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

NocTurn(非临床断层摄影用户研究网络)研究协调网络将促进非临床断层摄影公平数据指导原则的标准化和采用。非临床断层摄影在这里被广泛理解为各种研究学科中使用的数据收集技术，以利用波信号对实物和样本进行断层扫描。该项目将与100多个参与的公共和私营机构和团体协调，就数据再利用的共同核心要求，如元数据、存储和互操作性，进行必要的标准化。该项目的目标是增加断层扫描数据集的研究价值，促进跨学科合作，并为关联数据倡议和元数据聚合或分析创造新的机会。目前，断层扫描的非标准格式限制了数据的重复使用，因为数据集通常无法共享，一旦生成它们的专有软件过时，数据集很快就会过时。因此，诸如Morphobank、Morphosource和Phenome10K等公共资助的断层扫描数据的数字仓库倾向于仅托管来自数据生成管道中的特定步骤的输出，所述数据生成管道使用非专有文件格式，例如.TXT(用于元数据)、.TIFF(用于图像堆栈)和.stl(用于数字3D对象)。中间数据，包括探测器输出、重建算法参数、三维体文件和分段编辑器文件，通常被认为是瞬时的，因为它们没有跨平台实用程序。因此，表示这些数据的文件通常无法访问，因此不可能复制数据捕获和处理管道的每个步骤。这限制了方法的可重复性和数据的重复使用，并阻碍了图像处理的未来进步，这些进步可能会增加已有的数据。这些挑战可以通过更好地连接断层摄影界来克服，为此，一个由来自不同研究、教育和行业领域的100多名代表组成的网络已经聚集在一起，为这个项目开发和促进跨多学科数据共享的新标准的采用。该奖项由高级网络基础设施办公室由工程局和生物科学局联合支持。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.