dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods Synthesis. A pyrochlore-free ceramic sample of PMN was prepared by solid-state reaction according to the columbite route proposed in ref. 35. Powders of MgO and Nb 2 O 5 (with 2 mol% excess MgO) were reacted at 1000 °C for 8 h to form MgNb 2 O 6. This product was batched with a stoichiometric amount of PbO and heated in a covered crucible at 800 °C for 4 h to form phase-pure PMN. Prior to each heating, the powders were mixed and ground in a planetary ball mill in isopropanol using yttria-stabilized zirconia grinding media. Total-scattering measurements. Neutron total-scattering data were collected at 300 K and 200 K using the Polaris diffractometer at the ISIS facility (Science and Technology Facility Council, UK). The sample powder was loaded in a vanadium can with temperature control achieved using a displex cryostat.
The data were processed using the GUDRUN software to obtain the total-scattering function S(Q) and its Fourier transform, G(r) (Q max = 35 Å -1 ). X-ray total scattering was measured at the 11-ID-B beamline of the Advanced Photon Source (Argonne National Laboratory) using an incident-beam energy of ≈60 keV with the area detector positioned at 18 cm downstream from the sample. The sample was loaded in a 1mm kapton capillary. The X-ray data were processed using the PDFGetX3 software to obtain the corresponding total-scattering function and its Fourier transform (Q max = 25 Å -1 ). In both neutron and X-ray total-scattering measurements, the instrument resolution was characterized by measuring NIST Si SRM powder. Extended X-ray absorption fine structure (EXAFS) measurements were performed for the Pb L 3 and Nb K edges at the NIST beamline 06-BM at NSLS-II (Brookhaven National Laboratory).
The data were collected at room temperature in transmission. All the data were processed using Athena  36. Initial fitting was accomplished in the Artemis software  37. Scattering amplitudes and phases were calculated using FEFF8  38. For the Nb EXAFS, the k-space data were multiplied by k 2 prior to the Fourier transform, which was performed over the k-range from 3. 3 Å -1 to 13. 2 Å -1 ; the r-space fitting range was from 1 Å to 4. 2 Å, with both single-and multiple-scattering paths of a photoelectron included in the fit. For Pb, the k-space data were multiplied by k and the k-space range used in the transform extended from 2. 2 Å -1 to 10 Å -1 ; the r-space fit was performed from 1 Å to 3. 5 Å, with only the single-scattering paths included. Transmission electron microscopy. Transmission electron microscopy (TEM) was performed on the crushed powder dispersed on a lacey carbon-coated copper grid.
Selected area electron diffraction patterns and high-angle annular dark-field images were collected at 300 kV in the TEM and scanning TEM modes, respectively. Single-crystal X-ray diffuse scattering. X-ray single-crystal scattering measurements were performed at the beamline ID29 of ESRF. A rod-like 50-μm-thick crystal was mounted on a rotation stage and held in a cryostream flux for temperature control. Prior to the measurements, the sample was etched using hot concentrated hydrochloric acid. Diffuse-scattering patterns were recorded with the wavelength of 0. 954 Å over the angular range of 360°in 0. 1°increments on a This modulation, which is associated with the correlated oxygen displacements, reproduces the experimental results reported in ref. 18  , even though the present refinements did not include neutron diffuse scattering single-photon counting pixel detector, which allowed for the shutter-less data collection.
The CrysAlis software package was used to refine the orientation matrix and the final reciprocal-space reconstructions of 3D diffuse-intensity distributions were accomplished with locally developed computer software. Structure refinements. Structural refinements were performed using the development version of the RMCProfile software  24. This version makes extensive use of GPU computing (yet unavailable for the distribution package of RMCProfile), which is critical for achieving computational speeds required to handle large atomic configurations and calculations of 3D diffuse scattering involved in this study. The PMN structure was modeled using atomic configurations of 40 × 40 × 40 unit cells, which included 320,000 atoms (Mg/Nb ratio is equal to 0. 49998828) and covered interatomic distances up to 8 nm.
