Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond
<|reference_start|>Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond: The exponential growth of neuroscientific data necessitates platforms that facilitate data management and multidisciplinary collaboration. In this paper, we introduce Pennsieve - an open-source, cloud-based scientific data management platform built to meet these needs. Pennsieve supports complex multimodal datasets and provides tools for data visualization and analyses. It takes a comprehensive approach to data integration, enabling researchers to define custom metadata schemas and utilize advanced tools to filter and query their data. Pennsieve's modular architecture allows external applications to extend its capabilities, and collaborative workspaces with peer-reviewed data publishing mechanisms promote high-quality datasets optimized for downstream analysis, both in the cloud and on-premises.   Pennsieve forms the core for major neuroscience research programs including NIH SPARC Initiative, NIH HEAL Initiative's PRECISION Human Pain Network, and NIH HEAL RE-JOIN Initiative. It serves more than 80 research groups worldwide, along with several large-scale, inter-institutional projects at clinical sites through the University of Pennsylvania. Underpinning the SPARC.Science, Epilepsy.Science, and Pennsieve Discover portals, Pennsieve stores over 125 TB of scientific data, with 35 TB of data publicly available across more than 350 high-impact datasets. It adheres to the findable, accessible, interoperable, and reusable (FAIR) principles of data sharing and is recognized as one of the NIH-approved Data Repositories. By facilitating scientific data management, discovery, and analysis, Pennsieve fosters a robust and collaborative research ecosystem for neuroscience and beyond.<|reference_end|>
@article{goldblum2024pennsieve:,
    title={Pennsieve: A Collaborative Platform for Translational Neuroscience and
  Beyond},
    author={Zack Goldblum, Zhongchuan Xu, Haoer Shi, Patryk Orzechowski, Jamaal
  Spence, Kathryn A Davis, Brian Litt, Nishant Sinha, and Joost Wagenaar},
    journal={arXiv preprint arXiv:2409.10509},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.10509},
    primaryClass={cs.CY cs.DB cs.DL cs.ET}
}