Published July 4, 2022 | Version v1
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Storytelling with Heurist: Databasing on Demand

  • 1. Universität zu Lübeck; Universität Hamburg


Heurist is a data management platform for Humanities scholars, hosted at the University of Sydney since 2005 (Johnson 2008; Searle and Johnson 2014; “Home” n.d.). Using Heurist, researchers can create flexible, robust and sustainable research databases on the web. Though originally conceived as a data management tool, Heurist has evolved over the years into a platform for data-driven storytelling.

Heurist enables data-driven storytelling through its library of widgets. Heurist widgets can be embedded on a project’s website, allowing the researcher to present their data in an interactive and structured way. In the backend interface, the researcher can choose which records to make public, define ‘Filters’, ‘Rulesets’ and ‘Faceted Searches’ that enable visitors to navigate the database, and create ‘Custom Reports’ that determine how records are displayed. These Filters, RuleSets, Faceted Searches and Custom Reports can then be arranged on the website using Heurist widgets.

This talk comes from a panel on Heurist as a Platform for Data-Driven Storytelling. The speaker is Dr Sylvia Melzer, who introduces ‘databasing on demand’, a framework that allows users to rapidly convert their research data into a Heurist database for publication. 

As Dr Melzer explains, currently, thousands of files have been created in the field of humanities in which research data are documented electronically using e.g. TEI or EpiDoc. Importing such files into Heurist allows them to be easily searched, analysed and published. The challenge is to import large numbers of such files into an appropriately structured database in a short time. In this talk, she demonstrates how to create database instances on demand with Heurist using a large dataset of TEI and EpiDoc documents as an example.

The databasing on demand paradigm changes the way stories can be told using Humanities data. It allows researchers to rapidly transform static research data into live, sharebale data on the web with an intuitive interface.



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