dataesr/scientific-paragraphs-categorization huggingface dataset:
Words were then lemmatised, when various forms of a word are grouped together so that they can be analysed as a single word (e. g. , hunger, hungry), using the 'textstem' package  [78]. Speci c steps can be seen in (SI 6). Second, word frequency was used to determine which singular words were used most frequently across the three groups. For each group, the lexical density corresponding to the ratio of unique words relative to the total number of words in each group was calculated, where higher scores mean more unique words and less repetition. All text was considered together, irrespective of which box participants wrote it.
f) Analysis Missing Data Imputation: Descriptive statistics were calculated to show distributions for each variable (group, age, use of hormonal contraceptives). There were 118 missing observations for age and 255 for residence. To avoid power reduction and bias by excluding incomplete cases  [79] , multiple imputation was performed using chained random forest in the missRanger [80] package. This uses all other variables as co-variables and a random forest plot to predict each variable, iterating it until the average out-ofbag prediction error of the models no longer improves. The nal model for each question was run ve times, each on one of the imputed datasets, estimates pooled. The models were run on both complete cases and imputed datasets to check that results were similar [  SI 7]. Analysis: To investigate if the experimental treatment in uences the polarity of responses, we ran a two-part model [81] to account for the occurrence of zeros (neutral responses) in a categorical model (model 1), and the non-zero polarity scores (positive and negative) using a continuous distribution (model 2 + 3).
836, p < 0. 001, Fig. S1f ) cohorts. Furthermore, we could reveal that the two genes exhibited remarkably high and significant correlative co-expression scores in multiple expression datasets, including other cancer entities and control tissues (n = 39,090; p < 0. 001, Fig. 1e ). These observations prompted us to hypothesize that HHIP-AS1 and HHIP may indeed share a unique bidirectional promoter. Therefore, we performed a luciferase reporter assay and revealed the existence of forward and reverse activity of the promoter region in vitro (Fig. 1f ), suggesting that promoter hypomethylation may equally drive overexpression of both HHIP-AS1 and HHIP transcripts in SHHdriven tumors. Since HHIP-AS1 was not previously described in murine models of SHH-driven tumors and lncRNAs are only partially conserved through species  34  , we investigated to which extent HHIP-AS1 was conserved across vertebrates.
In order to decipher the molecular mechanism controlled by HHIP-AS1, we investigated the impact of HHIP-AS1 knockdown on both transcriptome and proteome in two independent cell models (Daoy and CHLA-266). Interestingly, our integrative proteogenomic analysis identified two candidates that were highly and consistently perturbed upon HHIP-AS1 knockdown in both models, namely hydroxysteroid 17-beta dehydrogenase 10 (HSD17B10) and DYNC1I2 (Fig. 3a , RNA sequencing data: NCBI GenBank #GSE140741 and proteomic data: ProteomeXchange PRIDE database #PXD016550). We decided to focus on DYNC1I2 based on its co-expression with HHIP-AS1 in primary MB samples (r = 0. 336; p < 0. 001, Fig. 3b ), as well as its high expression level specifically in SHH MB subgroup (Fig. S5a ) and its known functions in neurodevelopment and cell cycle progression  42.
Therefore, we correlated the expression of these miRNA candidates with DYNC1I2 using RNA sequencing data of 167 primary MB samples. Out of these six miRNAs, four were not detected in MB patient samples, while two miRNAs showed expression in MB patient samples, namely hsa-miR-425-5p (see binding site in Fig. 5a ) and hsa-miR-1915. Notably, hsa-miR-425-5p expression level showed no differences across molecular subgroups of MB patients and cell models (Fig. S7a  and b ), but demonstrated anti-correlation with DYNC1I2 expression in patient samples (r = -0. 312; p < 0. 001, Fig. S7c ) compared to hsa-miR-1915 (Fig. S7d ). After dividing the dataset into SHH and non SHH MB, the anti-correlation was only maintained in non SHH MB samples (r = -0. 303; p < 0. 001, Fig. S7e ). We therefore tested whether a functional relationship existed between hsa-miR-425-5p and DYNC1I2.
