Model Observational Data Research Articles

A Relational Data Model for Advancing Stormwater Infrastructure Management

To date, the knowledge benefits that can result from the growing abundance of measured stormwater data have yet to be fully realized within the industry. Obstacles due to data format and storage, retrieval, and quality control have limited the size and impact of accessing known data, resulting in data that are often siloed by project or objective, which inhibits advancements in our understanding, as well as decision making and information sharing across municipalities. Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI)’s observations data model (ODM) is a relational data model designed to organize disparate types of data; however, it does not provide avenues for the storage of stormwater monitoring–specific metadata. To facilitate stormwater infrastructure data analysis, the Villanova stormwater infrastructure data model (SIDM), constructed comparably to ODM1, was created by adding and removing tables from the ODM1 structure, along with other modifications to enable efficient data access. The novel data model presented here facilitates a comprehensive spatiotemporal analysis specific to stormwater systems to overcome traditional stormwater data siloes and harness the benefits of stormwater data through efficient avenues. The data model structure of the SIDM enables stormwater-specific data to be managed efficiently not only to provide data regarding function and performance but to advance the science around stormwater planning and management, as well as knowledge sharing across researchers, utilities, and municipalities. In this paper, we describe the logic of the design, benefits of the data model, and applications to specific green stormwater infrastructure installations. This has the potential to not only provide novel insight but open the door for applications of modeling advancement in physics-based models and beyond to artificial intelligence, data analysis, and data-informed decision making in the planning, design, operation, and maintenance of stormwater systems.

Rapid Prototyping of An Automated Sensor-to-Server Environmental Data Acquisition System Adopting A FAIR-Oriented Approach

Most existing environmental data acquisition systems are not designed to support automatic field data streaming to a data management system, but instead involve manual data exports therein. This paper introduces a FAIR-oriented (Findable, Accessible, Interoperable, and Reusable) approach and prototype of an automated sensor-to-web services and analytics wireless sensor network in which the aspects of data collection, transmission, and management as well as network organization are implemented automatically. The Python programming language was used to develop the necessary software components. The data and metadata supplied by custom-made stations are automatically stored in an extended instance of the Consortium of Universities for the Advancement of Hydrologic Sciences, Inc. (CUAHSI) Observations Data Model (ODM) to which a web interface is linked and makes the data available publicly in user’s preferred units via Web Services and Data Analytics at a central station. The system has been initially tested in outdoor environments and the experiments demonstrate that it is effective in not only reducing the workload of the post-deployment phase, but also has potential to reduce human errors.

Real-time data assimilation potential to connect micro-smart water test bed and hydraulic model

Abstract Recently, smart water application has gained worldwide attention, but there is a lack of understanding of how to construct smart water networks. This is partly because of the limited investigation into how to combine physical experiments with model simulations. This study aimed to investigate the process of connecting micro-smart water test bed (MWTB) and a ‘two-loop’ ENAPENT hydraulic model, which involves experimental set-up, real-time data acquisition, hydraulic simulation, and system performance demonstration. In this study, a MWTB was established based on the flow sensing technology. The data generated by the MWTB were stored in Observation Data Model (ODM) database for visualization in RStudio environment and also archived as the input of EPANET hydraulic simulation. The data visualization fitted the operation scenarios of the MWTB well. Additionally, the fitting degree between the experimental measurements and modeling outputs indicates the ‘two-loop’ ENAPNET model can represent the operation of MWTB for better understanding of hydraulic analysis.

Open source software for visualization and quality control of continuous hydrologic and water quality sensor data

It is common for in situ hydrologic and water quality data to be collected at high frequencies and for extended durations. These data streams, which may also be collected across many monitoring sites require infrastructure for data storage and management. The Observations Data Model (ODM), which is part of the Consortium of Universities for the Advancement of Hydrologic Science, Inc. (CUAHSI) Hydrologic Information System (HIS), was developed as a standard data model in which to organize, store, and describe point observations data. In this paper we describe ODM Tools Python, an open source software application that allows users to query and export, visualize, and perform quality control post processing on time series of environmental observations data stored in an ODM database using automated Python scripting that records the corrections and adjustments made to data series in the quality control process and ensures data editing steps are traceable and reproducible.

CohortExplorer: A Generic Application Programming Interface for Entity Attribute Value Database Schemas.

BackgroundMost electronic data capture (EDC) and electronic data management (EDM) systems developed to collect and store clinical data from participants recruited into studies are based on generic entity-attribute-value (EAV) database schemas which enable rapid and flexible deployment in a range of study designs. The drawback to such schemas is that they are cumbersome to query with structured query language (SQL). The problem increases when researchers involved in multiple studies use multiple electronic data capture and management systems each with variation on the EAV schema.ObjectiveThe aim of this study is to develop a generic application which allows easy and rapid exploration of data and metadata stored under EAV schemas that are organized into a survey format (questionnaires/events, questions, values), in other words, the Clinical Data Interchange Standards Consortium (CDISC) Observational Data Model (ODM).MethodsCohortExplorer is written in Perl programming language and uses the concept of SQL abstract which allows the SQL query to be treated like a hash (key-value pairs).ResultsWe have developed a tool, CohortExplorer, which once configured for a EAV system will "plug-n-play" with EAV schemas, enabling the easy construction of complex queries through an abstracted interface. To demonstrate the utility of the CohortExplorer system, we show how it can be used with the popular EAV based frameworks; Opal (OBiBa) and REDCap.ConclusionsThe application is available under a GPL-3+ license at the CPAN website. Currently the application only provides datasource application programming interfaces (APIs) for Opal and REDCap. In the future the application will be available with datasource APIs for all major electronic data capture and management systems such as OpenClinica and LabKey. At present the application is only compatible with EAV systems where the metadata is organized into surveys, questionnaires and events. Further work is needed to make the application compatible with EAV schemas where the metadata is organized into hierarchies such as Informatics for Integrating Biology & the Bedside (i2b2). A video tutorial demonstrating the application setup, datasource configuration, and search features is available on YouTube. The application source code is available at the GitHub website and the users are encouraged to suggest new features and contribute to the development of APIs for new EAV systems.