Challenges in Building an End-to-End System for Acquisition, Management, and Integration of Diverse Data From Sensor Networks in Watersheds: Lessons From a Mountainous Community Observatory in East River, Colorado

The U.S. Department of Energy's Watershed Function Scientific Focus Area (SFA), centered in the East River, Colorado, generates diverse datasets including hydrological, geological, geochemical, geophysical, ecological, microbiological and remote sensing data. The project has deployed extensive field infrastructure involving hundreds of sensors that measure highly diverse phenomena (e.g. stream and groundwater hydrology, water quality, soil moisture, weather) across the watershed. Data from the sensor network are telemetered and automatically ingested into a queryable database. The data are subsequently quality checked, integrated with the United States Geological Survey's stream monitoring network using a custom data integration broker, and published to a portal with interactive visualizations. The resulting data products are used in a variety of scientific modeling and analytical efforts. This paper describes the SFA's end-to-end infrastructure and services that support the generation of integrated datasets from a watershed sensor network. The development and maintenance of this infrastructure, presents a suite of challenges from practical field logistics to complex data processing, which are addressed through various solutions. In particular, the SFA adopts a holistic view for data collection, assessment and integration, which dramatically improves the products generated, and enables a co-design approach wherein data collection is informed by model results and vice-versa.U.S. Department of EnergyUnited States Department of Energy (DOE) [DE-AC02-05CH11231]; WatershedFunction Scientific Focus Area - U.S. Department of Energy, Office of Science, Office of Biological, and Environmental ResearchUnited States Department of Energy (DOE) [DE-AC02-05CH11231]; National Energy Research Scientific Computing Center (NERSC), U.S. Department of Energy Office of Science User FacilityUnited States Department of Energy (DOE) [DE-AC02-05CH11231]; Environmental Systems Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE) [DE-AC02-05CH11231]; [DE-SC0009732]; [DE-SC0018447]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Findings, Gaps, and Future Direction for Research in Nonpharmaceutical Interventions for Pandemic Influenza

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85533/1/Findings, Gaps, and Future Direction for Research in Nonpharmaceutical Interventions for Pandemic Influenza | CDC EID.pd

Enabling FAIR data in Earth and environmental science with community-centric (meta)data reporting formats.

Research can be more transparent and collaborative by using Findable, Accessible, Interoperable, and Reusable (FAIR) principles to publish Earth and environmental science data. Reporting formats-instructions, templates, and tools for consistently formatting data within a discipline-can help make data more accessible and reusable. However, the immense diversity of data types across Earth science disciplines makes development and adoption challenging. Here, we describe 11 community reporting formats for a diverse set of Earth science (meta)data including cross-domain metadata (dataset metadata, location metadata, sample metadata), file-formatting guidelines (file-level metadata, CSV files, terrestrial model data archiving), and domain-specific reporting formats for some biological, geochemical, and hydrological data (amplicon abundance tables, leaf-level gas exchange, soil respiration, water and sediment chemistry, sensor-based hydrologic measurements). More broadly, we provide guidelines that communities can use to create new (meta)data formats that integrate with their scientific workflows. Such reporting formats have the potential to accelerate scientific discovery and predictions by making it easier for data contributors to provide (meta)data that are more interoperable and reusable

Research findings from nonpharmaceutical intervention studies for pandemic influenza and current gaps in the research

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/85502/1/PIIS0196655310000398.pd