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The challenge of defining and cataloging the building blocks of the brain requires a standardized approach to naming neurons and organizing knowledge about their properties. The US Brain Initiative Cell Census Network, Human Cell Atlas, Blue Brain Project, and others are generating vast amounts of data and characterizing large numbers of neurons throughout the nervous system. The neuroscientific literature contains many neuron names (e.g. parvalbumin-positive interneuron or layer 5 pyramidal cell) that are commonly used and generally accepted. However, it is often unclear how such common usage types relate to many evidence-based types that are proposed based on the results of new techniques. Further, comparing different types across labs remains a significant challenge. Here, we propose an interoperable knowledge representation, the Neuron Phenotype Ontology (NPO), that provides a standardized and automatable approach for naming cell types and normalizing their constituent phenotypes using identifiers from community ontologies as a common language. The NPO provides a framework for systematically organizing knowledge about cellular properties and enables interoperability with existing neuron naming schemes. We evaluate the NPO by populating a knowledge base with three independent cortical neuron classifications derived from published data sets that describe neurons according to molecular, morphological, electrophysiological, and synaptic properties. Competency queries to this knowledge base demonstrate that the NPO knowledge model enables interoperability between the three test cases and neuron names commonly used in the literature., (© 2022. The Author(s).)

In this perspective article, we consider the critical issue of data and other research object standardisation and, specifically, how international collaboration, and organizations such as the International Neuroinformatics Coordinating Facility (INCF) can encourage that emerging neuroscience data be Findable, Accessible, Interoperable, and Reusable (FAIR). As neuroscientists engaged in the sharing and integration of multi-modal and multiscale data, we see the current insufficiency of standards as a major impediment in the Interoperability and Reusability of research results. We call for increased international collaborative standardisation of neuroscience data to foster integration and efficient reuse of research objects., (© 2021. The Author(s).)

There is great need for coordination around standards and best practices in neuroscience to support efforts to make neuroscience a data-centric discipline. Major brain initiatives launched around the world are poised to generate huge stores of neuroscience data. At the same time, neuroscience, like many domains in biomedicine, is confronting the issues of transparency, rigor, and reproducibility. Widely used, validated standards and best practices are key to addressing the challenges in both big and small data science, as they are essential for integrating diverse data and for developing a robust, effective, and sustainable infrastructure to support open and reproducible neuroscience. However, developing community standards and gaining their adoption is difficult. The current landscape is characterized both by a lack of robust, validated standards and a plethora of overlapping, underdeveloped, untested and underutilized standards and best practices. The International Neuroinformatics Coordinating Facility (INCF), an independent organization dedicated to promoting data sharing through the coordination of infrastructure and standards, has recently implemented a formal procedure for evaluating and endorsing community standards and best practices in support of the FAIR principles. By formally serving as a standards organization dedicated to open and FAIR neuroscience, INCF helps evaluate, promulgate, and coordinate standards and best practices across neuroscience. Here, we provide an overview of the process and discuss how neuroscience can benefit from having a dedicated standards body., (© 2021. The Author(s).)

The past decade has seen accelerating movement from data protectionism in publishing toward open data sharing to improve reproducibility and translation of biomedical research. Developing data sharing infrastructures to meet these new demands remains a challenge. One model for data sharing involves simply attaching data, irrespective of its type, to publisher websites or general use repositories. However, some argue this creates a 'data dump' that does not promote the goals of making data Findable, Accessible, Interoperable and Reusable (FAIR). Specialized data sharing communities offer an alternative model where data are curated by domain experts to make it both open and FAIR. We report on our experiences developing one such data-sharing ecosystem focusing on 'long-tail' preclinical data, the Open Data Commons for Spinal Cord Injury (odc-sci.org). ODC-SCI was developed with community-based agile design requirements directly pulled from a series of workshops with multiple stakeholders (researchers, consumers, non-profit funders, governmental agencies, journals, and industry members). ODC-SCI focuses on heterogeneous tabular data collected by preclinical researchers including bio-behaviour, histopathology findings and molecular endpoints. This has led to an example of a specialized neurocommons that is well-embraced by the community it aims to serve. In the present paper, we provide a review of the community-based design template and describe the adoption by the community including a high-level review of current data assets, publicly released datasets, and web analytics. Although odc-sci.org is in its late beta stage of development, it represents a successful example of a specialized data commons that may serve as a model for other fields., (© 2021. The Author(s).)

A central tenet in support of research reproducibility is the ability to uniquely identify research resources, i.e., reagents, tools, and materials that are used to perform experiments. However, current reporting practices for research resources are insufficient to identify the exact resources that are reported or to answer basic questions such as "How did other studies use resource X?" To address this issue, the Resource Identification Initiative was launched as a pilot project to improve the reporting standards for research resources in the methods sections of papers and thereby improve identifiability and scientific reproducibility. The pilot engaged over 25 biomedical journal editors from most major publishers, as well as scientists and funding officials. Authors were asked to include Research Resource Identifiers (RRIDs) in their manuscripts prior to publication for three resource types: antibodies, model organisms, and tools (i.e., software and databases). RRIDs are assigned by an authoritative database, for example a model organism database, for each type of resource. To make it easier for authors to obtain RRIDs, resources were aggregated from the appropriate databases and their RRIDs made available in a central web portal ( http://scicrunch.org/resources ). RRIDs meet three key criteria: they are machine readable, free to generate and access, and are consistent across publishers and journals. The pilot was launched in February of 2014 and over 300 papers have appeared that report RRIDs. The number of journals participating has expanded from the original 25 to more than 40 with RRIDs appearing in 62 different journals to date. Here, we present an overview of the pilot project and its outcomes to date. We show that authors are able to identify resources and are supportive of the goals of the project. Identifiability of the resources post-pilot showed a dramatic improvement for all three resource types, suggesting that the project has had a significant impact on identifiability of research resources.

