BisQue Deep Learning Cyberinfrastructure

This material is based upon work supported by the National Science Foundation under Grant No. 2411453. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

BisQue is an open-source platform for managing, exploring, and analyzing large-scale scientific multimodal data. BisQue combines images with structured metadata, annotations, sensor and experimental measurements, derived data products, models, workflows, and provenance. Its scalable web-services architecture supports data organization and search, interactive visualization, distributed storage, high-throughput analysis, and reproducible execution across diverse scientific domains.

BisQue now extends into BisQue Ultra, integrating large language model reasoning directly on top of the BisQue platform for more advanced, context-aware scientific analysis. Explore it on the BisQue Ultra tab.

BisQue brings together large-scale scientific imaging and multimodal data into a single, reproducible research environment — free to use and open to the community.

Data & Storage

  • Free and open-source, with no licensing costs for academic or research use
  • Scales to petabytes of data and millions of annotations without added infrastructure overhead
  • Distributed storage across local systems, iRODS, and S3, so institutions can plug in existing infrastructure
  • Support for more than 100 scientific image formats, from standard microscopy files to specialized multidimensional datasets

Analysis & Reproducibility

  • Integrated, high-throughput AI/ML and scientific analysis workflows that run at scale
  • Reproducible execution with full workflow and provenance tracking, so every result can be traced back to its inputs and parameters
  • Analysis modules implemented in MATLAB, Python, Java, and ImageJ, fitting into labs' existing toolchains
Explore the Analysis Modules → Purpose-built AI/ML modules that run inside BisQue — WildlifeMapper, Prairie Dog Detection, MethaneMapper, and Underwater & Marine Species.

Exploration & Annotation

  • Flexible textual, graphical, and structured metadata annotations for describing images and experimental measurements
  • Interactive access to very large multidimensional images directly in the browser, without needing to download full datasets first

Broader Impacts

This project is a multi-institutional collaboration spanning UC Santa Barbara, UC Riverside, and the Smithsonian Institution, committed to broadening participation in data-intensive science.

  • Cross-institutional collaboration across materials science, environmental science, and bioimaging research

  • Training and workforce development for students working with the platform

  • Broadened access to advanced analysis tools for a wider research community

  • Open-source distribution supporting long-term sustainability and community-driven development

Documentation & Source Code

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 continuously update the documentation, so check back often for updates.

Official Documentation Try BisQue Now Source Code on GitHub

Publications from This Award (2411453)

Publications and datasets resulting from this award will be listed here as they become available.

  • 2025Nag, S., Ghosh, U., Ta, C.-K., Bose, S., Li, J., and Roy-Chowdhury, A.K., "Conformal Prediction and MLLM aided Uncertainty Quantification in Scene Graph Generation," Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 11676–11686.  DOI →
  • 2025Zhang, B., Boulerice, J.T., Kuniyil, N., Mendiratta, C., Kumar, S., Shamon, H., and Manjunath, B.S., "RareSpot: Spotting Small and Rare Wildlife in Aerial Imagery with Multi-Scale Consistency and Context-Aware Augmentation," CVPR Workshop on Computer Vision for Animal Behavior Tracking and Modeling (CV4Animals). DOI →
  • 2025Islam, M.S., Dutta, A., Ta, C.-K., Rodriguez, K., Michael, C., Alber, M., Reddy, G.V., and Roy-Chowdhury, A.K. “DEGAST3D: Learning Deformable 3D Graph Similarity to Track Plant Cells in Unregistered Time Lapse Images.” IEEE/ACM Transactions on Computational Biology and Bioinformatics, 22(1): 343–354. DOI → 
  • 2025Islam, M.S., Nag, S., Dutta, A., Ahmed, S.M., Niloy, F.F., Bera, S., and Roy-Chowdhury, A.K., "ODES: Online Domain Adaptation with Expert Guidance for Medical Image Segmentation," Medical Image Computing and Computer Assisted Intervention – MICCAI 2025: 359–370.  DOI →
    • 2026Zhang, B., Boulerice, J.T., Mendiratta, C., Kuniyil, N., Kumar, S., Shamon, H., and Manjunath, B.S. “RareSpot+: A Benchmark, Model, and Active Learning Framework for Small and Rare Wildlife in Aerial Imagery.” Accepted for Publ;ication in Intl. Journal of Computer Vision (July 2026). arXiv preprint arXiv:2604.20000. DOI → 
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    Foundational & Related Publications

    • 2019Latypov, M.I., Khan, A., Lang, C.A. et al. Integr Mater Manuf Innov 8: 52. DOI →
    • 2019Polonsky, A.T., Lang, C.A., Kvilekval, K.G. et al. Integr Mater Manuf Innov 8: 37. DOI →
    • 2018Trigkakis D., Todorovic S., Preece J., Meier A., Elser J., Jaiswal P., Kvilekval K., Fedorov D., Manjunath B.S. PDF →
    • 2017D.V. Fedorov, K.G. Kvilekval, B.S. Manjunath, B. Doheny, S. Sampson, R.J. Miller. PDF →
    • 2016D. Fedorov, R.J. Miller, K. Kvilekval, B. Doheny, S. Sampson, B.S. Manjunath. View →
    • 2016J. Preece, J. Elser, P. Jaiswal, K. Kvilekval, D. Fedorov, B.S. Manjunath, R. Kitchen, X. Xu, D. Trigkakis, S. Todorovic, S. Carbon. PDF →
    • 2010Kvilekval K, Fedorov D, Obara B, Singh A, Manjunath BS. Bioinformatics 26(4):544–52 (PMID: 20031971). DOI →

    Contact

    B.S. Manjunath (PI), Frank Koenig Distinguished Chair in Signals and Systems, ECE Department, UCSB

    Investigators

    Tresa M. Pollock, Aloha Distinguished Professor, Materials Department, UC Santa Barbara
    Amit K. Roy-Chowdhury, Professor, Electrical and Computer Engineering, UC Riverside
    Beth L. Pruitt , Professor, BioEngineering, UC Santa Barbara
    Lacey Hughey, Smithsonian’s National Zoo & Conservation Biology Institute (NZCBI)
    Jesse T. Boulerice, Great Plains Science Program Smithsonian’s NZCBI
    Katherine Mertes, Smithsonian’s NZCBI
    Jared Stabach, Smithsonian’s NZCBI

    Project Personnel

    Connor Levenson — Research Staff Member, Vision Research Lab, UC Santa Barbara
    Chandrakanth Gudavalli — Research Staff Member, UC Santa Barbara

    Postdocs and Graduate Students 

    Amil Khan, Graduate Student, VRL (CS)
    Neal Brodnik, Project Scientist (Materials) (2024-25)
    Bowen Zhang, Gradiuate Student, VRL (ECE)
    Arindam Dutta, Graduate Student, ECE (UC Riverside, 2024-26)
    Shazid Islam, Graduate Student, ECE (UC Riverside)
    Charvi Mendiratta, Graduate Student, ECE 
    Pushpita Joardar, Graduate Student, ECE

    Undergraduate and Highschool Students

    Mihir Kondapalli, Nihir Kuniyil, Srinandha Murugesan, Ayush Garg, Saanvi Kotha, Dhruv Mittal, Rohini Vedam