Interoperability of neuroscience modeling software: Current status and future directions
Neuroinformatics, ISSN: 1539-2791, Vol: 5, Issue: 2, Page: 127-138
2007
- 51Citations
- 90Captures
- 1Mentions
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations51
- Citation Indexes51
- 51
- CrossRef45
- Captures90
- Readers90
- 90
- Mentions1
- Blog Mentions1
- Blog1
Conference Paper Description
Neuroscience increasingly uses computational models to assist in the exploration and interpretation of complex phenomena. As a result, considerable effort is invested in the development of software tools and technologies for numerical simulations and for the creation and publication of models. The diversity of related tools leads to the duplication of effort and hinders model reuse. Development practices and technologies that support interoperability between software systems therefore play an important role in making the modeling process more efficient and in ensuring that published models can be reliably and easily reused. Various forms of interoperability are possible including the development of portable model description standards, the adoption of common simulation languages or the use of standardized middleware. Each of these approaches finds applications within the broad range of current modeling activity. However more effort is required in many areas to enable new scientific questions to be addressed. Here we present the conclusions of the "Neuro-IT Interoperability of Simulators" workshop, held at the 11th computational neuroscience meeting in Edinburgh (July 19-20 2006; http://www.cnsorg.org). We assess the current state of interoperability of neural simulation software and explore the future directions that will enable the field to advance. © 2007 Humana Press Inc.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=34147171012&origin=inward; http://dx.doi.org/10.1007/s12021-007-0004-5; http://www.ncbi.nlm.nih.gov/pubmed/17873374; http://link.springer.com/10.1007/s12021-007-0004-5; http://www.springerlink.com/index/10.1007/s12021-007-0004-5; http://www.springerlink.com/index/pdf/10.1007/s12021-007-0004-5; https://dx.doi.org/10.1007/s12021-007-0004-5; https://link.springer.com/article/10.1007/s12021-007-0004-5
Springer Science and Business Media LLC
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