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https://github.com/SciSciCollective/pyscisci

Science of Science
https://github.com/SciSciCollective/pyscisci

Last synced: 2 months ago
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Science of Science

Host: GitHub
URL: https://github.com/SciSciCollective/pyscisci
Owner: SciSciCollective
License: mit
Created: 2020-03-24T01:41:21.000Z (almost 5 years ago)
Default Branch: master
Last Pushed: 2024-08-29T18:12:52.000Z (5 months ago)
Last Synced: 2024-09-29T21:59:22.962Z (3 months ago)
Language: Python
Size: 5.27 MB
Stars: 155
Watchers: 11
Forks: 21
Open Issues: 6
Metadata Files:
- Readme: README.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md

Awesome Lists containing this project

awesome-scisci - pySciSci - An unified interface to analyze several of the most common Bibliometric DataBases used in the Science of Science (Libraries, Softwares / Reviews)

README

        # ``pySciSci``

[![License: MIT](https://img.shields.io/badge/license-MIT-blue.svg)](https://github.com/SciSciCollective/pyscisci/blob/master/LICENSE)

[![Read the Docs](https://readthedocs.org/projects/pyscisci/badge/?version=latest)](https://pyscisci.readthedocs.io/en/latest/?badge=latest)

"The Science of Science (SciSci) is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field"[[1]](#1).

The ``pySciSci`` package offers a unified interface to analyze several of the most common Bibliometric DataBases used in the Science of Science, including:

| Data Set      | Example |

| ----------- | ----------- |

| [Microsoft Academic Graph](https://docs.microsoft.com/en-us/academic-services/graph/) (MAG)      | [Getting Started with MAG](/examples/Getting_Started/Getting%20Started%20with%20MAG.ipynb)       |

| [Clarivate Web of Science](https://clarivate.com/webofsciencegroup/solutions/web-of-science/) (WoS)   | [Getting Started with WOS](/examples/Getting_Started/Getting%20Started%20with%20WOS.ipynb)        |

| [DBLP](https://dblp.uni-trier.de) | [Getting Started with DBLP](/examples/Getting_Started/Getting%20Started%20with%20DBLP.ipynb) |

| [American Physical Society](https://journals.aps.org/datasets) (APS) | [Getting Started with APS](/examples/Getting_Started/Getting%20Started%20with%20APS.ipynb) |

| [PubMed](https://www.nlm.nih.gov/databases/download/pubmed_medline.html) | [Getting Started with PubMed](/examples/Getting_Started/Getting%20Started%20with%20PubMed.ipynb) |

| [OpenAlex](https://openalex.org/) | [Getting Started with OpenAlex](/examples/Getting_Started/Getting%20Started%20with%20OpenAlex.ipynb) |

The ``pySciSci`` package also provides efficient implementations of recent metrics developed to study scientific publications and authors, including:

| Publications Metrics |    |

| ----------- | ----------- |

| Measure | Example |

| Interdisciplinarity - Simpsons Index |  [Example of Interdisciplinarity](/examples/Method_Examples/Example%20of%20Interdisciplinarity.ipynb)|

| Interdisciplinarity - Shannons Index | [Example of Interdisciplinarity](/examples/Method_Examples/Example%20of%20Interdisciplinarity.ipynb) |

| Interdisciplinarity - RoaStirling Index | [Example of Interdisciplinarity](/examples/Method_Examples/Example%20of%20Interdisciplinarity.ipynb) |

| Disruption Index | [Example Publication Citations](/examples/Method_Examples/Example%20Publication%20Citations.ipynb) |

| Sleeping Beauty Coefficient | [Example Publication Citations](/examples/Method_Examples/Example%20Publication%20Citations.ipynb) |

| Novelty & Conventionality | [Example Novelty](/examples/Method_Examples/Example%20Novelty.ipynb) |

| Long Term Citation | [Example Publication Citations](/examples/Method_Examples/Example%20Publication%20Citations.ipynb) |

| Author Metrics |    |

| ----------- | ----------- |

| Measure | Example |

| H-index | [Example Career Analysis](/examples/Method_Examples/Example%20Career%20Analysis.ipynb) |

| G-index | [Example Career Analysis](/examples/Method_Examples/Example%20Career%20Analysis.ipynb) |

| Q-factor | [Example Career Analysis](/examples/Method_Examples/Example%20Career%20Analysis.ipynb) |

| Annual productivity trajectories |[Example Career Analysis](/examples/Method_Examples/Example%20Career%20Analysis.ipynb) |

| Author Pagerank | [Example of Scientific Credit](/examples/Network_Examples/Example%20of%20Diffusion%20of%20Scientific%20Credit.ipynb) |

| Collective credit allocation | [Example of Credit Allocation](/examples/Method_Examples/Example%20of%20Credit%20Allocation.ipynb)|

| Career Topic Switching | [Example Career Topic Switching](/examples/Method_Examples/Example%20Career%20Topic%20Switching.ipynb) |

| HotStreak | [Example Career Analysis](/examples/Method_Examples/Example%20Career%20Analysis.ipynb) |

