"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].
The pySciSci package offers a unified interface to analyze several of the most common Bibliometric DataBases used in the Science of Science, including:
- Microsoft Academic Graph (MAG)
- Clarivate Web of Science (WoS)
- DBLP
- American Physical Society (APS)
- PubMed
- OpenAlex
The pySciSci also provides efficient implemntations of recent metrics developed to study scientific publications and authors, including:
- H-index
- Disruption Index
- Author Pagerank
- Collective credit allocation
- Interdisciplinarity (RoaStirling)
- Annual productivity trajectories
- Sleeping Beauty Coefficient
- Q-factor
- Career Topic Switching
- Field Reference Share
- Field Reference Strength
- Novelty & Conventionality, ToDo ...
Advanced tools for constructing and analyzing network objects (both static and temporal):
- Citation network
- Co-citation network
- Co-authorship network
- Graph2vec network embedding
Natural Language Processing
- Publication matching
- Author matching
Visualization
- Career Timelines
Pull and install in the current directory:
pip install git+git://github.com/SciSciCollective/pyscisci
ToDo
Currently, the pySciSci is built ontop of pandas, and keeps entire dataframes in working memory. We have found that most large-scale analyzes require more computational power and RAM than available on a typical personal computer. Consider running on a cloud computing platform (Google Cloud, Microsoft Azure, Amazon Web Services, etc).
ToDo: explore Dask and pySpark implemenations for multiprocessing.
- HTML documentation is available readthedocs.
- Email: Alex Gates (ajgates42@gmail.com)
[1] Fortunato et al. (2018). Science of Science. Science, 359(6379), eaao0185.
[2] Wang & Barabasi (2021). Science of Science. Cambridge University Press.
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, GitHub: 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
- Kishore Vasan, GitHub: kishorevasan
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, University of Virginia, Charlottesville, VA; PI: Alexander Gates
- Network Science Institute, 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