dataesr/scientific-paragraphs-categorization huggingface dataset:
The molt of this collection towards a more formal BRC has been recently initiated. 8) The CBGP Small Mammal Collection, including specimens (skins and/or skulls) and their corresponding samples (i. e. , organ parts, blood or cells in ethanol, various buffers, or cryopreserved), and the Small Mammal Database gathering all the associated information have been the support of a number of studies over the last years. Among others, we may cite the description of the invasion of Senegal by the house mouse  (Dalecky et al. 2015)  based on the compilation of data on commensal rodents in Senegal over the period 1983-2014 in the frame of the JCJC ANR programme BENEMI^; a review on camouflage in a set of species of gerbil species based on analyses of their skin reflectance  (Boratyński et al. 2017) ; various studies on the phylogeny of supraspecific groups  (Ndiaye et al.
2012  or intraspecific phylogeography  (Hima et al. 2011; Dobigny et al. 2013) ; and a growing amount of works highlighting the role of small mammals as reservoirs of a number of zoonotic pathogens potentially transmissible to humans (e. g. ,  Dobigny et al. 2015 for Leptospira, Tatard et al. 2017 for Trypanosoma, Diagne et al. 2017a, b  for bacteria viruses in West Africa). This collection and associated database have represented the basic materials for an updated synthesis on West African rodents  (Granjon and Duplantier 2009) , and also serve as depository of information for the BWest African Observatory on small mammals as indicators of environmental changes( ObsMiCE), relying on a network of observation sites all over West Africa, and which is currently supported by IRD and OSU Pytheas fundings.
A case study based on soil samples will be developed during the project, because these resources are not well characterized at the time of sampling in terms of taxonomy. The main objective of BRC4Env is to develop the Information System of the network, currently constituted by shared tools and distributed services among BRCs, by making these tools interoperable and relevant regarding the Nagoya policy. A specificity of BRC4Env is the hosting of living collections of invertebrates. The Information System must provide and share information concerning sampling, rearing, phenotyping and molecular data, and barcoding. Here, we plan to set the following requirements: (i) centralization and standardization of metadata concerning the resources, (ii) secure storage and confidentiality by controlled access, (iii) tools to perform queries (in partnership with applications to be developed by AgroBRC-RARe).
A common database for registering the projects hosted by the BRCs of the network will be developed. The Information System of BRC4Env will offer shared information dedicated to the resources hosted by its BRCs. It will also promote and contribute to make easier the access, storage, archiving of datasets, and their subsequent valorization, and provide to end-users access to the productions of the network (publications, catalogues…) by subscription to News and RSS feed. Experience gained in other pillars of AgroBRC-RARe in these fields will be used in order to optimize the functional specificities of our IS and to select the most relevant technical solution. BRC4Env shares two main other objectives in the next future. -Internal practice harmonization. Considering the diversity of BRCs and collections, the network will encourage practice harmonization for improved management and enhanced traceability of its activities within its BRCs and make available a comprehensive set of harmonized procedures.
Acknowledgments The network BRC4Env thanks the  Research Infrastructure AgroBRC-RARe , the  French National Institute for Agricultural Research (INRA) , and the  GIS IBiSA  for supporting its activities (BRC4Env, GenoSol,  EP-Coll, and Colisa benefit of the IBiSA label). GenoSol and Biochem-Env are services of the BInvestment d'Avenir^infrastructure AnaEE-France , overseen by the  French National Research Agency (ANR)  ( ANR-11-INBS-0001 ).
This work presents a framework to facilitate reproducibility of research in video quality evaluation. Its initial version is built around the JEG-Hybrid database of HEVC coded video sequences. The framework is modular, organized in the form of pipelined activities, which range from the tools needed to generate the whole database from reference signals up to the analysis of the video quality measures already present in the database. Researchers can re-run, modify and extend any module, starting from any point in the pipeline, while always achieving perfect reproducibility of the results. The modularity of the structure allows to work on subsets of the database since for some analysis this might be too computationally intensive. To this purpose, the framework also includes a software module to compute interesting subsets, in terms of coding conditions, of the whole database.
Motivation and significance The domain of objective video quality algorithms suffers from the lack of reproducible research. Scientific progress is impacted by missing implementations of existing algorithms and missing test DOI of original article: http://dx. doi. org/10. 1016/j. dsp. 2017. 09. 013. data. The implementations are often missing because the individual authors hesitate to publish their code, thus requiring reimplementation of complex algorithms. As test data for the correctness of the algorithms is often also missing, a reimplementation may not be validated. As a consequence, comparisons published in the domain rely on uncertain data. The software described in this paper serves three purposes. Firstly, it provides an environment for calculating a reproducible large-scale dataset of compressed video sequences that can be used both as test data and for comparisons of algorithms.
