Neuroscience Information Framework Research Articles

NeuroML-DB: Sharing and characterizing data-driven neuroscience models described in NeuroML.

As researchers develop computational models of neural systems with increasing sophistication and scale, it is often the case that fully de novo model development is impractical and inefficient. Thus arises a critical need to quickly find, evaluate, re-use, and build upon models and model components developed by other researchers. We introduce the NeuroML Database (NeuroML-DB.org), which has been developed to address this need and to complement other model sharing resources. NeuroML-DB stores over 1,500 previously published models of ion channels, cells, and networks that have been translated to the modular NeuroML model description language. The database also provides reciprocal links to other neuroscience model databases (ModelDB, Open Source Brain) as well as access to the original model publications (PubMed). These links along with Neuroscience Information Framework (NIF) search functionality provide deep integration with other neuroscience community modeling resources and greatly facilitate the task of finding suitable models for reuse. Serving as an intermediate language, NeuroML and its tooling ecosystem enable efficient translation of models to other popular simulator formats. The modular nature also enables efficient analysis of a large number of models and inspection of their properties. Search capabilities of the database, together with web-based, programmable online interfaces, allow the community of researchers to rapidly assess stored model electrophysiology, morphology, and computational complexity properties. We use these capabilities to perform a database-scale analysis of neuron and ion channel models and describe a novel tetrahedral structure formed by cell model clusters in the space of model properties and features. This analysis provides further information about model similarity to enrich database search.

28. The Neuroscience Big Data Landscape and FAIR

The Neuroscience Information Framework (NIF) launched in 2008 to survey resources and databases for neuroscience (NIF Registry) and provide an information framework for promoting discovery and integration across databases (NIF data federation). Here we use NIF data to provide insight into the neuroscience data landscape, and how it measures up to the FAIR Data Principles, gaining broad adoption across biomedicine. FAIR aims to make data: Findable, Accessible, Interoperable and Reusable for humans and computers.

Resource Disambiguator for the Web: Extracting Biomedical Resources and Their Citations from the Scientific Literature.

The NIF Registry developed and maintained by the Neuroscience Information Framework is a cooperative project aimed at cataloging research resources, e.g., software tools, databases and tissue banks, funded largely by governments and available as tools to research scientists. Although originally conceived for neuroscience, the NIF Registry has over the years broadened in the scope to include research resources of general relevance to biomedical research. The current number of research resources listed by the Registry numbers over 13K. The broadening in scope to biomedical science led us to re-christen the NIF Registry platform as SciCrunch. The NIF/SciCrunch Registry has been cataloging the resource landscape since 2006; as such, it serves as a valuable dataset for tracking the breadth, fate and utilization of these resources. Our experience shows research resources like databases are dynamic objects, that can change location and scope over time. Although each record is entered manually and human-curated, the current size of the registry requires tools that can aid in curation efforts to keep content up to date, including when and where such resources are used. To address this challenge, we have developed an open source tool suite, collectively termed RDW: Resource Disambiguator for the (Web). RDW is designed to help in the upkeep and curation of the registry as well as in enhancing the content of the registry by automated extraction of resource candidates from the literature. The RDW toolkit includes a URL extractor from papers, resource candidate screen, resource URL change tracker, resource content change tracker. Curators access these tools via a web based user interface. Several strategies are used to optimize these tools, including supervised and unsupervised learning algorithms as well as statistical text analysis. The complete tool suite is used to enhance and maintain the resource registry as well as track the usage of individual resources through an innovative literature citation index honed for research resources. Here we present an overview of the Registry and show how the RDW tools are used in curation and usage tracking.

SciCrunch: A Cooperative And Collaborative Data, Information, And Resource Discovery Portal For Scientific Communities

SciCrunch: A Cooperative And Collaborative Data, Information, And Resource Discovery Portal For Scientific Communities

Neuroscience Gateway � Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research

Neuroscience Gateway � Cyberinfrastructure Providing Supercomputing Resources for Large Scale Computational Neuroscience Research

Semantic framework for mapping object-oriented model to semantic web languages.

