Maryann E. Martone - US grants

[unreadable] DESCRIPTION (provided by applicant): This application outlines plans for continued development of informatics tools for 3D structural and protein localization data from the cellular and subcellular domains, in the structural range of 1 nm3 to 10 pm3. This scale encompasses macromolecular complexes, organelles, multi-component structures like synapses and cellular structures such as spiny dendrites. These structures represent the heart of information processing in the nervous system and provide a bridge between the molecular information being assembled at one end of the biological continuum and the large-scale brain mapping being performed at the other. During the previous funding period, a web-accessible database, the Cell Centered Database or CCDB, was developed specifically for housing this type of high-resolution data. During this same period, we developed a novel approach for linking resources like the CCDB to other independent databases containing data taken at different scales or in different disciplines. The current application outlines plans for further expanding CCDB functionality, developing tools for situating its data into a larger spatial context using specially-developed brain atlas tools and promoting interoperability with other cell level databases. We will accomplish the following specific aims: (1) Extend CCDB by adding a wider variety of data, (2) Provide mechanisms to represent, explore and query cellular data at multiple spatial resolutions, (3) Develop techniques and tools to represent, query, compare and analyze expression patterns of proteins at different spatial scales and (4) Extend the knowledge-based mediation technique to scale up to a larger variety of interoperating sources. Development will take place in the context of core research on the structure of cells in the cerebella cortex, a highly ordered cytoarchitectural region, and the neostriatum, an area of exceeding neurochemical complexity with little cytoarchitectural differentiation. Detailed structural models will be built of the principle cells in these areas, detailing the distributions of key macromolecules like glutamate and dopamine receptors, which will serve as the basis for developing cell-level atlases for integrating properties at the level of the single cell. Although we focus on these two key brain areas, the resulting informatics tools wit1 be extensible to other challenging and clinically important areas of the nervous system, such as the hypothalamus and chemically coded nuclei. [unreadable] [unreadable]

The project's research involves development and deployment of an integrated and practical grid
architecture for data driven intra-operative volumetric simulation of brain deformation during
image guided therapy (IGT) and specifically for image guided neurosurgery (IGNS) to be
employed in the operating room of the future. Preliminary work has resulted in a prototype
dynamic data driven system, which resides locally in the operating room for IGNS that integrates
data acquisition, surgical navigation, quantitative monitoring and enhanced real-time control. The
proposed project will enable improvement in the safety and efficacy of brain tumor resection by
extending the current system with a tightly coupled capability for interactively steered and
adaptive data driven simulation of the dynamically shifting shape of the brain. Such simulation
capability, integrated with real-time intra-operative data acquisition capability and real-time
surgical resection, requires on-demand low latency fault tolerant robust and reliable access to
remotely located significant compute capacity and data management resources from the operating
room. Recent advances in grid infrastructure hold out the promise to pioneer such capability. The
overall objective of this projectl is the development of a novel grid architecture integrating real-
time data to allow intra-operative surgical decision-making and navigation during IGT. In
addition this will provide database infrastructure for post-surgical analysis and modeling of data
and image repositories. The project's information technology (IT) research is expected to have
broad impact on clinical practice within a period of a few years.

Area; Brain; CRISP; Cells; Communities; Comprehension; Computer Retrieval of Information on Scientific Projects Database; Data; Data Banks; Data Bases; Databank, Electronic; Databanks; Database Management Systems; Database, Electronic; Databases; Encephalon; Encephalons; Environment; Feedback; Funding; Goals; Grant; Image; Information Technology; Infrastructure; Institution; Investigators; Light; Link; Management Information Systems; Metadata; Microscopy; NIH; National Institutes of Health; National Institutes of Health (U.S.); Nervous System, Brain; Ontology; Photoradiation; Process; Purpose; Research; Research Infrastructure; Research Personnel; Research Resources; Researchers; Resolution; Resources; Retrieval; Scientist; Site; Source; System; System, LOINC Axis 4; Systems, Data Base Management; Transmission; United States National Institutes of Health; Work; clinical data repository; clinical data warehouse; computational tools; computerized tools; data modeling; data repository; electron tomography; imaging; instrument; molecular imaging; reconstruction; relational database; relational database management systems; tool; transmission process; web-accessible

This award will provide support for qualified undergraduates, graduate students, and postdoctoral researchers to attend the International Congress on Electron Tomography (ICET) in San Diego, CA, from November 5-8, 2006. This congress is the fourth in a series of workshops that was initiated in 1997 to begin to bring together practitioners developing and applying electron microscopic tomography. The meetings have provided a forum for intensive interactions and have catalyzed technical developments and an increased number of users. At this juncture, electron tomography is one of the most rapidly evolving approaches in multiscale 3D microscopy and is being employed for an increasingly broad range of applications, well beyond those envisioned at the time of the first conference.

