dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank  B. Dujon  and  B. Llorente  for their valuable feedback on the paper. This work was supported by the  Agence Nationale de la Recherche   ANR-16-CE12-0019  (to G. L. , J. S. and G. F. ) and  ANR-18-CE12-0004  (to G. L. and G. F. ). This work was also partially supported by the  ANR-15-IDEX-01  (to G. L. ),  Fondation pour la Recherche Médicale  ( EQU202003010413  to G. L. ), the European https://doi. org/10. 1038/s41588-023-01459- y Research Council ( ERC Consolidator  Grant  772505  to J. S. ), the  Guangdong Basic and Applied Basic Research Foundation  ( 2019A1515110762  to J. -X. Y. ),  Guangdong Pearl River Talents Program  ( 2019QN01Y183  to J. -X. Y. ), and  National Natural Science Foundation of China  ( 32070592  to J. -X. Y. ). J. S. is a Fellow of the  University of Strasbourg Institute for Advanced Study (USIAS)  and a member of the  Institut Universitaire de France.
Data availability All sequencing data and assembly/annotation files were deposited in the European Nucleotide Archive (https://www. ebi. ac. uk/ena/ browser/home) under the umbrella project PRJEB59869. The project accession for the raw sequencing data (fast5, nanopore fastq, Illumina fastq) is PRJEB50706. The assembly/annotation accessions are PRJEB59413, PRJEB59129, PRJEB59231, PRJEB59232 and PRJEB59230 for unphased nuclear, HP1, HP2, HP (for polyploids) and mitochondrial assemblies, respectively. Each accession for individual assemblies is indicated in Supplementary Table  1  (nuclear) and Supplementary Table  3  (mitochondrial).
Code availability All published and/or publicly available software used in the study, with their version numbers and their reference for downloading, are noted in the methods and Supplementary Methods sections as well as in the Reporting Summary. Custom scripts developed in this study are Telofinder (https://doi. org/10. 5281/zenodo. 8063924)  54  that is also available at https://github. com/GillesFischer Sorbonne/telofinder, the aneuploidy detection pipeline (https:// doi. org/10. 5281/zenodo. 8068318)  56  that is also available at https:// github. com/SAMtoBAM/aneuploidy_detection, the HP phasing pipeline (https://doi. org/10. 5281/zenodo. 8068328)  63  that is also available at https://github. com/SAMtoBAM/PhasedDiploidGenom-eAssemblyPipeline and the script for generating the nonredundant SV dataset (https://doi.
https://doi. org/10. 1038/s41588-023-01459-y Genome size variation in the ScRAP. a. Scatter plots showing the genome size of each strain, split by dataset, as a function of the number of Y' elements (left), Ty elements (middle and Y' + Ty elements (right). b. Number of TE sequences per strain across the 142 haploid/collapsed genome assemblies. All sequences from the 5 Ty families are pooled together by category. c. Number of complete Ty elements per strain across the 142 haploid/collapsed genome assemblies. d. Distribution of the 126 insertion sites across the 100 haploid or homozygous genomes considering either the complete Ty elements or all types of TE sequences (complete, truncated and solo-LTRs). e. Scatter plot.
Recently, parts of the design process have been complemented by elements of classical machine learning. Wang and coworkers combined machine learning, numerical optimization and experimental validation for the accelerated design of nanocrystalline soft magnetic materials  (Wang et al. , 2020). To relate magnetization, coercivity, and magnetostriction to the chemical composition and processing parameters, they carefully selected the features and the regression model. The underlying data were collected from relevant literature. In a second step, they applied an evolutionary algorithm to predict optimized material compositions and heat treatment conditions. Möller and coworkers used high-throughput density functional calculations to create a database of magnetic compounds in the ThMn 12 structure  (Möller et al.
Experimental data foundation The following described experimental data points are later used to train a machine learning model described in Section 2. 1. 3 predicting the intrinsic material properties saturation magnetization and magneto-crystalline anisotropy constant. 119 variations of (Nd,Ce,La,Pr) 13. 55 -  (Fe,Co,Ni)  80. 54 -B 5. 91 (at%) alloys were prepared by arc melting. These alloys were annealed at 1373 K for 24 h in Ar atmosphere. After a homogenizing heat treatment, alloy ingots were pulverized and sorted into particles with diameters of <20 μm in an inert atmosphere to make each particle single crystal. The powder density was determined using a pycnometer (Ulrtapyc1200e, Quantachrome Instruments, United States). The powder compositions were measured by ICP-AES (ICPS8100, Shimazu, Japan) and the main phase (2-14-1 phase) ratio was calculated from the obtained composition.
