dataesr/scientific-paragraphs-categorization huggingface dataset:
Results of our analyses controlling for interannual climate variability did not differ qualitatively from the results presented in the text (Supplementary Fig. 4 ). In addition, to test for temporal trends in stability and diversity responses to fertilization, we used data on overlapping intervals of four consecutive years. Results of our analyses using temporal trends did not differ qualitatively from the results presented in the text (Supplementary Fig. 6 ). Inference was based on 95% CIs. Second, we used SEM  29  with linear models, to evaluate multiple hypothesis related to key predictions from theories (Table  1 ). The path model shown in Fig. 1e  was evaluated for each treatment (control and fertilized), and we ran separate SEMs for each period of experimental duration (from 4 to 9 years of duration).
Acknowledgements The research leading to these results has received funding from the  European Union. Seventh Framework Programme  ( FP7/2007-2013)  under grant agreement no. 298935  to  Y. H. (with A. H. and E. W. S. ). This work was generated using data from the  Nutrient Network collaborative experiment , funded at the site scale by individual researchers and coordinated through  Research Coordination Network  funding from  NSF  to E. B. and E. W. S. (grant # DEB-0741952 ). Nitrogen fertilizer was donated to the  Nutrient Network by Crop Production Services, Loveland, CO. We acknowledge support from the  LTER Network Communications Office  and  DEB-1545288. M. L. was supported by the  TULIP Laboratory of Excellence  ( ANR-10-LABX-41 ), and by the  BIOSTASES Advanced Grant  funded by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no.
Data availability The data that support the findings of this study are available via GitHub (https://github. com/YannHautier/NutNetStabilityScaleUp). Data sources are provided with this paper. WorldClim global climate database is freely available through the World Data Center for Climate (WDCC; cera-www. dkrz. de), as well as through the CCAFS-Climate data portal (http://ccafs-climate. org).
Results We investigated the functional connectivity architecture of the human subcortex using resting-state fMRI acquired at 3T (n=1080) and 7T (n=183), and task-evoked fMRI acquired at 3T (n=725) from healthy adults participating in the Human Connectome Project (HCP)  19. Demographics are shown in Supplementary Table  S1. Two 3T resting-state fMRI sessions were acquired for each individual, with the first session used as the primary dataset (REST1) and the second used for replication (REST2). Gradients in functional connectivity were mapped using Laplacian eigenmaps and a newly developed procedure called gradientography. Formal model selection was used to determine whether gradient magnitude peaks were sufficiently large to warrant boundary delineation. The competing model posited a gradual spatial change in connectivity, without discrete boundaries.
Data and pre-processing Human neuroimaging data acquired as part of the Human Connectome Project S1200 release  19  was analyzed here. Participants were healthy young adults ranging between 22 and 37 years old. Some participants were genetically related. These relationships were controlled in statistical analyses by constraining permutations  59, 60. HCP datasets used in this study include: i) Two sessions (REST1 and REST2) of resting-state functional magnetic resonance imaging (rfMRI) acquired using multiband echo planar imaging (EPI) on a customized Siemens 3T MR scanner (Skyra system), where each session comprised two runs (left-to-right and right-to-left phase encoding) of 14min and 33s each (TR=720ms, TE=33. 1ms, voxel dimension: 2mm isotropic). The two runs were temporally concatenated for each session, yielding 29min and 6s of data in each session.
Concatenation of the two different phase encoded data ensured that any potential effect of phase encoding on gradient direction was counter balanced by the opposing phase encoding; ii) Task-evoked fMRI (tfMRI) acquired using the identical multiband EPI sequence as the resting-state fMRI, however, the run duration ranged between 2-5mins, depending on the specific task (7 tasks); iii) Resting-state fMRI acquired on a Siemens Magnetom 7T MR scanner using a multiband acquisition (TR=1000ms, TE=22. 2ms, voxel dimension: 1. 6mm isotropic), where participants completed four runs of approximately 16 minutes each. The first two runs (posterior-to-anterior and anterior-to-posterior phase encoding) were used in this study. We refer to this dataset as 7T-rfMRI. Details pertaining to the acquisition of rfMRI  26, 61  and tfMRI 30 are described elsewhere.