Electron Tomography is a new research area that produces 3-dimensional quantitative images from electron microscopy. Electron tomography is moving from a specialized experimental technique practiced by a few laboratories to one that is delivering critical new information to cell biologists, structural biologists and neuroscientists. Because electron tomography has been identified as a growth field, it is anticipated that the graduate students and postdoctoral researchers being trained today will go on to become the next generation lead investigators.

DESCRIPTION (provided by applicant): The Cell Centered Database (CCDB) was released in 2002 as a web-accessible database for high resolution 2D, 3D and 4D data derived from light and electron microscopic imaging, including correlated light and electron microscopy. The CCDB encompasses and hosts many types of data in the dimensional range lying between gross morphology and macromolecular structure - the so-called "mesoscale". These data and the software infrastructure that hosts and serves them are unique in scope, presenting to the community data sets laden with information which is technically difficult to obtain but very rich in content, making them particularly valuable for developing computational models of structures and physiological processes that occur in cells and tissues. Envisioned as a grid and/or web-based federated database system from the beginning of the project in 1998, the CCDB pioneered the production of a distributed, connectable repository system for managing and sharing data for a growing research community of microscopists. This proposal aims to provide a stable base of support for a growing community of users of the Cell Centered Database, by refining core services and establishing the facilities needed for CCDB data and software to be openly shared, modified, extended, edited and documented by the expanding participant group. The proposed work is organized into three specific aims: Aim 1. Open Source CCDB: The current system infrastructure will be translated from a centrally supported research product with defined community requirements to an industrymodeled open source project tailored for community wide contribution, customization and refinement. Aim 2: CDB Core Services: The principal components (schema, core services) of the CCDB will be packaged for community distribution and refinement via open source distribution model. Documentaiton will be provided along with specifications for expanded "community contribution" to a repository/collection of core CCDB services. Aim 3: CCDB Client Tools: Existing CCDB client tools will be packaged and distributed for use by the end-user community.

DESCRIPTION (provided by applicant): Reading the literature on any neurodegenerative condition quickly leads to a bewildering array of facts, some contradictory, regarding the correlations between known pathological and clinical features of the disease. To help unravel the multifaceted dimensions of diseases such as Parkinsonian disorders and cerebral ischemia, we describe a formal system for representing the characteristics of human neurological disease and animal models to promote understanding of disease mechanisms and to provide a more rigorous method to compare model systems and human disease. This collaboration between the National Center for Biomedical Ontologies (NCBO), the National Center for Microscopy and Imaging Research and the Biomedical Informatics Research Network project will develop tools that bridge between information contained in ontologies, distributed databases and images. This work builds upon efforts at NCBO to develop a Phenotype and Trait Ontology for expression of complex phenotypes in a machine-processable form, so that powerful algorithms can be employed to look for correlations and interesting patterns in the data. Ontologies for annotation of complex traits from animal models of Parkinsonian disorders and transient cerebral ischemia will be developed and applied to an analysis of multiscale imaging data. This ontology will provide the means to perform ontology-based image analysis and correlation of phenotypes across scales. This project forms a driving biological project for NCBO, bringing the need for ontology-based analysis of multiple spatial and temporal scales into the frameworks under development at NCBO, while at the same time contributing ontologies and data into the Open Biological Ontologies and Data repositories curated by NCBO. A core component of the proposal is the development of an integrated ontology-based environment for the that will help the scientist to understand the deep interrelationships among observed data and collected knowledge about neurological diseases such as Parkinson's. The proposed work thus aligns directly with the stated objectives of NCBO: create, access and browse relevant ontologies;use ontologies to annotate experimental datasets;access and draw inferences from the annotated data;and bridge between model systems and human disease. Eventhough tools developed as part of this proposal will be developed in the context of a specific driving biological project they will be broadly applicable to other imaging