Bar graphs are presented as the mean ± SD of three independent experiments. d Proliferation rate of Daoy and CHLA-266 with stable knockdown of HHIP-AS1 (sh-HHIP-AS1#1) measured by EdU incorporation in combination with or without transient inhibition of hsa-miR-425-5p. Bar graphs are presented as the mean ± SEM of at least ten independent experiments and corresponding controls were set to 100%. Student's two-sided t-test; ***p < 0. 001; **p < 0. 01; *p < 0. 05; n. s. not significant. Source data and exact pvalues are provided as a "Source Data file". mediated by binding of hsa-miRNA-425-5p to its 5'UTR. Together, our correlative data analysis approach in primary MB samples combined with in silico predictions and mechanistic validation, uniformly point to a previously poorly explored regulatory function of lncRNAs which consists in binding to a specific mRNA and thereby blocking endogenous miRNA binding sites.
html). Annotated species-specific exonic and intronic regions are highlighted in yellow or orange, respectively. Used RefSeq datasets were: zebrafish [danRer7], chicken [galGal3], rat [rn4], mouse [mm10], rhesus macaque [rhe-Mac2], chimpanzee [panTro3] and human [hg19]. Proteomic analyses Sample preparation. Proteins were extracted from frozen cell pellets as previously described  63. Briefly, cells were homogenized in urea buffer with a "TissueLyser" (Qiagen, Hilden, Germany) and subsequent sonication. After centrifugation for 15 min at 14,000 g and 4 °C, supernatants were collected. Protein concentration was determined via pierce 660 nm protein assay (Thermo Fisher Scientific) and 10 µg protein per sample were desalted through electrophoretic migration at 50 V for 10 min on a 4-12% bis-tris polyacrylamide gel (Thermo Fisher Scientific, #NP0322BOX).
Separation was achieved through a gradient from 4 to 40% solvent B (solvent A: 0. 1% (v/v) formic acid in water, solvent B: 0. 1% (v/v) formic acid, 84% (v/v) acetonitrile in water). Afterwards, peptides were ionized at a voltage of 1. 400 V and introduced into the mass spectrometer operated in positive mode. Mass spectrometry scans were recorded in profile mode in a range from 350-1700 m/z at a resolution of 60,000 while tandem mass spectra were recorded in the ion trap at normal scan rate. Tandem mass spectra were recorded with a data dependent Top 20 method and 35% normalized collision energy. Dynamic exclusion was activated with a repeat count of 1 for 45 s and only charge states 2+ and 3+ were analyzed. Computational mass spectrometric data analysis. Proteome discoverer (version 1. 4.
1. 14, Thermo Fisher Scientific) was applied for peptide/protein identification with mascot (version 2. 4, Matrix Science) as search engine employing the UniProt database (human; including isoforms; date 2016-11-01). A false discovery rate of 1% (p ≤ 0. 01) on peptide level was set as the identification threshold. Proteins were quantified with Progenesis QI for Proteomics (Version 2. 0, Nonlinear Dynamics, Waters Corporation).
Acknowledgements We thank especially  Prof Simone Fulda  and PD  Dr Till Milde  for their generous gifts of in vitro models. Furthermore, we thank  Emilie Indersie  for her support and  Christophe Alberti ,  Elodie Belloir , and  Adlin Thadal  for mouse colony management. We also thank  Kanokporn Faguier ,  Valérie Petit ,  Guillaume Bourmeau  and  Laetitia Besse  for their help and advice in Histology and Imaging. This work was supported by grants from  Deutsche Forschungsgemeinschaft  ( RE 2857/2-1  to M. R. ,  RE938/4-1  to G. R. , KFO  337  to M. R. ), the  German Cancer Aid (Deutsche Krebshilfe , grant no. 111537  to M. K. , S. M. P. , M. R. and G. R. ),  José-Carreras  foundation ( DJCLS 21R/2019  to U. F. , M. R. ),  Elterninitiative Kinderkrebsklinik  e. V. (M. R. , J. B. ), the  Gert-und-Susanna-Meyer foundation  (M.
Data availability Previously published gene expression and methylation data used in Fig. 1a -e, 3b, S1b-f, S5a, b, S7a, c-e are available under 'R2: Genomics Analysis and Visualization Platform (http://r2. amc. nl)'. RNA sequence data that support the findings of this study have been deposited in GenBank with the #GSE140741 accession code and proteomic data have been deposited in ProteomeXchange PRIDE database with the #PXD016550 accession code. The authors declare that the data supporting the findings of this study are available within the paper and its supplementary information files. Proteome discoverer (version 1. 4. 1. 14, Thermo Fisher Scientific) was applied for peptide/protein identification with mascot (version 2. 4, Matrix Science) as search engine employing the UniProt database (human; including isoforms; date 2016-11-01).
