Low-Cost, Open-Source, and Low-Power: But What to Do With the Data?

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

A data management and publication workflow for a large-scale, heterogeneous sensor network

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. © 2015, The Author(s

A webbased, interactive visualization tool for social environmental survey data

Understanding human motivations and actions related to environmental problems is central to modeling complex, human-natural systems. However, social science survey data on environmental issues are often presented in relatively static reports and figures and are not easily accessible for participatory deliberation. Federal data sharing mandates motivate innovative data visualization and sharing mechanisms. We developed an open-source, web-based Survey Data Viewer as a visual interface to explore quantitative social science survey data. We used the Python Django web framework and the D3.js visualization library to create and deploy the tool. The Viewer was implemented using a water-related survey administered to a large, random sample of Utah adults in public venues. The Viewer allows users to visualize question responses based on demographic variables with percentages and mean response levels. We developed a standardized template for encoding survey data and metadata that permits the generalization of the tool to other similar surveys