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HydroShare is an online, collaborative system being developed for open sharing of hydrologic data and models. The goal of HydroShare is to enable scientists to easily discover and access hydrologic data and models, retrieve them to their desktop or perform analyses in a distributed computing environment that may include grid, cloud or high performance computing model instances as necessary. Scientists may also publish outcomes (data, results or models) into HydroShare, using the system as a collaboration platform for sharing data, models and analyses. HydroShare is expanding the data sharing capability of the CUAHSI Hydrologic Information System by broadening the classes of data accommodated, creating new capability to share models and model components, and taking advantage of emerging social media functionality to enhance information about and collaboration around hydrologic data and models. One of the fundamental concepts in HydroShare is that of a Resource. All content is represented using a Resource Data Model that separates system and science metadata and has elements common to all resources as well as elements specific to the types of resources HydroShare will support. These will include different data types used in the hydrology community and models and workflows that require metadata on execution functionality. The HydroShare web interface and social media functions are being developed using the Drupal content management system. A geospatial visualization and analysis component enables searching, visualizing, and analyzing geographic datasets. The integrated Rule-Oriented Data System (iRODS) is being used to manage federated data content and perform rule-based background actions on data and model resources, including parsing to generate metadata catalog information and the execution of models and workflows. This presentation will introduce the HydroShare functionality developed to date, describe key elements of the Resource Data Model and outline the roadmap for future development.

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The power-law size distributions obtained experimentally for neuronal avalanches are an important evidence of criticality in the brain. This evidence is supported by the fact that a critical branching process exhibits the same exponent t~3=2. Models at criticality have been employed to mimic avalanche propagation and explain the statistics observed experimentally. However, a crucial aspect of neuronal recordings has been almost completely neglected in the models: undersampling. While in a typical multielectrode array hundreds of neurons are recorded, in the same area of neuronal tissue tens of thousands of neurons can be found. Here we investigate the consequences of undersampling in models with three different topologies (two-dimensional, small-world and random network) and three different dynamical regimes (subcritical, critical and supercritical). We found that undersampling modifies avalanche size distributions, extinguishing the power laws observed in critical systems. Distributions from subcritical systems are also modified, but the shape of the undersampled distributions is more similar to that of a fully sampled system. Undersampled supercritical systems can recover the general characteristics of the fully sampled version, provided that enough neurons are measured. Undersampling in two-dimensional and small-world networks leads to similar effects, while the random network is insensitive to sampling density due to the lack of a well-defined neighborhood. We conjecture that neuronal avalanches recorded from local field potentials avoid undersampling effects due to the nature of this signal, but the same does not hold for spike avalanches. We conclude that undersampled branching-process-like models in these topologies fail to reproduce the statistics of spike avalanches.

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Incluye Bibliografía

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The second issue of the Circular marks the first milestone in the life of the Network, because it fulfils our intent to publish it on a regular basis at least twice a year. The emphasis in this issue is on planning at the level of the river basin.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)