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Institution

University of Groningen

EducationGroningen, Groningen, Netherlands
About: University of Groningen is a education organization based out in Groningen, Groningen, Netherlands. It is known for research contribution in the topics: Population & Context (language use). The organization has 36346 authors who have published 69116 publications receiving 2940370 citations. The organization is also known as: Rijksuniversiteit Groningen & RUG.


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Journal ArticleDOI
Corinne Le Quéré1, Robbie M. Andrew, Pierre Friedlingstein2, Stephen Sitch2, Julia Pongratz3, Andrew C. Manning1, Jan Ivar Korsbakken, Glen P. Peters, Josep G. Canadell4, Robert B. Jackson5, Thomas A. Boden6, Pieter P. Tans7, Oliver Andrews1, Vivek K. Arora, Dorothee C. E. Bakker1, Leticia Barbero8, Leticia Barbero9, Meike Becker10, Meike Becker11, Richard Betts12, Richard Betts2, Laurent Bopp13, Frédéric Chevallier14, Louise Chini15, Philippe Ciais14, Catherine E Cosca7, Jessica N. Cross7, Kim I. Currie16, Thomas Gasser17, Ian Harris1, Judith Hauck18, Vanessa Haverd4, Richard A. Houghton19, Christopher W. Hunt20, George C. Hurtt15, Tatiana Ilyina3, Atul K. Jain21, Etsushi Kato, Markus Kautz22, Ralph F. Keeling23, Kees Klein Goldewijk24, Kees Klein Goldewijk25, Arne Körtzinger26, Peter Landschützer3, Nathalie Lefèvre27, Andrew Lenton28, Andrew Lenton29, Sebastian Lienert30, Sebastian Lienert31, Ivan D. Lima19, Danica Lombardozzi32, Nicolas Metzl27, Frank J. Millero33, Pedro M. S. Monteiro34, David R. Munro35, Julia E. M. S. Nabel3, Shin-Ichiro Nakaoka36, Yukihiro Nojiri36, X. Antonio Padin37, Anna Peregon14, Benjamin Pfeil10, Benjamin Pfeil11, Denis Pierrot8, Denis Pierrot9, Benjamin Poulter38, Benjamin Poulter39, Gregor Rehder40, Janet J. Reimer41, Christian Rödenbeck3, Jörg Schwinger10, Roland Séférian14, Ingunn Skjelvan10, Benjamin D. Stocker, Hanqin Tian42, Bronte Tilbrook28, Bronte Tilbrook29, Francesco N. Tubiello43, Ingrid T. van der Laan-Luijkx44, Guido R. van der Werf45, Steven van Heuven46, Nicolas Viovy14, Nicolas Vuichard14, Anthony P. Walker6, Andrew J. Watson2, Andy Wiltshire12, Sönke Zaehle3, Dan Zhu14 
University of East Anglia1, University of Exeter2, Max Planck Society3, Commonwealth Scientific and Industrial Research Organisation4, Stanford University5, Oak Ridge National Laboratory6, National Oceanic and Atmospheric Administration7, Cooperative Institute for Marine and Atmospheric Studies8, Atlantic Oceanographic and Meteorological Laboratory9, Bjerknes Centre for Climate Research10, Geophysical Institute, University of Bergen11, Met Office12, École Normale Supérieure13, Centre national de la recherche scientifique14, University of Maryland, College Park15, National Institute of Water and Atmospheric Research16, International Institute for Applied Systems Analysis17, Alfred Wegener Institute for Polar and Marine Research18, Woods Hole Oceanographic Institution19, University of New Hampshire20, University of Illinois at Urbana–Champaign21, Karlsruhe Institute of Technology22, University of California, San Diego23, Utrecht University24, Netherlands Environmental Assessment Agency25, Leibniz Institute of Marine Sciences26, University of Paris27, Hobart Corporation28, Cooperative Research Centre29, Oeschger Centre for Climate Change Research30, University of Bern31, National Center for Atmospheric Research32, University of Miami33, Council of Scientific and Industrial Research34, Institute of Arctic and Alpine Research35, National Institute for Environmental Studies36, Spanish National Research Council37, Goddard Space Flight Center38, Montana State University39, Leibniz Institute for Baltic Sea Research40, University of Delaware41, Auburn University42, Food and Agriculture Organization43, Wageningen University and Research Centre44, VU University Amsterdam45, University of Groningen46
TL;DR: In this paper, the authors quantify the five major components of the global carbon budget and their uncertainties, and the resulting carbon budget imbalance (BIM) is a measure of imperfect data and understanding of the contemporary carbon cycle.
Abstract: Accurate assessment of anthropogenic carbon dioxide (CO2) emissions and their redistribution among the atmosphere, ocean, and terrestrial biosphere – the "global carbon budget" – is important to better understand the global carbon cycle, support the development of climate policies, and project future climate change. Here we describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties. CO2 emissions from fossil fuels and industry (EFF) are based on energy statistics and cement production data, respectively, while emissions from land-use change (ELUC), mainly deforestation, are based on land-cover change data and bookkeeping models. The global atmospheric CO2 concentration is measured directly and its rate of growth (GATM) is computed from the annual changes in concentration. The ocean CO2 sink (SOCEAN) and terrestrial CO2 sink (SLAND) are estimated with global process models constrained by observations. The resulting carbon budget imbalance (BIM), the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a measure of imperfect data and understanding of the contemporary carbon cycle. All uncertainties are reported as ±1σ. For the last decade available (2007–2016), EFF was 9.4 ± 0.5 GtC yr−1, ELUC 1.3 ± 0.7 GtC yr−1, GATM 4.7 ± 0.1 GtC yr−1, SOCEAN 2.4 ± 0.5 GtC yr−1, and SLAND 3.0 ± 0.8 GtC yr−1, with a budget imbalance BIM of 0.6 GtC yr−1 indicating overestimated emissions and/or underestimated sinks. For year 2016 alone, the growth in EFF was approximately zero and emissions remained at 9.9 ± 0.5 GtC yr−1. Also for 2016, ELUC was 1.3 ± 0.7 GtC yr−1, GATM was 6.1 ± 0.2 GtC yr−1, SOCEAN was 2.6 ± 0.5 GtC yr−1, and SLAND was 2.7 ± 1.0 GtC yr−1, with a small BIM of −0.3 GtC. GATM continued to be higher in 2016 compared to the past decade (2007–2016), reflecting in part the high fossil emissions and the small SLAND consistent with El Nino conditions. The global atmospheric CO2 concentration reached 402.8 ± 0.1 ppm averaged over 2016. For 2017, preliminary data for the first 6–9 months indicate a renewed growth in EFF of +2.0 % (range of 0.8 to 3.0 %) based on national emissions projections for China, USA, and India, and projections of gross domestic product (GDP) corrected for recent changes in the carbon intensity of the economy for the rest of the world. This living data update documents changes in the methods and data sets used in this new global carbon budget compared with previous publications of this data set (Le Quere et al., 2016, 2015b, a, 2014, 2013). All results presented here can be downloaded from https://doi.org/10.18160/GCP-2017 (GCP, 2017).

