import sys; sys.path.append(_dh[0].split("knowknow")[0])
from knowknow import *
# lists html files in an automated way
def list_dir(d):
import urllib
cc = []
for fn in sorted(Path(".").glob("%s/**/*.html"%d)):
name = fn.name.split(".html")[0]
cc += ["<li> <a href='%s'>%s</a> </li>" % (urllib.parse.quote(bytes(fn)), name)]
cc = ['<ul>']+cc+['</ul>']
display(HTML("\n".join(cc)))
This Python package, knowknow
, is an attempt to make powerful, modern tools for analyzing the structure of knowledge open to anyone.
Although I hope we can continue to improve the methods and documentation written here, and I intend that this grow larger than myself, this package acts as a stabilizing force for the field, giving us all access to the common methods and data for analyzing these structures.
I have included every inch of code here, leaving no stone unturned. With every pip install knowknow-amcgail
, you download the following:
creating variables
, a collection of pre-processing algorithms for cleaning and summarizing Web of Science search results, or JSTOR Data for Research data dumps.analyses
, a set of descriptive notebooks which illustrate these datasetsFor more details and instructions on how to reproduce these analyses, see GitHub.
knowknow
¶sociology-wos
dataset.knowknow
¶sociology-wos
every paper in WoS in early 2020 whose journal is in the 'Sociology' category, and which have full data. See the Web of Science counter for more information.html)sociology-jstor
in-text citations and their contexts were extracted from >90k full-text Sociology articles indexed in JSTOR. See the JSTOR counter for more information.html)sociology-wos-all
list_dir("creating variables")
list_dir("analyses")
comments()