Secondly, the framework provides the possibility to provide different Peak Signal to Noise Ratio (PSNR) measures as an example for the calculation of more complex algorithms and for comparisons in scientific research as most researchers compare their work to PSNR. Thirdly, a subset selection algorithm that can be targeted to different research questions is provided that allows for running computationally expensive algorithms on parts of the large-scale database while retaining important characteristics for the analysis. This software package helps in developing and evaluating objective video quality measurement algorithms. Researchers can reproduce the test dataset and provide the results of their algorithm so that exact reproducibility of their algorithm can be guaranteed. Comparisons between different algorithms are also enabled because of the size of the large-scale dataset which reduces the probability of overtraining if the test design is carefully chosen.
Large scale encoding environment The ''Large Scale Encoding Environment'' performs the Hypothetical Reference Circuit (HRC) processing. At this moment, this module consists the version used in this evaluation of the HEVC standardization reference encoding package (http://hevc. kw. bbc. co. uk/svn/jctvc-a124/tags/HM-11. 1/) accompanied by a valuable set of configuration files and scripts in order to reproduce or extend the first version of the HEVC large scale database. Whenever the proposed video quality analysis framework needs improvement with more recent versions, more compression standards, other compression parameters, or network impairment simulations, then solely this block must be extended upon. Please note that more recent versions of the HEVC reference software may change the results of this reproducible dataset and should thus be considered a different dataset.
Quality measure The ''Quality Measure'' component consists of a collection of full reference quality measurements like Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Multi-Scale Structural Similarity (MS-SSIM), and Visual Information Fidelity (VIFp). These measurements are integrated in this Reproducible Video Quality Analysis software package using the Video Quality Measurement Tool (VQMT) from École Polytechnique Fédérale de Lausanne (EPFL).
Quality measure analysis The ''Quality Measure Analysis'' component focuses on extracting the relevant data from the full reference quality measurement database and processes it in order to perform the analysis. In this particular work we extracted frame-level MSE and PSNR values to compare the effect of temporal pooling through averaging either MSE or PSNR. Moreover, the variance of the frame-level PSNR is also computed and made available for the next visualization block. The module can be easily customized to handle different measures either present in the database or made available through files in the same format. Moreover, other indicators (e. g. , moving average), can be included in the output for the visualization block.
Software functionalities and illustrative examples The package contains scripts that are written in different language environments but can be executed under Linux and Windows platforms. The software package content is shown in Fig. 2. It contains 2 directories 'DATA' and 'SoftwareLibraries'. 'DATA' directory contains sub directories that contains the source data and the outputs of running the software that can be found in 'SoftwareLibraries'. First of all, the source content should be placed in (DATA/SRC). The current scripts deal with three resolutions (960 × 544, 1280 × 720, and 1920 × 1080). In the first module (Large-scale encoding environment), the file has to be run (Soft-wareLibraries/ENC_SRC/ENC_lin. py) to generate and encode the whole coding conditions (ENC_win. py is a Windows script).
The '. 265' and '. txt' output files will be placed in (DATA/ENC), see part A of Fig. 3. The '. 265' is the bitstream file and the '. txt' is the encoding information. The quality measure module calculates the objective In order to work on a subset of HRCs, the optional module ''Subset Selection'' has to be executed. The first step is to aggregate the input data for the MATLAB functions: 'getBitrateQuality-DrivenHRCs. m' and 'getContentDrivenHRCs. m' that can be found in (SoftwareLibraries/SUB_ENC). The input data takes the form of two matrices, the first one contains the PSNR measures and the second one is the calculated bitrate. These two matrices are formatted as follows: MxN where M represents the total set of the HRCs and N represents the number of source contents. They can be aggregated from the '.
txt' of the output of first module. This optional module saves the '. txt' and '. csv' files that contains the selected HRCs in the (DATA/SUB_ENC) folder, see parts C. 1 and C. 2 of Fig. 3. In the ''Quality Measure Analysis'', first sequence-level metrics are computed. The database already contains the PSNR for each frame, whereas the MSE can be computed by reversing the PSNR formula through 'script_create_other _psnr_data_and_msefile. py'. Next, 'script_create_psnr_stddev_per_frame. py' processes each HRC independently, automatically matching all the values (e. g. , PSNR and MSE) related to the same HRC even if stored in different files (e. g. , one per measure type) and computing the sequence-level indicators including the variance of the PSNR (σ 2 PSNR ) which is analyzed in details in our accompanying paper  [1].
Note that the software can be easily extended to include other temporal pooling strategies, or use other metrics such as SSIM. All the results are exported in a text file in comma separated value (csv) format, one line per HRC, see Fig. 4. Other utility software can perform HRC filtering operations ('script_filter _HRCs. py'), cumulative distribution function (CDF) computation while retaining all the original information ('script_compute_perpoint_cdf_function_ over_PSNR_G-PSNR_A. py'), computation of the linear interpolation functions ('script_linear_interpolation. py') and of the similarity of two point cloud distributions ('script_similarity. py'), by assigning points in one graph to the nearest one in the other, then computing statistics such as average number of assigned points and average distance.
