PARROT

PARROT provides a standardized database and methodological framework for archiving and presenting particle-technology tomographic datasets to support reproducibility, comparative analysis, and methodological development.


Key Features:

  • Archiving methodology: Focuses on a robust methodological framework for archiving tomographic datasets to ensure consistent data organization.
  • Data and metadata consistency: Emphasizes standardized data presentation and comprehensive metadata documentation.
  • Illustrative use cases: Includes three working-group-derived use cases that demonstrate how consistent, concise datasets enhance studies and serve as models for similar projects.

Scientific Applications:

  • Particle-technology research: Aggregates standardized tomographic datasets to facilitate reproducibility, comparative analysis, and the development of new methodologies in particle technology.
  • Method development and benchmarking: Supplies consistent datasets and use cases for benchmarking analytical and computational methods applied to tomography-based studies.
  • Cross-disciplinary adaptation: Provides a framework designed to be adaptable for research applications beyond particle technology.

Methodology:

The methodology emphasizes consistency in data presentation, comprehensive metadata documentation, and a robust archiving framework adaptable across disciplines.

Topics

Details

Cost:
Free of charge
Tool Type:
web application
Operating Systems:
Mac, Linux, Windows
Added:
6/15/2022
Last Updated:
6/15/2022

Operations

Publications

Ditscherlein R, Furat O, Löwer E, Mehnert R, Trunk R, Leißner T, Krause MJ, Schmidt V, Peuker UA. PARROT: A Pilot Study on the Open Access Provision of Particle-Discrete Tomographic Datasets. Microscopy and Microanalysis. 2022;28(2):350-360. doi:10.1017/s143192762101391x. PMID:35039098.

PMID: 35039098
Funding: - German Research Foundation: INST 267/129-1 (micro-CT), KR 4259/8-2 (modeling), PE 1160/22-2 (X-ray tomography), PE 1160/23-1 (cake filtration), SCHM 997/27-2 (stochastic modeling)