text: Ecosystems monitoring is essential to properly understand their development and the effects of events, both climatological and anthropological in nature. The amount of data used in these assessments is increasing at very high rates. This is due to increasing availability of sensing systems and the development of new techniques to analyze sensor data. The Enviro-Net Project encompasses several of such sensor system deployments across five countries in the Americas. These deployments use a few different ground-based sensor systems, installed at different heights monitoring the conditions in tropical dry forests over long periods of time. This paper presents our experience in deploying and maintaining these systems, retrieving and pre-processing the data, and describes the Web portal developed to help with data management, visualization and analysis.
  @cite_27: Many of today’s large-scale scientific projects attempt to collect data from a diverse set of sources. The traditional campaign-style approach to “synthesis” efforts gathers data through a single concentrated effort, and the data contributors know in advance exactly who will use their data and why. At even moderate scales, the cost and time required to find, gather, collate, normalize, and customize data in order to build a synthesis dataset can quickly outweigh the value of the resulting dataset. By explicitly identifying and addressing the different requirements for each data role (author, publisher, curator, and consumer), our data management architecture for large-scale shared scientific data enables the creation of such synthesis datasets that continue to grow and evolve with new data, data annotations, participants, and use rules. We show the effectiveness of our approach in the context of the FLUXNET Synthesis Dataset, one of the largest ongoing biogeophysical experiments.
  @cite_21: We present a science-centric evaluation of a 19-day sensor network deployment at Reventador, an active volcano in Ecuador. Each of the 16 sensors continuously sampled seismic and acoustic data at 100 Hz. Nodes used an event-detection algorithm to trigger on interesting volcanic activity and initiate reliable data transfer to the base station. During the deployment, the network recorded 229 earthquakes, eruptions, and other seismoacoustic events. The science requirements of reliable data collection, accurate event detection, and high timing precision drive sensor networks in new directions for geophysical monitoring. The main contribution of this paper is an evaluation of the sensor network as a scientific instrument, holding it to the standards of existing instrumentation in terms of data fidelity (the quality and accuracy of the recorded signals) and yield (the quantity of the captured data). We describe an approach to time rectification of the acquired signals that can recover accurate timing despite failures of the underlying time synchronization protocol. In addition, we perform a detailed study of the sensor network's data using a direct comparison to a standalone data logger, as well as an investigation of seismic and acoustic wave arrival times across the network.
  @cite_32: Data stream management is concerned with managing large streams of data arriving from data communications or sensor networks. Interest in this area is increasing, and it will grow rapidly over the coming decade, driven by rapid growth in the pervasiveness and bandwidth of digital-communications networks and an impending explosion in sensor networks. This special issue provides a snapshot of ongoing work in this area.
  @cite_3: Science is increasingly driven by data collected automatically from arrays of inexpensive sensors. The collected data volumes require a different approach from the scientists' current Excel spreadsheet storage and analysis model. Spreadsheets work well for small data sets; but scientists want high level summaries of their data for various statistical analyses without sacrificing the ability to drill down to every bit of the raw data. This demonstration describes our prototype data analysis system that is suitable for browsing and visualization - like a spreadsheet - but scalable to much larger data sets.
summary: Although it is common to think about sensor data management as stream data management, with the associated challenges (on-line aggregation, classification, ) @cite_32 , at least within environmental research, particularly in ground-based monitoring, this is not a frequent scenario. Most of the current applications based on sensor data use the perspective of historical (or an archive of) time series data. Applications using the stream data perspective are only beginning to appear, and the current applications that do require that perspective---e.g., volcano monitoring @cite_21 ---are still the exception. Data manipulation for most of the current applications is done after having the data collected and stored, applying a variety of analytical operations in an offline fashion @cite_3 @cite_27 .