The International Neuroinformatics Coordinating Facility: Evaluating the First Years

Schutter, Erik De

doi:10.1007/s12021-009-9054-1

Cited by 5 publications

(4 citation statements)

References 7 publications

(8 reference statements)

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“…Definitions of these terms are provided via downloadable documents available on the SchizConnect web portal. During development, we have aligned the SchizConnect domain model terms with the standards provided by the ontology community: XCEDE (XML-Based Clinical Experiment Data Exchange Schema, http://www.xcede.org/XCEDE.html) (Gadde et al, 2012), NeuroLEX (the Neuroscience Lexicon, http://neurolex.org/) (Larson and Martone, 2013), NIF (the Neuroscience Information Framework, http://neuinfo.org/) (Gardner et al, 2008) and INCF (the International Neuroinformatics Coordinating Facility, http://incf.org/) (Bjaalie and Grillner, 2007; De Schutter, 2009), wherever appropriate. The domain terms with links to existing ontology can be found on the SchizConnect website documentations page.…”

Section: Methodsmentioning

confidence: 99%

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

et al. 2016

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SchizConnect (www.schizconnect.org) is built to address the issues of multiple data repositories in schizophrenia neuroimaging studies. It includes a level of mediation—translating across data sources—so that the user can place one query, e.g. for diffusion images from male individuals with schizophrenia, and find out from across participating data sources how many datasets there are, as well as downloading the imaging and related data. The current version handles the Data Usage Agreements across different studies, as well as interpreting database-specific terminologies into a common framework. New data repositories can also be mediated to bring immediate access to existing datasets. Compared with centralized, upload data sharing models, SchizConnect is a unique, virtual database with a focus on schizophrenia and related disorders that can mediate live data as information are being updated at each data source. It is our hope that SchizConnect can facilitate testing new hypotheses through aggregated datasets, promoting discovery related to the mechanisms underlying schizophrenic dysfunction.

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Section: Methodsmentioning

confidence: 99%

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

et al. 2016

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“…Clearinghouse-type resources such as the Neuroscience Information Framework (NIF) (Gardner et al, 2008; Bandrowski et al, 2012), the International Neuroinformatics Coordinating Facility (INCF) (De Schutter, 2009), and Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) (Luo et al, 2009) provide portals to heterogeneous collections of neuroscience resources, but do not provide tools for integrating these resources into computations. The EPILEPSIAE (Evolving Platform for Improving the Living Expectations in Subjects Suffering from Ictal Events) project is a European effort to produce a high-quality, highly-annotated database of multi-channel continuous recordings of 300 subjects that includes EEG (Ihle et al, 2012).…”

Section: Related Workmentioning

confidence: 99%

MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB

Cockfield

Robbins

2013

Front. Neuroinform.

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Experiments to monitor human brain activity during active behavior record a variety of modalities (e.g., EEG, eye tracking, motion capture, respiration monitoring) and capture a complex environmental context leading to large, event-rich time series datasets. The considerable variability of responses within and among subjects in more realistic behavioral scenarios requires experiments to assess many more subjects over longer periods of time. This explosion of data requires better computational infrastructure to more systematically explore and process these collections. MOBBED is a lightweight, easy-to-use, extensible toolkit that allows users to incorporate a computational database into their normal MATLAB workflow. Although capable of storing quite general types of annotated data, MOBBED is particularly oriented to multichannel time series such as EEG that have event streams overlaid with sensor data. MOBBED directly supports access to individual events, data frames, and time-stamped feature vectors, allowing users to ask questions such as what types of events or features co-occur under various experimental conditions. A database provides several advantages not available to users who process one dataset at a time from the local file system. In addition to archiving primary data in a central place to save space and avoid inconsistencies, such a database allows users to manage, search, and retrieve events across multiple datasets without reading the entire dataset. The database also provides infrastructure for handling more complex event patterns that include environmental and contextual conditions. The database can also be used as a cache for expensive intermediate results that are reused in such activities as cross-validation of machine learning algorithms. MOBBED is implemented over PostgreSQL, a widely used open source database, and is freely available under the GNU general public license at http://visual.cs.utsa.edu/mobbed. Source and issue reports for MOBBED are maintained at http://vislab.github.com/MobbedMatlab/

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“…The work presented here was developed prior to the authors' involvement in the NineML effort, which is a model description language developed as part of the Large-Scale Network Modeling initiative of the International Neuroinformatics Coordinating Facility (http://www.incf.org/) [15]. The design of NineML is also divided in semantic layers, however its focus is on describing large-scale networks of integrate-and-fire neurons, and its design significantly diverges from the language presented here, which is oriented towards conductance-based models of ionic currents.…”

Section: Introductionmentioning

confidence: 99%

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

Raikov

Schutter

2012

PLoS Comput Biol

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We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language.

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The International Neuroinformatics Coordinating Facility: Evaluating the First Years

Cited by 5 publications

References 7 publications

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

SchizConnect: Mediating neuroimaging databases on schizophrenia and related disorders for large-scale integration

MOBBED: a computational data infrastructure for handling large collections of event-rich time series datasets in MATLAB

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

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