The study of archaeological ceramics is one of the most ubiquitous methods for understanding ancient culture, livelihood, and human habitation. The LEIA project's objective is to address these topics through the examination of ancient ceramic artifacts discovered in Mallorca, Spain. Python coding was used to scan these samples and conduct in-depth image analysis, to identify specific variations found across diverse specimens. In this independent study, I am particularly interested in applying this set of tools to see if I can distinguish how a ceramic vessel was created and to also focus on modifying code to expedite the time-consuming process of hands-on ceramic classification and specific archiving techniques. Through my work, I have carefully sawed samples, converted them into digital images, and helped interpret them using a digital application called Jupyter Notebooks. I have also contributed to the DigAR Lab's implementation of novel Python coding to look at different manufacturing methods. This code helps to identify (in) organic temper found in each piece (e.g. limestone, quartz, grass), to note color distribution observed, make size comparisons, and to determine rotational orientation. All of these offer insight into possible pottery-making methods, outside cultural influences, and various firing techniques. Furthermore, to ensure all pieces were treated with equal care, I worked with the lab to construct a step-by-step procedure, that has been refined through multiple trials to provide standard instructions, on how to properly use heavy machinery to cut these pieces without causing excess damage. Furthermore, this repeatable process will be used by archaeology students and staff during upcoming fieldwork in Spain (Summer of 2023) to document future findings. Overall, this ongoing study will investigate the power of this toolkit to recognize subtle differences that result from varied manufacture methods; if successful this could have major repercussions on how ceramic assemblages are studied.

Recently, archaeologists have been forced to reckon with the fact that the conditions in which they collect their data deeply affects the interpretation of that data. To this end, I have begun a project we are calling meta-archaeology, which offers a glimpse into the factors that contribute to and potentially distract from the collection and interpretation of archaeological data. Utilizing data from the Landscape, Encounters, and Identity in Archaeology Project (LEIAP) based in Mallorca, Spain, this study attempts to explore if weather conditions affect a surveyor’s ability to collect artifacts. The database for this project consists of datetime-stamped metadata associated with the GPS receivers used to map artifacts. To analyze this data, I use various Python packages (e.g. Pandas, Seaborn, Folium, and leiap, a custom package created for the project) to extract relevant weather data, merge the datasets using their datetimes, and subsequently find patterns in how weather affects archaeological data collection. This research hopes to show a correlation between data collection and factors like temperature, azimuth, and wind. For example, a higher average temperature for the day might result in a lower number of artifacts collected as surveyors are affected by the heat. These correlations can be used to structure field school practices to ensure the best possible data collection. In addition, these results can shape future research design to account for the conditions that affect what is or is not found during an archaeological survey.