dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomic coverage Description: The Fauna Europaea database contains the scientific names of all living European land and freshwater animal species, including numerous groups at various hierarchical levels, and the most important synonyms. More details about the conceptual background of Fauna Europaea and standards followed are described above. This data paper covers the Diptera-Brachycera content of Fauna Europaea, including 96 families, 11,751 species, 179 subspecies and 2,233 (sub)specific synonyms (see Fig. 4 ). Higher ranks are given below, the species list can be downloaded from the Fauna Europaea portal (see: Data resources).
Acknowledgements Many people have generously shared their expertise and contributed to the checklist by supplying miscellaneous taxonomic and/or faunistic data to one or more of the taxonomic specialists. This input is highly appreciated, and the contributors are here listed in alphabetical order:  S. Andersen  (Copenhagen),  H. Andersson  (Lund),  M. Báez  ( La Laguna ),  V. Beschovski  (Sofia),  R. M. Blackith  (Dublin),  Javier Blasco-Zumeta  (Zaragoza),  R. Contreras-Lichtenberg  (Vienna),  R. Danielsson  (Lund),  M. Dempewolf  (Amsterdam),  J. A. Dils  (Hoevenen),  R. H. L. Disney  (Cambridge),  A. Draber-Mońko  (Warszawa),  P. Dyte  (Datchet),  A. Freidberg  ( Tel Aviv ),  P. Gatt  (Rabat),  D. González-Mora  (Madrid),  I. Grichanov  ( St Petersburg ),  P. Grootaert (Brussels) ,  M. J.
Follow-up support was given by the  EC-FP5   EuroCAT  project ( EVR1- CT-2002-20011 ), by the  EC-FP6   ENBI  project ( EVK2- CT-2002-20020 ), by the  EC-FP6   EDIT  project ( GCE 018340 ), by the  EC-FP7   PESI  project ( RI-223806 ) and by the  EC-FP7   ViBRANT  project ( RI-261532 ). Continuing management and hosting of the Fauna Europaea services was supported by the  University of Amsterdam  ( Zoological Museum Amsterdam ) and  SARA/Vancis. Recently the hosting of  Fauna Europaea  is taken over by the  Museum für Naturkunde in Berlin , supported by the  EC-FP7  EU  BON  project (grant agreement  №308454 ).
tdwg. org/dwc/terms/ parentNameUsageID). Data resources Data package title: Fauna Europaea -Diptera-Brachycera Resource link: http://www. faunaeur. org/Data_papers/FaEu_Diptera-Brachycera_2. 6. 2. zip Alternative identifiers: http://www. faunaeur. org/experts. php?id=92 Number of data sets: 2 Data set name: Fauna Europaea -Diptera-Brachycera version 2. 6. 2 -species Character set: UTF-8 Download URL: http://www. faunaeur. org/Data_papers/FaEu_Diptera- Brachycera_2. 6. 2. zip Data format: CSV Column label datasetName version versionIssued rights rightsHolder accessRights taxonID parentNameUsageID scientificName acceptedNameUsage Character set: UTF-8 Download URL: Brachycera_2. 6. 2. zip Data format: CSV Column label datasetName version The full nameData set name: Fauna Europaea -Diptera-Brachycera version 2.
6. 2 -hierarchy Column description The name identifying the data set from which the record was derived (http:// rs. tdwg. org/dwc/terms/datasetName). Release version of data set. Issue data of data set version. Information about rights held in and over the resource (http://purl. org/dc/terms/ rights). A person or organization owning or managing rights over the resource (http:// purl. org/dc/terms/rightsHolder). Information about who can access the resource or an indication of its security status (http://purl. org/dc/terms/accessRights). An identifier for the set of taxon information (http://rs. tdwg. org/dwc/terms/ taxonID) An identifier for the name usage of the direct parent taxon (in a classification) of the most specific element of the scientificName (http://rs. tdwg. org/dwc/terms/ parentNameUsageID).
The full scientific name, with authorship and date information if known (http:// rs. tdwg. org/dwc/terms/scientificName). http://www. faunaeur. org/Data_papers/FaEu_Diptera-Column description The name identifying the data set from which the record was derived (http:// rs. tdwg. org/dwc/terms/datasetName). Release version of data set. versionIssued Issue data of data set version. Use license: Open Data Commons Attribution LicenseIP rights notes:rights resourceDescription An account of the resource, including a data-paper DOI (http://purl. org/dc/terms/ description).
