dataesr/scientific-paragraphs-categorization huggingface dataset:
Sixteen scans of 64K data points were recorded with a 90° pulse angle, a 6000 Hz spectral width, a 5. 46 s acquisition time, a 2 s recycle delay, 200 µs echo time and 400 loops, preceded by 16 dummy scans. The assignments of metabolites in the 1 H NMR spectra were based on chemical shifts, signal multiplicities, intensity ratios, comparison with database values (HMDB, http:// www. hmdb. ca, BMRB, http://www. bmrb. wisc. edu and Che-nomX NMR Suite 8. 3 library (ChenomX Inc. , Edmonton, Canada)) and with spectra of authentic compounds recorded in the same solvent conditions (in-house library) and by spiking the samples with corresponding compound standards when doubt remained. 2D NMR experiments (COSY, Correlation SpetroscopY; HSQC, Heteronuclear Single Quantum Correlation; HMBC, Heteronuclear Multiple Bond Correlation) with non-uniform sampling (NUS percent at 25% and 128 points for COSY and HSQC and 512 points for HMBC) were performed for selected samples for assignment verification.
COSY experiment observed 1 H nuclei in both direct and indirect dimensions where 16 dummy scans preceded a 64-scan series of 8. 97 µs 90° pulse in each dimension. Spectral width was 6000 Hz for each dimension, with 4K data points in f2 and 1K in f1. The HSQC experiment observed 1 H in direct dimension and 13 C in indirect dimension, 16 dummy scans preceded a 128-scan series of 8. 97 µs 90° pulse in direct dimension and 14. 50 µs 90° pulse in indirect dimension. Spectral width was 20,000 Hz in indirect dimension and 6,000 in the direct one, 2K data points were recorded in the direct dimension and 1K in the indirect one. The HMBC experiment observed 1 H in direct dimension and 13 C in indirect dimension, 16 dummy scans preceded a 128-scan series of 8. 50 µs 90° pulse in direct dimension and 14.
50 µs 90° pulse in the indirect dimension. Spectral width was 30,000 Hz in indirect dimension and 6000 in the direct one, 4K data points were recorded in the direct dimension and 2K in the indirect one. The 2D spectrum was reconstructed using the recursive multidimensional decomposition available within the Bruker software. Identification levels were based on the Metabolomics Standards Initiative  (Sumner et al. 2007). A representative 1 H NMR CPMG spectrum of each feed extract has been uploaded to the Zenodo repository (https ://zenod o. org/ recor d/14231 24; https ://doi. org/10. 5281/zenod o. 14231 24).
Data analyses Statistical analyses were performed with R statistical software (v. 3. 3. 2, R Development Core Team, 2008) and BioStatFlow web application (v. 2. 8, http://www. biost atflo w. org). To study the effect of feed on fish final body weight and FCR, the three supplemented plant-based feeds were compared to the COM and PB control feeds. First, a oneway ANOVA was carried out on the five feeds to assess their effect on final body weight. Then, the mean fish final body weight or FCR of each experimental feed was compared to the COM or PB control mean by using Student's t test. Differences were considered statistically significant when P < 0. 05. For feed analysis, a principal component analysis (PCA) was run on 473 1 H NMR spectral bins used as variables for a total of 23 feed samples after removal of one outlier sample in PB and one in COM feed.
2004 ). To check this hypothesis, the production of glucose from trehalose should be monitored in trout. Other mechanisms such as the effect of microbiota or differences in the digestibility of diets are probably involved in the growth performances of alternative feeds. Apparent digestibility of alternative feedstuffs should not be the main cause of growth performance differences as it seems quite high in yeast protein fraction (92. 1% of protein, unpublished data) and in published data on similar feeds including spirulina biomass and H. illucens larvae meal  (Hernández et al. 2012; Magalhães et al. 2017 ). However, our untargeted approach highlighted several compounds that are rarely described in the analysis of feeds, despite their potential metabolic activity. Their role deserves to be investigated into more detail.
