From Project to Infrastructure
The establishment of the National Research Data Infrastructure (NFDI) represents a cultural change in how scientific data is managed, preserved, and utilized across Germany. Central to the success of this initiative is the transition of consortia from project-based entities into reliable, long-term providers for different service areas. However, reliability is not an inherent property of infrastructure; it is the result of rigorous, transparent, and continuous Quality Assurance (QA). While the NFDI community has made significant strides in defining Data Quality through the application of the FAIR (Findable, Accessible, Interoperable, Reusable) principles [1], there remains a critical gap in the systematic Quality Assessment for its other services. This presentation addresses that gap by detailing the comprehensive Quality Management (QM) framework developed within the NFDI4Biodiversity consortium.
NFDI4Biodiversity operates at the intersection of diverse scientific disciplines, managing a vast and heterogeneous portfolio that includes among other things complex molecular data pipelines, ecological modelling tools, citizen science platforms, user support and extensive educational offerings. This diversity presents a significant challenge: a shared understanding of ‘quality’ is a prerequisite for unification; without it, quality remains a subjective and localized metric. Such fragmentation inevitably leads to inconsistent service delivery, whether in the form of inconsistent service delivery, technical downtime, outdated training materials, or delayed helpdesk responses. Ultimately, this undermines user confidence, posing a direct risk to the consortium’s credibility and the long-term sustainability of the national infrastructure. In reality, this stands in direct tension with the consortium’s mission statement, which explicitly commits to the core values of Trust, Transparency, User Orientation, and Quality [2]. To uphold these values, quality management must account for multiple dimensions and service-specific criteria. A one-size-fits-all approach is therefore insufficient; instead, a differentiated quality framework is required. To operationalize this, three core Service Areas have been delineated based on their distinct characteristics: Training, Helpdesk, and Services. The work program defines a specific objective for each area, functioning as an overarching quality target:
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Training: To enable scalable, needs-based capacity building for RDM.
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Helpdesk: To offer a central point of contact and individual user support, backed by a strong Helpdesk and expert network across consortia and RDM initiatives.
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Services: A consolidated, community-owned service portfolio [3].
These objectives dictate the relevant quality indicators. For instance, metrics for training services (e.g., pedagogical effectiveness, accessibility) differ fundamentally from those for helpdesk support (e.g., response time, resolution rate) or technical tools (e.g., uptime, interoperability). Fundamentally, quality assurance necessitates a dynamic governance model wherein operational processes are explicitly documented, subjected to periodic audit, and iteratively optimized in alignment with the Plan-Do-Check-Act (PDCA) paradigm. Only through such a differentiated and iterative framework can the consortium ensure that each service meets its specific standards while collectively reinforcing the consortium’s commitment to its foundational values.
The core of our presentation are the three Service Areas designed to harmonize these dimensions. At the highest level of the QM sits a general framing concept, which serves as the consortium’s “Quality Policy”, drawing inspiration from international industry standards such as ISO 9001 (Quality Management) [4] and ITIL (Information Technology Infrastructure Library) [5] and partner initiatives such as HeFDI (Hessian Research Data Infrastructure) [6] and de.NBI [7], but specifically adapted for the unique constraints and values of the academic Research Data Management (RDM) world.
Furthermore, we advocate for a pragmatic approach. Rather than overwhelming partners with complex requirements from the start, we began with easy-to-implement guidelines. This allows for quick improvements and builds the momentum necessary for deeper structural changes. Thus, we enable continuous improvement of our QM framework, consequently it is not a static document but a living system that evolves alongside the needs of the biodiversity community and the broader NFDI.
By sharing these concepts and the lessons learned during their development, this talk provides other NFDI consortia with a modular blueprint. This “blueprint” enables others to move beyond the technicalities of data and build a professionalized, reliable service infrastructure that earns and maintains the trust of the scientific community.