dataesr/scientific-paragraphs-categorization huggingface dataset:
STATISTICS The two first sessions of the OCaml MOOC started respectively in October 2015 and September 2016. Both lasted for seven weeks, numbered from 0 to 6, and the final date for the submission of the project was set at the end of each year. A final note was computed by assigning each week from 1 to 6 a maximum score of 0. 1 points, and a maximum of 0. 4 points to the project. To obtain the certificate, and pass the course, a note greater or equal to 0. 6 was required. The amount of work required from the students was high. There were 48 exercises for the main six weeks of the course, ranging from warm ups to advanced higher order functional manipulations of algebraic data structures, including trees and lists. For the final project, a choice was offered between two subjects: writing a solver for the popular Klotski puzzle, or building Markov chains from a text corpus for generating amusing text fragments.
RELATED WORK There has been extensive work on teaching programming using different programming languages, tooling and approaches, and it would be impossible to survey here all the related literature, so we focus here on related work that is relevant to teaching functional programming, in particular in a massive online course. The most famous and successful MOOC introducing functional programming is without doubt the one run in Scala by Martin Odersky  [Miller et al. 2014   Testing and grading the user programs locally inside the learner's browser is to the best of our knowledge entirely new, as is the library that allows easy and safe writing of test cases. The combination of these three innovations has allowed the learners that sticked to the course to obtain an outstanding level of proficiency, with 588 out of the 2418 active learners going through all the exercises and the final programming projects, and about 300 getting 100% of the final score.
SOURCE CODE AVAILABILITY Figure  23  shows the number of OCaml lines of code written for supporting the full OCaml MOOC. The source code of the whole exercices platform developed for the MOOC, containing the enhanced code editor based on ACE, the toplevel, and the automatic grader has been released as Open Source and a standalone demo in which a few exercises can be tried is available at https://try. ocamlpro. com/fun-demo/.
ACKNOWLEDGEMENTS The OCaml MOOC would not have been so successful without the collaborative effort of many people:  Ralf Treinen  and  Yann-Regis Gianas  spent countless hours working with the second author on the content and the videos of the course;  Çagdas Bozman  contributed significantly to the special version of TryOCaml used in the exercise environment; a team of volunteers at  Irill  and  LIP6 , with special thanks to  Emmanuel Chailloux , produced the english subtitles, and  Jean-Francois Monin  their french translation. Special thanks go to  Avner Bar-Hen , that has already run twice a MOOC on statistics: his guidance and advice have been precious in the statistical analysis of the data collected during the OCaml MOOC.
Pollock, Morris & Vesk (2012)  and  Jamil et al. (2013)  moved away from 'least-squares after data transformation' by using generalized linear mixed models (GLMM) for count or presence-absence data. Brown et al. (2014)  and  Warton, Shipley & Hastie (2015)  developed the same model under a simpler GLM framework without the extra random terms for species, and complemented it with resampling techniques, namely cross-validation for selection and bootstrapping of sites for statistical testing. Their method is thus essentially a community-level analysis in that the site (community) is the statistical unit on which the statistical inference is based. Their method of statistical testing is implemented via the functions 'traitglm' and 'anova. traitglm' in the R package 'mvabund'  (Wang et al. , 2012).
In detail, the four tests are: 1. anova. traitglm. The first test uses the 'anova. traitglm' function of version 3. 11. 5 of the R package mvabund  (Wang et al. , 2012)  with site-based resampling and calculation of the LR assuming a negative-binomial distribution (as in the data-generating models of 'Statistical models for trait-environment interaction' and 'Simulation models'). This function resamples sites by bootstrapping probability integral transform (PIT) residuals with all residuals across species of the same site kept together. The code for obtaining the significance of the interaction is simply: model1 <-traitglm(Y,E,T, composition=TRUE) anova. traitglm(model1, nBoot=nBoot) where Y, E and T contain the abundance data y ij , the environmental values e = e i and the trait values t = t j , respectively.
