dataesr/scientific-paragraphs-categorization huggingface dataset:
Behavioral assessment Children's skills in arithmetic and in a range of other cognitive and academic skills (vocabulary, reading, verbal and non-verbal IQ) were assessed in all six datasets. The instruments of interest are indicated in Table  S1. In all datasets, arithmetic skills were assessed using the calculation subtest of the Woodcock-Johnson III Tests of Achievement (WJ-III)  54  or the Basic Calculations Composite of the Comprehensive Mathematical Abilities Test (CMAT)  55. Both tests are untimed paper-and pencil tests in which children solve increasingly difficult arithmetic problems from the four operations (addition, subtraction, multiplication, division). An aggregate score including the four operations is used in the present study. Each correct answer is scored 1 and each incorrect answer is scored 0.
Vocabulary skills were assessed in each dataset using vocabulary subtests from either the Nouvelle Echelle Métrique de l'Intelligence-2 (NEMI-2)  56  or the Wechsler Abbreviated Scale of Intelligence (WASI)  57. In both subtests, children have to orally define words that are presented visually and orally. Each correct answer is scored 1 and each incorrect answer is scored 0. Reading skills were assessed in all datasets using the indice de precision (CM) of the Alouette reading test  58  (set #1 and set #2) or the Sight Word Efficiency Subtest (SWE) of the Test of Word Reading Efficiency (TOWRE)  59  (sets #3 to #6). In the Alouette test, children read a nonsensical text in 2 min. The CM is calculated by dividing the number of words correctly read by the number of words in the text (multiplied by 100).
In the TOWRE, children have to pronounce real words that are printed on paper within 45 s. The level of difficulty increases from single syllables to multiple syllables. A reading score is calculated based on reading accuracy and number of words read. Verbal IQ was measured in all datasets either using the comparison subtest of the NEMI-2  56  or the similarities subtest of the WASI  57. In these subtests, participants have to find the common characteristics between different verbal terms. Each correct answer is scored 1 and each incorrect answer is scored 0. Non-verbal IQ was measured in all datasets using the Matrix subtests of the NEMI-2  56  and of the WASI  57. In both tests, an incomplete matrix of shapes was shown to the children, who had to select the response option that completes the matrix.
MRI data acquisition In each of the six datasets, high-resolution anatomical scans were collected during the MRI session. In set #1 and set #2, brain imaging data were acquired using a 64-channel head coil and a Siemens 3 T Prisma Scanner (Siemens Healthcare, Erlangen, Germany). Parameters of the anatomical scan for set #1 and #2 were as follow: TR = 3500 ms, TE = 2. 24 ms, flip angle = 8°, matrix size = 256 × 256, slice thickness = 0. 90 mm, number of slices = 192, voxel size resolution = 0. 875 mm isotropic. In sets #3 to #6, brain imaging data were acquired using either a 16-channel or a 32-channel head coil and a Siemens 3 T Trio-Tim Scanner (Siemens Healthcare, Erlangen, Germany). Parameters of the anatomical scan for sets #3 to #6 were as follows: TR = 2300 ms, TE = 3. 36 ms, flip angle = 9 °, matrix size = 256 × 256, slice thickness = 1 mm, number of slices = 160, voxel size resolution = 1 mm isotropic.
VBM analyses Structural images were analyzed using the Computational Anatomy Toolbox (Cat 12)  60  within the Statistical Parametric Mapping Software Package (SPM 12)  61. Preprocessing of all images included the following steps. First, images were segmented into GM, WM, and cerebrospinal fluid (CSF) images using Tissue Probability Maps provided in the CAT12 toolbox  62. Second, images were spatially normalized using DARTEL registration  63  with an MNI template also provided by the CAT12 toolbox  60. At that point, normalized and segmented images were systemically checked for artifacts or orientation issues (one participant from set #1 had to be excluded at that stage). Third, to account for brain differences in size and volume, TIV was estimated. Data homogeneity was also checked for possible outliers (no participant was excluded).
This was also confirmed by likelihood-ratio tests  66. Therefore, we only allowed the intercepts to vary across sites and arithmetic tests in all models. All analyses were conducted using Jamovi version 2. 3. 24  68  including the jmv  69  and GAMLj modules  70. Furthermore, to quantify the strength of evidence for a lack of relation between arithmetic score and GMV in each ROI, we used the BayesFactor package  71  in R  72  to compute mixed-model Bayesian analyses with mean voxel intensity as dependent variable, arithmetic score, TIV, ADHD status, age, and sex as fixed effects, and both arithmetic test and scanning site as random effects. Default priors as well as random intercepts were used. The BF associated with the relation between GMV and arithmetic skill was estimated by comparing mixed-effect models differing only in the presence or absence of the arithmetic score covariate.
