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Physical samples and their associated (meta)data underpin scientific discoveries across disciplines, and can enable new science when appropriately archived. However, there are significant gaps in community practices and infrastructure that currently prevent accurate provenance tracking, reproducibility, and attribution. For the vast majority of samples, descriptive metadata is often sparse, inaccessible, or absent. Samples and associated (meta)data may also be scattered across numerous physical collections, data repositories, laboratories, data files, and papers with no clear linkages or provenance tracking as new information is generated over time. The Physical Samples Curation Cluster has therefore developed ‘A Scientific Author Guide for Publishing Open Research Using Physical Samples.’ This involved synthesizing existing practices, community feedback, and assessing real-world examples to identify community and infrastructure needs. We identified areas of work needed to enable authors to efficiently reference samples and related data, link related samples and data, and track their use. Our goal is to help improve the discoverability, interoperability, use of physical samples and associated (meta)data into the future. 

A fast, robust pipeline for strain mapping of crystalline materials is important for many technological applications. Scanning electron nanodiffraction allows us to calculate strain maps with high accuracy and spatial resolutions, but this technique is limited when the electron beam undergoes multiple scattering. Deep-learning methods have the potential to invert these complex signals, but require a large number of training examples. We implement a Fourier space, complex-valued deep-neural network, FCU-Net, to invert highly nonlinear electron diffraction patterns into the corresponding quantitative structure factor images. FCU-Net was trained using over 200,000 unique simulated dynamical diffraction patterns from different combinations of crystal structures, orientations, thicknesses, and microscope parameters, which are augmented with experimental artifacts. We evaluated FCU-Net against simulated and experimental datasets, where it substantially outperforms conventional analysis methods. Our code, models, and training library are open-source and may be adapted to different diffraction measurement problems.

 

The growth in scientific data volumes has resulted in a need to scale up processing and analysis pipelines using High Performance Computing (HPC) systems. These workflows need interactive, reproducible analytics at scale. The Jupyter platform provides core capabilities for interactivity but was not designed for HPC systems. In this paper, we outline our efforts that bring together core technologies based on the Jupyter Platform to create interactive, reproducible analytics at scale on HPC systems. Our work is grounded in a real world science use case - applying geophysical simulations and inversions for imaging the subsurface. Our core platform addresses three key areas of the scientific analysis workflow - reproducibility, scalability, and interactivity. We describe our implemention of a system, using Binder, Science Capsule, and Dask software. We demonstrate the use of this software to run our use case and interactively visualize real-time streams of HDF5 data.