Advanced tools for constructing and analyzing network objects (both static and temporal):

| Network Analysis |    |

| ----------- | ----------- |

| Measure | Example |

| Citation Network | |

| Author Citation Network | [Example of Diffusion of Scientific Credit](https://github.com/SciSciCollective/pyscisci/blob/master/examples/Network_Examples/Example%20of%20Diffusion%20of%20Scientific%20Credit.ipynb)|

| Co-citation network | [Example of Cocitation Network](https://github.com/SciSciCollective/pyscisci/blob/master/examples/Network_Examples/Example%20of%20Cocitation%20Network.ipynb)|

| Co-authorship network | |

| Co-mention network | [Example of Coword Mention Network](https://github.com/SciSciCollective/pyscisci/blob/master/examples/NLP_Examples/Example%20of%20Coword%20Mention%20Network.ipynb)|

| Graph2vec network embedding | [Example_Node2vec](https://github.com/SciSciCollective/pyscisci/blob/master/examples/NLP_Examples/Example_Node2vec%20(umap%2Csem_axis).ipynb)|

| Multiscale Backbone | [Example of Cocitation Network](https://github.com/SciSciCollective/pyscisci/blob/master/examples/Network_Examples/Example%20of%20Cocitation%20Network.ipynb) |

| Career Topic Switching | [Example Career Topic Switching](/examples/Method_Examples/Example%20Career%20Topic%20Switching.ipynb) |

Natural Language Processing

- Publication matching

- Author matching

Visualization

- Career Timelines [Example Career Analysis](/examples/Method_Examples/Example%20Career%20Analysis.ipynb)

## Installation

### Latest PyPI stable release

```

  pip install pyscisci

```

### Latest development release on GitHub

Pull and install in the current directory:

```

  pip install git+https://github.com/SciSciCollective/pyscisci

```

### Extras

- To enable all extra functionality run: pip install pyscisci[nlp,hdf]

- The requirement to only use hdf tables has been removed, thus the dependency on tables is moved to an extra: pip install pyscisci[hdf]

- Advanced NLP dependencies can be installed by running: pip install pyscisci[nlp]

## Computational Requirements

Currently, the ``pySciSci`` is built on top of pandas, and keeps entire dataframes in working memory.  We have found that most large-scale analyzes 

can be performed on a personal computer with extended RAM.  If you don't have enough computational power, consider a smaller database (DBLP or APS), or running on a cloud computing platform (Google Cloud, Microsoft Azure, Amazon Web Services, etc).

We also support basic Dask implementations for multiprocessing.  [An example notebook can be found here.](/examples/Getting_Started/Getting%20Started%20with%20a%20Dask%20Example.ipynb)

## Contributing

See the [contributing guide](/CONTRIBUTING.md) for detailed instructions on how to get started with our project.

## Help and Support

### Documentation

 - HTML documentation is available [readthedocs](https://pyscisci.readthedocs.io/en/latest/).

### Questions

 - Email: Alex Gates ([email protected])

 

## References

[1]

Fortunato et al. (2018).

[Science of Science](https://science.sciencemag.org/content/359/6379/eaao0185).

Science, 359(6379), eaao0185.

[2]

Wang & Barabasi (2021).

[Science of Science](https://science.sciencemag.org/content/359/6379/eaao0185).

Cambridge University Press.

## Credits

``pySciSci`` was originally written by Alexander Gates, and has been developed

with the help of many others. Thanks to everyone who has improved ``pySciSci`` by contributing code, bug reports (and fixes), documentation, and input on design, and features.

**Original Author**

- [Alexander Gates](https://alexandergates.net/), GitHub: [ajgates42](https://github.com/ajgates42)

**Contributors**

Optionally, add your desired name and include a few relevant links. The order

is an attempt at historical ordering.

- Jisung Yoon, GitHub: [jisungyoon](https://github.com/jisungyoon)

- Kishore Vasan, GitHub: [kishorevasan](https://github.com/kishorevasan)

Support

-------

``pySciSci`` those who have contributed to ``pySciSci`` have received

support throughout the years from a variety of sources.  We list them below.

If you have provided support to ``pySciSci`` and a support acknowledgment does

not appear below, please help us remedy the situation, and similarly, please

let us know if you'd like something modified or corrected.

**Research Groups**

``pySciSci`` was developed with full support from the following:

- [School of Data Science](https://datascience.virginia.edu/), University of Virginia, Charlottesville, VA; PI: Alexander Gates

- [Network Science Institute](https://www.networkscienceinstitute.org), Northeastern University, Boston, MA; PI: Albert-Laszlo Barabasi

**Funding**

``pySciSci`` acknowledges support from the following grants:

- Air Force Office of Scientific Research Award FA9550-19-1-0354

- Templeton Foundation Contract 61066