Digital Edition Publishing Cooperative for Historical Accounts


Historical financial records provide rich and highly structured administrative sources, containing substantial amounts of individual information. This individual information is often not in the core of research interest. Instead, the records acquire their significance in aggregation of the single entries. Pure transcription does not cover the full range of dimensions of such a source: the linguistic/textual, the quantifiable and the semantic dimension. For research purpose, historical sources are subject to a transformation process towards (linked) information sources that can be used in various research scenarios. The Project DEPCHA, an Andrew W. Mellon funded cooperation of five US partners and the Centre for Information Modelling at Graz University, aims to link the knowledge domain of economic activities to historical accounting records. For this purpose the so-called Bookkeeping-Ontology is developed. DEPCHA creates a publication hub for digital editions on the web, converting multiple formats into RDF and publishing these alongside the transcriptions and allows the usage of various retrieval and visualization functionalities, as well as interoperability and reuse of information in the sense of Linked Open Data.


Discover the data that lies hidden in DEPCHA.

This is the experimental part of DEPCHA, which is still under development. In this section, customized resource discovery strategies will be implemented to explore the entire DEPCHA dataset in different ways.


Structure transactions in accounting records.

According to that historical data must be prepared and structured based on a formal set of rules. The Bookkeeping-Ontology, a conceptual data model based on CIDOC-CRM, is developed in an ontology engineering process, involving historians, software developers and digital humanists. In a nutshell:The ontology formalizes the interpretation of a transaction as a combination of transfers of economic goods and money from one accounting object to another. In this way, semantic structures, representing transaction, can be defined from different sources and source types. Thus, workflows can be set up to normalize these semantics.


Learn how to prepare your data for DEPCHA.

To make this possible, it is necessary that the data is structured and normalized in a consistent way. The detailed tutorials on this page illustrate a variety of examples and annotation possibilities to ensure that data is structured appropriately for DEPCHA.