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. [email protected]")

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': '[email protected]'}

Here is an example to match affiliation to GRID dataset.

from affiliation_parser import match_affil
match_affil("Department of Health Science, Kochi Women's University, Kochi 780-8515, Japan. [email protected]")

Output is a dictionary consist of GRID ID

{'City': 'Kochi',
 'Country': 'Japan',
 'ID': 'grid.444150.0',
 'Name': "Kochi Women's University",
 'State': ''}

• unidecode
• numpy
• scikit-learn

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

$ python setup.py 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