Brown et al. (2014)  used a site-based cross-validation approach, treating species as fixed factors. To evaluate this approach, we simulated 1000 data sets as in the 'trait random' case of the Gaussian model with n = m = 30 and 10 traits and a single environmental variable. We applied the function traitglm of the R package mvabund with arguments method = ''cv. glm1path'' and composition = TRUE, then counted the number of simulated data sets in which the best model, i. e. , that gave the ''best predictive performance''  (Wang et al. , 2012) , contained any non-zero trait-environment coefficients. As a control experiment, we analyzed data from a model without any interaction of a latent trait with the observed variables. We also applied species-based cross-validation to the same data sets to determine whether this led to similar results.
Case study As an illustration, we re-analysed the data of  Choler (2005)  who investigated the shift in Alpine plant traits along a snow-melt gradient. This is the aravo data set in the R package ade4  (Dray & Dufour, 2007). It has 82 species and 75 sites. Here we considered only one trait and one environmental variable, namely Spread (maximum lateral spread of clonal plants) and Snow (mean snowmelt date in Julian day averaged over 1997-1999). Because the distribution of Spread is right-skewed, it was logarithmically transformed before analysis. First, we performed statistical tests based on the squared fourth-corner correlation, using the R package ade4  (Dray & Dufour, 2007). Second, we performed regression-based analyses as described in  Brown et al. (2014)  and  Warton, Shipley & Hastie (2015).
In such analyses, the abundance of the species is the response variable for which we must specify a distribution. We used the default distribution of the R package mvabund  (Wang et al. , 2012) , the negative-binomial distribution, and checked whether this distribution is tenable for these data. Following  Warton, Shipley & Hastie (2015) , we did this by plotting the Dunn-Smith residuals of the model 'site+species+species:Snow' against the fitted values. The plot (Fig. S1 ) does not show any clear pattern that would suggest an ill-specified mean-variance relation. We therefore continued analysis with this default distribution. We then applied the anova. traitglm test and the other three tests in 'Statistical tests on trait-environment interaction. ' We used 999 resamples in each test. Third, as an alternative to statistical testing, we applied predictive modelling as in 'Predictive modelling by lasso.
DISCUSSION Our simulations showed that resampling of sites is simply not sufficient to generate a valid statistical test on trait-environment association under a GLM framework. This holds true not only for advanced bootstrap methods, such as is implemented in the anova. traitglm function of the R package mvabund  (Wang et al. , 2012) , but also for permutation tests using either GLM (Fig. 1 ) or simple methods such as CWM and fourth-corner (Peres-Neto,  Dray & ter Braak, 2016). The reason is that sites are not the only random statistical units; species are random units as well  (Ives & Helmus, 2011) , particularly when we want to determine which traits interact with particular environmental features in conditioning species distributions. Species and sites form two crossed random factors and both need to be considered in models.
) (Fig. 1, grey lines) up to values around 0. 50. The sites test results in similar p-values. The species test results in much lower p-values than the sites test when b tx = 0, but similar p-values if b tx = b ze (solid and dotted blue lines in Fig. 1, respectively). The latter is expected as, when b tx = b ze , the model is perfectly symmetric in species and sites. The max r/c test, which takes the maximum of the p-values per simulated dataset, results in p-values around or below 0. 05 if b tx = 0 but has a slightly inflated type I error rate if b tx = b ze > 0. 4 (solid and dotted black lines in Fig.
Here we characterized the variation in mammalian gut microbiome compositions in light of host diet and phylogeny. We are defining diet with a coarse granularity, using nine large dietary categories from the EltonTraits database  23. A percentage for each species is assigned to each of the nine categories (see 'Host phylogeny and dietary data' in the Methods and Supplementary Table  1 ). Hence, this study focuses on the impact that large dietary shifts had on microbiomes at the scale of mammalian evolution. Furthermore, we employ 'host phylogeny' as a composite term that encompasses all traits that change roughly clock-like along the phylogeny of hosts and that might influence microbiome compositions, such as genetic or immunological factors  24. We hypothesized that major changes in diet and host phylogeny may have driven vertical and horizontal inheritance of bacterial lineages at different bacterial phylogenetic scales  6, 25.
