voigt (ORCiD:0000-0001-9486-3189,
# github.com/michaelavoigt) and maintained by the Open Access team of
# TU Berlin University Library
###############################################################################
import numpy as np
import collections
import pickle
import weakref
from prettytable import PrettyTable
import urllib.request
import urllib.error
import json
import os
import re
import itertools
# ----------------- 1. Enable/Disable Functionalities -------------------------
# Detailed instructions for querying databases and preparing DOAJ data
# are included in the manual (in German).
# These variables are used to run only certain parts of the script. Set
# them all to 'True' to run the whole script.
# doAnalysis: If True - do some statistics and analysis of the final data. If
# False - disable this feature.
# doReadIn: If True - read in the database data from the text files and save it
# to file 'finalList'. If False - data is loaded from file 'finalList'
doAnalysis = True
doReadIn = True
# Variable that determines whether to contact CrossRef. Possible values:
# 1: Contact the CrossRef API to add missing ISSNs. Write results to file.
# 2: Read results from previously created file instead
# 0: Disable this feature
contactCR = 1
# Variable that determines whether to contact Unpaywall in order to retrieve
# data on green and hybrid OA. Possible values:
# 1: Contact the Unpaywall-API to retrieve OA-article-data. Write results to file.
# 2: Read results from previously created file instead
# 0: Disable this feature
contactOaDOI = 1
# Decide what to do when a corresponding/first author of a publication can't be
# determined manually. Possible values:
# 1: Write all of those articles to a file (docsToBeChecked.txt).
# Once you have determined which aricles have a first/corresponding author
# from a relevant institution, save the data in a tab-delimited, utf8-
# encoded text-file called 'docsChecked.txt', with the following format:
# each line corresponds to an article and the three columns contain (in this
# order): title, DOI, name of institution (which should be spelled identical
# to the name of the institution as set up in this script = 'inst.name')
# Place this file in the subfolder 'input-files'
# 2: Load this information into the script and have it included in the final
# results and the statistic.
# 0: Disable this feature completely.
checkToDo = 1
# Clarify which years of publication are of interest to you
yearMin = 2019
yearMax = 2019
# Enter your email here. It's needed to contact Unpaywall
myEMail = '[email protected]'
# ----------------- 2. Setting up Classes and Functions -----------------------
# Set up class for institutions
class Inst(object):
instances = []
nameVar1 = None
def __init__(self, name, nameVariants):
self.__class__.instances.append(weakref.proxy(self))
self.name = name
self.nameVar = nameVariants
# Set up class for databases
class Database(object):
instancesdb = []
content = None
def __init__(self, name, idNummer):
self.__class__.instancesdb.append(weakref.proxy(self))
self.name = name
self.idNummer = idNummer
def save_publications_data_to_file(document_list, filename_out):
print('save data to ' + filename_out)
if os.path.exists(filename_out):
os.remove(filename_out)
with open(filename_out, 'w', encoding='utf-8') as f:
ch = 'authors\ttitle\tOA-Status\tDOI\tjournal\tISSN\teISSN\tpublisher\tyear\t' + \
'affiliations\tall identified name variants\tcorresponding author\t' + \
'found name variant\te-mail\tsubject\tDOAJ subject\tfunding\tlicence\t' + \
'databaseID\tnotes\toaDOI[is_oa]\toaDOI[journal_is_oa]\toaDOI[host_type]\t' + \
'oaDOI[license]\tAPC Amount\tAPC Currency'
f.write(ch + '\n')
for d in document_list:
document_data = []
for i in d.arry():
if i is None:
result_string = 'None'
else:
result_string = re.sub('[\t\n\r]', ' ', str(i).strip())
document_data.append(result_string)
f.write('\t'.join(document_data))
f.write('\n')
# Set up class for documents
class Document(object):
nameVariant = None
allNameVariants = None
doajSubject = None
lizenz = None
oaStatus = None
APCValue = None
APCCurrency = None
checks = ''
oaDOI1 = ''
oaDOI2 = ''
oaDOI3 = ''
oaDOI4 = ''
def __init__(self, authors, title, DOI, journal, ISSN, eISSN, publisher,
