NumPy is the fundamental array package underpinning the Scientific Python ecosystem. BIDS hosts a team of four core developers that work with the NumPy community to develop the library in preparation for the next decade of data science.
NumPy contains, among other things, the following:
Data Science Discovery Program
The AstroPy Project is a community effort to develop a single core package for astronomy in Python and foster interoperability between Python astronomy packages. The core package has nearly 100 contributors to date and has become one of the most widely used pieces of software in astronomy. The ecosystem also emcompasses a growing list of affiliated packages providing more specialized functionality.
NIMBLE: Numerical Inference for Hierarchical Models Using Bayesian and Likelihood Estimation
NIMBLE is a system for building and sharing analysis methods for statistical models, especially for hierarchical models and computationally intensive methods. NIMBLE is built in R but compiles your models and algorithms using C++ for speed.
Data Science Studies Berkeley
This working group designs and conducts research projects across disciplines and methods to understand the challenges posed by data scientists’ practices in the academic context. In collaboration with our partner institutions, it also develops innovative quantitative and qualitative metrics to measure the evolution of data science environments.
Working Spaces and Culture
Our hope and expectation is that BIDS will unite people who are developing data science opportunities in an environment where daily collaboration will help grow a real community of practice through targeted activities and shared physical space. This working group investigates how working space and culture can be used to better engage researchers and promote cross-disciplinary collaboration.
Reproducibility and Open Science
This working group studies the cultural, educational, legal, and technological barriers to reproducible and open research. Through example, we document and demonstrate what advantages reproducibility has for the scientific process and how individuals and teams can improve their productivity by adopting tools and workflows that support reproducibility.
Software Tools and Environments
As science becomes more data driven, software plays an increasingly important role. However, faculty, students, and postdocs in many scientific domains are not equipped to develop and deliver the advanced software they require. The charge of this working group was to fill this gap, with an emphasis on bridging the culture of academic research with that of open source software.