Neutron-and X-ray total-scattering data in both real and reciprocal spaces were fitted simultaneously with the two EXAFS data sets and the 3D X-ray single-crystal diffuse-intensity distribution; in addition, the neutron Bragg profile was included in the fit. The X-ray data were calculated considering the Q-dependence of the X-ray scattering cross-sections. In both the neutron and X-ray cases, the calculated total-scattering data were corrected for the instrument resolution in reciprocal and real spaces prior to comparison with the corresponding experimental signals. The resolution corrections accounted for the actual peak profile shapes, which is especially important for the time-of-flight neutron data that exhibit significant peak asymmetry. The accuracy of these corrections is key for obtaining meaningful results while fitting a pair-distribution function to longer distances.
Acknowledgements The ceramic PMN sample used in this study was prepared by  Russell Maier. The octahedral deformation-mode analysis was developed by  Anton Gagin. We thank the  STFC (UK)  for access to the  ISIS neutron facility. Portions of this research were carried out at the  National Synchrotron Light Source II (NIST beamline 6-BM ) and  Advanced Photon Source  (beamline 11-ID-B), the  U. S. Department of Energy (DOE) Office of Science User Facilities , operated for the  DOE Office of Science by Brookhaven National Laboratory and Argonne National Laboratory , respectively, under Contracts Nos DE-SC0012704 and  DE-AC02-06CH11357 , respectively.
The FALL3D model FALL3D is an Eulerian atmospheric dispersal model that solves the advection-diffusion-sedimentation equation for a set of particle classes (bins), each characterized by a particle size, density and shape factor. Given an eruption source term and meteorological variables, FALL3D solves the 4D ash concentration for each particle class, from which the total and the fine ash column mass loadings are diagnosed performing a vertical integration. The meteorological fields must be furnished off-line by a Numerical Weather Prediction (NWP) model forecast or from a re-analysis dataset. The source term determines the amount of tephra injected to the atmosphere, its vertical distribution along the eruption column, and the fraction of mass associated to each particle bin. This term can be parameterized using different schemes available in the model for the Mass Eruption Rate (MER) (e.
Code and data availability. FALL3D model is available through an open license (http://datasim. ov. ingv. it/models/fall3d. html). The ETKF-FALL3D code is written in python. The code and the required data to run a sample experiment are available through an open license at https://doi. org/10. 5281/zenodo. 3066310. Atmospheric state data from the Global Forecasting System produced by the National Centre for Environmental Prediction is available through the University Corporation for Atmospheric Research data archive (https://rda. ucar. edu/datasets/ds335. 0/, Accessed 07 Apr 2019).
Offline Lagrangian analysis of AMOC upper limb pathways with ARIANE We used the ARIANE tool (version 2. 2. 6; Blanke and Raynaud, 1997; Blanke et al. , 1999)   ARIANE is a freely available Fortran software that infers Lagrangian particle trajectories from simulated threedimensional volume-conserving velocity fields saved on a C grid by offline advecting virtual fluid particles along analytically computed streamlines. No additional sub-grid-scale Lagrangian diffusion parameterization was implemented. The obtained trajectories thus represent volume transport pathways, which may experience along-track tracer and density changes that reflect the sub-grid-scale parameterizations of the underlying ocean general circulation model (OGCM), including vertical tracer mixing in the mixed layer. For a detailed discussion of this concept please refer to  van Sebille et al.
This knowledge is crucial for a better assessment of the potential impact of the observed warming in the extended Agulhas region and the estimated increase in Agulhas leakage over the last decades not only on the strength but also on the stability of the AMOC. Finally, even though in this study we focused on the importance of the ratio of the DP and AC contributions for the upper limb of the AMOC, the ratio of the two inflows generally impacts the thermohaline structure of the whole South Atlantic, also with consequences on a more regional scale, for instance for the Benguela upwelling region  (Tim et al. , 2018)  or the tropical Atlantic  (Lübbecke et al. , 2015; Castellanos et al. , 2017). Code and data availability. The observational-based Eulerian data sets used as OGCM input and to validate the hindcast simulation are all freely available online.
copernicus. eu/, last access: 20 April 2019). The Lagrangian software employed, AR-IANE version 2. 2. 6, is available at http://www. univ-brest. fr/lpo/ ariane (last access: 1 July 2010). For reproducibility of all major results of the Lagrangian analyses performed for this study, the customized code of the ARIANE software, the simulated Lagrangian trajectories  (Rühs, 2019) , and post-processing scripts are made available through GEOMAR (https://data. geomar. de/thredds/ catalog/open_access/ruehs_et_al_2019_os/catalog. html).