Funding We thank the  Phileo Lesaffre Animal Care and French ANRT  (Grant No. CIFRE 2016/0775 ) for funding  Simon Roques , and  FUI 2014  (Grant No. 2014-NINAQUA) NINAqua  and  MetaboHUB  (Grant No. ANR-11-INBS-0010 ) projects for financing.
However, the vision underlying these recent developments, i. e. , designing meaningful geomorphic features for computation from SRTM elevation data, is far reaching and shows the potential of these data for automated extraction of geomorphic signatures over territories devoid of adequate landform or landscape mapping. More specifically,  Dragut and Eisank (2012)  emphasized that using SRTM elevation as a single input variable can lead to reasonable results (through objectoriented classification). Iwahashi and Pike (2007)  introduced elevation-derived surface texture among three input geometric criteria  (Pike, 1988)  of an automated algorithm. But several aspects of the texture concept  (Haralick et al. , 1973)  have not been considered by these last authors who limited themselves to the frequency analysis of pits and peaks for processing SRTM elevation data.
Previous studies in French Guiana have proven that the mapping of landforms and landscapes is relevant for understanding spatial patterns of biodiversity and forest resources  (Slaymaker et al. , 2009; Sabatier, 2006; Guitet et al. , 2015b). For this, a detailed geomorphological object-based map of landscapes is already available over French Guiana ca. 83,000 km 2  (Guitet et al. , 2013). This map was established from SRTM digital elevation data using an object-based identification of individual landforms coupled with a multivariate analysis and expert delineation of broader entities, called landscape classes. Extending this quite heavy mapping process to larger parts of the Guiana shield in neighboring countries is limited by insufficient availability of experts and trained operators as often in tropical countries.
Data Analyses were performed from the SRTM data acquired by NASA in February 2000 (see  Farr et al. , 2007  for technical details) at the increased resolution of 1 arcsec (published for open access by NASA in 2013). Such data approximates the elevation at the top of vegetation. The vertical accuracy of SRTM data has been assessed as being about 10 m for the 3 arcsec version  (Bourgine and Baghdadi, 2005). We used data at 1 arcsec as digital elevation model (DEM) as variations of canopy height are small over the dense humid tropical forest. Indeed, canopy height variation among five broad forest types in French Guiana were barely detectable from the 3 arcsec SRTM  (Fayad et al. , 2014). The elevation data may, however, include spatial variation in vegetation height in coastal areas where landscapes include forest and nonforest mosaics.
By changing window size, the FOTO method allows for multiplescale analyses with the same initial data (here SRTM elevation image). In addition to window size, using overlapping sliding windows allows increasing the nominal output resolution (i. e. , distance between the centers of two successive overlapping windows). As already observed  (Couteron et al. , 2006) , texture gradients provided by FOTO change progressively with window size, and complementary information is to be expected only when using contrasted sizes. As landform sizes may be very variable and we are interested in landforms of 100 m to 10 km, we have done dozens of tests with different scales (i. e. , different sizes of windows, from 300 * 300 m to 15 * 15 km). We used the existing work on landform delineation carried out in French Guiana  (Guitet et al.
By design, it does not take into account the absolute elevation or its range of variation (local relief) as to decouple the study of 2D spatial variations from their grey level scaling. This decoupling, which we initially adopted to get robustness against instrumental artifacts of grey levels in air photographs  (Couteron, 2002) , echoes a classical distinction in geomorphology between descriptors of vertical (relief) and horizontal (texture/grain) variations  (Mark, 1975). FOTO is implementable via a program provided by N. Barbier that was compiled from Matlab® (The Mathworks Inc. ) functions (written by N. Barbier and P. Couteron). The program uses geotiff format images and yields the matrix of r-spectra as well as maps of window scores along the three main PCA axes (geotiff format), which show texture variations.
From a practical point of view, applying the FOTO method to SRTM data allowed us to describe the geomorphic context of Amapa and French Guiana in an understandable way for nongeomorphologist end-users of the maps in the fields of land planning and natural resource management. Textural analysis based on 2D discrete Fourier transform proved useful and efficient to define and map geomorphometric features that were not used before. The agreement with existing sources of comparisons, including field experience of some of us, supports the relevance of the approach as does the complementarity with older works  (IBGE, 2004 (IBGE, , 2015;; Iwahashi and Pike, 2007; Guitet et al. , 2013). We suggest this approach could be usefully applied for the entire Guiana shield and tested in other contexts following a similar flow of operation provided that the multiscale combination of textural analysis would be adapted and parameterized (through the adjustment of window sizes) to the specificities of each context.