year, affiliations, corrAuth, eMail, subject, funding, dbID):
self.authors = authors
self.title = title
self.DOI = DOI
self.journal = journal
self.ISSN = ISSN
self.eISSN = eISSN
self.publisher = publisher
self.year = year
self.affiliations = affiliations
self.corrAuth = corrAuth
self.eMail = eMail
self.subject = subject
self.funding = funding
self.dbID = dbID
# Return first three consonants of the author's name concatenated with the
# first 19 consonants of the title
def konsonanten(self):
d = ' '.join([''.join([i for i in self.authors if i in co]).lower()[0:3],
''.join([i for i in self.title if i in co]).lower()[0:19]])
return d
# Return all values associated with a certain publication
def arry(self):
return [self.authors, self.title, self.oaStatus, self.DOI,
self.journal, self.ISSN, self.eISSN, self.publisher, self.year,
self.affiliations, self.allNameVariants, self.corrAuth,
self.nameVariant, self.eMail, self.subject, self.doajSubject,
self.funding, self.lizenz, dbNameID[self.dbID], self.checks,
self.oaDOI1, self.oaDOI2, self.oaDOI3, self.oaDOI4,
self.APCValue, self.APCCurrency]
# Function that takes consonants from a title and turns them into a string
# INPUT: title of a publication (string)
# OUTPUT: first twenty consonants of the title (string)
def kons(title):
d = ''.join([item for item in title if item in co]).lower()[0:19]
return d
# Function that checks if an ISSN/eISSN is in the DOAJ and adds doaj-data
# to the document
# INPUT: List of documents to be checked, case = 1 in general, case = 2 if
# finalList is being read in from a file
def checkISSN(docList, case):
for item in docList:
test1 = (item.ISSN in eissns or item.ISSN in issns)
test2 = (item.eISSN in eissns or item.eISSN in issns)
test3 = False
if test1:
t1 = [rec for rec in doaj if rec[0] == item.ISSN]
if t1 == []:
t1 = [rec for rec in doaj if rec[1] == item.ISSN]
test3 = int(t1[0][8]) <= int(item.year)
elif test2:
t1 = [rec for rec in doaj if rec[0] == item.eISSN]
if t1 == []:
t1 = [rec for rec in doaj if rec[1] == item.eISSN]
test3 = int(t1[0][8]) <= int(item.year)
if test3:
if (item.ISSN is not None and test1) \
or (item.eISSN is not None and test2):
if case == 1:
item.oaStatus = 'gold'
item.checks += 'Identified via DOAJ '
if t1[0][4] != '':
item.APCValue = t1[0][4]
item.APCCurrency = t1[0][5]
elif case == 2:
doc = [x for x in finalList if x.DOI == item.DOI]
doc[0].oaStatus = 'gold'
doc[0].checks += 'Identified via DOAJ '
if t1[0][4] != '':
doc[0].APCValue = t1[0][4]
doc[0].APCCurrency = t1[0][5]
if test1:
j = np.where(doaj == item.ISSN)
elif test2:
j = np.where(doaj == item.eISSN)
item.doajSubject = str(doaj[j[0], 3]).strip("['").strip("']")
item.publisher = str(doaj[j[0], 6]).strip("['").strip("']").strip()
if '\n' in item.publisher:
item.publisher = ''.join([s for s in item.publisher
if s != '\n'])
item.lizenz = str(doaj[j[0], 7]).strip("['").strip("']")
return
# Function to identify corresponding authors (= first authors) in Inspec data
# INPUT: list of authors of a publication, list of their affiliations
# OUTPUT: first author; associated affiliation (string)
def inspecCorrAuth(auth, affil):
authorList = auth.split('; ')
affilList = affil.split('; ')
if authorList[0] in affilList:
firstAuth = affilList.index(authorList[0])
else:
return None
while firstAuth < len(affilList) and affilList[firstAuth] in authorList:
firstAuth += 1
if firstAuth + 1 == len(affilList):
tempor = affilList[-1]
elif firstAuth < len(affilList):
tempor = affilList[firstAuth].split('.')[:-2]
return authorList[0] + '; ' + ''.join(tempor)
# Function that takes a list of documents and contacts CrossRef to find
# missing ISSNs/eISSNs
# INPUT: List of documents that have a DOI but no ISSN of eISSN
def askCR(missISSN):
print('Begin contacting CrossRef')
c = 0
reCheck = []
baseurl = 'http://api.crossref.org/works/'
for doc in missISSN:
doi = doc.DOI
myurl = baseurl + doi
try:
response = urllib.request.urlopen(myurl)
cr_data = json.load(response)
cr_data_msg = cr_data["message"]
if "ISSN" in cr_data_msg:
c += 1
reCheck.append(doc)
doc.ISSN = str(cr_data_msg["ISSN"][0])
if len(cr_data_msg["ISSN"]) > 1:
doc.eISSN = str(cr_data_msg["ISSN"][1])
except urllib.error.HTTPError as err:
fehler = "Sorry, something went wrong with CrossRef (HTTP Error)."
