dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison of Freesurfer labels Table  4  shows generalised Dice similarity coefficients for 70 cortical grey matter labels, or 16 deep grey-matter labels. These gDSCs reflect similarities between label obtained from the first in-vivo structural scan and labels obtained using either the repeat in-vivo structural scan or synthetic structural image. For the HCP-M dataset, median gDSCs were typically lower for synthetic images than repeat in-vivo data (p = 0. 01 for both comparisons using uncorrected Wilcoxon Signed-Rank tests). The worst-case performances of in-vivo and synthetic pipelines were similar on this dataset. For the HCP-S dataset, differences between in-vivo and synthetic pipeline gDSCs were not statistically significant (p = 0. 65 for cortical GM and p = 0. 51 for deep GM using uncorrected Wilcoxon Signed-Rank tests).
Similarly, Cheng et al. [26]  applied thresholding to processed diffusion weighting images to identify grey-matter-and white-matter-like tissues, then applied constants to combine into a T1-like image. These latter approaches both generated T1-like images, but their utility is potentially limited by relying qualitative thresholding approaches to tissue segmentations, and a lack of a physical basis for conversion to the structural image. We have expanded on these ideas by using a wellestablished physical model that allows precise scanning parameters to be simulated; this is demonstrated here by the synthesis of specific T1w MPRAGE and T2w spin-echo sequences from both single-shell (HCP-S dataset) and multi-shell (HCP-M dataset, Hospital dataset) diffusion scans. We demonstrated that the synthetic images generated for these 32 participants, including those displaying pathology, were both visually convincing and had tissue contrasts in line with images acquired in-vivo.
However, this is unlikely to be of serious concern for tractography-based studies which are more reliant on an accurate grey-matter/white-matter interface, which was generally of reasonable quality (Fig  3 ), particularly for the 2mm HCP-S dataset (Table  4 ). Indeed, tractography derived from in-vivo structural and synthetic-structural parcellations overlapped well (median Dice � 0. 90) for all three types of diffusion acquisition tested in this study. A common use for tractography is to identify a tract from which microstructural measures, such as FA or MD, can be sampled. Here, FA and MD sampled from tractography of the in-vivo and synthetic-image pipelines differed well below the level of practical significance for most forms of analysis (Fig  5 ). An alternative to the proposed method, available only to longitudinal diffusion MRI studies, is to replace a corrupted structural image with another acquired at another time point.
We tested the efficacy of this alternative for the HCP-S and HCP-M datasets by running the Freesurfer-based pipeline with T1w and T2w scans acquired at a second time point. In general, this did not produce meaningfully better results, in terms of tractography Dice scores, MD, or FA, than using a synthetic image. Visual inspection suggested that performance of this in-vivorepeat run sometimes suffered from slight mis-registrations between the real structural images and diffusion images, reminding that the negative impact of the lower-resolution synthetic images is somewhat counter balanced by their guaranteed perfect alignment to the diffusion image. We also calculated generalised Dice coefficients (gDSC) for 70 cortical GM labels and 16 deep GM labels, at the GM/WM interface (Table  4 ). For the HCP-S dataset, gDSC scores for the synthetic and in-vivo repeat pipelines were comparable.
Results for the HCP-M dataset, however, implied that groups who work with high resolution (1. 25mm) diffusion data whose pipelines rely on a particularly accurate cortical parcellation would do best to rely on an invivo structural image (if available), rather than a synthetic structural image. The degree to which the differing parcellation accuracies will affect tractography depends on the specifics of the tractography. For example, when we performed tractography of the corticothalamic tracts in this study, we used cortical and thalamic ROIs from Freesurfer. The ROIs produced by the in-vivo pipeline had higher gDSC scores (median gDSCs of 0. 73 for cortical GM, 0. 64 for thalamus) than the synthetic pipeline (0. 58 cortical GM, 0. 48 thalamus). Nevertheless, these differences did not meaningfully affect the physical location of the tractography or the microstructural measurements taken from it (Fig  5 ).
