Data Repository and Publication
iUTAH researchers are developing datasets and models to support iUTAH research goals. We are using HydroShare to support the full data life cycle for these resources. HydroShare gives participants a venue for sharing and publication of their data products, and it supports discovery and access of heterogeneous datasets with clear metadata that is compliant with recognized standards.
Sensor Data Management
The CI team actively supports the streaming sensor data from the monitoring sites in the GAMUT network. The CI Team is assisting in the telemetry connections to each of the iUTAH monitoring sites, automated loading of sensor data into relational databases, and manages the tools for public web-based access and for data management and post processing. See this published paper for a full description of our sensor data workflow.
Web-Based Data Access and Visualization
The iUTAH CI Team has developed a web application for visualizing, summarizing, and exporting data collected by environmental sensors at iUTAH GAMUT monitoring sites. Features include faceted searching to browse available data series, a map interface to identify data available at different sites, and different plot types with descriptive statistics. The web-based data visualization is found at http://data.iutahepscor.org/tsa/.
Environmental monitoring networks such as GAMUT need to track physical inventory and related actions. We developed a data model and a web interface for entering, storing, and retrieving information on equipment and site visits, calibrations, and deployments. The web application is available via GitHUB: https://github.com/UCHIC/ODM2Sensor/
Sensor Data Quality Control
Sensor data streams typically include values that are not representative of environmental conditions and need editing due to fouling, drift, or unknown causes. The CI Team developed ODM Tools Python, an open source software application for post processing of sensor data. The software provides the functionality to move raw data to quality controlled products while capturing information about transformations to ensure that the full provenance of the data is recorded. ODM Tools Python is available at https://github.com/UCHIC/ODMToolsPython. We published a paper on the software here.
Survey Data Viewer
Social science survey data is useful for understanding perceptions regarding environmental issues. These data are often presented in static figures and reports with exploration limited to the original investigator. We developed a web-based viewer to provide access to survey data to various users. Features of the viewer include several view options and the ability to cross-tabulate data based on demographic variables. The Viewer is available at http://data.iutahepscor.org/surveys/ and development is documented at https://github.com/ODM2/SurveyDataViewer.
Hydrologic model coupling efforts have become an increasingly important and central focus of modern water resources research. iUTAH is uniquely positioned with research initiatives requiring innovative approaches to resolve the human, natural, and social aspects of water use in Utah. We are developing a software coupling framework to enable cross-disciplinary, collaborative, and asynchronous research. This framework borrows conceptually from other projects (e.g. OpenMI and CSDMS) to form a data-centric modeling platform. We aim to bridge the gap between observed hydrologic data and simulation computations by leveraging open data standards (WaterML and NetCDF) and relational database management systems (ODM2). We will support coupled water models by providing tools for (1) data discovery, collection, and analysis, (2) model design and execution, and (3) results analysis and archival. To learn more about this coupled modeling platform, visit the Environmental Model Integration Project code repository.
Streaming and Loading Sensor Data
Sensor data collected at remote field sites are typically logged and stored in text-based files. We are developing software to permit automated loading of sensor data into relational databases. The functionality includes mapping of text-based files with metadata associated with time series data and scheduling regular loads of the data to ODM2 databases. See https://github.com/ODM2/ODM2StreamingDataLoader for more information.
Specimen Based Data
Monitoring networks frequently collect specimens of water, soil, and air for subsequent analysis. The documentation for these data are distinct and more extensive than that of time series data. We are collaborating with the ODM2 and CZO Data communities for the development of data modeling standards and specifications for specimen-based data and working on tools to help manage iUTAH specimen-based datasets.