dataesr/scientific-paragraphs-categorization huggingface dataset:
Volume is determined from the silhouette as the solid of revolution. The experiments covered a wide range of conditions, including isothermal experiments at temperatures of 1050 -1500 K, and linear heating ramps at rates of 0. 04 -0. 25 K. s -1. The data from optical dilatometry are confined to porosity (), while the tomography provides 3-dimensional data that are used to measure both the total porosity (), and the porosity that is connected across the sample (or segmented domain)   (). After the heating experiments, we repeated the thermal analysis step on a few sub-samples, and no mass loss was observed, demonstrating the samples did equilibrate volatiles during the in situ experiments. Supplementing our datasets collected at relatively low   and a small difference between   and   (small initial supersaturation), we re-analyse the data from Gardner et al.
Acknowledgements We acknowledge  European Research Council Advanced  Grant  EVOKES 247076 , UK  NERC  grants  NE/N002954/1  and No. NE/M018687/1 , a fellowship from the  Institute of Advanced Study at Durham University  (to J. Gardner) and from the  Centre for Advanced Study at LMU, Munich  (to F. Wadsworth), a  Royal Society University Research Fellowship  (to H. Tuffen) and  Royal Society International Exchange  grant (to  H. Tuffen  and  M. Heap ), and the  VUELCO consortium  funded under the  EU 's  FP7  grant agreement  282759. The  Paul-Scherer-Institute (Swiss Light Source ) awarded beamtime under proposals No. 20141231  and No. 20150413 at the TOMCAT beamline. Thanks to  Yan Lavallée  and  Jackie Kendrick  for valuable discussions and for supporting J. Schauroth's contribution to this work.
Experiment 1 Experiment 1 allowed testing of the presence of the US devaluation effect reported by De  Houwer et al. (2000)  in a procedure devoid of any regression to the mean effect. This experiment was originally aimed at exploring unrelated hypotheses. Therefore, we collected additional measures (i. e. , inter-individual difference measures) that are not reported here. The data from the experiment as well as the R script created to manage, analyze, and represent the data visually are available on Open Science Framework (OSF link: https://osf. io/82mcs/).
Analytical Strategy For each participant, we computed both for the CSs and for the USs the average difference in ratings between post-conditioning ratings and normative ratings. Normative ratings for the CSs were collected in a pre-test (see  Pleyers et al. , 2007)  and normative ratings for the USs consisted in IAPS ratings. The mean differences in evaluations were averaged, separately for each US valence (positive and negative). The data were analyzed using the 'aov' and 'anovaBF' functions in R (R Development Core Team, 2017; from the R package 'BayesFactor',  Morey, Rouder, & Jamil, 2015)  for the frequentist and Bayesian analyses respectively. We report the Bayes factors associated with the model comparison made in the frequentist analyses. An augmented model containing the tested factor was compared to a constrained model that did not contain the tested factor.
Experiment 2 The role of the CS-US pairing was examined in Experiment 2 by including control USalone stimuli in the conditioning procedure. As a further asset, Experiment 2 relied on idiosyncratic rather than normative US evaluations. In this case, participants served as their own control, although USs were still selected on the basis of prior normative ratings (therefore precluding regression to the mean effects). We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the experiment. The raw data from the experiment as well as the R script created to manage, analyze, and visually represent the data are available on Open Science Framework (OSF link: https://osf. io/82mcs/).
Experiment 3 Experiment 3 was designed to further investigate the role of CS-US pairing in US devaluation. In addition, this experiment provides a formal test for the contribution of US habituation. This was achieved by comparing evaluations for USs that were presented and rated only once versus USs that were either paired with CSs or presented alone in the conditioning procedure. If US habituation plays a role, ratings should be less extreme for USalone and US-paired stimuli than for USs presented and rated only once. We report how we determined our sample size, all data exclusions, all manipulations, and all measures in the experiment. The raw data from the experiment as well as the R script created to manage, analyze and visually represent the data, are available on Open Science Framework (OSF link: https://osf.
