The FLUXNET2015 dataset provides ecosystem-scale data on CO 2 , water, and energy exchange between the biosphere and the atmosphere, and other meteorological and biological measurements, from 212 sites around the globe (over 1500 site-years, up to and including year 2014). These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. The dataset is already being used in a number of applications, including ecophysiology studies, remote sensing studies, and development of ecosystem and Earth system models. FLUXNET2015 includes derived-data products, such as gap-filled time series, ecosystem respiration and photosynthetic uptake estimates, estimation of uncertainties, and metadata about the measurements, presented for the first time in this paper. In addition, 206 of these sites are for the first time distributed under a Creative Commons (CC-BY 4.0) license. This paper details this enhanced dataset and the processing methods, now made available as open-source codes, making the dataset more accessible, transparent, and reproducible.

[1] We propose the Breathing Earth System Simulator (BESS), an upscaling approach to quantify global gross primary productivity and evapotranspiration using MODIS with a spatial resolution of 1-5 km and a temporal resolution of 8 days. This effort is novel because it is the first system that harmonizes and utilizes MODIS Atmosphere and Land products on the same projection and spatial resolution over the global land. This enabled us to use the MODIS Atmosphere products to calculate atmospheric radiative transfer for visual and near infrared radiation wave bands. Then we coupled atmospheric and canopy radiative transfer processes, with models that computed leaf photosynthesis, stomatal conductance and transpiration on the sunlit and shaded portions of the vegetation and soil. At the annual time step, the mass and energy fluxes derived from BESS showed strong linear relations with measurements of solar irradiance (r 2 = 0.95, relative bias: 8%), gross primary productivity (r 2 = 0.86, relative bias: 5%) and evapotranspiration (r 2 = 0.86, relative bias: 15%) in data from 33 flux towers that cover seven plant functional types across arctic to tropical climatic zones. A sensitivity analysis revealed that the gross primary productivity and evapotranspiration computed in BESS were most sensitive to leaf area index and solar irradiance, respectively. We quantified the mean global terrestrial estimates of gross primary productivity and evapotranpiration between 2001 and 2003 as 118 AE 26 PgC yr À1 and 500 AE 104 mm yr À1 (equivalent to 63,000 AE 13,100 km 3 yr À1 ), respectively. BESS-derived gross primary productivity and evapotranspiration estimates were consistent with the estimates from independent machine-learning, data-driven products, but the process-oriented structure has the advantage of diagnosing sensitivity of mechanisms. The process-based BESS is able to offer gridded biophysical variables everywhere from local to the total global land scales with an 8-day interval over multiple years.Citation : Ryu, Y., et al. (2011), Integration of MODIS land and atmosphere products with a coupled-process model to estimate gross primary productivity and evapotranspiration from 1 km to global scales, Global Biogeochem. Cycles, 25, GB4017,

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The combination of low-cost sensors, low-cost commodity computing, and the Internet is enabling a new era of data-intensive science. The dramatic increase in this data availability has created a new challenge for scientists: how to process the data. Scientists today are envisioning scientific computations on large scale data but are having difficulty designing software architectures to accommodate the large volume of the often heterogeneous and inconsistent data. In this paper, we introduce a particular instance of this challenge, and present our design and implementation of a MODIS satellite data reprojection and reduction pipeline in the Windows Azure cloud computing platform. This cloud-based pipeline is designed with a goal of hiding data complexities and subsequent data processing and transformation from end users. This pipeline is highly flexible and extensible to accommodate different science data processing tasks, and can be dynamically scaled to fulfill scientists' various computational requirements in a cost-efficient way. Experiments show that by running a practical large-scale science data processing job in the pipeline using 150 moderately-sized Azure virtual machine instances, we were able to produce analytical results in nearly 90X less time than was possible with a high-end desktop machine. To our knowledge, this is one of the first eScience applications to use the Windows Azure platform.

SUMMARYThe Internet, Web 2.0 and Social Networking technologies are enabling citizens to actively participate in 'citizen science' projects by contributing data to scientific programmes via the Web. However, the limited training, knowledge and expertise of contributors can lead to poor quality, misleading or even malicious data being submitted. Subsequently, the scientific community often perceive citizen science data as not worthy of being used in serious scientific research-which in turn, leads to poor retention rates for volunteers. In this paper, we describe a technological framework that combines data quality improvements and trust metrics to enhance the reliability of citizen science data. We describe how online social trust models can provide a simple and effective mechanism for measuring the trustworthiness of community-generated data. We also describe filtering services that remove unreliable or untrusted data and enable scientists to confidently reuse citizen science data. The resulting software services are evaluated in the context of the CoralWatch project-a citizen science project that uses volunteers to collect comprehensive data on coral reef health.