DOI: 10.57760/sciencedb.o00133.00019
title: Consortium for Reliability and Reproducibility(CoRR) -Enhanced
title: Consortium for Reliability and Reproducibility(CoRR) -Enhanced

description: CoRR (https://fcon_1000.projects.nitrc.org/indi/CoRR/html/index.html) was to create an open science resource for the imaging community that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. In order to accomplish this, we have aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world. This enables the:1.Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics2.Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs3.Creation of a standard/benchmark test-retest dataset for the evaluation of novel metricsIn 2024, research focused on the reliability of magnetic resonance imaging (MRI) remains a central priority for our team. To enhance the comprehensiveness of brain imaging reliability assessment, we have consolidated single-site datasets into a large multi-site dataset comprising 23 sub-datasets from 11 different sites. We have standardized the preprocessing pipeline, expanded the measurement design from a single repetition to include 1 to 9 repetitions, and upgraded the selection of metrics from a single indicator to encompass both global and local measures. Notably, all preprocessed brain imaging data have been made publicly available through SciDB, offering valuable data and technical support for the evaluation of brain reliability.
description: CoRR (https://fcon_1000.projects.nitrc.org/indi/CoRR/html/index.html) was to create an open science resource for the imaging community that facilitates the assessment of test-retest reliability and reproducibility for functional and structural connectomics. In order to accomplish this, we have aggregated resting state fMRI (R-fMRI) and diffusion imaging data from laboratories around the world. This enables the:1.Establishment of test-retest reliability and reproducibility for commonly used MR-based connectome metrics2.Determination of the range of variation in the reliability and reproducibility of these metrics across imaging sites and retest study designs3.Creation of a standard/benchmark test-retest dataset for the evaluation of novel metricsIn 2024, research focused on the reliability of magnetic resonance imaging (MRI) remains a central priority for our team. To enhance the comprehensiveness of brain imaging reliability assessment, we have consolidated single-site datasets into a large multi-site dataset comprising 23 sub-datasets from 11 different sites. We have standardized the preprocessing pipeline, expanded the measurement design from a single repetition to include 1 to 9 repetitions, and upgraded the selection of metrics from a single indicator to encompass both global and local measures. Notably, all preprocessed brain imaging data have been made publicly available through SciDB, offering valuable data and technical support for the evaluation of brain reliability.