Skip to main content

A package for calculating scientometric indices

Project description

Scientometrics

Scientometrics is a Python package designed to calculate various bibliometric indices used to measure the impact and productivity of researchers. These indices include the H-index, G-index, and X-index.

Table of Contents

Installation

To install the package, clone the repository and install the required dependencies.

git clone https://github.com/yourusername/scientometrics.git
cd scientometrics
pip install -r requirements.txt

Usage

To use the package, you can import the desired index calculation functions from the indices module.

from scientometrics.indices import h_index, g_index, x_index

# Example usage for h-index and g-index
citations = [10, 8, 5, 4, 3]

h = h_index(citations)
g = g_index(citations)

# Example usage for X-index with edge list
edge_list = [
    (1, 'entity1', 10),
    (2, 'entity2', 8),
    (3, 'entity1', 5),
    (4, 'entity3', 4),
    (5, 'entity2', 3)
]

x = x_index.calculate(edge_list)

# print the results
print(f"H-index: {h}")
print(f"G-index: {g}")
print(f"X-index: {x}")

Indices

H-index

The H-index is a measure that aims to quantify the productivity and citation impact of a researcher. It is defined as the maximum value of h such that the given researcher has published h papers that have each been cited at least h times.

G-index

The G-index is an index for quantifying scientific productivity based on publication record. It is calculated as the largest number g such that the top g articles received (together) at least g^2 citations.

X-index

The X-index is a hybrid metric that combines aspects of both the H-index and the G-index. It aims to provide a balanced measure of both productivity and citation impact.

License

This project is licensed under the MIT License. See the LICENSE.txt file for details.

Contributing

Contributions are welcome! Please open an issue or submit a pull request on GitHub. Feel free to modify the git clone URL and other details according to your specific project setup.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

scientometrics-0.1.0.tar.gz (4.9 kB view details)

Uploaded Source

Built Distribution

scientometrics-0.1.0-py3-none-any.whl (6.0 kB view details)

Uploaded Python 3

File details

Details for the file scientometrics-0.1.0.tar.gz.

File metadata

  • Download URL: scientometrics-0.1.0.tar.gz
  • Upload date:
  • Size: 4.9 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.1.0 CPython/3.12.2

File hashes

Hashes for scientometrics-0.1.0.tar.gz
Algorithm Hash digest
SHA256 f555b1255dd90b66077a96b2868fe100ff19205d3052b541ebaf21388d3c3ae1
MD5 90e5f70e70966916c4151e2af884d24c
BLAKE2b-256 c94458f0a6d843d40006689fb5af42d93634c88db0ea3ea0fd0d407297958c1a

See more details on using hashes here.

File details

Details for the file scientometrics-0.1.0-py3-none-any.whl.

File metadata

File hashes

Hashes for scientometrics-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 a344ecfcb4980acd644b9d3ef45ba1d217b51549027e132030886c255d2ecac5
MD5 c34d5ef2c68f8cf5e2962f947e051867
BLAKE2b-256 e026ef6f5074bfcbe54b796c706a13cb9c8ef4041e86fd1cb18922c54f08bdd3

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page