[Paper Review] Pennsieve: A Collaborative Platform for Translational Neuroscience and Beyond

Zack Goldblum, Zhida Xu+7 more|arXiv (Cornell University)|Sep 16, 2024

Science, Research, and MedicineMedicine3 citations

TL;DR

Pennsieve is an open-source, cloud-based scientific data management platform designed for translational neuroscience and multidisciplinary research. It enables FAIR-compliant data curation, collaborative workspaces, and advanced metadata management, supporting multimodal datasets across 350+ high-impact datasets (125 TB total, 35 TB public), with integration into major initiatives like NIH SPARC and HEAL.

ABSTRACT

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.

Motivation & Objective

• Address the growing challenge of managing large-scale, multimodal neuroscience datasets across disparate formats and repositories.
• Overcome data silos in neuroscience by enabling interoperable, FAIR-compliant data sharing and integration.
• Support collaborative, multidisciplinary research through secure, version-controlled workspaces and peer-reviewed data publishing.
• Facilitate reproducible research by enabling detailed metadata, annotations, and code integration with datasets.
• Provide a scalable, modular platform that supports both cloud and on-premises deployment for diverse research institutions.

Proposed method

• Implement a modular, cloud-native architecture using microservices and serverless functions to extend platform capabilities.
• Support custom metadata schemas and graph-based metadata modeling to enable complex data relationships and semantic linking.
• Integrate domain-specific data viewers for timeseries, imaging, genomic, and computational model data with overlay annotation tools.
• Expose a comprehensive RESTful API to enable third-party application integration and custom data pipeline development.
• Adopt the FAIR principles through persistent DOIs, standardized metadata, access controls, and machine-readable data descriptions.
• Deploy collaborative workspaces with role-based access and versioned dataset publishing for reproducibility and auditability.

Experimental results

Research questions

• RQ1 How can a scalable, cloud-based platform improve data integration and collaboration in large-scale, multimodal neuroscience research?
• RQ2 To what extent can a FAIR-compliant data management system reduce data silos and enhance dataset reusability across institutions?
• RQ3 What role does customizable metadata and annotation infrastructure play in enabling cross-modal data discovery and analysis?
• RQ4 How does a modular, API-first architecture support extensibility and integration with external scientific tools and workflows?
• RQ5 Can a unified platform effectively serve diverse neuroscience initiatives, including clinical, preclinical, and computational research?

Key findings

• Pennsieve hosts over 350 publicly available, high-impact datasets across 125 TB of scientific data, with 35 TB accessible to the public.
• The platform is integrated into major national initiatives, including NIH SPARC, NIH HEAL PRECISION Human Pain Network, and RE-JOIN, serving over 80 research groups globally.
• Pennsieve supports diverse data modalities, including EEG, MEG, MRI, microscopy, gene expression, 3D models, and computational simulations.
• The platform enables peer-reviewed data publishing with version control, persistent DOIs, and structured metadata, enhancing dataset reproducibility and citation.
• Integrated data viewers and annotation tools allow users to visualize and annotate timeseries, imaging, and genomic data directly on the platform.
• Pennsieve is recognized as an NIH-approved data repository and adheres to FAIR principles, ensuring long-term findability, accessibility, and reusability of neuroscience data.