dataesr/scientific-paragraphs-categorization huggingface dataset:
iucnredlist. org/). We assigned each taxon to one or more geographic regions from Western Europe (W), The Caucasus & Balkans (T), North America (A), Japan & Korea (North and South) (J), China (C), Mongolia (M), Eastern Russia (E) and India & Nepal (I). Ancestral range estimation. We used the R package 'BioGeoBEARS' 1 to estimate ancestral ranges in Corylus. This package allows for inferences using three biogeographic models almost identical to the DEC model 2 , the DIVA model 3 and the Bayesian Inference of Historical Biogeography for Discrete Areas (BayArea) model  26. 'BioGeoBEARS' allowed us to estimate the importance of founder events in the biogeographic history of a group by introducing a new parameter (j) for jump dispersal (founder-event speciation). We performed two sets of analyses, the first where there were no restrictions on area combinations and the second where an "areas-allowed" matrix was used (Supplementary Table  S1 ); this matrix summarises the geographical connectedness of different areas (including Eastern Russia & North America to incorporate a late tertiary Bering land bridge  27  ) and this information is used to restrict area combinations.
Results phylogenetic inference. We collated data from four outgroup taxa and 16 Corylus taxa. Sequences from 13 partitions, consisting of eight chloroplast loci and five nuclear loci, were downloaded from Genbank. Sequences for each taxon were taken from five different sources on average (min: 2, max: 7) which will reduce any error caused by incorrectly labelled or identified GenBank sequences because identification mistakes are unlikely to be shared among sources. Furthermore, almost half (~44%) of sequences had a voucher associated, which suggests that these samples are reliable. We further assessed potential error in our sequence data by building gene trees (Appendix S2) for each partition. Individual gene trees had varying levels of phylogenetic signal (see supplementary data) and bootstrap support was generally low, probably due to the relatively small amount of sequence data in each partition.
Close inspection revealed several instances where sequences had long terminal branches and unusual topological placement. We removed C. heterophylla from the trnl-trnf alignment and C. sieboldiana var. mandshurica from our 5S-rRNA alignment. We initially removed C. sieboldiana from the atpB-rbcl alignment but subsequently found the entire alignment to converge very poorly during our Bayesian tree inference, so it was removed completely. The presence/absence of each locus for each species can be found in Supplementary Table  S2  and the accession numbers of all sequences used can be found in Supplementary Table  S3. Independent runs reached stationarity at the same point and after combination ESS values were greater than 200 for almost all parameters. Support for MCC trees within the genus was higher in the nrDNA (72% PP > 0.
9) than cpDNA (61%) (Fig. 2a, b ). Estimated crown node ages for Corylus ranged from 13. 87-23. 33 (10. 26-32. 47) Ma depending on calibrations and DNA dataset (Table  2 ). The nrDNA tree gives a noticeably younger age of Corylus than the cpDNA tree (Table  2 ). The recovered topologies were identical among different calibration approaches (Fig. 2 ; Supplementary Fig. S1 ). They revealed strong support of non-monophyly of varieties of C. heterophylla in both trees and C. sieboldiana/C. ferox in the cpDNA tree only. The topologies of the nrDNA (Fig. 2a ) and cpDNA (Fig. 2b ) trees differed extensively from each other. Some of these differences could be related to low support values but a number are well-supported incongruences. Most conspicuously, the two species found in North America (C. cornuta & C.
Ancestral range estimation. Model selection revealed that the DEC + j model with an "areas-allowed" matrix fit the data best for the nrDNA tree (Fig. 3b ; Supplementary Table  S4 ) and the BayArea + j model with an "areas-allowed" matrix fit best for the cpDNA tree (Fig. 3d ; Supplementary Table  S4 ). In the nrDNA model Corylus most likely originated in east Asia, in a range containing China and India & Nepal while in the cpDNA model the most likely root state was only North America. Due to concerns about the validity of models with the jump dispersal parameter  29  we also present the best fitting models without this parameter. In the DEC nrDNA model (Fig. 3a ) China is the root state while it is China & Eastern Russia in the cpDNA BayArea model (Fig. 3c ). Much of the speciation in the genus appears to have taken place in east Asia, particularly in China.