A BF < 3 was considered anecdotal evidence, a 3 < BF < 10 was considered substantial evidence, a 10 < BF < 30 was considered strong evidence, a 30 < BF < 100 was considered very strong evidence, and a BF > 100 was considered extreme evidence that our data are more likely under the alternate than the null hypothesis (i. e. , BF 10 ) or under the null hypothesis than the alternate hypothesis (i. e. , BF 01 ). Finally, the relation between VBM and the arithmetic score was also analyzed using an exploratory linear regression model across the whole brain. Note that this analysis was exploratory because the whole-brain regression did not take into account the nested structure of the data. The variable of interest was arithmetic score and covariates were measures of TIV, ADHD status, age, sex, and scanning site.
Fourth, an adapted volume-based diffeomorphic DARTEL algorithm was applied to the surface for spherical registration  76. The cortical thickness data were finally smoothed with a 15 mm full-width half-maximum Gaussian kernel. CT data were analyzed using linear regression models across the whole sample of participants. This analysis was also exploratory because it did not take into account the nested structure of the data. The variable of interest was arithmetic score and covariates were measures of TIV, ADHD status, age, sex, scanning site, as well as measures of language and IQ. This analysis did not show any relation between arithmetic score and CT (voxel-level threshold of p ≤ 0. 001, cluster-level threshold of p ≤ 0. 05, family-wise error corrected for multiple comparisons).
ACKNOWLEDGEMENTS This work was supported by a grant from the  German Academic Exchange Service  [ 91773742 ] and by a grant from the  Agence Nationale de la Recherche  ( ANR-18-CE93-0001 ) and conducted at the  Lyon Neuroscience Reserach Center (CRNL). This paper was finalized while the first author was supported by a grant from  AMPIRIC  ( Aix-Marseille -Pole d'Innovation, de Recherche, d'Enseignement pour l'E ́ducation ). The authors would like to thank the  Brain Development Laboratory (PI :  James R. Booth ) at  Vanderbilt University  for sharing part of the data the manuscript is based on.
DATA AVAILABILITY Raw MRI data are available on Open Neuro for sets #3 (https://openneuro. org/ datasets/ds001486/versions/1. 3. 1), #4 (https://openneuro. org/datasets/ds002424/ versions/1. 2. 0), #5 (https://openneuro. org/datasets/ds001894/versions/1. 4. 1), and #6 (https://openneuro. org/datasets/ds002886/versions/1. 1. 0). Individual behavioral and ROI data for all datasets are available from Zenodo, as well as ROI masks shown in Fig. 1  (https://doi. org/10. 5281/zenodo. 7866555).
Fermi-LAT data analysis Fermi-LAT is a pair conversion telescope onboard the Fermi Gamma-Ray Space Telescope and is sensitive to γ rays in the energy range from ∼20 MeV to several hundred GeV  (Atwood et al. 2009 ). Here, we analysed 12 yr and 5 months of data, taken between August 4, 2008 and December 31, 2020. We used the 'Pass 8' IRFs  (Atwood et al. 2013 ) and selected events passing the P8R3_SOURCE event selection (event class 128, event type 3). Because the angular resolution of Fermi-LAT substantially worsens below 1 GeV, we restricted the analysis to events above this energy. To suppress γ rays originating from the Earth's limb, we furthermore excluded events with zenith angles above 90 •. The data were analysed using Fermitools 4  version 2. 2. 0 and Fermipy 5  version 1. 1. 5 (Wood et al.
2017). The analysis was carried out in a region of interest (ROI) of 10 • × 10 • , centred on the nominal position of 4FGL J1810. 3-1925e provided in the 4FGL-DR2 catalogue (H. E. S. S. Collaboration 2020;  Ballet et al. 2020). The events and exposure maps were binned using spatial bins of 0. 1 • × 0. 1 • size and five bins per decade in energy. γ ray source models were then fitted to the data with a likelihood fit as described in Sect. 2. 1. 2, where we used for the sources in the ROI the models provided in the 4FGL-DR2 catalogue by default. In addition, we included standard templates for isotropic and Galactic diffuse γ-ray emission 6. In the likelihood analysis, we have fixed the parameters of all source models with a TS value smaller than 25 or with fewer than 700 predicted events. We left free the normalisation parameter for all sources within 1 • of 4FGL J1810.
