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.
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.txtTo 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}")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.
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.
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.
This project is licensed under the MIT License. See the LICENSE.txt file for details.
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.
