Having consulted with the rest of the database team, we have decided the previous system and protocols for encoding Mentions and Alt Names/Relationships within EndNote is not conducive to efficient encoding. Last week, I explored the utilization of Excel for the implementation of secondary protocols—both to describe data more effectively and to improve user experience. Excel manages better machine and user readability through its structure. By having users describe data by simply filling in fields, we can avoid the additional encoding of syntax that complicates user experience—as in the case of the EndNote protocols.
To implement Excel encoding, we created a spreadsheet to contain the metadata associated with the record. These sheets describe the metadata associated with Alternative Names, Mentions, and Relationships. (Figure 1) The components of this spreadsheet mirror the properties and construction of the EndNote protocols but are presented and encoded according to a different structure. Within the spreadsheet, we designated sheets and the columns within for different aspects of descriptive data. Both Alternative Names and Relationships are entirely document specific; they describe connections that are true regardless of what letter contains the information being described. For Mentions, users will begin by declaring the digital record they are describing. This then associates the record with the following descriptive metadata encoded by the teams. From here, the remainder of the columns contain the information that would have been encoded in the EndNote fields with indicator tags. (Figure 2)
The configuration of the sheets and columns now replace the need for encoding syntax. Comments communicate to the user how to encode data within the cells, such as this example from the Alt Names that states the additional fields are only necessary if a standardized name is not available. (Figure 3) This functionality not only facilitates an easier user interaction, but creates a more effective data set as well. To achieve this, data must be consistently updated from the term lists generated by individual teams within EndNote. Populating fields within the EndNote library creates term lists which are available for export—further into a CSV, XML, or JSON file, then made machine-readable for database relational language. This exported list can then be imported into Excel to populate dropdown menus that help enforce standardization across names and teams. Updating these lists is crucial to maintaining standardization protocols. Consistent updating of the term lists can be achieved by creating a workflow process that closely follows the weekly upload of team libraries to a cloud location for backup. Describing these terms in Excel will prove to be more effective than EndNote encoding for both the database structure and the user.
Questions were also raised about interventions necessary with the letter images to improve user readability. A select few records, such as Abigail Pemberton’s, have drastically slanted lines. We approached these as potential barriers to user interaction as aspects which complicate legibility might prevent users from attempting transcription of the letter. Consequently, it might be worth exploring digital interventions with specific letters to increase legibility and hopefully, user interaction. (Figure 4) Issues which complicate legibility include not only slanted lines but also cramped line spacing and ink bleed. We are now exploring applications of the Warp or Straighten functions in an image editing software such as Photoshop to mitigate the complications presented by slanted lines. There is also the potential to add “slices” of background color between cramped lines and to adjust color levels for mitigation of ink bleedthrough. It remains to be seen if the time spent doing this presents enough of a legibility improvement to warrant intervention.