2, middle-left column). A qualitative assessment indicated that Freesurfer parcellations obtained from synthetic datasets were similar to those obtained from in-vivo data, as shown in Fig 3. Notably, near the white matter/ grey matter interface, from which tractography is typically seeded, Freesurfer parcellations were qualitatively similar between corresponding synthetic and in vivo images. However, the synthetic images at 2mm resolution often produced inaccurate segmentation of the pial surface by Freesurfer (Fig 3, S1Table in S1 File).
Acknowledgments Data were provided in part by the  Human Connectome Project, WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University. The authors are grateful to the  Dr Katie McMahon ,  Ms Clare Berry , and the team at the  Herston Imaging Research Facility  for their generous scan time allowances, and technical support acquiring some of the data presented here. The authors acknowledge the facilities of the  National Imaging Facility , a National Collaborative Research Infrastructure Strategy (NCRIS) capability, at the  Herston Imaging Research Facility.
Acknowledgements. We would like to thank swisstopo for acquiring, archiving, scanning, and distributing the TerrA images. This work would not have been possible without over 100 years of efforts. We are particularly grateful to  Philip Joerg  and  Matthias Zesiger  for providing additional details on the images' metadata and acquisition. We are grateful to  Roxana Zehtabchi  for early developments of the data-processing workflow. We would like to thank  Timothée Produit  and  Adrien Gressin  ( HEIG-VD ) for sharing initial TerrA viewsheds and for discussions on the TerrA archive. Financial support. Amaury Dehecq ,  Romain Hugonnet ,  Matthias Huss , and  Daniel Farinotti  received funding from the  Swiss National Science Foundation  (grant no. 184634 ). This project was funded by the  Federal Office of Meteorology and Climatology Me-teoSwiss  in the framework of  GCOS Switzerland.
Code and data availability. The source code of the photogrammetric analysis is available at https://doi. org/10. 5905/ethz-1007-552 (Mannerfelt, 2022a). The code for post-processing, uncertainty, and mass balance is available at https://doi. org/10. 5905/ethz-1007-553 (Mannerfelt, 2022b). The Python package xDEM was partly developed for this project and is available at https://doi. org/10. 5281/zenodo. 4809697 (xdem contributors, 2021). All intermediate and output data for this analysis are available at https://doi. org/10. 5281/zenodo. 6675911  (Mannerfelt et al. , 2022).
Spatial grains: A 400 m 2 ; B 1000 m 2 ; C 1 ha. The maps on the left show the median estimated species richness at the corresponding spatial grain for each 2. 5 arcminute grid cell of the World, averaged over 99 boosted regression tree models based on different resampled datasets. Colors are on a log 2 scale. The maps on the right show the distribution of hotspots (red) and coldspots (blue), i. e. , areas where species richness is above the 95th or below the 5th global percentile, respectively. Here, we explore alpha diversity patterns across multiple spatial grains globally. We leverage methodological advances in modeling biodiversity across scales  9, 12, 18, [30] [31] [32]  using the sPlot database, a global initiative that aggregates and harmonizes local-scale species cooccurrence data from hundreds of independent datasets and vegetation surveys  15, 16.
The sPlot database incorporates more than 1 million vegetation plots and covers both natural and semi-natural ecosystems on all continents and in all biomes  15. We focused on terrestrial vascular plants only, since data on bryophytes, lichens, vascular epiphytes and aquatic habitats are too scattered in the sPlot database. We applied machine learning (boosted regression trees) to model the relationships between vascular plant species richness at different grains and 20 global datasets on current and past climate, soil and topography. Our models allowed relationships between alpha diversity and environmental variables to vary across grains by including interaction terms between plot size and other predictors  9. To simultaneously quantify uncertainty and to account for the uneven distribution of data across biomes and vegetation formations in our database, we averaged our results over 99 model runs, each based on a stratified resampling of the data (Supplementary Fig.
