  The unprecedented rate at which synchrotron radiation facilities are
producing micro-computed (micro-CT) datasets has resulted in an overwhelming
amount of data that scientists struggle to browse and interact with in
real-time. Thousands of arthropods are scanned into micro-CT within the NOVA
project, producing a large collection of gigabyte-sized datasets. In this work,
we present methods to reduce the size of this data, scaling it from gigabytes
to megabytes, enabling the micro-CT dataset to be delivered in real-time. In
addition, arthropods can be identified by scientists even after implementing
data reduction methodologies. Our initial step is to devise three distinct
visual previews that comply with the best practices of data exploration.
Subsequently, each visual preview warrants its own design consideration,
thereby necessitating an individual data processing pipeline for each. We aim
to present data reduction algorithms applied across the data processing
pipelines. Particularly, we reduce size by using the multi-resolution
slicemaps, the server-side rendering, and the histogram filtering approaches.
In the evaluation, we examine the disparities of each method to identify the
most favorable arrangement for our operation, which can then be adjusted for
other experiments that have comparable necessities. Our demonstration proved
that reducing the dataset size to the megabyte range is achievable without
compromising the arthropod's geometry information.
