Big-Data Electron-microscopy for Novel Community Hypotheses: Measuring And Retrieving Knowledge (BENCHMARK)

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Project Summary In an effort to better understand structural organization and anatomy of nervous systems at unprecedented spatial resolution, recent efforts, including BRAIN Initiative funded projects, have collected increasingly larger datasets using Electron Microscopy (EM) and X-Ray Microtomography (XRM). We can now image neural tissue across a range of different scales, potentially forming the basis for the next generation of brain atlases at submicron and nanometer resolution. However, there is huge variability in data collection approaches, as well as ongoing research into evolving imaging technology, experimental protocols, data storage, and post- processing methods. Different resolutions, contrasts, staining, image corrections, data compression, machine learning algorithms, and metadata are all being developed. To enable comparison, meta-analysis, and registration with other datasets and imaging modalities, new standards for EM and XRM data are required, similar to those pursued in light microscopy, magnetic resonance imaging, and other domains. In this time period of growth in EM and XRM imaging, and its increased adoption and utilization for neuroscientific investigations, it is a critical time to implement standards that ensure interoperability, sustainability, and availability of these expensive datasets. This will be critical to enable openness, sharing between laboratories, and reproducible results on these large and expensive datasets. This proposal aims to develop standards for large scale EM and XRM structural data, as well as standards for annotations and links to complementary data sources. This will enable validation, sharing, and replication, greatly amplifying investment in other BRAIN initiative projects in this community. Our team will bring together a community of researchers into two complementary Working Groups (WGs) for Image and Experimental Metadata Standards and Annotation Standards. This community of interest will collaboratively develop standards and disseminate results in conjunction with BRAIN initiative projects and archives. Finally, this project will build tools to query and retrieve image and annotation data, including motif discovery, through a community portal and open source tools. This will allow scientists to reproducibly analyze data, test hypotheses, and share data products and results with the community. We will emphasize collaboration with existing standards across communities and the development and integration of software tools supporting the standards to ensure adoption.