USGS Science Support Framework

  • Data Collection Part 1: How to avoid a spreadsheet mess - Lessons learned from an ecologist

    Most scientists have experienced the disappointment of opening an old data file and not fully understanding the contents. During data collection, we frequently optimize ease and efficiency of data entry, producing files that are not well formatted or described for longer term uses, perhaps assuming in the moment that the details of our experiments and observations would be impossible to forget. We can make the best of our sometimes embarrassing data management errors by using them as ‘teachable moments’, opening our dusty file drawers to explore the most common errors, and some quick fixes to improve day-to-day approaches to data.
     

  • Data Collection Part 2: Relational databases - Getting the foundation right

  • Data Sharing and Management within a Large-Scale, Heterogeneous Sensor Network using the CUAHSI Hydrologic Information System

    Hydrology researchers are collecting data using in situ sensors at high frequencies, for extended durations, and with spatial distributions that require infrastructure for data storage, management, and sharing. Managing streaming sensor data is challenging, especially in large networks with large numbers of sites and sensors.  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 presentation I will describe tools that have been developed for research groups and sites conducting long term monitoring using in situ sensors.  Functionality includes the ability to track equipment, deployments, calibrations, and other events related to monitoring site maintenance and to link this information to the observational data that they are collecting, which is imperative in ensuring the quality of sensor-based data products. I will present these tools in the context of a data management and publication workflow case study for the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) network of aquatic and terrestrial sensors.  iUTAH researchers have developed and deployed an ecohydrologic observatory to monitor Gradients Along Mountain to Urban Transitions (GAMUT). The GAMUT Network measures aspects of water inputs, outputs, and quality along a mountain-to-urban gradient in three watersheds that share common water sources (winter-derived precipitation) but differ in the human and biophysical nature of land-use transitions. GAMUT includes sensors at aquatic and terrestrial sites for real-time monitoring of common meteorological variables, snow accumulation and melt, soil moisture, surface water flow, and surface water quality. I will present the overall workflow we have developed, our use of existing software tools from the CUAHSI Hydrologic Information System, and new software tools that we have deployed for both managing the sensor infrastructure and for storing, managing, and sharing the sensor data.