On the Emerging Supremacy of Structured Digital Data in Archaeology: A Preliminary Assessment of Information, Knowledge and Wisdom Left Behind

Abstract

While the epistemological affordances and varied impacts of different media on archaeological knowledge production have been scrutinized by many practitioners in recent decades, sources of digital structured data (e.g., spreadsheets, traditional relational databases, content management systems) have seen far less critical enquiry. Structured digital data are often venerated for their capacities to facilitate interoperability, equitable data exchange, democratic forms of engagement with, and widespread reuse of archaeological records, yet their constraints on our knowledge formation processes are arguably profound and deserving of detailed interrogation. In this article, we discuss what we call the emerging supremacy of structured digital data in archaeology and seek to question the consequences of their ubiquity. We ground our argument in a case study of a range of texts produced by practitioners working on the Çatalhöyük Research Project. We attempt to map short excerpts from these texts to structured data via the CIDOC Conceptual Reference Model. This exercise allows making preliminary observations about the representational affordances and resistances of texts (which can be considered as a type of semi- or unstructured data) and structured data. Ultimately, we argue that the push to create more and more structured and structurable data needs to be tempered by a more inclusive digital practice in archaeology that protects difference, incommensurability, and interpretative nuance.

Data Legacies, Epistemic Anxieties, and Digital Imaginaries in Archaeology

“…Similarly, data friction could provide a mechanism with which to investigate the range of issues surrounding the creation, manipulation, archiving, and subsequent reuse of data. Such studies would usefully sit alongside calls for similar ethnographic-style studies to examine the construction of the ontologies used in archaeology [40] and to investigate the emphasis on structured data and structuring mechanisms [41], for instance.…”

“…This goes beyond standardized digital communications protocols and persistent identifiers to include shared languages for knowledge representation and common vocabularies, for instance. These ontologies, high-level classes of information, and associated standards are not widely discussed in archaeology [40], and recent studies demonstrate that the structures created with such tools do not necessarily capture the nuances of archaeological data (e.g., [41]). Consequently, the kinds of cultural changes required to develop open approaches in archaeology and beyond via stronger data mandates from funders, incentivization of open data practices, and appropriate funding for data infrastructures (e.g., [33,37,42]) are not the whole story: the open-data imaginary risks fetishizing data sharing without fully considering the implications of what is shared, how, and for whom.…”

“…Manovich, for example, highlights the variability of digital media which can exist in different, potentially infinite versions, and the way that it can be transcoded, or translated, into different formats [83] (pp. [36][37][38][39][40][41][42][43][44][45][46][47]. So rather than a static unchanging entity, data is unstable, capable of translation from one mode to another, from one format to another, from one category to another, for one purpose or another.…”

“…Data in proprietary file formats is translated into open formats for archival purposes, which preserves the data but may lose relationships or other characteristics in the conversion, requiring accompanying documentation to fill the gap. Semi-structured or unstructured data may be incorporated within a structured data model, in the process smoothing out variability or omitting aspects considered to be unimportant for the study at hand, or which simply do not fit within the schema (e.g., [41]). The provision of directly captured data, typically from instruments, may be set aside in favor of processed datasets (e.g., [112] (Section 3.1)) although not in all cases (e.g., [113] (Section 3.1)).…”

“…[22] (p. 41). She highlights the way in which algorithms are experienced and encountered as part of the "'force relations' that give people a 'reason to react'" and the consequent feedback loop through which the algorithm is moulded in turn [22] (p. 41).…”