print(fehler)
print(str(c) + ' ISSNs added via CrossRef')
return reCheck
# Contacts the Unpaywall-API to retrieve data on green / hybrid (& gold) OA
# status. Also retrieve publisher data if provided.
# INPUT: List of publications that have a DOI but whose ISSN is not listed in
# the DOAJ
# OUTPUT: Results printed to file oaDOI-response.txt: one line per publication,
# containing the following information: DOI, is_oa, journal_is_oa,
# host_type [repository or publisher], license, publisher, oaStatus
def askOaDOI(needInfo):
print('Begin contacting Unpaywall')
baseurl = 'https://api.unpaywall.org/v2/'
relKeys = {1: 'is_oa', 2: 'journal_is_oa', 3: 'host_type', 4: 'license',
5: 'publisher'}
replies = [[0 for x in range(7)] for y in range(len(needInfo))]
i = 0
errDOIs = []
for doc in needInfo:
doi = doc.DOI
replies[i][0] = doi
myurl = baseurl + doi + '?email=' + myEMail
try:
response = urllib.request.urlopen(myurl)
response = json.load(response)
for item in relKeys:
if relKeys[item] in response:
replies[i][item] = response[relKeys[item]]
# if the key cannot be found in the response (on the top level)
# but there is an entry 'best_oa_location' that has an entry for
# that key then take that value.
elif 'best_oa_location' in response:
subresponse = response['best_oa_location']
if subresponse is not None:
replies[i][item] = subresponse[relKeys[item]]
if replies[i][1] and replies[i][2]:
doc.oaStatus = 'gold'
doc.checks += 'Identified via Unpaywall '
elif replies[i][1] and not replies[i][2] \
and replies[i][3] == 'repository':
doc.oaStatus = 'green'
doc.checks += 'Identified via Unpaywall '
elif replies[i][1] and not replies[i][2] \
and replies[i][3] == 'publisher':
if 'cc' in str(replies[i][4]):
doc.oaStatus = 'hybrid'
doc.checks += 'Identified via Unpaywall '
doc.oaDOI1 = replies[i][1]
doc.oaDOI2 = replies[i][2]
doc.oaDOI3 = str(replies[i][3])
doc.oaDOI4 = str(replies[i][4])
doc.lizenz = str(replies[i][4])
doc.publisher = str(replies[i][5])
replies[i][6] = doc.oaStatus
except urllib.error.HTTPError as err:
fehler = "Sorry, Unpaywall doesn't know this DOI (HTTP Error)."
print('DOI ', doi, ': ', fehler)
errDOIs += [doi]
except:
print('DOI ', doi + ': ++++++++ other error!! ++++++++ ')
i += 1
if i % 500 == 0:
print('Now received responses for ', i, ' documents from Unpaywall')
ch = 'DOI\tis_oa\tjournal_is_oa\thost_type\tlicense\tpublisher\toaStatus'
np.savetxt('output-files/oaDOI-response.txt', replies, delimiter='\t',
header=ch, comments='', fmt='"%s"')
np.savetxt('output-files/DOIs-oaDOI-error.txt', errDOIs, delimiter='\t',
header='DOIs causing error at Unpaywall-API', comments='',
fmt='"%s"')
print('Saved Unpaywall-responses to file "oaDOI-responses.txt"')
