In [1]:
import sys; sys.path.append(_dh[0].split("knowknow")[0])
from knowknow import *
In [2]:
# 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)))

KnowKnow

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 datasets
  • A connector to pre-computed cooccurrence sets, hosted on OSF

For more details and instructions on how to reproduce these analyses, see GitHub.

Projects built on knowknow

  • amcgail/citation-death applies the concept of 'death' to attributes of citations, and analyzes the lifecourse of cited works, cited authors, and the authors writing the citations, using the sociology-wos dataset.

Datasets built with knowknow

Under the hood

Creating Variables

Analyses

In [2]:
comments()