Medyckyj-Scott D1
1NZ Bioeconomy Science Institute, Palmerston North, Aotearoa / New Zealand
Biography:
David Medyckyj–Scott is Research Data Manager for the newly created New Zealand Bioeconomy Science Institute. His background is broad covering geography, psychology, computer science as well a research data management. Prior to his current position he held several pivotal roles including Geospatial and Research Data Manager at a UK national data centre (University of Edinburgh). He has led the design, implementation and operation of nationally significant geospatial and environmental data services in both the UK and New Zealand. He was Technical Director at the NZ National Land Resource Centre and Head of Data Management at Manaaki Whenua Landcare Research.
Abstract:
Research organisations increasingly need to identify their most valuable data assets. But there is limited guidance on how to do this. Can we create a framework for assessing data to determine whether it is high value?
Established in 2025, the New Zealand Bioeconomy Science Institute (BSI) wanted an understanding of its high-value research data assets to improve their visibility and accelerate internal research and collaboration. To do this, we needed a way to identify datasets that are valuable beyond their original project or immediate team, because, for example, they have significant national scientific, cultural, or commercial importance.
Despite its importance, research on how to assess the value of research data is limited. Building on work presented by Susanne den Boer at an RDA Value of Research Data BOF in 2025, the author developed a framework that can be used in a research institute context. The framework comprises three assessment dimensions: Extrinsic Characteristics (why a dataset matters), Intrinsic Characteristics (properties of the dataset content), and Enabling Capacity Characteristics (factors that enable a dataset’s value to be realised and sustained).
The framework has been tested in a survey of BSI researchers as the first step towards creating a catalogue of high-value research data. The survey identified candidate high-value datasets across multiple scientific domains but highlighted limitations of the framework in practice.
The presentation will describe the framework, its application, and the ongoing challenges of identifying high-value research data. The framework and lessons learned should be valuable to other organisations undertaking similar exercises.
