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DATASHUTTLE: AUTOMATED DATA MANAGEMENT FOR EXPERIMENTAL NEUROSCIENCE

Joseph Ziminskiand 5 co-authors

Sainsbury Wellcome Centre & Gatsby Computational Neuroscience Unit, University College London

FENS Forum 2026 (2026)

Barcelona, Spain

Board PS05-09AM-027

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Board: PS05-09AM-027

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PS05-09AM-027

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Abstract

Experimental data are often stored using custom folder structures and naming conventions, which hinders data sharing, reproducibility, and the development of community tools. However, applying strict folder organization standards during the fast-paced acquisition of complex experimental data is difficult, and can place a significant burden on researchers.

To address this, we have developed datashuttle, an open source software for managing neuroscience project folders. Datashuttle automates the creation and validation of standardized project folders, and provides flexible data transfer between acquisition, storage, and analysis machines. Standardized folder-naming makes transferring subsets of data straightforward, facilitating management of large and complex datasets. The software can be used via a graphical user interface or through a Python API, allowing integration into existing acquisition scripts.

Datashuttle emphasizes ease of adoption by implementing a lightweight folder specification (NeuroBlueprint) based on existing community specifications. We aim for datashuttle to be a low-barrier entry point for data standardisation, acting as a stepping stone towards comprehensive schemas such as the Brain Imaging Data Structure (BIDS) and Neurodata Without Borders (NWB). Together, datashuttle lowers the barrier for experimental data standardisation to support reproducibility and collaboration in neuroscience.

Diagram illustrating the NeuroBlueprint data organization and transfer workflow using datashuttle. On the left, a BIDS-like project folder structure shows rawdata organized by subject and session, with datatype folders for electrophysiology, behavior, functional imaging, and anatomy, and a separate derivatives folder for processed data. On the right, acquisition machines upload data via datashuttle to central storage, which is then downloaded to an analysis machine for processing. Arrows indicate bidirectional data flow between acquisition systems, central storage, and analysis environments, highlighting standardized and reproducible data management in the Neuroinformatics Unit.

DATASHUTTLE: AUTOMATED DATA MANAGEMENT FOR EXPERIMENTAL NEUROSCIENCE

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