One Archaeology: A Manifesto for the Systematic and Effective Use of Mapped Data from Archaeological Fieldwork and Research

Abstract

The Infrastructure for Spatial Information in Europe (INSPIRE) Directive (2007) requires public organisations across Europe to share environmentally-related spatial datasets to support decision making and management of the environment. Despite the environmental focus of INSPIRE, it offers limited guidance for archaeological datasets. Most primary data is created outside, but ultimately curated within, the public sector. As spatial evidence from fieldwork activities is not considered by the Directive, it overlooks a range of barriers to sharing data, such as project-based fieldwork, a lack of data standards, and formatting and licencing variations. This paper submits that these challenges are best addressed through the formalised management of primary research data through an archaeological Spatial Data Infrastructure (SDI). SDIs deliver more efficient data management and release economic value by saving time and money. Better stewardship of archaeological data will also lead to more informed research and stewardship of the historic environment. ARIADNE already provides a digital infrastructure for research data, but the landscape and spatial component has been largely overlooked. However, rather than developing a separate solution, the full potential of spatial data from archaeological research can and should be realised through ARIADNE.

Documentation of Archaeology-Specific Workflow for Airborne LiDAR Data Processing

“…(4.3) Spatial data archiving issues are currently widely debated inside and outside of archaeology, but as of yet there are no generally accepted practices [17,87]. The starting premise is that all data, including raw and point-cloud data, should be archived in a persistent repository to make the whole process of processing repeatable (Figure 1).…”

“…Unfortunately, this has become the norm rather than the exception. We see researchers processing their own data, referring to the airborne LiDAR-derived data visualization (LiDAR visualizations for short [17]) as a "LiDAR image" without providing any further information. Sometimes, the situation is further exacerbated by editorial guidelines and/or the peer review process, which consider the description of the airborne LiDAR data processing to be irrelevant.…”

“…Or, in the words of The London Charter for the Computer-Based Visualisation of Cultural Heritage, metadata and paradata are used to clarify the relationship between research sources, implicit knowledge, explicit reasoning, and visualization-based outcomes [22]. In addition, documenting is also the cornerstone of FAIR data sharing [25,26], especially for archaeological geospatial data [17].…”

Linking Theories, Past Practices, and Archaeological Remains of Movement through Ontological Reasoning

“…At present (2020), the imperative for integrating this morass of digital information to produce coherent, compelling, data-embedded archaeological narratives is frequently argued in connection to the archaeology's ability to contribute to debates on societal, climate, and environmental issues [3]. This emerging drive to re-articulate archaeological data to address contemporary agendas has implications for the development of new practices of archaeological knowledge creation.…”

“…Over the past two decades, the basis for the creation of archaeological knowledge about past societies archaeology has fundamentally changed. This shift has been driven by multiple factors, notably the acceleration of development-led archaeology [1][2][3] and the integration of large digital observational datasets, particularly those acquired through remote sensing methods for earth observation and monitoring [4,5], the proliferation of scientific methods applied to gather diverse data [6], and the increasing digitization of archaeological data [3,7,8]. Within these broader trends, emerging applications of machine learning methods for the identification of features of archaeological interest in remote sensing derived datasets (e.g., [9,10]) are causing a particularly important change in knowledge production practices.…”