dataesr/scientific-paragraphs-categorization huggingface dataset:
Through combining land use data HYDE 3. 2, we calculated the total cropland and pasture areas within each country where manure application amount was larger than zero. We then computed mean manure N application rates on pastures, annually over the period 1961-2016 (Fig. 2 ). We calculated the national-level ratio of manure application to production (R a2p y, j ) by combining gridded manure production data in  Zhang et al. (2017)  and the grid cell area. To spatialize the national-level manure N application amounts to gridded maps of application rates in each grid area, we multiplied the R a2p y, j in grids where pasture areas were larger than zero with the time-series gridded spatial distribution maps of manure production rate in  Zhang et al. (2017)  during 1961-2014 and based on the spatial distributions of global pastures in land use data HYDE 3.
2  (Klein Goldewijk et al. , 2017). The above-mentioned processes are represented by following equations: R a2p y, j = T Mapp y,j g=n in country j g=1 (R Mprod y, g × A g ) , ( 1 ) where year is from 1961 to 2016, and country number is 165. R a2p y, j is the ratio (unitless) of manure application to production in the year y and country j. T Mapp y,j is the national total manure application amount (kg N yr -1 ) derived from the FAO database for each year. A g is the area of each grid (km 2 ). R Mapp y, g = R a2p y, j × R Mprod y, g , (2) where R Mapp y, g is the gridded manure application rate (kg N km -2 yr -1 ) in year y and country j. As the national-level manure application amount was not available during 1860-1960, we assumed that R a2p y, j is the same as for 1961. Combining with the gridded spatial maps of manure production rates in  Zhang et al.
(2017) , we generated the datasets of spatialized manure application rates to global pastures during 1860-1960. Finally, we calculated manure application amounts in each country by combining R Mapp y, g and grid areas to compare with national-level deposition amounts from the FAOSTAT database during 1961-2016. As we calculated national-level manure application amounts during 1860-1960 using R a2p y, j in 1961, these data served as national total manure N application amounts to adjust R Mapp y, g during 1860-1960. The adjustment procedure is represented in the following equations: CT Mapp y,j = g=n in country j g=1 (R Mapp y, g × A g ), ( 3 ) where year is from 1860-2016. CT Mapp y,j (kg N yr -1 ) is the calculated national-level manure application amounts in the year y and country j. If CT Mapp y, j is less or more than T Mapp y, j , an adjustment is needed to keep calculated national total amounts consistent with amounts from the FAOSTAT database.
Global manure N deposition on pastures and rangelands To develop global distribution maps of manure N deposition by grazing animals, we first obtained country-level statistics of "manure left on pasture" over the period 1961-2016 from the FAOSTAT (  2018 ) "Livestock manure" domain of FAOSTAT agri-environmental indicators  (FAO, 2018). We then obtained the national-level ratio of manure deposition to production (R d2p y, j ) by combining country-level FAOSTAT datasets of manure left on pasture and gridded total manure production datasets based on  Zhang et al. (2017). Then, we used spatial distributions of global permanent meadows and pastures, including pastures and rangelands, based on HYDE 3. 2 grassland data  (Klein Goldewijk et al. , 2017)  and gridded maps of deposition rates, to spatialize the national-level manure N deposition at the global scale.
For example, we multiplied the R d2p y, j ratio in grids within which the pastures and rangelands area was larger than zero, with the time-series gridded spatial distribution maps of manure production rates in  Zhang et al. (2017)  during 1961-2014 (Fig. 2 ). The above-mentioned processes are represented by the following equations: R d2p y, j = T Mdep y, j g=n in country j g=1 (R Mprod y, g × A g ) , (6) where year (y) is from 1961 to 2016 and country number (j ) is 157. R d2p y, j is the ratio (unitless) of manure deposition to production in the year y and country j. T Mdep y, j is national total manure deposition amount (kg N yr -1 ) derived from the FAOSTAT database for each year. R Mprod y, g is the gridded manure N production rate (kg N km -2 yr -1 ) in the year y and grid g. R Mdep y, g = R d2p y, j × R Mprod y, g , (7) where R Mdep y, g is the gridded manure deposition rate (kg N km -2 yr -1 ) in the year y and country j.
