The dataARC tool is designed to enable synthesis research by bridging together heterogeneous datasets in a manner that allows non-specialist to access and view the data and the logic behind connections between different datasets. The datasets in dataARC are linked temporally, spatially, and conceptually. Linking diverse datasets is not a simple task. For example, how an archaeologist thinks about and references time is different from how a saga specialist or paleoecologist does. One might reference a calendrical date or discrete event, while another organizes their data in terms of periods of several hundred or even several thousands of years. Reconciling differences in how researchers from different disciplinary backgrounds reference time and location (space) was a necessary part of the dataARC team’s work.
Going beyond basic connections based on time and place, the project team has developed a shared data model and conceptual framework to build semantic links across the disciplines. The practical outcome of this work is a set of concept maps that provide the basis for semantically-enabled search and discovery, a growing collection of scope notes which describe key concepts within the project’s shared framework, and formal mappings between queries on datasets and concepts which reveal how different researchers think about the relationships between data and concepts.
The purpose of the concept maps, scope notes, and data-to-concept mappings (referred to within the project as combinators) is to provide improved access to and understanding of these diverse, specialized datasets to students, researchers, and anyone interested in the human ecodynamics of the North Atlantic. The tool is designed to encourage users to consider the relevance of data from multiple domains to their research questions, particularly at the early data discovery phase of a project. For example, a researcher that specializes in pollen analysis who begins to explore the data related to concepts of land management and outfield areas in Iceland through the dataARC tool would find data from SEAD connected through the tool, and would also be presented with data from NABONOSEAD and combinators which highlight the implications of faunal data found at archaeological sites across the North Atlantic for the concepts they used in their search. Analyzing and interpreting ‘raw’ faunal data (or any other domain’s basic data) requires training and therefore this ‘raw’ data may not be most suitable or accessible to someone looking to make an initial foray into a new area of interdisciplinary work. For this reason, the dataARC team focused on making data accessible at a more synthesized level, as seen in the middle of the figure to the left, through what we call 'combinators'. These are meaningful combinations of data developed or used by domain specialists which they consider to be more interpretable (with some caution!) by non-specialists. While the raw data are still archived and accessible at partnered repositories, for example, SEAD or Tephrabase, dataARC will provide results at the combinator level, allowing for researchers to access data that can more easily contribute to interdisciplinary synthesis research. Read more about how the project team has developed combinators and the associated dataARC concept map on the Concepts page.