Host phylogenetic distances between our 33 mammals were deduced from a time-calibrated ultrametric phylogenetic tree of mammals  30  , which was recently updated  31, 32. Diet distances were deduced from EltonTraits  23  , a database that compiles dietary attributes for all mammalian species. Phylogeny and diet shape microbiomes at different scales. Consistent with our hypothesis, BDTT was able to disentangle the effects that the major dietary shifts had on gut microbiome compositions from those of host phylogeny. This was possible because host phylogeny shapes gut microbiome compositions mainly near the leaves of the bacterial tree (Mantel test; R 2 ¼ 0. 38 at 100 Myr ago, P valueo0. 001; R 2 ¼ 0. 03 at 1,000 Myr ago, P value40. 05), while host diet mostly determines the distribution of more ancient bacterial lineages among hosts (Mantel test; R 2 ¼ 0.
Methods Mammalian microbiome data set. We analysed a data set of mammalian gut microbiota containing 33 mammals 11 that represent 10 mammalian orders and cover various diets (carnivorous, herbivorous and omnivorous)  30. These data are ideal because they cover a large diversity of mammalian clades and were produced at the same time within a single study, avoiding biases introduced with variation in DNA extraction protocols, choice of 16S primers and sequencing platform. The data consist of amplicons of the V2 region of the 16S rRNA gene. As conspecific samples were originally available in this data set for seven host species, only one individual per species was retained for further analyses (and chosen at random) to focus on the evolution of gut microbiota at the interspecies level only. We also ran analyses including the other conspecific samples to control for the effect of intrahost species variability in gut microbiome compositions.
46) to dereplicate 16S sequences. Chimera sequences were removed from the data, using the UCHIME algorithm  47  and the reference Gold database, containing 5,181 16S sequences. We subsequently used the de novo algorithm, also implemented in UCHIME, to further remove chimeras. We used the Ribosomal Database Project Classifier  48  to assign a taxonomy to each unique 16S sequence. We retained bacterial sequences with an assignment probability 40. 8 at the phylum level (85% of all sequences). The final data set comprises 44,444 unique bacterial sequences. Phylogenetic reconstruction. To use BDTT on our data, we reconstructed the phylogenetic tree of all 16S unique sequences. First, we added the V2 region of the 16S rDNA genes from 139 Cyanobacteria, 115 Rickettsiales, 21 Chlorobium and 24 Chromatiales to the set of 44,444 stool sequences.
These external 16S sequences were used to incorporate calibrations when computing the divergence times (see below) and were subsequently removed for all our analyses. We aligned all 16S reads with the sina program (version 1. 2. 11) using the Silva database  49  , producing an alignment of 751 sites. We then removed sites containing 495% of gaps from the alignment. The final alignment contains 257 positions. To limit computational burden, we used FastTree  50  to reconstruct the ML phylogenetic tree of all 44,444 unique stool sequences plus 299 external sequences. We used the GTR model and the default CAT approximation to model rate heterogeneity across sites. We used the RDP taxonomy to constrain the topology of the phylogeny, forcing all phyla and all classes within the Proteobacteria (alpha, beta, gamma, delta, epsilon) to be monophyletic.
We used the EltonTraits 1. 0 database to compute dietary distances  23. EltonTraits compiles dietary attributes for a large number of mammals, including the 33 species present in our data set. Nine dietary items (Invertebrate, Vertebrate (excluding fishes), Rotting carcass, Fish, Unknown Vertebrate, Fruits, Nectar, Seed, other plant materials (for example, grass, ground vegetation, seedlings, weeds, lichen, moss, small plants, reeds, cultivated crops, forbs, vegetables, fungi, roots, tubers, legumes, bulbs, leaves, above ground vegetation, twigs, bark, shrubs, herbs, shoots, aquatic vegetation, aquatic plants)) are defined and each species is assigned a percentage for each item depending on its diet. We used Euclidian distance to build the diet distance matrix. As our dietary categories represent fuzzy variables (proportions between 0 and 1), we also computed diet distances using the Manly index, implemented in the dist.
