dataesr/scientific-paragraphs-categorization huggingface dataset:
The ecopathlite function called by the createensemble function is the one that reproduces the main calculations performed by the Ecopath module of the EwE software (Fig. 1 ). This function is a 'stripped-down' version of the Ecopath algorithms allowing an estimation of missing parameters by solving the system of n equations with n unknowns (see equations (  1 ) and (  2 )). Users can also choose whether they want ensemble members that respect the biomass.
Calculation of an ensemble of values for ENA indices Finally, the indices function was developed in this present work to calculate a set of 13 ENA indices (Fig. 1 ; Table  2 ) for each ensemble member generated by the createensemble function. The mathematical formulas for these indices required a harmonization between the EwE and LIM ecosystem modelling communities. We compared the formulas in use in EwE with those currently in use by modelers working with linear inverse models (LIMs) in Matlab  (Leguerrier et al. , 2007; Johnson et al. , 2009; Niquil et al. , 2011; Saint-Béat et al. , 2013)  (Table  2 ). Most formulas were shared in common between both communities and were as such already available in Matlab. Ecological interpretations of ENA indices are summarized in Table  2. Full details regarding their links with ecosystem ecology theories can be found, for instance, in  Ulanowicz (2004) ,  Kones et al.
Description of the Bay of Biscay Ecopath model A full description of the Bay of Biscay Ecopath parameterization can be found in  Lassalle et al. (2011). The model considered for this zone was restricted to the central part of the shelf between Author-produced version of the article published in  Ecological modelling, 2015, 313, 23-40. The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. ecolmodel. 2015. 05. 036 ©. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ the 30-m and 150-m isobaths with a surface area of 102,585 km 2 (Fig. 2 ). The model represented a typical year between 1994 and 2005, i. e. before the collapse of the European anchovy (Engraulis encrasicolus) and the subsequent five-year closure of the fishery for this species.
The Bay of Biscay Ecopath ensemble and ENA ensemble The ENAtool routine was used to generate 1000 balanced ensemble members based on the uncertainty values assigned to each input parameter according to Pedigree scores (Table  1 )  (Lassalle et al. , 2014) ; for this particular food web, the search for 1000 balanced ensemble members took between three and five days to run on a single-processor machine. For each ENA index listed in Table  2 , the single value obtained with the EwE software was graphically compared to the 1000 values derived from the ENAtool routine as to whether it falls between the boxplot whiskers. Then, the coefficient of variation between the mean value and the single Ecopath estimate was calculated. The 'balance' constraint can move the parameter distribution of the balanced ensemble members away from the initial sampling distribution.
It could make a crucial difference as to whether the ensemble experiment applied to the Bay of Biscay is simply adding error bars onto the input to the ENA index equations, or if it is adding error bars and shifting the mean/median value of the inputs variables. As such, an additional 1000-member ensemble based on the Bay of Biscay input dataset and Pedigree scores was generated, with keeping both balanced and unbalanced members. Author-produced version of the article published in  Ecological modelling, 2015, 313, 23-40. The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. ecolmodel. 2015. 05. 036 ©. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ Then, the ensemble mean parameter values of these two ensembles were statistically compared using two-sample Kolmogorov-Smirnov goodness-of-fit tests (alpha = 0.
Results First, based on the exploratory statistical comparisons of the parameter distributions between the balanced ensemble and the mixed ensemble (i. e. balanced and unbalanced), 52 of the basic estimates parameters shifted mean and 169 of the non-zero diet components shifted too. Author-produced version of the article published in Ecological modelling, 2015, 313, 23-40. The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. ecolmodel. 2015. 05. 036 ©. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ For the pre-existing Ecopath model of the Bay of Biscay continental shelf, the value derived from the EwE software for each ENA index was compared to the range of values obtained following the application of the ENAtool routine to this model with a nset of 1000 and levels of uncertainty in accordance with Pedigree scores (Table  3 ).
Discussion The present work provides EwE modellers, and more broadly ecosystem ecologists, with a routine that generates distributions of values for a set of well-known indices synthesizing structural and functional properties of ecosystems by taking into account uncertainty in model input parameters. In the first place, reanalyzing the Bay of Biscay continental shelf food web in the light of the most probable estimates of uncertainty around input parameters for this ecosystem supported the main ENA-derived ecological conclusions. Indeed, ENA index distributions all encompassed the single ENA values derived from the EwE software with mean values in the same range as the initial Ecopath estimates (Table  3 ). The Bay of Biscay ensemble approach as such supported and strengthened the main conclusion of a detritus-based, and relatively mature Author-produced version of the article published in  Ecological modelling, 2015, 313, 23-40.
