dataesr/scientific-paragraphs-categorization huggingface dataset:
The original ChIPseq sample data are available at White Lupin Genome Website (http://www. whitelupin. fr). To identify repeats associated with CENH3-containing chromatin, reads from the ChIPseq experiment obtained by sequencing DNA from isolated chromatin prior to (the input control sample) and after immunoprecipitation with the CENH3 antibody (1:200 dilution, the ChIP sample) were separately mapped to the repeat clusters. The mapping was based on read similarities to contigs representing individual clusters, using BLASTn with parameters '-m 8 -b 1 -e 1e-20 -W 9 -r 2 -q -3 -G 5 -E 2 -F F' and custom Perl scripts for parsing the results. Each read was mapped to a maximum of one cluster, based on its best similarity detected among the contigs. Ratio of ChIP/input reads assigned to individual clusters was then used to identify repeats enriched in the ChIP sample as compared to the input.
Data generation with short-reads technology. We selected 14 white lupin accessions to evaluate a broader range of the genetic diversity and determine population structure and linkage disequilibrium. More information about these accessions can be found in Supplementary Note 4. Young leaves of 30 plants were used to extract genomic DNA of each accession using the QIAGEN Genomic-tip 100/G kit following the supplier's recommendations. The accessions were sequenced using Illumina technology using paired-end 2 × 150 bp short-reads. It was generated a total of 310. 95 Gb of data with average sequencing depth of 45. 99× (Supplementary Table  8 ). Mapping and SNP detection. Cutadapt 55 was used to remove Illumina Truseq adapters from the sequencing data and to remove bases with a quality score lower than 30, in both 5' and 3' end of the reads.
2. 51  63. Then, a phylogenetic tree was prepared using the iTOL v 4. 3  64. A principal component analysis (PCA) was also performed in R (http://www. R-project. org/) function 'prcomp'. A Bayesian model-based clustering method implemented with STRUCTURE v2. 3. 4  22  was used to investigate the population structure using all the filtered SNPs. The program was run 10 times for each K value, ranging from 1 to 5, with a 1000 burn-in time and 1000 iterations. The optimal K value was determined based on the ΔK from the Structure Harvester v0. 6. 94  65  program, through Evanno's test  23. De novo assembly of GRAECUS and P27174. Long-read sequencing was realized using Oxford Nanopore technology, using a GridION 18. 04. 1-0, with a software Minknow 1. 10. 24-1 at platform at Get-PlaGe core facility (INRA, Toulouse, France).
High MW DNA was used to prepare a library with the Ligation Sequencing Kit 1D (sqk-lsk109). DNA was sequenced using a single ONT MinION R9. 4 flowcell (FLO-MIN106) for 48 h and base-calling was performed using Albacore 2. 1. 10-1. This produced 1,280,206 sequences for GRAECUS, corresponding to 12. 45 Gb of data with a N50 length of 13. 6 Kb (27. 6 x of sequencing depth). For the accession P27174 this produced a total of 1,738,579 reads corresponding to 14. 59 Gb of data with N50 length of 11. 8 Kb (32. 36 x of sequencing depth). The de novo assembly of the two genotypes were performed using CANU  15. For P27174-4, two round of correction have been made prior to the assembly step, using the parameters correctedErrorRate = 0. 16 and corMax-EvidenceErate = 0. 15. For GRAECUS, only one round of correction have been made, using minOverlapLength = 400, correctedErrorRate = 0.
16 and corMax-EvidenceErate = 0. 15. The Illumina paired-end data described in 3. 1 were used to polish two times the two genome assemblies using Pilon  53. BUSCO v 3. 0. 0 66 was run on the set of predicted transcripts. The assessment software detected for GRAECUS 96. 8% of complete gene models (1142 complete single copy and 188 duplicated respectively) plus 9 additional fragmented gene models. For P27174 97. 8% of complete gene models (1125 complete single copy and 220 duplicated respectively) plus 4 additional fragmented gene models. Structural variation of these two accession were performed using Assemblytics  24  based on whole genomes alignments generated with MUMmer  25. Details of the de novo genome assembly and analysis of structural variation of these two accessions are provided in Supplementary Note 4.
