# Data management in balance – a decade of balancing pragmatism, sustainability and innovation at plant research center IPK Gatersleben

## TL;DR

This paper outlines how IPK Gatersleben developed a sustainable and innovative data management system over 15 years, balancing practical needs with scientific standards.

## Contribution

The paper introduces an ambidextrous data management approach that combines operational efficiency with agile innovation.

## Key findings

- A central research data infrastructure was established before the FAIR criteria were defined.
- Pilot working groups and data champions improved participation and compliance across departments.
- Agile methods enabled the adoption of technical innovations while maintaining daily operational needs.

## Abstract

The Leibniz Institute of Plant Genetics and Crop Plant Research (IPK) Gatersleben is a leading international plant science institute specializing in biodiversity and crop plant performance research. Over the last decade, all phases of the research data lifecycle were implemented as a continuous process in conjunction with information technology, standardization, and sustainable research data management (RDM) processes. Under the leadership of a team of data stewards, a research data infrastructure, process landscape, capacity building, and governance structures were successfully established. As a result, a generic research data infrastructure was created to serve the principles of good scientific practice, archiving research data in an accessible and sustainable manner, even before the FAIR criteria were formulated. In this paper, we discuss success stories as well as pitfalls and summarize the experiences from 15 years of operating a central RDM infrastructure. We present measures for agile requirements engineering, technical and organizational implementation, governance, training, and roll-out. We show the benefits of a participatory approach across all departments, personnel roles, and researcher profiles through pilot working groups and data management champions. As a result, an ambidextrous approach to data management was implemented, referring to the ability to efficiently combine operational needs, support daily tasks in compliance with the FAIR criteria, while remaining open to adopting technical innovations in an agile manner.