The impact of atmospheric reactive nitrogen (N r ) deposition on carbon (C) sequestration in soils and biomass of unfertilised, natural, semi-natural and forest ecosystems has been much debated. Many previous results of this dC/dN response were based on changes in carbon stocks from periodical soil and ecosystem inventories, associated with estimates of N r deposition obtained from large-scale chemical transport models. This study and a companion paper (Flechard et al. ,   2019)  strive to reduce uncertainties of N effects on C sequestration by linking multi-annual gross and net ecosystem productivity estimates from 40 eddy covariance flux towers across Europe to local measurement-based estimates of dry and wet N r deposition from a dedicated collocated monitoring network. To identify possible ecological drivers and processes affecting the interplay between C and N r inputs and losses, these data were also combined with in situ flux measurements of NO, N 2 O and CH 4 fluxes, soil NO 3 -leaching sampling, as well as results of soil incubation experiments for N and greenhouse gas (GHG) emissions, surveys of available data from online databases and from the literature, together with forest ecosystem (BASFOR) modelling.
Such approaches were tested and implemented in this study, as part of a large-scale effort, within the NitroEurope Integrated Project  (NEU, 2013; Sutton and Reis, 2011) , to quantify N r deposition and N losses from ecosystems, in parallel and coordinated with the CarboEurope Integrated Project  (CEIP, 2011)  to estimate the net C and 205 GHG balance, for forest and semi-natural ecosystems in Europe. The main aim of this paper is to build tentative C, N and GHG budgets for a wide range of European monitoring sites, and to critically examine uncertainties and knowledge gaps therein, prior to an assessment in the companion paper  (Flechard et al. , 2019)  of the dC/dN response of C sequestration from the same datasets. To this end, we compiled the C, N and GHG flux data from NEU, CEIP and other complementary datasets, using a combination of in situ measurements, empirical 210 relationships, ecosystem modelling, literature and database surveys, at the scale of the CEIP and NEU flux monitoring networks.
This study presents the methodologies and data, including atmospheric deposition from gas, aerosol and precipitation N r concentration monitoring, soil NO 3 -leaching measurements and modelling, GHG and N r emission estimates from chamber measurements and laboratory-based soil bioassays, EC tower-based C budgets, as well as historical published data. Forest ecosystem modelling (BASFOR) is used to simulate C, N and GHG fluxes, with the double objective to compare 215 with actual measurements and to fill some gaps in the datasets. Wherever possible, alternative measurements, datasets or modelled data are shown alongside the primary data in order to provide an estimate of the uncertainty in the different terms. {Insert Figure  1  here} 2 Materials and methods.
Atmospheric deposition 245 To obtain realistic estimates of total (dry + wet) N r deposition at the 40 sites of the network, it was necessary to measure ambient air concentrations of the main N-containing chemical species at each location, due to the large spatial heterogeneity in gas phase concentrations, especially for NH 3. The requirement for local measurements of wet deposition was relaxed because this is much less spatially variable. For both dry and wet components, measurements had to be complemented by models, either to calculate fluxes based on local concentration data at each site, or to obtain local estimates from a large-250 scale CTM when data were missing. Atmospheric inorganic N r concentrations, available from the NEU (2013) database, were measured monthly for 2-4 years in the gas phase (NH 3 , HNO 3 , HONO) and in the aerosol phase (NH 4 + , NO 3 -), using DEnuder for Long-Term Atmospheric sampling (DELTA) systems  (Sutton et al.
, 2001; Tang et al. , 2009). Concentrations of nitrogen dioxide (NO 2 ), not covered by DELTA sampling, were measured by chemiluminescence at a few sites only, and were otherwise taken from gridded 255 concentration outputs of the European-scale EMEP CTM (details given below). The N r data initially reported in  Flechard et al. (2011)  covered the first 2 years of the NEU project  (2007) (2008) ; here, the data from the entire 4-yr NEU monitoring period  (2007) (2008) (2009) (2010)  were used and averaged to provide a more robust long-term 4-year estimate of N r dry deposition. The inferential modelling method was used to calculate dry deposition for N-containing gas and aerosol species, whereby measured ambient N r concentrations were multiplied by a vegetation-, meteorology-and chemical species-dependent 260 deposition velocity (V d )  (Flechard et al.
Soil gaseous and leaching losses Nitrogen losses to the atmosphere (gaseous emissions) and to groundwater (N leaching), which are especially hard to quantify and thus typically cause large uncertainties in ecosystem N budgets, were estimated by direct flux measurements or by indirect empirical methods. Soil NO and N 2 O emissions were measured in the field using closed static and dynamic chamber methods, as part of NEU (e. g. EN2, EN10, EN16, DB2, SN3, SN8, SN9) and/or collected from the literature (e. g. 300 EN2, EN10, EN14, EN16, DB2,  Pilegaard et al. , 2006; long term data at EN2 in Luo et al. , 2012). Such data were available for N 2 O at seven forest sites and four semi-natural sites, and at five forest sites for NO (Table  S6 ). Manual static chamber N 2 O measurements were made manually at a typically bi-weekly (growing season) or monthly (winter half-year) frequency at many sites.