A total of 2,048,118,650 paired-end reads of 150 pb were sequenced using an Illumina NovaSeq6000 Sequencer. To remove low quality sequences, the RNA-seq reads were checked and trimmed using Cutadapt  55  with a minimum quality score of 30 in both 3′ and 5′ end, with the nextseq-trim option enabled. Illumina TruSeq adapter sequences have also been removed. The resulting reads shorter than 35 pb have been discarded. The quality checked RNA-seq reads were then mapped on white lupin reference genome using Hisat2 68 software. Transcripts were assembled and quantified using Stringtie software. Gene counts were extracted and imported in the R package DESeq2 69. These counts have been normalized according to the size factor computed by DESeq2. For small RNA sequencing, 24 independent root RNA-seq libraries were constructed using NEXTflex™ Small RNA-Seq kit according to the manufacturer's protocol.
Acknowledgements We thank  F. Parcy  and  H. Rouached  for critical reading of the manuscript and for helpful discussions. We thank  J. B. Magnin-Robert  ( INRAE Agroécologie, Dijon, France ),  Nathalie Harzic (Jouffray-Drillaud, France), Paolo Annicchiarico (Council for Agricultural Research and Economics, Milan, Italy) , and  David McNaughton  ( Soya UK, UK ) for providing seeds from the different accessions. This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  ( Starting Grant LUPINROOTS -grant  agreement No  637420  to B. P. ). We thank  R. Flores  ( INRAE URGI University, Paris Saclay Versailles, France ) for making the Legume synteny data publicly available in the frame of the  Institut Carnot Plant2Pro Project 'SyntenyViewer'.
We acknowledge  L. Combes-Soia  and  V. Labas  ( Physiologie de la Reproduction et des Comportements, INRAE, CNRS, Université de Tours, France ),  O. Bouchez  ( GeT-PlaGe, INRAE Auzeville, Castellan Toulosan, France ), andC. Poncet and  V. Gautier  ( Genotyping Platform Gentyane, Clermont-Ferrand, France ). This work was partly performed in the LRSV and LIPM, which belong to the  TULIP LABEX  ( ANR-10-LABX-41 ). This work was also partly performed in IPS2, which belong to the  SPS LABEX  ( ANR-10-LABX-40 ). We also thank the  VILLUM Foundation  for the Young Investigator grant awarded to F. G. -F. (Project  15476 ) and funding from  Innovate UK  to M. N. A. M. thanks  FAPEAL/CNPq (Brasil)  for postdoctoral fellowship.
In addition, we provide abundances of the elements Fe, Al, and Ni, as well as an overall [α/Fe] ratio obtained using a new pipeline based on the GAUGUIN optimization method that is able to deal with variable signal-to-noise ratios. The Rave DR6 catalogs are cross matched with relevant astrometric and photometric catalogs, and are complemented by orbital parameters and effective temperatures based on the infrared flux method. The data can be accessed via the Rave Web site a) or the Vizier database.
2015; and Gaia-ESO, Gilmore et al. 2012). For a recent review on abundances derived from large spectroscopic surveys we refer to  Jofré et al. (2019). This publication addresses the determination of stellar atmospheric parameters, chemical abundances, and distances in the context of the Rave survey, which over its 10 year observing campaign amassed this information based on more than half a million spectra. Together with the accompanying paper  (Steinmetz et al. 2020, henceforth DR6-1) , which is focusing on Rave spectra, error spectra, spectral classification, and radial velocity determinations, it constitutes the sixth and final data release (DR6) of Rave. In particular, DR6 provides a new set of stellar atmospheric parameters employing parallax information from Gaia DR2  (Gaia Collaboration et al.
2018) , and a robust determination of the α-enhancement [α/Fe]. The paper is structured as follows: in Section 2 we give a brief overview of the Rave survey and its collected data. Section 3 presents an update on the stellar atmospheric parameter determination and introduces a new catalog of stellar stellar atmospheric parameters inferred using a Bayesian pipeline with Gaia DR2 parallax priors following the procedure outlined in  McMillan et al. (2018). In Section 4, a new optimization pipeline GAUGUIN is presented  (Bijaoui et al. 2010; Bijaoui et al. 2012; Guiglion et al. 2016 ) in order to extract [α/Fe] ratios as well as individual abundances of Fe, Al, and Ni. Section 5 describes how orbital parameters of stars are derived from Rave combined with Gaia DR2 astrometric information. Rave data validation including a comparison of Rave stellar stellar atmospheric parameters and abundances with external observational data sets is done in Section 6.
