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Jeffery S. Horsburgh

Utah State University | Associate Professor

Subject Areas: Hydrology, Water quality, Hydroinformatics

 Recent activity

ABSTRACT:

HydroShare is a web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). Within HydroShare, users can create and share data and models using a variety of file formats and flexible metadata. HydroShare enables users to formally publish these resources as well as create linkages between published data and model resources and peer reviewed journal publications that describe them. Ability to link published data and models with the papers that describe them is a great step in the direction of scientific reproducibility, but is only a first step. HydroShare supports further transparency in the scientific process by enabling scripting of analytical steps via a RESTful application programming interface (API). Using this API, HydroShare users can develop scripts to read data from HydroShare, perform an analytical step (e.g., data processing or visualization), and then write results back to HydroShare. The script itself can then be shared as part of the published dataset in HydroShare, or it can be shared as a Jupyter Notebook that can be executed within the HydroShare environment. Scripts or Jupyter Notebooks can then be executed by others to reproduce the analysis used by the original authors. In this presentation, we discuss how HydroShare can enable best practices for linking publications with data and models and for promoting reproducibility in environmental analyses through sharing of data, models, and scripts that encode the scientific workflow. The HydroShare system is available at http://www.hydroshare.org. Source code for HydroShare is available at https://github.com/hydroshare.

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ABSTRACT:

This resource includes a data file containing high resolution water use data from a residential dormitory building on Utah State University's campus. The included iPython notebook demonstrates an analysis aimed at answering the question of how weekend (Saturday and Sunday) water demands are different than weekday (Monday - Friday) demands. The code in the Notbook does the following:

1. Resamples the data to hourly by summing incremental volumes
2. Bins the hourly data into weekday versus weekend data
3. Aggregates the data to average hourly and standard deviation by using the "groupby" function
4. Plots the aggregated weekday versus weekend data for visual comparison

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ABSTRACT:

New abstract.

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ABSTRACT:

There are now many ongoing efforts to develop low-cost, open-source, low-power sensors and datalogging solutions for environmental applications. Many of these have advanced to the point that high quality scientific measurements can be made using relatively inexpensive and increasingly off-the-shelf components. With the development of these innovative systems, however, comes the ability to generate large volumes of high-frequency monitoring data and the challenge of how to log, transmit, store, and share the resulting data. This presentation will focus on a new, web-based system http://data.envirodiy.org that was designed to enable citizen scientists to stream sensor data from a network of EnviroDIY Mayfly Arduino-based dataloggers. This system enables registration of new sensor nodes through a website. Once registered, any Internet connected device (e.g., cellular or WIFI) can then post data to the data.envirodiy.org website through a web service programming interface. Data are stored in a back-end data store that implements Version 2 of the Observations Data Model (ODM2). Live data can then be viewed and downloaded from the data.envirodiy.org website in a simple text format. While this system was purpose built to support an emerging network of Arduino-based sensor nodes deployed by citizen scientists in the Delaware River Basin, the architecture and components are generic and could be used by any Internet connected device capable of making measurements and formulating an HTTP POST request to send them to data.envirodiy.org.

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ABSTRACT:

A demo Composite Resource with some watersheds for the Blacksmith Fork watershed and some additional content.

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Resources
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Composite Resource 0
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Geographic Feature 0
Geographic Raster 0
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MODFLOW Model Instance Resource 0
Multidimensional (NetCDF) 0
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Time Series Time Series

ABSTRACT:

This dataset contains observations of water temperature in the Little Bear River at Mendon Road near Mendon, UT. Data were recorded every 30 minutes and represent the average values over the preceeding time interval. The values were recorded using a HydroLab MS5 multi-parameter water quality sonde connected to a Campbell Scientific datalogger. Values represent quality controlled data that have undergone quality control to remove obviously bad data.

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ABSTRACT:

Anticipated changes to climate, human population, land use, and urban form will alter the hydrology and availability of water within the water systems on which the world’s population relies. Understanding the effects of these changes will be paramount in sustainably managing water resources, as well as maintaining associated capacity to provide ecosystem services (e.g., regulating flooding, maintaining instream flow during dry periods, cycling nutrients, and maintaining water quality). It will require better information characterizing both natural and human mediated hydrologic systems and enhanced ability to generate, manage, store, analyze, and share growing volumes of observational data. Over the past several years, a number of hydrology domain cyberinfrastructures have emerged or are currently under development that are focused on providing integrated access to and analysis of data for cross-domain synthesis studies. These include the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), the Critical Zone Observatory Information System (CZOData), HydroShare, the BiG CZ software system, and others. These systems have focused on sharing, integrating, and analyzing hydrologic observations data. This presentation will describe commonalities and differences in the cyberinfrastructure approaches used by these projects and will highlight successes and lessons learned in addressing the challenges of big and complex data. It will also identify new challenges and opportunities for next generation cyberinfrastructure and a next generation of cyber-savvy scientists and engineers as developers and users.