The seven tfMRI tasks were chosen to tap a broad range of cognitive and affective processes and activated a wide range of neural systems  30. Participants that had completed at least two runs in each dataset were included. Supplementary Table  S1  shows the sample size and basic participant demographics for each dataset. The second 3T-rfMRI session (REST2) was used for replication. Minimally preprocessed volumetric rfMRI and tfMRI data were sourced from the online HCP repository. Details of the minimal preprocessing pipeline can be found elsewhere  62. The pipeline includes correction of head motion and spatially structured physiological noise with ICA+FIX  63, 64. ICA-based denoising methods outperform, or equal, the performance of alternative approaches on most benchmarks  65. After eliminating spatial artefacts and distortions, the functional images were spatially aligned to the MNI (Montreal Neurological Institute) standard space using the FNIRT nonlinear registration algorithm.
MNI standard space herein always refers to the MNI ICBM 152 nonlinear 6th generation stereotaxic registration model. Additional nuisance regression and temporal filtering were not performed as part of the pipeline. However, head motion registration parameters estimated as part of spatial alignment were curated, reduced to a single summary measure known as framewise displacement  66  and included as a confound in statistical analyses to control for individual variation in head motion  67, 68. The subcortical signal-to-noise ratio (SNR) and sensitivity of the blood-oxygenation level dependent (BOLD) signal was lower compared to the cortex because T2* signal decay times are shorter in subcortical regions compared to cortical regions, due to iron enrichment of subcortical tissue  34. Therefore, to enhance subcortical SNR and BOLD sensitivity, the minimally preprocessed data were subjected to two additional operations (Supplementary Fig  S8a ).
The Wishart filter was performed over all gray matter voxels for each individual separately in all datasets. In addition, task block regressors were created for each task condition based on the time of onset, the duration and the relative magnitude of each stimulus, and then convolved with hemodynamic response functions (spm_hrf. m function from SPM12, https://www. fil. ion. ucl. au. uk/spm). The relevant regressors corresponding to each task were then regressed from the tfMRI data, to ensure that functional connectivity was disambiguated from the potential confound of task co-activation effects  71. The residuals remaining from this regression were used for all subsequent analyses.
The final mask comprised a total of !=7,984 voxels (63. 87cm 2 ). The subthalamic nuclei, substantia nigra and comparably small subcortical nuclei were not included because the spatial resolution of the fMRI data was deemed insufficient to accurately resolve putative substructures within these nuclei (e. g. pars reticulata and compacta). Sub-millimetre fMRI and multi-echo planar imaging would benefit investigation of the functional architecture of these exceedingly small nuclei  75. A binary gray matter mask was delineated using the MNI152 probabilistic gray matter atlas. Voxels belonging to cortical gray matter were included in the gray matter mask as well as any voxels comprising the final subcortex mask. fMRI data was consistently absent for a majority of individuals in a small proportion of voxels comprising the gray matter mask.
Homogeneity estimation A valid functional parcellation should comprise regions that are internally homogenous with respect to the functional connectivity profile of each constituent voxel. The regional homogeneity of Scale I-IV parcellations was compared to the extent of homogeneity achieved with alternative subcortex parcellations as well as the homogeneity that could be expected due to chance. The fMRI data for a region was represented as a matrix of dimension ! × #, where ! and # denote the number of voxels comprising the region and the number of time frames, respectively. Regional homogeneity was defined as the variance explained by the first principal component of this matrix, which was estimated using PCA. Homogeneity thus quantified the extent of synchrony in fMRI activity across voxels comprising a region.
While homogeneity can be computed with respect to functional connectivity 3 , we performed estimation directly on the fMRI time series acquired in the second session (REST2). This ensured that validation was performed using independent data and an independent quantity. Homogeneity was computed separately for each region and each individual. For each individual, homogeneity was then averaged across all regions to yield an overall estimate of homogeneity for the parcellation as a whole, referred to as parcellation homogeneity (Supplementary Fig S11a). Parcellation homogeneity increased from Scale I to IV (Fig 5a, red dotted line). This was because small regions were inherently more homogeneous than large regions 3. To determine whether the observed homogeneity values were larger than expected due to chance, the subcortex was parcellated randomly, such that the total number of regions and the distribution of region size were matched between the random and observed parcellations (Supplementary Fig  S11b ).