SURVEY DATA AND THEIR REDUCTION The motivation, history, specifications and performance of the Rave survey are presented in detail in the data release papers DR1, DR2, DR3, DR4, and DR5  (Steinmetz et al. 2006; Zwitter et al. 2008; Siebert et al. 2011a; Kordopatis et al. 2013a; Kunder et al. 2017 ) and a comprehensive summary is given in DR6-1  (Steinmetz et al. 2020 ). Here, we only summarize the main properties of the Rave survey. Rave was initiated in 2002 as a kinematically unbiased wide-area survey of the southern hemisphere with the primary goal to determine radial velocities of Milky Way stars  (Steinmetz 2003). Thanks to the 6dF multi-object spectrograph on the 1. 23m UK Schmidt telescope at Siding Spring in Australia, up to 150 spectra could be simultaneously acquired over a field of view of 5.
The data reduction of Rave follows the sequence of the following pipeline: 1. quality control of the acquired data on site with the RAVEdr software package (paper DR6-1, Section 3. 1). 2. reduction of the spectra (DR6-1, Section 3. 1). 3. spectral classification (DR6-1, Section 4). 4. determination of (heliocentric) radial velocities with SPARV ('Spectral Parameter And Radial Velocity', DR6-1, Section 5). 5. determination of stellar atmospheric parameters T eff log g, and [m/H] 1  with MADERA ('MAtisse and DEgas used in RAve', (Section 3. 1). 6. determination of the effective temperature using additional photometric information (InfraRed Flux Method (IRFM), Section 3. 2). 7. modification of the Rave stellar atmospheric parameters T eff log g, and [M/H] derived spectroscopically with additional photometry and Gaia DR2 parallax priors using BDASP (Bayesian Distances Ages and Stellar Parameters, DR6-2 Section 3.
3). 8. determination of the abundance of the elements Fe, Al, and Ni, and an overall [α/Fe] ratio with the pipeline GAUGUIN (Section 4). 9. recalibration of the stellar atmospheric parameters T eff log g, and [M/H], for giant stars based on K2 asteroseismic information (Section 3. 4) followed by the determination of the chemical abundances [Fe/H] and [Mg/H] using the GAUFRE pipeline  (Valentini et al. 2013). The output of these pipelines is accumulated in a PostgreSQL data base and accessible via the Rave website http://www. rave-survey. org (Section 7 and DR6-1, Section 7).
2008)  and Turbospectrum  (Plez 2012)  under the assumption of local thermodynamic equilibrium (LTE). The atomic data was taken from the VALD 2  database  (Kupka et al. 2000) , with updated oscillator strenghts from  Gustafsson et al. (2008). The line-list has been calibrated primarily on the Solar spectrum of  Hinkle et al. (2003)  and with adjustments to fit also the Arcturus spectrum to an acceptable level (see  Kordopatis et al. 2011 , for further information). Furthermore, the grid excludes the cores of the Calcium triplet lines, as they can suffer, depending on spectral type, from non-LTE effects or emission lines owing to stellar activity. The grid has three free parameters: effective temperature, T eff , logarithm of the surface gravity, log g, and metallicity 3  , [m/H]. These free parameters are hence the parameters that MADERA determines.
Distances, Ages, Stellar Atmospheric Parameters with Gaia priors Rave DR6 includes for the first time stellar atmospheric parameters derived using the Bayesian framework demonstrated in  McMillan et al. (2018) , along with derived distances, ages, and masses, which have also been derived for previous data releases. We refer to the method as the BDASP code. This follows the pioneering work deriving (primarily) distances by  Burnett & Binney (2010)  and  Binney et al. (2014). This method takes as its input the stellar atmospheric parameters T eff (taken from the IRFM, Section 3. 2), log g taken from the MADERA pipeline, an estimate of the overall metallicity taken from the MADERA (see below), J, H, and K s magnitudes from 2MASS, and, for the first time, parallaxes from Gaia DR2. For a detailed description of the method, the interested reader should refer to  McMillan et al.
