As Death by Numbers has evolved and developed, there have been some slight changes to our workflow, which caused us to reconsider how to work through and present our data. One of those shifts came about because we set up our workflows using early 18th century bills as a model, before shifting to work with the bills from the mid and late 17th century. As a team, we quickly realized that the older bills were falling apart and had more missing information than the bills produced later.

We are in the process of building out a data API to support the data work we’re undertaking with the transcription of the plague bills. We anticipate hundreds of thousands of rows of data by the end of our transcription process, and we wanted an easy and efficient way to work with that data. As part of our work in data-driven historical research at RRCHNM, we are building a data API to store and access data from databases.

Figure 1. Bills of Mortality Workflow. Once items are added to DataScribe and the datasets are ready for transcription, the transcription workflow begins. The project owner can assign users one of two roles: reviewer or transcriber. Reviewers can edit all records and items, regardless of the item’s status. For Bills of Mortality, Reviewers include the staff members on the project and our Digital History Research Assistants. Transcribers can only edit records and items which are locked to them.

In our last post, we explained how we used Tropy to organize photographs of bound bills into items, concluding with the export of the item metadata using the Tropy CSV Export plugin. This post covers the other part of the process of going from digital images to items in a datascribe item set. If you look at the workflow image, we’ll be describing work that takes place in the “Image Processing and CSV Creation” and “Omeka S Item Creation” areas.

Have you ever wondered how a complex project like Death by Numbers comes together? This post is the first in a series about the workflow that takes us from archival sources to transcriptions formatted for computational analysis. Let’s begin with digitization. Figure 1. diagram of image preparation workflow showing process from digitization to image processing and CSV creation to omeka s item creation to datascribe transcription. Digitization of Original Documents There are many ways historic documents become digital objects, and the BoM project is built on documents from a variety of sources.

So you want to start transcribing data from historical documents? The task seems easy! However, there are quite a few issues that can pop up which can create problems for other parts of the project. Below are some of the expected errors our transcribers on Death By Numbers frequently run into and some tips on how to handle them. The job may sound intimidating with all the potential pitfalls, but we have suggested solutions from all the tips and tricks our team has picked up over the past few months.