Methods to Evaluate Lifecycle Models for Research Data Management

TL;DR

This paper analyzes 90 research data lifecycle models, proposing two approaches to evaluate their quality despite terminological inconsistencies, highlighting the challenges and possibilities in assessing research data management models.

Contribution

It introduces two methods for assessing research data lifecycle models, addressing terminological issues and demonstrating empirical evaluation feasibility.

Findings

Terminological issues hinder direct model comparison

Empirical evaluation of models is feasible

Two approaches to assess model quality are proposed

Abstract

Lifecycle models for research data are often abstract and simple. This comes at the danger of oversimplifying the complex concepts of research data management. The analysis of 90 different lifecycle models lead to two approaches to assess the quality of these models. While terminological issues make direct comparisons of models hard, an empirical evaluation seems possible.