Collaborative Research: Cross-Cutting Improvements: Non-Clinical Tomography Users Research Network (NoCTURN)

University Of Florida, Gainesville FL

Investigators

Abstract

The NoCTURN (Non-Clinical Tomography Users Research Network) research coordination network will improve standardization and adoption of FAIR data guiding principles for non-clinical tomography, broadly understood here as data-gathering technologies used in a wide variety of research disciplines to obtain sectional scans of physical objects and samples by use of wave signals. The project will coordinate with more than one hundred participating institutions and groups, both public and private, on needed standardization of common, core requirements for data reuse, such as metadata, storage, and interoperability. The goal of the project is to increase the research value of tomographic datasets, foster interdisciplinary collaborations, and create new opportunities for linked data initiatives and metadata aggregation or analysis. The nonstandard formats of tomography scans currently limit data reuse because datasets often cannot be shared and quickly become obsolete once the proprietary software that generated them is deprecated. As a result, digital warehouses for publicly funded tomography data such as Morphobank, Morphosource, and Phenome10K tend only to host outputs from specific steps in the data generation pipeline that utilize non-proprietary file formats such as .TXT (for metadata), .TIFF (for image stacks), and .STL (for digital 3D objects). Intermediate data, including detector outputs, reconstruction algorithm parameters, 3D volume files, and segmentation editor files, often are considered to be transient because they have no cross-platform utility. As a result, files representing these data generally are not made accessible, rendering it impossible to replicate each step of the data-capture and processing pipeline. This curtails methodological repeatability and data reuse, and it forestalls future advances in image processing that could augment data already in hand. These challenges can be overcome through better connectivity across the tomographic community and to that end a network of more than one hundred representatives from diverse fields of research, education, and industry ranging from established practitioners at the forefront of tomographic science as well as early career scholars have come together for this project to develop and foster adoption of new standards for data sharing across multiple disciplines. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Directorate for Engineering and the Directorate for Biological Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.