The article deals with and discusses two main approaches in building semantic structures for electrophysiological metadata. It is the use of conventional data structures, repositories, and programming languages on one hand and the use of formal representations of ontologies, known from knowledge representation, such as description logics or semantic web languages on the other hand. Although knowledge engineering offers languages supporting richer semantic means of expression and technological advanced approaches, conventional data structures and repositories are still popular among developers, administrators and users because of their simplicity, overall intelligibility, and lower demands on technical equipment. The choice of conventional data resources and repositories, however, raises the question of how and where to add semantics that cannot be naturally expressed using them. As one of the possible solutions, this semantics can be added into the structures of the programming language that accesses and processes the underlying data. To support this idea we introduced a software prototype that enables its users to add semantically richer expressions into a Java object-oriented code. This approach does not burden users with additional demands on programming environment since reflective Java annotations were used as an entry for these expressions. Moreover, additional semantics need not to be written by the programmer directly to the code, but it can be collected from non-programmers using a graphic user interface. The mapping that allows the transformation of the semantically enriched Java code into the Semantic Web language OWL was proposed and implemented in a library named the Semantic Framework. This approach was validated by the integration of the Semantic Framework in the EEG/ERP Portal and by the subsequent registration of the EEG/ERP Portal in the Neuroscience Information Framework.

51. Initiatives and projects for collaboration in neuroinformatics

Sharing of electrophysiology data, related metadata, processing methods and workflows is one of the crucial tasks in neuroinformatics. International Neuroinformatics Coordinating Facility (INCF) develops and maintains computational infrastructure for neuroscientists and INCF Programs address infrastructural issues of high importance to the neuroscience community. The INCF program “Standard for data sharing – Electrophysiology Task Force” deals with collection of requirements for developing the data format that could be accepted and widely shared within the community. To enable collaboration through the sharing of neuroscience data, INCF also introduced the INCF Dataspace that associates data sources in a distributed system based on iRods solution. The Czech National Node for Neuroinformatics (CNNN) is currently focused on two main topics. The first one deals with higher reliability of interactions of human subjects, artificial systems and their alliances. Theoretical knowledge is applied in transportation area; relations between the components of the EEG alpha rhythm and attention levels of relevant subjects are investigated. The Node also continues in building of complex hardware and software infrastructure for research in electrophysiology. A catalog server connected to INCF Dataspace and a node server for EEG/ERP domain (a subnode of the catalog server) were established. The Node members also work in an international group developing the Ontology for describing Experimental Neurophysiology (OEN). The EEG/ERP Portal (EEGBase) is a web-based system that enables researchers to store, manage, share, and process data and metadata from EEG/ERP experiments ( EEG/ERP Portal (EEGBase), 2014 ). The portal also enables users to work in research groups, define metadata, and run methods for discrete signal processing. Users may have different roles in the system and the related level of authority. The EEG/ERP Portal was registered as a neuroscience resource within the Neuroscience Information Framework (NIF); direct access to the services implemented in the EEG/ERP Portal is ensured through the NIF interface. Off-line and mobile versions of the portal are also available. A programmable hardware stimulator that allows users to create various experiments and use the combined stimulation (acoustic and visual) was designed and developed. The stimulator is portable and can be connected to conventional equipment. Acknowledgments The work was supported by the European Regional Development Fund (ERDF), Project “NTIS - New Technologies for Information Society”, European Centre of Excellence, CZ.1.05/1.1.00/02.0090.