Finally, we calculated the manure deposition amount for each country through combining R Mdep y, g and grid area to compare with the national-level deposition amounts from the FAOSTAT database, using the following equation: CT Mdep y, j = g=n in country j g=1 (R Mdep y, g × A g ), (8) where CT Mdep y, j (kg N yr -1 ) is the calculated national-level manure deposition amount in the year y and country j. If CT Mdep y, j is less or more than T Mdep y, j , an adjustment was made to keep calculated national total amounts consistent with those from the FAOSTAT database. In this case, CT Mdep y, j is roughly equal to T Mdep y, j using Eq. (  8 ); thus no adjustment was needed. Since the national-level manure deposition amounts are not available during 1860-1960, we assumed that R d2p y, j is the same as that in 1961.
Overview of global N inputs to pastures and rangelands The global N cycle has been significantly perturbed by human activity since at least the industrial revolution. Intense agricultural activities, such as synthetic N fertilizer production and use, and intensive livestock production, were identified as major drivers of such change. In this context, improving estimates of global anthropogenic N inputs to pastures and rangelands and their consequences, including on N 2 O emissions, is important  (Galloway et al. , 2008; Tian et al. , 2016; Xu et al. , 2017). In this study, we generated global datasets of fertilizers N inputs from livestock, both synthetic and from manure, during the period 1860-2016. Pastures and rangelands experienced substantial land expansion over the period of  1860 -1998 (Klein Goldewijk et al.
Extension of FAO information Our work extends the relevant FAO national-level statistics in order to provide input drivers for process-based model simulations (e. g. , NMIP;  Tian et al. , 2018 Tian et al. , , 2019)). We furthermore separated N application rates between pastures and cropland, based on previous published work. We likewise extended information available in FAOSTAT by providing spatialized manure N application rates to pastures and a spatialized national-level manure N deposition dataset from 1860 to 2016.
Comparison with other studies We compared our datasets with other existing data sources (Table  2 ). Our estimate of world total manure N use on pastures was 58 % and 171 % higher than that estimated by Stehfest and  Bouwman (2006)  and  Liu et al. (2010) , respectively. However, our estimate was 39 % and 87 % lower than estimates by  Bouwman et al. (2002 Bouwman et al. ( , 2013, respectively), respectively). Critically, pasture area data varied significantly across different studies. For example, Bowman et al. (  2013 ) divided grasslands into mixed and pastoral systems, and estimated grasslands area based on the country-or regional-level grazing intensity (Table  2 ). In addition, synthetic fertilizers were applied to the area of mixed agricultural systems (grassland and cropland) and manure N was assumed to be applied to both mixed and pastoral systems.
The HYDE 3. 2 land use dataset divides the global grazing area into intensively managed grasslands (pastures), and less intensive and unmanaged grasslands (rangelands)  (Klein Goldewijk et al. , 2017). In this study, we rather assumed that all manure N was applied to pastures, the latter estimated from the HYDE database (798 Mha). Hence, pasture area defined in Bowman et al. (  2013 ) was more than 4-fold higher than the data we used. Consequently, the spatial distribution and annual total N application differed substantially compared with that in  Bowman et al. (2013). Similarly, the estimates of N fertilizer use in pastures showed large variations across studies (Table  1 ). This study obtained country-level N fertilizer amounts applied to pastures from the national-level ratios provided by  Lassaletta et al.
Limitations and uncertainties This study attempts to provide an overall estimate of N inputs to global rangelands and pastures during the period 1860-2016. However, before these data are used in global models, uncertainties of these datasets need to be addressed. First, the different definitions of grassland systems used by the scientific community introduce uncertainties of the spatial patterns and annual total amounts of N inputs. Chang et al. (2016)  generated global maps of grassland management intensity from 1901 based on modeled net primary production and the use of grass biomass generated by  Herrero et al. (2013). Their total grassland area substantially differed from pasture area developed by HYDE 3. 1  (Chang et al. , 2016). In this study, we used HYDE 3. 2 to generate N inputs to global grasslands, defined more appropriately by using the FAO land use definition of permanent meadows and pastures.