This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. Primary support for the subproject and the subproject's principal investigator may have been provided by other sources, including other NIH sources. The Total Cost listed for the subproject likely represents the estimated amount of Center infrastructure utilized by the subproject, not direct funding provided by the NCRR grant to the subproject or subproject staff. DATABASE AND KNOWLEDGE INFRASTRUCTURE FOR NCMIR The objective of this project is to develop our current information technology infrastructure for managing distributed microscopy imaging data at NCMIR to serve the requirements of the mesoscale technological research and development and associated collaborative activities described under the previous aims. Through the Cell Centered Database (CCDB), Telescience, and the Neuroscience Information Framework (NIF), NCMIR has been developing a robust infrastructure for storing, sharing, searching, and disseminating microscopy image information via the CCDB and a secure web portal. NCMIR also utilizes the CCDB, and its sister project, the Whole Brain Catalog as a means of disseminating NCMIR data.

The University of California-San Diego and Yale University are awarded collaborative grants to develop a Neuroscience Gateway (NSG) that will facilitate access and use of high performance computing (HPC) resources by neuroscientists. Computational modeling of cells and networks has become an essential part of neuroscience research, and investigators are using models to address problems of ever increasing complexity, e.g. large scale network models and optimization or exploration of high dimensional parameter spaces. The NSG will catalyze such research by lowering or eliminating the administrative and technical barriers that currently make it difficult for investigators to use HPC resources. It will offer computer time to neuroscience users through an administratively and technologically streamlined process with a simple web portal-based environment for uploading models, specifying HPC job parameters, querying running job status and receiving job completion notices, and retrieving and storing output data. The URL of the NSG portal is http://www.nsgportal.org. It will also provide a community forum for neuroscientists to collaborate and share data. The NSG architecture will transparently distribute user jobs to appropriate HPC resources provided by NSF supercomputer centers. The NSG team will collaborate with developers of neural simulation software to optimally install, test, and benchmark these applications on HPC machines, and allow developers to test new versions before release.

This project will have a transformative impact by enabling access by members of the computational neuroscience community to HPC resources for research and instruction involving compute-intensive simulations (typically large neural networks). Many of these investigators and students would otherwise find it very difficult, if not impossible, to implement and study models that press or exceed the storage and speed capabilities that are under their direct control. Computational modeling offers opportunities for students and researchers at institutions with limited resources for wet lab or experimental infrastructure to participate in leading edge science. Projects such as this, which facilitate access to HPC resources, democratize participation in science by mitigating the financial barriers to research infrastructure. The NSG will enable students and researchers, who lack access to HPC resources and are thus at a significant disadvantage compared to very few who have it, by removing the barriers for progress for many including historically underrepresented groups. The PIs of this project will target the promotion of the NSG to underrepresented minority scientists and minority serving institutions through active participation in summer training academies and a network of previously mentored female and minority students, some now employed at minority serving institutions.

DESCRIPTION (provided by applicant): This proposal outlines plans for continued development and maintenance of the OpenCCDB software suite, a set of tools for web-based management, visualization and annotation of large multidimensional light and electron microscopic imaging data sets. Modern analytical and molecularly-based biomedical investigations increasingly draw upon multiscale and multimodal cell-centered imaging to gain insight into spatially constrained interactions of molecular machinery of cells and tissues. The OpenCCDB was developed under the auspices of the Cell Centered Database (CCDB0 project, an on-line repository and platform for sharing microscopic imaging data of cells and tissues. The CCDB project was predicated on developing solutions for federating access to very large 3D imaging data sets, produced by diverse instrumentations and maintained across diverse technological platforms. The CCDB model is built on flexible and expandable data stores that can be fielded globally but accessed through CCDB's central metadata system and linked through data federations like the Neuroscience Information Framework (http://neuinfo.org). During the last funding period, the American Society for Cell Biology launched its own data repository, with support from ARRA funding. The Cell: An Image Library currently contains > 37,000 exemplary imaging data sets from light and electron microscopy, all available through an elegant cell-centered user interface. To provide a more unified and sustainable resource for research and education, the ASCB Council and Executive Committee voted in 2011 to partner their effort with the group at UCSD, combining their curatorial and interface design expertise and accomplishments with UCSD's database and software development expertise and accomplishments. In so doing, ASCB transitioned control of the development and future sustainability of their user portal environment, The Cell:An Image Library to the CCDB team at UCSD. Immediately, this new alliance applied CCDB's open source software framework, OpenCCDB, to the task of extending the functionality of The Cell: An Image Library system, adding new capability to upload and explore massive datasets, facilitate data sharing, and completely federate its underlying database with the CCDB. The resultant integrated environment offers the scientific community a more comprehensive solution to propel cell-centric biomedical research and education than that offered previously. In this proposal, we outline aims to develop and deploy additional personalized data management and annotation tools within the context of The Cell: An Image Library, by combining the power and tools of a social networking site with specialized tools for web-based management, sharing, annotation and federation of very large microscopic imaging data sets. PUBLIC HEALTH RELEVANCE: This renewal application outlines plans to harden and further develop the code base of the Open CCDB software platform in support of the new joint cell-centered microscopy resource established through the merging of the Cell Image Library and the Cell Centered Database.

