3 datasets found
  1. d

    Remote Approach-9: Using Sciunit in CyberGIS-Jupyer for water for the...

    • search.dataone.org
    Updated Dec 30, 2023
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    Young-Don Choi (2023). Remote Approach-9: Using Sciunit in CyberGIS-Jupyer for water for the reproducibility of SUMMA modeling [Dataset]. https://search.dataone.org/view/sha256%3A337ae7dc69972c74f6ade6c00792a67b9c6a2440f0ef8e157240f40765c7f459
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    Dataset updated
    Dec 30, 2023
    Dataset provided by
    Hydroshare
    Authors
    Young-Don Choi
    Area covered
    Description

    This HydroShare resource provides the Jupyter Notebooks for the reproducibility of SUMMA modeling using Sciunit in CyberGIS-Jupyter for water in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

    To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

  2. Data from: Efficient Regionalization for Spatially Explicit Neighborhood...

    • figshare.com
    Updated Apr 14, 2020
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    Ran Wei; Sergio Rey; Elijah Knaap (2020). Efficient Regionalization for Spatially Explicit Neighborhood Delineation [Dataset]. http://doi.org/10.6084/m9.figshare.11254304.v1
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    Dataset updated
    Apr 14, 2020
    Dataset provided by
    figshare
    Figsharehttp://figshare.com/
    Authors
    Ran Wei; Sergio Rey; Elijah Knaap
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Efficient Regionalization for Spatially Explicit Neighborhood Delineation## AbstractNeighborhood delineation is increasingly relied upon in urban social science research to identify the most appropriate spatial unit. In problems of this type, the true number of neighborhoods (typically called the k parameter) is unknown and analysts often require algorithmic approaches determine kendogenously. Existing approaches for neighborhood delineation that do not require pre-specification ofa k-parameter, however, are either nonspatial or lead to noncontiguous or overlapping regions. In this paper, we propose the use of max-p-regions for neighborhood delineation so that the geographic space can be partitioned into a set of homogeneous and geographically contiguous neighborhoods. In addition, we developed a new efficient algorithm to address the computational challenges associated with solving the max-p-regions so that it can be applied for large-scale neighborhood delineation. This new algorithm is implemented in the open-source Python Spatial Analysis Library (PySAL). Computational experiments based on both simulated and realistic data sets are performed and the results demonstrate its effectiveness and efficiency.## InstructionsThe files in this archive demonstrate the code for the new max-p algorithm.To run the demonstration, please do the following:1. extract the archive unzip maxp.zip2. Install Anaconda python distribution3. conda env create -f environment.yml4. conda activate maxp5. jupyter notebook6. Select the notebook demo.ipynb

  3. d

    Run Ensemble RHESSys models on HPC through CyberGIS Computing Service on...

    • search.dataone.org
    • beta.hydroshare.org
    • +1more
    Updated Apr 15, 2022
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    YOUNGDON CHOI; Zhiyu/Drew Li; Iman Maghami; Anand Padmanabhan; Shaowen Wang; Jonathan Goodall; David Tarboton (2022). Run Ensemble RHESSys models on HPC through CyberGIS Computing Service on CyberGIS-Jupyter for Water (CJW) [Dataset]. https://search.dataone.org/view/sha256%3A4518645384b9afc366c9babb873e7643083672320169b017576326941a07a8eb
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    Dataset updated
    Apr 15, 2022
    Dataset provided by
    Hydroshare
    Authors
    YOUNGDON CHOI; Zhiyu/Drew Li; Iman Maghami; Anand Padmanabhan; Shaowen Wang; Jonathan Goodall; David Tarboton
    Description

    RHESSys (Regional Hydro-Ecological Simulation System) is a GIS-based, terrestrial ecohydrologic modeling framework designed to simulate carbon, water and nutrient fluxes at the watershed scale. RHESSys models the temporal and spatial variability of ecosystem processes and interactions at a daily time step over multiple years by combining a set of physically based process models and a methodology for partitioning and parameterizing the landscape. Detailed model algorithms are available in Tague and Band (2004).

    This notebook demonstrates how to configure an ensemble RHESSys simulation with pyRHESSys, submit it to a supported HPC resource (XSEDE COMET or UIUC Virtual Roger) for execution through CyberGIS Computing Service, visualize model outputs with various tooks integrated in the CyberGIS-Jupyter for Water (CJW).

    The model used here is based off of a pre-built RHESSys model for the Coweeta Subbasin 18 (0.124 𝑘𝑚2 ), a subbasins in Coweeta watershed (16 𝑘𝑚2 ), from the Coweeta Long Term Ecological Research (LTER) Program.

    How to run the notebook: 1) Click on the OpenWith button in the upper-right corner; 2) Select "CyberGIS-Jupyter for Water"; 3) Open the notebook and follow instructions;

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Click to copy link
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Close
Cite
Young-Don Choi (2023). Remote Approach-9: Using Sciunit in CyberGIS-Jupyer for water for the reproducibility of SUMMA modeling [Dataset]. https://search.dataone.org/view/sha256%3A337ae7dc69972c74f6ade6c00792a67b9c6a2440f0ef8e157240f40765c7f459

Remote Approach-9: Using Sciunit in CyberGIS-Jupyer for water for the reproducibility of SUMMA modeling

Explore at:
Dataset updated
Dec 30, 2023
Dataset provided by
Hydroshare
Authors
Young-Don Choi
Area covered
Description

This HydroShare resource provides the Jupyter Notebooks for the reproducibility of SUMMA modeling using Sciunit in CyberGIS-Jupyter for water in the manuscript of "Comparing Approaches to Achieve Reproducible Computational Modeling for Hydrological and Environmental Systems" in Environmental Modeling and Software.

To find out the instructions on how to run Jupyter Notebooks, please refer to the README file which is provided in this resource.

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