This dataset exactly followed the FAOSTAT data during 1960-2015, and combined population density data to reconstruct land use prior to 1960. Pastures and rangelands defined in HYDE 3. 2 were based on the intensity of human management. Although  Bouwman et al. (2013)  indicated that grassland areas in their study were also calculated based on the grazing intensity, their total area (pastures and rangelands) and spatial patterns were obviously different from HYDE 3. 2 (Table  2 ). Thus, a better understanding of land use is vital to reduce the uncertainty of estimating N input rates and amounts in pastures and rangelands. Second, the FAOSTAT database provides country-level manure N applied to soils; however, this dataset could not be directly applied to study N cycles on pastures since applications to cropland and pasture soils are not differentiated.
Hence, N fertilizer application datasets were generated considering crop-specific fertilizer rates and cropland area in each grid  (Lu and Tian, 2017; Mueller et al. , 2012; Nishina et al. , 2017; Potter et al. , 2010). In reality, N fertilizer application on pastures of each country is not homogeneous. In this study, we assumed that N fertilizer application rate in each country was constant, which means fertilizer was applied evenly in each grid with a pasture area larger than zero. Last, inside each relevant land use cell pastures and rangelands may be characterized by different livestock density and deposition rates, which is not considered in our current datasets. The final manure N deposition would be highly affected by the proportion of each type of management in the grid cell. Thus, it is necessary to consider these in the future research.
Data availability The 0. 5 • × 0. 5 • gridded global datasets of manure nitrogen deposition, manure nitrogen application and nitrogen fertilizer application in grassland systems are available at https://doi. org/10. 1594/PANGAEA. 892940  (Xu et al. , 2018). Data are in ASCII format. A Supplement is added to the list of all other parameters used in this study to calculate these three datasets in global grassland systems.
Conclusions In the context of increasing livestock production, manure and fertilizer N inputs to permanent meadows and pastures (pasture and rangeland areas) globally have increased rapidly since the industrial revolution. However, datasets of global N inputs are still incomplete. This is the first study that has attempted to consider major sources of anthropogenic N inputs in permanent meadows and pastures and hence generated time-series gridded datasets of manure and fertilizer N application rates and manure deposition rate during 1860-2016. Our datasets indicated a rapid increase of total N inputs to pastures and rangelands globally during this period, especially in the past half a century. The hotspots of grassland N application shifted from European countries to southern Asia, specifically China and India in the early 21st century, which indicated the spatial transformation of environmental problems.
In this study, we have obtained N data from various sources to fill the data gap; however, large uncertainties still remain in our datasets (e. g. , N application rate within each country, annual manure application amounts). More information is needed to improve these datasets in our further work. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/essd-11-175-2019-supplement. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. This study has been supported by the National Key R & D Program of China (grant number: 2017YFA0604702, 2018YFA0606001), NOAA grants (NA16NOS4780207, NA16NOS4780204), the National Science Foundation (1210360, 1243232), the STS program of the Chinese Academy of Sciences (KFJ-STS-ZDTP-010-05) and SKLURE grant (SKLURE2017-1-6).
Only recently, we have reported on the molecular dynamics of lipoproteins under HHP. [35, 36]  These data revealed that HHP has a significant impact on the atomic motions and the flexibility of LDL particles, strongly depending on the actual lipid composition. The aim of the present systematic study was to clarify the structural impact of HHP on LDL particles by applying small angle X-ray scattering (SAXS) and small angle neutron scattering (SANS) techniques. More precisely, we investigated LDL samples isolated from normolipidemic donors; tested the influence of the core lipid composition using an LDL sample with a TG-rich lipid core, which is typically found in blood samples of hypertriglyceridemic patients  [15, [37] [38] [39] [40] ; and finally addressed the behavior of oxidatively stressed LDL.
High Hydrostatic Pressure SAXS Measurements SAXS data were measured for different forms of LDL comprising normolipidemic (N-LDL), triglyceride-rich (TG-LDL), and oxidized (Ox-LDL) nanoparticles. N-LDL and TG-LDL were directly isolated from plasma of individual donors in their native state. The Ox-LDL sample was prepared through in vitro oxidation of the native sample and subsequent inhibition of the oxidation process prior to the onset of extensive oxidative damage. [3] The chemical compositions of the samples used for SAXS measurements are given in Table  S1  in the Supporting Information. Differential scanning calorimetry (DSC) curves were measured to obtain the actual lipid core phase transition temperature T m (Figure  S1 , Supporting Information). All calorimetric data are summarized in Table  S2  in the Supporting Information.