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ABSTRACT:

How do you manage, track, and share hydrologic data and models within your research group? Do you find it difficult to keep track of who has access to which data and who has the most recent version of a dataset or research product? Do you sometimes find it difficult to share data and models and collaborate with colleagues outside your home institution? Would it be easier if you had a simple way to share and collaborate around hydrologic datasets and models? HydroShare is a new, web-based system for sharing hydrologic data and models with specific functionality aimed at making collaboration easier. Within HydroShare, we have developed new functionality for creating datasets, describing them with metadata, and sharing them with collaborators. In HydroShare we cast hydrologic datasets and models as “social objects” that can be published, collaborated around, annotated, discovered, and accessed. In this presentation, we will discuss and demonstrate the collaborative and social features of HydroShare and how it can enable new, collaborative workflows for you, your research group, and your collaborators across institutions. HydroShare’s access control and sharing functionality enable both public and private sharing with individual users and collaborative user groups, giving you flexibility over who can access data and at what point in the research process. HydroShare can make it easier for collaborators to iterate on shared datasets and models, creating multiple versions along the way, and publishing them with a permanent landing page, metadata description, and citable Digital Object Identifier (DOI). Functionality for creating and sharing resources within collaborative groups can also make it easier to overcome barriers such as institutional firewalls that can make collaboration around large datasets difficult. Functionality for commenting on and rating resources supports community collaboration and quality evaluation of resources in HydroShare.

This presentation was delivered as part of a Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Cyberseminar in June 2016. Cyberseminars are recorded, and archived recordings are available via the CUAHSI website at http://www.cuahsi.org.

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Test Resource
Created: Jan. 19, 2017, 1:28 p.m.
Authors: Amber Jones · Jeffery S. Horsburgh · Aanderud, Zach ·

ABSTRACT:

This is a test resource created to demonstrate HydroShare functionality.

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ABSTRACT:

This resource contains the final data files and R scripts used in our analysis of water use across two high-traffic, public restrooms on Utah State University's campus. We used an inexpensive, open source, water metering system that uses off-the-shelf electronic components and inexpensive analog meters to measure water use quantity and behavior at high temporal frequency (< 5 s). We demonstrated this technology in the two restrooms at Utah State University before and after installing high efficiency, automatic faucets and toilet flush valves. We also integrated an inexpensive sensor to count user traffic to the restrooms. Sensing and recording restroom visits and water use events at high frequency allowed us to monitor water use behavior and identify water fixture malfunctions, such as undesired leaks. Results also show average water use per person, variability in water use by different fixtures (faucets versus urinals and toilets), variability in water use by fixtures compared to manufacturer specifications, gender differences in water use, and the difference in water use related to retrofit of the restrooms with high efficiency fixtures. The inexpensive metering system can help institutions remotely measure and record water use trends and behavior, identify leaks and fixture malfunctions, and schedule fixture maintenance or upgrades based on their operation, all of which can ultimately help them meet goals for sustainable water use.

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Streamflow Data for the South Fork Little Bear River, UT
Created: March 29, 2017, 5:26 p.m.
Authors: Jeffery Horsburgh · Amber Spackman Jones · David K. Stevens · David Tarboton · Nancy O. Mesner

ABSTRACT:

This resource contains stage data collected at multiple sites in the South Fork of the Little Bear River in Cache County, UT. These data resulted from a project funded by the National Science Foundation originally aimed at testing sensor networks and environmental cyberinfrastructure within a hydrologic observatory test bed. The time series of continuous stage measurements and manually read stage plate readings are contained in the files labeled "Stage". Manual discharge measurements, surveyed water surface elevations, and corresponding quality controlled stage values are contained in the files labeled "StageDischarge". These values can be used to derive a stage-discharge relationship for estimating discharge at each site. Notes regarding quality control are provided in each file.

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ODM2 IPython Notebook Examples
Created: May 5, 2017, 2:59 p.m.
Authors: Jeffery Horsburgh

ABSTRACT:

This resource provides three examples of how to use ODM2 and its Python application programming interface (API). The three examples are as follows:

1. ODM2_Example1.ipynb: Create a blank ODM2 database and load controlled vocabulary terms from http://vocabulary.odm2.org in preparation for data loading.
2. ODM2_Example2.ipynb: Parse water quality sample data from an ODM2 Excel Template file using the ODM2 Python API and write data to an ODM2 database instance and a YAML Observations Data Archive (YODA) file.
3. ODM2_Example3.ipynb: Read water quality sample data from an ODM2 database instance using the ODM2 Python API for manipulation or visualization of the data.

Instructions for running each of these Notebooks using HydroShare's JupyterHub server are in the "readme.pdf" file in the Content section below.

The data used by these examples are packaged in the "data" folder within the content section below. Code for generating the ODM2 schema, loading the controlled vocabularies, the ODM2 Python API, and the YODA Tools utilities has been packaged for convenience in the "code" folder in the content section below. Each of these are available in their respective GitHub repositories see http://github.com/ODM2/.

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ABSTRACT:

This presentation describes Version 2 of the Observations Data Model (ODM2). ODM2 is an information model for describing and encoding spatially-discrete Earth observations. ODM2 and its related software ecosystem follow an open development model and can be found in GitHub at http://github.com/ODM2.