Note that we plot all stars in common, irrespective of their possible classification. This simplification does not affect the general statistics of the comparison samples appreciably, but could be important when comparing individual objects, as discussed in DR6-1, Section 4. The comparison shows an excellent agreement in the derived values for log g and T eff. Indeed, the advantage of having Gaia DR2 parallax information available for Rave results in log g estimates that are at least comparable to those derived with higher resolution spectroscopy. We compared Rave results also with new unpublished GALAH values of log g and [Fe/H], which make use of Gaia astrometry. This new information makes the log g values of both datasets almost identical (∆ = 0. 03, σ = 0. 10) and decreases the iron abundances for GALAH by ∼ 0.
This research was supported in part by the  National Science Foundation  under Grant No. NSF   PHY-1748958  to KITP. This work has made use of data from the  European Space Agency (ESA) mission Gaia  (https: //www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. 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. Based on data products from observations made with  ESO Telescopes at the La Silla Paranal Observatory  under programme ID  188. B-3002. This work has also made use of observations obtained with the  Apache Point Observatory 3. 5-meter telescope, which is owned and operated by the  Astrophysical Research Consortium.
Materials and Procedure The entire procedure included four cognitive tasks: an episodic memory task, a semantic memory task, an executive functioning task, and a visual perception task. Task order was randomly assigned for each participant. See Figure  1  for examples and a schematic representation. The episodic memory task was separated into two parts: an encoding phase and a retrieval phase. During the encoding phase, participants were presented with 40 unrelated pairs of words for 2,500 ms duration in a randomized order. Words were extracted from the French Lexique database  (New, Pallier, Brysbaert, & Ferrand, 2004)  according to the following criteria: nouns or adjectives with six letters, two syllables, and between 20 and 100 occurrences per million. During the retrieval phase, immediately after the end of the encoding phase, participants were presented with a cue word seen during the encoding phase and had to select which one of the two other presented words was paired with this cue word.
Data and Statistical Analyses As described above, we focused on both metacognitive bias and metacognitive sensitivity. In our initial preregistration, we aimed to measure metacognitive bias by subtracting mean task performance from mean confidence because we anticipated that firstorder performance would differ across the four tasks. Metacognitive sensitivity was proposed to be measured by the area under the type II ROC curve. We decided to deviate from both of these planned analyses for several reasons (see online supplemental material for preregistered analyses). Regarding metacognitive bias, we reasoned that there was some ambiguity in the absolute meaning of the scale label 0% confident, given that chance level in 2AFC tasks is 50%. We therefore decided to measure the average confidence level across trials without subtracting mean task performance, which would rely on subjects having interpreted a scale value of 0% confidence as 50% performance (chance).
M and M refer to the mean and the standard deviation of log(meta-d=/d=). MiMj is the correlation coefficient for log(meta-d=/d=) between tasks i and j. The HMeta-d toolbox (https://github. com/metacoglab/HMeta-d) uses Markov chain Monte Carlo sampling to estimate posterior distribution over model parameters using the JAGS program  (Plummer, 2003). We modified the HMeta-d code to allow estimation of parameters in R using rjags. As in the HMeta-d toolbox, we discarded early samples of the posterior distributions and ran three chains to diagnose convergence problems. Convergence diagnostics were computed with the coda package using the potential scale reduction factor R ˆ  (Gelman & Rubin, 1992). Material, raw data, model, and analysis scripts are available in OSF (https://osf. io/b5ype/). Significance of group-level parameters was estimated by calculating whether the 95% highest density intervals (HDIs) on the posterior distributions of the correlation coefficients MiMj overlapped with zero, which is a Bayesian analogue of a frequentist confidence interval because it is the smallest interval containing 95% of the Markov chain Monte Carlo samples  (Kruschke, 2014).
However, as also suggested by previous work  (Morales et al. , 2018) , both domain-specific processes and a more domain-general resource may make independent contributions to confidence formation. To conclude, we find that contrary to previous results, both metacognitive bias (measured by confidence level) and metacognitive efficiency share common resources across domains. This observation of a domain-general signature of metacognitive efficiency was obtained after ensuring that task structures were similar across domains (2AFC), that experimental power was sufficient, and that performance-controlled measures of metacognition were used (meta-d=/d=). The percentage of explained variance, however, suggests that both domain-specific and domain-general resources are involved in metacognitive efficiency, which is consistent with previous neuroimaging data  (Morales et al.