Likewise, the BayArea + j model suggested Corylus originated in North America and soon after dispersed to The Caucasus & Balkans. There were some similarities between the different datasets. For example, most of the diversification appears to have taken place in east Asia and The Caucasus & Balkans was colonised twice in all models. In the best-fitting models without the jump dispersal parameter (Fig. 3a, c ) we find a considerable increase in the number of times that Eastern Russia is part of an ancestral range. One major source of this increase is the movement of lineages from East Asia to North America, which go through Eastern Russia as this is the only area that connects the two continents in our models. The "areas-allowed" matrix restricts the possible combinations of areas that can make up an ancestral range.
Discussion evolutionary History of Corylus. In this study we have improved our understanding of interspecific and intraspecific relationships in the genus Corylus. We used fossil calibrations to estimate that the genus likely originated in Miocene or Oligocene (Table  2 ). This large date range is due to difference in node ages between cpDNA and nrDNA and wider 95% HPD intervals for time-calibration based on uniform priors. Our results were similar to previous estimates of Corylus age in several studies  [30] [31] [32]  , but not as young those in Grimm and Renner  33  who also took their data from GenBank but used different fossil calibrations. 3a. The species currently occurring in each region are labelled in adjacent boxes. Arrows on lines represent the direction of lineage dispersal. Solid lines represent a complete range shift where the ancestral region is no longer part of the lineage's range.
Dotted lines represent lineages that spread to a new region while also remaining in their ancestral region. There may be multiple dotted lines per lineage. The shape of the lines between regions does not represent the dispersal path. Vicariance events and range contractions (not inferred) are not represented on the figure. Table  3. Results from Biogeographic Stochastic Mapping conducted on the set of best-fitting models for each tree detailing the mean and standard deviations of the frequency of each biogeographic event type after 500 simulations. Range contractions are not considered. Acronyms are used for within-area speciation (WAS) and within-area subset speciation (WASS). Using sequence data from GenBank is a common practice (e. g. 33 ) but may lead to the use of misidentified samples that could adversely affect phylogenetic tree inference  34.
Our initial dataset included up to 13 different loci from as many as seven different sources per taxon (Supplementary Table  S3 ). We assessed each of our loci by inspecting gene trees for any unusual topology or extreme branch lengths, removing several sequences and a partition in the process. Almost half of our sequences had vouchers associated and any errors in identification are unlikely to be shared by sequences from different sources. In the event of an error the other loci in the dataset will help to minimize the effect of an incorrectly labelled locus. Therefore we believe that the potential impact of misidentified GenBank sequences is minimal. With the addition of novel nuclear and chloroplast loci, our trees broadly reflect the findings of earlier studies. Whitcher and Wen 10 used ITS1, ITS2, 5.
ferox, which was absent from the study. This study also included a tree based on the chloroplast marker matK that grouped species from North America together as in our cpDNA tree (Fig. 2b ), corroborating our inference and providing additional evidence to support the idea that cpDNA and nrDNA have different histories in Corylus. A recent study by Yang et al. 12  used SNP data to infer the evolutionary history of Corylus. Their study included most of the species used here as well as C. wangii and C. californica but their dataset lacked C. maxima and the variety C. heterophylla var. thunbergii. While Yang et al. 12  generated more sequence data they concatenated all SNPs into a single locus and appear not to have accounted for the lack of invariant sites, which is known to bias phylogenetic inference and overestimate branch lengths  35, 36.
Furthermore, they attempted to partition their dataset with recombination tests and evidence for recombination was used as a reason to remove SNPs from the dataset, resulting in a different tree topology  12. These recombination tests were likely inappropriate as they were conducted on the concatenation of SNPs rather than contiguous sequences. Yang et al. 12  find clades similar to those found here and in Whitcher and Wen  10  but we note that C. avellana is placed either closely related to the Colurnae subsection (as in our nrDNA tree) or the Corylus subsection depending on tree inference approach taken, casting doubt on the reliability of their inferences. Divergence events in Corylus were generally found to be older in Yang et al. 12  than in our study and could be attributed to biases caused by inferring branch lengths with SNP data only.
