dataesr/scientific-paragraphs-categorization huggingface dataset:
Taxonomic composition of the real dataset Taxonomic assessment results with the representation of phyla in sampling sites and their relative abundances indicated that the pipeline type had little influence (Fig. 7 ). The total numbers of phyla were around 54, 51 and 52 for BIOCOM-PIPE (similar to ASV definition, as all sequences were affiliated), FROGS and mothur, respectively (unclassified phylum excluded) (Additional file 11: Table  S3 ). Alphaproteobacteria was the most abundant phylum for the three pipelines; as regards the major taxa, the very same phyla were found, but were ranked differently (Fig. 6 ). Mothur assigned no phylum to the medium group, while all sequences were assigned with FROGS, and 0. 31% and 0. 62% were left unclassified by BIOCOM-PIPE and mothur, respectively (Fig.
Discussion BIOCOM-PIPE is a new pipeline designed to characterize microbial diversity from environmental DNA metabarcoding data. Faced with many tools already available, we chose to work closely with various stakeholders (biologists, computational scientists, statistics experts) to suggest alternative approaches to reduce errors and biases from metabarcoding datasets. This expertise is based on 10 years of work aimed at developing standard operating procedures for molecular preparation and sequencing in close collaboration with the GenoSol platform  [47]. BIOCOM-PIPE was designed to remain manageable for both beginners and advanced users. Once the fully completed "project file" and the raw datasets are placed in the appropriate folder, the pipeline can be run directly with all default parameters, or with specific parameters defined by an advanced user.
), but too stringent filters can lead to the loss of rare microorganisms  [26, 48, 49, 51]. Here, FROGS seems to be the more efficient to form the lowest number of OTUs, but it was unable to detect some strains, as one representative sequence per OTU is selected and taxonomically assigned after clustering, leading to biases in community composition  [49]. BIOCOM-PIPE and mothur showed better results in terms of taxonomic affiliation. It is noteworthy that BIO-COM-PIPE can be more flexible, as it is possible to optimize the "hunting-recovering" parameters to adapt the stringency of the detection, leading to the deletion of the "best" potential chimera. Moreover, the latest taxonomic database updates for Archaea, Bacteria and Fungi are also available from SILVA r132, and µgreen-db for photosynthetic microeukaryotes and cyanobacteria.
This will make it easier for supervisors, colleagues or users themselves to choose the parameters of the analysis. Moreover, having a structured folder with all summary results and parameters allows for better archiving. It is currently very difficult to compare studies carried out with different bioinformatic analyses  [2]. To address this issue while providing great flexibility, a post-clustering step using ReClustOR was added to BIOCOM-PIPE, compared to classical pipeline analysis  [17]. In order to overcome the problems of OTU stability and reliability posed by the classical clustering methods, this post-clustering step was implemented to improve the quality of the reconstructed OTUs. Moreover, thanks to an available open-reference clustering method, ReClustOR can define and/or enrich a reference OTU database with analyzed projects without carrying out the complete analysis each time new sequences are added.
As a consequence, such a post-clustering step is a clear benefit that increases the reproducibility and reliability of results among different clustering analyses, and also between samples and datasets  [17]. To highlight BIOCOM-PIPE specificities compared to other pipelines, a subsampled dataset from the RMQS metabarcoding project was used  [13]. The key steps to reduce the number of sequences vary between pipelines, especially during the chimera step. At the end of the analysis, BIOCOM-PIPE kept the highest number of sequences for the subsampling step, which made it possible to set this step to 10,000 sequences versus 8000 with FROGS or mothur. A higher number of OTUs was observed with mothur, in line with previous studies  [7]. It is worth noting that BIOCOM-PIPE counted the lowest number of singletons thanks in particular to the use of ReClus-tOR, which allowed for better sequence assignment.
Another improvement can be achieved by providing an alternative method to characterize fungal communities by analyzing the internal transcribed spacer (ITS). Therefore, it will be necessary to integrate another aligner than Infernal (e. g. , MAFFT program  [61] ) for multiple sequence alignment since no covariance model for fungal ITS sequences exists. Finally, if we keep focusing on linking sequencing preparation and bioinformatic analysis more closely, it would also be interesting to integrate a component to check the validity of sequencing runs and the different pipeline steps. About run effectiveness, an upstream analysis of internal standards with various tests (e. g. , richness, community structure, taxonomy) could be used to validate or not the sequenced samples. An internal standard reference database could be created, compared and enriched to highlight a drift.
