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ZENODO
Software . 2025
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Software . 2026
Data sources: ZENODO
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Reverse Engineering Lab - Data Collection Platform

Authors: van Lierde, Simon; Donati, Franco;

Reverse Engineering Lab - Data Collection Platform

Abstract

Changelog [0.1.0] - 2025-07-31 Note: This is an early development release. The API is not production-ready and the current frontend is a demo UI for development purposes only. Description Initial release of the Reverse Engineering Lab platform for open product composition research. This platform supports systematic data collection for disassembled durable goods. Features API and Backend RESTful API built with FastAPI Async database operations with SQLModel ORM PostgreSQL database integration Automated database migrations with Alembic Swagger API documentation Authentication and Access Control User authentication and authorization OAuth integration (Google and GitHub) User organization and role management Admin API routes and SQLAdmin interface for data management Data Management Support for products, components, and materials Hierarchical product and material structures Product-type and material categorization Development and Deployment Automated code quality checks with pre-commit Ruff for linting and code style Pyright for static type checking Containerized deployment with Docker Dependency management with uv Media and Storage File and image storage management Image and video upload for products Raspberry Pi Camera Plugin for remote image capture Current Limitations Frontend is a basic demo UI API is incomplete and does not have test suite Not ready for production use License Code: AGPL-v3+ Data: ODbL For questions, contact info@cml-relab.org.

Please see https://docs.cml-relab.org/ for documentation on the platform. If you use this software, please cite it as below.

Keywords

data collection, machine learning, life cycle assessment, circular economy, computer vision, industrial ecology

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average