How Neurodesk enables collaborative and open neuroimaging research
Poster No:
1814
Submission Type:
Abstract Submission
Authors:
Thuy Dao1, Aswin Narayanan2, Joshua Scarsbrook1, Edan Hamilton1, Steffen Bollmann1
Institutions:
1University of Queensland, Brisbane, Queensland, 2Australian National Imaging Facility, Brisbane, Queensland
First Author:
Co-Author(s):
Introduction:
Neuroimaging data is crucial for studying brain and behavior relationship, but acquiring high-quality data is costly and demands specialized expertise. To address these challenges, open-access datasets and data sharing between labs have become common practice. Data repositories like OpenNeuro (Markiewicz et al., 2021) provide valuable platforms for sharing data openly.
However, preparing datasets for public access is a complex task. It involves preprocessing data, organizing it according to data standardization like the Brain Imaging Data Structure (BIDS) (Gorgolewski et al., 2016), and uploading it to repositories - tasks that require significant effort and expertise.
Using public datasets is also challenging. Researchers need specialized tools to download and adapt data to their own analysis pipelines. Moreover, neuroimaging data analysis often relies on specialized software, which can be difficult to install and might yield different results across computing environments. These hurdles can block progress and reduce the reproducibility of scientific work.
The growing ecosystem of open science tools-ranging from data conversion utilities to standardized processing platforms-has improved the accessibility and usability of open data, fostering a new wave of scientific collaboration (Harding et al., 2023). However, the principles of openness come with responsibilities to address ethical issues, particularly when involving sensitive patient data. As data sharing policies vary globally, cross-border collaborations require a flexible and comprehensive platform to support different data sharing models while ensuring reproducibility on any infrastructure.
However, preparing datasets for public access is a complex task. It involves preprocessing data, organizing it according to data standardization like the Brain Imaging Data Structure (BIDS) (Gorgolewski et al., 2016), and uploading it to repositories - tasks that require significant effort and expertise.
Using public datasets is also challenging. Researchers need specialized tools to download and adapt data to their own analysis pipelines. Moreover, neuroimaging data analysis often relies on specialized software, which can be difficult to install and might yield different results across computing environments. These hurdles can block progress and reduce the reproducibility of scientific work.
The growing ecosystem of open science tools-ranging from data conversion utilities to standardized processing platforms-has improved the accessibility and usability of open data, fostering a new wave of scientific collaboration (Harding et al., 2023). However, the principles of openness come with responsibilities to address ethical issues, particularly when involving sensitive patient data. As data sharing policies vary globally, cross-border collaborations require a flexible and comprehensive platform to support different data sharing models while ensuring reproducibility on any infrastructure.
Methods:
One such platform is Neurodesk (Renton et al., 2024) leveraging software containers to enhance reproducibility and accessibility of the data consumption and production workflow (Figure 1). It provides tools like dcm2niix (Li et al., 2016), heudiconv (Heudiconv, n.d.), bidscoin (Zwiers et al., 2022), and sovabids (Sovabids, n.d.) for standardizing data into the BIDS format. The data can then be processed with compatible pipelines including BIDS compliant tools. Additionally, Neurodesk integrates DataLad (Halchenko et al., 2021) to upload and share data in repositories like OSF (Foster & Deardorff, 2017) and OpenNeuro (Markiewicz et al., 2021). The comprehensive suite of tools within Neurodesk fosters a wider dissemination of open science for the community.
Results:
In addition to enabling access to data from open repositories, Neurodesk supports two flexible models for private data collaboration: centralized and decentralized (Figure 2). In the centralized collaboration model, a Neurodesk instance with common storage can be shared among collaborators. Data can be easily accessed within the group via the shared storage, ensuring that only those with granted access can view and process the data. This model simplifies collaborative workflows by centralizing both the data and tools in one environment while securing data with appropriate authentication. In the decentralized model, separate Neurodesk installations are maintained at each institution, allowing researchers to process sensitive local data without sharing it directly. This approach complies with stricter privacy policies where their data governance does not allow the sharing of data outside of the institution. Results, rather than raw data, can then be shared among collaborators. Consistent execution of analysis pipelines across any Neurodesk setup simplifies collaboration, making it ideal for collaborations, hackathons and workshops.
Conclusions:
Neurodesk lowers the barriers to open data by providing a browser-based virtual desktop, command-line interface, and Jupyter notebooks. These tools are compatible with personal computers, cloud systems, and high-performance computing (HPC) facilities, enabling large-scale computation and fostering collaboration among research teams.
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Workflows 2
Keywords:
Computing
Data analysis
Data Organization
Open Data
Open-Source Code
Open-Source Software
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Gorgolewski, K. J. (2016). The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Scientific Data, 3(1), 160044. https://doi.org/10.1038/sdata.2016.44
Halchenko, Y. (2021). DataLad: Distributed system for joint management of code, data, and their relationship. Journal of Open Source Software, 6(63), 3262. https://doi.org/10.21105/joss.03262
Harding, R. J. (2023). The Canadian Open Neuroscience Platform—An open science framework for the neuroscience community. PLOS Computational Biology, 19(7), e1011230. https://doi.org/10.1371/journal.pcbi.1011230
Heudiconv. (n.d.). [Computer software]. https://github.com/nipy/heudiconv
Li, X. (2016). The first step for neuroimaging data analysis: DICOM to NIfTI conversion. Journal of Neuroscience Methods, 264, 47–56. https://doi.org/10.1016/j.jneumeth.2016.03.001
Markiewicz, C. J. (2021). The OpenNeuro resource for sharing of neuroscience data. eLife, 10, e71774. https://doi.org/10.7554/eLife.71774
Renton, A. I. (2024). Neurodesk: An accessible, flexible and portable data analysis environment for reproducible neuroimaging neuroimaging. Nature Methods. https://doi.org/10.1038/s41592-023-02145-x
Sovabids. (n.d.). [Computer software]. https://github.com/yjmantilla/sovabids
Zwiers, M. P. (2022). BIDScoin: A User-Friendly Application to Convert Source Data to Brain Imaging Data Structure. Frontiers in Neuroinformatics, 15, 770608. https://doi.org/10.3389/fninf.2021.770608