DESCRIPTION (provided by applicant): This proposal will expand and leverage NIA's investment, as part of NIH's Blueprint for Neuroscience Research, in the Neuroscience Information Framework (NIF; http://neuinfo.org) to create an Aging Integrative Platform Pilot. NIF is a unique resource where diverse biomedical data (i.e. behavioral, clinical, genetic, imaging, etc.), and resources are concurrently available through a semantic search and knowledge environment. The current NIF data federation searches over 350 million data records, in over 110 databases, with new databases being aligned and added weekly through the use of curation assistance tools. NIF has gained extensive experience in understanding the requirements to maximize the value of diverse data through the application of uniform curation standards and links to our ontologies. Through its investment in NIF, NIA has a cost-effective platform available with an experienced curation team to provide a data extraction and data archiving platform for its assets and aging related assets in general. This directly addresses one of the purposes of the RFA, namely the archiving and dissemination of data sets to enable secondary analyses in order to further advance research. The current NIF provides a discovery portal geared towards neuroscience; however, its underlying system is generic and can be easily adapted to provide specialized views for different communities. Under this proposal, we will accelerate the integration of NIA-relevant data resources and use these as the driver to provide a search portal and knowledge environment that is specifically geared towards aging. The data sets assimilated into this pilot will be usable as part of the overall NIF system, available through web services, linked open data and services to link data to the literature, without additional work as NIF already adds these services to each federated data set. Once integrated within the NIF, these resources become known and usable to a much wider array of researchers. PUBLIC HEALTH RELEVANCE: This application directly addresses the urgent need for the development of efficient capabilities for integration of and computation on biomedical, sociobehavioral and clinical data by providing a knowledge environment which provides the necessary infrastructure to take full advantage of existing datasets. This proposal focuses on the archiving and dissemination of data sets to enable secondary analyses thereby further advancing research and meeting NIH's goal of seamless exchange of resources to promote knowledge discovery.

DESCRIPTION (provided by applicant): This application outlines plans for the establishment of the NIDDK Interconnectivity Network Coordinating Center (INCC) to expand and enhance the current NIDDK Consortium Interconnectivity Network (dkCOIN) community and infrastructure. The current dkCOIN was established in recognition of the need to interconnect research communities, both basic and clinical, by providing seamless access to large pools of data relevant to the mission of NIDDK. The aims of the dkCOIN project are similar in scope to those of the existing Neuroscience Information Framework (NIF), a project established in 2008 by the NIH Blueprint for Neuroscience Research Institutes to provide a landscape analysis of the myriad of tools and data available via the web, and to create a portal where these resources could be collectively accessed. The NIF was designed to break down silos of information through its novel data federation technology and its concept based search. NIF takes a global view of resources such that there are no neuroscience resources or metabolic resources, only biomedical resources that contain information of more or less relevance to different communities. Thus, while the NIF user interface is domain-specific; the NIF information system and the resources it federates are broadly applicable to biomedical science. In this proposal, we outline plans to extend and enhance dkCOIN through the use of the NIF infrastructure, data federation and expertise. Through this merger, we can immediately add considerable value to the current dkCOIN by bringing in NIF's expansive data federation, resource catalog and semantic search services. The portal will continue to present the data and tools according to the needs and customs of the NIDDK community, but the backend will tap into an expanded resource pool that cuts across all domains of biomedical science, rather than a restricted set. New development will focus on the creation of workflows using tools in use by the NIDDK community with this vast array of integrated data. These tools and workflows will link to cloud computational and storage resources, in order to ensure that the network is sustainable. New development will also focus on more effective means to connect NIDDK researchers with resources that are available to them to support their research projects. Development will be driven by use cases supplied by NIDDK-supported researchers to ensure that it meets NI DDK's objectives. We believe that the strategy outlined here provides a cost effective and innovative means to ensure that researchers have access to the data and tools they require regardless of where they reside, and provides a sustainable model for future similar efforts.