Conclusions Although many complex questions remain unanswered concerning microbial ecology  [62] , tremendous advances have been made from a technical point of view, with the constant emergence of new protocols or sequencers, but also better knowledge of microbial diversity. This craze boosted scientists to develop algorithms and tools to analyze metabarcoding data over and over again. BIOCOM-PIPE is an interesting alternative for the scientists who wish to benefit from an efficient and flexible tool. This pipeline is robust, based on longstanding expertise, and can compare large datasets with great ease. Its original features use various rarely used methods or tools, not because there are not effective but likely due to a lack of hindsight. Like any tool, it is constantly evolving and may also contain errors.
Acknowledgements We thank all the soil surveyors and technical assistants involved in sampling the sites. Thanks are also extended to  Annie Buchwalter  for correction and improvement of English language in the manuscript. We also thank the  Center for Computing Research and Technology (Paris, France)  who let us use the Cobalt supercomputer. We are grateful to the  INRA MIGALE bioinformatics facility (MIGALE, INRA, 2018. Migale bioinformatics Facility , https ://doi. org/10. 15454/1. 55723  90655 34329 3E12 ) for providing help and/or computing and/or storage resources. We finally want to thank  Sophie Sadet-Bourgeteau  to let us use her published dataset as an illustrative example for users who want to test BIOCOM-PIPE.
Availability and requirements BIOCOM-PIPE package is available in Zenodo repository (https ://doi. org/10. 5281/ zenod o. 36781 29). A virtual machine encompassing the complete pipeline into an UBUNTU system is also available (https ://doi. org/10. 5281/zenod o. 39459 64). All raw datasets for the example are publicly available in the EBI database system (in the Short Read Archive) under project accession No. PRJEB21351. Project name: BIOCOM-PIPE. Project home page: Not defined. Operating system(s): Linux and UNIX. Programing language: Perl, Python, C, JavaScript, HTML/CSS. Other requirements: all third-party tools listed in the User Guide. License: CC. Any restrictions to use by non-academics: Invention disclosure statement (DI-RV-17-0037).
Funding This study was granted by the ADEME (French Environment and Energy Management Agency) and by 'France Génomique' through the involvement of the Genoscope technical facilities. Because of the involvement of the technical facilities at the GenoSol platform of the infrastructure "Analyses et Expérimentations sur les Écosystèmes" (ANAEE) (France), it also received a grant from the French state through the National Agency for Research under the program "Investments for the Future" (reference ANR-11-INBS-0001). RMQS soil sampling was supported by a French scientific group of interest on soils: the "GIS Sol, " involving the French Ministry of Ecology, Sustainable Development and Energy (MEEM); the French Ministry of Agriculture (MAP); the French Institute for Forest and Geographical Information (IGN); the Environment and Energy Management Agency (ADEME); the French Institute for Research and Development (IRD); and the National Institute for Agronomic Research (INRA).
In recent years, dedicated probabilistic simulation tools have been developed, to predict the progression of marine operations and the suitability of resourcing strategies against synthetically produced metocean data. Embedding a stochastic metocean model within these simulation tools allows the users to first input observed weather data for a finite number of years and generate many synthetic realisations of the weather to assess the progression of marine operations. ] [6]  To cope with the variation in the offshore weather conditions at each project, it is vital that a stochastic weather model is adaptable to account for seasonal or monthly variations to produce realistic weather time series. The Markov-switching autoregressive (MS-AR) model included in the METIS MATLAB toolbox, developed by Monbet and Ailliot,  7  has been investigated in this study and configured to produce monthly realisations of observed time series.
An ARIMA model uses the process of differencing many times, to reduce the forecast to an autoregressive moving average process (ARMA). An ARMA model relies on a set number of previous values in a given time series and memory function described denoted by the 'MA' or moving average, to produce forecasts. TGP can be described in three steps. For a univariate process, it starts by transforming the observed data into a Gaussian variable, then generating a second-order random time series with the same spectral properties. This second-order time series can then be re-transformed back into a full univariate time series, and it is noted that auto and partial correlation functions may reveal significant differences between the original and synthetically produced data. It is remarked that while these methods are less sensitive to noisy data, they are reliant on large data sets to generate estimates and have difficulty reproducing an accurate distribution of stormy and calm conditions within the generated time series.
It is identified that the unconditional distribution of the wave heights in the observed data were not Gaussian and therefore a Box-Cox transformation is applied to reduce skewness and suit the Gaussian distribution. Bivariate ARMA-GARCH models are investigated to account for the dependency between wave heights and the strength of the wind. A vector error correction (VEC) bivariate ARMA-GARCH model is used by the authors, which includes a conditional covariance matrix of the error term, which restricts the prediction of the bivariate outcomes based on positivity of the covariance matrix. For both univariate and bivariate models, the Gaussian, student t and skewed t distributions of the error term are investigated. Assessments of the models are completed using continuous ranked probability, maximum absolute error for point forecast accuracy and a brier score to statistically assess the probability of 1.