ktab function in the 'ade4' R package  53. This control revealed no impact on our results, so we only present results obtained with the Euclidian distance matrix. Measuring correlation profiles with diet and host phylogeny. For each time period, we correlated b-diversities to host dietary and phylogenetic distances. We used (i) Mantel test  54  (with Pearson's correlations, that is, a linear model) and (ii) a non-linear model (Generalized Dissimilarity Modeling (GDM) approach  55  ) to measure correlation coefficients between b-diversity and host phylogenetic or dietary distances. All Mantel tests were run with the MRM (Multiple Regression on distance Matrices) function from the 'ecodist' R package  56. This function has the interesting property of allowing the user to run regression models with multiple predictors, a property that we used to measure the combined effect of host phylogeny and host diet in the prediction of gut community dissimilarities (see below in this section).
We correlated the residuals of the linear regression between the amount of phylosymbiosis and time (SES-age) to a quantification of dietary shifts. To measure these dietary shifts, we computed dietary distances between successive nodes in the mammalian phylogeny. We used ancestral diets reconstructed both with the trait-based approach using the dietary data of 1,534 mammals and with the microbiome-based approach. Each approach provides a probability distribution for each dietary category at each ancestral mammalian node. We computed the dietary distance between two consecutive nodes by computing a distance between the two probability distributions. When computing this distance, we accounted for the fact that omnivory is the union of carnivory and herbivory. Each node has three probabilities (summing to one) that correspond to the three diets (omnivory, carnivory and herbivory).
Detecting co-speciating bacterial lineages. We measured the amount of co-speciation and host-swap events per OTU at the same time slice that we used to measure the phylosymbiosis signal. We only conserved OTUs that are present in at least four hosts and we used the rarefied data matrix. We extracted the individual alignments for each OTU from the global alignment previously used to reconstruct the phylogenetic tree of all 16S sequences. Replicate sequences were treated as follows. When identical sequences were observed multiple times in a single host, only one sequence was retained in the alignment. Replicate sequences observed in multiple hosts were all conserved in the OTU alignment. We used the ALE algorithm  [39] [40] [41]  to detect the co-speciation and host-shift events along the phylogenetic tree of 16S sequences.
Acknowledgements This work was funded by  The Center for Microbiome Informatics and Therapeutics  and is also supported by the  National Science Foundation  under Grant No. 0821391. F. M. , S. L. and W. T. received support from the  European Research Council  under the European Community's  Seven Framework Programme FP7 /2007-2013 Grant Agreement no. 281422  (TEEMBIO). F. M. , S. L. and W. T. belong to the  Laboratoire d'E ´cologie Alpine , which is part of  Labex OSUG@2020  ( ANR10 LABX56 ). Part of the computations was performed using the CiGRI platform of the CIMENT infrastructure (https://ciment. ujfgrenoble. fr),which is supported by the  Rhone-Alpes region  ( GRANT CPER07_13 CIRA ) and  France-Grille  (http://www. france-grilles. fr). We are thankful to  Laure Gallien  for her guidance in the use of the VirtualCom R package.
Code availability. BDTT has been implemented in R. All codes needed to run BDTT, as well as illustrative examples, are available here: https://github. com/ FloMazel/BDTT. Data availability. The data that support the findings of this study (the multiple sequence alignment of the processed 16S data that we used for all phylogenetic analyses, the OTU table of unique sequences, the calibrated and non-calibrated bacterial phylogenetic trees used in the BDTT analyses, the matrix of host phylogenetic distances and the matrix of host dietary distances) are available from: https://github. com/mgroussi/MammalianGuts.
The Canadian Sea Ice and Snow Evolution (Can-SISE) Network is a climate research network focused on developing and applying state-of-the-art observational data to advance dynamical prediction, projections, and understanding of seasonal snow cover and sea ice in Canada and the circumpolar Arctic. This study presents an assessment from the CanSISE Network of the ability of the second-generation Canadian Earth System Model (CanESM2) and the Canadian Seasonal to Interannual Prediction System (CanSIPS) to simulate and predict snow and sea ice from seasonal to multi-decadal timescales, with a focus on the Canadian sector. To account for observational uncertainty, model structural uncertainty, and internal climate variability, the analysis uses multi-source observations, multiple Earth system models (ESMs) in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and large initial-condition ensembles of CanESM2 and other models.