The Future of Research Communication and e-Scholarship conference (FORCES2015) will be held in Oxford, UK in January 2015. This is the third in a series organized by FORCE11, a group from the research community and publishing communities dedicated to transforming scholarly communicaton through technology. The group involves researchers, librarians, publishers, technologists, funders and administrators for a trans-disciplinary summit and discussion. The funds will provide an opportunity for students and postdoctoral fellows to participate. Meeting materials are posted on the web, including videos of speaks, tweat and references to blogs and other articles about the conference.

? DESCRIPTION: The Neuroscience Information Framework (NIF; http://neuinfo.org) is currently managed, maintained, and hosted by researchers in the Center for Research in Biological Systems (CRBS) at the University of California, San Diego. Our group is the principal developer of the NIF system and has overseen its growth since 2008 from a modest catalog of 300 resources developed during the first phase of NIF, to the largest source of neuroscience resources on the web. As defined here, resources include data, databases, software/web-based tools, materials, literature, networks, terminologies, or information that would accelerate the pace of neuroscience research and discovery. NIF was instantiated because many of these valuable tools and services were largely unknown to the scientific community they were meant to serve. With the launch of major brain initiatives in the US and Europe, the amount of neuroscience data and tools will continue to increase. NIF can be viewed as a cost effective PubMed and PubMed Central for digital assets, e.g., databases, software tools, alternative media, to make them collectively searchable and present a unified discovery environment for biomedical researchers. The NIF is heavily used, as measured by the number of visitors per month (more than 40,000) to the NIF web resources and the large number of repeat users (~35%) that visit the NIF discovery portal on a regular basis. NIF's services, standards and products are also heavily used as our web services are now regularly receiving more than 15 million hits per month. NIF has developed a novel sustainability plan that provides for continued enhancement and population of the resource. NIF has developed a reputation as a trusted community partner, allowing us to gain cooperation with large segments of the neuroscience community, as well as publishers, non-profit and government organizations. Through these networks, we've been able to launch major initiatives, help launch new collaborative efforts in basic and clinical neuroscience, and implement standards to help transform the way that we cite and track research resources. The work to be performed during the award period will be directed towards operating and maintaining the current NIF system while providing necessary strategic enhancements and providing broad outreach and dissemination efforts to encourage utilization of the NIF. A core new area of development will be centered around providing analytic tools to explore and identify lacunas of knowledge in Neuroscience. The initial work will focus on developing analytic heatmaps for activation foci from the neuroimaging literature.

Project Summary A detailed census of the structure and role of cell type specific data in the brain is recognized as one of the most promising avenues for advancing our understanding of the human brain in health and disease. The primary goal of this project is to build a foundational community resource for housing single-cell centered data content in the brain. By adopting a three-tiered data, tools, and knowledge paradigm we will lead, together with data generation partners, in the development and deployment of fundamental data models, common community standards, and scientific results to improve our understanding of the diverse cell types in the mammalian brain and its three dimensional organizational logic. This project proposes a unified solution to the development of its resource beginning with a data collection, quantification, and mapping framework for managing data and information across diverse repositories. The first step is to support the acquisition of fundamental data types from data partners by developing the data models and framework for importing structured data into the BRAIN Cell Data Center (BCDC) in consistent data description standards that describe and facilitate best practices for community use of multi-modal single cell data and its content. Toward achieving this end, we will partner with the Neuroscience Information Framework (NIF), International Neuroinformatics Coordinating Facility (INCF), Neuroscience without Borders (NWB), and other standards promoting organizations to leverage and extend existing standards to allow external data contributors to utilize the BCDC. For these data to be coherent, spatial common coordinate frameworks for mapping are essential, and the project develops and extends Allen Institute Mouse Common Coordinate Framework to enable external mapping of key single cell modalities in a 2D and 3D anatomic context for access and visualization at cell, nuclei, and/or population level. The entire resulting data, tools, and knowledge will be made publicly available in a unified and integrated web-accessible Cell Registry and Portal supporting data retrieval, search, visualization, and analysis of cell specific data and knowledge synthesis. The BCDC front end will provide data feature summaries, tools, and resulting knowledge, and links to raw data and high-resolution images, together with a programmable API/SDK that allows the community to build their own analysis applications and contribute tools. Finally, we will amplify public impact through strong BICCN partnership management, communication, standards committees, and analysis working groups, and develop infrastructure for maximizing community usage, publishing standards and tutorials, and coordinating education and outreach.