Multi-grain global maps of local species richness We modeled forest and non-forest ecosystems jointly but focus on each broad formation separately in the main text. Modeling them separately yielded similar results (not shown). For forests, we generated estimates for the three grain sizes most commonly used for sampling forests: 400 m 2 , 1000 m 2 and 1 ha. At the finest grain Fig. 2  | Global distribution of estimated vascular plant alpha diversity in nonforest ecosystems. Spatial grains: A 10 m 2 ; B 100 m 2 ; C 1000 m 2. The maps on the left show the median estimated species richness at the corresponding spatial grain for each 2. 5 arcminute grid cell of the World, averaged across 99 boosted regression tree models based on different resampled datasets. Colors are on a log 2 scale. The maps on the right show the distribution of hotspots (red) and coldspots (blue), i.
The hotspot in the western Congo Basin increased in size (Fig. 1C ). The temperate region contained no hotspots at this grain. The coldspot in southern Australia expanded to the eastern coast, while the coldspot in central Africa disappeared. The uncertainty in alpha diversity estimates, quantified as the ratio between IQR and median across the 99 resampled subsets, was highest in the boreal regions of Canada, Central and Eastern Siberia, the Amazon and Sundaland (Supplementary Fig. 2 ). For non-forest ecosystems, we used an alternative set of grains: 10 m 2 , 100 m 2 and 1000 m 2 , to match the most frequently used plot sizes in our database. At the finest grain (10 m 2 ), the median estimated alpha diversity across the 99 resampled subsets ranged from 0 to 68 vascular plant species (median across all grid cells = 14, IQR = 7; Fig.
3  | Relative influence of environmental and biogeographic variables on alpha diversity of vascular plants. Points represent the median relative importance of a predictor across 99 runs of a boosted regression tree model that jointly models vascular plant species richness in forest and non-forest formations. The bars connect the 2. 5th and the 97. 5th percentiles of the relative importance distribution across runs. The vertical dashed line separates variables with relative influence higher or lower than expected, i. e. , those variables whose relative influence is higher or lower than 100% divided by the number of variables (n = 20). datasets arising from the clustered nature of our database  34. We found no major bias or trend in residuals across grain sizes, biomes or geographical regions (Supplementary Fig.
Coarse-grained alpha diversity was calculated at 1 ha and 1000 m 2 for forest and non-forest ecosystems, respectively. We only show alpha diversity estimates where (A) forests would grow under current climate conditions and without human influence  208  , or B the land cover 'herbaceous vegetation' occurs, based on a consensus map integrating multiple global remote sensing-derived land-cover products  209. Color codes are based on quartile distributions of species richness at the two grains. Parallel hatching represents data-poor regions, i. e. , regions farther than 500 km from any vegetation plots. Values are averaged over 7700 km 2 hexagons. SR: species richness. Source data are provided as a Source Data file. (149 species per 1000 m 2 )  42  and Colombia (313 species per 1000 m 2 )  43.
Species richness data The vegetation-plot database 'sPlot' (www. idiv. de/splot) collates 110 national or regional vegetation-plot datasets. Vegetation-plot records provide geo-referenced information on the presence and cover/ abundance of all vascular plants co-occurring within a delimited area. The sPlot database version 2. 1 contains records from 1,121,244 vegetation plots surveyed between 1885 and 2015. These comprise 23,586,216 occurrence records for 58,066 vascular plant taxa, whose names have been standardized to a common nomenclature  15. When the formation to which a plot belonged was not specified (n = 137,146 plots), we used the growth form of the recorded species  61  to classify a plot as forest or non-forest as in ref. 22. That is, we defined a plot record as forest if the sum of the cover values of all tree taxa was >25% of the sum of the cover values of all species in that plot, and as nonforest, if the sum of cover values of all low-growing taxa other than trees and shrubs was >90% of the sum of the cover values of all species in that plot.
The data in the sPlot database are geographically biased since plots are unevenly distributed across geographical regions and formations (Supplementary Fig. 1 ), with relatively few data from the wet tropics. We therefore made a special effort to improve the data coverage in these regions by searching for publications and databases that report species richness, plot size and spatial coordinates of vegetation plots in the tropics. We focused on plots for which the full assemblage of vascular plants (with or without epiphytes) was sampled. However, such data were particularly scarce in many regions (e. g. , the central Amazon, Western Ghats and Sundaland). For these regions, we also included data reporting woody species richness only (along with the diameter at breast height-DBH-used as the minimum sampling threshold).