0+00. 0, and is furthermore spatially coincident with dense molecular clouds as indicated by 12 CO (J = 3-2) observations by the James Clerk Maxwell Telescope (JCMT;  Castelletti et al. 2016 ) and CS observations by the Mopra telescope  (Voisin et al. 2019). Moreover, the contour lines from the FUGIN 12 CO (J = 1-0) survey  (Umemoto et al. 2017 ) illustrate that molecular gas is present throughout the region. In Appendix D, we provide a map of the FUGIN 12 CO data with the two components of HESS J1809-193 overlaid. Finally, we show in Fig. 4  the energy spectra and flux points obtained for the two components of HESS J1809-193, and compare these to previously obtained results from the literature. We note that, when comparing the fitted PL models for components A and B (solid lines), the spectrum of component B appears somewhat harder than that of components A (Γ = 1.
The primary proton spectrum is described using an ECPL model (see Eq. (  2 )) and we have assumed a distance to the source of 3 kpc. We used the wrapper class for Naima models implemented in Gammapy, so that they could be fitted directly to the H. E. S. S. data sets (as opposed to fitting them to the extracted flux points only), using the same likelihood framework as before (cf. Sect. 2. 1. 2). The fit results are presented in Table  5 , and the resulting spectra displayed in Fig. 10. The same spatial models as in the previous analysis (cf. Sect. 3. 1) were assumed and compatible best-fit parameters were obtained for them. The pp model for component A prefers a relatively hard spectral index of Γ p,A = 1. 76 ± 0. 17. Integrating the primary spectrum above 1 GeV yields a total required energy of W p,A ∼ 3.
2 × 10 50 (n/1 cm -3 ) -1 erg, which represents -unless high ISM densities are invoked -a significant fraction of the canonically assumed kinetic energy released in a supernova explosion of ∼10 51 erg (e. g. Ginzburg 1975). In this context, we note that while the FUGIN CO data indeed show the presence of molecular gas beyond the dense clouds discovered by  Castelletti et al. (2016)  and  Voisin et al. (2019) , they also indicate a gradient in the gas density across the extent of HESS J1809-193 (see the map in Appendix D). This gradient is not reflected in the Fig. 10. SED of HESS J1809-193 with hadronic (pp) models. The H. E. S. S. and Fermi-LAT data points have been derived in this work, the HAWC data points (shown for comparison only) are taken from  Goodman (2022). The lines show the predicted γ-ray spectra for hadronic models fitted to each of the two H.
It would, however, leave the X-ray PWN without a counterpart at TeV energies (component A being associated with electrons injected long ago only), which would be unexpected when comparing with other PWN systems  (Kargaltsev et al. 2013). Our analysis of Fermi-LAT data has confirmed the presence of an extended source, J1810. 3-1925e, that based on its location and morphology appears to be associated to component A of HESS J1809-193. However, the spectrum of J1810. 3-1925e does not connect smoothly to that of component A of HESS J1809-193, implying the need for a spectral hardening around 100 GeV. While our presented model is not able to describe this feature, we note that the overall shape of the SED is reminiscent of that of another well-known PWN system, Vela X, which also exhibits a break at around 100 GeV  (Tibaldo et al.
Acknowledgements. The support of the  Namibian authorities and of the University of Namibia  in facilitating the construction and operation of H. E. S. S. is gratefully acknowledged, as is the support by the  German Ministry for Education and Research (BMBF) , the  Max Planck Society , the  German Research Foundation (DFG) , the  Helmholtz Association , the  Alexander von Humboldt Foundation , the  French Ministry of Higher Education, Research and Innovation ,  the Centre National de la Recherche Scientifique  ( CNRS/IN2P3  and  CNRS/INSU) , the  Commissariat à l'Énergie atomique et aux Énergies alternatives (CEA) , the  U. K. Science and Technology Facilities Council (STFC) , the  Irish Research Council (IRC)  and the  Science Foundation Ireland (SFI) , the  Knut and Alice Wallenberg Foundation , the  Polish Ministry of Education and Science , agreement no.
