Package: knowknow-amcgail
This Python package,knowknow, is an attempt to make powerful, modern tools for analyzing the structure of knowledge open to anyone.
I recognize that parallel efforts exist along these lines, includingCADRE, but this package is still the only resource foranyoneto analyze Web of Science datasets, and the methods can be incorporated into CADRE byanyone.Projects built on knowknowamcgail/citation-deathapplies the concept of 'death' to attributes of citations, and analyzes the lifecourse of cited works, cited authors, and the authors writing the citations, using thesociology-wos-74bdataset.amcgail/lost-forgottendigs deeper into . An online appendix is availablehere, and the paper published inThe American Sociologistcan be foundhere.Datasets built with knowknowSociologysociology-wos(Harvard Dataverse) every paper in WoS in early 2020 whose journal is in the 'Sociology' category, and which have full data.in progresssociology-jstorin-text citations and their contexts were extracted from >90k full-text Sociology articles indexed in JSTOR.Installation (from PyPI)Install Python 3.7+InstallBuild Tools for Visual StudioRunpip install knowknow-amcgailInstallation (from GitHub)Install Python 3.7+Clone this repository to your computerCreate a virtualenv forknowknowIn the virtualenv, executepip install -r requirementsOn Windows, I needed to install the latest versions ofnumpy,scikit-learnandscipyvia .whlFor Windows, download fromthis site, install withpip install <fn.whl>Getting StartedTo get started with knowknow, you need to
1) specify where knowknow should store data and code ("init")
2) either create a new project or copy an existing one, and
3) start a JupyterLab environment.The following commands will help you perform these actions, getting you started conducting or reproducing analyses usingknowknow.python -m knowknow init.
Run this command first.
It will prompt you for the directory to store data files and the directory where code will be stored.python -m knowknow start <PROJ-NAME>For instance,python -m knowknow start citation-death.
Start a JupyterLab notebook in a knowknow code directory.
If the directory doesn't exist, knowknow creates the directory.[Recommended] Interfacing with GitHubIn order to use the following commands you must installGit.
This allows you to use others' code, and to publish your own code for others to use.python -m knowknow clone <URL>For instance,python -m knowknow clone https://github.com/amcgail/lost-forgotten.
Clone someone else's repository.In order to make your own changes to others' code, or to share your code with the world, do the following:Create aGitHubaccount and log in.InstallGitHub Desktop, which is a simple connector between Git on your computer and GitHub, in the cloud.
3a) [Share your code] In GitHub Desktop, chooseFile -> Create Repository, navigate to the folder containing knowknow code. This folder was created by knowknow using thestartcommand. Now press "Publish Repository" in the upper right to add this code to your GitHub account.
3b) [Contribute to others' code] In GitHub,forkthe repository you would like to contribute to. This creates a personal copy of that repository in your GitHub account. Then clone this copy into knowknow's code directory using theclonecommand, or using GitHub desktop. Once you are satisfied with your updates, and they are pushed back to GitHub, submit a "pull request" to the original repository to ask them to review and merge your changes.Auto-downloading Data and CodeData files will be automatically downloaded during code execution, if they are not alredy in thedatadirectory you specified with theinitcommand. This may take up significant bandwidth -- the data files for the Sociology dataset are ~750MB.Code specified by theknowknow.reqiurefunction will be automatically downloaded by knowknow into thecodedirectory you specified with theinitcommand.Be sure you trust whoever wrote the code you download.Running arbitrary code from random strangers on your computer is a security risk.DevelopingIf you want to contribute edits of your own, fork this repository into your own GitHub account, make the changes, and submit a request for me to incorporate the code (a "pull request"). This process is really easy with GitHub Desktop (tutorial here).There is a lot to do! If you find this useful to your work, and would like to contribute (even to the following list of possible next steps) but can't figure out how, please don't hesitate to reach out. My website ishere,Twitter here.Possible projectsThe documentation for this project can always be improved. This is typically through people reaching out to me when they have issues. Pleasefeel free.completeAn object-oriented model for handling context would prevent the need for so much variable-passing between functions, reduce total code volume, and improve readability.ongoingDifferent datasets and sources could be incorporated, if you have the need, in addition to JSTOR and WoS.complete - you can now upload data files to Harvard's DataverseIf you produce precomputed binaries and have an idea of how we could incorporate the sharing of these binaries within this library, pleaseDM meor something. That would be great.ongoing, future workAll analyses can be generalized to any counted variable of the citations. This wouldn't be tough, and would have a huge payout.huge project, uncertain payoutIt would be amazing if we could make a graphical interface for this.user simply imports data, chooses the analyses they want to run, fill in configuration parameters and press "go"the output is a PDF with the code, visualizations, and explanations for a given analysisbehind the scenes, all this GUI does is runnbconvertalso could allow users to regenerate any/all analyses for each dataset with the click of a buttoncould provide immediate access to online archives, either to download or upload similar count datasets