To enable comparison between 3T and 7T parcellations, the 3T parcellations were upsampled to the same spatial resolution as 7T (1. 6mm isotropic) and the spatial similarity between each scale of 3T and 7T parcellations was estimated using the normalized mutual information (NMI)  91. NMI is an information theoretic measure that quantifies the extent to which knowledge of a parcellation reduces the uncertainty in the location of regions in a second parcellation. NMI ranges between zero and one, with higher NMI indicating greater spatial correspondence between parcellations. reproducibility and minimize circularity, the SVM was trained and evaluated using data from the second session (REST2). One hundred individuals were randomly selected to train the SVM and the trained SVM was then applied to delineate a personalized parcellation for the remaining 921 individuals.
Specifically, the group-consensus eigenmaps and the gradient magnitude images were recomputed using only the first quarter of time frames (T=278) from each run of the resting-state fMRI data (278 × 2 in total), thereby matching the average number of time frames across the seven tasks. We found that the gradient magnitude images computed using the shortened and full-length resting-state fMRI acquisitions were highly correlated (Spearman correlation: r>0. 98 for both REST1 and REST2). This suggests that differences in the number of volumes between the task and resting-state fMRI acquisitions do not explain the task-evoked reconfiguration of connectional gradients and diversity curves.
Other confounding factors such as blood pressure, hemoglobin, height and weight were not included due to missing responses for several individuals. ICA was performed using the Icasso package  99  for MATLAB, which provides an implementation of the FastICA algorithm  100. Under ICA, the matrix of residuals (behaviors × individuals), denoted with F, was factorized into the product F = K *( L, where K *( denotes the mixing matrix (behaviors × components) and L denotes the estimated independent sources (components × individuals). The inverse of K *( is denoted K (components × behaviors) and referred to as the de-mixing matrix. To estimate the de-mixing matrix, FastICA was performed with default parameter settings, including a cubic nonlinearity and deflationary orthogonalization. Bootstrapping was used to sample individuals with replacement and ICA was performed independently on each bootstrapped sample using randomly chosen initials conditions.
A higher =M suggests compact clusters, whereas a lower =M suggests that clusters are not well separated. These operations were performed internally in the Icasso package and further details are available in the package's documentation. In addition to these internal procedures, we performed a sampling and matching process to ensure stability and reliability of the estimated independent components. Specifically, the ICA as described above was repeated for 10 trials, yielding an ensemble of de-mixing matrices, Once the reference trial was determined, the components in each of the other trials were reordered to match the reference, as dictated by the optimal one-to-one matching computed with the Hungarian algorithm. The polarity of the component was potentially flipped to ensure consistency with the reference trial.
The reordered de-mixing matrices K ( , K ! , … , K () were then averaged across trials to yield a single de-mixing matrix that represented a consensus across the 10 trials. The independent components L ( , L ! , … , L () were reordered and then averaged in the same way. proportion of permutations for which the largest component identified was larger than or equal in size to the observed component. The NBS was repeated independently for each of the five behavioral dimensions and for the two alternative hypotheses of positive and negative correlation. This resulted in a total of ten multiple comparisons. The false discovery rate was controlled at 5% across these multiple comparisons. The NBS was performed on functional connectivity mapped using 3T-rfMRI data from the second acquisition (REST2), given that the new atlas was delineated using the first acquisition (REST1).