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Composite Resource Composite Resource

ABSTRACT:

This dataset includes data collected using a mobile sensing platform during baseflow and stormflow conditions in the Northwest Field Canal, located in Logan, UT. Data were collected by floating a payload of sensors in a longitudinal transect down the length of the canal and recording latitude, longitude, and several water quality variables, including fluorescent dissolved organic matter (FDOM), observations from custom fluorometers designed for calculating the fluorescence index (FI), dissolved oxygen, temperature, pH, specific conductance, and turbidity. The methods used in collection and processing of these data are described in detail in the methods document included within this resource.

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Composite Resource Composite Resource

ABSTRACT:

This dataset includes grab sample data collected during baseflow and stormflow conditions in the Northwest Field Canal (NWFC), located in Logan, UT. Grab sample data includes results from samples that were analyzed using dissolved organic carbon concentration analysis and excitation emission matrix spectroscopy to determine organic matter concentration and characteristics. Methods used in sample collection and analysis are described in detail within the methods document included as part of this resource.

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Composite Resource Composite Resource

ABSTRACT:

This dataset includes time series data collected during baseflow and stormflow conditions in the Northwest Field Canal, located in Logan, UT. Time series data includes fluorescent dissolved organic matter (FDOM), observations from custom fluorometers used to calculate the fluorescence index in situ, turbidity, and rainfall. Methods used for deploying sensors, collecting data, and processing for quality control are described in the methods document contained within this resource.

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Composite Resource Composite Resource
A demo composite resource
Created: Nov. 4, 2017, 4:09 p.m.
Authors: Jeffery Horsburgh

ABSTRACT:

A demo Composite Resource with some watersheds for the Blacksmith Fork watershed and some additional content.

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Composite Resource Composite Resource
Low cost, open-source, and low-power: But what to do with the data?
Created: Dec. 8, 2017, 8:11 p.m.
Authors: Jeffery Horsburgh · Anthony Keith Aufdenkampe · David Arscott · Sara Geleskie Damiano · Shannon Hicks

ABSTRACT:

There are now many ongoing efforts to develop low-cost, open-source, low-power sensors and datalogging solutions for environmental applications. Many of these have advanced to the point that high quality scientific measurements can be made using relatively inexpensive and increasingly off-the-shelf components. With the development of these innovative systems, however, comes the ability to generate large volumes of high-frequency monitoring data and the challenge of how to log, transmit, store, and share the resulting data. This presentation will focus on a new, web-based system http://data.envirodiy.org that was designed to enable citizen scientists to stream sensor data from a network of EnviroDIY Mayfly Arduino-based dataloggers. This system enables registration of new sensor nodes through a website. Once registered, any Internet connected device (e.g., cellular or WIFI) can then post data to the data.envirodiy.org website through a web service programming interface. Data are stored in a back-end data store that implements Version 2 of the Observations Data Model (ODM2). Live data can then be viewed and downloaded from the data.envirodiy.org website in a simple text format. While this system was purpose built to support an emerging network of Arduino-based sensor nodes deployed by citizen scientists in the Delaware River Basin, the architecture and components are generic and could be used by any Internet connected device capable of making measurements and formulating an HTTP POST request to send them to data.envirodiy.org.

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Composite Resource Composite Resource
Testing Existing Composite Resource
Created: May 9, 2018, 5:20 p.m.
Authors: Jeffery Horsburgh

ABSTRACT:

New abstract.

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Composite Resource Composite Resource

ABSTRACT:

This resource includes a data file containing high resolution water use data from a residential dormitory building on Utah State University's campus. The included iPython notebook demonstrates an analysis aimed at answering the question of how weekend (Saturday and Sunday) water demands are different than weekday (Monday - Friday) demands. The code in the Notbook does the following:

1. Resamples the data to hourly by summing incremental volumes
2. Bins the hourly data into weekday versus weekend data
3. Aggregates the data to average hourly and standard deviation by using the "groupby" function
4. Plots the aggregated weekday versus weekend data for visual comparison

···
Composite Resource Composite Resource

ABSTRACT:

HydroShare is a web-based hydrologic information system operated by the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI). Within HydroShare, users can create and share data and models using a variety of file formats and flexible metadata. HydroShare enables users to formally publish these resources as well as create linkages between published data and model resources and peer reviewed journal publications that describe them. Ability to link published data and models with the papers that describe them is a great step in the direction of scientific reproducibility, but is only a first step. HydroShare supports further transparency in the scientific process by enabling scripting of analytical steps via a RESTful application programming interface (API). Using this API, HydroShare users can develop scripts to read data from HydroShare, perform an analytical step (e.g., data processing or visualization), and then write results back to HydroShare. The script itself can then be shared as part of the published dataset in HydroShare, or it can be shared as a Jupyter Notebook that can be executed within the HydroShare environment. Scripts or Jupyter Notebooks can then be executed by others to reproduce the analysis used by the original authors. In this presentation, we discuss how HydroShare can enable best practices for linking publications with data and models and for promoting reproducibility in environmental analyses through sharing of data, models, and scripts that encode the scientific workflow. The HydroShare system is available at http://www.hydroshare.org. Source code for HydroShare is available at https://github.com/hydroshare.

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