shepard is a multi-database storage system for highly heterogenous experimental research data. Simple connection options via standardized interfaces enable the automated recording of data including an annotation with meta information.
Guided by the vision of a seamlessly digital integrated chain of highly varying processes, the shepard system (storage for heterogeneous product and research data) is being developed at Augsburgs Center for Lightweight Production Technology. One focal point is the interdisciplinary utilization of all generated data, e.g. for AI methods for data analysis or contextual curation of data.
Shepard is a scalable system for a highly flexible automated storage and linking of heterogeneous data (including measuring data, simulation results, CAD data) and related metadata (e.g. provenance information or semantic classification of the data) along most varying real and digital process chains. It is intended to provide a simple and sustainable way for all users for storing, retrieving, analyzing, and sharing research data, enabling comprehensive collaboration, and thereby representing the basis for a consistent research data management from experiment to publication.
Due to development and prototypical use for a structured acquisition of experiments in a wide variety of disciplines (from virtual simulation workflows, through production technology up to flight experiments or optical test range) the system already covers many domains in the research context, especially at the common fields of the DLR.
Simple connection options via standardized interfaces enable the automated recording of data including an annotation with meta information. These interfaces are also used for evaluation and provide the basis for connecting any AI framework. The provided web interface enables a comfortable use of shepard's basic functions. More complex applications can easily be connected via the provided REST API.