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Recent advancements in photography hardware and software, such as the GIGAmacro Photography System, allow collections workers to capture thousands of high-resolution, wide focal-depth photographs a day with minimal manual effort. The front-end work of camera setup is the most time-consuming task, with the bulk time spent specifying where in the tray the camera should photograph. The GIGAmacro software package does not include a tool to reduce or help automate this setup, so we developed our own. The tool we designed is an intuitive user interface that is linked to scripted processes to semi-automate the setup process. On average, this tool has decreased our camera setup time by 98.5%. The development process involved a feedback loop of gathering comments and suggestions, implementing features, and testing with different end-users. The resulting auto-assist tool is designed to be accessible for workers with varying levels of experience and is wholly contained in one Excel document for portable use. We chose to develop our camera setup tool in Excel due to broad user familiarity and presence of necessary supporting components. Both advantages greatly shortened development time. Additionally, Excel allowed us to change measurement or calculation numbers for the camera on the fly without having to recompile and/or install a new executable. The files that contain the camera setup runs (now numbering in the hundreds after two years) can be saved and reloaded easily on any of our network computers. This Excel-based, custom tool complements the extensive automated process that GIGAmacro provides. The tool fits into the front-end workflow of the entire digitization process, reduces manual setup time by almost two orders of magnitude, and can be employed by other research collections interested in digitizing thousands of microfossils. The software tool is freely available at https://github.com/alex-zimmerman/GigaMacroAssist along with user notes on how to employ and/or adapt the tool in other collections. 

Collections digitization relies increasingly upon computational and data management resources that occasionally exceed the capacity of natural history collections and their managers and curators. Digitization of many tens of thousands of micropaleontological specimen slides, as evidenced by the effort presented here by the Indiana University Paleontology Collection, has been a concerted effort in adherence to the recommended practices of multifaceted aspects of collections management for both physical and digital collections resources. This presentation highlights the contributions of distributed cyberinfrastructure from the National Science Foundation-supported Extreme Science and Engineering Discovery Environment (XSEDE) for web-hosting of collections management system resources and distributed processing of millions of digital images and metadata records of specimens from our collections. The Indiana University Center for Biological Research Collections is currently hosting its instance of the Specify collections management system (CMS) on a virtual server hosted on Jetstream, the cloud service for on-demand computational resources as provisioned by XSEDE. This web-service allows the CMS to be flexibly hosted on the cloud with additional services that can be provisioned on an as-needed basis for generating and integrating digitized collections objects in both web-friendly and digital preservation contexts. On-demand computing resources can be used for the manipulation of digital images for automated file I/O, scripted renaming of files for adherence to file naming conventions, derivative generation, and backup to our local tape archive for digital disaster preparedness and long-term storage. Here, we will present our strategies for facilitating reproducible workflows for general collections digitization of the IUPC nomenclatorial types and figured specimens in addition to the gigapixel resolution photographs of our large collection of microfossils using our GIGAmacro system (e.g., this slide of conodonts). We aim to demonstrate the flexibility and nimbleness of cloud computing resources for replicating this, and other, workflows to enhance the findability, accessibility, interoperability, and reproducibility of the data and metadata contained within our collections.