Supplementary Material Software: the foto. exe program has been created by Nicolas Barbier from earlier functions written by Nicolas Barbier and Pierre Couteron in Matlab® language. It can be run on a Windows 64-byte personal computer and is freely downloadable from http://doi. org/10. 5281/zenodo. 1216005 or directly requested (nicolas. barbier@ird. fr or pierre. couteron@ird. fr). Maps: digital versions of the Landform types and Landscape units maps presented in this paper are freely available from http://doi. org/10. 5281/zenodo. 1226381.
Acknowledgements This paper was principally funded by the  BIOMAP project (Guyamazon III program , http://www. guyamazon. org/guyamazon-iii-2015-2018/biomap) with  FAPEAP  as funder for Brazil and  IRD, Cirad, CNRS, Région Guyane for France , and supported by the Observatory of the dynamics of interactions between societies and environment in the Amazon (Odyssea, funding from the  European Union 's  Horizon 2020 Research and innovation program  under the  Marie Skłodowska-Curie  grant agreement No  691053 ). Synergies from the  3DFORMOD  project 'Combining remote sensing and 3D forest modelling to improve tropical forests monitoring of  GHG emissions' (EU-H2020 -FACCE ERA-GAS , grant agreement No  696356 ) are also acknowledged. We thank  Dr. Richard A. Marston  as editor of this paper along with  Dr.
The small agricultural area Thysse Kaymor is one of these catchments that have not been the object of modelling studies at the catchment scale, despite that many hydro-climatic data have been collected. Five nested catchments have been monitored during the period 1982-1992, including rainfall, runoff, and water content and hydrodynamic properties of the soils. The role of the agriculture on the runoff was clearly shown by  [11] , who claimed that the runoff conditions are first high before the tillage, and then reduce along the rainy season due to the growth of the crops. They also found that the Normalized Difference Vegetation Index (NDVI) could be a good predictor of the runoff conditions. However, modelling was only performed at the plot scale  [12] , and not at the catchment scale. The aim of this study is to assess the skill of two well-known event-based models-the Soil Conservation Service (SCS) model and the Green-Ampt (GA) model in reproducing the flood processes in a semi-arid agricultural catchment of Senegal.
Rainfall and Runoff Data Ndiba rainfalls were continuously registered from seven rain gauges (Precis-Mecanique with tipping buckets, surface 400 cm 2 , graphical recording with daily rotation) in and around the catchment (Figure  1 ). The mean rainfall for the catchment during the period 1983-1990 was 612. 5 mm, and years 1983 and 1984 were particularly dry, as the annual rainfall was less than 500 mm. These years were characterized by a severe drought that affected West Africa since the 1970's. The rainy season occurred during summer months, the maximal rainfall generally occurred in August and its mean value is 183 mm. Gauging information was available at the hydrometric station of Ndiba (altitude = 13 m). The water levels were continuously recorded by a mechanical OTT 10 stream gauge, between 1983 and 1990.
Sixty-four gauging have been performed between 1985 and 1988, up to the water level 160 cm, corresponding to a discharge of 58 m 3 •s -1. Those gauging were correctly located around a unique and reliable rating curve. Rainfall and runoff data for Ndiba catchment were extracted from the SIEREM database (http://www. hydrosciences. fr/sierem/) for the period 1983-1990, with a 5-min time interval. Twenty-eight rainfall-runoff events were selected for the study, and were delimited by periods of 48 h when rain intensity did not exceed 1 mm•h -1 ; in addition, the events were definitely selected if the maximal rainfall amount was more than 10 mm and the peak flow more than 1 m•s -1. Table  1  shows their main characteristics. The durations of the events ranged from 0. 5 to 14. 25 h. The rainfall intensity was generally important at the beginning of the precipitations and decreased along the event.