Statistical analysis was performed using Kruskal-Wallis Test with Dunn's Multiple Comparison Test; ***p < 0. 001. Red dots = SHH-driven entities; black dots = non SHH-driven tumors and control tissue. c H3K27ac ChIP-sequencing profile on HHIP-AS1 and HHIP loci in the four MB subgroups 64. Bar graph indicates the expression level of HHIP-AS1 in the corresponding MB subgroups. Error bars represent ± SEM. d Scatter plot representing the degree of DNA methylation (B value) of the potential promoter region in relation to HHIP-AS1 expression levels in SHH MB (red dots) and other MB subgroups (black dots). Statistics were done by Pearson correlation. e Scatter plot displaying expression correlation of HHIP and HHIP-AS1 across datasets (n = 351). Mean expressions of both transcripts from 39,090 samples were analyzed in their respective datasets and plotted with error bars representing the SEM for both genes.
Data capturing multiple axes of tree size and shape, such as a tree's stem diameter, height and crown size, underpin a wide range of ecological research-from developing and testing theory on forest structure and dynamics, to estimating forest carbon stocks and their uncertainties, and integrating remote sensing imagery into forest tories contain a metadata file describing each field of the Tallo database and a look-up table with the full list of bibliographical sources from which records were obtained. R code and ancillary data needed to replicate the three case studies presented in this paper can be found on the GitHub repository.
To overcome this challenge, we developed the Tallo database, a collection of 498,838 georeferenced and taxonomically standardized records of individual trees for which stem diameter, height and/or crown radius have been measured. These data were collected at 61,856 globally distributed sites, spanning all major forested and non-forested biomes. The majority of trees in the database are identified to species (88%), and collectively Tallo includes data for 5163 species distributed across 1453 genera and 187 plant families. The database is publicly archived under a CC-BY 4. 0 licence and can be access from: https://doi. org/10. 5281/zenodo. 6637599. To demonstrate its value, here we present three case studies that highlight how the Tallo database can be used to address a range of theoretical and applied questions in ecology-from testing the predictions of metabolic scaling theory, to exploring the limits of tree allometric plasticity along environmental gradients and modelling global variation in maximum attainable tree height.
K E Y W O R D S allometric scaling, crown radius, forest biomass stocks, forest ecology, remote sensing, stem diameter, tree height Building on previous efforts to compile regional and global tree allometry databases  (Falster et al. , 2015; Feldpausch et al. , 2011; Jucker et al. , 2017; Loubota Panzou et al. , 2021) , here we bring together the world's largest open access collection of trees for which stem diameter, height and/or crown radius have been measured-the Tallo database (Figure  1 ). Tallo includes nearly 500,000 georeferenced and taxonomically standardized records from more than 5000 tree species acquired at over 60,000 sites worldwide, including data from all major terrestrial biomes and some of the world's largest ever recorded trees. After describing the key steps involved in the acquisition and standardization of the data, we showcase some of the potential applications of the Tallo database through a series of three case studies.
| Data aggregation We compiled data on trees for which stem diameter (D, in cm), total tree height (H, in m) and/or crown radius (CR, in m) were measured. D was measured at breast height (1. 3 m aboveground) or otherwise just above buttress roots using either diameter tape or callipers. As is common practise, for multi-stemmed trees a single pooled value of D was calculated by summing the diameter values of all individual stems (D i ) using the quadratic diameter:  et al. , 2021; Paul et al. , 2016). While care was taken to identify records from multi-stemmed individual, it is possible that for records compiled from existing databases a small number of multi-stemmed trees were mistakenly treated as separate individuals. D = � ∑ D 2 i (de Souza H and CR-which we mostly derived from 2 to 8 orthogonal crown radii measurements or otherwise from crown projection areas-were measured using a variety of approaches, including laser or ultrasonic range finders, clinometers, as well as tape measures and telescopic poles for smaller trees.
For a very small subset of trees with fully sun-exposed crowns, H and CR were measured using a combination of high-resolution aerial photos and airborne LiDAR  (Cano et al. , 2019). Previous work comparing tree height measurements derived using laser range finders and clinometers-the two most common methods used to take tree biometric measurementshas shown that the two approaches provide consistent estimates, with laser rangefinders allowing for greater precision but with a tendency to slightly underestimate total tree heights  (Larjavaara & Muller-Landau, 2013). In addition to the data on tree size and shape, we also recorded the latitude and longitude of the site where each tree was measured, and any available taxonomic information. Data were obtained from a range of sources, including the published literature, online databases and unpublished data collected by coauthors of this study (see Appendix S1 in Supporting Information for a complete list of data sources).
In compiling these data, we excluded records from heavily managed and industrial tree plantations, as well as agroforestry systems. Care was also taken to avoid double counting trees, which could occur either because the same data had been obtained from multiple sources or because trees were measured more than once as part of successive forest inventories. Specifically, for data obtained from public databases we made sure none had been compiled from the same primary sources. Additionally, for the small subset of trees that had been measured more than once, we only retained data from the most recent census.
| Taxonomy Species names were cross-referenced and harmonized against those of The Plant List (TPL; http://www. thepl antli st. org), using a combination of the taxonstand package in R  (Cayuela et al. , 2012; R Core Development Team, 2021)  and the Taxonomic Name Resolution Service  (Boyle et al. , 2013). While TPL has been static since 2013, it was chosen as a reference as it remains widely used in ecology. Future versions of Tallo will align to the World Flora Online (http://www. world flora online. org) as this becomes the new standard for plant taxonomy. Taxa that did not match TPL were reviewed manually and any misspellings or synonyms that had not been automatically detected were corrected. The small number of species for which no direct match to TPL was found (n = 43, 0. 8% of the total) were checked against the Global Tree Search database (GTS; https://tools.
bgci. org/global_tree_search. php), a curated list of over 60,000 plant species that meet the IUCN's Global Tree Specialist Group definition of a tree. In all, 11 species (0. 2% of the total) did not match either TPL or GTS, but because all of these could be traced back to published records online, we retained them in the database. Finally, genus names were used to assign each tree to its family and group trees into major divisions of vascular plants (i. e. angiosperms and gymnosperms) following the classification of Kew Royal Botanic Gardens (http://data. kew. org/vpfg1 992/genfi le. html). Having standardized taxonomic names, we then removed any records from species that did not meet our working definition of trees-perennial woody seed plants with a single dominant stem, that are self-supporting and undergo secondary growth.
| Geographical coordinates Each tree in the database is associated with a set of geographical coordinates, recorded in decimal degrees of latitude and longitude. These range in precision between 1 and 3 decimal places (approximately 0. 1-11 km at the equator), with the majority of trees geolocated with a precision of ≤1 km. To facilitate the integration of the Tallo database with other large-scale spatial datasets, we used the CoordinateCleaner package in R to flag and correct common issues known to affect georeferenced records obtained from online databases  (Zizka et al. , 2019). (Fick & Hijmans, 2017)  at a spatial resolution of 30 arc-seconds (approximately 1 km). Panel (e) shows a phylogenetic tree constructed from all species in the Tallo database (n = 5163). Branch tips have been colour coded to reflect the number of trees sampled for each species and the position of several seed plant families on the tree has been labelled.
The phylogenetic tree was generated using the V. PhyloMaker package in R  (Jin & Qian, 2019) , the backbone of which is a phylogeny of 79,881 taxa of seed plants developed by  Smith and Brown (2018). First, we checked for any coordinates that were either invalid or had zero values for longitude or latitude. Data from two locations with a longitude of zero were retained after being checked against primary sources. Next, we checked for coordinates that did not fall on land by overlaying them onto a map of the world's coastlines obtained from the Natural Earth database at 1:10, 1:50 and 1:110 million scales (https:// www. natur alear thdata. com). A small number of locations (n = 13 sites, 0. 02% of the total) were located at sea at all three scales. These records were all from the Balearic Islands in Spain and were manually corrected to the nearest land point using the 1:10 million scale map as a reference.
| Data quality control As a quality control measure, we first removed any trees recorded as dead or damaged and then filtered the database to exclude trees with D < 1 cm and H < 1. 3 m. We then used Mahalanobis distance as implemented in the OutlierDetection package in R to identify trees with unrealistically large or small H and CR values given the size of their trunk, their biome association and their functional group (see Appendix S2 for details). These outliers could be the result of a data entry error (e. g. shift in decimal place or mistaken conversion between m, cm and mm) or possibly reflect a tree with substantial damage to its crown which went unrecorded. In total, 508 trees were identified as outliers based on H (Figure  S1 ) and a further 490 based on CR (Figure  S2 ). These records were retained in the Tallo database but flagged as outliers, allowing them to be easily removed by users depending on the application.
| DATABA S E OVERVIE W AND ACCE SS The Tallo database includes a total of 498,838 trees from 61,856 unique sites across the world for which D, H and/or CR have been measured (Figure  1a ), including 311,326 trees where all three dimensions are recorded. These represent all major forested and non-forested biomes (Table  1 ) and cover a gradient of over 45°C in mean annual temperature and more than 7000 mm in annual rainfall (Figure  1d ). Trees in the database span multiple orders of magnitude in size (Figure  1b-c  and Table  1 ), with D ranging from 1. 0 to 770. 0 cm, H from 1. 3 to 115. 8 m and CR from 0. 05 to 24. 25 m. The majority of trees (88%) are identified to species, and overall the Tallo database includes records for 5163 species, 1453 genera and 187 plant families (Figure  1e ).
The version of the Tallo database described in this article is publicly archived on Zenodo under a CC-BY 4. 0 licence so that it can be freely used, shared and modified so long as appropriate credit is given (https://doi. org/10. 5281/zenodo. 6637599). Major version updates will be periodically uploaded to Zenodo, in addition to which we will also maintain an up-to-date version of the database on GitHub (https://github. com/selva -lab-repo/TALLO). Tallo should be referenced by citing this paper and users are also encouraged to report the version of the database they have accessed and to cite the original data sources whenever possible. The database is stored as a csv file which contains the individual tree morphological data, the geographical coordinates of each tree, any available taxonomic information, an identifier flagging any records classified as outliers and a reference code linking to the source from which records were obtained.
TA B L E 1 Breakdown of the Tallo database by biome, including number of tree records and species, as well as the median and range of stem diameter (D, in cm), tree height (H, in m) and crown radius (CR, in m) values. Biome classifications follow those of  Olson et al. (2001)   3), while CR is assumed to scale isometrically with H (i. e. CR ∝ H 1 ). However, there is substantial evidence that real-world scaling relationships can depart substantially and systematically from the theoretical predictions of MST due to the environmental context in which a tree is growing (e. g. water availability, competition for light, browsing, disturbance regime), as well as its evolutionary history  (Jucker et al. , 2017; Lines et al. , 2012; Moncrieff et al. , 2011; Muller-Landau et al. , 2006; Shenkin et al.
, 2020). Using Tallo, we tested whether crown allometric scaling relationships can be reconciled with the predictions of MST or if instead they vary systematically and predictably among major plant lineages (i. e. angiosperms and gymnosperms) and biome types. We modelled H-D, CR-D and CR-H scaling relationships using a power-law function by fitting linear mixed-effects regressions to log-log transformed data and allowing both the normalization constant (intercept) and scaling exponent (slope) to vary among biomes for both angiosperm and gymnosperm lineages. Models were fit using the lme4 package in R and took the following general form: lmer(log(Y) ~ log(X) + (log(X)|Biome:Lineage)) Additionally, we also tested whether biome-level scaling exponents varied in relation to the degree of aridity experienced by trees within a biome, quantified as the ratio between mean annual precipitation and potential evapotranspiration.
Aridity data for this analysis were obtained at 30-arc second resolution from the Global Aridity Index and PET Database  (Trabucco & Zomer, 2019)  and matched to each tree based on its geographical coordinates. We found that agreement with MST varied considerably among the two plant lineages and biomes, as well as the allometric relationship being examined (Figure  2 ). Overall, observed H-D scaling exponents were lower than the 2 3 predicted by MST (exponent estimate ±95% confidence intervals = 0. 581 ± 0. 068). This was especially true for angiosperm trees, whose H-D scaling exponents were on average substantially lower than those of gymnosperms across biomes (0. 537 and 0. 637, respectively). However, for both lineages departure from MST was most pronounced in arid biomes, whereas H-D scaling exponents of trees growing in nonwater limited ecosystems such as tropical and temperate forests were consistent with the predictions of MST (Figure  2a ).