First-principles data foundation The following material database is later used to improve the machine learning model described in Section 2. 1. 3 predicting the intrinsic material properties saturation magnetization and magneto-crystalline anisotropy constant by appending predicted exchange integral information, Curie temperature and magnetization at 0 K. In this work no attempt to compute the anisotropy constant with first-principles is presented. The material database for the magnetization and Curie temperature (T C ) of (Nd 1-α-β-γ Pr α La β Ce γ ) 2 -(Fe 1-δ-ζ Co δ Ni ζ ) 14 -B is constructed using the AkaiKKR program package 2   (Akai, 1989) , where the Korringa-Kohn-Rostoker (KKR) Green's function method  (Korringa, 1947; Kohn and Rostoker, 1954)  is implemented within the local density approximation (LDA) of the density functional theory  (Hohenberg and Kohn, 1964; Kohn and Sham, 1965).
Experimental lattice parameters are used for R 2 T 14 B (R = Nd, Pr, La, Ce; T = Fe, Co, Ni) when available in literature  (Herbst, 1991). Otherwise, the lattice parameters are estimated by integrating first-principles calculation data and available experimental data, as described in Appendix D in  (Harashima et al. , 2021). The lattice parameters for non-stoichiometric systems are determined from those for stoichiometric systems according to Vegard's law.
Input data for building the machine learning model presented are two data sets: i) Data from ab-initio simulations relating chemical composition to the computed properties such as the spontaneous magnetization, local magnetic moments, the lattice constants, Curie temperature, and the exchange integrals (see Section 2. 1. 2). ii) Experimental measurements provide the spontaneous magnetization and the anisotropy field for different chemical compositions and temperatures (see Section 2. 1. 1). The ab-initio data set and the experimental data do not cover the same combinations of chemical compositions and it is hard to visualize or compute the overlap between two multidimensional sets of partially dependent and independent chemical concentrations of 2-14-1 systems. Our approach is to apply a manifold method exclusively to the ab-initio dataset which transforms each entry to a 2-dimensional projection plane.
Each data point receives a repulsive and attractive force between all others, where points close in chemical concentrations attract each other whereas others are pushed away. After relaxation groups of composites will have formed and can be visualized in a 2-dimensional embedding space. In machine learning this is often used to categorize data points in a certain amount of classes, but in our case we use it to gain insight into the overlap of two datasets. After the ab-initio data has been used to calibrate this transformation function, the experimental points are transformed with the same function to verify if those newly added points are far from the ab-initio entries. The result of such a Uniform Manifold Approximation & Projection (UMAP)  (McInnes et al. , 2018)  is shown in Figure  2.
One can see that an overlap exists within this projection space which is small but sufficiently encapsulates the ab-initio points. The first principals calculations database consists of many simulations where Co or Ni atoms replace Fe. Using the ab-initio data, we train a first model that predicts abinitio data from the chemical composition (see i) in Figure  3 ). We use this partial least squares regressor to augment the experimental data with predicted values for the ab-initio data (see ii) in Figure  3 ). In addition, we add estimates for M s (T) and M 3 s (T) estimating the spontaneous magnetization at temperature T with Kuz'min's equation  (Kuz'min, 2005)  from the ab-initio-calculated magnetization at 0 K and the Curie temperature. By adding the ab-initio data to the experimental data set, we increase the total number of features which are then used to predict the temperature dependent magnetization and anisotropy with a second partial least squares regressor (see iii) in Figure  3 ).
Exchange constant A and anisotropy constant K 1 of Nd 2 Fe 14 B at room temperature are 8 pJ/M and 4. 9 MJ/m 3  , respectively  (Coey, 2010, p. 401). Hence δ 0 = 1. 3 nm defines the meshsize close to microstuctural features where the nucleation of reversed domains is likely to happen. While some growth of the meshsize towards the center of the core phase is wanted to speed-up computation, we limited the maximum meshsize to 2δ 0 for unsupervised simulations as it is difficult to automatically detect errors due to artificial pinning of domain walls. This fine discretation limits the maximum modelsize even on today's computing hardware. We developed an automated simulation process comprising geometry generation, mesh generation, mesh quality checks, computing the hysteresis properties, and storing the results in a database.