ACKNOWLEDGEMENTS WR acknowledges the support by the  Excellence Initiative of the German Research Foundation  ( EXC 294 ), by a grant from the  Ministry of Science, Research and the Arts of Baden-Württemberg  (Az:  33-7532. 20 )and by a starting grant of the  European Research Council  ( Programme 'Ideas 'call identifier:  ERC-2011-StG 282105 ). SA is grateful for the support by the  International Max Planck Research School for Molecular and Cellular Biology (IMPRS-MCB ). SZ thanks the  China Scholarship Council (CSC)  for a PhD fellowship.
This allowed closing of most of the original gaps, significantly reducing the number of contigs. The genome sequence of L. plantarum WJL was determined using a 150-bp paired-end library with Illumina MiSeq technology (Illumina, San Diego, CA) at ProfileXpert (Lyon, France). A total of 1,711,744 reads were generated and assembled into 38 contigs using Ray (4), ranging from 560 bp to 1,586,832 bp. Gap closing and resequencing of low-quality regions were conducted by Sanger sequencing to arrive at a high-quality finished genome sequence. Thirty-eight gap-closing primer pairs were designed between contigs using the Primer3 software  (5). The endpoint PCRs were conducted on a Veriti Applied Biosystems thermocycler (Life Technologies, Carlsbad, CA). Twenty-five gaps were closed, leading to a total of 13 contigs.
ACKNOWLEDGMENTS The Illumina sequencing was conducted by ProfileXpert:  Génomique & Microgénomique , Lyon, France. This work was funded by an  ERC  starting grant ( FP7/2007-2013-N°309704 ). The lab of F. L. is sponsored by the  EMBO YIP program , the  ATIP/AVENIR program ,  Fondation FINOVI , and the  Fondation Schlumberger pour l'Education et la Recherche.
Introduction High-throughput technologies, increasingly used in diverse contexts such as brain activity modeling, astronomy, or gene expression analysis, share the common property to generate a huge volume of data, which makes possible the large-scale analysis of complex systems. Such data are generally characterized by their high dimension, as the number of features can reach several thousands, whereas the sample size is usually about some tens. More and more authors also point out their heterogeneity, as the true signal and some confusing factors (uncontrolled and unobserved) are often observed at the same time. For example, in Omics data used for quantitative issues in systems biology, both these factors and the joint contribution of subsets of features to common biological pathways generate a biologically meaningful dependence structure among features.
Alternatively, one can handle the fitting and selection issues at the same time by relaxing the 0 -penalization by the 1 -penalization. This leads to the LASSO estimator β(λ) of logistic regression parameters  (Tibshirani 1996) : β(λ) = argmin β ⎛ ⎝ D(β) + λ m j=1 |β j | ⎞ ⎠ ( 10 ) where the tuning parameter λ is chosen to control the sparsity of the estimator: larger values of λ lead to more zero components in β(λ). The choice of the tuning parameter can be achieved by minimization of the cross-validated residual deviance or misclassification rate, as implemented in the R package glmnet  (Friedman et al. 2010). LASSO is computationally feasible for large m as the optimization problem in (10) is convex. For variable selection or prediction purpose, two-stage procedures such as adaptive LASSO  (Zou 2006 ) can be applied, generally improving the control of the number of false positives, but at the cost of a lack of power.
As mentioned by  Van de Geer (2010) , LASSO makes strong assumptions on the covariance matrix, mainly that correlations between variables are weak. Consequently, a major and still open question remains the application of the procedure while coping with large correlations between variables. Let us illustrate the impact of dependence on the stability of a standard variable selection procedure (LASSO) by a simulation study, comparing the dependent and independent cases. In the dependent case, let us consider a two-group variable Y , taking the value 0 for n 0 = 30 sampling items and 1 for the n 1 = 30 other items. A (n 0 + n 1 ) × m dataset, with m = 500, of normal m-profiles x is generated, with mean μ 0 = 0 for the sampling items with Y = 0, and the components of μ 1 are also zero except for the m 1 = 100 last variables of the profile, for which the mean is δ = 0.
