Institution
Wageningen University and Research Centre
Education•Wageningen, Netherlands•
About: Wageningen University and Research Centre is a education organization based out in Wageningen, Netherlands. It is known for research contribution in the topics: Population & Sustainability. The organization has 23474 authors who have published 54833 publications receiving 2608897 citations.
Topics: Population, Sustainability, Agriculture, Climate change, Gene
Papers published on a yearly basis
Papers
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TL;DR: 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, and is detailed in this paper.
Abstract: The FLUXNET2015 dataset provides ecosystem-scale data on CO2, 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.
681 citations
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TL;DR: A novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems.
Abstract: [1] There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices.
678 citations
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TL;DR: An overview of scientific issues that need to be addressed with priority in order to improve the risk assessment of nanotechnologies and nanoparticles in food products is given.
677 citations
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University of Duisburg-Essen1, University of Düsseldorf2, Harvard University3, University of Warsaw4, University of Melbourne5, St. Vincent's Institute of Medical Research6, Johns Hopkins University7, Swiss Institute of Bioinformatics8, The Turing Institute9, Western General Hospital10, University of British Columbia11, BC Cancer Agency12, ETH Zurich13, Delft University of Technology14, Leiden University Medical Center15, Broad Institute16, Georgia State University17, Heidelberg Institute for Theoretical Studies18, Karlsruhe Institute of Technology19, Centrum Wiskunde & Informatica20, Utrecht University21, University of Amsterdam22, Imperial College London23, Radboud University Nijmegen24, University Medical Center Groningen25, Wageningen University and Research Centre26, University of Connecticut27, Wellcome Trust Sanger Institute28, University of Cambridge29, European Bioinformatics Institute30, Max Planck Society31, Saarland University32, Zuse Institute Berlin33, German Cancer Research Center34, Leiden University35, I.M. Sechenov First Moscow State Medical University36, Princeton University37, Memorial Sloan Kettering Cancer Center38
TL;DR: This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years in single-cell data science.
Abstract: The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands-or even millions-of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.
677 citations
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Helmholtz Centre for Environmental Research - UFZ1, Finnish Environment Institute2, James Hutton Institute3, European Centre for Nature Conservation4, Humboldt University of Berlin5, Wageningen University and Research Centre6, University of Antwerp7, Szent István University8, Polish Academy of Sciences9, Estonian University of Life Sciences10, University of Grenoble11
TL;DR: To realise their full potential, NBS must be developed by including the experience of all relevant stakeholders such that 'solutions' contribute to achieving all dimensions of sustainability.
677 citations
Authors
Showing all 23851 results
Name | H-index | Papers | Citations |
---|---|---|---|
Walter C. Willett | 334 | 2399 | 413322 |
Albert Hofman | 267 | 2530 | 321405 |
Frank B. Hu | 250 | 1675 | 253464 |
Willem M. de Vos | 148 | 670 | 88146 |
Willy Verstraete | 139 | 920 | 76659 |
Jonathan D. G. Jones | 129 | 417 | 80908 |
Bert Brunekreef | 124 | 806 | 81938 |
Pedro W. Crous | 115 | 809 | 51925 |
Marten Scheffer | 111 | 350 | 73789 |
Wim E. Hennink | 110 | 600 | 49940 |
Daan Kromhout | 108 | 453 | 55551 |
Peter H. Verburg | 107 | 464 | 34254 |
Marcel Dicke | 107 | 613 | 42959 |
Vincent W. V. Jaddoe | 106 | 1008 | 44269 |
Hao Wu | 105 | 669 | 42607 |