Characterizing cellular diversity at different levels of biological organization and across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also essential to manipulate cell types in controlled ways and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data-generating centers, data archives, and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain with demonstration of prototype feasibility for human and nonhuman primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed by the BICCN, and to accessing and using these data and extensive resources, including the BRAIN Cell Data Center (BCDC), which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted toward Findable, Accessible, Interoperable, and Reusable (FAIR) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

Materials science research deals primarily with understanding the relationship between the structure and properties of materials. With recent advances in computational power and automation of simulation techniques, material structure and property databases have emerged (Curtarolo et al., 2012;Jain et al., 2013;Kirklin et al., 2015), allowing a more data-driven approach to carrying out materials research. Recent studies have demonstrated that representing these databases as material networks can enable extraction of new materials knowledge (Hegde, Aykol, Kirklin, & Wolverton, 2018;Isayev et al., 2015) or help tackle challenges like predictive synthesis (Aykol, Hegde, et al., 2019) that require relational information between materials. Materials databases have become very popular because they enable their users to do rapid prototyping by searching near globally for figures of merit for their target application. However, both scientists and engineers have little in the way of visualization of aggregates from these databases, that is, intuitive layouts that help understand which materials are related and how they are related. The need for a tool that does this is particularly crucial in materials science because properties like phase stability and crystal structure similarity are themselves functions of a material dataset, rather than of individual materials.In these new approaches, materials can be represented with a graph structure that has nodes standing in for materials, and links between them encoding the appropriate relationships of interest, such as thermodynamic co-existence, chemical similarity or co-occurrence in text, to name a few. MaterialNet is an open-source web application designed to explore the topology of such material networks, while also displaying information about each material, highlighting its immediate neighborhood within the graph, and offering several auxiliary tools to help drill down into the details contained within the dataset. Such graph-encoded datasets for a collection of materials can become large and complex: for example, the materials stability network exemplified in this paper contains on the order of 20,000 materials, with on the order of 200,000 links between them (Aykol, Hegde, et al., 2019). Large graphs of this sort demand interactive visualization, empowering materials researchers to explore the data, a key user requirement highlighted by field experts . To the best of our knowledge, there exists no other interactive visualization tool for materials networks. MaterialNet provides interactive "maps" of the materials space exposed in large material databases, helping researchers navigate this space with a particular research task in mind, as showcased in Figure 1. A live deployment of MaterialNet can be accessed at

Metabolomics involves the comprehensive measurement of metabolites from a biological system. The resulting metabolite profiles are influenced by genetics, lifestyle, biological stresses, disease, diet and the environment and therefore provides a more holistic biological readout of the pathological condition of the organism (Beger et al., 2016; Wishart, 2016). The challenge for metabolomics is that no single analytical platform can provide a truly comprehensive coverage of the metabolome. The most commonly used platforms are based on mass-spectrometry (MS) and nuclear magnetic resonance (NMR). Investigators are increasingly using both methods to increase the metabolite coverage. The challenge for this type of multi-platform approach is that the data structure may be very different in these two platforms. For example, NMR data may be reported as a list of spectral features, e.g., bins or peaks with arbitrary intensity units or more directly with named metabolites reported in concentration units ranging from micromolar to millimolar. Some MS approaches can also provide data in the form of identified metabolite concentrations, but given the superior sensitivity of MS, the concentrations can be several orders of magnitude lower than for NMR. Other MS approaches yield data in the form of arbitrary response units where the dynamic range can be more than 6 orders of magnitude. Importantly, the variability and reproducibility of the data may differ across platforms. Given the diversity of data structures (i.e., magnitude and dynamic range) integrating the data from multiple platforms can be challenging. This often leads investigators to analyze the datasets separately, which prevents the observation of potentially interesting relationships and correlations between metabolites detected on different platforms. Viime (VIsualization and Integration of Metabolomics Experiments) is an open-source, web-based application designed to integrate metabolomics data from multiple platforms. The workflow of Viime for data integration and visualization is shown in Figure 1. User Interface Features and Architecture Data Upload Data upload can be a cumbersome step in many data analysis packages. Often the data must be provided in a specified format in order to be properly read and the details of the

Characterizing cellular diversity at different levels of biological organization across data modalities is a prerequisite to understanding the function of cell types in the brain. Classification of neurons is also required to manipulate cell types in controlled ways, and to understand their variation and vulnerability in brain disorders. The BRAIN Initiative Cell Census Network (BICCN) is an integrated network of data generating centers, data archives and data standards developers, with the goal of systematic multimodal brain cell type profiling and characterization. Emphasis of the BICCN is on the whole mouse brain and demonstration of prototypes for human and non-human primate (NHP) brains. Here, we provide a guide to the cellular and spatial approaches employed, and to accessing and using the BICCN data and its extensive resources, including the BRAIN Cell Data Center (BCDC) which serves to manage and integrate data across the ecosystem. We illustrate the power of the BICCN data ecosystem through vignettes highlighting several BICCN analysis and visualization tools. Finally, we present emerging standards that have been developed or adopted by the BICCN toward FAIR (Wilkinson et al. 2016a) neuroscience. The combined BICCN ecosystem provides a comprehensive resource for the exploration and analysis of cell types in the brain.

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