Normalized Difference Vegetation Index For cultivated catchments, the growth of the vegetation often induces a non-stationarity of the hydrological behavior of the catchment, and consequently, of the model parameters  [22, 23]. Such variability requires to be related to a reliable descriptor of the growth of the vegetation. The Normalized Differential Vegetation Index (NDVI) was used here as a proxy to monitor the seasonal evolution of vegetation, as shown by  [11]. The NDVI daily time series have been retrieved from the NASA's land long term data record (LTDR) project (available online at: https://ltdr. modaps. eosdis. nasa. gov), which aims to produce a global set of data at a 0. 05 • spatial resolution, which were collected from AVHRR and MODIS instruments for climate studies  [24]. The algorithms used to derive the Normalized Differential Vegetation Index (NDVI) from daily surface reflectance are described in  [25, 26].
Rainfall-Runoff Modelling The models were performed within the Atelier Hydrologique Spatialisé (ATHYS) modelling platform, which was developed at the Hydrosciences Montpellier laboratory  [27, 28]. ATHYS brings together a large set of distributed models within a consistent and easy-to-use environment, including processing of hydrometeorological and geographical data. ATHYS is an open software, which can be downloaded for Windows or Linux (www. athys-soft. org). The distributed models operated over a grid mesh of regular squared cells. The basic information was brought by a Digital Elevation Model (DEM), which supplied elevation, slope, upstream area, and flowpath for each cell. Here, we used the Advanced Spaceborne Thermal Emission and Reflection Parameter (ASTER) DEM, projected to a UTM horizontal resolution of 30 m.
The Green-Ampt parameters were in agreement with the measured, e. g. , hydraulic conductivity at saturation, or estimated values. But, Green-Ampt finally scored less than SCS, because a high sensitivity to the K s parameter. At this point, however, the predictive scores of the models are still low when considering the NS values that were obtained with the best predicted values of S or K s. The main reason seems to be that the tillage practices are not actually taken into account in the models, nor in the predictors (e. g. , NDVI) of the parameters of the models. This seems to be the main point to study for the improvement of the predictive scores of the models. In addition, added value could be brought by considering the whole nested set of catchments in Thysse Kaymor. To conclude, it is worth noting the interest of such existing large data sets dating from the 80's, even before, Thysse Kaymor being an example among other.
To do this, we apply a resampling scheme on a large dataset (N = 1,400) to create a test condition and compare this with an independent evaluation condition. The test condition corresponds to subsampling participants into studies and combine these using meta-analyses. The evaluation condition corresponds to a high-powered group analysis. We observe the best performance when using mixed effects models in individual studies combined with a random effects meta-analysis. Moreover the performance increases with the number of studies included in the meta-analysis. When peak height is not taken into consideration, we show that the popular ALE procedure is a good alternative in terms of the balance between type I and II errors. However, it requires more studies compared to other procedures in terms of activation reliability.
More specifically, we test the effect of pooling subjects at the individual study level using either a fixed effects, ordinary least squares (OLS) or mixed effects group level model on the outcome of the meta-analyses methods mentioned above. As in  Eickhoff et al. (2016a)  we also evaluate the effect of the number of studies in the meta-analysis (K). Extending on their work, we consider the case for K = 10,  12, 14, 16, 18, 20, 30, and 35  when using ALE as well as effect size based CBMA's using a fixed and random effects model. We consider two performance measures: the balance between false positives and true positives and the activation reliability as a proxy for replicability. We approach this problem by applying a resampling scheme on a large dataset from the IMAGEN project  (Schumann et al.
, 2010)  and create meta-analyses (i. e. , test conditions) which we compare against a high powered large sample size study as a reference (i. e. , an evaluation condition). In the following section, we discuss the dataset, give a theoretical overview of the three models to pool subjects at study level and discuss the three models for coordinate-based metaanalysis. In the sections thereafter, we present the design of the study with the chosen performance measures and discuss our findings.
