A high-throughput multispectral imaging system for museum specimens

We present an economical imaging system with integrated hardware and software to capture multispectral images of Lepidoptera with high efficiency. This method facilitates the comparison of colors and shapes among species at fine and broad taxonomic scales and may be adapted for other insect orders with greater three-dimensionality. Our system can image both the dorsal and ventral sides of pinned specimens. Together with our processing pipeline, the descriptive data can be used to systematically investigate multispectral colors and shapes based on full-wing reconstruction and a universally applicable ground plan that objectively quantifies wing patterns for species with different wing shapes (including tails) and venation systems. Basic morphological measurements, such as body length, thorax width, and antenna size are automatically generated. This system can increase exponentially the amount and quality of trait data extracted from museum specimens., Processed data
These data include but are not limited to all parameters generated during image processing, gridded multispectral reflectance, wing shapes, and the measurements of body size and antennae. The detailed data structure can be found on the GitHub repository.
 Map of archived materials, protocols, and tutorials
To prevent potential conflicts, scripts for different purposes on the cluster and on the local machine are provided in different protocols on Protocols.io and repositories on GitHub. Here, the summary of online protocols and source codes are organized as follows. Inclusion in [Protocol] indicates the corresponding step-by-step instruction on Protocols.io; inclusion in [Cluster] indicates the script will run better on the cluster; inclusion in [Local] indicates the script is designed for local machines with relatively low CPU and memory demands.
 ●    Raw data: files described in the following format [[Folder/File name]]: descriptions
●       [[Methodology_imaging_record..., The pipeline was mainly developed under Matlab and R, but the data formats (e.g. *.mat, *.json) can still be operated in Python or other interface.