  Major advancements in fields as diverse as biology and quantum computing have
relied on a multitude of microscopic techniques. All optical, electron and
scanning probe microscopy advanced with new detector technologies and
integration of spectroscopy, imaging, and diffraction. Despite the considerable
proliferation of these instruments, significant bottlenecks remain in terms of
processing, analysis, storage, and retrieval of acquired datasets. Aside from
the lack of file standards, individual domain-specific analysis packages are
often disjoint from the underlying datasets. Thus, keeping track of analysis
and processing steps remains tedious for the end-user, hampering
reproducibility. Here, we introduce the pycroscopy ecosystem of packages, an
open-source python-based ecosystem underpinned by a common data model. Our data
model, termed the N-dimensional spectral imaging data format, is realized in
pycroscopy's sidpy package. This package is built on top of dask arrays, thus
leveraging dask array attributes but expanding them to accelerate
microscopy-relevant analysis and visualization. Several examples of the use of
the pycroscopy ecosystem to create workflows for data ingestion and analysis
are shown. Adoption of such standardized routines will be critical to usher in
the next generation of autonomous instruments where processing, computation,
and meta-data storage will be critical to overall experimental operations.