Acknowledgements. The OGCM and trajectory simulations were performed at the  High Performance Computing Center in Hanover (HLRN). The project received funding from the following:  Cluster of Excellence 80 "The Future Ocean " within the framework of the Excellence Initiative by the  Deutsche Forschungsgemeinschaft (DFG)  on behalf of the German federal and state governments (grant  CP1412 , SR); the  German Federal Ministry of Education and Research (BMBF)  for the  SPACES-AGULHAS  project (grant  03F0750A , AB and FUS); the  European Union   Horizon 2020 research and innovation program  as part of the  AtlantOS  project (grant  633211 , SS); and the  SAMOC  project (grant  11-ANR-56-004 , SS). The authors further wish to thank  Susan Lozier  and  Erik van Sebille  for inspiring discussions that added value to this paper,  Bruno Blanke  and  Nicolas Grima  for realizing and helping to tackle the Lagrangian ARIANE software,  Willi Rath  for technical support,  Jonathan Durgadoo  for discussing Lagrangian analyses techniques, and two anonymous reviewers for their constructive criticism, in particular regarding the framing and discussion of our work.
, 2008). Several entropy-based features have been proposed to determine the complexity of biosignals, including, Sample Entropy, Approximate Entropy, Rényi entropy and Singular Value Decomposition Entropy (SvdEn)  (Lake et al. , 2002; Zhang et al. , 2012; Caraiani, 2014; Cornforth et al. , 2014). SvdEn is an extension of principle component decomposition analysis, defined in this work as the number of attributes required to characterize a biosignal (sEMG, CoP joint angle dynamics). Biosignals are represented in matrix form in order to extract singular values from the matrix representation of the signals, and entropy is determined from the data associated with each singular value. In this case entropy is the aggregate measure of the information content for the data associated with the selected singular values describing the sources of the biosignals  (Caraiani, 2014).
Data Processing Initial data processing was completed using Visual 3D (Version 6, C-Motion, Germantown, MD), where joint angle and ground reaction force data underwent fourth-order Butterworth low-pass filter with cut-off frequencies at 18 and 50 Hz, respectively. A Cartesian local coordinate system defined the yaxis as lateromedial, x-axis as posterior-anterior, and z-axis as external-internal rotation (Supplementary Material-Item 1). Intersegmental joint angles were calculated at the ankle and knee joints. Dry surface EMG electrodes containing a pre-amplification attachment (1,000 Hz, Trigno TM 16 channel wireless EMG system, Delsys Inc, USA) were positioned in accordance with SENIAM guidelines to obtain accurate muscle myoelectric activity readings. Skin preparation involved shaving of the skin at the application site to remove both hair and loose epidermal cells, and wiping with an alcohol impregnated wipe  (Hermens et al.
, 1999). All sensors were tested during the initial calibration phase to ensure a suitable signal was being obtained. Raw sEMG data was rectified, and filtered with a high band pass filter cut off frequency set at 20 Hz to reduce noise interference  (Wang et al. , 2013). The low band pass filter cut-off frequency was set at 450 Hz, followed by a Chebyshev filter, to obtain a linear envelope of the EMG. Each individual time series was normalized to 1001 frames to allow direct comparison between all trials (Figure  2 ). Joint angles and ground reaction forces while performing the YBT were recorded with an eight-camera Vicon 3-D motion analysis system (100 Hz, model, Vicon, Oxford Metrics, UK) and single AMTI force plate (1,000 Hz, BP600900-2K, AMTI, USA). All data was time synchronized and recorded with Vicon Nexus (v1, Oxford Metrics, UK).