In total, we found information for an additional set of 1914 vegetation plots from 53 papers (Supplementary References). Of these, only 170 vegetation plots contained species richness information for all vascular plants. Finally, we scanned the Global Index of Vegetation-plot Databases  62  to retrieve additional datasets from the tropics, which were not included in sPlot 2. 1. We obtained permission to use 11 local datasets, totaling 7929 additional vegetation plots (7385 with species richness data for all vascular plants). In total, our database contained 412,452 vegetation plots  41, 59,  (Supplementary Fig. 1 , Supplementary Data 1).
Data cleaning and geographical resampling To further mitigate the remaining geographical bias in vegetation-plot distribution and to account for the fact that plot sizes vary markedly across regions and vegetation types (Supplementary Fig. 8 ), we applied a stratified resampling strategy that we repeated 99 times. We defined each stratum as a unique combination of realm  197  , biome  15  , broad formation (two classes: forest and non-forest), and plot size as a factor variable with four levels (small: ≤150, medium: 150-600, large: 600-1200, very large: >1200 m 2 ). These intervals were chosen to encompass the grains used for predictions (i. e. , 10, 100, 400, 1000 and 1 ha, see below) while accounting for the fact that some plot sizes are more routinely used than others. For each stratum, we randomly sampled (without replacement) up to 100 vegetation plots in each iteration.
If a stratum had fewer than 100 vegetation plots, we retained all of them. This procedure resulted in the selection of 17,972 plots in each iteration. The total number of plots used across the 99 iterations was 170,272. Altogether, these plots provided 9,953,940 occurrence records for 53,271 vascular plant taxa, i. e. , ~15% of the estimated ~350,000 vascular plant species that exist. This figure is slightly underestimated, since for 1893 plots (59,299 occurrence records) only aggregated alpha diversity data were available, but no specieslevel data. Not all vegetation plots were complete with respect to the sampled functional groups. Most records from tropical forest plots contained either only tree data, or only data on trees and shrubs (Supplementary Fig. 9 ). Excluding these plots would not be optimal, as it would have greatly reduced the spatial coverage of our dataset.
Since most of these incompletely sampled plots were from the tropics, excluding them would also create the risk of introducing a strong spatial bias into our model. Therefore, we retained these plots in the dataset and included a new predictor variable called 'plants recorded' (three levels: 'complete vegetation', 'trees and shrubs only', 'trees only') in our statistical models (see below). Specifically, a plot belonged to the 'only trees' level if it only contained information on woody species with a diameter at breast height (DBH) larger than 5 cm. It belonged to the 'only trees and shrubs' level when it either contained information on all woody species (both trees and shrubs) but not herbs, or if the minimum DBH threshold used for sampling woody individuals was less than or equal to 5 cm.
As most of these incompletely sampled plots were in the tropics, we simulated the occurrence of incomplete plots also in the other biomes when resampling the full database. This was achieved by selecting some plots with complete vegetation information and recalculating their species richness when accounting for 'only trees', i. e. , discarding all information on the occurrence of shrub and herb species, or for 'only trees and shrubs', i. e. , discarding information on herbs. We limited this procedure to biomes with >10,000 plots with complete vegetation information (i. e. , subtropics with winter rain, subtropics with year-round rain, temperate mid-latitudes). In these biomes, 20% of all the plots selected randomly within each resampling iteration (623 on average) were transformed this way.
Statistical modeling We used boosted regression trees (BRTs) to model the relationships between species richness and the explanatory variables. BRTs are nonparametric machine-learning models based on decision trees in a boosting framework. BRTs have few prior assumptions, are relatively robust against overfitting, missing data, and collinearity, and are very flexible in detecting nonlinear relationships and interactions among predictors  204. We parameterized our BRTs as follows. We first set a tree complexity of 5 and a bag fraction of 0. 5. We then systematically tested the combination between learning rates (from 0. 00025 to 0. 1) and the number of trees returning the highest 10-fold cross-validated model fit, using the gbm. step routine from the dismo package  205. For each explanatory variable, we calculated its relative influence (i.
