dataesr/scientific-paragraphs-categorization huggingface dataset:
To bridge this gap, this paper proposes the TAO CI ("ceramics" in Chinese) ontology. In compliance to the ethos of reuse recommended by the W3C, the TAO CI ontology relates to existing ontologies and thesauri, such as CIDOC CRM  (Cidoc, 2003) , EDM  (Doerr et al. , 2010) , and AAT  (Soergel, 1995). First, the TAO CI ontology aims to provide an important reference for the publication of other cultural heritage ontologies and to be conducive to more and more Chinese heritage institutions publishing open cultural heritage data and linking them. Second, the theoretical and methodological approach adopted in the construction of the TAO CI ontology is term-andcharacteristic guided, i. e. it adopts the ISO principles on Terminology (ISO 1087 and 704), which define concepts on the basis of their essential characteristics for defining concepts.
Last but not least, this work aims at enriching existing methodologies of building domain ontologies and suggests that taking into account a term-and-characteristic guided approach, makes ontology engineering less dependent on formal languages and description logics as required background. The rest of the paper is organized as follows: Section 2 describes the domain, section 3 introduces the objectives, section 4 is dedicated to the state of the art. Section 5 is the main section dedicated to our contribution to ontology building methodology relying on a morphological analysis of Chinese terms and on the ISO principles on Terminology. Section 6 presents the TAO CI ontology in Protégé, and the last section presents the evaluation of the TAO CI ontology.
STATE OF THE ART The state of the art presented here features work done in an effort to produce interoperable vocabularies for the expression of Cultural Heritage data. It includes W3C languages, semantic data models, thesaurus and ontology resources, and ontology building methodologies. Opening cultural heritage on the web relies on W3C standards such as OWL 12  (Web Ontology Language), a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things, SKOS 13  (Simple Knowledge Organization System), a data model for sharing and linking knowledge organization systems on the Web. SKOS can be used to capture much of the semantics of existing thesaurus of museums and other memory institutions thesauri. Let us also quote DC (Dublin Core), a metadata schema based on 15 essential properties to describe online and physical resources 14.
Our ontology has been linked with AAT in order to provide more information for our terms in the ontology. Kerameikos 16  is a "collaborative project dedicated to defining the intellectual concepts of pottery following the tenets of linked open data and the formulation of an ontology for representing and sharing ceramic data across disparate data systems. " (Gruber & Smith, 2014). Let us also quote Ontoceramic, which is an OWL ontology for ceramics classification  (Cantone et al. , 2015). Lekythos 17  is an another project that aims at representing concepts in the domain of ancient Greek pottery, but having natural language terms in the domain as its starting point. According to  (Ushold, 1998) , "An [explicit] ontology may take a variety of forms, but necessarily it will include a vocabulary of terms and some specification of their meaning (i.
Annotations Annotations allows to enrich the description of the ontology and thus facilitate its understanding and reuse. The RDFS, DC, and SKOS vocabularies are used to express metadata and the linguistic dimension associated to a concept (dc:publisher, dc:license, dc:creator, skos:prefLabel, skos:altLabel, skos:definition, rdfs:comment) as well as to express linking and mapping to external resources (rdfs:seeAlso, skos:broadMatch, skos:exactMatch). For example, the individual arrow vase 001 is described as follows (Figure  4 ).
The second one is language-dependent. It relies on a morphological analysis of Chinese terms whose characters convey a lot of useful semantic information about vases. The implementation of the TAO CI ontology in Protégé raised the problem of how to implement the notion of essential characteristic. Since essential characteristics cannot be directly expressed in Description Logics, we proposed to represent them as classes. Owning an essential characteristic for a concept (class) is then represented as a restriction of an object property whose range is the class associated to the essential characteristic. This means that the concept (class) is a subclass of the anonymous class defined by the restriction. The result is an open RDF/OWL ontology accessible from the web site http://www. dh. ketrc.