The simulation runs completely unsupervised without human interaction. Thus, coercivity data can be sampled in a highthroughput like fashion. The sampler explores the design space and selects a point in feature space. A python script was used to create the multi-phase grain geometry (as sketched in Figure  1 ) using the opensource software Salome 3. MeshGems 4  was used to create a tetrahedral representation of the geometry. The grains have a brick like shape as shown by transmission electron microscopy  (Sepehri-Amin et al. , 2013). Such multi-phase grains consist of a core and a shell phase enclosed within a ferromagnetic grain boundary phase. Characteristic features of the core/shell geometry were the thickness of a weakly soft magnetic grain boundary phase (2 nm-50 nm), the thickness of the shell (2 nm-50 nm), the extensions of the grain (60 nm-100 nm in c-direction and 100 nm-150 nm in a-and b-direction), and the misorientation of the grain with respect to the field direction (0°-45°).
Materials optimization through machine learning Using micromagnetic simulations we created a database which related chemical composition, grain geometry, and hysteresis properties. We implemented different methodologies to sample the design space. Initially, sample points for training were generated using a Bayesian optimizer  (Häse et al. , 2018)  for maximizing coercivity. In a second stage we applied an active learning scheme. The machine learning predictor for coercivity was used to evaluate the objective function (high coercivity and low Nd + Pr content) of a genetic algorithm. The Pareto points were saved and put into a database for recomputation of the hysteresis properties with the micromagnetic solver. Figure  5  shows the active learning scheme to create additional samples to improve the micromagnetic simulation database.
The feature vector which contains geometry and intrinsic magnetic properties is augmented with the minimum Stoner-Wohlfarth switching field of the core and shell materials. In Figure  6  we show the prediction performance as a residual plot and true vs. predicted values for the coercive field of single coreshell grains with defect shells. We included the calibration errors of all micromagnetic simulations as final performance measures: mean absolute error (MAE), root mean squared error (RMSE) and the explained variance (R2). These should not be confused with the predictor performance values during fine tuning of the neural network, which are computed via a 5-fold cross validation. The number of units of the single hidden layer was the only parameter optimized, which gave best results with nine units for the multi-layer perceptron (MLP) regressor inside the sklearn package 5.
4) The principal component score obtained by 3) is displayed by selecting axes. By coloring each point based on a specific numerical value, for example, it is possible to see whether there is a correlation between the material performance value and the principal component scores. We think that the individualization of analysis and the preprocessing of inhomogeneous data are the bottlenecks in MI, and in order to solve these problems, we have built a common platform for data analysis and storage named "WAVEBASE" 6. This system is built on a cloud, and users can upload measured data from a web browser. The uploaded data is uniformly pre-processed, features extracted, and visualized in the system. For example, for image analysis, processing is performed by the function 1)-4) above. Data from other techniques such as XRD, small-angle X-ray scattering (SAXS), infrared (IR) and mass spectroscopy can be analyzed in the same way.
Synthetic microstructure generation A power spectrum as shown in Figure  7C  characterizes the microstructure of a magnet. We aim to generate synthetic microstructures for micromagnetic simulations that resemble the grain structure of a given magnet. Instead of matching the SEM image with an image derived from the synthetic microstructure we try to create a synthetic microstructure which gives a power spectrum that is similar to that computed from a SEM image. For this purpose, we combine synthetic microstructure generation, neural-network-based image segmentation and a Bayesian optimizer. We use the software package Neper 7 , which applies Laguerre tessellation for the generation of polycrystalline grains with realistic shape and size distribution  (Quey and Renversade, 2018). After slicing the three-dimensional synthetic structure, we create a threecolored image where the different colors represent the main NdFeB phase, the grain boundary phase, and a secondary phase such as for example Nd-oxides.
