Fast and simple parser for MEDLINE and Pubmed Open-Access affiliation string. We can parse multiple fields from the affiliation string including department, affiliation, location, country, email and zip code from affiliation text.
We also provide function to match affiliation string to GRID dataset.
Here is an example to parse affiliation
from affiliation_parser import parse_affil
parse_affil("Department of Health Science, Kochi Women's University, Kochi 780-8515, Japan. watanabe@cc.kochi-wu.ac.jp")output is a dictionary
{'full_text': "Department of Health Science, Kochi Women's University, Kochi , Japan. ",
'department': 'Department of Health Science',
'institution': "Kochi Women's University",
'location': 'Kochi , Japan',
'country': 'japan',
'zipcode': '780-8515',
'email': 'watanabe@cc.kochi-wu.ac.jp'}Here is an example to match affiliation to GRID dataset.
from affiliation_parser import match_affil
match_affil("Department of Physics, Chulalongkorn University, Thailand") # string
match_affil([
"Department of Physics, Chulalongkorn University, Thailand",
"Department of Physics, Mahidol University, Thailand"
]) # or list of stringOutput is list (or a list of list) of dictionary consist of GRID ID and matching score
[{
'score': 0.783,
'index': 515,
'grid_id': 'grid.7922.e',
'institution': 'Chulalongkorn University',
'city': 'Bangkok',
'state': nan,
'country': 'Thailand'},
...
}]use pip install -r requirements.txt in order to install required packages
Clone the repository and install using setup.py or simple copy affiliation_parser
folder to your workspace.
git clone https://github.com/titipata/affiliation_parser
cd affiliation_parser
pip install .I put some snippet on how to produce quick summarization from MEDLINE data here.
Total number of publications per country
Number of publications over time from selected countries