The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. ecolmodel. 2015. 05. 036 ©. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ with known uncertainties on parameters. If no Pedigree scores were filled for the EwE model, model builders or experts of the study area should be interviewed regarding the quality of data used during model construction. This was even more strongly suggested for field biomasses (B) and diet compositions (DC) that appeared as the most influential input parameters (Table  3 ). This last result can be also interpreted as an uncertainty analysis, showing that less constrained biomasses and diet compositions in input matrices both had a marked influence on ecosystemlevel EwE model outputs such as ENA indices.
The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. ecolmodel. 2015. 05. 036 ©. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ based on fluxes of matters and energy, and trophic networks at the scale of the ecosystem was recently listed as a critical way to improve the implementation of European policies  (Reyjol et al. , 2014). In this scope, by using the ENAtool routine and by applying variations more specifically to the diet compositions, the robustness of this relationship 'SOI/anthropogenic impacts' is planned to be statistically tested in an upcoming comparative study before presentation as a potential indicator of "Good Environmental Status". Attention will have to be paid to the topology and the degree of aggregation among functional groups in the compared models as these two factors were demonstrated to influence ENA values  (Johnson et al.
Conclusion Author-produced version of the article published in  Ecological modelling, 2015, 313, 23-40   Kearney (2012)  are given in italics. In agreement with the developer, some modifications were made to these functions to enhance their applicability to all operating systems and to all EwE model versions. These modifications were specified in the name of the function by "_mod". The functions that were specifically built for the present work were marked in bold. The origins of formulas used in the indices functions are listed in Table  2. 2. Author-produced version of the article published in  Ecological modelling, 2015, 313, 23-40. The original publication is available at http://www. sciencedirect. com/ doi : 10. 1016/j. ecolmodel. 2015. 05. 036 ©. This manuscript version is made available under the CC-BY-NC-ND 4.
0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ Graphics are organized following the order of Table  2. 3 )). Values were derived from pre-defined tables provided by  Christensen et al. (2005)  associating a Pedigree score to each given level of uncertainty for each basic input parameter. Blank cells correspond to parameters left to be estimated by the model, where the parameter did not apply (e. g. Q/B for primary producers), or where the EwE software did not allow setting Pedigree scores (e. g. P/B of primary producers). To run the ENAtool routine, blank cells were replaced by zeros. presented for EwE software v. 6 as well as for the linear inverse modelling approach. For each ENA index, its single value calculated using the EwE model of the Bay of Biscay continental shelf of  Lassalle et al.
The generalized intra-model ensemble routine: ENAtool In keeping with our goal to provide a single user-friendly tool for ENA index ensemble generation, we have packaged together a master Matlab script (ENAtool. m) and two data input templates, all of which are available via the Supplementary Materials. The ENAtool. m script grew out of, and now incorporates several sub-functions from, the Matlab implementation of Ecopath (Kearney, 2015; DOI:10. 5281/zenodo. 17837), with additional routines added to calculate ENA indices from the resulting model ensemble. The key calculations performed by this tool are as follows. All the Matlab functions called during the ENAtool routine operate only on Ecopath data.
Acknowledgments This research has been mainly supported by the project  ANTROPOSEINE (ANalysis  of the TROPhic structure and cOntribution of habitats from the  SEINE River estuary)  financed by the  GIP-Seine Aval  (http://seine-aval. crihan. fr/web/). It was also supported by the project  DEVOTES (DEVelopment Of innovative Tools for understanding marine biodiversity and assessing good Environmental Status ) funded by the  European Union  under the 7th  Framework Programme, 'The Ocean for Tomorrow' Theme  (grant agreement no. 308392 ; www. devotes-project. eu). The authors wanted to thank the two anonymous reviewers for their constructive remarks on the manuscript.
2. 2 2 The generalized intra-model ensemble routine: ENAtool In keeping with our goal to provide a single user-friendly tool for ENA index ensemble generation, we have packaged together a master Matlab script (ENAtool. m) and two data input templates, all of which are available via the Supplementary Materials. The ENAtool. m script grew out of, and now incorporates several sub-functions from, the Matlab implementation of Ecopath (Kearney, 2015; DOI:10. 5281/zenodo. 17837), with additional routines added to calculate ENA indices from the resulting model ensemble. The key calculations performed by this tool are as follows. All the Matlab functions called during the ENAtool routine operate only on Ecopath data.
Mixmod is a well-established software package for fitting a mixture model of multivariate Gaussian or multinomial probability distribution functions to a given data set with either a clustering, a density estimation or a discriminant analysis purpose. The Rmixmod S4 package provides a bridge between the C++ core library of Mixmod (mix-modLib) and the R statistical computing environment. In this article, we give an overview of the model-based clustering and classification methods, and we show how the R package Rmixmod can be used for clustering and discriminant analysis.
