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							<h1>Open Science & Data Sharing</h1>
<h2><a href="fair_data.html">FAIR Guiding Principles for Scientific Data Management and Stewardship</a></h2>
<p>The FAIR principles (Findable, Accessible, Interoperable, Reusable) are a set of guidelines to improve the management and sharing of research data. These principles ensure that data is prepared in a way that optimizes its reuse and reproducibility.</p>
<h2><a href="bids_data.html">BIDS Format for Neuroimaging Data</a></h2>
<p>BIDS (Brain Imaging Data Structure) is a standardized way to organize and describe neuroimaging and behavioral data. It simplifies the process of data sharing, analysis, and collaboration in the neuroscience community. BIDS is designed to make data easier to use and to facilitate reproducible research.</p>
<h2><a href="nipoppy_data.html">NiPoppy Framework</a></h2>
<p>NiPoppy is a robust, automated pipeline designed for the quality assessment of neuroimaging data. It is particularly useful for researchers working with large datasets, ensuring that data quality is consistently evaluated and maintained.</p>
<h2><a href="datalad_data.html">DataLad: Data Management and Version Control for Neuroimaging</a></h2>
<p>DataLad is an open-source tool designed to facilitate data management and version control for large datasets, particularly in the field of neuroimaging. It combines the benefits of Git and and other data management tools to provide a comprehensive solution for tracking and sharing datasets.</p>
<h2><a href="open_neuro.html">OpenNeuro Data Platform</a></h2>
<p>OpenNeuro is an open science resource that provides a platform for sharing, discovering, and analyzing neuroimaging data. It encourages researchers to share their datasets publicly, following the FAIR principles, which enhances data accessibility, reproducibility, and collaboration within the neuroscience community. OpenNeuro supports various neuroimaging modalities, including MRI, EEG, MEG, and more, and allows researchers to access and contribute to a growing repository of standardized and well-curated datasets.</p>
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