metaknowledge | Python library for doing bibliometric and network analysis | Natural Language Processing library

by UWNETLAB Python Version: 3.4.1 License: GPL-2.0

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | metaknowledge Summary

metaknowledge is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. metaknowledge has no bugs, it has no vulnerabilities, it has build file available, it has a Strong Copyleft License and it has low support. You can download it from GitHub.

metaknowledge is a Python3 package that simplifies bibliometric research using data from various sources. It reads a directory of plain text files containing meta-data on publications and citations, and writes to a variety of data structures that are suitable for quantitative, network, and text analyses. It handles large datasets (e.g. several million records) efficiently. You can find the documentation.

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Quality

Security

License

Reuse

Support

metaknowledge has a low active ecosystem.

It has 144 star(s) with 37 fork(s). There are 15 watchers for this library.

It had no major release in the last 12 months.

There are 6 open issues and 9 have been closed. On average issues are closed in 229 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of metaknowledge is 3.4.1

Quality

metaknowledge has 0 bugs and 0 code smells.

Security

metaknowledge has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

metaknowledge code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

metaknowledge is licensed under the GPL-2.0 License. This license is Strong Copyleft.

Strong Copyleft licenses enforce sharing, and you can use them when creating open source projects.

Reuse

metaknowledge releases are available to install and integrate.

Build file is available. You can build the component from source.

Top functions reviewed by kandi - BETA

kandi has reviewed metaknowledge and discovered the below as its top functions. This is intended to give you an instant insight into metaknowledge implemented functionality, and help decide if they suit your requirements.

Compute the diffusion network
Return the value of a field
Make a node ID
Return the alternative name for a tag
Write the graph to a file
Write edge list
Plot a directed graph
Return a list of values
Write tnet edge list
Adds an element to the collection
Parses a scopus file
Return a list of citations
Get a list of citations
Parse MEDLINE file
Parses the record
Creates a citation
Drop nodes by count
Parse a single record
Parse NSF file
Drops edges from the graph
Drop nodes by degree
Parse wos file
Adds diffusion counts from source to target
Generates a time series of each year
Parse a ProQuest file
Read a graph from a CSV file

Get all kandi verified functions for this library.

metaknowledge Key Features

No Key Features are available at this moment for metaknowledge.

metaknowledge Examples and Code Snippets

No Code Snippets are available at this moment for metaknowledge.

Community Discussions

Trending Discussions on metaknowledge

alternatives to conda install for quick experimenting?

QUESTION

alternatives to conda install for quick experimenting?

Asked 2020-May-20 at 07:47

Yesterday, I wanted to work through a tutorial that uses metaknowledge. (Python 3 under Anaconda; Win 10.)

So, conda install -c conda-forge metaknowledge into an almost fresh env and, a day later, I am 22% of my way through examining conflicts.

Is there a smarter way to proceed?

how much faster would this be if I conda create every time I wanted to play with a new package?
miniconda?
mamba?

...

ANSWER

Answered 2020-May-20 at 07:47

You can install packages using pip inside conda even though it is not the preferred method for packages that exist in conda. This method may avoid whatever is causing your conda install to be unusably slow.

According to the docs:

There is no need to worry about creating a venv or virtualenv (older and newer styles of python virtual environments) for pip to install the package into because a conda environment is already a virtualenv
Once you are in the conda environment you want to install the package into, just pip install

This question has some related tips.

Using a fresh conda environment also might help if the slowness is due to having a vast number of packages, or a problematic package, already in the environment. The purpose of virtual environments is to isolate the set of packages installed so other projects can't be impacted, and so you are clear which packages you are potentially using. Whether you create a fresh environment for each experiment, or do all your experiments in a common environment which gradually accumulates packages in it, is up to you.

Source https://stackoverflow.com/questions/61905395

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install metaknowledge

You can download it from GitHub.
You can use metaknowledge like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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