Project Summary As the NIDDK Information Network (dkNET) Coordinating Unit we must develop strategies to ensure that dkNET continuously evolves to meet community needs. As the use of a wide-array of methods that generate large amounts of complex data become more common in biomedical research, there is a growing need to develop an interdisciplinary workforce that can use and further refine computational and statistical approaches to interrogate this high-content data. The New Investigator Bioinformatics Pilot program will: (i) facilitate the ability of Early Stage and New Principal Investigators with computational and bioinformatics expertise to pursue research questions in Diabetes, Endocrinology or Metabolic Diseases, or (ii) allow Early Stage and New PIs currently pursuing research in these NIDDK topic areas to explore incorporating computational, statistical, and/or bioinformatics approaches into their research projects. The overall goal of this dkNET Pilot Award program is to provide support for these early career investigators to obtain feasibility data in support of future grant applications.

This proposal outlines plans for the creation of K-CORE, a new core for SPARC that will provide curation of SPARC datasets, knowledge management and services for the SPARC Knowledge Graph. The latter is a semantic store that combines information on SPARC datasets annotated according to the SPARC Minimal Information Standard (MIS) with functional and anatomical topologically based connectivity knowledge for the autonomic nervous system (ANS). Included in the ANS are the sympathetic, parasympathetic and enteric nervous systems, with reference to sensory and motor pathways as necessary. Technology development in SPARC is provided by the SPARC Data and Resource Center (DRC), currently comprising DAT-CORE, which provisions the data platform, MAP-CORE, which develops 2 and 3D mapping tools and a map-based GUI, and SIM-CORE which fields the simulation platform. To date, the curation and knowledge management functions have been conducted under the purview of MAP-CORE, led by Dr. Peter Hunter at the Auckland Bioinformatics Institute with a subcontract to UCSD. However, these services are sufficiently broad and cross cutting that they should be managed through a separate core. The K-CORE team has been providing curation and knowledge management services for SPARC since 2018 and thus are deeply familiar with the project.

Project Summary: A Data-driven Framework for Brain Transcriptomic Cell Type Definition, Ontology, and Nomenclature Defining the complete census of neuronal and non-neuronal cell types in the brain is a major priority for the NIH BRAIN Initiative, since cellular complexity is a major barrier to understanding brain function and the mechanistic underpinnings of disease. Single cell transcriptomics has revolutionized this field with the scale and information content to characterize complex tissues, and is leading quickly to a brain-wide classification of cell types in mouse, monkey and human. Transcriptomics is also uniquely suited to allow quantitative comparisons across species, across developmental time, and between brain and other organs, and is the common denominator with other large-scale efforts to characterize the entire human body in the Human Cell Atlas and HuBMAP consortia. The opportunity is now here to create a new quantitative framework for defining cell types in the brain, generating a new data-driven cell type ontology and a nomenclature convention similar in concept to the reference genomes that unify genomics. Importantly, the design principles should be extensible beyond brain to other organs so that the schema can be adopted across the other major consortium projects, but also to incorporate other important cellular phenotypes important for neurobiological function. The proposed project aims to bring together a team of experts in single-cell transcriptomics, informatics, ontology development and computational biology who are also leaders and members of the major cell type consortia to develop a data-driven framework of brain cell types. First, the project aims to develop standards for quantitative definitions of transcriptomic-based cell types from the BRAIN Initiative Cell Census Network (BICCN) datasets, and tools for mapping other datasets (other data types or data from other researchers) to this reference. This will create reference data structures for features of transcriptomic cell types and taxonomies that will be deployed through the BICCN portal. Secondly, the project aims to build on prior work on developing cell and phenotype ontologies to develop a new, data-driven formal cell ontology for the whole brain reference. Part of this ontology is a nomenclature convention for systematic naming of cell types that allows similar naming of homologous cell types across species. Finally, a major goal is to engage the international cell type community in developing and refining these standards and reference classification to ensure their usefulness and widespread adoption. This will involve active engagement of the community through a working group structure, and periodic domain expert workshops with the BICCN, HCA, HuBMAP and INCF consortia. All standards, ontologies and tools will be deployed on the BICCN portal with mechanisms for community feedback and vetting.