Integration of Multidisciplinary Sensory Data: A Pilot Model of the Human Brain Project Approach

Miller, Perry L.; Nadkarni, Prakash M.; Singer, Michael S.; Marenco, Luis N.; Hines, Michael L.; Shepherd, Gordon M.

doi:10.1136/jamia.2001.0080034

Cited by 23 publications

(16 citation statements)

References 25 publications

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“…For local and public access levels, owners of the data may make the data freely available to all with the required permissions, or they may choose to give access only to the descriptive data and low-resolution image views. If a user retrieves a data set tagged in this fashion, an e-mail is sent notifying the owner who then has the choice of granting permission for downloading of the original data, similar to systems employed by other databases (Miller et al, 2001;Roland et al, 2001). Data may only be used for noncommercial purposes and the CCDB and the owner of the data should be acknowledged in any resulting articles.…”

Section: For Details) (F) Confocal Light Microscopic Image Of a Labementioning

confidence: 99%

“…To achieve such a lofty goal, databases like the CCDB, which on their own cover only a small portion of the biological spectrum, will have to be linked to other databases. Creating these linkages for biological data presents a major challenge to the informatics community and one that is proceeding on several fronts (e.g., Gardner et al, 2001;Miller et al, 2001;Rachedi et al, 2000). In our own work, we are linking the CCDB to databases containing structural and functional information at different scales, from whole brains to molecules, using a novel mediator-based architecture which allows for the incorporation of additional knowledge in order to bridge between sources (Gupta et al, 2000;Lud€ a ascher et al, 2001).…”

Section: Future Directionsmentioning

confidence: 99%

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A cell-centered database for electron tomographic data

Martone

Gupta

Wong

et al. 2002

Journal of Structural Biology

106

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Section: For Details) (F) Confocal Light Microscopic Image Of a Labementioning

confidence: 99%

Section: Future Directionsmentioning

confidence: 99%

A cell-centered database for electron tomographic data

Martone

Gupta

Wong

et al. 2002

Journal of Structural Biology

106

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“…The current system consists of the KIND mediator with the following sources registered: 1) a prototype of the CCDB; 2) the EF-Hand Calcium binding protein database, a database of sequence data available from Vanderbilt University (http://structbio.vanderbilt.edu/cabp_database/); 3) the NeuronDB database from the SenseLab project (Shepherd et al, 1998;Miller et al, 2001); 4) TAXON, a database created from taxonomy information available from the National Center for Biotechnology Informatics (NCBI).…”

Section: Federation Of Brain Datamentioning

confidence: 99%

“…The question of whether equal rewards will accrue from similar efforts for histological data, whole brain imaging, physiological data, etc., remains to be answered. Nevertheless, at the very least, neuroinformatics efforts will provide organizing frameworks and computational tools to manage neuroscience data, from the lab notebook to published data (Gardner et al, 2001;Miller et al, 2001). In the best case, it will provide new opportunities to integrate large amounts of biological data into unified theories of function and aid in the discovery process (Chicurel, 2000;Fox and Lancaster, 2002).…”

Section: Introductionmentioning

confidence: 99%

Federation of Brain Data through Knowledge-guided Mediation

Martone

Gupta

Ludäscher

et al. 2003

Neuroscience Databases

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Abstract:In this chapter, we describe a data integration framework for multi-resolution data and some of the tools we have created for achieving integration of distributed data sources. The chapter is divided into two parts. In the first part, we describe a recently developed database for high resolution 3D data on cell structure and protein localization, the Cell Centered Database or CCDB. In the second part, we describe how we can link data sources such as the CCDB to other databases using a mediation-based approach. By incorporating additional knowledge into the system, we are able to link together sources that are related conceptually, even if they do not contain similar elements in their schemata. For each of the tools and resources described, we indicate its current state of development and availability.

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“…Each SenseLab database has an easily extensible structure achieved through the EAV/CR (EntityAttribute-Value with Classes and Relationships) data schema , Miller et al 2001. ModelDB is integrated with NeuronDB (Marenco et al 1999), another SenseLab database that stores neuronal properties derived from the neuroscience literature (http:// senselab.med.yale.edu/senselab/NeuronDB).…”

mentioning

confidence: 99%

ModelDB: Making Models Publicly Accessible to Support Computational Neuroscience

et al. 2003

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Computational neuroscience as a scientific discipline must provide for the ready testing of published models by others in the field. Unfortunately this has rarely been fulfilled. When exact reproduction of a model simulation is achieved, it is often a long and difficult process. Too often, missing or typographically incorrect equations and parameter values have made it difficult to explore or build upon published models. Compounding this difficulty is the proliferation of platforms and operating systems that are incompatible with the author's original computing environment.Because of these problems, most models are never subjected to the rigorous testing by others in the field that is a hallmark of the scientific method. This not only impedes validation of a model, but also prevents a deeper understanding of its inner workings, especially through modification of the parameters. Furthermore, modular pieces of the model, e.g. ion channels or the morphology of a cell, cannot be reused to build new models and propel research forward.ModelDB (http://senselab.med.yale.edu/modeldb) is intended to address these issues (Peterson et al, 1996;Shepherd et al, 1998). ModelDB is a database of computational models, either classics in the field or published in recent years. It focuses on models for different types of neurons, and presently contains over 60 models for 15 neuron types. In addition to compartmental models, it contains models covering from ion channels and receptors through axons and dendrites through neurons to networks. Models can be accessed by author, model name, neuron type, concept, e.g. synaptic plasticity, pattern recognition, etc, or by simulation environment.ModelDB is a member of a major neuroscience database collection called SenseLab. Each SenseLab database has an easily extensible structure achieved through the EAV/CR (EntityAttribute-Value with Classes and Relationships) data schema , Miller et al 2001. ModelDB is integrated with NeuronDB (Marenco et al 1999), another SenseLab database that stores neuronal properties derived from the neuroscience literature (http:// senselab.med.yale.edu/senselab/NeuronDB). Use of the models is free to all. Contributing to the database is also open to all. Contributions are tested for quality-control purposes before being made public. Here we describe how to find, run, and submit models to ModelDB. Browsing ModelDBThe use of ModelDB typically starts with a computational neuroscientist who wishes to test the results of a simulation by a published model, and use that as a starting point for further research. Instead of recreating the model from scratch, the user goes to the ModelDB home page (Fig.1, top), to find the model by any of the various ways already mentioned. All the information about a model is shown on a single page (Fig.1, bottom), which also contains tools for finding related models in the database.The files required to run a simulation are stored in a compressed archive in zip format and can be browsed and/or downloaded (Fig.1, bottom, lower left column)...

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Integration of Multidisciplinary Sensory Data: A Pilot Model of the Human Brain Project Approach

Cited by 23 publications

References 25 publications

A cell-centered database for electron tomographic data

A cell-centered database for electron tomographic data

Federation of Brain Data through Knowledge-guided Mediation

ModelDB: Making Models Publicly Accessible to Support Computational Neuroscience

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