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Database Commons

a catalog of worldwide biological databases

Database Profile

Dyslexia Data Consortium Repository

General information

URL: https://dyslexia.computing.clemson.edu/
Full name: Dyslexia Data Consortium Repository
Description: The Dyslexia Data Consortium Repository is a multisite neuroimaging database integrating raw and processed MRI data with harmonized behavioral, clinical and demographic information related to reading development and dyslexia. It supports BIDS-based data sharing, image processing and quality control, neuroimaging metric generation, controlled data download and in-platform analysis.
Year founded: 2023
Last update: 2025
Version: v1.0
Accessibility:
Accessible
Country/Region: United States

Contact information

University/Institution: Clemson University
Address:
City: Clemson
Province/State: South Carolina
Country/Region: United States
Contact name (PI/Team): Dyslexia Data Consortium Team
Contact email (PI/Helpdesk): dyslexia.consortium@gmail.com

Publications

41060539
Dyslexia Data Consortium: A Comprehensive Platform for Neuroimaging Data Sharing, Analysis, and Advanced Research in Dyslexia. [PMID: 41060539]
Phatangare RV, Eckert MA, Luo L, Vaden KI, Wang JZ, Dyslexia Data Consortium .

Neuroimaging studies have and continue to advance our understanding of the neurobiology of dyslexia. Integration of data from these studies has the potential to replicate findings, deepen understanding through theoretically focused research, and provide for unexpected discovery. This data integration can be important for questions where a sufficiently large and well-defined group of participants is necessary for sufficient experimental power, particularly for a complex disorder where age, language background, and cognitive profiles can impact imaging results. We have developed a data-sharing platform to provide a data repository, image processing resources, and data analysis tools, with an emphasis on data harmonization across retrospective datasets ( https://dyslexiadata.org ). Here, we summarize data sharing, download, imaging metrics, and quality and privacy considerations in the design of and resources available through this repository. By providing access to a relatively large multisite dataset, researchers can test hypotheses about reading development and disability, test novel data analysis methods, even within the platform, and advance understanding of dyslexia.

Neuroinformatics. 2025:23(4) | 0 Citations (from Europe PMC, 2026-07-25)
38352916
Dyslexia Data Consortium Repository: A Data Sharing and Delivery Platform for Research. [PMID: 38352916]
Roshan Bhandari, Rishikesh V Phatangare, Mark A Eckert, Kenneth I Vaden, James Z Wang

Specific learning disability of reading, or dyslexia, affects 5-17% of the population in the United States. Research on the neurobiology of dyslexia has included studies with relatively small sample sizes across research sites, thus limiting inference and the application of novel methods, such as deep learning. To address these issues and facilitate open science, we developed an online platform for data-sharing and advanced research programs to enhance opportunities for replication by providing researchers with secondary data that can be used in their research (https://www.dyslexiadata.org). This platform integrates a set of well-designed machine learning algorithms and tools to generate secondary datasets, such as cortical thickness, as well as regional brain volume metrics that have been consistently associated with dyslexia. Researchers can access shared data to address fundamental questions about dyslexia and development, replicate research findings, apply new methods, and educate the next generation of researchers. The overarching goal of this platform is to advance our understanding of a disorder that has significant academic, social, and economic impacts on children, their families, and society.

Brain Inform (2023). 2023:13974() | 1 Citations (from Europe PMC, 2026-07-25)

Ranking

All databases:
6447/6935 (7.051%)
Health and medicine:
1642/1765 (7.025%)
Raw bio-data:
520/588 (11.735%)
Genotype phenotype and variation:
943/1020 (7.647%)
Metadata:
661/728 (9.341%)
6447
Total Rank
1
Citations
0.333
z-index

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Record metadata

Created on: 2024-07-16
Curated by:
liu yuxi [2026-07-22]
Miaomiao Wang [2024-08-30]
Haochen Liu [2024-07-18]
Wenzhuo Cheng [2024-07-16]