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Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
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Lightweight Distributed Provenance Model for Complex Real-world Environments.

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This summary is machine-generated.

This study introduces a lightweight provenance model for tracking research object lineage across organizations. The model enables distributed provenance chains, enhancing traceability, reproducibility, and FAIR data principles in complex research environments.

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Area of Science:

  • Biotechnology
  • Data Science
  • Digital Curation

Background:

  • Research objects like datasets and biological materials often have fragmented lifecycles due to multi-organizational handling.
  • Interconnecting these fragmented records creates distributed provenance chains, crucial for research traceability and reproducibility.
  • Ensuring research objects are Findable, Accessible, Interoperable, and Reusable (FAIR) necessitates robust provenance tracking.

Purpose of the Study:

  • To define a lightweight provenance model based on W3C PROV standards.
  • To enable the generation of distributed provenance chains in complex, multi-organizational research settings.
  • To demonstrate the model's application in a real-world biotechnology research pipeline.

Main Methods:

  • Developed a lightweight provenance model adhering to W3C PROV specifications.
  • Applied the model to a comprehensive research pipeline, from specimen acquisition to AI model training.
  • Integrated provenance data for biological specimens, histological examinations, and associated digital data (images, annotations, clinical data).

Main Results:

  • Successfully generated distributed provenance chains across multiple stages of a research pipeline.
  • Demonstrated the model's capability to capture lineage information in a complex, multi-organizational environment.
  • The proposed model serves as the conceptual foundation for the emerging ISO 23494 standard for biotechnology provenance.

Conclusions:

  • The lightweight provenance model effectively facilitates the creation of distributed provenance chains.
  • This approach enhances traceability, reproducibility, and FAIRness for research objects in complex environments.
  • The model's adoption as a foundation for ISO 23494 highlights its significance in the biotechnology domain.