Corrigendum: Software and hardware infrastructure for research in electrophysiology

This article adds Yann Le Franc as a co-author of the technology report article “Software and Hardware Infrastructure for Research in Electrophysiology,” describes his individual contribution to the article, and presents changes in two paragraphs in Section 2.4, where an additional reference is also provided. Moreover, the members from the OEN group who work and cooperate on building OEN are personally acknowledged. Co-author: Yann Le Franc Affiliation: e-Science Data Factory S.A.S.U., Paris, France; Ludwig-Maximilians-Universitat Munchen, Planegg-Martinsried, Germany; University of Antwerp, Antwerpen, Belgium Authors' Contributions Yann Le Franc contributed to the Figures 3,4 and 8 and to the work on the Ontology for describing Experimental Neurophysiology (OEN). Section 2.4 Old version: The group follows the best practices for creating ontologies, for example, it cooperates with community of researchers who design and create ontologies, uses existing data formats and repositories (odML, HDF5), and reuses existing resources (terms, ontologies—NEMO, OBI). For the general description of experimental neurophysiology, the terms from ontologies NEMO and OBI are relevant. However, the set of the domain terms is still not complete in these ontologies (information stored in the EEG/ERP Portal cannot be fully described by these ontologies) and OEN will be finally an extension of OBI (e.g., the granularity of OBI for devices and related information will be extended). New version: The group follows the best practices for creating ontologies, for example, it cooperates with community of researchers who design and create ontologies, uses existing data formats and repositories (odML, HDF5), and reuses existing resources (terms, ontologies—NEMO, OBI). For the general description of experimental neurophysiology, the terms from ontologies NEMO and OBI are relevant. However, the set of the domain terms needed to describe the information stored in the EEG/ERP Portal is not yet complete in these ontologies. OEN aims at defining these missing terms and at term, should be used to propose an extension of OBI's neurophysiology model (e.g., the granularity of OBI for devices and related information will be extended). Old version: Terminologies within OEN have been primarily developed in the odML format. Subsequently, an OWL file has been constructed aided by Ontofox (Xiang et al., 2010). The current developer's version of OEN is available at https://github.com/G-Node/OEN. New version: The OEN device branch development is based on the odML terminology (Grewe et al., 2011), concepts gathered by the Neuroscience Information Framework (NIF) and concepts used in the EEGBase data model to describe setups and setup configurations. The gathered terms are currently mapped with the aforementioned ontologies. Subsequently, an OWL file has been constructed to contain OEN terms and the mapped terms. Existing terms in other ontologies will be imported using the MIREOT approach (Courtot et al., 2011), aided by Ontofox (Xiang et al., 2010). The current developer's version of OEN is available at https://github.com/G-Node/OEN.

Extending the NIF DISCO framework to automate complex workflow: coordinating the harvest and integration of data from diverse neuroscience information resources.

This paper describes how DISCO, the data aggregator that supports the Neuroscience Information Framework (NIF), has been extended to play a central role in automating the complex workflow required to support and coordinate the NIF’s data integration capabilities. The NIF is an NIH Neuroscience Blueprint initiative designed to help researchers access the wealth of data related to the neurosciences available via the Internet. A central component is the NIF Federation, a searchable database that currently contains data from 231 data and information resources regularly harvested, updated, and warehoused in the DISCO system. In the past several years, DISCO has greatly extended its functionality and has evolved to play a central role in automating the complex, ongoing process of harvesting, validating, integrating, and displaying neuroscience data from a growing set of participating resources. This paper provides an overview of DISCO’s current capabilities and discusses a number of the challenges and future directions related to the process of coordinating the integration of neuroscience data within the NIF Federation.

The scourge of neuroanatomical nomenclature: use of neuroanatomical ontologies within the Neuroscience Information Framework (338.2)

The brain is perhaps unique among major organ systems in the multiplicity of naming schemes for its major and minor regions. It has been divided based on topology of major features, cyto‐ and myelo‐architecture, developmental boundaries, supposed evolutionary origins, histochemistry, gene expression and functional criteria. The gross anatomy of the brain reflects the underlying networks only superficially, and thus any parcellation reflects a somewhat arbitrary division based on one or more of these criteria. A core aim of the Neuroscience Information Framework (http://neuinfo.org) is to establish a semantic framework for searching and integrating across data contained in diverse information systems. As anatomy is foundational to a common understanding of the brain, NIF and several other foundational projects, e.g., Neuronames, have worked to establish the means to translate among these different nomenclatures. In this presentation, I will discuss strategies employed by NIF, in collaboration with the International Neuroinformatics Coorindating Facility (INCF) to establish semantic interoperability of brain regions through atlasing and ontologies. I will show how these ontologies are used within the NIF to help with cross‐query of distributed data and literature and discuss the use of our semantic Wiki, Neurolex (http:/neurolex.org) as a platform for communities to contribute their nomenclatures.

