For much of its history, archaeological research has relied on site-specific projects, regional comparisons, and theory building from case studies. However, recent research themes concerning the emergence of complex social-ecological systems and long-term land-use legacies require a new approach to archaeological data. Large-scale syntheses of archaeological data provide an effective way forward to address these new research themes. In more concise terms, “big questions” require “big data” to help answer them. The archaeological information collected by the USDA Forest Service is one such “big dataset” and represents an incalculable investment in time, resources, and expertise. This article explores this concept and presents an R package (ArchaeoSRP) designed to extract archaeological information from USDA Forest Service site record files. We demonstrate the functionality of this R package through a case study examining the archaeological data for the Cle Elum Ranger District, within Central Washington's Okanogan-Wenatchee National Forest. Our results reveal the efficiency of using automated methods to extract, organize, and synthesize district-level archaeological data, which, in turn, reveal patterns of precontact and historic land use that were otherwise not distinguishable.

Today, there is a growing movement to use accumulated archaeological information to contribute to discussions of general issues facing human societies, including our own. In this regard, the archaeological record is most unique and helpful when viewed at broad comparative scales. Most relevant data for these sorts of analyses are collected through the cultural resource management (CRM) process. Still, by and large, interpretation remains limited to individual projects, and data integration across projects is nearly nonexistent. What would it take for CRM to achieve real data integration? In this article, we discuss these issues and suggest one potential solution. The most pressing need we identify is for data products that integrate the primary data emanating from CRM at broad spatial and temporal scales, which are suitable for research by archaeologists and other social scientists. We argue that the time is right for the discipline to invest in organizations that produce such products.