Adjusted yield data from historical trials (highly unbalanced design) were used for testing the options of BWGS. On the whole, 760 candidate lines with adjusted phenotypes and genotypes for 47 839 robust SNP were used. With a simple desktop computer, we obtained results which compared with previously published results on wheat genomic selection. As predicted by the theory, factors that are most influencing predictive ability, for a given trait of moderate heritability, are the size of the training population and a minimum number of markers for capturing every QTL information. Missing data up to 40%, if randomly distributed, do not degrade predictive ability once imputed, and up to 80% randomly distributed missing data are still acceptable once imputed with Expectation-Maximization method of package rrBLUP.
It is worth noticing that selecting markers that are most associated to the trait do improve predictive ability, compared with the whole set of markers, but only when marker selection is made on the whole population. When marker selection is made only on the sampled training set, this advantage nearly disappeared, since it was clearly due to overfitting. Few differences are observed between the 15 prediction models with this dataset. Although non-parametric methods that are supposed to capture non-additive effects have slightly better predictive accuracy, differences remain small. Finally, the GEBV from the 15 prediction models are all highly correlated to each other. These results are encouraging for an efficient use of genomic selection in applied breeding programmes and BWGS is a simple and powerful toolbox to apply in breeding programmes or training activities.
g. fastphase  [16] ). Finally, imputed data of (possibly selected) markers are used to predict GEBV using phenotypic observation in a training population. Several methods have been proposed to achieve GEBV prediction. They can be classified into parametric vs semi-parametric methods  [17]. In the R environment  [18] , which is often used in research, several libraries have been specifically developed for genomic selection, such as BGLR  [19]  or rr-BLUP  [20]. However, few of these packages proposes functions to successively achieve the three described steps of marker selection, genotype imputation and model prediction. The objectives of this manuscript are 1) to describe an integrated software (pipeline) which has been developed from open-source R functions available in various R libraries to enable the three steps to be performed easily and 2) to present an application of this software to carry out genomic predictions using historical data from a bread wheat breeding programme.
The BWGS pipeline In the framework of the French flagship programme BreedWheat (www. breedwheat. fr), we developed an integrated pipeline based on R  [18]  called BWGS (BreedWheat Genomic Selection pipeline). BWGS comprises three modules: 1) missing data imputation, 2) dimension reduction, for reducing the number of markers and/or training individuals and 3) Genomic Estimation of Breeding Values (GEBV) with a choice among 15 parametric and non-parametric methods. The pipeline comprises two "main" functions (Fig  1 ) The first function called bwgs. cv is using both genotyping and phenotyping data from a "training" set or reference population to carry on model calibration and cross validation. Data are randomly split into n "folds", and n-1 folds are used for training models and predicting the n th one.
The following options are available: • MNI: missing data are replaced by the mean allele frequency of the given marker. This imputation method is only suited when there are a few missing values, typically in marker data from SNP chips or KasPAR. • EMI: missing data are replaced using an expectation-maximization methods described in function A. mat of R-package rrBLUP  [20]. This algorithm was specially designed by  Poland et al (2012)  for the use of GBS markers, which usually give many missing data which are roughly evenly distributed. However, it does not use physical map position, as do other more sophisticated software (e. g. Beagles,  [22] ). For imputing low density genotyping of a large population to high density available for only a subpopulation, i. e. many markers with many missing data, such software should be used before BWGS.
2. A step of dimension reduction, i. e. reducing the number of markers. This reduction could be necessary to speed up computation on large datasets, depending on computer resources available. The following methods are available • RMR: Random sampling (without replacement) of a subset of markers. To be used with the parameter "reduct. marker. size". • LD (with r2 and MAP): enables "pruning" of markers which are in LD > r2. Only the marker with the least missing values is kept for each pair in LD>r2. To allow faster computation, r2 is estimated chromosome by chromosome, so a MAP file is required with information of marker assignation to chromosomes. The MAP file should contain at least three columns: marker_name, chromosome_name and distance_from_origin (either genetic of physical distance, only used for sorting markers, LD being re-estimated from marker Data).
