Pittsburgh Supercomputing Center, Carnegie Mellon University, Pittsburgh, PA, 15213, USA.
Center for Biologic Imaging, University of Pittsburgh, Pittsburgh, PA, 15213, USA.
Sci Data. 2024 Nov 11;11(1):1212. doi: 10.1038/s41597-024-03761-8.
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.
显微镜技术和计算技术的进步使研究人员能够以微米级的精度对大脑进行数字重建。因此，像大脑倡议细胞普查网络（BRAIN Initiative Cell Census Network，BICCN）这样的社区努力已经生成了数千个全脑成像数据集，以追踪神经元回路并全面绘制细胞类型图。这些数据包含了超出初始分析的有价值的信息，为变异研究和特定脑区细胞类型的稳健分类开辟了道路。然而，这些成像数据的规模和异质性使得个体研究人员在存储、共享和分析这些数据时遇到了困难，在更广泛的社区范围内也不切实际。在这里，我们介绍了大脑图像库（Brain Image Library，BIL），这是一个为神经科学社区服务的公共资源，它为大脑显微镜数据提供了一个持久的集中式存储库。BIL 目前拥有数千个大脑数据集，并提供了一个集成的分析生态系统，允许在无需下载的情况下进行探索、可视化和数据访问，从而鼓励科学发现和数据重用。