This effort includes Canadian contributions to the International Polar Year (e. g. Kulkarni et al. , 2012) , to the development of Earth system model (ESM) and climate-prediction systems  (Merryfield et al. , 2013a; Sigmond et al. , 2013; van den Hurk et al. , 2016) , and to leadership of ongoing field and remote sensing efforts  (King et al. , 2015). As part of Canada's larger effort in snow and sea ice research, the focus here is on seasonal and longer timescale prediction of terrestrial snow, sea ice cover, and related climate variability. The purpose of this paper is to evaluate the ability of Canada's current ESM and climate-prediction system to carry out this kind of prediction in the context of the development of new observational products. This work was undertaken by the Canadian Sea Ice and Snow Evolution Network (CanSISE), a core project of the Climate Change and Atmospheric Research Program of the Natural Sciences and Engineering Research Council of Canada (CCAR/NSERC) 1.
Models and data used In Sect. 3, our analysis will focus on CanESM2  (Arora et al. , 2011; Scinocca et al. , 2016). This is the ESM used by the Canadian Centre for Climate Modelling and Analysis (CC-Cma) of Environment and Climate Change Canada (ECCC) for its contribution to Phase 5 of the Coupled Model Intercomparison Project (CMIP5). CanESM2 combines atmosphere, ocean, land-surface (including snow), sea ice, and carbon-cycle components in a coupled framework in which all model components interact. The system can simulate the past and projected state of global temperature, circulation, carbon dioxide concentrations, etc. under the influence of external forcing, but independently of assimilated ocean and atmospheric initialization data. As summarized in  Arora et al. (2011) , the atmospheric and oceanic components are the fourth-generation atmospheric and oceanic general circulation models CanAM4 and CanOM4, the prognostic carboncycle components are the Canadian Model of Ocean Carbon (CMOC) and the Canadian Terrestrial Ecosystem Model (CTEM), the land-surface component (including the snow which are in the Climate Research Division), and the Pacific Climate Impacts Consortium (PCIC).
Like CanESM2, Can-SIPS is also a multi-component interactive system. However, unlike CanESM2, when operating as a prediction system CanSIPS starts from an initial state that approximates the real-world state at a given initial time. CanSIPS includes (1) a data assimilation system that estimates realistic initial states of the atmosphere, ocean, land, and sea ice to start the forecasts; (2) two separate coupled climate models (the earliergeneration Canadian Coupled Model 3, CanCM3; and the later-generation Canadian Coupled Model, CanCM4) that advance the simulated system from this initial condition (using an ensemble size of 10 for each model); and (3) diagnostic systems to analyze the output and generate useful forecasts for operational use within ECCC's Meteorological Service of Canada (e.
Further progress in this kind of process representation will be achieved in part through internationally coordinated intercomparison efforts associated with CMIP6, including the Land Surface, Snow and Soil Moisture Model Intercomparison Program (LS3MIP;  van den Hurk et al. , 2016)  and the Earth System Model Snow Intercomparison Project (ESM-SnowMIP). Besides ongoing work on sea ice mechanics and its relationship, through wind driving, to sea ice drift, Can-SISE research is also currently characterizing snow cover on sea ice in models and observations, which also serves as a potential source of model error in the timing and amplitude of sea ice growth and melt. CanSISE demonstrates the utility of entraining a network of researchers bridging observational and modelling communities to focus on a related set of processes in evaluation of Earth system models and climate-prediction systems.
For many years now, IRSN has developed its own Monte Carlo continuous energy capability, which allows testing various nuclear data libraries. In that prospect, a validation database of 1136 experiments was built from cases used for the validation of the APOLLO2-MORET 5 multigroup route of the CRISTAL V2. 0 package. In this paper, the k eff obtained for more than 200 benchmarks using the JEFF-3. 1. 1 and JEFF-3. 2 libraries are compared to benchmark k eff values and main discrepancies are analyzed regarding the neutron spectrum. Special attention is paid on benchmarks for which the results have been highly modified between both JEFF-3 versions.