return
# Function that takes data in WoS-format and transforms the data into a list of
# Document-objects.
# !This function is not really needed in this script as it is right now!
# It's being included for the purpose of easily adding new databases to the
# script. To do this export article data from Citavi in the WoS-format and use
# this function to do the read-in for the data
# INPUT: (list of records in WoS-format, database ID (integer))
# OUTPUT: list of Document-objects
def wosFormat(wosRecords, ind):
records = []
i = 0
with open(wosRecords, 'r', newline=None) as f:
for line in f:
if i == 0:
newDoc = Document('', '', None, None, None, None,
None, None, '', None, None, None, None, ind)
i += 1
le = len(line)
if line[0:2] != ' ':
kuerzel = line[0:2]
if line[0:2] == 'TI':
newDoc.title = line[3:le].strip('\n').strip('\r')
elif line[0:2] == 'SO':
newDoc.journal = line[3:le].strip('\n').strip('\r')
elif line[0:2] == 'PY':
newDoc.year = line[3:le].strip('\n').strip('\r')
elif line[0:2] == 'SN':
if '-' not in line:
vorl = line[3:le].strip('ISSN ').strip('\n').strip('\r')
newDoc.ISSN = vorl[0:4] + '-' + vorl[4:8]
elif ',' in line:
newDoc.ISSN = line.strip('ISSN ').strip('\n').strip('\r')[0:9]
else:
newDoc.ISSN = line[3:le].strip('ISSN ').strip('\n').strip('\r')
elif line[0:2] == 'DI':
newDoc.DOI = line[3:le].strip('\n').strip('\r')
elif line[0:2] == 'AF':
newDoc.authors = line[3:le].strip('\n').strip('\r')
elif kuerzel == 'AF' and line[0:2] == ' ':
newDoc.authors += '; '
newDoc.authors += line[3:le].strip('\n').strip('\r')
elif line[0:2] == 'FN':
records.append(newDoc)
newDoc = Document('', '', None, None, None, None,
None, None, '', None, None, None, None, ind)
records.append(newDoc)
return records
# Checks a given name of an institution against a list of approved name
# variants and returns the names of institutions that have been identified.
# INPUT: (text string with name of an institution, case 0 = corresponding
# author | case 1 = general affiliations)
# OUTPUT: (bool stating if instition is part of approved list, names of
# institutions that have been found)
def listCheck(institution, case):
val = [None] * len(institutions)
variant = None
for i in range(0, len(val)):
if case == 0:
instList = institutions[i].nameVar
elif case == 1:
instList = institutions[i].nameVar1
valu = [None] * len(instList)
for j in range(0, len(instList)):
valu[j] = all(word in institution for word in instList[j])
if any(valu):
if variant is None:
variant = institutions[i].name
else:
variant += '; ' + institutions[i].name
val[i] = True
return (any(val), variant)
# Function that takes data in PubMed-format and transforms it into a list of
# Documents.
# INPUT: (PubMed-records, database ID (integer))
# OUTPUT: list of Documents
def pubmedFormat(pmRecords, ind):
records = []
i = 0
authorCount = 0
newDoc = None
with open(pmRecords, 'r', newline=None) as f:
for line in f:
lengths = len(line)
if line[0:2] != ' ':
kuerzel = line[0:4]
if line[0:4] == 'PMID':
authorCount = 0
if i > 0:
records.append(newDoc)
newDoc = Document('', '', None, None, None, None,
None, None, '', None, None, None, None, ind)
i += 1
elif line[0:2] == 'TI':
newDoc.title = line[6:lengths].strip('\n').strip('\r')
elif kuerzel == 'TI ' and line[0:2] == ' ':
newDoc.title += ' '
newDoc.title += line[6:lengths].strip('\n').strip('\r')
elif line[0:2] == 'IS' and line[-5:-2] == 'nic':
newDoc.eISSN = line[6:15]
elif line[0:2] == 'IS' and line[-5:-2] == 'ing':
newDoc.ISSN = line[6:15]
elif line[0:3] == 'FAU' and authorCount > 0:
newDoc.authors += '; '
newDoc.authors += line[6:lengths].strip('\n').strip('\r')
authorCount += 1
elif line[0:3] == 'FAU' and authorCount == 0:
newDoc.authors = line[6:lengths].strip('\n').strip('\r')
newDoc.corrAuth = newDoc.authors + '; '
authorCount += 1
elif authorCount == 1 and line[0:2] == 'AD':
newDoc.corrAuth += line[6:lengths].strip('\n').strip('\r')
newDoc.affiliations = line[6:lengths].strip('\n').strip('\r')
elif line[0:2] == ' ' and kuerzel == 'AD ' and authorCount == 1:
newDoc.affiliations += line[5:lengths].strip('\n').strip('\r')
newDoc.corrAuth += line[5:lengths].strip('\n').strip('\r')
elif line[0:2] == ' ' and kuerzel == 'AD ' and authorCount > 1:
newDoc.affiliations += line[5:lengths].strip('\n').strip('\r')
elif authorCount > 1 and line[0:2] == 'AD':
newDoc.affiliations += '; '
newDoc.affiliations += line[5:lengths].strip('\n').strip('\r')
elif line[0:2] == 'JT':
newDoc.journal = line[6:lengths].strip('\n').strip('\r')
elif line[0:2] == 'DP':
newDoc.year = line[6:10]
elif line[0:3] == 'LID' and 'doi' in line:
newDoc.DOI = line[6:lengths].strip('\n').strip('\r').strip(' [doi]')
records.append(newDoc)
return records
# Read in table mapping RIS-fields of databases to document-attributes
risFields = np.genfromtxt('RIS-fields.csv', delimiter=';', dtype=None, encoding='utf-8')
# Valid characters for ISSNs
numX = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', 'X']
def concatenate_tags(publication, tag_list):
# just concatenate the strings belonging to the tags in the tag_list; separate values with '; '
result_list = []
for tag in tag_list:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
result_list.extend(publication[tag])
if result_list:
return '; '.join(result_list)
else:
return None
def get_first_author_and_first_affiliation_as_string(newDoc):
result_list = []
att_authors = getattr(newDoc, 'authors')
att_affiliations = getattr(newDoc, 'affiliations')
if att_authors:
# if more than one author: normally separated by ;
first_author = att_authors.split('; ')[0]
result_list.append(first_author)
if att_affiliations:
# if more than one affiliation: normally separated by ;
first_affiliation = att_affiliations.split('; ')[0]
result_list.append(first_affiliation)
return '; '.join(result_list)
# Read in RIS-files
# INPUT: RIS-records, database-ID
# OUTPUT: List of documents
def risFormat(risRecords, ind):
records = []
#
# read file, put data in data structure
#
publication_data = []
with open(risRecords, 'r', newline=None) as f:
for line in f:
if line.strip(): # ignore empty lines
tag = line[0:2]
data = line[6:-1].strip()
# ignore lines that do not match the pattern "XX - data"
if not (tag.isupper() and line[2] == ' '):
continue
# ignore line with Tag 'TY' for database CINAHL
if ind == 12 and tag == 'TY':
continue
# Start of a new record?
if (ind != 12 and tag == 'TY') or (ind == 12 and tag == 'ID'):
publication_data.append({})
# enter line in data list
# is there an entry for that tag?
if not tag in publication_data[-1]:
publication_data[-1][tag] = []
publication_data[-1][tag].append(data)
#
# Auswertung
#
# get col from file RIS-fields.csv
ris_tags = {}
for col_nr_db in range(len(risFields[0])):
if str(ind) == risFields[0][col_nr_db]:
break
# get RIS-Tags for fields
for line_nr in range(1, len(risFields)):
attribute = risFields[line_nr][0]
if not attribute in ris_tags:
ris_tags[attribute] = []
if risFields[line_nr][col_nr_db]: # falls Feld nicht leer
ris_tags[attribute].append(risFields[line_nr][col_nr_db])
#
# extract data for each publication
#
for publication in publication_data:
newDoc = Document('', '', None, None, None, None, None, None, '', None, None, None, None, ind)
records.append(newDoc)
# take fields as is; concatenate with ; if several tags
for attribute in ['authors', 'title', 'journal', 'publisher']:
result_string = concatenate_tags(publication, ris_tags[attribute])
if attribute == 'title' and result_string:
result_string = result_string.strip('.')
setattr(newDoc, attribute, result_string)
result_string = None
# affiliations
attribute = 'affiliations'
if ind in [9, 13, 17]: # BSC, EBSCO, SportDiscus
# nehme Affiliatin aus 'AD', wenn vorhanden, sonst 'N1'
for tag in ris_tags[attribute]:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
# Affiliationen stehen häufig im Notiz-feld N1; ist aber Fallback, nehme N1 nur,
# wenn es kein anderes Feld gibt!
if tag != 'N1':
attribute_string = '; '.join(publication[tag])
setattr(newDoc, attribute, attribute_string)
break # danach keine weiteren Tags mehr auswerten!
