3  Public Interface

The long term goal of Otto’s efforts to get the University Museum inventory complete was to develop an online interface for students, researchers, and community members to search the collections. Using R, Craig built a system for publishing to the web records that were created in PastPerfect. The interface is hosted on the University Museum subdomain and the underlying code is hosted at a GitHub repository and available for evaluation by interested parties. This document is a recipe that explains how to export records from PastPerfect and generate a searchable web interface using Craig’s code. The hope is that others will attempt to use this and evaluate it with the long term aim of developing free and open source tools for communicating about museum collections.

3.1 Export Data from Past Perfect

To generate a web based inventory using scripts created by Craig:

• Clone the repository with git clone https://github.com/n8craig/um-inventory.git to the desired location on the local machine. See the relevant GitHub documentation for cloning a repository.

• Begin by copying the PastPerfect Images directory to the data/Images directory of the um-inventory R project that was cloned in the prior step. Copying the files can take more than 10 minutes. While the files are copying over, begin exporting data from PastPerfect.

• Open PastPerfect and click on the Reports tab.

  

• In the Reports screen, click Objects and then Objects Detail - Complete

  

• Click Export Excel, click ok to the dialogues that follow and wait for Excel to open.

• Save the resulting file as a .xlsx and use a descriptive name.

3.2 Integrating Data into R Project, Sampling, and Rendering

• Place the exported .xlsx file into the data directory of the um-inventory R project.

• Edit the index.qmd file of the um-inventory project to reflect the file name of the export file.

• Before proceeding, ensure that the Images directory finished copying to the um-inventory project directory. Once that is complete, proceed to the next step.

• For the df-sample code chunk, set eval: true to sample the data. Change the values for n to make a larger or smaller sample. Craig finds 200 a good sample for beta testing.

• Render the project sampling the data set. If the project renders properly, and one is ready to render a full version, then set eval: false to df-sample and rerun to compile the complete project. As of December 2022, with ~4500 records, rendering the entire database takes about an hour.

• After rendering is complete, it is a good practice to compress the images in the output _site directory.¹ Doing so reduces the load for users and saves space on the server. For this task, the Caesium Image Compressor is recommended. Craig found that JPG compression of 75 works well. Two directories need to be processed: ./_site/data/images_full/ and ./_site/data/images_thumbs/. Importantly, the files in images_full also need to be resized while those in images_thumbs do not.² Therefore, compression is best run in two batches.

  • Within Caesium add images_full.

    

  • The following compression settings work well:

    

  • On the Output tab, ensure that Same folder as input is checked.

  • When ready, compress the images. Since the files are being overwritten, Caesium will return a warning. Click OK and proceed.

  • Once compression is complete, close the completion notice, and click the red Clear items icon.

    

  • With Caesium’s queue clear, add the images_thumbs folder, navigate to the Resize tab, and select No Resize from the dropdown list.

    

  • Leave all other settings the same and compress the images.

• After image compression, the site is ready to send to the server.

3.3 Server Hosting

3.3.1 Netlify

• Once rendered and compressed, larger projects can be published to Netlify with the terminal command: quarto publish netlify --no-render. To re-render at publish, leave off the last argument. However, Craig generally renders in one step, checks the output, and if the result is suitable publishes without re-rendering.

• Quarto will ask what account to publish with and upon answering will begin uploading files.

3.3.2 univmuseum.nmsu.edu

• The contents of the _site directory can be pushed to any web server. Craig hosts the project at univmuseum.nmsu.edu which is under the NMSU’s Cascade CMS instance. Many university websites deploy Cascade, so these procedures may be of more general interest to university museums who wish to host external content on a Cascade instance.

• Zip the contents of _site.

• Sign into Cascade and select University Museum as the active site.

• Navigate to ./pages/collections/inventory/

• Delete the contents of the directory. This may take some time as Cascade is slow.

• Once Cascade says the deletion process is complete, refresh the page to confirm.

• While still inside inventory, in the upper left hand corner of Cascade, click + Add Content and select file.

• Drag the zip file created above onto the file drop located in the Add Content interface.

• When asked whether to extract the file, select Yes and wait for the button in the upper right hand corner of the interface to read unpack. Once it does, click the button to unpack the files. Be prepared that Cascade is slow to unpack files.

• Resist the temptation to refresh the page and wait for Cascade to give a Zip Archive Report. Once the report is displayed, click Close.

• Refresh the page to confirm that the files are present in the directory.

• Check the box to the left above the file list to select all the files. Then click the publish button and confirm that you want to publish all of the files.

• Navigate into the site data directory

• Then from the upper right hand of the Cascade interface, click the Publish button to publish the directory. Give Cascade a good 5 minutes to update the site.

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1. Ideally image compression would be performed in the programming pipeline and could be accomplished with the magick package.

2. This is also something that should be incorporated into the project’s programmed pipeline.