dataesr/scientific-paragraphs-categorization huggingface dataset:
In the past, SHADOW used hybrid data from  Henke et al. (1993)  (up to 30 keV) and  Cromer (1983). DABAX files contain many tabulations available in the literature. The library xraylib, used by default in OASYS, accesses a selected tabulation of data as described by  Schoonjans et al. (2011). The inclusion of anomalous scattering factors in calculations of crystal diffraction profiles using the dynamical theory of diffraction is essential, as it contributes to the peak intensity. The list of atoms in the unit cell is tabulated in the DABAX file crystals:dat. The same data are also integrated in xraylib. We added the new entry YB 66 in DABAX, with information on the composition of the crystal unit cell.
The new crystal preprocessor for OASYS and SHADOW The preprocessor file in SHADOW and CRYSTAL contains the elements of the structure factor [equation (  1   ) ]. The idea is to group atoms that are identical in all parameters except in (x, y, z) in a single 'prototypical' atom. With this, the number of terms in the sum is reduced and the calculation is faster. The new code bragg_calc2 is used for generating the crystal material file used in both SHADOW and OASYS (CRYSTAL and FH widgets). This code can use two libraries for material constants (xraylib and DABAX), at the users' choice. When calculating YB 66 , DABAX is the only option, as it was upgraded with its crystal unit-cell structure (in the file crystals:dat) and f 0 ionic states (in the file f0 InterTables:dat). The format of the new crystal material file evolved from the original one to minimize changes of source code; the old and new formats are shown in Figs.
New crystals in OASYS widgets In the OASYS suite, the widgets that perform crystal calculations are FH and CRYSTAL in the XOPPY add-on, and BRAGG pre-processor in the ShadowOUI add-on. FH and CRYSTAL widgets are used for calculating crystal structure parameters, for instance, the structure factor, Darwin width, Bragg angle, etc. and the X-ray reflectivity for crystals. Previously these applications accepted crystals composed of neutral atoms with single scale temperature factors for all atomic sites. A new software component bragg calc2 has been introduced to calculate arbitrary crystals including charged atoms, and isotropic or anisotropic temperature factors in different sites. Fig. 4  shows the reflectivity of mica 111 at 8 keV calculated with the new bragg calc2 and compared with calculations using Stepanov's X-ray server.
Diffraction profiles The calculated structure factors for YB 66 are compared in Table  3  with those of  Richards & Kasper (1969). The updated CRYSTAL widget can calculate the reflectivity for an arbitrary crystal. The reflectivity plot of YB 66 004 (see Fig. 6 ) takes into account the different anisotropic temperature factors at 14 symmetric sites and ionic atoms at 8. 04 keV. No data for YB 66 reflectivities are available in the literature for comparison. The YB 66 400 reflection is used in the following section in  Comparison of crystal profile calculations of mica 111 at 8 keV using the new code (circles) and Stepanov's X-ray server (solid line). Table  3  Comparison of calculated structures factors with those of  Richards & Kasper (1969)  for YB 66. Richards & Kasper (1969). SHADOW ray-tracing simulation calculations, and the results are compared with the measurement data from the references.
Demonstration of ray-tracing simulations for YB 66 crystal The SHADOW code is a well known ray-tracing engine for beamline design in the synchrotron radiation community. Almost all current beamlines benefitted from the help of SHADOW. We use SHADOW3 code interfaced in the ShadowOUI add-on of OASYS to demonstrate the ray-tracing results of a DCM with neutral atomic crystal and ionic atomic crystals represented by new SHADOW preprocessor files, in comparison with the data in the references. Both preprocessor files are created with the updated BRAGG widget. To check the consistency of the modifications included in SHADOW, we performed ray tracing of a DCM with InSb (111) crystal pair with the old and new preprocessor file formats at a photon energy around 1. 8 keV. The results  [Figs. 7(a)  and 7(b)] show an excellent agreement in reflectance profile and full width at half-maximum (FWHM).
6 eV by ray-tracing calculations for the JUMBO beamline at SSRL  (Cerino et al. , 1980)  where the abnormal reflectivity was measured  (Wong et al. , 1999). Flux measurement at JUMBO was made using a gold mesh detector  (Cerino et al. , 1980) ; therefore the quantum efficiency of gold  (Krumrey et al. , 1988)  must be taken into account in the calculation. For creating the YB 66 preprocessor files around 2080 eV it is important to use a dense energy grid. The anomalous dispersion factor ( f 0 , f 00 ) must be sampled correctly, which is guaranteed using Henke data  (Henke et al. , 1993)  from the CXRO website (https://henke. lbl. gov/optical_ constants/sf/y. nff), available in DABAX. For the JUMBO beamline, there was a Pt-coated mirror at an incidence angle of 89 followed by a DCM of YB 66 (004),  1 10Þ crystal which is a normalized plot between the histogram intensity on the photon energy traced after a beryl crystal and that of the source.
Ray-tracing simulations were performed for a JUMBO monochromator retrieving values consistent with the experimental results. In summary, the tools available in OASYS for crystal reflectivity and ray-tracing crystal monochromators are upgraded to account for any crystal, once the unit cell is known. The new open source software tools developed here are available for supporting accurate calculations in the design and optimization of new X-ray monochromators. The OASYS workspaces and scripts implementing the simulations presented in this paper are available at https:// github. com/91902078/yb66.
Acknowledgements The authors would like to acknowledge the  Singapore Synchrotron Light Source  for providing the facility necessary for conducting the research. The laboratory is a National Research Infrastructure under the  National Research Foundation, Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of  National Research Foundation, Singapore. The authors are grateful to  Arthur Leung  and  Herbert O. Moser  for interesting discussions.
Co-limitation towards lower latitudes shapes global forest diversity gradients The latitudinal diversity gradient (LDG) is one of the most recognized global patterns of species richness exhibited across a wide range of taxa. Numerous hypotheses have been proposed in the past two centuries to explain LDG, but rigorous tests of the drivers of LDGs have been limited by a lack of high-quality global species richness data. Here we produce a high-resolution (0. 025° × 0. 025°) map of local tree species richness using a global forest inventory database with individual tree information and local biophysical characteristics from ~1. 3 million sample plots. We then quantify drivers of local tree species richness patterns across latitudes. Generally, annual mean temperature was a dominant predictor of tree species richness, which is most consistent with the metabolic theory of biodiversity (MTB).
Here we quantified the relative contribution of a wide range of environmental factors across space on local tree species richness in forested areas around the world. To accomplish this, we standardized a global tree species richness (that is, as alpha diversity) database (Fig. 1 ) and quantified the relative contribution of 47 explanatory variables including bioclimatic conditions (for example, annual mean temperature), vegetation and survey attributes (for example, sample plot size), topographic covariates (for example, terrain roughness), soil covariates (for example, bulk density) and anthropogenic spatial features (for example, size of roadless areas) in an attempt to test whether local co-limitation exists when multiple subordinate drivers co-dominate (Figs. 2  and 3 ). We conducted a three-stage analysis (Fig.
1  and Methods in Supplementary Information for details) based on two independent ground-sourced forest inventory datasets (Phase I and Phase II; Extended Data Fig. 1 ). The main dataset (Phase I) consisted of 1,255,444 sample plots, while the validating dataset (Phase II) consisted of 22,131 sample plots, most of which are located in unsampled and under-sampled regions of the Phase I dataset. Together, our sample data covered 424 of the 435 (97%) forested ecoregions worldwide (Extended Data Fig. 1 ), with a total of ~55 million sample trees representing more than 32,000 species.
1 ). To address this gap, we argue that there needs to be a shared responsibility among forestry agencies at various levels of government, scientists, indigenous communities and other biodiversity monitoring groups to improve sample coverage of forest inventories in developing countries. Innovative biodiversity funding mechanisms, for example, forest inventories funded by carbon initiatives such as REDD+, should be incorporated into a comprehensive global forest biodiversity database. Meanwhile, the severe shortage of experts and database management infrastructures, especially in developing countries, poses another major challenge to address this gap  41. The education and training of new generations of forest scientists, taxonomists and foresters can bring tangible benefits to biodiversity monitoring while improving local economies as well.
Methods As illustrated in Fig. 1 , we conducted data analyses and modelling in three stages. Stage 1. For this study, we compiled individual in situ tree data from all the regional and national GFBi forest inventory datasets (Supplementary Table  2 ) into a standardized GFBi dataframe, that is, the GFBi tree list. In this standardized GFBi dataframe, each row represents an individual tree, and columns represent nine key tree-and plot-level attributes. These attributes are tree ID (FID), a unique number assigned to each individual tree; plot ID (PLT), a unique string assigned to each plot; plot coordinates (LAT and LON); tree species name (SPCD); DBH or above buttress; year of measurement; and dataset name (DSN), a unique number assigned to each forest inventory dataset (Supplementary Table  2 ).
With a total of 56 million trees surveyed, GFBi individual-based dataframe represents 1/50,000 of the approximately 2. 7 trillion trees  47  worldwide. Because all trees in each sample plot were identified and measured, GFBi data make it possible to quantify forest community structure, composition and species distribution. To ensure consistency and maximum accuracy in species names, we standardized observations from different forest inventory datasets with the following protocol. First, all multi-stem trees were divided so that each stem represents an individual tree. The scientific names were extracted from original datasets, keeping only the genus and species (authority names were removed). Next, all the species names were compiled into five general species lists, one for each continent.
We verified individual species names against 23 online taxonomic databases or web application programming interfaces (API) using the gnr_resolve() function from the 'taxize' package  48  of R  49. We then manually verified and corrected all the names that did not match with the majority of the online taxonomic databases, that is, the names with a matching score lower than 0. 9. For individuals denoted by morphospecies, we assigned each a unique name comprising the genus name and a unique species code. The unique species code consisted of the string 'spp' , plus the dataset name followed by a unique number denoting if two individuals belong to the same species. For example, 'Aidia sppCDi1' and 'Aidia sppCDi2' represented two different species under the genus 'Aidia' , and both species have been observed in a forest inventory of the Democratic Republic of  the Congo named 'CDi'.
To maximize our species coverage, a tree was defined in this study as a perennial plant with an elongated woody stem that supports branches and leaves, including woody angiosperms, gymnosperms and taller palms (Arecaceae). Tree ferns (Cyatheales) and bamboos (Bambusoideae) were excluded from our analysis. From the GFBi individual tree-level dataframe, we derived a global species abundance matrix. The global species abundance matrix consisted of the number of individuals by species (column vectors) within individual sample plots (row vectors). The global species abundance matrix consisted of two complementary datasets: Phase I dataset contained 1,255,444 sample plots and Phase II dataset contained 22,131 sample plots, most of which are located in unsampled and under-sampled regions of Phase I dataset.
We quantified for each sample plot tree species richness (S), which is the total number of tree species in a community. Due to the difference in plot size (standard deviation = 0. 09 ha) and threshold DBH values (standard deviation = 2. 52 cm) across GFBi sample plots, we developed machine learning models to standardize tree species richness for a common basis of 1 ha in area and 5 cm in threshold DBH. The models incorporated both plot area (A) and threshold DBH (D) as predictors to account for the underlying species-area relationship  [50] [51] [52]  and species-individual size distribution  53  in a rarefaction-based approach  54. This standardization approach justifies compiling direct tree species diversity estimates from GFBi in situ data of different sources and sampling protocols  [55] [56] [57]  , an issue highlighted in earlier large-scale-although less extensive-forest biodiversity studies  57, 58.
b, Width of the 95% confidence interval (C. I. ) for the estimated tree species richness per hectare. All map layers are displayed at a 0. 025° × 0. 025° resolution with an equirectangular projection (Plate-Carrée) for better illustration of the latitudinal gradients. 160° W 120° W 80° W 40° W 0°40° E 80° E 120° E 160° E 160° W 120° W 80° W 40° W 0°40° E 80° E 120° E 160° E 80° N 40° N 0°4 0° S 80° (plot size, basal area, threshold diameter and percent forest canopy cover); and anthropogenic variables (human footprint, roadless areas and size of protected areas) (Supplementary Table  1 ). We extracted all geospatial covariate values from raster datasets to point locations of GFBi plots using ArcMap 10. 3 (ref. 59 ) and R 3. 4. 1 (ref. 49 ), to build a standardized plot-level dataframe. Stage 2.
We trained random forests (RF)  60  , an ensemble learning method that detects general trends present in the data using a multitude of decision trees, to estimate standardized community-level tree species diversity. The RF algorithm applies the general technique of bootstrap aggregating (bagging) with a modified tree learning algorithm that selects at each candidate split in the learning process a random subset of the features (that is, feature bagging). Because a random subset of variables is chosen for each tree, the RF algorithm based on bagged tree ensembles avoids overfitting  60  and mitigates the multicollinearity issue  61  posed by high correlations between some of the predictors variables (Fig. 3 ). Using subsamples of GFBi data as the training set (that is, training dataframe) with response S, bagging repeatedly for B times selects a random sample with replacement of the training set and trains a regression tree f b.
We decided to use ecoregions as spatial blocks because (1) unlike political units such as countries and provinces, ecoregions are delineated based on ecological and bioclimatic conditions; and (2) with a total of ~700 terrestrial ecoregions across the world, each ecoregion encompasses 1,800 sample plots on average, which is a large enough sample size for training RF models. This process was repeated until all the forested ecoregions across the world had been tested. SCV was more rigorous than RCV because samples from an entire ecoregion rather than random samples were withheld for validation. PSV was the most rigorous among the three validation processes. For PSV, we have collated an independent sample dataset from 22,131 forest sample plots, which we named Phase II sample plots to highlight their independence from the original GFBi dataset (that is, Phase I sample plots).
Stage 3. Global map of local tree species richness. To map community-level tree species richness over the global forest range, we first derived the global forest range map from version 1. 3 of the Global Forest Change database  70 (years 2000-2015). To ensure consistency with the definition of forest  71  by the Food and Agriculture Organization of the United Nations (FAO), the global forest range in this study was defined as forested areas with ≥10% tree crown coverage per unit area. The tiled 'treecover2000' , 'loss' and 'gain' datasets were integrated to obtain current forest cover estimates for the year 2015. To minimize processing artefacts, the ~1 arcsecond spatial resolution tiles were spatially aggregated to an even multiple of their native resolution that approximated the resolution of our covariates.
Articles Nature ecology & evolutioN percent forest cover per ~30 arcsecond pixel. Artefacts in the original data led to 0. 08% of all terrestrial pixels having forest cover estimates greater than 100% and 1. 9% of terrestrial pixels having estimates less than 0%. These values were truncated to 100% and inflated to 0%, respectively. Finally, the global forest range consisted of those pixels with a percent forest cover ≥10% in 2015. In total, each map consisted of 9,944,908 pixels of 0. 025° × 0. 025° (hereafter, the pixel) of forested areas. This range is rather conservative and potentially underestimates many remnant forests in drylands and grasslands  72. We then estimated tree species richness at a 1 ha scale for all pixels within a continent based on the final RF model trained for that continent, using both Phase I and Phase II data.
Spatially explicit local environmental covariate data across the global forest range were used for the imputation, except that plot size and threshold DBH were set as 1 ha and 5 cm, respectively. For ecoregions with extremely low sample coverage, we further fine tuned the RF model using samples of similar environment characteristics from other continents. More specifically, we first identified two ecoregions of extremely low sample coverage, that is, the temperate forests in South America and the tropical forests in Oceania, as there were fewer than 1,000 sample plots for the entire biome on those continents. We then trained a new RF model for each ecoregion, using all the sample data from the same biome across the world and fine tuned the mapping data for that ecoregion using the biome-specific RF model.
Data availability The global map of tree species richness is available under license CC BY 4. 0, with the identifier: 10. 6084/m9. figshare. 17232491. This map can be downloaded in two formats. One is a geoTIFF file (S_mean_raster. tif) containing the fully geo-referenced map of tree species richness worldwide at a 0. 025° × 0. 025° resolution. The other is a comma-separated file (S_mean_grid. csv) with the following attributes: S is local average tree species richness per ha and x, y are centroid coordinates of all 0. 025° × 0. 025° pixels. The global map of co-limitation is available under license CC BY 4. 0, with the identifier: 10. 6084/ m9. figshare. 17234339. The metadata of the entire training dataframeincluding the characteristics and references of all the in situ Phase I and Phase II datasets and the definitions, units and summary statistics of the environmental covariates-is available under license CC BY 4.
0 with the identifier: 10. 6084/ m9. figshare. 19733449. v1. The public version of the training dataframe, including the plot-level species richness and all the covariates, which is needed to reproduce the models and results presented here, is available at: https://doi. org/10. 6084/ m9. figshare. 20055488. The maps and dataframe are also available on the international web research platform: Science-i (https://science-i. org/). Raw forest inventory data are commonly subject to a wide array of confidentiality clauses in regard to open access policies. Despite recent efforts to make some of these data fully open  76, 77  , some governments and private data owners, especially those from the developing countries generally have decided to keep their data confidential. This decision is based on well-founded arguments to protect certain trees or forests (because of their large size or protected taxonomic status) from illegal logging or trespassing and to protect landowners' privacy against the misuse of plot information such as the geographic coordinates.
acknowledgements The team collaboration and manuscript development are supported by the web-based team science platform: science-i. org,with the project number  202205GFB2. We thank the following initiatives, agencies, teams and individuals for data collection and other technical support: the  Global Forest Biodiversity Initiative (GFBI)  for establishing the data standards and collaborative framework;  United States Department of Agriculture, Forest Service ,  Forest Inventory and Analysis (FIA) Program ;  University of Alaska Fairbanks ; the  SODEFOR, Ivory Coast; University Félix Houphouët-Boigny (UFHB, Ivory Coast ); the  Queensland Herbarium and past Queensland Government Forestry and Natural Resource Management departments  and staff for data collection for over seven decades; and the  National Forestry Commission of Mexico (CONAFOR).
064976/2014-01 ); R. Ávila  and  S. van Tuylen ,  Instituto Nacional de Bosques (INAB), Guatemala , for facilitating Guatemalan data; the  National Focal Center for Forest condition monitoring of Serbia (NFC) ,  Institute of Forestry, Belgrade, Serbia ; the  Thünen Institute of Forest Ecosystems (Germany)  for providing National Forest Inventory data; the FAO and the  United Nations High Commissioner for Refugees (UNHCR)  for undertaking the  SAFE (Safe Access  to Fuel and Energy) and CBIT-Forest projects; and the  Amazon Forest Inventory Network (RAINFOR) , the  African Tropical Rainforest Observation Network (AfriTRON)  and the ForestPlots. netinitiative for their contributions from Amazonian and African forests. The Natural Forest plot data collected between January 2009 and March 2014 by the  LUCAS programme for the New Zealand Ministry  for the Environment are provided by the  New Zealand National Vegetation  Survey Databank https://nvs.
landcareresearch. co. nz/. We thank the  International Boreal Forest Research Association (IBFRA) ; the  Forestry Corporation of New South Wales, Australia ; the  National Forest Directory of the Ministry of Environment and Sustainable Development of the Argentine Republic (MAyDS)  for the plot data of the Second National Forest Inventory (INBN2); the  National Forestry Authority and Ministry of Water and Environment of Uganda for their National Biomass Survey (NBS)  dataset; and the  Sabah Biodiversity Council  and the staff from  Sabah Forest Research Centre. All TEAM data are provided by the  Tropical Ecology Assessment and Monitoring (TEAM) Network , a collaboration between  Conservation International , the  Missouri Botanical Garden , the  Smithsonian Institution  and the  Wildlife Conservation Society , and partially funded by these institutions, the  Gordon and Betty Moore Foundation  and other donors, with thanks to all current and previous TEAM site manager and other collaborators that helped collect data.