Programming All complexity measures were calculated using a Python script based on the complexity. py script available through the Neurokit package (https://pypi. org/project/neurokit/) for purposes of the current analysis. sEMG signals were filtered with a script downloaded from Scientifically Sound (https:// scientificallysound. org/2016/08/22/python-analysing-emgsignals-part-4/) prior to determining HDf and SvdEn.
Data analysis Three different data analysis methods have been used in this work to analyse the experimental fluorescence decay traces: GLA, PCA and MCR-ALS. GLA methods approximate experimental data by a discrete sum-of-exponentials function. On the other hand, both PCA and MCR-ALS are soft-modelling methods that are not based on the compliance of any model based on rate constants. PCA has been used to observe similarities and dissimilarities among fluorescence decay traces measured for different sequences and pH values. As shown below, PCA does not take into account any chemical or physical information during the mathematical analysis. MCR-ALS, instead, is oriented to describe the number and the evolution of structural species found for every particular DNA sequence as a function of pH. The method uses information on natural properties of decay traces and concentration values within the process modelling task.
In PCA, the s components are ordered based on the variance explained in descending order. Therefore, the first component explains the largest percentage of variance in matrix D. The second component is orthogonal to the first one and explains the largest percentage of variance not described by the first component and so forth. In this work, the score plots allow displaying each row in the dataset, related to a decay curve, and, as a consequence, help in finding similarities and dissimilarities amongst the dynamic behaviour of the considered DNA sequences at the different pH values, whereas the loadings provide qualitative information about the relevant decay curve features to differentiate amongst DNA sequences. Prior to PCA, the data were preprocessed by autoscaling, which uses column mean-centring followed by division of column standard deviation.
This is why C and S T matrices calculated with MCR-ALS are different from U and V T matrices calculated with PCA. A good advantage of MCR-ALS is that no prior or little information is needed about the nature and composition of the compounds involved in the process of interest. MCR-ALS aims at resolving the bilinear model shown in Equa-tion (3) by using the sole information contained in the raw dataset D. The MCR-ALS modus operandi can be summarized in the following steps: 1. Determination of the number of components in the raw dataset D; 2. Generation of initial estimates of either the evolving concentration profiles, C, or spectra, S T ; 3. Iterative least-squares calculation of the spectra matrix, S T , and the matrix of concentration profiles, C, under constraints until convergence is achieved (i.
In this study, non-negativity was applied to the concentration profiles and to the pure decay traces in S T , since both kinds of profiles are known to be formed by positive values. As we commented above, from the MCR-ALS analysis we can get two matrices C and S T. In order to know better the lifetimes associated with the pure resolved decays, the pure decay traces obtained from MCR-ALS analysis were fitted to the minimum number of exponential terms by means of the curve fitting tool (cftool) of Matlab without considering the contribution of the IRF, which could not be exported from the instrument. Fitting error is then minimized using an algorithm based on a non-linear leastsquares approach. The quality of the fit in the models is as-sessed by the value of R 2 (over 0. 999) and by testing the significance of each fitted exponential term comparing the value of the lifetime fitted with the associated error through a t-test.
In order to further examine the conformational dynamics of F-TT-Q, F-AA-Q, F-nmyc-Q and F-nmycM-Q sequences in open form (single-stranded structure) and presenting i-motif conformation, global decay fitting as described in the data analysis section has been performed. In every global fitting, all decay traces recorded for the same sequence at a particular pH value were analysed together and a single set of lifetimes was obtained. First of all, to evaluate pH-dependent contributions to the decays that could potentially come from the fluorophore, the fluorescence decays of the control sequence F-T10 at the different pH values were analysed. All decay profiles from F-T10 could be fitted by a triexponential function convolved by the IRF with lifetimes of ∼1. 88, 0. 80 and 0. 20 ns with slight difference around these values depending on the sequence (see Supplementary Table  S1 ).
Numerous meta-analyses have been conducted in the last three decades to assess the productive and environmental benefits resulting from a diversification of cropping systems. These metaanalyses assessed one or several diversification strategies (e. g. , rotations, cover crops, agroforestry) according to various outcomes (e. g. , productivity, profitability, biodiversity). To date, no dataset has provided a comprehensive synthesis of existing experimental data on crop diversification. We present here a dataset containing 2382 effect sizes published in 99 meta-analyses covering 3736 experimental studies worldwide (https://figshare. com/s/ c15a93e96c95f89ddd89). We also provide an extensive appraisal of the quality of each meta-analysis and a quantification of the redundancy of primary studies between meta-analyses.