(2009)  was 325 applied to predict DIN leaching based on key variables (throughfall inorganic N r deposition DIN TF , organic horizon C/N ratios, MAT). The algorithm, developed from the extensive Indicators of Forest Ecosystem Functioning (IFEF) database (>300 European forest sites), simulates the non-linearity of DIN leaching with respect to DIN TF and soil C/N ratio, with critical thresholds for the onset of leaching of DIN TF = 0. 8 g (N) m -2 yr -1 and C/N = 23, respectively. Since the algorithm requires DIN TF as input, as opposed to total (above canopy) N dep , in the present study we applied a reduction factor of 0. 85 330 from N dep to DIN TF (i. e. a canopy retention of 15% of atmospheric N), which was calculated as the average of all available individual DIN TF / N dep ratios in the IFEF database.
, 2000). Briefly, post-processing of the raw high frequency EC data included typically: de-spiking to remove outliers; 2-D rotation of the coordinate system; time lag optimization by maximization of the covariance between CO 2 concentration and vertical component of wind speed (w); block-averaging over the flux averaging interval of 30 minutes. Corrections were applied for various methodological artefacts, including notably i) flux losses at the different frequencies of flux-carrying eddies, caused e. g. by attenuation/damping in the inlet/tubing system  (Ibrom et al. 355 2007; Fratini et al. 2012) , path averaging, sensor separation, analyzer response time, high-and low-pass filtering; ii) effects of temperature fluctuations and dilution by water vapor on measured fluctuations in concentrations of CO 2  (Webb-Pearman-Leuning corrections; Webb et al.
, 1980) ; iii) CO 2 storage below sensor height. Quality assurance and quality control procedures were further developed and agreed upon within CEIP, including statistical tests, non-stationarity, integral turbulence characteristics  (Foken et al. , 2004) , and footprint evaluation  (Göckede et al. , 2008). Friction velocity (u * ) threshold filtering was implemented using the moving point test according to  Papale et al. (2006)  and as described in  REddyProc (2019) , in order to discard flux data from periods of low turbulence. Different EC post-processing softwares were used at the different sites within the project, such that the data were not evaluated in exactly the same way across the CEIP network, but a reasonably good overall agreement was found among the different softwares, within 5-10% difference for 30-minute CO 2 flux values  (Mauder et al.
, 2008; Mammarella et al. , 2016). 365 Similarly, for the gap-filling of the 30-minute flux time series, during periods of instrument malfunction or unsuitable measurement conditions (low turbulence, insufficient fetch, etc. ), and for the partitioning of NEP into GPP and R eco , a number of alternative algorithms have been developed in the past, based on different sets of principles  (Falge et al. , 2001; Barr et al. , 2004; Reichstein et al. , 2005; Lasslop et al. , 2010). The gap-filling and partitioning algorithm used by default in this study was the generic online REddyProc (2019) software, implemented also in the European Fluxes Database Cluster. 370 REddyProc was based on i)  Reichstein et al. (2005)  for the filling of gaps in the NEE flux data on the basis of information from environmental conditions; ii)  Reichstein et al.
(2005)  for the nighttime data based R eco parameterization (using an Arrhenius-type function of temperature); and iii) on  Lasslop et al. (2010)  for the daytime data based GPP evaluation (using a rectangular hyperbolic light-response curve for NEE and including a temperature sensitivity of respiration and limitation of GPP by vapour pressure deficit). 375 In this study, for all CEIP flux sites, we have retrieved the fully analysed and validated half-hourly (level-3) and daily to annual (level-4) CO 2 flux (NEP, GPP, R eco ) data as available, initially from the CEIP database, later from the European Fluxes Database Cluster (2012) or from the GHG-Europe portal  (GHG-Europe, 2012). For these data, although the evaluation methods were not necessarily harmonized between sites, we hold that the data available in the database were obtained using the best possible, state-of-the-art evaluation methods at the time of retrieval.