elif tag == 'N1':
attribute_string = '; '.join(publication[tag])
# Affiliationen stehen im Notizfeld zusammen mit anderen Angaben
# wird mit "Affiliations: " eingeleitet
# angaben danach eingeleitet mit : 'Source Info:', 'Issue Info:', 'Document Type:' oder 'Release Date:'
m = re.search('Affiliations?: +(.+?) +(Source Info:|Issue Info:|Document Type:|Release Date:|No. of Pages:)', attribute_string)
if m:
substring = m.group(1)
if ind in [17]: # SportDiscus
# split into affiliations; each is marked by : 1 ,: 2 , etc,
affiliations_list = re.split(": [0-9]+ ", ': ' + substring)
else:
# split into affiliations; each is marked by 1: , 2: , etc,
# important: there must be whitespace ahead of the number; otherwise the match might be incorrect
affiliations_list = re.split("\s[0-9]+\s*:\s*", ' ' + substring)
new_affiliations_string = ''
for aff in affiliations_list:
if aff.strip(): # ignore empty entries
aff = aff.strip(' ;') # strip ; from end
if new_affiliations_string:
new_affiliations_string += '; '
new_affiliations_string += aff
setattr(newDoc, attribute, new_affiliations_string)
else:
setattr(newDoc, attribute, concatenate_tags(publication, ris_tags[attribute]))
# DOI
attribute = 'DOI'
result_string = ''
if ind in [13]:
for tag in ris_tags[attribute]:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
if tag != 'N1':
for attribute_string in publication[tag]:
if 'doi.org' in attribute_string or attribute_string.startswith('10.'):
result_string = attribute_string
break # nehme nur die erste DOI
if result_string: # falls Ergebnis, ddanach keine weiteren Tags mehr auswerten!
break
else: # N1
attribute_string = '; '.join(publication[tag])
m = re.search('DOI: +([^\s]+)', attribute_string)
if m:
result_string = m.group(1).strip(' .')
if result_string.strip():
result_string = result_string.strip()
if 'doi.org' in result_string:
result_string = result_string[result_string.find('doi.org') + 8:]
setattr(newDoc, attribute, result_string)
else:
result_string = concatenate_tags(publication, ris_tags[attribute])
if result_string and 'doi.org' in result_string:
result_string = result_string[result_string.find('doi.org') + 8:]
setattr(newDoc, attribute, result_string)
result_string = None
# ISSN, eISSN
attribute = 'ISSN'
for tag in ris_tags[attribute]:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
for attribute_string in publication[tag]: # falls es mehrere Tags gibt
if attribute_string[0:4] != '978-':
# anInt = filter(lambda x: x in numX, attribute_string.split())
anInt = ''.join(filter(lambda x: x in numX, attribute_string))
if anInt != '':
# das erste Tag
if getattr(newDoc, attribute) in ('', None):
setattr(newDoc, attribute, anInt[0:4] + '-' + anInt[4:8])
if len(anInt) > 8:
setattr(newDoc, 'eISSN', anInt[8:12] + '-' + anInt[12:16])
# zweites Tag = Elektronisch
else:
setattr(newDoc, 'eISSN', anInt[0:4] + '-' + anInt[4:8])
# year
attribute = 'year'
for tag in ris_tags[attribute]:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
attribute_string = publication[tag][0] # nehme das erste Tag, ignoriere weitere
if len(attribute_string) > 4:
result = int(attribute_string[:4]) # beginnt mit der Jahreszahl
else:
result = int(attribute_string)
setattr(newDoc, attribute, result)
# corrAuth
attribute = 'corrAuth'
if ris_tags[attribute]: # if defined in RIS-fields.csv
if ind in [16]: # Scopus
result_list = []
for tag in ris_tags[attribute]:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
for attribute_string in publication[tag]:
m = re.search('Correspondence Address: (.+)$', attribute_string)
if m:
result_list.append(m.group(1))
if result_list:
result_string = '; '.join(result_list)
else: # nothing found in tags
result_string = get_first_author_and_first_affiliation_as_string(newDoc)
setattr(newDoc, attribute, result_string)
result_string = None
result_list = None
else:
setattr(newDoc, attribute, concatenate_tags(publication, ris_tags[attribute]))
else:
# take first author + first affiliation
# but only if the database has information about affiliations (otherwise useless)
if getattr(newDoc, 'affiliations'):
setattr(newDoc, attribute, get_first_author_and_first_affiliation_as_string(newDoc))
# eMail
attribute = 'eMail'
if ind in [9, 13, 17]: # BSC, EBSCO, SportDiscus
# Mail address in notes N1
result_string = ''
attribute_string_list = []
for tag in ris_tags['affiliations']:
if tag in publication: # wenn Tag in Publications-Daten vorhanden
# Affiliationen stehen häfig im Notiz-feld N1; ist aber Fallback, nehme N1 nur, wenn es kein anderes Feld gibt!
attribute_string_list.extend(publication[tag])
if tag != 'N1':
break # danach keine weiteren Tags mehr auswerten!