The Mixmod package  (Mixmod Team 2008)  is primarily devoted to clustering using mixture modeling and, to a lesser extent, to discriminant analysis (supervised and semi-supervised situations). Many options are available to specify the models and the strategy to be run. Mixmod is dealing with 28 multivariate Gaussian mixture models for quantitative data and 10 multivariate multinomial mixture models for qualitative data. Estimation of the mixture parameters is performed via the EM, the Stochastic EM or the Classification EM algorithms. These three algorithms can be chained and initialized in several different ways leading to original strategies (see Section 2. 3). The model selection criteria BIC, ICL, NEC and crossvalidation are proposed according to the modeling purpose (see Section 2. 4 for a brief review).
Mixmod, developped since 2001, is a package written in C++. Its core library (mixmodLib) can be interfaced with any other softwares or libraries, or can be used in command line. It has been already interfaced with Scilab and Matlab  (Biernacki et al. 2006). It was lacking an interface with R (R Development Core Team 2012). The Rmixmod package provides a bridge between the C++ core library of Mixmod and the R statistical computing environment. Both cluster analysis and discriminant analysis can be now performed using Rmixmod. Userfriendly outputs and graphs allow for a relevant and nice visualisation of the results. There exists a wide variety of packages in R dedicated to the estimation of mixture model. Among them let us cite bgmm  (Biecek et al. 2012) , flexmix  (Leisch 2004; Grün and Leisch 2007, 2008) , mclust  (Fraley and Raftery 2007a,b) , mixtools  (Benaglia et al.
2009 ) but none of them offer the large set of possibilities of the newcomer Rmixmod. This paper reviews in Section 2 the Gaussian and multinomial mixture models and the Mixmod library. An overview of the Rmixmod package is then given in Section 3 through a description of the main function and of other related companion functions. The practical use of this new package is illustrated in Section 4 on toy datasets for model-based clustering in a quantitative and qualitative setting (Section 4. 1) and for discriminant analysis (Section 4. 2). The last section (Section 5) evokes future works of the Mixmod project. The package is available from the Comprehensive R Archive Network at http://cran. r-project. org/package= Rmixmod. x i is an individual in X 1 ×. × X d. The space X j (j = 1,. , d) depends on the type of data at hand: It is R for continuous data and it is {1,.
Supervised classification In discriminant analysis, data are composed by n observations x = {x 1 ,. , x n } (x i ∈ X 1 ×. × X d ) and a partition of x into K groups defined with the labels z. The aim is to estimate the group z n+1 of any new individual x n+1 of X 1 ×. × X d with unknown label. Discriminant analysis in Mixmod is divided into two steps. The first step consists of a classification rule from the training data set (x, z). The second step consists of assigning the other observations to one of the groups. Semi-supervised classification Usually all the labels z i are completely unknown (unsupervised classification) or completely known (supervised classification). Nevertheless, partial labeling of data is possible, and it leads to the so-called semi-supervised classification. The Mixmod library handles situations where the data set x is divided into two subsets x = (x , x u ) where x = {x 1 ,.
Supervised classification In the supervised setting, note that only the model (not the number of mixture components) has to be selected. Two criteria are proposed in this situation: BIC and cross-validation. For BIC, the completed log-likelihood (10), where z is fixed to its known value, has to be used. The cross-validation criterion (CV) is valid only in the discriminant analysis (supervised) context. The model leading to the highest CV criterion value is selected. Cross validation is a resampling method which can be summarized as follows: Consider random splits of the whole data set (x, z) into V independent datasets (x, z) (1) ,. , (x, z) (V ) of approximatively equal sizes n 1 ,. , n V. (If n/V is an integer h, we have n 1 =. = n V = h. ) The CV criterion is then defined by CV = 1 n V v=1 i∈Iv δ(ẑ i ( θ(v) ), z i ) ( 16 ) where I v denotes the indexes i of data included in (x, z) (v) , δ corresponds to the 0-1 cost and ẑi ( θ(v) ) denotes the group to which x i is assigned when designing the assignment rule from the entire data set (x, z) without (x, z) (v).
Mixmod library implementation and related packages The Mixmod library The Mixmod core library (mixmodLib) is the main product of the Mixmod software package. Developped since 2001, it has been downloaded from the Mixmod web site http://www. mixmod. org about 300 per year. Distributed under GNU GPL license, mixmodLib has been enhanced and improved for years  (Biernacki et al. 2006 ). An important work has been done to improve performance of the mixmodLib which can today treat very large data sets quickly with accuracy and robustness. Currently, some arbitrarily large "hard" limits for the sample size and for the variable number are respectively fixed to 1 000 000 and 10 000. It is possible to change them but it requires to recompile the source code. The user must also be aware that to reach these limits in practice will essentially depend on its computing resources.
