The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

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Publication Details

Journal
Scientific Data
Published
2024-11-11
DOI
https://doi.org/10.1038/s41597-024-03761-8
Citations
7
Primary Topic
Cell Image Analysis Techniques
Type
article
Field-Weighted Citation Impact
4.64

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article

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Megan Smith, Mariah L. Kenney, Rozita Laghaei, Alan M. Watson et al.
7 citations
Scientific Data
Cell Image Analysis Techniques
4.64
article

The Brain Image Library: A Community-Contributed Microscopy Resource for Neuroscientists

Megan Smith, Mariah L. Kenney, Rozita Laghaei, Alan M. Watson, Iaroslavna Vasylieva, Greg Hood, Luke Tuite, Ivan Cao-Berg, Alexander J. Ropelewski
article en
7 citations

Abstract

Advancements in microscopy techniques and computing technologies have enabled researchers to digitally reconstruct brains at micron scale. As a result, community efforts like the BRAIN Initiative Cell Census Network (BICCN) have generated thousands of whole-brain imaging datasets to trace neuronal circuitry and comprehensively map cell types. This data holds valuable information that extends beyond initial analyses, opening avenues for variation studies and robust classification of cell types in specific brain regions. However, the size and heterogeneity of these imaging data have historically made storage, sharing, and analysis difficult for individual investigators and impractical on a broad community scale. Here, we introduce the Brain Image Library (BIL), a public resource serving the neuroscience community that provides a persistent centralized repository for brain microscopy data. BIL currently holds thousands of brain datasets and provides an integrated analysis ecosystem, allowing for exploration, visualization, and data access without the need to download, thus encouraging scientific discovery and data reuse.

Scientific DataVol. 11(1)
University of Pittsburgh (US), Pittsburgh Supercomputing Center (US), Carnegie Mellon University (US)
National Science Foundation, National Institute of Mental Health, National Institute on Drug Abuse, Office of Advanced Cyberinfrastructure
Quality Education
Openalex Percentile: Top 6%
Cell Image Analysis Techniques
4.64
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