IV. IMPACT ON THE NEUTRALINO RELIC DENSITY We finally come to the discussion of the impact of the corrections presented in Sec. III on the neutralino relic density Ω χ0 1 h 2. To this end, we have implemented the corrections into the numerical code DM@NLO, which is used as an extension to micrOMEGAs. In practice, this means that the Boltzmann equation is still integrated using the latter, while the cross section calculation of the relevant processes (see Tab. II) is performed by DM@NLO instead of CalcHEP. In the following, we will illustrate the impact of the corrections by comparing the relic density obtained using the full DM@NLO NLO calculation to the values obtained using the tree-level calculation of micrOMEGAs / CalcHEP.
ACKNOWLEDGMENTS The authors would like to thank  A. Pukhov  for providing us with the possibility to interface our DM@NLO code with micrOMEGAs. J. H. was supported by the  DFG Emmy Noether  Grant No. HA 8555/1-1 , and earlier by the  Labex ILP  (reference  ANR-10-LABX-63 ) part of the Idex SUPER, and received financial state aid managed by the  Agence Nationale de la Recherche (ANR) , as part of the  programme Investissements d'avenir  under the reference  ANR-11-IDEX-0004-02. The work of B. H. is supported by  Investissements d'avenir, Labex ENIGMASS , contrat  ANR-11-LABX-0012. The work of S. S. was supported by the  DFG  through  RTG 2149 Strong and Weak Interactions -from Hadrons  to  Dark Matter. The figures presented in this paper have been generated using MatPlotLib [59].
) is complete in terms of methods available, but performs very poorly  [7]. On the other hand, some languages (C, python) or sofware (Oracle) are very powerful but oer very few analytical tools and require serious programming skills. Furthermore, both types of software are integrated into obsolete interfaces that completely ignore the principles of HCI and are poorly adapted to non-expert users (Figure  S1 ). The R++ project (Figure  1 ) aims to develop modern statistical analysis software integrated into a user-friendly interface. In this paper, we present the methodology that led us to the design of R++. Section 2 briey recalls the concept of exploratory design and video prototyping. Sections 3 and 4 details elements that have been improved. Section 5 is the conclusion.
Methods To conceive the user interface of R++, we used the exploratory design method and video prototyping: during 1 hour, statisticians and computer specialists brainstormed on: What are the dicult, tedious, time-consuming or highly error-prone tasks with your current statistical software? This round table meeting was done in an open mode: the participants intervened and a moderator took notes on the board. At the end of this rst step, we selected two or three of the themes that were most frequently mentionned. In the second step, we sought solutions for the previously selected themes: Imagine the statistical analysis software of your dreams: how would it work? For 10 minutes, participants were asked to write down three ideas, plus a far-fetched idea. The objective of this out-of-the-box idea was to avoid self-censorship: indeed, a participant may have a good idea but not say it for fear of being ridiculous.
In doing so, it helped avoiding the said problem. When everyone had found their three plus one ideas, they were presented to the rest of the group and then debated, combined, and hopefully improved. Finally, during the third hour, the participants created low-delity prototypes (Figure  S2 ). With the help of paper, felt pens, post-its, and cut-outs, they created a scenario that they lmed using a telephone. These prototypes were then presented to the entire group. This provided the rst feedback. In total, we organized eight general sessions without specic instructions. We also organized thematic sessions where the agenda was announced in advance (e. g. merging databases). Only the statisticians directly concerned by the problem came to these sessions. The course of the session was then modied: 1) search for solutions 2) presentation of videos related to the theme discussed during the general sessions 3) creation of video prototypes.