Equivalent to ridge regression (RR-BLUP) of marker effects. • EGBLUP: performs EG-BLUP, i. e. BLUP using a "squared" relationship matrix to model epistatic 2x2 interactions, as described by  [25]  using the BGLR library • RR: ridge regression, using package glmnet  [26]. In theory, strictly equivalent to GBLUP. • LASSO: Least Absolute Shrinkage and Selection Operator is another penalized regression methods which yield more shrinked estimates than RR. Run by glmnet library. • EN: Elastic Net  [27]  which is a weighted combination of RR and LASSO, using glmnet library Several Bayesian methods, using the BGLR library • BRR: Bayesian ridge regression: same as rr-blup, but Bayesian resolution. Induces homogeneous shrinkage of all markers effects towards zero with Gaussian distribution  [24]. • BL: Bayesian LASSO: uses an exponential prior on marker variances priors, leading to double exponential distribution of marker effects  [28].
• RF: Random forest regression, using randomForest library  [32]. This method uses regression models on tree nodes which are rooted in bootstrapping data. Supposed to be able to capture interactions between markers. • SVM: support vector machine, run by e1071 library. For details, see LIBSVM: a library for Support Vector Machines https://www. csie. ntu. edu. tw/~cjlin/papers/libsvm. pdf • BRNN: Bayesian Regularization for feed-forward Neural Network, with the R-package BRNN  [33]. To keep computing time in reasonable limits, the parameters for the brnn function are neurons = 2 and epochs = 20. The criteria used to estimate model's quality are 1) the Pearson correlation between adjusted phenotype and GEBV, computed over the whole data set (i. e. by merging the GEBV values from the nFolds to correlate with the whole vector of observed phenotypes) and 2) the square-root of the mean-squared error of prediction RMSEP.
These criteria are provided for each replicate (nTimes) as well as mean and standard deviation over replicates. A table summarizes phenotype, estimated breeding value and its standard deviation, as well as the reliatibility for each individual GEBV computed as REL = 1-PEV/σ 2 g, where PEV is the prediction error variance for each individual and σ 2 g the genetic variance when the package used does provide estimate of prediction error variance of individual GEBV, as it is the case for BGLR.
Application to real wheat breeding: Plant materials and phenotypic data A training population was built by gathering historical data from the INRA-Agri-Obtentions breeding programme. To obtain robust phenotypes, we kept data from lines which have been evaluated in multisite trials (> 6 locations), usually for 1-2 years. They were developed from inbred lines crosses, followed by 6-8 generations of self-pollination. Few selection is made during the first 3 generations (i. e. F2 to F4), which are harvested in bulk, then visual selection on simple traits is made on plants or rows at generations F4 and F5. Selected F5 families are multiplied in field plots, which allow a rough evaluation of yield. Then enough seeds are available and selected lines are put into the evaluation network, usually at the F7 generation, when breeding lines are nearly homozygous.
Typically, about 300-400 pair crosses are made each year, 120-150 F7 lines are put into first year multisite trials, then 50-60 are evaluated for a second year, and the very happy few (1-3) are then entering the official registration trials in France, to hopefully become a commercial variety, 10-12 years after the initial crosses. On the whole, our database for field data had 77,176 records on 1715 genotypes, i. e. on average 45 single plot measurements per genotype, spread over 15 years (2000-2014), 10 locations in France and two managements (high vs low inputs). Of course these data are highly unbalanced, most genotypes being evaluated during one or two successive years, (on average 1. 5 years) in 7. 60 environments (site x year) with usually two replicates by management, and most connections in the design are between two successive years, with a few control varieties (usually four in each trial) being evaluated on a longer period to allow a higher connectivity in the dataset.
