The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

Gardner, Daniel; Akil, Huda; Ascoli, Giorgio A.; Bowden, Douglas M.; Bug, William J.; Donohue, Duncan; Goldberg, David H.; Grafstein, Bernice; Grethe, Jeffrey S.; Gupta, Amarnath; Halavi, Maryam; Kennedy, David N.; Marenco, Luis N.; Martone, Maryann E.; Miller, Perry L.; Müller, Hans‐Michael; Robert, Adrian; Shepherd, Gordon M.; Sternberg, Paul W.; Essen, David C. Van; Williams, Robert W.

doi:10.1007/s12021-008-9024-z

Cited by 195 publications

(168 citation statements)

References 19 publications

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“…3), or contributing lab, and is also searchable by general keywords, or more specifically by morphometry or metadata, including information on the imaging protocol and parameters as well as the method used for reconstruction. Being the largest web-accessible collection of neuronal reconstructions and associated metadata, it is one of the integrated resources in the Neuroscience Information Framework (NIF), a recent initiative sponsored by the National Institutes of Health (NIH) for integrating access to and promoting the use of webbased neuroscience resources (131,132). Moreover, it mirrors many other repositories, in line with its primary goal of achieving and maintaining dense coverage of all publically available neuronal reconstructions.…”

Section: Morphology Databasesmentioning

confidence: 99%

Neuron tracing in perspective

2010

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The study of the structure and function of neuronal cells and networks is of crucial importance in the endeavor to understand how the brain works. A key component in this process is the extraction of neuronal morphology from microscopic imaging data. In the past four decades, many computational methods and tools have been developed for digital reconstruction of neurons from images, with limited success. As witnessed by the growing body of literature on the subject, as well as the organization of challenging competitions in the field, the quest for a robust and fully automated system of more general applicability still continues. The aim of this work, is to contribute by surveying recent developments in the field for anyone interested in taking up the challenge. Relevant aspects discussed in the article include proposed image segmentation methods, quantitative measures of neuronal morphology, currently available software tools for various related purposes, and morphology databases. ' 2010 International Society for Advancement of Cytometry

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Section: Morphology Databasesmentioning

confidence: 99%

Neuron tracing in perspective

2010

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“…SciCrunch was built based on the extensible Neuroscience Information Framework platform described previously ( 12, 13, RRID:nif-0000-25673), and the portal infrastructure for RII was developed under an award from NIDDK to create a dkNET portal (RRID:nlx_153866), while the customization of the portal was done by Monarch staff. The data is aggregated from the SciCrunch tool registry, the antibody registry, as well as the model organism community databases and stock centers ( Table 1).…”

Section: Workflowmentioning

confidence: 99%

The Resource Identification Initiative: A cultural shift in publishing

Bandrowski¹,

Brush

Grethe³

et al. 2015

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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 allow humans and algorithms to identify the exact resources that are reported or answer basic questions such as “What other studies used 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 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 (including software and databases). RRIDs represent accession numbers assigned by an authoritative database, e.g., the model organism databases, 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 ( www.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. Here, we present an overview of the pilot project and its outcomes to date. We show that authors are generally accurate in performing the task of identifying resources and supportive of the goals of the project. We also show that identifiability of the resources pre- and post-pilot showed a dramatic improvement for all three resource types, suggesting that the project has had a significant impact on reproducibility relating to research resources.

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“…Neuroscience Information Framework (NIF) is a dynamic repository of Web-based neuroscience resources: data, a neuroscience ontology, materials, and tools (Gardner, et al, 2008). Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC) users can find and compare neuroimaging tools for functional and structural neuroimaging analyses (Luo, Kennedy & Cohen, 2009).…”

Section: Background and Overviewmentioning

confidence: 99%

A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience

et al. 2015

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Abstract:The CARMEN Virtual Laboratory (VL) is a cloud-based platform which allows neuroscientists to store, share, develop, execute, reproduce and publicise their work. This paper describes new functionality in the CARMEN VL: an interactive publications repository. This new facility allows users to link data and software to publications. This enables other users to examine data and software associated with the publication and execute the associated software within the VL using the same data as the authors used in the publication. The cloud-based architecture and SaaS (Software as a Service) framework allows vast data sets to be uploaded and analysed using software services. Thus, this new interactive publications facility allows others to build on research results through reuse. This aligns with recent developments by funding agencies, institutions, and publishers with a move to open access research. Open access provides reproducibility and verification of research resources and results. Publications and their associated data and software will be assured of long-term preservation and curation in the repository. Further, analysing research data and the evaluations described in publications frequently requires a number of execution stages many of which are iterative. The VL provides a scientific workflow environment to combine software services into a processing tree. These workflows can also be associated with publications and executed by users. The VL also provides a secure environment where users can decide the access rights for each resource to ensure copyright and privacy restrictions are met.

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The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

Cited by 195 publications

References 19 publications

Neuron tracing in perspective

Neuron tracing in perspective

The Resource Identification Initiative: A cultural shift in publishing

A Digital Repository and Execution Platform for Interactive Scholarly Publications in Neuroscience

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