High Hydrostatic Pressure SANS Measurements SANS data were measured for N-LDL, Ox-LDL, and TG-LDL samples (for details see the Experimental Section). The chemical compositions of the SANS samples are given in Table  S4  in the Supporting Information. The corresponding DSC curves are shown in Figure  S3  in the Supporting Information and the calorimetric data are summarized in Table  S5  in the Supporting Information. SANS data were collected at several pressure points (20, 300, 600, 1200, 2000, 3000 bar) and at two temperatures, well below (10 °C) and above T m (40 °C). The SANS curves (Figure  4 ) show a direct correlation between elevated pressure levels and the shape of the scattering curves.
The bell-shaped curves of the Kratky plots indicate globular particles. A plateau or increase in the high q-region would indicate extended molecules or unfolded particles. Thus, our data clearly show that a globular like particle shape is present over the whole range of experimental conditions. This implies that the structural integrity of LDL is not impaired even at the highest pressure point. Next, the pair distance distribution functions p(r) were calculated from the SANS curves for all experimental conditions (Figure  S6 , Supporting Information). With SANS, in contrast to SAXS, only the overall shape of the particle can be seen and already very small changes in the relative positions of surface molecules can lead to detectable changes in the p(r) function. From the p(r) functions a D max value of 25 nm was derived and did not change within the investigated temperature and pressure range.
Ab Initio Shape Determination of LDL under HHP Ab initio low resolution shapes of the SANS data were calculated with the program DAMMIN or DAMMIF from the ATSAS package. [61, 62]  In this case, a predefined search volume, here 25 nm, is filled with densely packed spheres of defined radius (1 nm) to determine a configuration which approximates the scattering curve best. Simulated annealing was used to accomplish the global minimization of the target function (see the Experimental Section for details). The fitting procedures were carried out for the N-LDL, Ox-LDL, and TG-LDL samples at the two measured temperatures (10 and 40 °C) and at the two most extreme pressure points and at intermediate pressure (1200 bar). An exemplary result of a DAMMIN fit to the experimental SANS data is shown in Figure  S7  in the Supporting Information.
The pressure-induced phase transition is a highly conserved feature, which can be induced in all LDL samples. The LDL samples kept their structural integrity over the whole pressure range and exhibited a fully reversible behavior upon high pressure exposure. The shift of a peak in the p(r) functions, which predominantly corresponds to the protein and phospholipid headgroups, with increasing pressure gives a hint for an altered molecular arrangement in the outer surface layer of LDL. This assumption is confirmed by the SANS data analysis. The ab initio shape reconstructions indicate a flattening effect under HHP stress conditions, which is visible in the 3D shapes of the particles, in the changes of the maximum diameters of the axes of inertia and in the reduction in volume. Altogether, our data provide evidence that LDL samples experience structural adaptations under extreme pressure stress conditions, which cannot be induced by a decrease in temperature.
Differential Scanning Calorimetry (DSC) The core lipid transition temperature was determined on a Microcal VP-DSC differential scanning calorimeter (Malvern Instruments Ltd. , Malvern, UK). Each sample (≈2 mg apoB-100 per mL) was scanned reversibly for three times from 5 to 50 °C (pre-equilibrated at 5 °C for 30 min before each heating scan) and subsequently up to 95 °C with a scanning rate of 1 °C min -1. The collected data were baseline subtracted and mass normalized to obtain heat capacity (kJ moL -1 of CE) versus temperature curves. Calorimetric data were processed with the data analysis software Microcal LLC DSC (OriginLab Corporation, Northampton, MA, USA). The curves were interpreted in terms of the core lipid phase transition temperature (T m ), observable as the maximum of change in heat capacity, as well as the calorimetric enthalpy (ΔH cal ) calculated by integration of peak areas using baseline extrapolation.