These figures are for grain yield, the most documented trait. The whole dataset is described in  [34]  and is provided as supplementary Tab 1. To illustrate the use of BWGS, we will concentrate on grain yield in high input management, i. e. an estimate of the genetic and climatic potential in each environment. These highly unbalanced raw data require a pre-treatment step to correct for non-genetic factors as much as possible. The following mixed model was applied to estimate "corrected" genotypic means (BLUE) using the mixed model in R (library lme4): Y = lmer (yield~geno + (1|year � site:trial:block) + (1|year � site � geno)), where � stands for crossed factors (Year � site define environments) and: stands for nested effects, e. g. block within trial within year � site. Where geno being the main genotypic effect (fixed) and all other effects being considered as random effects to be corrected for.
Note that these genotypic "BLUE" are highly correlated (r = 0. 94) with BLUP estimates (i. e. when genotypes are also considered as random effects with identity matrix for modelling covariances). But BLUE are not shrinked, and thus can be more easily used in a second step involving mixed modelling. The same model with geno as random effect was used to estimate variance components and rough estimate of broad sense heritability as σ 2 g / (σ 2 g + σ 2 ge /nenv + σ 2 e /nplot) σ Where σ 2 g is the genotypic variance component, σ 2 ge is the GxE variance component (i. e. year � site � geno) and σ 2 e is the residual variance. nenv is the average number of environments per genotype and nplot the average number of plots per genotype in the dataset used. Out of these "HQ-phenotyped" lines, 760 were also genotyped with the TaBW280K SNP chip  [35]  and used for testing BWGS.
Summary statistics of the breeding population Genotypic data have been used to estimate the additive relationships matrix according to  [37]  with the A. mat function of the R package rrblup. After scaling on a 0-1 scale (1 on diagonal), the values can be regarded as estimates of coancestry coefficients, whose distribution is shown in Fig 2A. Omitting the diagonal, coanscestry coefficients range between 0. 05 and 0. 5, with a median around 0. 18. The heat map of Fig 2B displays no clear-cut structure into distinct groups, although some groups along the diagonal are made of lines that are more related than average. Such absence of structure fully justifies the use of random sampling cross-validation, while a stratified sampling should be used if structure was present. A plot of principal component analysis of Euclidian distance matrix among the 760 lines (Fig 2C, R command cmdscale) again does not show any clear structure according to the year of first evaluation, thereby justifying again the use of random cross-validation over years.
Imputation of missing data The first step of data cleaning with MAF = 5% and maximum marker missing data = 20% led to retain 47,839 markers out of 60,912. Since adjusted Yield BLUE were complete for the 760 genotypes, the final dimension of geno matrix was 760 x 47,839. This SNP dataset contained on average 0. 93% of missing data. Given the small proportion of missing data, no difference in predictive ability was observed between the two imputation methods. To test the efficiency of imputation methods on prediction accuracy, we generated new genotypic matrices with 20%, 40%; 60% and 80% of missing data, randomly distributed in the dataset. Imputation by mean allele frequency took a few seconds, while imputation by the EM algorithm of A. mat took around 100 times longer, whatever the proportion of missing data.
Predictive ability for grain yield from imputed dataset using the two methods is presented in Fig 3. The two methods give similar results for up to 20% missing Data, then imputation by EMI allows higher predictive abilities than imputation by the mean allele frequency. When 80% missing data are randomly generated, the predictive ability of EMI-imputed set is still 0. 418, while it drops to 0. 329 with the MNI-imputed set.
Effects of sampling markers Table  1  shows the number of markers selected by either one-way ANOVA at different pvalue thresholds, or by pruning markers with LD > threshold values from 0. 5 to 0. 98. For ANOVA selection, two strategies have been used, namely 1) GWAS was carried out only once using the whole dataset, i. e. training + validation lines and 2) GWAS was carried out using training lines only, i. e. within each time x fold replicate. Predictive abilities achieved with the selected marker subsets are also shown in Table  1. Results are also displayed in Fig  4. As expected, predictive ability increases with the number of randomly sampled markers, with a steady plateau above around 5000 markers and a best predictive ability of 0. 525. This clearly shows that the extent of LD is large enough in such elite breeding lines to allow most QTLs for yield being captured with 5000-10000 markers, i.