, 2021) , for which dozens of them may have genetically contributed to present-day rose diversity. In this study, we reconstruct the history of the rose breeding in Europe. To do so, we assembled a unique dataset, composed of large phenotypic data, genetic data from 204 roses genotyped with a 69k SNP array, plus whole-genome sequence data from 32 roses. The sequenced accessions are either botanical roses or varieties bred in Europe between 1800 and 1910, and include star varieties at that time. Botanical roses are considered as wild species, but they are maintained in rose collections and are assumed to be non-intentionally selected. By combining population genomics and quantitative genetics analyses, we (i) described the genomic makeup of the rose varieties, (ii) provided an approximate number of generations of selection, (iii) estimated the levels of genetic diversity and investigated their evolution throughout the period, (iv) identified potential footprints of artificial selection, and (v) performed the largest collection of GWAS analyses to date to uncover the genetic bases of many horticulturally important traits.
A rapid shift from a European to an Asian genetic background The same large collection of rose varieties was genotyped at 68,893 SNPs using the Rose Genotyping Array (WagRhSNP Axiom Array,  Koning-Boucoiran et al. , 2015) , a SNP array developed from modern cut and garden roses. After clone-correction, a final set of 204 rose varieties was used for the subsequent analyses (see Materials and methods, Fig. S3  and Table  S1 ). The first axis of a Principal Component Analysis (PCA) explains more than a quarter of the total variance (29. 3%) and isolates the two ancient genetic backgrounds: an Asian one (in yellow) and an European one (in blue, Fig. 2A ). The whole dataset forms a gradient of population structure between the two backgrounds, which is consistent with the frequent crosses between European and Asian roses throughout the 19th century.
The second axis isolates some samples, including most of the botanical samples, from the Asian-European gradient. This population structure is consistent with a previous report based on the genotyping at 32 microsatellite markers of an even larger collection of varieties and the identification of 16 genetic groups (Fig. 2A ,  Liorzou et al. , 2016). Independently from this first dataset, we resequenced 15 varieties and collected publicly available rose genomes to generate a dataset of 32 samples, composed of ancient Asian, ancient European, early European x Asian, hybrid tea and botanical samples. After joint-genotyping and SNP filtering, we generated a dataset of 77,862,879 filtered SNPs on the 32 samples. PCA based on a pruned set of 50,369 randomly selected SNPs (Fig. 2B ) is consistent with an Asian-European gradient of population structure, as previously identified with the SNP array (Fig.
2A ). The proportion of variance explained by the first axes is however lower based on the sequencing data, likely due to SNP ascertainment bias associated with the design of the array, since the SNP set was established based on the analysis of more modern varieties, therefore emphasizing more on the European-Asian gradient (i. e. the axis 1). Based on the WGS data, some axes can be built thanks to a single botanical individual (e. g. axes 2 and 4, Fig. S6 ), suggesting that a large proportion of the diversity is indeed only present in the botanical accessions. Family relationships were then inferred using KING  (Manichaikul et al. , 2010)  based on the pairwise kinship values calculated on the sequencing data. Among the 32 samples, only a few individuals are not connected to at least another individual of the dataset through a third degree relationship (i.
Genetic diversity erosion Nucleotide diversity was estimated over non-overlapping 100-kbp sliding windows on the four groups (namely, ancient European, ancient Asian, early European x Asian and hybrid tea roses), each containing a total set of 16 chromosome sets after considering the inferred ploidy based on the distribution of allele balance (Table  S2 ). Among all the dataset, the genetic diversity in roses was relatively high with an average π of 1. 47x10 -2 (Fig. 2D ). Significant differences in diversity were observed between groups, with higher diversity in the ancient European (1. 67x10 -2 ) than in the ancient Asian samples (1. 20x10 -2 ). The "early European x Asian" group exhibits an averaged nucleotide diversity of 1. 52x10 -2 , a value that slightly exceeds the expectation assuming both the previous estimates and the genetic makeup of this group (50.
The largest rose GWAS catalog to date In addition to reconstructing the history of rose breeding, we conducted large Genome-Wide Association Studies (GWAS) with the aim of identifying associations between the genotypes at the 204 varieties and the different phenotypes. Given the large number of phenotypes we have investigated, as well as the variety of scenarios possible regarding dominance in polyploids  (Rosyara et al. , 2016) , a dedicated website portail has been created in order to explore all the results (hereafter referred to as the rose GWAS browser, https://roseGWASbrowser. github. io/). The online browser allows to select a category of traits (e. g. petal counts or colors, plant architecture, scent), then a specific trait (e. g. 2phenylethanol content), and select a specific scenario regarding the polyploid gene action, among additive, simplex dominant and duplex dominant scenarios.