m = re.search('(Email Address|email): ([^\s;]+)', '; '.join(attribute_string_list))
if m:
result_string = m.group(2).strip(' ,;.')
if result_string:
setattr(newDoc, attribute, result_string)
result_string = None
elif ind in [14, 16]: # Embase, Scopus
result_list = []
for tag in ris_tags['corrAuth']: # part of the field that contains the corresponding author
if tag in publication: # wenn Tag in Publications-Daten vorhanden
for attribute_string in publication[tag]:
m = re.search('(E-mail|email): (.+)$', attribute_string)
if m:
result_list.append(m.group(2))
if result_list:
setattr(newDoc, attribute, '; '.join(result_list))
result_list = None
return records
# List of consonants
co = ('b', 'c', 'd', 'f', 'g', 'h', 'j', 'k', 'l', 'm', 'n', 'p', 'q', 'r',
's', 't', 'v', 'w', 'x', 'y', 'z', 'B', 'C', 'D', 'F', 'G', 'H', 'J',
'K', 'L', 'M', 'N', 'P', 'Q', 'R', 'S', 'T', 'V', 'W', 'X', 'Y', 'Z')
# Duplicate check
# This function takes a set of data (= masterList) and compares incoming new
# data with it. All duplicates are removed from the new data which is then
# added to the masterList. This action is repeated for each database.
# Comparisons are done via DOI-matching and then title/author-matching.
# doiList = list that contains all DOIs from masterList.
# konsMast = a list of strings which consist of the first three consonants of
# the authors' names and the first 19 consonants of the title for the data
# in the masterlist.
# INPUT: (iterating integer, list containing article data,
# list of DOIs (strings), list of strings for title/author-matching)
# OUTPUT: list containing Documents with duplicates removed
def dubletten(iterates, masterList, dois, kons):
nowList = datenbanken[iterates].content
if iterates == 1:
doiList = [x.DOI for x in masterList]
konsMast = [item.konsonanten() for item in masterList]
else:
doiList = dois
konsMast = kons
f = collections.Counter(konsMast)
i = len(nowList)
print(datenbanken[iterates].name, ' - number of records: ', i)
nowList = [item for item in nowList if item.DOI is None
or item.DOI.strip('"') == ''
or item.DOI.strip('"') not in doiList]
j = len(nowList)
print(datenbanken[iterates].name, \
' - number of records removed via DOI-matching: ', i-j)
nowList = [item for item in nowList if item.authors is not None
and item.konsonanten() not in f]
k = len(nowList)
print(datenbanken[iterates].name, \
' - number of records removed via title/author-matching: ', j-k)
print(datenbanken[iterates].name, \
' - number of records added to masterList: ', k)
if k > 0:
masterList += nowList
doiList += [x.DOI for x in nowList]
konsMast += [item.konsonanten() for item in nowList]
iterates += 1
if iterates < len(datenbanken):
masterList = dubletten(iterates, masterList, doiList, konsMast)
return masterList
# -------------------- 3. Set up Institutions ---------------------------------
# Set up institutions. Format for name variants:
# [[var1,var2],[var3]] is equivalent to: (var1 AND var2) OR (var3)
# Careful: the name variant used when querying the database is not
# necessarily the same name variant used in the raw data.
# TU
TUnames = [['Tech', 'Univ', 'Berlin'], ['Berlin', 'TU'],
['Berlin', 'Inst', 'Technol']]
TU = Inst('TU', TUnames)
# Charité
Cnames = [['Charit', 'Univ'],
['Campus', 'Virchow', 'Berlin'],
['Campus', 'Franklin', 'Berlin'],
['Campus', 'Buch', 'Berlin'],
['Campus', 'Mitte', 'Berlin'],
['Charit', 'Berlin'],
['Berlin', 'Inst', 'Health'],
['Berlin', 'Inst', 'Gesundheitsforschung'],
['medizin', 'Berlin'],
['Medical', 'Univ', 'Berlin'],
['Medical', 'School', 'Berlin']]
Charite = Inst('Charité', Cnames)