Context This experiment is the first in a series conducted as part of Audrey Mazancieux's doctoral research program, the details of which can be found on OSF. The aim of the research program is to assess whether there are domain-general resources contributing to metacognition. More broadly, the research straddles a wellestablished metacognitive tradition grounded in memory research (hence the focus on memory) and the more recent field inspired by psychophysical experiments examining metaperception (hence the signal-detection inspired modeling approach). The research reported here benefited from a collaborative visit by A. M. to S. M. F. 's laboratory in 2018 after the study had been preregistered and the data had been collected. This greatly influenced the choice of hierarchical modeling approach, leading to the changes between our preregistered analysis plans and the results presented here.
This work was in part supported by funding from  Initiative d'Excellence (IDEX)  held by  University Grenoble Alpes  to  Audrey Mazancieux  to support a research visit in  Stephen M. Fleming 's laboratory. Stephen M. Fleming  is supported by a  Sir Henry Dale Fellowship jointly  funded by the  Wellcome Trust and Royal Society  ( 206648/Z/17/Z ). Chris J. A. Moulin  gratefully acknowledges the support of the  Institut Universitaire de France.
This paper introduces three independent new methods and the results obtained with them. The first two algorithms enhance the data selection and reconstruction process, while the third is a novel analysis method, referred to as multiscale method in the following, that uses the arrival directions of upgoing muon neutrinos recorded with the ANTARES neutrino telescope in 6 years of data taking. The goal of this analysis is to identify the most significant, spatially confined excess over the background of atmospheric neutrinos without relying on assumptions of any emission model of potential neutrino sources. The analysis method is sensitive to a clustering of events in an extended sky region, and its sensitivity increases with the size of the signal region. The multiscale method does not rely on any property derived from modelling or simulations, but computes all required estimates from the observed data.
Model-independent multiscale source search The model-independent multiscale source search aims to identify the region of arbitrary position, size, shape and and distribution of neutrino events within the region with the most significant excess of neutrino events in the sky with respect to the background expectation. The method itself 2  is independent from the ANTARES neutrino telescope or the fact that the analysed data are neutrino candidates. The only assumption made in this method is that the density of background events at any declination is approximately independent of the right ascension. Unlike many techniques known from image processing, it is designed to operate also on the sparse dataset of neutrinos. While many other fields of astronomy can operate on image-like measurements, these neutrino data would fill only very few grid points of a skymap.
The search grid In the first step a spherical grid is defined that allows for describing the sky map in terms of sets of integers assigned to the grid points (see subsequent steps). The distance of adjacent grid points is chosen to be 0. 5 • , commensurate with the angular resolution of reconstructed neutrino-induced muon tracks in ANTARES (see Fig. 2 ). The grid consists of 165016 grid points. Figures  4, 5  and 6  show the spherical grid, with grid points in blue and 12000 randomly generated neutrino events in white. This number of neutrinos is close to the expectation for the data analysis and they are distributed according to the visibility of ANTARES. In order to better illustrate the steps of the analysis method, two artificial point sources with 12 and 18 events, respectively, have been added at a declination of -70 • , see Figures  6  and 7.
IceCube IC40 In order to perform an independent cross check of the result obtained using ANTARES data, the publicly available IC40 dataset  [26]  published by the IceCube Collaboration has also been analysed. This analysis searches specifically for an excess in the area of the large cluster found in ANTARES data. To achieve this, it only considers clusters found in the IC40 data that overlap with the ANTARES cluster by at least 51% of their area. The value of 51% is chosen here because this means that they are more related to the cluster from ANTARES than to other regions. On the other hand, requiring an overlap of close to 100% would exclude e. g. clusters that extend beyond the shape of the ANTARES cluster. Since the requirement for overlap implicitly confines the size of the cluster, the clustersize in grid points, N c , is no reasonable metric to assess the significance of a cluster in this evaluation.
Therefore the metric that gave the second best results in the investigations introduced in Section 3. 8 has been used. It is the mean value of the √ N c grid points 5  with the highest values within a cluster. The result obtained by this adapted search on the IC40 dataset is shown in Figure  22. A cluster is found within the expected shape with a post-trial significance of 2. 1σ. The position of the found cluster overlaps with the search template by 78% of its size. The observation that not all features in the skymaps in Figures 21 and 22 match exactly is to be expected, as ANTARES has its highest sensitivity at lower energies compared to IceCube. Moreover, random fluctuations of atmospheric neutrinos do influence the results.