SUMMARY: The research data life cycle from project planning to data publishing is an integral part of current research. Until the last decade, researchers were responsible for all associated phases in addition to the actual research and were assisted only at certain points by IT or bioinformaticians. Starting with advances in sequencing, the automation of analytical methods in all life science fields, including in plant phenotyping, has led to ever‐increasing amounts of ever more complex data. The tasks associated with these challenges now often exceed the expertise of and infrastructure available to scientists, leading to an increased risk of data loss over time. The IPK Gatersleben has one of the world's largest germplasm collections and two decades of experience in crop plant research data management. In this article we show how challenges in modern, data‐driven research can be addressed by data stewards. Based on concrete use cases, data management processes and best practices from plant phenotyping, we describe which expertise and skills are required and how data stewards as an integral actor can enhance the quality of a necessary digital transformation in progressive research.

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.

Open Science is reshaping how research is conducted, yet the sustainable adoption of FAIR (Findable, Accessible, Interoperable, Reusable) research data management (RDM) requires more than technical infrastructures. In agrosystem sciences, heterogeneous data and diverse institutional contexts make cultural change particularly challenging. FAIRagro, the NFDI consortium for agrosystem sciences in Germany, developed the Community Engagement and Empowerment Cycle to address three persistent gaps in FAIR implementation: limited engagement, insufficient integration practice, and the lack of guidance. The Cycle comprises four components – Identify, Translate, Communicate and Anchor – and operates across individual, institutional and global levels. This paper presents selected measures implemented within the Cycle and discusses their interplay and impact, including Helpdesk support, Use Cases, training activities and communication formats. These are examined in light of five core assumptions: infrastructure is necessary but insufficient; cultural change requires coordinated socio-technical interventions; connectors are essential for bridging domain science and RDM; shared values and trust are major preconditions; and that sustainable change depends on shared ownership and active responsibility. The paper also reflects on structural challenges and the need for internationally aligned FAIR ecosystems. It outlines effective approaches to community involvement, highlights remaining obstacles and offers pathways for advancing FAIR RDM in agrosystem science and beyond.

The NFDI-consortium FAIRagro has established a systematic framework for evaluating the FAIRness of Research Data Infrastructures (RDI) within the German agrosystem research landscape. While FAIR principles are widely accepted, their practical implementation by RDIs remains challenging. By operationalizing the FAIR principles into a reproducible multi-dimensional scoring methodology, this initiative addresses the critical need for a transparent and citable benchmark of RDIs that moves beyond simple compliance. This paper details the underlying assessment criteria, comprising 20 core metrics, the iterative community-driven validation process, and the integration of these metrics into the FAIRagro Search Hub. This framework evaluates RDIs, like repositories or databases, instead of sampling hosted data sets, across the four distinct categories of FAIR independently, yielding granular, pillar-specific ratings. Unlike aggregate scoring models, which can inadvertently mask technical deficiencies by averaging performance across categories, this multi-dimensional approach ensures that a repository’s distinct strengths and bottlenecks remain fully visible. Our findings demonstrate that standardized scoring not only clarifies data accessibility for users but also highlights specific operational gaps, allowing repository providers to identify precisely where the service implementation can be enhanced. By establishing this data-driven service in the agronomy domain, we provide a scalable template for the broader NFDI and EOSC ecosystems to foster a culture of excellence in research data stewardship.

The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the continuous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research supervising and will train data curators in our community. Data stewards have core competencies in research data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing.