dataesr/scientific-paragraphs-categorization huggingface dataset:
Though it is widely accepted that Indonesia is a hotspot of plant biodiversity, the number of occurrence records collected there was relatively low, even after searching for datasets in several languages (Fig. 2 ; this data deficiency is also described by  Collen et al. (2008) ;  Raes et al. (2009)  and  Cahyaningsih et al. (2021) ), which may have led to underestimated ranges and species numbers in this region. Third, our approach is dependent on the quality of the independent reference checklist used for data cleaning. In Kew's regional-level distribution database, mainland China is only divided into nine regions: China North-Central, China South-Central, China Southeast, Hainan, Inner Mongolia, Manchuria, Qinghai, Tibet, and Xinjiang. North-Central China, Inner Mongolia, and South-Central China in particular cover extensive areas with high intra-regional environmental variability  (Ren et al.
, 2007). In contrast, Kew's database divides North and Central America into much smaller regions, leading to more accurate validation of distribution maps in these regions. Therefore, a good reference checklist for data cleaning is important for enhancing map quality at finer scales. When working at smaller spatial scales, finer regional checklists are recommended to remove outliers. While most maps are accurate, we identified some artefacts, particularly in maps of tropical and subtropical species, where ranges are overestimated or underestimated, or where artefactual linear range borders are observed, especially in Lithocarpus and Quercus. We expect that the main reason for these artefacts is insufficient data for rare and narrow-ranged species. Since our pipeline is automated and therefore easily applicable to data updates, future versions of the presented maps will increase in accuracy as data coverage increases.
Solutions for supplementing and completing datasets might come from national forest inventories (Serra-Diaz et al. , 2017) and citizen science projects (e. g. iNatualist and eBird;  Bradter et al. , 2018) , and a data merging workflow, such as the one developed in this study, could be used to add these data to already existing datasets. Furthermore, although our pipeline could reduce uncertainty, an improvement in public databases is still necessary and requires the support of taxonomists and improved AI identification technology.
Conclusion In conclusion, our study highlights the power of combining multiple occurrence and range datasets, as well as the crucial importance of improved data cleaning methods and the collection of additional data through innovative approaches in biodiversity science (e. g. citizen science projects and online observation reporting), for the global mapping of species distribution ranges. The maps generated here are provided to the scientific community open access, and future efforts will include expanding the mapping to more families and regularly updating existing maps as more data become available. The mapping approach developed here will further the field of macroecology and the study of global distribution patterns and may significantly aid future conservation efforts. performed the data analysis.
Notes S2 The effect of occurrence numbers on pseudo-absence strategies. Notes S3 Details of map validation. Notes S4 Family-level distribution patterns. Notes S5 A primary analysis between species richness and environmental factors. Table  S1  Databases and operations used to access occurrences, and numbers of accessible occurrences of Fagales and Pinales from each database. S3  Summary of occurrence processing of species of Fagales and Pinales. Table  S4  Summary of occurrence record processing of Betulaceae, Casuarinaceae, Fagaceae, Juglandaceae, Myricaceae, Nothofagaceae, and Ticodendraceae (Fagales) and of Araucariaceae, Cupressaceae, Pinaceae, Podocarpaceae, Sciadopityaceae, and Taxaceae (Pinales). Table  S5  Missing species and accessible supplementary references. Table  S6  True skill statistic (TSS) values of seven different pseudo-absence strategies (random, target-group, geographic, density, geographically stratified, environmentally stratified, environmentally semi-stratified) and five model complexity levels for each of the generalized linear models (GLMs), generalized additive models (GAMs), generalized boosting machines (GBMs), and random forest (RF) models.
Correcting for species names and occurrence data cleaning left us a dataset encompassing 2372 272 records, Ó 2022 The Authors New Phytologist Ó 2022 New Phytologist Foundation New Phytologist (2022) www. newphytologist. com 1326 (98. 1%) of the 1351 (excluding hybrid species) Fagales species of the Catalogue of Life (Tables.
Acknowledgements The authors thank  Benjamin Fl€  uck and  Alexander Skeels  from  ETH Z€ urich ,  Yunyi Shen  from the  University of Wisconsin-Madison ,  Tong Lyu  from  Peking University , and Xiaoting Xu from  Sichuan University  for technical support; and  Melissa Dawes  for feedback and proofing the manuscript. This work was supported by a  China Scholarship Council  grant awarded to LL, a  Swiss National Science Foundation  grant awarded to LP (no. 310030_188550 ) and an  ETH postdoctoral fellowship  granted to LMB.
Data availability The codes for data cleaning, parameter optimization, and mapping described in this study are openly available in GitLab at https://gitlab. ethz. ch/gdplants/gdplants. The modified environmental layers used for SDM mapping are openly available in EnviDat at https://www. envidat. ch/dataset/sdm-env-layersgdplants (doi: 10. 16904/envidat. 309). The species distribution maps generated in this study are openly available in EnviDat at https://www. envidat. ch/dataset/species-distribution-maps-gdplants (doi: 10. 16904/envidat. 308).
0 were retained. For indels, variants with FS equal to or less than 200, ReadPosRankSum equal to or greater than -20. We used the threshold QualByDepth (QD) as equal to or greater than 2 for both SNPs and indels were kept. The first version of homozygous variants was used to recalibrate the bam files of each sample using BaseRecalibrator and BQSR, then variant calling was again performed based on recalibrated bam files to get the final version of homozygous SNPs and indels written in the Variant Call Format (VCF) files. SNPs with read depth > 10, no missing data, and no heterozygous sites were retained, resulting in about 1. 8 million SNPs. These SNPs were then used to calculate the. Principal Coordinate Analysis (PCA) of the genetic distance with TASSEL 5. 0 and in R-3. 6. 3  50. A customized script in R-3.
All lines, including one susceptible check (L390) and parental lines, were grown in the greenhouse as previously mentioned and fertilized weekly. At the 4-6 true leaf stage, the F 2 populations were screened with the Pss4 strain by drench inoculation as described above. Plants were scored using a standardized 0 to 5 rating scale twice weekly for two weeks after inoculation. The scores were used to calculate the area under the disease progress curve (AUDPC) and the deviation from expected segregation ratios of resistance in the two F 2 populations was determined using the χ 2 test in R-3. 6. 3  50. Sequencing data were submitted to the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA).
Acknowledgements The authors thank  Dr. Po-Xing Zheng  for his assistance in bioinformatics analysis, our interns  Ms. Szu-ping Chen  under the supervision of  Dr. Kai-yi Chen  at  National Taiwan University  and  Ms. Chia-chi Yeh  under the supervision of  Dr. Hung-ling Yeh  at  National Chiayi University  for their technical assistance in this project.
Data availability The Illumina sequencing data have been deposited at NCBI under BioProject PRJNA725647. (reviewer linkhttps:// datav iew. ncbi. nlm. nih. gov/ object/ PRJNA 725647? revie wer= d15n1 ajijj hsspo v22ta 9s50fa) All other data are available at the World Vegetable Center repository, HARVEST (worldveg. org/harvest3).
Microscopy and image analyses Immunofluorescence images were acquired using an IXplore Spinning disk confocal microscope (Olympus) equipped with a digital camera (Hamamatsu; sCMOS Orca flash 4. 0 v3). For analysis of each sample, photographs of at least 3 different fields around the lesion were taken. For c-Caspase 3, NeuN and GFP, labelled cells were counted by fluorescence densitometry and reported as a percentage of total nuclei. For OLIG2, Phospho-NF-H and IBA1 immunostainings, mean gray values were measured. The surface covered by GFAP marker was expressed as a percentage over the whole field. The number of CD31 or GLUT1-positive vessels was quantified per field. Measurements were done using ImageJ software (NIH).
Statistical analysis and reproducibility Statistical analyses were performed with the statistical software Prism 8 software (GraphPad). Results were reported as mean ± standard error of the mean (s. e. m. ). No statistical method was used to predetermine sample size. The number of mice used in each experiment is indicated in the figure legends (n). When multiple values were obtained for a given analysis in an animal (e. g. for immunofluorescence assays where at least 3 different fields around the lesion were taken), all values are presented on the graph as individual points. When required, normality of the data was tested using Shapiro-Wilk test and a statistical method to correct for multiple comparison was used as recommended by Prism software. A value of p < 0. 05 was considered as statistically significant.
Afterwards, SEM images at points P6 and P7 show a slow transition from a connected island-like morphology towards particle-like features. Finally, points P8 and P9 of S3 are further away from the center and are covered by relatively spherical particles. Figure  1c  shows the particle size distributions at points P1, P8, and P9, as determined with the image processing software ImageJ. At P1, the average particle size (radius) is 15 ± 10 nm, and moving across the island film to P8 leads to larger particles with 20 ± 13 nm. The largest particle density with the smallest particles is observed at P9, with an average particle size of 10 ± 6 nm. The average particle sizes at points P2 to P7 cannot be easily determined since the substrate is covered with connected Au islands. An important parameter for SERS enhancement is the gap between the particles/islands.
Finally, at point P9, the average gap size is 11 ± 3 nm, which is similar to P1. This morphology is similar for different fabricated substrates within the limits of the production process of inhomogeneous samples, as can be seen in the supporting information in Figure  S1. Afterwards, SEM images at points P6 and P7 show a slow transition from a connected island-like morphology towards particle-like features. Finally, points P8 and P9 of S3 are further away from the center and are covered by relatively spherical particles. Figure  1c  shows the particle size distributions at points P1, P8, and P9, as determined with the image processing software ImageJ. At P1, the average particle size (radius) is 15 ± 10 nm, and moving across the island film to P8 leads to larger particles with 20 ± 13 nm.
The extinction maxima of these spectra are shown in Figure  3c  by the black line as a function of the horizontal position. The values are determined using an in-house MATLAB script that also considers the asymmetry of the curve, since in curves 10 to 12, the maxima are no longer discernible. The spectral shape of the extinction spectra suggests that this substrate covers the morphology change between points P1 and P5 of the substrate shown in Figures  1  and 2. For this substrate, there is a red shift from approximately 550 nm at position 1 to 670 nm at position 12. This means that the particle sizes are small at the beginning and gradually increase in size until a connected Au island film is obtained. Note that adding water droplets for the SERS measurements is expected to systematically shift the LSPR resonances to longer wavelengths.
Since HD 111520 is such an unusual system, we have obtained multiwavelength Gemini Planet Imager (GPI) data to better characterize the disk morphology and further investigate its observed asymmetry. We have conducted an empirical analysis of our GPI data through measuring the disk structure in Section 3. 1, as well as the surface brightness profiles of each band and disk color in Section 3. 2. We then discuss possible explanations for the disk's asymmetries based on the results of our empirical analysis in Section 4.
(Wolff et al. 2014). Calibration sources are measured prior to the observing sequence, minimizing flexure offsets. For the J and H bands a standard box aperture method is used to extract the flux into a wavelength calibrated data cube  (Maire et al. 2014 ). In the K1 band, the satellite spots had weaker flux compared to the background, which interferes with the calibrations necessary after cube extraction. The microlens PSF extraction method was then used to extract the data from the detector  (Draper et al. 2014; Ingraham et al. 2014 ). The S/ N improved enough for the pipeline to find the satellite spots in subsequent steps in at least the central wavelength slice of each K1-band integral field spectrometer (IFS) cube. Sky subtraction at the 2D detector was attempted but this step appeared to introduce more noise.
To create these noise maps, we use U f to calculate the standard deviation at each radius in 1-pixel wide stellocentric annuli. Here, we are assuming that U f contains no disk signal, which would be expected for an optically thin debris disk causing single scattering. To better understand the prior HST results, we reprocessed the archival Space Telescope Imaging Spectrograph (STIS) data from GO 12998 (PI: Padgett). HD 111520 was acquired behind the WEDGEA1. 0 mask position and imaged in two consecutive orbits with two different roll angles separated by 32°. Each orbit accumulated five 419 s integrations. PSF subtraction was accomplished by differencing the two calibrated and registered _sx2. fits images from each other. The two PSF-subtracted images were rotated to the north and the data were averaged.
(2020). We perform the fitting for all three bands, which will allow us to determine which parameters can be well constrained and which ones cannot. For the fitting procedure, MCMC via the Python package emcee (Foreman-Mackey et al. 2013) is utilized. Ring models are first generated from the equation of an ellipse with a given disk radii (R d ), x and y disk offset (δ x , offset along the major axis, and δ y , offset along the minor axis), disk inclination (i), and position angle (PA) measured from east to north through rotating the data. Note that for the disk offsets, a negative value means a disk offset toward the left or down, while a positive value means a disk offset toward the right or up. These models are then compared to the data points using a χ 2 function. The results from this fitting procedure for all three bands can be found in Table  2.
Surface Brightness Previous analysis of GPI observations for HD 111520 report a 2:1 brightness asymmetry in the H band  (Draper et al. 2016 ). We measured the surface brightness of our multiwavelength data to better constrain the brightness asymmetry across all three bands, as well as to measure the disk color. Here, the vertical offset profile is used to approximate the peak surface brightness location along the disk. The surface brightness is integrated along 2 × 12 pixel bins at a series of radii from the star, where 12 pixels is similar to the measured FWHM of the disk. For the polarized intensity, 1σ uncertainties are measured using noise maps derived from the U f data in each band. In contrast, 1σ uncertainties for the total intensity are measured using the standard deviation of similar sized bins located at the same radial distance but outside the disk.
HST Observations While this paper is mainly focused on the micron-sized dust grains of the debris disk around HD 111520 as observed with GPI, we also take a deeper look into observations with HST STIS. HST data are sensitive to the smallest dust grains set on highly eccentric parabolic orbits by radiation pressure that form the most extended component of debris disks, also known as the disk halo  (Hughes et al. 2018). These observations can be seen plotted in Figure  4 , with the GPI data in the Spec H band plotted in the center. Like the dust grains observed with GPI, the disk halo also features strong asymmetries. This includes an even larger brightness asymmetry of 5:1, a radial asymmetry between the two sides, as well as a bifurcation or fork feature observed in the NW extension  (Padgett & Stapelfeldt 2015; Draper et al.
esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement. This research made use of the SIMBAD and VizieR databases, operated at  CDS, Strasbourg, France. We thank support from  NSF   AST-1518332 ,  NASA NNX15AC89G , and  NNX15AD95G/NEXSS. K. A. C. and B. C. M. acknowledge a  Discovery Grant  from the  Natural Science and Engineering Research Council of Canada. Facility: Gemini:South. Software: pyKLIP (Wang et al. 2015),  Gemini Planet Imager Pipeline  (Perrin et al. 2014, http://ascl. net/1411. 018),emcee (Foreman-Mackey et al. 2013, http://ascl. net /1303. 002 ),corner (Foreman-Mackey 2016, http://ascl. net /1702. 002 ),matplotlib (Hunter 2007; Droettboom et al. 2017), iPython (Perez & Granger 2007), Astropy (Astropy Collaboration et al 2018), NumPy (Oliphant 2006; https://numpy.
Concerning seed coat color, most lines had a seed coat color (170 overall), including cream  (22) , canella  (15) , brown and dark brown (66), red (4), purple  (10) , and black (53). There were 141 lines with a white seed main color. Regarding growth habit, 237 lines showed determinate bush habits and 74 indeterminate climbing habits. Table  1. List of the 12 pod traits analyzed using the software Tomato Analyzer. The code assigned to each character is indicated in parentheses. The green pods were collected at the beginning of the R8 stage, pods developed, and seeds grew.
Core-SBP Evaluation The Core-SBP was further evaluated in the field during summer 2021 to verify its phenotypic diversity. Data for lines SBP080, SBP265, and SBP326 were not available due to pest damage. The Core-SBP maintained a wide variation for the 12 pod traits, NSP, and 25-seed weights (Table  2 ). For example, the PL ranged from 7. 07 cm (SBP029) to 19. 01 cm (SBP041), PSH from 0. 66 cm (SPB_150) to 1. 91 cm (SBP333), and NSP from 4. 1 (SBP318) to 8 (SBP014). HCPC analysis using the phenotypic data collected in the 2021 field study also grouped the Core-SBP lines into four main clusters (see Figure  S4 ). The two main components revealed by Principal Components Analysis (PCA) explained 77. 9% of the variance. Clusters A and D contained lines with extreme values for most traits, while those in B and.
Core-SBP Evaluation The Core-SBP was further evaluated in the field during summer 2021 to verify its phenotypic diversity. Data for lines SBP080, SBP265, and SBP326 were not available due to pest damage. The Core-SBP maintained a wide variation for the 12 pod traits, NSP, and 25-seed weights (Table  2 ). For example, the PL ranged from 7. 07 cm (SBP029) to 19. 01 cm (SBP041), PSH from 0. 66 cm (SPB_150) to 1. 91 cm (SBP333), and NSP from 4. 1 (SBP318) to 8 (SBP014). HCPC analysis using the phenotypic data collected in the 2021 field study also grouped the Core-SBP lines into four main clusters (see Figure  S4 ). The two main components revealed by Principal Components Analysis (PCA) explained 77. 9% of the variance. Clusters A and D contained lines with extreme values for most traits, while those in B and C exhibited intermediate values (see Table  S3 ).
Pod morphology included 12 dimension traits (see Table  1 ), NSP, and 25-seed weight. Pod dimensions were measured longitudinally (PLP, PLA, PLW, PL, PLC, PL/PLC) and in the cross-section (PSP, PSA, PSW, PSH, PSH/PSW, PSC) for 10 fresh pods (at the beginning of developmental stage R8) per line with the help of Tomato Analyzer software  [34]. The NSP was manually recorded from 10 dry pods, and the 25-seed weight was evaluated four times per line in dry seeds (14% water content). Fresh pod color and main seed coat color were qualitatively recorded in five (green, yellow, mottled green, mottled yellow, and purple) and nine (white, yellow, cream, canella, brown, dark brown, red, purple, and black) classes, respectively. Finally, the growth habit was recorded when the plants started flowering, by considering the four main classes  [35] : (i) determinate bush; (ii) indeterminate bush; (iii) indeterminate prostrate, and (iv) indeterminate climbing.
Statistical Analysis The phenotypic variation in the quantitative morphological traits was visualized by frequency distributions generated by ggplot2  [36]. Descriptive statistical analyses of the phenotypic data were conducted in R  [37]  using the package Rcmdr  [38]. Means derived from the different evaluations were adjusted using least-squares means with the help of the package LSmeans  [39] , and normality was analyzed by Shapiro-Wilk tests. Correlation coefficients among the traits were also investigated using the package corrplot  [40]. An HCPC analysis was carried out to identify the main clusters using 14 morphological characteristics of pods. The HCPC approach allowed us to combine the three standard methods used in multivariate data analyses: PCA, hierarchical clustering, and partitioning clustering.
1 (https://phytozome-next. jgi. doe. gov/, accessed on 15 November 2021). Fresh young trifoliate leaves from each line were collected, frozen, and homogenized into a fine powder to isolate the genomic DNA using the SILEX method  [46]. Significant associations between markers and pod traits were investigated using Student's t-test (marker with two alleles) or ANOVA (markers with more than two alleles). To detect the association between qualitative traits and InDel markers, Chi-contingency tests were used. Significant differences (p < 0. 05) among the alleles of the markers indicated the involvement of the tagged region in the control of the trait. Statistical analysis was carried out using package Rcmdr in R  [37, 38].
Funding: This project has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 774244  (BRESOV]. M J is supported by the  FPI  Grant  PRE2019-091249  financed by  MCIN/AEI/10. 13039/501100011033  and  FSE  " El FSE invierte en tu futuro ". IPS2 benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
Randomisation and masking Participants were block-randomised (random block size between 2, 4, 6, and 8, ratio of 1:1:1:1) within female/20μg, female/30μg, male/20μg, and male/30μg using the blockrand R package by the study statistician  [18]. The age distribution in each group was then verified to be not different in the four groups using a Kruskal-Wallis test. The obtained p-value was 0. 54. Participants and laboratory staff were masked to the administered vaccine dose (20μg versus 30μg). To ensure participant blinding to the vaccine dose, randomisation lists were kept out of sight, vaccines were prepared, and syringes were filled beforehand.