Besides, μ 1 = β to keep the same β as in the dependent case. For each case, 1000 datasets are simulated. The same LASSO selection procedure is implemented using glmnet, where the penalty parameter is selected by minimization of a ten fold cross-validation residual deviance. Histograms of the numbers of selected features in both scenarios of dependence are reproduced in Fig. 2. The rank in |β| of each selected feature is also deduced and the accuracy of the selection is assessed by the mean rank of the subset of selected features. Histograms of these mean ranks statistics are also provided in Fig. 3. The first striking impact of dependence is related to the number of selected features (Fig. 2 ), which is much larger when the features are correlated. Moreover, whereas in the independent case, no erroneous selection of null features is reported in the simulations, in 12.
Consequently, they suggest an ad hoc procedure which consists, for each k-factor model (Ψ k , B k ), to estimate the variance of the number of false positives when the tests are calculated with the k-factor-adjusted residuals: ê -Ẑk Bk. The algorithm described in this section is implemented in the R package FADA available from the R repository CRAN, providing functions for decorrelation, feature selection, and estimation of a classification model. In the following two sections, we illustrate, on real data and by simulations, that this new factor adjustment algorithm improves variable selection, both in terms of classification or prediction performance and reproducibility of the selected variables.
Methods Variable selection is performed using the R package glmnet  (Friedman et al. 2010 ) which provides a function to fit a twogroup logistic regression model via 1 -regularized maximum likelihood  (Tibshirani 1996). The sample being small, the choice of the tuning parameter is done thanks to Leave-One-Out cross-validation. LASSO is known to be non consistent when performed on correlated data  (Bach 2008 ). However, the following example aims to illustrate how a lack of stability can be observed on real data and how factor adjustment can stabilize a usual selection procedure. The procedure is first applied on the complete dataset (with n = 15 observations). The performance of the procedure is evaluated through the number of selected variables and the cross-validation error. Then to illustrate instability of variable selection, the same procedure is applied, removing successively one of the observations.
The aim is to evaluate the sensibility of the procedure to changes in the data. The results of the selection procedure are compared to those obtained on the complete data considering the number of selected variables and the overlap with the subset of variables initially selected using the complete data. Finally, the same procedure is applied on the factoradjusted data. Factor adjustment is performed with the method presented in Sect. 3. 3. The minimization of the variance inflation criterion suggests to keep q = 1 common factor for the complete data and for each incomplete dataset.
Results Selection procedure on the complete dataset The results of the selection procedure on the complete dataset (for raw and factor-adjusted data) are reported in Table  1. The number of features selected by the LASSO procedure is lower when considering the factor-adjusted data. Moreover, the decorrelation step of factor adjustment leads to a better performance of the selection procedure, i. e. a lower prediction error. In the following, I raw (resp. I FA ) denotes the subset of selected features when the selection procedure is applied on the complete raw (resp. complete factor-adjusted) data. Selection procedure on the incomplete datasets The selection procedure is then applied on the 15 sub-datasets, removing successively one of the observations from the complete raw data (resp. complete factor-adjusted data).
Methods According to the simulation study in Sect. 5, shrinkage discriminant analysis (SDA) appears to be the most efficient method regarding the prediction error and the precision of selection. Thus, SDA is conducted on the whole dataset using the R package sda. The results are compared to the following three-step procedure. (1) A decorrelation step is performed on the whole dataset using FADA R package then, (2) to decrease the dimension of the dataset and to avoid high computation time in step (3), a rough selection is performed through standard t tests on decorrelated data and the first 3000 CpG sites are selected for the next step. (3) Variable selection and classification model are finally performed by SDA on the factor-adjusted sub-dataset. Prediction errors are computed through a tenfold cross-validation with 20 repetitions so that the model is estimated on 200 splits of the data.
Results Ten factors are extracted from the whole dataset for factor adjustment at step (1). On the sub-dataset composed of the first 3000 CpG sites, one factor is extracted [step (2)]. Table  3  reports the prediction error and the precision of selection for the two compared procedures. When applied on factoradjusted data, SDA leads to slightly lower prediction error rate than standard SDA but, most importantly, less variables are selected to achieve this precision.
Impact of the dependence design: a simulation study In order to study the performance of factor adjustment for classification and variable selection, we propose a more intensive simulation study. Considering several scenarios of dependence between variables [independence, block dependence, factor structure, and Toeplitz design, in the manner of  Meinshausen and Bühlmann (2010) ], some well-known classification methods are applied on simulated datasets. The stability of original procedures is compared to their factoradjusted versions.
