ModelView for ModelDB: Online Presentation of Model Structure
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ModelDB (modeldb.yale.edu), a searchable repository of source code of more than 950 published computational neuroscience models, seeks to promote model reuse and reproducibility. Code sharing is a first step; however, model source code is often large and not easily understood. To aid users, we have developed ModelView, a web application for ModelDB that presents a graphical view of model structure augmented with contextual information for NEURON and NEURON-runnable (e.g. NeuroML, PyNN) models. Web presentation provides a rich, simulator-independent environment for interacting with graphs. The necessary data is generated by combining manual curation, text-mining the source code, querying ModelDB, and simulator introspection. Key features of the user interface along with the data analysis, storage, and visualization algorithms are explained. With this tool, researchers can examine and assess the structure of hundreds of models in ModelDB in a standardized presentation without installing any software, downloading the model, or reading model source code.
KeywordsModelDB Repository Visualization Code analysis
We thank the laboratory of GM Shepherd for valuable suggestions for improving ModelView’s usability, P. Miller, L. Marenco, and N.T. Carnevale for comments on the manuscript, and Nicole Flokos for her contributions to the NeuronWeb library. This research was supported by NIH T15 LM007056, NIH R01 NS11613, and NIH R01 DC009977.
Conflict of interests
The authors declare that they have no conflict of interest.
- Davison, A.P., Brüderle, D., Eppler, J., Kremkow, J., Muller, E., Pecevski, D., Perrinet, L., & Yger, P. (2008). PyNN: a common interface for neuronal network simulators. Frontiers in Neuroinformatics, 2.Google Scholar
- Davison, A.P., Mattioni, M., Samarkanov, D., & Sumatra, T.B. (2014). A toolkit for reproducible research. In V. Stodden, F. Leisch, & R.D. Peng (Eds.) Implementing reproducible, research (pp. 57–79). Boca Raton: Chapman & Hall/CRC.Google Scholar
- Gleeson, P., Crook, S., Cannon, R.C., Hines, M.L., Billings, G.O., Farinella, M., Morse, T.M., Davison, A.P., Ray, S., Bhalla, U.S., Barnes, S.R., Dimitrova, Y.D., & Silver, R.A. (2010). NeuroML: a language for describing data driven models of neurons and networks with a high degree of biological detail. Plos Computational Biology, 6(6), e1000815.PubMedCentralCrossRefPubMedGoogle Scholar
- Le Novere, N., Bornstein, B., Broicher, A., Courtot, M., Donizelli, M., Dharuri, H., Li, L., Sauro, H., Schilstra, M., Shapiro, B., Snoep, J.L., & Hucka, M. (2006). BioModels database: a free, centralized database of curated, published, quantitative kinetic models of biochemical and cellular systems. Nucleic Acids Research, 34(suppl 1), D689–D691.PubMedCentralCrossRefPubMedGoogle Scholar
- Podlaski, W.F., Ranjan, R., Seeholzer, Markram, H., Gerstner, W., & Vogels, T. (2013). Visualizing the similarity and pedigree of NEURON ion channel models available on ModelDB. Program No. 678.31. Neuroscience 2013 Abstracts. San Diego: Society for Neuroscience. Online.Google Scholar
- Rivest, R. (1992). The MD5 message-digest algorithm. RFC1321, Internet engineering task force.Google Scholar