From this artificial microstructure image the power spectrum can be computed. In order to obtain three-colored microstructure images from SEM images we apply automated image segmentation. A convolutional neural network is trained to map a SEM image to a three-color segmented image. We use the wellestablished unet  (Ronneberger et al. , 2015; Choudhary et al. , 2022)  for the network architecture. Instead of manually labeling SEM images we use synthetically generated data for training of the network. Threecolored images which were obtained from synthetic microstructures are randomized by adding noise. Figure  8A  shows input output pairs used for training the neural network. Figure  8B  shows a SEM image and the segmented three-colored image. The segmentation of the SEM images ensures close similarity in the input color space for the computation of the power spectra between the experimental and the synthetic images.
We then apply a Bayesian optimizer 8 to find a set of input parameters for polycrystalline 6 https://www. toyota. co. jp/wavebase, accessed 17-10-2022. 7 https://neper. info/, accessed 24-10-2022. 8 https://scikit-optimize. github. io/stable/, accessed 24-10-2022. Frontiers in Materials 08 frontiersin. org  structure generation with the aim to create a synthetic microstructure which is close to the microstructure of a real magnet. The magnet's microstructure is characterized by the average of power spectra PSD exp obtained from SEM images as described above. To characterize the three-dimensional synthetic grain structure, we compute the average of the power spectra of nine slices through the magnet PSD syn. The Euclidean norm between the powerspectra f = |PSD exp -PSD syn | will be minimized during optimization.
As nucleation is expected to start close to the surface of the multi-phase grain  (Grönefeld and Kronmüller, 1989; Fischbacher et al. , 2018) , K 1 of the shell phase is always higher or equal than K 1 of the core phase. The exchange constant A is assumed to be 8 pJ/m for all three phases. We then reduced K 1 (and consequently M s ) in the core and shell phases until the coercive field μ 0 H c was reduced to 1 T. One suitable phase combination identified was K 1 = 3. 77 MJ/m 3 , μ 0 M s = 1. 45 T for the shell phase and K 1 = 0. 7 MJ/m 3 , μ 0 M s = 1. 04 T for the core phase. The inverse design approach then predicted (Nd 0. 29 La 0. 36 Pr 0. 35 ) 2 Fe 14 B for the shell phase and (La 0. 33 Ce 0. 67 ) 2 (Fe 0. 75 Co 0. 25 ) 14 B for the core phase at 300 K. Since this approach can be seen as temperature independent, the same can be done with the 433 K dataset.
Here we compare the computed demagnetizing factors with those of commercial sintered NdFeB magnets. Depending on the heat treatment the demagnetizing factor ranges from 1. 0 to 1. 5. The grain size distribution for the sintered magnet is shown in Figure  17. In order to obtain the grain size distribution we used four SEM images, we manually labeled the grain boundaries and derived the surface area of the grains with the image processing tool ImageJ  (Schneider et al. , 2012). The resulting grain size distribution from 2D images was corrected using the method of Saltykov  (Saltykov, 1961). For this purpose we applied the software GrainSizeTools (Lopez-Sanchez, 2018). The average grain size is 6. 42 μm which is close to the value 6. 68 μm obtained by matching the experimental and synthetically computed power spectra.
Though standard statistical methods are available for these tasks, further improvement of the methodologies to test the robustness of regression models and to quantify model uncertainties are needed. Simulated data as input for machine learning models may create a biased model. For intrinsic magnetic data, fusion as presented previously by  (Harashima et al. , 2021)  helps to reduce the error introduced by the approximations of the simulations. On the other hand, the predictor performance for estimating intrinsic magnetic properties can be improved when the experimental datasets are augmented with ab initio data as shown in Section 2. 1. 3. At the mesoscopic length scale, at which grain morphology, secondary phases, and grain boundary phases are relevant, building of synthetic microstructures that resemble real magnets is a prerequisite for the critical assessment of simulation results.
Acknowledgments The financial support by the  Austrian Federal Ministry for Digital and Economic Affairs , the  National Foundation for Research, Technology and Development, Austria  and the  Christian Doppler Research Association, Austria  is gratefully acknowledged. Data of first-principles computations used in this work was computed using the facilities of the  Supercomputer Center of the Institute for Solid State Physics at the University of Tokyo , and computational resources of supercomputer Fugaku provided by the  RIKEN Center for Computational Science  (Project ID:  hp220175 ).