Software and hardware infrastructure for research in electrophysiology

As in other areas of experimental science, operation of electrophysiological laboratory, design and performance of electrophysiological experiments, collection, storage and sharing of experimental data and metadata, analysis and interpretation of these data, and publication of results are time consuming activities. If these activities are well organized and supported by a suitable infrastructure, work efficiency of researchers increases significantly. This article deals with the main concepts, design, and development of software and hardware infrastructure for research in electrophysiology. The described infrastructure has been primarily developed for the needs of neuroinformatics laboratory at the University of West Bohemia, the Czech Republic. However, from the beginning it has been also designed and developed to be open and applicable in laboratories that do similar research. After introducing the laboratory and the whole architectural concept the individual parts of the infrastructure are described. The central element of the software infrastructure is a web-based portal that enables community researchers to store, share, download and search data and metadata from electrophysiological experiments. The data model, domain ontology and usage of semantic web languages and technologies are described. Current data publication policy used in the portal is briefly introduced. The registration of the portal within Neuroscience Information Framework is described. Then the methods used for processing of electrophysiological signals are presented. The specific modifications of these methods introduced by laboratory researches are summarized; the methods are organized into a laboratory workflow. Other parts of the software infrastructure include mobile and offline solutions for data/metadata storing and a hardware stimulator communicating with an EEG amplifier and recording software.

The Antibody Challenge

The Antibody Challenge

The Neuroscience Information Framework (NIF): A Unified Semantic Framework and Associated Tools for Discovery, Integration, and Utilization of Biomedical Data and Resources on the Web

The Neuroscience Information Framework (NIF): A Unified Semantic Framework and Associated Tools for Discovery, Integration, and Utilization of Biomedical Data and Resources on the Web

Orchestration of web services in the NIF project: using the Kepler workflow engine for data fusion

Event Abstract Back to Event Orchestration of web services in the NIF project: using the Kepler workflow engine for data fusion Vadim Astakhov1*, Anita Bandrowski1, Amarnath Gupta1, Jeffery Grethe1 and Maryann Martone1 1 UCSD, United States "We report on progress of employing the Kepler workflow engine and service oriented architecture (SOA) to prototype application integration workflows that integrate data and web services developed by the Neuroscience Information Framework (NIF). One prerequisite of the scientific enterprise consists of searching for effective and useful data and resources, i.e., reagents, neuro-anatomy features, genes, or proteins. Finding relevant resources is becoming not a challenge of scarcity, but one of overabundance; in fact relevant data can be found anywhere among thousands of neuroscience-relevant information resources created by a range of information providers including, research groups, funding agencies, vendor groups, and public data initiatives. NIF provides a graphical user interface, GUI, to locate and access ontologically aligned and semantically fused heterogeneous federated information. NIF also atomized the various functions that serve the user interface and put them out as services that can be used like "Lego blocks" to query the data, build entirely new interfaces or tools. Currently, we use Kepler to orchestrate communication among various NIF services and provide a transparent layer for data fusion. Kepler combines data and processes into a configurable, structured set of steps that helps to implement semi-automated workflows. Kepler provides a development environment with a graphical user interface for designing workflows composed of a linked set of components called Actors, which can be executed under different Models of Computation. In this work, we report on specific workflows that perform data fusion and orchestration of diverse web services. This "Brain data flow"(See figures below) outputs categorized counts of information from 150 data sources about brain regions. Obtaining a similar set of data from the NIF GUI, requires manually writing down result counts that are the result values for each database for each query. Kepler, unencumbered by the current configuration of the user interface can be asked to pull a different set of data from the result set, in this case the number of results, and place that into a table. This table can then be easily turned into a graphic that helps users see which databases are information rich given a particular query. In this example, Kepler loops and recovers the same set of information for all of the brain parts and all databases, producing a massive matrix ( http://tinyurl.com/6nkfe9f )." Figure 1 Keywords: General neuroinformatics, DATA FUSION, web services, ontology, computational model Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Astakhov V, Bandrowski A, Gupta A, Grethe J and Martone M (2014). Orchestration of web services in the NIF project: using the Kepler workflow engine for data fusion. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00019 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Vadim Astakhov, UCSD, San Diego, United States, astakhov@ncmir.ucsd.edu Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Vadim Astakhov Anita Bandrowski Amarnath Gupta Jeffery Grethe Maryann Martone Google Vadim Astakhov Anita Bandrowski Amarnath Gupta Jeffery Grethe Maryann Martone Google Scholar Vadim Astakhov Anita Bandrowski Amarnath Gupta Jeffery Grethe Maryann Martone PubMed Vadim Astakhov Anita Bandrowski Amarnath Gupta Jeffery Grethe Maryann Martone Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