Data We use preprocessed data from the IMAGEN project  (Schumann et al. , 2010). This is a large genetic-neuroimaging study on reinforcement-related behavior in adolescents with the goal to identify its predictive value for the development of frequent psychiatric disorders across Europe. The database contains fMRI data from 1,487 adolescents aged between 13 and 15 years, acquired across several research centers on 3 Tesla scanners from different manufactures. The data are stored and preprocessed at the Neurospin 1 center (France) using SPM8 2 (Statistical Parametric Mapping: Wellcome Department of Cognitive Neurology, London, UK). The scanning sessions of interest involved a global cognitive assessment. In a fast-event related design, participants had to do a series of alternating cognitive/motor tasks.
Preprocessing included slice-timing correction, movement correction, coregistration to the segmented structural T1weighted images, non-linear warping on the MNI space using a custom EPI template and spatial smoothing of the signal with a 5 mm Gaussian Kernel (Imagen fMRI data analysis methods, revision2, July 2010). In the first level analysis, all experimental manipulations were modeled using a general linear model (GLM) with a standard autoregressive [AR(1)] noise model and 18 estimated movement parameters as nuisance terms. This resulted in a statistical map for each parameter estimate and a map reflecting the residual variance of the model fit. In this study, we use for each participant (1) the contrast map or the difference between the parameter estimate maps for MATH and LANGUAGE and (2) a variance (squared standard error) image for that contrast derived from the residual variance map.
g. , patients vs. controls) by incorporating additional regressors in the group design matrix. As statistical tests are performed in all voxels simultaneously, adjustments for multiple testing need to be imposed. Several methods are available to estimate β G and var(β G ) in model (2). We consider the Ordinary Least Squares (OLS), Fixed Effects (FE) and Mixed Effects (ME) approaches. In this study, we use the FSL (RRID:SCR_002823) software library  (Smith et al. , 2004)  and therefore only outline the implementation of these methods as described in  Woolrich et al. (2004). For a discussion of different implementations in other software packages, see  Mumford and Nichols (2006).
Between Study Variability We observe no substantial differences between the fixed and random effects meta-analysis in most results. Since we are working with one large database of a homogenous sample executing the same paradigm, between study variability is limited. To illustrate, we refer to section 8 in the Supplementary Material depicting the distributions across all voxels of betweenstudy variability and within-study variability, averaged over all folds. To investigate this further, we look at the between study variability, estimated by τ 2 in the weights (U vm in Equation  10 ) of the random-effects meta-analysis for K = 10. In Figure  14 , we display the average t-map (over 7-folds) of the reference images over 4 slices along the z-axis. We then plot the estimated τ 2 from the random effects meta-analyses combined with the statistically significant voxels depicting the weighted averages of the random effects meta-analysis.
DISCUSSION In this paper, we studied how (1) the balance between false and true positives and (2) activation reliability for various coordinatebased meta-analysis (CBMA) methods in fMRI is influenced by an analytic choice at the study level. We applied a resampling scheme on a large existing dataset (N = 1,400) to create a test condition and an independent evaluation condition. Each test condition corresponds to a combination of (a) a method for pooling subjects within studies and (b) a meta-analytic method for pooling studies. For (a), we considered OLS, fixed effects and mixed effects modeling in FSL and for (b) we considered an activation likelihood estimation (ALE), a fixed effects coordinatebased meta-analysis and a random effects coordinate-based meta-analysis. We generated meta-analyses consisting of 10-35 studies.
First, we did not investigate adaptive smoothing kernels such as the anisotropic kernel described in  Radua et al. (2014). This type of kernel incorporates spatial information of the brain structure. These kernels are promising as they potentially result in a better delineation of the activated brain regions in a metaanalysis rather than the Gaussian spheres we observed in our results. Second, our results are characterized by low between-study heterogeneity since each study is created by sampling from the same dataset. In a real meta-analysis, we expect higher between study variability as it will include studies with a range of different scanner settings, paradigm operationalizations, pre-processing pipelines (such as differences in normalization) and sample populations. In previous versions of this manuscript, we tested (1) sampling subjects in Figure  2  according to the scanning site involved in the IMAGEN project and (2) clustering subjects based on their individual effect size maps into individual studies to achieve higher between-study variability.