The reduction of diversity of domesticated species has often been viewed as a less vital topic as   (Hibrand-Saint Oyant et al. , 2018). 2-4: Manhattan plots of the q-values along the genome for the three years of scoring  (2: 2014, 3: 2015, 4: 2016). SNPs with q-values < 0. 20, 0. 10 and 0. 05 are shown in yellow, orange and red, respectively. 5: Local density in highly associated SNPs (p-value < 0. 01) for each of the three years of scoring  (2014, 2015 and 2016   Hibrand-Saint Oyant et al. , 2018)  are not shown here (these results are however available online, https://rosegwasbrowser. github. io/blackspot).
Advances and challenges for GWAS in roses As a tool for the future of rose breeding, we have released the largest GWAS catalog for roses (https://roseGWASbrowser. github. io/). Our GWAS browser allows the website visitors to explore the association with more than 25 traits, on the basis of our panel of 204 accessions. The objective is to make future breeding more effective for some traits, including the resistance to the blackspot disease (Fig. 4 ). This trait represents a typical example of an important target need in order to find sources of genetic resistances in roses, in the context of a tightening environmental legislation associated with the use of chemicals due to their deleterious environmental impacts and health risks. Despite the great advance made with the release of the GWAS catalog, the direct insights appear however relatively limited to date.
Genotyping and sequencing data Genotyping was performed on 3x96-plates at the Gentyane facility (INRAE Clermont-Ferrand, France) using the Affymetrix Axiom technology. To further check the quality of the genotyping (see below), we included technical replicates (21 duplicate samples, as well as a triplicate one, shown in yellow in Table  S1 ). Regarding sequenced samples, library preparations and sequencing were performed at the Vienna BioCenter Core Facilities (VBCF, Vienna, Austria). For the library preparations, the facility used the Ultra II DNA kit for Illumina (New England Biolabs), which includes an enzymatic shearing as a first step. Quality control was performed prior to sequencing. Quality control of the obtained NGS libraries were then assessed prior to the sequencing using the Fragment Analyzer technology.
Phenotyping All phenotyping data were collected in the rose garden (Roseraie Loubert, Rosiers sur Loire, Gennes-Val-de-Loire, France). The number of petals was scored for at least two and at most five flowers. We used averaged petal counts across the different flowers. For the recurrent blooming, the plants were scored in the rose garden every week during the blooming period (spring -autumn) and this experiment was replicated during three years. We have previously developed a statistical method on the flowering curve using Gaussian mixture models and created indicators, as Reg. Mag for the magnitude of reblooming (for details, see  Proïa et al. 2016). A value of 0 means that the rose flowers only in spring (onceflowering roses), higher is value (above 0), higher is the reblooming capacity (see Supplementary Note 1).
Genotyping We used a two-round genotyping procedure to ensure highly accurate calls, in particular regarding the GWAS analysis. First, we performed a QC analysis under Axiom Analysis Suite based on all the genotypes (288). Based on this genotyping, 4 individuals exhibited very low DQC (<0. 25) and were excluded. Based on all the other individuals (with DQC > 0. 85, 284 samples), we generated a first genotype matrix with FitPoly assuming a ploidy=4 for all samples. Given that roses are grafted, all samples with a close genetic proximity with the rootstock cultivar used in the Loubert's rose garden were considered as sampling errors and were excluded from the dataset (7 in total). In addition to these previously excluded genotypes, we excluded 7 additional genotypes with a DQC < 0. 94 and a QC Calling rate < 0.
89, resulting in a dataset of 270 individuals. For this new dataset, we performed detailed QC and then converted the output in a FitPoly format thanks to FitPolyTools (version 1. 1; unpublished R package by Roeland Voorrips). To take into account the variable ploidy level between individuals, with the vast majority of the accessions used expected to be either diploid or tetraploid, we generated a final genotyping matrix using FitPoly assuming ploidy=4 for all individuals, which means that diploid individuals with heterozygous alleles are generally expected to be called AABB. The rose SNP array targets 68,893 SNPs, with probes for both strands, leading to a total number of 137,786 probes (for details, see  Koning-Bourcoiran et al. , 2015). After SNP filtering, we have obtained a dataset of 92,007 probes, corresponding to 56,467 of the 68,894 unique SNPs.