Methodology This article applies the homogeneous MS-AR model with Gaussian innovations, embedded within the METIS MATLAB tool box, for application in offshore planning and simulation tools. This section describes the observed meteorological data sets, the MS-AR model tested, the model selection process and a summary of the steps to produce the final time series. It is important to note that the theoretical and computerbased MS-AR models have been adopted for this study. In this article, we present a generic methodology that can be used to generate annual time series of U and H s and the steps used to assess the results. The main steps of the overall method are shown in Figure  1. A brief summary of these steps are as follows. We begin with observed data that are resampled to three hourly intervals and compiled into monthly sets to obtain stationery, non-seasonal collections.
MS-AR model This section describes the homogeneous MS-AR model used to generate wind speed (U) and wave height time series (H s ). The model is included with the METIS MATLAB toolbox and was obtained directly from the authors. 7 The description of the model is based on the information included in the METIS toolbox documentation  7  and a description of the most standardised MS-AR model, included in Section 2 of Ailliot and Monbet. 9 The following description summarises the functionality of the MS-AR model and should provide sufficient detail to support the reader's understanding of the computational steps. Wind and wave data and recorded time series commonly demonstrate non-stationary, which is characterised by daily factors, seasonal fluctuations and interannual variations. The issue of seasonality in a wind time series can be treated by dividing the data into months and fitting a separate model to each month, which assumes that the same month across each year is an individual representation of a common stochastic process.
9 Segmentation of monthly wind data sets from the original 3-h meteorological file was necessary to inform the MS-AR algorithm. Within the METIS toolbox, three different MS-AR models are provided: Gaussian MS-AR model, Gamma or Lognormal MS-AR model and a Non-homogeneous gamma MS-AR model, which was developed to handle bivariate processes for wind speed and direction. The Gaussian MS-AR model was selected for review in this study as Monbet and Ailliot state that the formulas for the estimation step can be explicitly described, supporting overall interpretation in this study. As the majority of the available metocean data sets did not include comprehensive wind speed and directional observations, the bivariate case was not applicable. To fundamentally describe the MS-AR model, it is important to consider the different evolutions in the wind speed that can occur over a month.
It should be noted that the example shown in Figure  4  is taken from the UK North East data set and that the month of March was one of few months to demonstrate the dependence of up to six lags, which for three hourly samples corresponds up to 18 h in the past. The same process was followed for significant wave height, and it was identified that up to three lags were statistically significant. Once a ceiling value for the AR model order is identified, the process of checking the MS-AR model types can be completed. This involves running the EM algorithm in the METIS toolbox to obtain the parameters L, N and k from model combinations pf1 À 6g, Mf1,. , 6g. In all, 36 model types were assessed for both U and H s across each month at all three sites. The BIC score was produced for each model combination across all months, and the model with the lowest BIC score was selected to produce the synthetic weather series.
However, the English Channel showed the largest absolute percentage difference. On average, the modelled data exhibit more windows for the first 6-7 months. It should be noted that this does not necessarily mean more workability in the simulated data and is more likely due to the fragmentation of weather windows, which are believed to be less prominent in the observed data. Nevertheless, this outcome shows that despite the larger deviations in the winter months, there would likely be a similar amount of time to complete the required marine operations in each month. However, if the English Channel time series were comprised within a dedicated planning and simulation tool, it is likely the modelled data set would exhibit an increase in waiting times or more frequent interruptions, than the observed data set.
In Figures  7  and 8 , the CDF of the simulated data follows the observed years of annual data. Looking at the lower wind speeds and wave heights in both figures shows that the model has a tendency to over predict the occurrence of U and H s below 3 m/s and 0. 4 m, respectively. This observation is consistent with similar observations completed by Ailliot and Monbet, 8 who note that this behaviour will have limited impact for applications that are insensitive to lower magnitudes, such as the intended application in this article. Furthermore, climate change trends could be incorporated to adjust the resulting simulated U and H s time series, drawing on publicly available projections such as Copernicus Climate Change Service. 18 It is proposed that these trends could be used to modify the monthly simulated time series before to pairing the individual U and H s realisations.
However, it should be noted that current climate models anticipate limited change in wind and wave climates for the regions investigated in this article. In Figure  9 , a right skew tail is shown, which indicates that some extreme wind speeds may have been produced in the simulation compared to the observed data set. However, the concentration of the points is quite low, especially for the most extreme points. Figure  10  shows a much slighter skew than was observed with the wind speed. This behaviour at the tails is expected to have little impact for marine planning applications as these extreme values are likely to span well beyond the operational constraints for these tasks. To assess the validity of the overall methodology, the final annual compositions of U and H s were initially compared using weather window persistence histograms and average monthly weather window durations for a crew transfer task.
