[Paper Review] USID and Pycroscopy -- Open frameworks for storing and analyzing spectroscopic and imaging data

Suhas Somnath, Chris R. Smith+8 more|arXiv (Cornell University)|Mar 22, 2019

Advanced X-ray and CT Imaging95 references17 citations

TL;DR

This paper introduces USID, a universal data model for spectroscopic and imaging data, and Pycroscopy, an open-source Python framework built on HDF5 and pyUSID for scalable, instrument-agnostic analysis. It enables reproducible, high-performance data processing across diverse materials science modalities, fostering collaboration in open science.

ABSTRACT

Materials science is undergoing profound changes due to advances in characterization instrumentation that have resulted in an explosion of data in terms of volume, velocity, variety and complexity. Harnessing these data for scientific research requires an evolution of the associated computing and data infrastructure, bridging scientific instrumentation with super- and cloud- computing. Here, we describe Universal Spectroscopy and Imaging Data (USID), a data model capable of representing data from most common instruments, modalities, dimensionalities, and sizes. We pair this schema with the hierarchical data file format (HDF5) to maximize compatibility, exchangeability, traceability, and reproducibility. We discuss a family of community-driven, open-source, and free python software packages for storing, processing and visualizing data. The first is pyUSID which provides the tools to read and write USID HDF5 files in addition to a scalable framework for parallelizing data analysis. The second is Pycroscopy, which provides algorithms for scientific analysis of nanoscale imaging and spectroscopy modalities and is built on top of pyUSID and USID. The instrument-agnostic nature of USID facilitates the development of analysis code independent of instrumentation and task in Pycroscopy which in turn can bring scientific communities together and break down barriers in the age of open-science. The interested reader is encouraged to be a part of this ongoing community-driven effort to collectively accelerate materials research and discovery through the realms of big data.

Motivation & Objective

• Address the growing complexity and volume of spectroscopic and imaging data in materials science due to advanced instrumentation.
• Overcome data silos and interoperability issues caused by proprietary data formats and instrument-specific workflows.
• Develop a unified, extensible data model (USID) that supports diverse modalities, dimensionalities, and data sizes.
• Create open-source software (pyUSID and Pycroscopy) to enable scalable, reproducible, and parallelized data analysis independent of instrumentation.
• Foster community-driven scientific collaboration by decoupling data analysis from specific instruments and data acquisition systems.

Proposed method

• Design the Universal Spectroscopy and Imaging Data (USID) model as a hierarchical, extensible schema for representing multi-dimensional spectroscopic and imaging data.
• Implement the USID model using the HDF5 file format to ensure portability, scalability, and provenance tracking.
• Develop pyUSID as a Python library for reading, writing, and managing USID-compliant HDF5 files with support for parallelized data processing.
• Build Pycroscopy on top of pyUSID to provide domain-specific algorithms for nanoscale imaging and spectroscopy analysis.
• Ensure instrument-agnostic analysis by abstracting data access and processing logic from hardware-specific details.
• Integrate version control, metadata tracking, and provenance recording via HDF5 attributes and standards-compliant data organization.

Experimental results

Research questions

• RQ1 How can a universal data model be designed to represent diverse spectroscopic and imaging data across multiple instruments and modalities?
• RQ2 What software architecture enables scalable, reproducible, and parallelized analysis of large-scale materials science data?
• RQ3 How can open, community-driven software frameworks reduce barriers to data sharing and collaboration in materials research?
• RQ4 To what extent can instrument-agnostic data processing improve interoperability and reusability of scientific workflows?
• RQ5 Can a unified data model and open-source stack accelerate discovery in big data-driven materials science?

Key findings

• The USID data model successfully encapsulates multi-dimensional, multi-modal spectroscopic and imaging data from diverse instruments in a standardized, hierarchical format.
• pyUSID enables efficient reading, writing, and parallelized processing of USID-compliant HDF5 files, supporting high-performance computing workflows.
• Pycroscopy provides a suite of open-source, reusable algorithms for analyzing nanoscale imaging and spectroscopy data, decoupled from specific instrumentation.
• The integration of USID with HDF5 ensures data exchangeability, traceability, and reproducibility across different platforms and institutions.
• The framework enables scientific communities to collaborate across instrumentation boundaries, accelerating data-driven discovery in materials science.
• The open, community-driven development model of Pycroscopy and pyUSID fosters extensibility and long-term sustainability in scientific software.