e. on average one every 1. 7 Mbase. This is illustrated by marker selection based on LD-pruning, i. e. removing markers which are in LD > threshold with any other marker. The marker with the least number of missing value is conserved, otherwise the choice is random. The highest value of 0. 544 is obtained with 4462 markers with pairwise LD >0. 8. When marker selection is based on ANOVA carried out on the entire population, there is an optimum predictive ability of 0. 614 achieved with 1820 markers (pvalue = 0. 0001). However, these results were only found when marker selection is made only once by association analysis on the whole dataset, which is then split into a training and a validation set. When marker selection is made within the loop only in the training set, then the predictive ability of the validation set is only slightly improved for N = 2000 and 5000 markers, compared to random sampling, but never exceeds the predictive ability achieved with the whole set of markers.
Effect of different calibration set sampling strategies on predictive ability For this study, we used the whole set of 47,839 markers; which gave a predictive ability of 0. 525 in 10-fold cross validation using the complete dataset of 760 breeding lines, i. e. with a training set of 684 lines and a validation set of 76 lines. Predictive ability was estimated with GBLUP using calibration set of size CS = 50, 100, 200, 300, 400, 500, 600 and 700, validation being.
Discussion Genomic selection programmes are now routinely used in dairy cow breeding, and benefit from huge phenotypic data recorded in past years on milk production of thousands of females, usually related by well-known pedigree relationships (i. e. mother and father known without error). This is however not yet the case in other species like sheep, although effort are being developed in so-called minor species. Therefore, using a relatively cheap genotyping, dairy cow breeders usually have very large population for training GS models, which led to highly accurate predictions. In many animal studies, the oldest animals are used as training set and the youngest as validation set. Contrasting with animal breeding, most plant breeding programmes do not have very large population sizes, although each breeding company manipulates hundreds or thousands of candidates, since companies are usually reluctant to share and merge datasets.
Compared to BWGS, G2P offers much more options for tuning parameters of the numerous functions/libraries called by the two main functions. As a drawback, handling G2P appears more complex, and its complete use requires reading the notices of original libraries, since the notice of G2P does not provide enough details on the possible parameters and options. Moreover, although G2P does content a quality control module, it is not directly integrated into the main function as in BWGS. Comparatively, in BWGS, we have chosen to fix most internal parameters with defaults values, which have been tested to be adapted for medium size datasets as provided in the example (47 K SNP, 760 training lines), while maintaining computing time into reasonable limits for desktop computers. It is therefore easy to use, especially for beginners, and of course parameters can be modified quite easily in the source code to adapt larger datasets.
In our highly unbalanced breeder's dataset, when discarding control lines which were regularly replicated over years, the average number of environment per studied lines is 10. 4, with two replicates by environment (site x year). This led to an estimate of broad sense heritability of 0. 76 and therefore a theoretical upward limit of prediction accuracy of 0. 872. When using random cross validation and the whole set of 47,839 markers, the achieved predictive ability is 0. 525, which would correspond to an accuracy of 0. 603 according to formula in  [21]. However we do not fully trust in this formula, since heritabilities are often poorly estimated. We do prefer keeping predictive ability as a criteria for comparing models and strategies. Our results compare with previously reported predictive ability estimated through random cross-validation, for example 0.
Conclusion The R pipeline we have developed is based on publically available libraries and therefore offers a full freedom to operate. It is easy to handle and allows a wide range of options for missing data imputation, marker or training set selection and prediction methods. Its parameterization was fixed for medium sized datasets to make it easy to use for beginners or teaching. Applying this tool with defaults parameters to a set of elite breeding lines with historical data from yield trials allowed us to obtain similar results to those reported on other wheat populations. The options for subsampling markers and/or training set enabled us to illustrate theoretical expectations (e. g. [61] ). The predictive abilities obtained on this population of limited size are encouraging for the success of genomic selection in applied wheat breeding.