Model validation We assessed model performance in three ways. First, we averaged the tenfold cross-validation across resampled sets obtained from the BRT output. Second, for each of the 99 resampled sets, we selected all plots not used in the specific set and calculated Pearson's correlation between species richness observed in a given plot and the respective BRT prediction at a grain corresponding to the plot area. As a third approach, we performed a spatially-constrained cross-validation  210. We did this because our plots were spatially clustered and our spatial predictors had high spatial autocorrelation (2320 km on average across all the quantitative predictors, based on 5000 random samples, Supplementary Fig. 14 ). This means that even selecting plots completely independent of the training dataset does not ensure proper validation of our models, as the training and the test data remain spatially dependent.
This violation of the fundamental assumption of model validation, namely the independence between training and test data, has been shown to affect many mapping models created with 'Big Data' approaches  34. To avoid this problem, we divided the world into square spatial blocks whose size corresponds to the average spatial autocorrelation range of the quantitative predictors (i. e. , 2320 km, n = 84, Supplementary Fig. 14 ). For each resampling, we randomly assigned each block to five folds using the function spatialBlock in the R package blockCV 33 , which selects the most even spread of vegetationplot data across folds in 99 iterations (Supplementary Fig. 15 ). We then refitted our BRT model five times for each resampling, each time using four out of five folds for training and the remaining fold for validation, and averaged Pearson's correlation coefficient between the observed and predicted species richness across folds.
We also repeated this process separately for each biome separately, i. e. , sequentially withholding all data located within a fold and a given biome for validation. We then reported the distribution of these correlation coefficients across the resampled sets, both when considering all plots, and when disaggregating by biomes. Finally, we checked the model residuals for spatial autocorrelation by fitting variogram models to the residuals using the function variogram from the R package gstat 211. All analyses were performed in R 3. 6. 3  212. Map boundaries derive from R package rnaturalearth  213.
Acknowledgements The authors are grateful to thousands of vegetation scientists who sampled vegetation plots in the field or digitized them into regional, national or international databases. We appreciate the support of the  German Research Foundation  for funding sPlot as one of the iDiv research platforms ( DFG FZT 118 ,  202548816 ). F. M. S. also acknowledges financial support within the funding programme Open Access Publishing by the  German Research Foundation (DFG) , and within the  Rita-Levi Montalcini  ( 2019 ) programme, funded by the  Italian Ministry of University. In our analyses, we used the iDiv High-Performance Computing (HPC) cluster, for which we in particular acknowledge the support of  Christian Krause. M. C. was supported by the  Czech Science Foundation  (project no.
B. H. & J. K. N. 'D. acknowledge the  DynRecSe  Project ( C2D   AMRUGE ). E. A. -D. acknowledges the  Red Col-Tree (Red de Monitoreo del bosque en Colombia). Ji. D. was funded by  MŠMT Inter -excellence LTAUSA 18007,  Czech Science Foundation  ( 21-26883S ). S. K. acknowledges funds by the 2019-2020 BiodivERsA joint call for research proposals, under the  BiodivClim ERA-Net COFUND program  (FeedBaCks, 193907). R. L. P. acknowledges funds by  GeoPark Perú. J. J. acknowledges funds from various projects of  Senacyt, Fonacon and Usac. O. P. , A. M. , and R. V. acknowledge support from the  UK Department for International Development , a  Research Fellowship  to OP from the  UK Natural Environment Research Council , the  Mellon and MacArthur Foundations ' support to the  Missouri Botanical Garden , and the  European Research Council  for enabling our ForestPlots.
netcontribution to this project. C. V. was partly supported by the  Fondation pour la Recherche sur la Biodiversité (FRB)  and  Electricité de France (EDF)  in the context of the  CESAB  project ' Causes and consequences of functional rarity from local to global scales ' (FREE). A full list of funding sources for each dataset listed in Supplementary Data 1 is available in Bruelheide et al. 15.