Exploring Mammalian Brain Connectivity using NeuroLex

Event Abstract Back to Event Exploring Mammalian Brain Connectivity using NeuroLex Anita Bandrowski1*, Amarnath Gupta1, Auroni Gupta1, Stephen Larson1 and Maryann Martone1 1 University of California San Diego, United States Supported by the Neuroscience Information Framework (NIF) and the International Neuroscience Coordination Facility (INCF), the NeuroLex is a dynamic lexicon of neuroscience terms and terminological relationships created by the neuroscience community. The terms in NeuroLex cover multiple spatial scales from the whole brain to subcellular structures to neuroscience-relevant molecules like neurotransmitters. It represents partonomic relationships in the brain and captures information such as cellular and subcellular synaptic targets of neurons. Herein, we describe the use of the NeuroLex as a graph-structured, ontological database and explore the connectivity structure of the mammalian brain as represented in this database. In some cases, the connections between two brain regions are represented directly. We allow the neuroscience community to fill in a property that asserts a direct brain region level connection such as the red nucleus is connected to the cerebellum, and references for this statement are encouraged. However due to the wealth of cellular data in the NeuroLex, we can also determine which neurons are defined as parts of brain regions, which properties they have, and add significant information about the projection. For example, a connection can be inferred to be inhibitory because a neuron within a brain region is a "projection neuron" that releases GABA as a neurotransmitter, two properties provided in the neuron list. Using this approach, we can capture a great deal of information that does not need to be directly coded. These data will inform the NIF instance data search functions, such as individual connectivity statements made based on published reports aggregated in CoCoMac or BAMS, and resting MRI connectivity maps. We have computed several network characteristics of this connectivity network including its degree distribution, several centrality measures of its nodes and edges, as shown in the Figure. In the poster, we will also present a multiscale analysis of the network, by combining it with the ontological relationships representing the partonomy of the brain. Figure 1 Keywords: General neuroinformatics, neurolex, Brain regions, neuronal network, mammalian brain development Conference: 5th INCF Congress of Neuroinformatics, Munich, Germany, 10 Sep - 12 Sep, 2012. Presentation Type: Poster Topic: Neuroinformatics Citation: Bandrowski A, Gupta A, Gupta A, Larson S and Martone M (2014). Exploring Mammalian Brain Connectivity using NeuroLex. Front. Neuroinform. Conference Abstract: 5th INCF Congress of Neuroinformatics. doi: 10.3389/conf.fninf.2014.08.00012 Copyright: The abstracts in this collection have not been subject to any Frontiers peer review or checks, and are not endorsed by Frontiers. They are made available through the Frontiers publishing platform as a service to conference organizers and presenters. The copyright in the individual abstracts is owned by the author of each abstract or his/her employer unless otherwise stated. Each abstract, as well as the collection of abstracts, are published under a Creative Commons CC-BY 4.0 (attribution) licence (https://creativecommons.org/licenses/by/4.0/) and may thus be reproduced, translated, adapted and be the subject of derivative works provided the authors and Frontiers are attributed. For Frontiers’ terms and conditions please see https://www.frontiersin.org/legal/terms-and-conditions. Received: 21 Mar 2013; Published Online: 27 Feb 2014. * Correspondence: Dr. Anita Bandrowski, University of California San Diego, La Jolla, United States, bandrow@gmail.com Login Required This action requires you to be registered with Frontiers and logged in. To register or login click here. Abstract Info Abstract The Authors in Frontiers Anita Bandrowski Amarnath Gupta Auroni Gupta Stephen Larson Maryann Martone Google Anita Bandrowski Amarnath Gupta Auroni Gupta Stephen Larson Maryann Martone Google Scholar Anita Bandrowski Amarnath Gupta Auroni Gupta Stephen Larson Maryann Martone PubMed Anita Bandrowski Amarnath Gupta Auroni Gupta Stephen Larson Maryann Martone Related Article in Frontiers Google Scholar PubMed Abstract Close Back to top Javascript is disabled. Please enable Javascript in your browser settings in order to see all the content on this page.