For the four non-CEIP flux sites, 380 flux evaluation closely followed CEIP protocols; in the case of DB4 the EddyPro (v6. 2) software was used, which was based on a synthesis of calculation and correction methods from CEIP and other FLUXNET flux networks around the globe. The EC-CO 2 flux measurements used in this study mostly spanned the 5-year period of  CEIP (2004 CEIP ( -2008)) , except for a dozen sites where measurements continued until 2010, i. e. the end of NEU and of atmospheric N r sampling. Older EC data (since the mid-late 1990's) were also available at DB5, EN6 and EN13. Data collection started and ended later at DB4, at 385 which both EC-CO 2 flux and DELTA-N r measurements spanned the 7-year period 2009-2015. Data analyses presented in the paper, based on inter-annual mean CO 2 budgets and mean N r deposition, assume that five or more years of monitoring yield reasonably robust estimates of long-term fluxes for the different sites, and that the small time shift between CEIP and NEU project periods (2-3 year overlap) does not affect the results significantly.
Soil CO 2 and CH 4 fluxes In situ soil CO 2 efflux (SCE) measurements by opaque (static or dynamic) manual chambers were carried out at 24 of the forest sites, with typically weekly to monthly sampling frequency, with fluxes being measured continuously (hourly) by automated chambers at a few sites (e. g. EN2). The SCE is usually considered a proxy for CO 2 production by soil respiration 395 (R soil ), though the two may not be equal as part of the CO 2 production is dissolved into pore water and may reach the atmosphere only later, either on-site, or even off-site if dissolved CO 2 (DIC) leaches to groundwater. Annual R soil data, scaled-up from SCE measurements, are available for 18 forest sites and were collected from the CEIP or GHG Europe databases and/or from various peer-reviewed publications for the different sites (see Table  S7 ).
Ancillary soil, plant and ecosystem measurements Ancillary data were collected mainly for the purpose of assembling input parameters and calibration datasets for forest ecosystem (BASFOR) modelling (see below). Texture (% clay, % sand, % silt), pH, soil organic carbon concentration (SOC) 425 and C/N ratios were measured in soils of 35 sites as part of the bioassay experiments described previously, but were otherwise also documented in the CEIP database and in papers previously published for the majority of sites. For the forest sites, ecosystem data for soil water content (SWC), porosity, saturation water content ( SAT ), field capacity ( FC ) and wilting point ( WP ), and for canopy height (H), leaf area index (LAI), diameter at breast height (DBH), basal area (BA), number of trees per unit area or stand density (SD) and thinning events, were obtained from CEIP and other project (e.
g. 430 FLUXNET) databases and complemented by various publications. Such was also the case for ecosystem carbon stocks in soil organic matter (CSOM) and in roots (CR), stems (CS), branches (CB), leaves (CL) and litter layers (CLITT), for which the global database assembled by  Luyssaert et al. (2007)  provided additional data. At sites for which published values of  FC and  WP were not available, default estimates were inferred from soil texture by means of van Genuchten (1980) pedotransfer functions, using tabulated values from the German soil description handbook  (Eckelmann et al. , 2005) 435  Foliar C and N contents (LeafC, LeafN) were measured as part of NEU for EN1, EN2, EN5, EN8, EN10, EN15, EN16, DB2  (Wang et al. , 2013) , DB4, SN3, SN4, SN8 and SN9, or were otherwise taken from CEIP, GHG Europe and FLUXNET databases as well as various publications; in total, leaf C/N measurements were available for 31 sites.
General description The BASic FORest model, BASFOR, is a process-based, deterministic forest ecosystem model, which simulates the growth and biogeochemistry (C, N and water cycles) of temperate deciduous and coniferous stands at a daily time step  (van Oijen et al. , 2005; Cameron et al. , 2013 Cameron et al. , , 2018)). Model code and documentation are available on GitHub  (BASFOR, 2016). 445 Interactions with the atmospheric and soil environments are simulated in some detail, including the role of management (thinning or pruning). BASFOR is a one-dimensional model, i. e. no horizontal heterogeneity of the forest is captured, and BASFOR does not simulate some variables which are important in forest production, such as wood quality or pests and diseases. Nine state variables for the trees describe i) C pools: leaves, branches, stems, roots, reserves (CL, CBS or collectively CLBS, 450 CR, CRES; kg (C) m -2 ); ii) N pool in leaves (NL; kg (N) m -2 ); and iii) Stand density (SD, trees m -2 ), tree phenology (only for deciduous trees): accumulated chill days (chillday; d) and accumulated thermal time (Tsum; °C d).
