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

NoCTURN（非临床断层扫描用户研究网络）研究协调网络将提高非临床断层扫描的标准化和采用FAIR数据指导原则，这里广泛理解为在各种研究学科中使用的数据收集技术，通过使用波信号获得物理对象和样本的断层扫描。该项目将与100多个参与的机构和团体进行协调，包括公共和私营机构，就数据重用的共同核心需求（如元数据、存储和互操作性）所需的标准化进行协调。该项目的目标是提高层析数据集的研究价值，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。断层扫描的非标准格式目前限制了数据的重用，因为数据集通常不能共享，一旦生成它们的专有软件被弃用，数据集就会很快过时。因此，公共资助的断层扫描数据（如Morphobank、Morphosource和Phenome10K）的数字仓库往往只托管数据生成管道中特定步骤的输出，这些步骤利用非专有文件格式，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，包括检测器输出、重建算法参数、3D卷文件和分割编辑器文件，通常被认为是暂时的，因为它们没有跨平台的实用程序。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用，并且它预示着图像处理的未来发展，可以增加手头的数据。这些挑战可以通过层析成像社区之间更好的连接来克服，为此，来自不同研究、教育和行业领域的100多名代表组成了一个网络，从层析成像科学前沿的成熟从业者到早期职业学者，他们聚集在一起为这个项目制定和促进采用跨多学科数据共享的新标准。该奖项由先进网络基础设施办公室颁发，并得到工程局和生物科学局的联合支持。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.