BIDS Machine Shop

As more scientific fields move to intersect with computation, a need arises for software tools that can bridge the gap between the matter under investigation and computational principles/software engineering. Many scientists are specialists trained in their respective domains, so finding contributors with the necessary practical experience to implement computational tools—be it for statistical analysis, data wrangling, machine learning, visualization, or data management—can be difficult.  The aim of the BIDS Machine Shop is to serve the scientific community on the UC Berkeley campus by providing human resources to close this gap.

Structure

A lab (or scientist) brings a software request coupled with a domain problem to the Machine Shop. Requests should envision the development of a minimal viable product of a software tool that fills a specified niche with an estimated development time of one to three months.

The Machine Shop reviews proposals and allocates resources as available to develop a proof-of-concept tool. The development takes place in collaboration with one or more representatives from the lab (perhaps graduate students under the principle investigator) who will act as liaisons and collaborators and will be available to the team for domain-specific questions.

The team develops a prototype through continuous feedback from and interaction with the liaison, which is eventually released under a permissive open source license.

Optionally, the team/lab also writes a short report on the tool and publishes it on an open access platform, such as Arxiv.

Researchers interested in incubating a project with the BIDS Machine Shop should get in touch with Stéfan van der Walt.

Current BIDS Machine Shop Sub-Projects

OSS DevKit

Tooling to improve open source development with GitHub.

OCR Templates

Investigate, compare, and document open source Optical Character Recognition approaches and best practices, with the purpose of making these techniques more accessible to researchers.

Bee WING identification

Combine image processing & machine learning to identify species of bees based on photos of their wings.

BIDS Affiliates

Leader

Stéfan van der Walt

Berkeley Institute for Data Science
Research Fellow