Simulation design Let us datasets simulated according to a multivariate normal distribution, each dataset being composed of m = 1000 variables and n = 30 observations. Besides, let us consider a binary variable Y such that the observations are split into two arbitrary groups of size n 0 = n 1 = n/2. The m-dimensional profiles X are normally distributed with mean μ 0 = 0 m in the first group (Y = 0), where 0 m ∈ R m is the zero vector, and μ 1 in the second group (Y = 1). A subset I of 50 variables is randomly chosen to be group predictive. For these variables, μ 1 has non-zero components: μ 1 j = δ for j ∈ I and μ 1 j = 0 otherwise. The value of δ is set to 0. 55 or 0. 47, which matches with high and moderate signal strength, as introduced by  Donoho and Jin (2008). Thousand datasets are simulated considering each of the four following scenarios for the covariance matrix Σ: (A) The m variables are normally and independently distributed with variance 1 so that Σ is the m-diagonal matrix I m.
Methods The following selection procedures are applied on each simulated dataset: (LASSO) 1 -regularized logistic regression using the R package glmnet  (Friedman et al. 2010) ; (SLDA) Sparse linear discriminant analysis, which is an 1 -penalized LDA using the R package SparseLDA  (Clemmensen et al. 2011) , the stop parameter was set to 10; (SDA) Shrinkage discriminant analysis, which is a James-Stein regularized version of LDA, using the R package sda  (Ahdesmäki and Strimmer 2010). Note that SDA consists finally in a correlation adjustment of the scores used for feature selection in DDA; (DDA) Shrinkage diagonal discriminant analysis, which assumes within-group independence among features, using the R package sda  (Ahdesmäki and Strimmer 2010). Estimation of the DDA model is here regularized using a ridge approach.
Several cutoffs are implemented in the R package sda to conduct DDA and SDA such as the False Non-Discovery Rate (FNDR) or Higher Criticism  (Donoho and Jin 2008). Both lead to similar results in this simulation study and the results reported here concern the FNDR cutoff. Each procedure is applied both on raw data and on factoradjusted data, using the decorrelation method presented in Sect. Prediction errors are calculated on an independent balanced test dataset consisting of 10,000 sampling items, generated according to each structure of dependence. Performances of methods are assessed by calculating, for each simulated dataset, the prediction error calculated on the test dataset, the number of selected features, and the proportion of truly selected variables (or positive predictive value, reported hereafter as "precision").
3. 3: for each simulated dataset, covariance parameters Ψ and B and latent factors Z are estimated on training datasets and factor-adjusted training data (decorrelation step) are computed using formula x -Bz introduced in expression (14). Estimates Ψ and B are used to estimate latent factors of testing data and factor-adjusted testing data are computed in the same way. Classification methods are finally trained on decorrelated training samples and assessed on decorrelated testing sample.
Transcriptional profiles of 3 0 RR-deficient B cells. Transcriptional profiles evidenced differences between splenic B cells of 3 0 RR-deficient and wt mice (Supplementary Table  3 ). Several genes implicated in interferon-related functions, inflammation, cell trafficking and B-cell homing were affected in resting B splenocytes of 3 0 RR-deficient mice (accession number GSE45230, Gene Expression Omnibus database). Since the cis-regulatory element 3 0 RR is not expected to modulate genes outside the IgH locus, these differences rather involve the recently reported decreased accumulation of marginal zone B cells in 3 0 RR-deficient mice  32. Accordingly, functions of the affected genes fit well with roles of marginal zone B cells in infection, inflammation and immunity. After 3 days LPS þ IL4 stimulation, some genes were differentially affected (Supplementary Table  4 ).
Acknowledgements This work was supported by grants from  Conseil Regional du Limousin, Association pour la Recherche sur le Cancer  ( SL 220100601332  and  PJA 20141201649 ),  ANR  (Projets Blanc 2011),  Ligue Contre le Cancer  ( comitede la Correze ), and ' Lions Club de la Corre  `ze, Zone 33 district 103 Sud'. Epigenetic experiments were supported by a specific grant from  INCa-Cance  ´ropole GSO (2014). M. Cogneis supported  Institut Universitaire de France  and  Fondation pour la Recherche Medicale. P. Rouaud  had a fellowship from  ARC  ( DOC20130606964 ). We acknowledge the technological expertise of the  Nice Sophia-Antipolis Functional Genomics Platform , supported by  MICROENVIMET ,  FP7-HEALTHF2-2008-201279 , the  ARC , and the  INCa. We thank  Dr P. Barbry  for helpful discussions and support during this work.