SciCrunch: A cooperative and collaborative data and resource discovery platform for scientific communities

SciCrunch was designed to break down the traditional types of portal silos created by different communities and to enable re-use of annotations and curation efforts across different biomedical domains. The SciCrunch platform (SciCrunch.org) can enable a research community to easily develop their own community-specific resource portal to provide access to resources, such as databases, materials, and tools, relevant to their research area. Communities can select existing resources to include in their portal and also add new resources through the data ingestion pipeline. The SciCrunch platform uses a set of ontologies to enable data integration and semantic expansion of user queries to enhance search results across these disparate resources. Data within a portal can be accessed via the portal Web interface or programmatically via Web services. The SciCrunch platform provides data used in the Resource Identification Portal, the NIDDK Information Network, the Neuroscience Information Framework, the NCBO Resource Index, and the Monarch Initiative. Keywords— semantic web; neuroscience; diabetes; genotype; phenotype; resource identification

Describing neurophysiology data and metadata with OEN, the Ontology for Experimental Neurophysiology

Describing neurophysiology data and metadata with OEN, the Ontology for Experimental Neurophysiology

A Unified Research Resource Layer; Experiences from the Neuroscience Information Framework

A Unified Research Resource Layer; Experiences from the Neuroscience Information Framework

A knowledge based approach to matching human neurodegenerative disease and animal models

Neurodegenerative diseases present a wide and complex range of biological and clinical features. Animal models are key to translational research, yet typically only exhibit a subset of disease features rather than being precise replicas of the disease. Consequently, connecting animal to human conditions using direct data-mining strategies has proven challenging, particularly for diseases of the nervous system, with its complicated anatomy and physiology. To address this challenge we have explored the use of ontologies to create formal descriptions of structural phenotypes across scales that are machine processable and amenable to logical inference. As proof of concept, we built a Neurodegenerative Disease Phenotype Ontology (NDPO) and an associated Phenotype Knowledge Base (PKB) using an entity-quality model that incorporates descriptions for both human disease phenotypes and those of animal models. Entities are drawn from community ontologies made available through the Neuroscience Information Framework (NIF) and qualities are drawn from the Phenotype and Trait Ontology (PATO). We generated ~1200 structured phenotype statements describing structural alterations at the subcellular, cellular and gross anatomical levels observed in 11 human neurodegenerative conditions and associated animal models. PhenoSim, an open source tool for comparing phenotypes, was used to issue a series of competency questions to compare individual phenotypes among organisms and to determine which animal models recapitulate phenotypic aspects of the human disease in aggregate. Overall, the system was able to use relationships within the ontology to bridge phenotypes across scales, returning non-trivial matches based on common subsumers that were meaningful to a neuroscientist with an advanced knowledge of neuroanatomy. The system can be used both to compare individual phenotypes and also phenotypes in aggregate. This proof of concept suggests that expressing complex phenotypes using formal ontologies provides considerable benefit for comparing phenotypes across scales and species.

The Neuroscience Information Framework (NIF): A Unified Semantic Framework and Associated Tools for Discovery, Integration, and Utilization of Biomedical Data and Resources on the Web

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