Acknowledgements We thank  Dr Matthew Glasser  for provision of the Wishart filter. We thank  Dr Luca Cocchi  for provision of the additional validation dataset (Supplementary  S3 ). All other data were provided by the  Human Connectome Project ,  WU-Minn Consortium  ( 1U54MH091657 ; Principal Investigators:  David Van Essen  and  Kamil Ugurbil ) funded by the 16  National Institutes of Health (NIH)  institutes and centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
Data and code availability Analyses were undertaken on publicly available human neuroimaging datasets acquired and maintained by the Human Connectome Project 19. These datasets are available at: https://db. humanconnectome. org/. The new atlas is openly available in the form of NIFTI (Neuroimaging Informatics Technology Initiative) and CIFTI (Connectivity Informatics Technology Initiative) files. See Supplementary Table S5 for further details. To facilitate mapping of whole-brain connectomes, we have also integrated the new atlas into several well-known cortex-only parcellation atlases and the combined cortex-subcortex atlases are made openly available. In addition to these key resources, custom MATLAB code to compute Laplacian eigenmaps, gradient magnitudes, diversity curves and other computational analyses undertaken as part of this study are openly available at: https://github.
com/yetianmed/subcortex All additional software packages used for this study are freely and openly available: Diffusion Toolkit and TrackVis: http://trackvis. org/ NBS: https://www. nitrc. org/projects/nbs/ Icasso: https://research. ics. aalto. fi/ica/icasso/ NeuroMArVL: https://immersive. erc. monash. edu/neuromarvl/ PALM: https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/PALM/ExchangeabilityBlocks.
An estimated 76% of global stream area is occupied by channels with widths above 30m. Sentinel-2 imagery with resolutions of 10m could supply information about the composition of river corridors at national and global scales. Fuzzy classification models that infer sub-pixel composition could further be used to compensate for small channel widths imaged at 10m of spatial resolution. A major challenge to this approach is the acquisition of suitable training data useable in machine learning models that can predict land-cover type information from image radiance values. In this contribution, we present a method which combines unmanned aerial vehicles (UAVs) and Sentinel-2 imagery in order to develop a fuzzy classification approach capable of large-scale investigations. Our approach uses hyperspatial UAV imagery in order to derive high-resolution class information that can be used to train fuzzy classification models for Sentinel-2 data where all bands are super-resolved to a spatial resolution of 10m.
We use a multi-temporal UAV dataset covering an area of 5. 25km 2. Using a novel convolutional neural network (CNN) classifier, we predict sub-pixel membership for Sentinel-2 pixels in the fluvial corridor as divided into classes of water, vegetation and dry sediment. Our CNN model can predict fuzzy class memberships with median errors from À5% to +3% and mean absolute errors from 10% to 20%. We also show that our CNN fuzzy predictor can be used to predict crisp classes with accuracies from 95. 5% to 99. 9%. Finally, we use an example to show how a fuzzy CNN model trained with localized UAV data can be applied to longer channel reaches and detect new vegetation growth. We therefore argue that the novel use of UAVs as field validation tools for freely available satellite data can bridge the scale gap between local and regional fluvial studies.
Despite progress in manned (e. g. Carbonneau et al. , 2004; Dugdale et al. , 2013 Dugdale et al. , , 2015;; Frechette et al. , 2018)  or unmanned image acquisitions (e. g. Tamminga et al. , 2015; Woodget et al. , 2015; Carbonneau et al. , 2018) , image data for entire catchments and nations is currently constrained to multi-year surveys done by national and/or regional environmental agencies and, at best, delivers imagery with a spatial resolution on the order of 0. 5m and temporal resolutions of several years or decades. Sub-metric spatial resolution imagery acquired at daily or weekly temporal frequency is available in the commercial realm but purchasing costs for entire nations or continents are beyond the most generous budgets. A potential alternative is the use of public domain multispectral satellite data such as the European Union (EU) Copernicus Sentinel-2 or the joint NASA/USGS Landsats 7 and 8.
These offer a reasonable temporal repeat frequency but at much lower spatial resolutions. Sentinel-2 has four bands acquired natively at a spatial resolution of 10m. Downing et al. (2012)  estimate that mean width of fifth-order streams is roughly 30m and that streams with orders 5-12 (the highest order of the Amazon River) occupy 76% of global stream area. Sentinel-2 data should therefore at least be capable of detecting such streams and thus be sensitive to most rivers of the world. However, it is clear that medium to small streams (dimensions below ~100m) will have few pixel samples that will leave a coarse digital representation of these complex landscapes. Fuzzy classification is a well researched topic that aims to infer sub-pixel scale compositions by assigning to each pixel a membership percentages for each available class  (Foody and Cox, 1994; Foody, 1997; Foody et al.
