BisQue is a web-based platform specifically designed to provide researchers with organizational and quantitative analysis tools for up to 5D image data. Users can extend BisQue by creating their own modules that take advantage of cutting edge machine learning algorithms. BisQue’s extensibility stems from two core concepts: flexible metadata facility and an open web-based architecture. Together these empower researchers to create, develop and share novel multimodal data analyses.
The official documentation covers the BisQue cloud service running live at UCSB, module development for the platform, and the BQAPI. If you have any questions, feel free to reach out. We will be continuously updating the documentation so check back often for updates!
- Latypov, M.I., Khan, A., Lang, C.A. et al. Integr Mater Manuf Innov (2019) 8: 52. https://doi.org/10.1007/s40192-019-00128-5
- Polonsky, A.T., Lang, C.A., Kvilekval, K.G. et al. Integr Mater Manuf Innov (2019) 8: 37. https://doi.org/10.1007/s40192-019-00126-7
- Amil Khan (BisQue Team)
- Satish Kumar (BisQue Team)
- Griffin Danninger
- Mike Goebel
- Docker
- ExtJS (UI)
- Imaris Convert (Image Service)
- OpenSlide (Image Service)
- Bio-Formats (Image Service)
- FFMpeg (Image Service)
- libTIFF (Image Service)
- TurboGears (backend)
- SQLAlchemy (backend)
- Kristian Kvilekval, Dmitry Fedorov, Christian Lang
- NSF SI2-SSI No.1664172, NSF MCB Grant No. 1715544
- Cyverse at University of Arizona