# TODO: Univ Med Berlin = Charité?
# FU
FUnames = [['Berlin', 'FU'], ['Berlin', 'Free Univ'],
['Berlin', 'Freie', 'Univ'], ['Univ', 'Libre', 'Berlin']]
FU = Inst('FU', FUnames)
# TODO: not recognized
#
# Frei Univ Berlin
# Frei Universität Berlin
# Frei Universitaet Berlin
# HU
HUnames = [['Berlin', 'HU'],
['Berlin', 'Humboldt', 'Univ']]
HU = Inst('HU', HUnames)
# TODO: not recognized
#
# Humboldt University
# Humboldt-Universität
# Humboldt University
# Humbolt Univ Berlin
# UdK
UdKnames = [['Univ', 'Arts', 'Berlin'],
['Univ', 'Kunst', 'Berlin'],
['Berlin', 'UdK']]
UdK = Inst('UdK', UdKnames)
# TODO: not recognized
#
# Universität der Künste, Berlin
# Universität der Künste Berlin
# Beuth
Bnames = [['Beuth', 'Berlin']]
Beuth = Inst('Beuth', Bnames)
# HTW
HTWnames = [['HTW', 'Berlin'],
['Tech', 'Wirt', 'Berlin']]
HTW = Inst('HTW', HTWnames)
# HWR
HWRnames = [['HWR', 'Berlin'],
['Wirt', 'Recht', 'Berlin'],
['Berlin', 'Economics', 'Law', 'School']]
HWR = Inst('HWR', HWRnames)
# TO DO: not recognized
# Berlin Sch Econ & Law, Berlin
# Alice Salomon
ASHnames = [['Alice', 'Salomon', 'Berlin'], ['ASH', 'Berlin'],
['Universidad Alice Salomon']]
ASH = Inst('ASH', ASHnames)
# Create list of institutions
institutions = [x for x in Inst.instances]
# If the name of the institution is very generic, a second set of name
# variants can be defined here. These are used when searching strings with
# more than one affiliation in them
for item in institutions:
item.nameVar1 = item.nameVar
TU.nameVar1 = [['Technische Universitat Berlin'],
['Technische Universitaet Berlin'],
['Technische Universität Berlin'],
['Berlin Institute of Techn'],
['Tech Univ Berlin'],
['Berlin Univ Technol'],
['Univ Technol Berlin'],
['TU Berlin'],
['Tech. Univ. Berlin'],
['Technical Univ. of Berlin'],
['Berlin Inst Technol'],
['Technical University Berlin'],
['Technische Universitaet de Berlin'],
['Technical University of Berlin'],
['Berlin University of Technology']]
# TODO: not recognized
#
# Tech Univ, Fachgebiet Bauinformat, Berlin, Germany
# Technol Univ Berlin
# Tech Univ, Berlin
# Berlin Tech Univ
# TU, Berlin
# Technical University (TU) Berlin
# Technischen Universität Berlin
# Technische Universität, Berlin
# Technische Univerisitaet Berlin
# Berlin, TU
# Technical University, Berlin
# Tech. Univ., Berlin
# Technical University in Berlin
# ------------- 4. Read in Text Files and Extract Information -----------------
# Set up databases
# The order of the databases here determines the order in which they are
# considered. Therefore databases with good/complete metadata should be near
# the top
dbWoS = Database('Web of Science', 1)
dbSF = Database('SciFinder', 2)
dbPM = Database('PubMed', 3)
dbScopus = Database('Scopus', 16)
dbInspec = Database('Inspec', 5)
dbTEMA = Database('TEMA', 4)
dbPQ = Database('ProQuest', 7)
dbBSC = Database('Business Source Complete', 9)
dbGf = Database('GeoRef', 10)
dbCIN = Database('CINAHL', 12)
dbLisa = Database('LISA', 15)
dbCAB = Database('CAB Abstracts', 11)
dbEm = Database('Embase', 14)
dbSD = Database('SportDiscus', 17)
dbIEEE = Database('IEEE', 6)
dbEB = Database('EBSCO', 13)
# List the databases
datenbanken = [x for x in Database.instancesdb]
# Create dictionary containing dbID mapped onto database name
leDat = len(datenbanken)
dbNameID = {datenbanken[i].idNummer: datenbanken[i].name for i in range(leDat)}