884 citations

Journal ArticleDOI
Jens Kattge1, Gerhard Bönisch2, Sandra Díaz3, Sandra Lavorel  +751 moreInstitutions (314)
TL;DR: The extent of the trait data compiled in TRY is evaluated and emerging patterns of data coverage and representativeness are analyzed to conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements.
Abstract: Plant traits-the morphological, anatomical, physiological, biochemical and phenological characteristics of plants-determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait-based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits-almost complete coverage for 'plant growth form'. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait-environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives.

882 citations

Journal ArticleDOI
Roel Aaij, Bernardo Adeva1, Marco Adinolfi2, A. A. Affolder3  +700 moreInstitutions (63)
TL;DR: In this paper, the performance of the various LHCb sub-detectors and the trigger system are described, using data taken from 2010 to 2012, and it is shown that the design criteria of the experiment have been met.
Abstract: The LHCb detector is a forward spectrometer at the Large Hadron Collider (LHC) at CERN. The experiment is designed for precision measurements of CP violation and rare decays of beauty and charm hadrons. In this paper the performance of the various LHCb sub-detectors and the trigger system are described, using data taken from 2010 to 2012. It is shown that the design criteria of the experiment have been met. The excellent performance of the detector has allowed the LHCb collaboration to publish a wide range of physics results, demonstrating LHCb's unique role, both as a heavy flavour experiment and as a general purpose detector in the forward region.

880 citations

Journal ArticleDOI
Nabila Aghanim1, Yashar Akrami2, Yashar Akrami3, Frederico Arroja4  +251 moreInstitutions (72)
TL;DR: In this paper, the authors present the cosmological legacy of the Planck satellite, which provides the strongest constraints on the parameters of the standard cosmology model and some of the tightest limits available on deviations from that model.
Abstract: The European Space Agency’s Planck satellite, which was dedicated to studying the early Universe and its subsequent evolution, was launched on 14 May 2009. It scanned the microwave and submillimetre sky continuously between 12 August 2009 and 23 October 2013, producing deep, high-resolution, all-sky maps in nine frequency bands from 30 to 857 GHz. This paper presents the cosmological legacy of Planck, which currently provides our strongest constraints on the parameters of the standard cosmological model and some of the tightest limits available on deviations from that model. The 6-parameter ΛCDM model continues to provide an excellent fit to the cosmic microwave background data at high and low redshift, describing the cosmological information in over a billion map pixels with just six parameters. With 18 peaks in the temperature and polarization angular power spectra constrained well, Planck measures five of the six parameters to better than 1% (simultaneously), with the best-determined parameter (θ*) now known to 0.03%. We describe the multi-component sky as seen by Planck, the success of the ΛCDM model, and the connection to lower-redshift probes of structure formation. We also give a comprehensive summary of the major changes introduced in this 2018 release. The Planck data, alone and in combination with other probes, provide stringent constraints on our models of the early Universe and the large-scale structure within which all astrophysical objects form and evolve. We discuss some lessons learned from the Planck mission, and highlight areas ripe for further experimental advances.

879 citations

Journal ArticleDOI
TL;DR: In this paper, a model for understanding the causes of research shopping is proposed, and potential strategies for managing it are investigated, including attribute-based decision-making, lack of channel lock-in and crosschannel synergy.

878 citations


Authors

Showing all 36692 results

NameH-indexPapersCitations
Ronald C. Kessler2741332328983
Nicholas J. Wareham2121657204896
André G. Uitterlinden1991229156747
Lei Jiang1702244135205
Brenda W.J.H. Penninx1701139119082
Richard H. Friend1691182140032
Panos Deloukas162410154018
Jerome I. Rotter1561071116296
Christopher M. Dobson1501008105475
Dirk Inzé14964774468
Scott T. Weiss147102574742
Dieter Lutz13967167414
Wilmar B. Schaufeli13751395718
Cisca Wijmenga13666886572
Arnold B. Bakker135506103778
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
2023166
2022543
20214,487
20203,990
20193,283
20182,836