This research employs machine learning (Mask Region-Based Convolutional Neural Networks [Mask R-CNN]) and cluster analysis (Density-based spatial clustering of applications with noise [DBSCAN]) to identify more than 20,000 relict charcoal hearths (RCHs) organized in large “fields” within and around State Game Lands (SGLs) in Pennsylvania. This research has two important threads that we hope will advance the archaeological study of landscapes. The first is the significant historical impact of charcoal production, a poorly understood industry of the late eighteenth to early twentieth century, on the historic and present landscape of the United States. Although this research focuses on charcoal production in Pennsylvania, it has broad application for both identifying and contextualizing historical charcoal production throughout the world and for better understanding modern charcoal production. The second thread is the use of open data, open source, and open access tools to conduct this analysis, as well as the open publication of the resultant data. Not only does this research demonstrate the significance of open access tools and data but the open publication of our code as well as our data allow others to replicate our work, to tweak our code and protocols for their own work, and reuse our results.

It is possible to purchase human remains via Instagram. We present an experiment using computer vision and automated annotation of over ten thousand photographs from Instagram, connected with the buying and selling of human remains, in order to develop a distant view of the sensory affect of these photos: What macroscopic patterns exist, and how do these relate to the self-presentation of these individual vendors? Using Microsoft’s Azure cloud computing and machine learning services, we annotate and then visualize the co-occurrence of tags as a series of networks, giving us that macroscopic view. Vendors are clearly trying to mimic ‘museum’-like experiences, with differing degrees of effectiveness. This approach may therefore be useful for even larger-scale investigations of this trade beyond this single social media platform.

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Bibliothèque et Archives Nationales du Québec digitally scanned and converted to text a large collection of newspapers to create a resource of tremendous potential value to historians. Unfortunately, the text files are difficult to search reliably due to many errors caused by the optical character recognition (OCR) text conversion process. This digital history project applied natural language processing in an R language computer program to create a new and useful index of this corpus of digitized content despite OCR related errors. The project used editions of The Equity, published in Shawville, Quebec since 1883. The program extracted the names of all the person, location and organization entities that appeared in each edition. Each of the entities was cataloged in a database and related to the edition of the newspaper it appeared in. The database was published to a public website to allow other researchers to use it. The resulting index or finding aid allows researchers to access The Equity in a different way than just full text searching. People, locations and organizations appearing in the Equity are listed on the website and each entity links to a page that lists all of the issues that entity appeared in as well as the other entities that may be related to it. Rendering the text files of each scanned newspaper into entities and indexing them in a database allows the content of the newspaper to be interacted with by entity name and type rather than just a set of large text files. Website: http://www.jeffblackadar.ca/graham_fellowship/corpus_entities_equity/

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