Research Data Management
Digital Data Drives Discovery
Researchers in ctd.qmat generate large amounts of research data — from numerical measurements, plots, and images to lab notes, protocols, and software. This data is a valuable scientific resource. When managed effectively in an open system, it can remain accessible to the scientific community in the long term. To support this goal, ctd.qmat is partnering with RZUW (JMU Würzburg’s Information Technology Centre) to build a research data platform aligned with the FAIR principles — Findable, Accessible, Interoperable, and Reusable — the current benchmark for modern scientific data management.
Distributed and Collaborative
Our ambition is to turn collaborative data management into everyday practice. We are therefore developing an integrated infrastructure that enables ctd.qmat researchers to store, share, cite, analyze, and extract new insights from research data — all within a single platform. Multiple tools and workflows are brought together in a user-friendly way to ensure long-term accessibility across hardware generations and vendors.
Building on Proven Resources
Whenever possible, we rely on established solutions rather than reinventing the wheel. This includes open-source software — developed transparently and readily adaptable — as well as proven web services and modern data-storage technologies, all centrally operated by RZUW. These services work behind the scenes to provide researchers with a unified, cloud-like environment — a modern data mesh — that supports data-driven research from a single entry point.
Modern Infrastructure
Our infrastructure combines Kubernetes and a distributed object store — the same architecture used by major cloud providers such as Amazon, Google, and Microsoft.
The open-source platform Kubernetes and a Kubernetes cluster are used to coordinate and manage services running inside containerized environments, monitor resource health, and provide vendor- and hardware-independent flexibility and scalability.
A Ceph object store with a capacity of 1.5 petabytes offers secure, redundant storage based on the widely used AWS S3 protocol. It supports fast HTTP/HTTPS access, scales easily with growing data volume, and ensures robust long-term reliability.
Web Services and Tools
The platform supports a suite of web services now widely adopted at ctd.qmat.
JupyterHub and BinderHub
These open-source tools enable fully reproducible computing environments — including code, software, and interactive elements — that can be launched directly in a browser without installing additional software. Links allow environments to be shared effortlessly with colleagues.
GitLab
Already in long-standing use, GitLab provides a shared environment for collaborative software development, workflow management, and automated testing.
NOMAD Oasis
A local instance of the open-source NOMAD platform developed within the FAIRmat NFDI consortium. It combines an electronic lab notebook, Jupyter-based analysis tools, and a materials science data repository with rich, searchable metadata.
eLabFTW
Provides digital lab notebooks with timestamped documentation of measurements and experiments.
Overleaf
Collaborative LaTeX editing for writing and publishing scientific documents.
High-Performance Computing
All ctd.qmat members can access Julia 2, the JMU-operated high-performance computing system.
More Information and Support
Contact the Data Management Team:
datamanagement.ctd.qmat@listserv.dfn.de
We are happy to assist with data management challenges and welcome suggestions for expanding the infrastructure.
Additional peer support is available on the Discord server of the Würzburg Chapter of de-RSE (Research Software Engineers).