8 research outputs found
A novel modelâdata fusion approach to terrestrial carbon cycle reanalysis across the contiguous U.S using SIPNET and PEcAn state data assimilation system v. 1.7.2
The ability to monitor, understand, and predict the dynamics of the terrestrial carbon cycle requires the capacity to
robustly and coherently synthesize multiple streams of information that each provide partial information about different pools
and fluxes. In this study, we introduce a new terrestrial carbon cycle data assimilation system, built on the PEcAn modeldata
eco-informatics system, and its application for the development of a proof-of-concept carbon "reanalysis" product that
harmonizes carbon 5 pools (leaf, wood, soil) and fluxes (GPP, Ra, Rh, NEE) across the contiguous United States from 1986-
2019. We first calibrated this system against plant trait and flux tower Net Ecosystem Exchange (NEE) using a novel emulated
hierarchical Bayesian approach. Next, we extended the Tobit-Wishart Ensemble Filter (TWEnF) State Data Assimilation (SDA)
framework, a generalization of the common Ensemble Kalman Filter which accounts for censored data and provides a fully
Bayesian estimate of model process error, to a regional-scale system with a calibrated localization. Combined with additional
10 workflows for propagating parameter, initial condition, and driver uncertainty, this represents the most complete and robust
uncertainty accounting available for terrestrial carbon models. Our initial reanalysis was run on an irregular grid of â 500 points
selected using a stratified sampling method to efficiently capture environmental heterogeneity. Remotely sensed observations
of aboveground biomass (Landsat LandTrendr) and LAI (MODIS MOD15) were sequentially assimilated into the SIPNET
model. Reanalysis soil carbon, which was indirectly constrained based on modeled covariances, showed general agreement
15 with SoilGrids, an independent soil carbon data product. Reanalysis NEE, which was constrained based on posterior ensemble
weights, also showed good agreement with eddy flux tower NEE and reduced RMSE compared to the calibrated forecast.
Ultimately, PEcAnâs carbon cycle reanalysis provides a scalable framework for harmonizing multiple data constraints and
providing a uniform synthetic platform for carbon monitoring, reporting, and verification (MRV) and accelerating terrestrial
carbon cycle research.Published versio
Towards a Social Trust-Aware Recommender for Teachers
Fazeli, S., Drachsler, H., Brouns, F., & Sloep, P. B. (2014). Towards a Social Trust-aware Recommender for Teachers. In N. Manouselis, H. Drachsler, K. Verbert & O. C. Santos (Eds.), Recommender Systems for Technology Enhanced Learning (pp. 177-194): Springer New York.Online communities and networked learning provide teachers with social learning opportunities, allowing them to interact and collaborate with others in order to develop their personal and professional skills. However, with the large number of learning resources produced everyday, teachers need to find out what are the most suitable ones for them. In this paper, we introduce recommender systems as a potential solution to this . The setting is the Open Discovery Space (ODS) project. Unfortunately, due to the sparsity of the educational datasets most educational recommender systems cannot make accurate recommendations. To overcome this problem, we propose to enhance a trust-based recommender algorithm with social data obtained from monitoring the activities of teachers within the ODS platform. In this article, we outline the re-quirements of the ODS recommender system based on experiences reported in related TEL recommender system studies. In addition, we provide empirical ev-idence from a survey study with stakeholders of the ODS project to support the requirements identified from a literature study. Finally, we present an agenda for further research intended to find out which recommender system should ul-timately be deployed in the ODS platform.NELLL, EU 7th framework Open Discovery Spac