Focus on graphs The second highly problematic point concerns graphs. Traditional software produces graphs that are generally austere, xed, and can only be manipulated by command lines. Statisticians are often forced to use additional software (Photoshop, Gimp) that they do not master well. In order to solve this, the following solutions have been chosen: Interactive graphs allow, when you click on a part of the graph (for example on an outlier) to select the corresponding data. This action also sorts the database so that the selected data is present at the top of the column. This makes it possible to access specic values very quickly. Some graphs are mandatory parts of an analysis. Their production has been automated: univariate analysis (Figure  2  and S3) but also statistical test graphs (Figure  3 ).
Conclusions In this article, we have presented the methodology that led us to design R++, a statistical analysis software integrated into a user-friendly interface. There are still many areas in which statistical analysis could benet from HCI contributions. In Figure  2 , the appearance of the graphs limits the position of the displayed data. User feedback could allow us to determine if it is a blocking element. If so, could solutions such as perspective wall  [8]  help? Or could Focus+Context  [9]  be adapted to the statistical context? Similarly, the graph interface requires you to leave the data page. Would it be possible to use a more direct conguration solution, such as Magic Lenses? [10]. Another interesting eld, one of the pillars of science is the possibility of being able to reproduce an analysis.
It is composed by three main components: (i) an image-based breast and tumor characterization tool, (ii) a predictive model after breast conservative therapy and radio-biological model, and (iii) three different clinical decision support systems (i. e. guideline-based, experience-based and patient similarity based) that coexist and complement each other to give most personalized and best evidence-based recommendations to the BUs. All these are supported by DESIMS (i. e. DESiree Information Management System), a Security and Access Control module and an image system for image and models visualization.
Some systems provide purely CPG-based decision support, that rely on latest evidence but give generic recommendations or are not able of coping with particular clinical cases that are complex or there is not sufficient evidence reported yet on the CPGs to cope with them. Hence, these systems may become inefficient and not effective at all when supporting clinicians in the decision-making process. In this context, the web-based software ecosystem DESIREE has been developed for a better coordination and information management during the BU. It is composed by several interoperable modules that rely on a Digital Breast Cancer Patient Model built using standard terminologies and codifications (i. e. NCI Thesaurus, UMLS, SNOMED CT) and formalized using semantic web technologies (i. e. ontologies).
This model is capable of defining all relevant information characterizing a breast cancer patient to be used in the decision-making process along with the experience generated in this process. This experience is used later on for going beyond the state of the art of the reported knowledge in the model based on previous cases and outcomes, improving and boosting the process of knowledge discovery. In the demo, the three main modules that compose DESIREE will be presented: (i) an image-based breast and tumour characterization tool, which extracts valuable information for diagnosis, treatment, prognosis and follow up from the heterogenous set of image data  [1]  (ii) a predictive model after breast conservative therapy and radio-biological model, which provides useful results to the clinician and patient from the very first step before conservative therapy  [2]  (iii) three different clinical decision support systems that coexist and complement each other to give most personalized and best evidence-based recommendations to the BUs  [3] : -a guideline based CDSS, which implements various international (e.
5 m for each pit. In more detail, we measured four kinds of soil thicknesses: LA which corresponds to plowed horizon, S which is cambic horizon, C1 and C2 which correspond to calcaric material (C1 is cryoturbed limestone and C2 is sandy calcaric material). Based on these data combined with pF data for each horizons characterized, soil mapping was generated with krigeage models providing soil layer thicknesses but also WHC estimation at every point of the platform. As microplots are georeferenced, an estimation of the WHC was performed for each of them by computing the mean of the WHC points contained in the corresponding area. Data management was operated by PostgreSQL software, a relational database management system extended with PostGIS software to add support for geographic objects.
Phenotyping Gantry and Sensor Bay A set of eight fully automated phenotyping gantries were installed over the moving rainout shelter rails in order to acquire frequent crop canopy measurements via remote sensors, thus ensuring non-invasive measurements and the collection of a large amount of phenotyping data. Each 25 m wide gantry is able to lift a payload at a 6 m height, allowing data acquisition on any type of crop, even tall maize cultivars. These data are obtained with the sensors installed on a high throughput phenotyping bay, mounted on the gantries during experimental campaigns. It allows smoothed screening from 0. 1 to 3 m. s -  and centimetric controlled repositioning of canopy sizing from 0 to 3 m. Each sensor head can carry a set of sensors with no limit of power consumption and up to 150 kg.