Small-Angle X-Ray Scattering (SAXS) Synchrotron SAXS experiments were performed at the SAXS beamline at ELETTRA (Trieste, Italy). [69] Scattering patterns were recorded with the 2D photon counting detector system Pilatus 1M (DECTRIS Ltd, Baden-Daettwil, Switzerland). The scattering intensity was measured as a function of the scattering vector q (q = 4π sinθ/λ, where 2θ is the scattering angle and λ is the wavelength), covering a q-range of 0. 07-5. 35 nm -1 (λ = 0. 154 nm). The position calibration of the detector was performed using the diffraction pattern of silver behenate. Reference measurements (empty cell, buffer) were carried out with the same adjustments. The exposure time for all measurements was 60 s per image. Calibration and primary data treatment were performed with the Fit2D software.
Small-Angle Neutron Scattering (SANS) SANS experiments were performed at the SANS-II beamline at the Paul-Scherrer Institut (Villigen, Switzerland). [73]  To enable data collection over a broad q-range (0. 06-2. 06 nm -1 , λ = 0. 531 nm) three different sample-to-detector distances (1. 5, 3. 0, and 6. 0 m) were applied for each sample. All measurements were performed with D 2 O buffer (10 × 10 -3 M NaPi, 3. 4 × 10 -3 M EDTA, pH 7. 4 (pD 7. 8). Reference measurements (detector efficiency, empty cell, buffer, cadmium) were carried out with the same adjustments and were used for primary data treatment with GRAS ans P, a Matlab script application developed by Charles Dewhurst (ILL) for the graphical inspection, analysis, and reduction of SANS data. Appropriate high hydrostatic pressure equipment including a temperature control unit was available at the beamline.
Small-Angle Scattering Data Analysis Procedures SANS data were reduced with GRAS ans P, whereas SAXS data were further processed and reduced in Igor Pro Version 6. 2. 2. 2 (WaveMetrics, Inc. , Lake Oswego, OR, USA). The radius of gyration (R g ) for each experimental condition was determined by using the Guinier approximation in the low q-region. According to the equation In (I(q)) = In (I(0)) -R g 2 q 2 3 the R g can be determined from the slope of the linear regression of the Guinier plot (qR g < 1. 3). Kratky plots were depicted to interpret samples according to their compactness and flexibility. The pair distance distribution function p(r), which gives information about the molecular particle shape and about intraparticle scattering distribution was calculated by indirect Fourier transformation of the scattered intensity I(q) by using the program GIFT (PCG Software package, Version 4.
05. 12). [54,55] For a particle of uniform electron density the p(r) is given by p(r) = 1 2π 2 ∫ 0 ∞ I(q) q r sin(qr)dq. Ab initio low resolution shape reconstruction was done by simulated annealing. The technique utilizes a single phase dummy atom model to fill a space of predefined size with dummy atoms of specified radius. Through an iterative process a theoretical scattering curve is approximated to the actually measured data. The SANS curves were processed with the program DAMMIN/DAMMINF from the ATSAS software package. [61] For each scattering curve at least ten independent runs of DAMMIN with identical parameters were carried out (search volume sphere diameter = 25 nm, dummy atom packing radius = 1 nm, number of knots = 15, expected shape unknown) resulting in independent low resolution shape reconstructions.
The ab initio shapes were aligned and an averaged model was built. These tasks were done with the program suite DAMAVER from the ATSAS package. [62] The dimensions of the averaged models were obtained with PyMOL. [75] The overall dimensions of the aligned models through the Inertia Axis Aligned Bounding Box (IABB box) were determined by applying two additional scripts. [75]  For that reason each model was aligned along its axes of inertia and the maximal dimensions of these axes were taken. shown in a continuous order from the lowest pressure point (bottom curve) to the highest pressure point (top curve). The SAXS curves were shifted vertically for better visibility. The characteristic peak at a scattering vector q = 1. 7 nm -1 is marked by an arrow. The curves are shifted vertically for better visibility.
Acknowledgements B. L. and M. G. contributed equally to this work. This work has been supported by the  Austrian Science Fund  ( FWF Project No. I 1109-N28  to R. P. ) and by the  Agence Nationale de la Recherche  ( ANR ; project number  ANR-12-ISV5-0002-01LDLPRESS  to J. P. ). This work is based on experiments performed at  ELETTRA Sincrotrone Trieste, Trieste, Italy  and at the Swiss spallation neutron source SINQ,  Paul Scherrer Institute, Villigen, Switzerland. The authors would like to thank  Hanna Lindermuth  for excellent technical support. The authors declare no competing financial interest.