Data The dataset includes the values of effect sizes of 99 meta-analyses based on a total of 3736 unique primary studies. The dataset covers seven strategies of crop diversification (Table  1 ) and 114 countries over five continents (Fig. 1 ). More than 50 species are included in our dataset, but most of the data concerns six species (Maize, Wheat, Barley, Soybean, Bean, and Cowpea -Fig. 2 ). Our database also reports a quality assessment of the selected meta-analyses based on an extended and updated version of the quality checklist of  [1]. The data collected are grouped into six separate but inter-related tables. The table 'Effect_size' contains the effect sizes reported in the 99 selected meta-analyses. Two other tables pertain to the extraction and classification of meta-information on 'Effect_Size'; 'Description_Meta-analyses' compiles the references and the publication information on each meta-analysis, and the 'Primary_Studies' table reports information on each of the 4972 primary studies (of which 3736 are unique) included in the 99 meta-analyses.
g. , soil carbon, biodiversity), agricultural production (e. g. , crop yield, incidence of plant diseases) and economic profitability. -The database can be used to identify knowledge gaps, i. e. combinations of crop diversification strategies and outcomes with a low number of published meta-analyses. -The database includes an in-depth quality appraisal of 99 meta-analyses on crop diversification which can help weighting evidence in future scientific evidence assessments. yield, soil quality and biodiversity respectively. The strategy 'Associated plant species' is the strategy including the highest number of effect size values (25% of all effect sizes), followed by 'Intercropping' (24% of all effect sizes), 'Rotation' (18% of all effect sizes), and 'Agroforestry' (13% of all effect sizes). Each effect size is described by its type (e.
Literature search A systematic search of peer-reviewed journals and grey literature was carried out on May 2018 using six databases: Web of Science (http://apps. webofknowledge. com), CAB abstract (http://www. cabdirect. org), Greenfile (http://www. greeninfoonline. com), Environment Complete Database (https://www. ebsco. com), Agricola (http://agricola. nal. usda. gov), and Google Scholar (http://www. scholar. google. com). Our search equation was defined as follows; (meta-analysis OR meta analysis) AND (cropping system OR crop* OR agriculture) AND ((rotation OR Diversification OR intercrop* OR cover crop OR mixture) OR (organic AND (system OR agriculture)) OR (conservation AND (system OR agriculture)) OR no till* OR agroforestry OR agroecology). No restriction was applied to the date and language of publication in the article title, abstract and keywords, or to the geographical localization of the studies.
Extraction of data and quality assessment All effect sizes related to crop diversification in each of the selected meta-analysis were extracted. Here, an effect size is defined as a quantitative measure of the effect of a crop diversification strategy compared to a reference cropping system (i. e. , less diversified) on one or several response variables (e. g. , crop yield, soil carbon content, biodiversity index, plant disease incidence). Let Y T and Y C be the values of one response variable in the diversified treatment and control, respectively. An effect size is a function of Y T and Y C. Depending on the considered meta-analysis, the effect size can either be the ratio of Y T to Y C (or a log ratio, odds ratio) or the difference between Y T and Y C (standardized or not). Each selected meta-analysis presents the estimated values of one or several effect sizes for one or several groups of primary studies corresponding to different regions, crop types, etc.
When several estimated values were available, they were extracted altogether with the characteristics of the groups of primary studies used for their estimation (e. g. , name of the region, type of crop, etc. ). When available, information characterizing the uncertainty of estimated effect sizes were also systematically extracted (e. g. , sample size, confidence interval, p-value). Data were extracted from tables, text, supplementary information or directly from graphics using the WebPlotDigitizer software  [10]. Information to assess the quality of each meta-analysis was also extracted using 20 criteria spanning the quality of the literature review, data extraction, data analyses, and interpretations. Each meta-analysis was read carefully at least three times, to identify relevant data. In case of ambiguity, a second reader was asked to re-analyze the article.