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While some archaeological research requires destructive methodologies such as excavation, archaeology faces a race against time as rapid development, urbanization, war, and antiquities trading erase so much of the material record of the past. For these reasons, archaeology needs better approaches to document and meaningfully preserve the digital record of the past. Our project explores archaeological data creation and management practices in three geographical areas (Africa, Europe, and South America) in order to better understand how to better align these practices with the data reuse needs of a broader research community. With funding from the National Endowment for the Humanities, the Secret Life of Data (SLO-data) project follows the lifecycle of data from the field to the digital repository to better understand opportunities and challenges in data interpretation, publication and preservation. Our “slow data” approach focuses not on maximizing the speed and quantity of data, but rather on emphasizing curation, contextualization, and hopefully communication and broader understanding. Our work will guide archaeologists in creating higher-quality and more easily understood data and will expand data publishing services provided by Open Context (opencontext.org).
The project team includes researchers at Stanford University, OCLC Research, the University of Michigan, and the Institute for Field Research. During the first year of work, our team conducted 21 interviews at the participating field sites. We analyzed the transcripts using a codebook and coding protocols that we developed as a team during a week-long meeting in September 2016. We are using these interviews, as well as field observations, sample excavation data, documentation, and guidelines to establish a baseline for each excavation project that describes current data collection and management practices, including the features and functions of the tools they use. Analysis of these studies guides our current work to recommend changes (both technical and organizational) that will improve data creation and management practices.