There are now many ongoing efforts to develop low-cost, open-source, low-power sensors and datalogging solutions for environmental monitoring 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 paper describes a new web application that was designed to enable citizen scientists to stream sensor data from a network of Arduino-based dataloggers to a web-based Data Sharing Portal. This system enables registration of new sensor nodes through a Data Sharing Portal website. Once registered, any Internet connected data-logging device (e.g., connected via cellular or Wi-Fi) can then post data to the portal through a web service application programming interface (API). 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 using multiple visualization tools, downloaded from the Data Sharing Portal in a simple text format, or accessed via WaterOneFlow web services for machine-to-machine data exchange. This system was built to support an emerging network of open-source, wireless water quality monitoring stations developed and deployed by the EnviroDIY community for do-it-yourself environmental science and monitoring, initially within the Delaware River Watershed. However, the architecture and components of the ODM2 Data Sharing Portal are generic, open-source, and could be deployed for use with any Internet connected device capable of making measurements and formulating an HTTP POST request.

It is common for hydrology researchers to collect data using in situ sensors at high frequencies, for extended durations, and with spatial distributions that produce data volumes requiring infrastructure for data storage, management, and sharing. The availability and utility of these data in addressing scientific questions related to water availability, water quality, and natural disasters relies on effective cyberinfrastructure that facilitates transformation of raw sensor data into usable data products. It also depends on the ability of researchers to share and access the data in useable formats. In this paper, we describe a data management and publication workflow and software tools for research groups and sites conducting long-term monitoring using in situ sensors. Functionality includes the ability to track monitoring equipment inventory and events related to field maintenance. Linking this information to the observational data is imperative in ensuring the quality of sensor-based data products. We present these tools in the context of a case study for the innovative Urban Transitions and Aridregion Hydrosustainability (iUTAH) sensor network. The iUTAH monitoring network includes sensors at aquatic and terrestrial sites for continuous monitoring of common meteorological variables, snow accumulation and melt, soil moisture, surface water flow, and surface water quality. We present the overall workflow we have developed for effectively transferring data from field monitoring sites to ultimate end-users and describe the software tools we have deployed for storing, managing, and sharing the sensor data. These tools are all open source and available for others to use.

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<p>Collecting and managing high temporal resolution (< 1 minute) residential water use data is challenging due to cost and technical requirements associated with the volume and velocity of data collected. It is well known that this type of data has potential to expand our knowledge of residential water use, inform future water use predictions, and improve water conservation strategies. However, most studies collecting this type of data have been focused on the practical application of the data (e.g., developing and applying end use disaggregation algorithms) with much less focus on how the data were collected, retrieved, quality controlled, and managed to enable data visualization and analysis. We developed an open-source, modular, generalized cyberinfrastructure system to automate the process from data collection to analysis. The system has three main architectural components: first, the sensors and dataloggers for water use monitoring; second, the data communication, parsing and archival tools; and third, the analyses, visualization and presentations of data produced for different audiences. For the first component, we present a low-cost datalogging device, designed for installation on top of existing, analog, magnetically driven, positive displacement, residential water meters that can collect data at a user configurable time resolution interval. The second component consists of a system developed using existing open-source software technologies that manages the data collected, including services and databasing. The final element includes software tools for retrieving the data that can be integrated with advanced data analytics tools. The system was used in a single family residential water use data collection case study to test the scalability and performance of its functionalities within our design constraints. Testing with a base system configuration, our results show that the system requires approximately six minutes to process a single day of data collected at a four second temporal resolution for 500 properties. Thus, the system proved to be effective beyond the typical number of participants observed in similar studies of residential water use and would scale well beyond this even with the modest system resources we used for testing. All elements of the cyberinfrastructure developed are freely available in open source repositories for re-use.</p>