The overarching goal of the NIF (Neuroscience Information Framework) project is to be a one-stop-shop for Neuroscience. This paper provides a technical overview of how the system is designed. The technical goal of the first version of the NIF system was to develop an information system that a neuroscientist can use to locate relevant information from a wide variety of information sources by simple keyword queries. Although the user would provide only keywords to retrieve information, the NIF system is designed to treat them as concepts whose meanings are interpreted by the system. Thus, a search for term should find a record containing synonyms of the term. The system is targeted to find information from web pages, publications, databases, web sites built upon databases, XML documents and any other modality in which such information may be published. We have designed a system to achieve this functionality. A central element in the system is an ontology called NIFSTD (for NIF Standard) constructed by amalgamating a number of known and newly developed ontologies. NIFSTD is used by our ontology management module, called OntoQuest to perform ontology-based search over data sources. The NIF architecture currently provides three different mechanisms for searching heterogeneous data sources including relational databases, web sites, XML documents and full text of publications. Version 1.0 of the NIF system is currently in beta test and may be accessed through http://nif.nih.gov.

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface.

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience's Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov , http://neurogateway.org , and other sites as they come on line.

A critical component of the Neuroscience Information Framework (NIF) project is a consistent, flexible terminology for describing and retrieving neuroscience-relevant resources. Although the original NIF specification called for a loosely structured controlled vocabulary for describing neuroscience resources, as the NIF system evolved, the requirement for a formally structured ontology for neuroscience with sufficient granularity to describe and access a diverse collection of information became obvious. This requirement led to the NIF standardized (NIFSTD) ontology, a comprehensive collection of common neuroscience domain terminologies woven into an ontologically consistent, unified representation of the biomedical domains typically used to describe neuroscience data (e.g., anatomy, cell types, techniques), as well as digital resources (tools, databases) being created throughout the neuroscience community. NIFSTD builds upon a structure established by the BIRNLex, a lexicon of concepts covering clinical neuroimaging research developed by the Biomedical Informatics Research Network (BIRN) project. Each distinct domain module is represented using the Web Ontology Language (OWL). As much as has been practical, NIFSTD reuses existing community ontologies that cover the required biomedical domains, building the more specific concepts required to annotate NIF resources. By following this principle, an extensive vocabulary was assembled in a relatively short period of time for NIF information annotation, organization, and retrieval, in a form that promotes easy extension and modification. We report here on the structure of the NIFSTD, and its predecessor BIRNLex, the principles followed in its construction and provide examples of its use within NIF.

This paper describes the NIF LinkOut Broker (NLB) that has been built as part of the Neuroscience Information Framework (NIF) project. The NLB is designed to coordinate the assembly of links to neuroscience information items (e.g., experimental data, knowledge bases, and software tools) that are (1) accessible via the Web, and (2) related to entries in the National Center for Biotechnology Information's (NCBI's) Entrez system. The NLB collects these links from each resource and passes them to the NCBI which incorporates them into its Entrez LinkOut service. In this way, an Entrez user looking at a specific Entrez entry can LinkOut directly to related neuroscience information. The information stored in the NLB can also be utilized in other ways. A second approach, which is operational on a pilot basis, is for the NLB Web server to create dynamically its own Web page of LinkOut links for each NCBI identifier in the NLB database. This approach can allow other resources (in addition to the NCBI Entrez) to LinkOut to related neuroscience information. The paper describes the current NLB system and discusses certain design issues that arose during its implementation.

The thorough characterization of transgenic mouse models of human central nervous system diseases is a necessary step in realizing the full benefit of using animal models to investigate disease processes and potential therapeutics. Because of the labor- and resource-intensive nature of high-resolution imaging, detailed investigation of possible structural or biochemical alterations in brain sections has typically focused on specific regions of interest as determined by the researcher a priori. For example, Parkinson's disease researchers often focus imaging on regions of the brain expected to exhibit pathology such as the substantia nigra and striatum. Because of limitations in acquiring and storing high-resolution imaging data, additional data contained in the specimen is not usually acquired or disseminated/reported to the research community. Here we present a method of imaging large regions of brain at close to the resolution limit of light microscopy using a mosaic imaging technique in conjunction with multiphoton microscopy. These maps are being used to characterize several genetically modified animal models of neurological disease by filling the information "gap" among techniques such as magnetic resonance imaging and electron microscopic analysis.

The creation of structured shared data repositories for molecular data in the form of web-accessible databases like GenBank has been a driving force behind the genomic revolution. These resources serve not only to organize and manage molecular data being created by researchers around the globe, but also provide the starting point for data mining operations to uncover interesting information present in the large amount of sequence and structural data. To realize the full impact of the genomic and proteomic efforts of the last decade, similar resources are needed for structural and biochemical complexity in biological systems beyond the molecular level, where proteins and macromolecular complexes are situated within their cellular and tissue environments. In this review, we discuss our efforts in the development of neuroinformatics resources for managing and mining cell level imaging data derived from light and electron microscopy. We describe the main features of our web-accessible database, the Cell Centered Database (CCDB; http://ncmir.ucsd.edu/CCDB/), designed for structural and protein localization information at scales ranging from large expanses of tissue to cellular microdomains with their associated macromolecular constituents. The CCDB was created to make 3D microscopic imaging data available to the scientific community and to serve as a resource for investigating structural and macromolecular complexity of cells and tissues, particularly in the rodent nervous system.