It contains about 80 C++ classes and can be used in command line or can be interfaced with any other software or library (in accordance with the terms of the GNU GPL license). Some of these C++ classes (top level classes) have been created to interface easily mixmodLib. Clustering can be performed with the top level XEMClusteringMain class (using XEMClus-teringInput and XEMClusteringOutput classes) and Discriminant Analysis with the XEM-LearnMain class (using XEMLearnInput and XEMLearnOutput classes) for the first step and the XEMPredictMain class (using XEMPredcitInput and XEMpredictOutput classes) for the second step (prediction). The Rmixmod package uses also the Rcpp package  (Eddelbuettel and François 2011)   "BIC", "ICL", "NEC", c("BIC", "ICL", "NEC"). Default is "BIC". weight Numeric vector with n (number of individuals) rows.
Non-graphical functions The show, the print and the summary methods have been redefined for the Rmixmod S4 classes Strategy, Model, GaussianParameter, MultinomialParameter, MixmodResults, MixmodCluster, MixmodLearn and MixmodPredict. The Rmixmod package provides two other utility functions: 1. nbFactorFromData(): Allow to get the number of levels of each column of a data set; 2. sortbyCriterion(): After calling the mixmodCluster() or mixmodLearn() method, results will be sorted into ascending order according to the first given criterion (descending order for CV criterion). This method is able to reorder the list of results according to a given criterion. The input parameters are • object: A Mixmod object; • criterion: A string containing the criterion name. Most of these functions will be illustrated in Section 4.
Continuous variables: Geyser dataset The outputs and graphs of clustering with Rmixmod are illustrated on the well-known geyser dataset  (Azzalini and Bowman 1990). It is a data frame containing 272 observations from the Old Faithful Geyser in the Yellowstone National Park taken from the Modern Applied Statistics in S library  (Venables and Ripley, 2002). Each observation consists of two measurements: The duration (in minutes) of the eruption and the waiting time (in minutes) to the next eruption. In this example we ignore the partition and we want to estimate the best Gaussian mixture model fitting the data set. The following code provides a way to do it by running a cluster analysis with a list of clusters (from 2 to 8 clusters), all the Gaussian models, the BIC, ICL and NEC model selection criteria, and strategy2 defined in Section 3.
Further works The Rmixmod package interfaces almost every functionality of the Mixmod library. Some particular initializations strategies and models to deal with high-dimensional data have not been implemented in the package. But initialization strategies of most interest are available in Rmixmod and the package HDclassif  (Bergé et al. 2012)  has been recently released to the clustering and the discriminant analysis of high-dimensional data. We have proposed some tools to visualize outcomes but data visualization in Rmixmod can be enhanced. In addition, supervised and semi-supervised classification currently implemented could be greatly improved by including a variable selection procedure for instance (see  Maugis et al. 2011). Moreover, we encourage users to contribute by suggesting new graphics or other utility functions.
: system expert connect to its own schema/database instance to access the appropriate tables and manage its conditions Conditions Metadata in COOL The conditions data are accessed by reconstruction programs by means of a set of metadata: Global Tag : a string identifying a set of leaf tags (this string is the same in every Det-System/DBinstance) Node : a string identifying a DataSet type Tag : a string identifying a set of IOVs (Interval Of Validity) for a given DataSet type IOVs : time related parameters identifying the validity in time for a given payload inside a DataSet type JEE RESTful services 1) Access to multiple data sources. 2) Web services implemented via REST ((REpresentational State Transfer) ✤ provide client language independent services 3) PL/SQL performance faster than COOL API methods ✤ provides tools for quick comparison of COMA and COOL content ✤ enables faster synchronisation from COOL to COMA 4) Provides methods to clients not available via the COOL API 5) Monitoring functionalities implemented as scheduled tasks ✤ IOV coverage for system tags ✤ Detect missing Condition information in run ranges ✤ Compute total number of IOVs per tag A.
Formica 1 , E. Gallas 2 on behalf of ATLAS collaboration metadata retrieval are possible only via the COOL API, so retrieving information from several schemas requires an equivalent number of independent connections to the database META-DATA extraction in unique set of tables (COMA DB) Conditions meta-data (with the exception of IOVs and payload data) have been extracted and merged into an independent well normalised relational schema: The COMA Database includes all ATLAS Conditions Database Node and Tag metadata (along with other operations metadata) which provides information from several schemas (and systems) in the same query.
Statistical considerations It is not simple to process datasets from microarray analyses due to the multiple levels of analysis and interpretation, from pre-treatment of the rough data to the filtering of statistically significant hits at the gene and gene-set levels. The different methods proposed and their different parameterisations can be combined into millions of analysis workflows that lead to different lists of significantly detected genes. While this technology offers great potential for generating data, several limitations should be considered during analysis of that data downstream, some of which are common to all fields of investigation and others that are more specific to surveys conducted on fishes. First of all, as one statistical test is performed for each gene, thousands of tests are performed on a single array, which generates a very large number of false positive and false negative DEGs.