Conclusion This article has presented a metocean modelling methodology using an MS-AR model with Gaussian innovations to produce stochastic wind speed and wave height time series for inclusion in marine risk planning tools. The METIS MATLAB toolbox developed by Monbet and Ailliot 7 provided the main simulation framework which was reconfigured to produce annual sets of wind and wave height data. The versatility of the MS-AR model for differing metocean regimes has been discussed, and we have implemented a BIC scoring process to select the most suitable model type for each weather parameter. The MS-AR model was originally intended to produce wind speed outputs, yet it is demonstrated that realistic wave height simulations can be produced. Two approaches to pair the individual wind speed and wave height simulations have shown their validity to finalise the composition of simulated realisations.
Due to the deprecation of APIs in the Android operating system, developers have to update usages of the APIs to ensure that their applications work for both the past and current versions of Android. Such updates may be widespread, non-trivial, and time-consuming. Therefore, automation of such updates will be of great benefit to developers. AppEvolve, which is the state-of-the-art tool for automating such updates, relies on having before-and after-update examples to learn from. In this work, we propose an approach named CocciEvolve that performs such updates using only a single after-update example. CocciEvolve learns edits by extracting the relevant update to a block of code from an after-update example. From preliminary experiments, we find that CocciEvolve can successfully perform 96 out of 112 updates, with a success rate of 85%.
[19]  reported that, in order for AppEvolve to perform a successful update, the target code requiring update has to be written syntactically similar to the before-and after-update example. They demonstrated that AppEvolve's performance can be improved significantly if the app code is manually rewritten to have syntactic similarities to the before-and after-update example. In this work, we propose CocciEvolve. CocciEvolve outperforms AppEvolve in the following aspects: (1) CocciEvolve eliminates the shortcoming of AppEvolve by normalizing both the after-update example and the target app code to update. In this way, CocciEvolve ensures that both of them are written similarly, thereby preventing unsuccessful updates caused by minor differences in the way the code is written. (2) CocciEvolve requires only a single after-update example for learning how to update an app that uses a deprecated API.
Consider an after-update example in Figure  1. The if and else blocks correspond to the code using the replacement and deprecated API methods, respectively. The code in the if block runs only on versions of Android after the deprecation. Thus, the code in else block can be considered as the code that uses the deprecated method before the update. (3) Transformations made by CocciEvolve are expressed in the form of a semantic patch by leveraging Coccinelle  [8]. Semantic patch has a syntax similar to a diff that is familiar to software developers. Therefore, the modifications process are more readable and understandable to developers. The contributions of our work are: • We propose CocciEvolve, an approach for updating Android deprecated-API usages using only a single after-update example. • We perform automatic code normalization of both the update example and the target code to be updated, addressing the challenge of updating code that is semantically equivalent but syntactically different, which was a limitation of prior work.
• We have evaluated CocciEvolve with a dataset of 112 target files to update that we obtained from Github. The 112 files use 10 deprecated APIs used in the original evaluation of AppEvolve. We show that CocciEvolve can successfully update 96 target files. The remainder of this paper is structured as follows. Section 2 provides some preliminaries. Section 3 details our proposed approach. Section 4 describes our preliminary experiments and results. Section 5 presents related work. Finally, we conclude in Section 6.
AppEvolve then computes the common core from the produced generic patches. The common core is the longest subsequence of edits across the patches. In the API-Usage Update phase, AppEvolve applies generic patches in ascending order of their distance to the common core. To apply a generic patch, AppEvolve tries to match context variables to variables in the app. If matches are found, AppEvolve applies edits in the generic patch and returns the updated app if the edits are successful. Coccinelle4J. Coccinelle4J  [6]  is a recent Java port of Coccinelle, which is a program matching and transformation tool  [8, 14]. Given the source code of a program and a semantic patch describing the desired transformations, Coccinelle4J transforms the parts of the source code that match the semantic patch. Written in the Semantic Patch Language (SmPL), a semantic patch has two parts: (1) context information, including the declaration of metavariables; and (2) changes to be made to the source code.
A metavariable can match program elements of the type indicated in its declaration. Modifications are expressed using fragments of Java as follows: (1) code that should be removed by annotating the start of the lines with -; and (2) code that should be added by annotating the start of the lines with +. Unannotated lines add context to the semantic patch. Figure  2  showed an example of a semantic patch. The line surrounded by @@ declares the metavariable.
Source File Normalization Different software developers may have different programming styles, thus, semantically-equivalent code may be expressed in different syntactic forms. As a result, equivalent usages of one API may vary in its expression. Figure  4  shows an example of such cases in which getCurrentHour() is expressed differently. Therefore, it is necessary to perform source code normalization. Focusing on source code that is related to the API usage, we perform the following code normalization steps: • An API call contained.