The are been corrected for the estimated background contributions, shown as gray area. The predictions from DJANGOH and RAPGAP are weighted to achieve good agreement with the data. The ratio of data to prediction is shown at the bottom of each figure tions dealt with multijet production and the determination of the strong coupling constant α s (M Z ) at low Q 2  [10]  and at high Q 2  [11]. In this paper double-differential measurements are presented of absolute and normalised inclusive jet, dijet and trijet cross sections in the Breit frame. Two different jet algorithms, the k T  [12]  and the anti-k T  [13]  algorithm, are explored. The cross sections are measured as a function of Q 2 and the trans-verse jet momentum P jet T for the case of inclusive jets. Dijet and trijet cross sections are measured as a function of Q 2 and the average jet transverse momentum.
Experimental method The data sample was collected with the H1 detector at HERA in the years 2003 to 2007 when HERA collided electrons or positrons  2  of energy E e = 27. 6  GeV with protons of energy E p = 920 GeV, providing a centre-of-mass energy of √ s = 319 GeV. The data sample used in this analysis corresponds to an integrated luminosity of 351 pb -1 , of which 160 pb -1 were recorded in e -p collisions and 191 pb -1 in e + p collisions.
H1 Fig. 12  Ratio of NLO predictions with various PDF sets to predictions using the MSTW2008 PDF set as a function of Q 2 and P T. For comparison, the data points are displayed together with their statistical uncertainty, which are often outside of the displayed range in this enlarged presentation. All PDF sets used are determined at NLO and with a value of α s (M Z ) = 0. 118. The shaded bands show the PDF uncertainties of the NLO calculations obtained from the MSTW2008 eigenvector set at a confidence level of 68 % ment phase space for which the results are quoted. Extending the event selection to a larger phase space helps to quantify migrations at the phase space boundaries, thereby improving the precision of the measurement. The actual measurement is performed in the NC DIS phase space given by 150 < Q 2 < 15 000 GeV 2 and 0.
Unfolding The jet data are corrected for detector effects using a regularised unfolding method which is described in the following. The matrix based unfolding method as implemented in the TUnfold package  [52]  is employed. A detector response matrix is constructed for the unfolding of the neutral cur-rent DIS, the inclusive jet, the dijet and the trijet measurements simultaneously  [53]. The unfolding takes into account the statistical correlations between these measurements as well as the statistical correlations of several jets originating from a single event. The corrections for QED radiation are included in the unfolding procedure. Jet cross sections and normalised jet cross sections at hadron level are determined using this method. The hadron level refers to all stable particles in an event with a proper lifetime larger than cτ > 10 mm.
Experimental uncertainties Statistical and other experimental uncertainties are propagated by analytical linear error propagation through the unfolding process  [52]. Systematic uncertainties are estimated by varying the measurement of a given quantity within the experimental uncertainties in simulated events. For each 'up' and 'down' variation, for each source of uncertainty, a new migration matrix is obtained. The difference of these matrices with respect to the nominal unfolding matrix is propagated through the unfolding process  [52]  to obtain the size of the uncertainty on the cross sections. To avoid fluctuations of the systematic uncertainties caused by limited number of data events, in most cases uncertainties are obtained by unfolding simulated data. The following sources of systematic uncertainties are taken into account: • The uncertainty of the energy scale of the HFS is subdivided into two components related to the two-stage calibration procedure described in Sect.
Electroweak corrections Only virtual corrections for γ -exchange via the running of α em (μ r ) are included in the pQCD calculations. The electroweak corrections c ew account for the contributions from γ Z -interference and Z -exchange. They are estimated using the LEPTO event generator, where cross sections can be calculated including these effects (σ γ,Z ) and excluding them (σ γ ). The electroweak correction factor c ew is defined for each bin i by c ew i = σ γ,Z i /σ γ i. It is close to unity at low Q 2 and becomes relevant for Q 2 → M 2 Z , i. e. mainly in the highest Q 2 bin from 5000 < Q 2 < 15 000 GeV 2. In this bin the value of c ew is around 1. 1 for the luminosity-weighted sum of e + p and e -p data corresponding to the full HERA-II dataset. The electroweak correction has some P T -dependence, which, however, turns out to be negligible for the recorded mixture of e + p and e -p data.