, 1997; Zhang and Foody, 2001; Ling et al. , 2019). Fuzzy classification approaches could therefore be a way to mitigate for the relatively low spatial resolution of Sentinel-2 data. Supervised classification, of either the fuzzy or crisp (i. e. assigning a single integer class number to each land-cover type representing the semantic class of a landform) variety, of large-scale river corridors does pose logistic challenges. Traditionally, ground-truth played a critical part in supervised classification of remotely sensed imagery  (Curran and Williamson, 1985; Steven, 1987). However, the obvious logistic implications of large-scale ground-truth data acquisition has somewhat weakened this practice and we find an increasing usage of on-screen image interpretation as the basis for the production of training areas for supervised classification algorithms (e.
g. Thanh  Noi and Kappas, 2018). Low-cost drones, unmanned aerial vehicles (UAVs), might provide a cost-effective approach to the acquisition of ground-truth data for satellite data analysis. Carbonneau et al. (2018)  have demonstrated that low-altitude imagery could be used as ground-truth data for grain size mapping algorithms. Similar to this idea, this article proposes to use UAV imagery in order to derive suitable training data for fuzzy classification algorithms applicable to Sentinel-2 imagery. Using one of the largest UAV datasets in the published literature, we have developed a novel convolutional neural network (CNN) fuzzy classification algorithm tailored to Sentinel-2 imagery. We focus mainly on the fuzzy classification of river corridors and consider three end-member classes: water, vegetation and dry exposed sediment.
CNN models trained with UAV-derived labels from two rivers can be satisfactorily transferred to two new rivers, imaged on two separate Sentinel-2 tiles. Finally, CNN models trained on one part of a given river perform well when classifying other parts of the same river. Furthermore, we have tested comparator methods such as linear unmixing and dense neural network (DNN) fuzzy classifiers and have made two important findings. First, models that are trained with UAV-derived label data always perform better than models trained without the benefit of field data. Second, linear unmixing and DNN fuzzy predictors cannot match the performance of our novel CNN approach. As a final demonstration of the potential of our novel approach, we give the reader access to fuzzy classifications for 294 linear kilometres along the river corridors of our study rivers.
Drone acquisitions and Sentinel-2 imagery Ground-truth data are derived from 16 UAV surveys carried out in Italy using a DJI Phantom 4 Pro drone. The data were acquired during 2017 and 2018 and spread across eight sites located on four rivers. In northern Italy, imagery was acquired for two sites on the River Sesia, near the towns of Arborio and Caresana. The River Sesia starts in the Alpine foothills as an island-braiding channel and then, nearer its confluence with the Po, evolves into a single thread meandering channel. For the River Po, we have one sampling site. The River Po is the longest river in Italy and consists of a single-thread channel with local wandering. In central Italy, imagery was acquired for three sites on the River Paglia in Umbria. This river is a small single-thread channel with rare occurrences of localized wandering.
In addition to the UAV imagery, Sentinel-2 data was downloaded from the European Space Agency (ESA) Open-Access Copernicus hub. First, we identify suitable cloud free imagery, nearest in time within a maximum 15days of the drone acquisition. Second, we verify the discharge from the nearest upstream gauging station and precipitation records in order to avoid rain threedays prior to acquisition (dry land) and to control hydraulic conditions. Once these conditions are met, we download the full Sentinel-2 tiles at level 2A (with full atmospheric correction). In total, eight tiles were needed to match all the UAV surveys. Within these tiles, we discard Sentinel-2 bands designed to sample atmospheric quantities 3121 UAV-BASED FUZZY CLASSIFICATION OF SENTINEL-2 and we will only use bands 2, 3, 4, 5, 6, 7, 8, 8A, 11 and 12 (total of 10 bands).