Funding The work was partly carried out within the framework of the " Christian Doppler Laboratory  for magnet design through  physics informed machine learning " funded by the  Christian Doppler Research Association. This work was supported by  MEXT  as " Program for Promoting Researches on the Supercomputer Fugaku"  ( DPMSD , Grant ID:  JPMXP1020200307 ). The  LANEF framework  (No. ANR-10-LABX-51-01 ) is acknowledged for its support with mutualized infrastructure. Institut  NEEL received funding from the  Toyota Motor Corporation.
Literature search We systematically searched Google Scholar (for suitability for meta-analyses see  Gehanno et al. , 2013; Martín-Martín et al. , 2018; Walters, 2007)  with the following term but you are free, as Carpenter did in 2013. We provide an overview of the search process in Figure  1. The database searches achieved 1760 hits. We also searched articles by scanning reference sections of Meta-analysis flow diagram (adapted from PRISMA 2020) found articles and using the "related articles" and "cited by" options in Google Scholar. Based on reviewer feedback, we asked for unpublished studies in the ADRIPS, EADM, and EASP social networks, without any additional results. After adjusting for duplicates, 81 sources remained. To minimize possible potential publication bias, we contacted all identified authors in person and requested unpublished manuscripts.
Finally, we exclude studies with missing statistics or statistics that are not reported: Studies that do not report crucial measures such as the number of participants or standard needed for calculating the effect size deviation will be excluded from the sample. We briefly read through all articles to examine whether they met our inclusion criteria. A total of 7 articles were qualified for the exclusion, leading to a total sum of 22 identified articles with codable data. Finally, a total of 52 samples were included in this metaanalysis leading to a sum of 74 effect sizes. We provided a list of all included experiments in Table  1. We used a data extraction sheet that was already successfully used in other meta-analyses (e. g. ,  Fillon et al. , 2021; Yeung et al. , 2021). The coding process for the pre-tests was completed by two coders to ensure high inter-rater reliability.
Robustness We did not pre-register an estimation of Robustness. Still, we ran a script to create a Graphical Display of Study Heterogeneity (GOSH) to assess the robustness of effect size found. We provide the R script in supplementary rather than in the Rmarkdown because of the  time consumption used in the analysis. On our recent computer, the analysis took between 3 and 4 hours. One test of robustness includes the leave-one-out analysis, a method of analysis  (Olkin et al. , 2012)  made to see the influence of one effect size on heterogeneity. Another possibility is to estimate the influence of a subgroup in meta-analyses, which leads to a very high number of meta-analyses to perform to find the whole combination of effect sizes that could influence the robustness of the analysis. In fact, with 74 effect sizes found, it leads to 1.
Before and After Carpenter (2013) Finally, we wanted to see if any differences were made between the studies before and after the Carpenter (2013) analysis to see if they lead to a different effect size found. Carpenter  (Carpenter, 2013)  found an average effect size of r =. 13. Once our overall effect size (g = 0. 45) was transformed in correlation, we found an r = 0. 22 of the technique, two times higher than Carpenter found. This result still holds for the analysis we made of the identical dataset used by Carpenter. Why do we have so much difference? We found several errors in the Carpenter  (Carpenter, 2013)  analysis. For example, Carpenter used one experiment  (Dufourcq-Brana, 2007)  two times. Also, Carpenter made ambiguous and not reported decisions in his study. For example, for the experiments with two measures (e.
g. ,  Guéguen et al. , 2002; Marchand et al. , 2009; Pascual, 2002) , he decided to take one of them and did not make transparent the reason why. In our analysis, we decided to merge them except if one variable is not includable as reported in our preregistration. We found several errors in the original papers, some tests were not compatible with the reported number of participants (e. g. ,  Guéguen et al. , 2013; Pascual et al. , 2002, study 10). All the discrepancies found are made open in the commentary columns in the dataset. For the publication bias section, Carpenter only used the Trim-and-Fill technique, leading to no missing studies. We do not know how researchers used the algorithm (bilateral or left-centered, as recommended) and the Trim-and-Fill plot is not available. Also, researchers did not report the heterogeneity (I² or tau²) found in the article, while giving the percentage of variability explained by sampling error.