Acquisition, Data Calibration The level and stability of the sensing chain including illumination, geometric configuration, light transmission and sensor response functions were set up to optimize signal to noise ratio of low level data and were documented. Every day of acquisition, controls were performed systematically against a secondary calibration surface and tracked by the National Institute of Standards and Technology (NIST) through Spectralon R (Labsphere, NH, United States) in accordance with good practice for uncertainty management (GUM). This data were used to correct the white balance of RGB cameras and to calculate physical units of reflectance. Acquisition was optimized to maximize the sampling within the microplot and to allow full acquisition of the platform in 1 day with a two-sensors bay.
Processing and Interpretation For RGB cameras, a white balance process was first applied to adjust intensities of the red, green, and blue channels at a same intensity on a reference gray panel. This standardization was important for a robust color based image analysis. The first use a RGB images was the calculation of the green cover fractions (GCF) at 0 • and 45 •. A support vector machine algorithm trained on a reference dataset, was used to classify for each image the green and non-green pixels and determine the percentage of green elements for a given viewing angle  (Dutartre et al. , 2015). The green cover fractions at 0 • and 45 • were used to estimate the Green Area Index (GAI) and Average Leaf Angle (ALA). Both variables were estimated by inverting a simple Poisson model using the measured gap fractions Po, calculated as (1-GCF).
The model used to relate Po to GAI is: Po θp = e - G(θp,θl) cos(θp). GAI Where θp is the viewing angle, θl is the mean leaf angle and G(θp,θl) is the function that expresses the projected area of the leaves for a particular configuration. We assumed that the G function follows an ellipsoidal leaf angle distribution  (Campbell, 1986 (Campbell, , 1990)). The model was inverted using a look up table minimization procedure  (Weiss et al. , 2000 (Weiss et al. , , 2004) )  to retrieve the more likely combination of GAI and ALA. For spectroradiometers, a calibration measurement on a spectrally characterized reference surface was done before each data acquisition session. The reflectance at the crop level was then obtained by dividing the canopy reflectance by the calibration measurement. At the plot level, the averages of the normalized reflectances were computed.
5 Plant height (cm) was estimated from the analysis of the 3D point cloud generated from the combination of the LiDAR scans of height (z) and the (x,y) positioning of the sensor, recorded by the gantry's encoders. The plot mean height was calculated using the algorithm developed by  Madec et al. (2017). It first consisted in clustering the point cloud to separate the ground from the vegetation. The maximum peak in the z-distribution of the non-vegetation points was assigned as the ground level. This distance was subtracted from the 3D point cloud resulting into a distribution of the height values. The height of the canopy was then defined as the height value corresponding to 99. 5% of the cumulated height distribution of the vegetation points. After statistical and physical validation against expected intermediate values and validation of biophysical values in order to control non-divergence in case of inversion techniques, data were uploaded and shared through a dedicated database named PhenX  (Piquemal, 2017)  allowing statistical analyses following good practices in plant experiments.
This could explain poor grain filling and smaller grain on WD than on WW (Figure  10C ) and it is consistent with LIDAR, tensiometers and modeling with CHN. As blue, green and orange curves represent the mean data of each modality (respectively, WW N+, WW N0, and WD N+), gray curves represent all varieties, showing genetic variability. Using spectroradiometer data, as for other HTP variable acquired with time sequences, the area under the curve (AUC) of the MTCI index was calculated during grain filling (between flowering and maturity stage). Figure  13  shows that there was a positive and strong relationship between this trait and the quantity of nitrogen in the grain. Looking at parameters of curves such as the maximum value, Figure  14  shows a correlation between the maximum height of the plant (at the beginning of grain filling) and yield.