Brief description
In modern architectural research, structured and easily accessible data are a central prerequisite for scientific progress. Yet such data are often lacking: datasets are unstructured, difficult to find, or limited by unclear rights and missing standards. Combined with heterogeneous data sources and low levels of data literacy, this leads to error‑prone and inefficient workflows. Visual data—such as drawings or CAD models—pose additional challenges. As a result, data often remain fragmented and unstructured, and are rarely published in repositories due to the high effort required and the lack of suitable representation formats.
The Architectural Research Data Management Pipeline (ARDMP) addresses these issues directly. Building on the NFDI4ING Seed Fund project “RDM‑Workflows for Construction Engineering and Architecture”, we are developing a low‑threshold web application that helps researchers manage their data efficiently and make them available in accordance with the FAIR principles.
What the ARDMP Offers
The pipeline integrates existing NFDI services and enables researchers to:
- transform research data into interconnected knowledge graphs
- enrich datasets with authority data
- prepare and publish them as FAIR Digital Objects
This creates a coherent, practice‑oriented solution for the entire research data lifecycle—from data capture to publication.
Project Goals
The ARDMP pursues two central goals:
- Enabling efficient data management: Through workflows, templates, and automated processes, the pipeline supports researchers in structuring data, files, and sources, thereby improving the quality of available data in architectural research.
- Developing practical, community‑aligned solutions: Close collaboration with researchers and NFDI services ensures that the pipeline meets real‑world needs and is tailored to the requirements of the research community.
Project status
ongoing, 01.01.2026 – 31.12.2028
Project Members
Cooperations
Prof. Achim Menges, Institute for Computational Design and Construction, Universität Stuttgart (Tobias Schwinn and Hana Svatos-Raznjevic)
Further informations
Funding
Funded by the German Research Foundation (DFG)
