Digital representation of Circular Economy (CE) data points at the nano level using the Asset Administration Shell (AAS).
Important: The AAS modeling of data points contains some in-progress IDTA submodels, and due to data privacy constraints, it is not possible to publish them at this time. Once the official submodel publication becomes available, the complete AAS modeling of data points will be accessible in this repository.
The transition to a Circular Economy requires structured, interoperable data across product life cycles. The Asset Administration Shell (AAS), as the Industry 4.0 digital representation standard, provides this foundation — yet CE-relevant data points remain insufficiently defined.
This repository accompanies a methodology to identify and classify nano level CE data, map them to modular AAS submodels, and produce a reusable template for Digital Product Passports (DPPs) and digital twins. The approach enhances data exchange, supports future CE requirements, and is scalable to higher CE levels (micro, meso, macro).
Keywords: Circular Economy; CE Data Points; Digital Product Passport (DPP); Asset Administration Shell (AAS); Data Space; Sustainable Manufacturing.
This repository provides the list of CE data points at the nano level identified and consolidated through CE standards (e.g., ISO 59000 series), sustainability reporting frameworks (GRI, ESRS, SASB, VDI), scientific literature on circularity indicators and lifecycle assessments, existing IDTA submodel templates, and expert workshops.
The data points are categorized into three main groups: Product Information, Supply Chain, and Documentation. Each data point is further characterized by attributes such as type of information (static or dynamic), data source, involved stakeholders, and its mapping to a corresponding AAS submodel (standard, extended, or newly defined).
The data point set is subject to ongoing revision and refinement through collaborative review loops. A formal publication describing the methodology and use case is in preparation; reference details will be added here once available.
This project is licensed under the GNU General Public License v3.0 — see the LICENSE file for details.
This work is funded by the European Union – NextGenerationEU and BMFTR Guideline for Funding Projects to Establish Data Competence Centers in Science within the framework of the DACE project (16DKZ2056E).
The findings and opinions stated reflect the opinion of the authors and not the opinion of the European Union nor the BMFTR.
Developed at the German Research Center for Artificial Intelligence (DFKI) in collaboration with project partners.