Differences between plant functional types Forests (F) and short semi-natural (SN) vegetation showed similar relationships with GPP as a function of N r deposition, increasing with a broadly similar slope at low N dep values, then levelling off beyond 2g (N) m -2 yr -1 , except for the fact that 660 GPP was lower by typically 200-500 g (C) m -2 yr -1 in SN compared with F sites, for a given N dep level (Fig. 4 ). The behaviour was different for NEP, where the slope against N dep in the range 0-2 g (N) m -2 yr -1 was much steeper for F than for SN, which occurred because R eco values are of the same order for F and SN at a given N dep level. No systematic difference was observed between the forest PFT, based on the available data, in the apparent relationships of the C fluxes vs. N dep. However, this may be a result of the small number -and large diversity -of deciduous broadleaf (DB) and evergreen 665 broadleaf (EB) forest sites in the dataset, compared with evergreen needleleaf (EN) sites (Table  1 ).
Carbon fluxes and pools derived from forest ecosystem modelling In the BASFOR base run (Fig. 6 ), reasonable overall model performance was achieved for GPP, ecosystem C pools, H, 675 DBH, LAI and LeafN, while more scatter was present for R eco , NEP and ET. In particular, in apparent contrast to GPP, R eco stands out as a more challenging variable to model. Predictably, because BASFOR was calibrated using a subset of 22 sites from this dataset  (Cameron et al. , 2018) , the range and mean values of modelled R eco were close to mean observations by EC across the study sites, but differences between sites were poorly reproduced with much scatter around the 1/1 line and a low R 2. One possible reason was that BASFOR assumed that autotrophic respiration (R aut ) is a constant fraction of GPP, which 680 may be an over-simplification  (Collalti and Prentice, 2019).
5%), but on average still a factor of three smaller than the warming by N 2 O emissions equal to 3. 9% of NEP (range 0-18. 5%). By contrast to forests, at semi-natural, short vegetation sites N 2 O or CH 4 emissions had a larger impact on the net GHG 705 balance, where most (seven out of nine) sites showed non-CO 2 GHG contributions larger than 10% of NEP. Three of these seven sites were unfertilised, extensively grazed upland (SN2, SN5, SN6) grasslands (small N 2 O sources), while three sites (SN3, SN7, SN8) were CH 4 -emitting peatlands or wetlands (EC-CH 4 and chamber flux data from  Drewer et al. , 2010; Hendriks et al. , 2007; Juszczak and Augustin, 2013 and Kowalska et al. , 2013). At SN3 and SN8, the small to moderate NEP sinks were turned by large CH 4 emissions into net GHG sources (net warming budgets of +127 and +242 g CO 2 -C Eq m -2 yr - 710 1 , respectively), though not into actual net C sources (Fig.
The quality of the individual data sets is, however, not uniformly high. Some of the data were measured in situ with known uncertainty, while others were simulated, derived from 725 laboratory experiments and adapted to the field situation using measured time series of soil T and soil moisture, or taken from existing databases and literature. Also, data may not be fully comparable between sites (different methods used), nor even fully representative of each site (spatial heterogeneity). In the following sections, we discuss limitations of the measured, empirical and simulated data, both in terms of the component C and N fluxes, their budgets and interactions, as well as the challenges faced when attempting to establish empirical/statistical evidence for possible N effects on carbon 730 sequestration in natural and semi-natural terrestrial ecosystems in Europe.
, 2003). Typical stem N content values, proposed for N uptake calculations in the Convention on Long-range Transboundary Air Pollution 795 (CLRTAP) manual for critical loads mapping, are 1 and 1. 5 g N kg -1 dry matter for conifers and deciduous trees, respectively, for steady state conditions (CLRTAP, 2017). Tree species traits may therefore, in our study, have exacerbated an existing DIN leaching predisposition resulting from edaphic factors and pollution climate. At the lower end of the N dep range, the dataset is consistent with previous studies, which have shown that DIN leaching is unlikely to occur in forests where N dep < 1 g (N) m -2 yr -1 (de  Vries et al. , 2009) , although under these conditions there may still be significant N losses as 800 NO and N 2 O (Fig. 3 ). The best empirical fit for the relationship of the sum DIN + NO + N 2 O to N dep was slightly non-linear (Fig.