SlicerMorphCloud: An interactive, cloud-based platform for quantitative analysis of high-resolution 3D scans of biological specimens.

Ali Maga, University of Washington

0000-0002-7921-9018

ACCESS Allocation Request BIO180006

Abstract:

Quantitative study of morphology is central to many fields within the biological sciences, as anatomical systems hold important clues on how organism move, feed, reproduce and interact with the environment in which they live in. Through these studies, biologists can understand what changes have happened through time; get a good glimpse of the variability within a species, and even predict whether they are likely to survive with global environmental challenges. Morphological data is best acquired using 3D imaging technologies. These technologies can range from surface scanners to high-resolution 3D microscopes that use UV or X-ray to collect data. Biologists then process and visualize these datasets in computers to either collect data for research or use them as visual aids in the classroom. However, some of these imaging modalities produces datasets that are much larger than typical personal computers can handle; or may require specialized software that may not be available to everyone due to licensing or cost issues. Both can have the potential to create sharing and usage obstacles for publicly funded data, impeding teaching, scientific exploration and collaboration. This project provides equitable and convenient access to publicly funded cyberinfrastructure (JetStream2) and data resources (MorphoSource) for biologists, who otherwise may not benefit from their availability; either because they lack the computational resources or the technical expertise to use them. The project team will also train hundreds of early career scientists, whose research and teaching will benefit from the easy manipulation and use of digital biological specimens. Additionally, bootcamp opportunities will transform motivated grad students and post-docs into future inventors of new analytical tools and functionalities, which will grow the community of morphologists working in digital environments. MorphoCloud will make working with digital biological specimen data as routine as working with genomics data, so that biologists can focus on their scientific questions rather than get bogged down with complex workflows, technicalities, and limited resources. Proposed cloud services and analytical tools will also make wider-scale, multi-lab collaborations friction-free, via derived data exchange of 3D phenotypes.

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Allocations:

2024	Indiana Jetstream2 CPU	200,000.0 SUs
2024	Indiana Jetstream2 GPU	1,286,640.0 SUs
2024	Indiana Jetstream2 Large Memory	50,000.0 SUs
2024	Indiana Jetstream2 Storage	10,000.0 GB
The estimated value of these awarded resources is $284,665.96. The allocation of these resources represents a considerable investment by the NSF in advanced computing infrastructure for the U.S. The dollar value of the allocation is estimated from the NSF awards supporting the allocated resources.
2022	Indiana Jetstream2 CPU	710,080.0 SUs
2022	Indiana Jetstream2 GPU	1,121,280.0 SUs
2022	Indiana Jetstream2 Large Memory	1,269,760.0 SUs
2022	Indiana Jetstream2 Storage	20,000.0 GB
The estimated value of these awarded resources is $395,163.20. The allocation of these resources represents a considerable investment by the NSF in advanced computing infrastructure for the U.S. The dollar value of the allocation is estimated from the NSF awards supporting the allocated resources.

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