Impact Since the main focus of our work is providing efficient tools for reproducible research in the context of video quality analysis, the software has been designed and verified to exactly reproduce all the data in the database as well as all the results presented in our accompanying paper  [1]. However, we would like to underline that the same piece of software, with only slight modifications, can be effectively used to pursue new research questions. For instance, for the PSNR analysis part, it is extremely easy to reuse the software with a different per-frame quality metric such as SSIM or VIFP, or testing the different results of various temporal pooling strategies. At the same time, new synthetic quality indicators can be computed and tested, e. g. , by applying moving averages, mean, variances, etc.
Therefore, while the software itself might appear relatively simple, in our opinion it can greatly facilitate the pursuit of new research questions that can be addressed by mining into the large set of data currently available in the database. Note that three widespread frame-level video quality metrics, i. e. , PSNR, SSIM, VIFP, are already available in the database and can be immediately used for analysis, whereas other measures can be added with limited implementation effort. The impact of this work had up to now can be seen in the following list of publications. These publications use the proposed reproducible research framework in order to run different kinds of analysis that benefit the video coding and video quality communities. • Using the ''Large Scale Encoding Environment'' and the ''Quality Measure'' components from the provided pipeline, in  [3] , the authors provide a way to analyze quality measures on the sequence level to highlight the unusual behavior of these measurements.
The concept of agreement between the quality measures is introduced to compare the source of disagreement for different source contents. One conclusion that can be drawn from  [3]  is that the VIF and the SSIM agree more often on the ordering. • The impact of the software pipeline can also be seen in  [4]. In this paper, the authors used the large scale database resulting from the ''Large Scale Encoding Environment'' component of this paper to compare the behavior of different quality measures in loss-impaired sequences and tried to predict the behavior of the objective measures with the help of content characteristics. The authors were thus able to reuse the provided 160 h of video sequences without having to invest resources to encode these sequences again. For this work, the HEVC reference decoder, present in this software package, has been modified to handle packet losses.
Conclusions The presented framework has been designed in order to facilitate reproducibility of research in video quality evaluation. It is intended to provide building blocks to be expanded or reused by other researchers to test their own measures or ideas and easily compare the results in a reliable and consistent way. The framework also aims at simplifying the use of subsets of the database when testing on a large scale is computationally infeasible. To the best of our knowledge, this is the first attempt to bring exact reproducibility end-to-end in video quality evaluation research field. We hope that this framework will be further expanded through the contribution of reference implementations of new measures, indexes or simply by expanding the database by means of new content, coding conditions, etc.
Acknowledgments Some of the computational resources have been provided by  HPC@POLITO  (http://www. hpc. polito. it). Some parts of this work are supported by the  Marie Skłodowska-Curie  under the  PROVI-SION (PeRceptually Optimized VIdeo CompresSION ) project bearing Grant Number  608231  and  Call Identifier :  FP7-PEOPLE-2013-ITN. The research activities described in this paper were partially funded by  Ghent University, imec ,  Flanders Innovation & Entrepreneurship (VLAIO) , the  Fund for Scientific Research Flanders (FWO-Flanders) , and the  European Union. Some aspects of this work were carried out using the  STEVIN Supercomputer Infrastructure at Ghent University.
coli sensu stricto strains to four and seven phylogroups, respectively. PCR assays for Escherichia clade assignment  [1]  and more recently for E. albertii  [12, 13]  and E. fergusonii  [13]  assignment have also been reported. With the growing number of available complete genomes (more than 10 000 E. coli genomes are available at the NCBI RefSeq database to date), it would be useful to have 'in silico PCR assays' that would allow us to assign strains to a specific species/phylogroup directly from the strain's complete sequence. Several groups have now reported such in silico Clermont phylo-typing but none of them provides precise methodology or a validation step  [14] [15] [16]. In addition, one group has reported discrepant results between the core genome phylogenetic tree and the in silico Clermont typing for E.
General intention The ClermonTyping is an in silico method that aims to reproduce the results of the E. coli phylo-typing by PCR proposed by  Clermont et al. in 2000 [11]  and updated in 2013  [5]  as well as the various PCR assays developed to identify the Escherichia clades  [1]  and E. albertii  [12]. In addition, we have developed in the present study an E. fergusonii-specific PCR. We designed the ClermonTyping with two main goals in mind: (i) it had to be fast and easy to use for all members of the scientific community anywhere and anytime, and (ii) the results had to be as concordant as possible with the in vitro method. ClermonTyping comes in two flavours: a command line set of scripts and executables and a website, the ClermonTyper, with a very user-friendly interface that will allow anyone to assign/phylo-type Escherichia sp.
Outline of the method The method takes a DNA FASTA/multi-FASTA formatted Escherichia sp. genome. A BLAST database is then created using this query genome and the BLASTn algorithm is called with specific parameters to find matches for a set of primers described in Table  S1  (available in the online version of this article)  [17]. The BLAST result is then interpreted as the PCR would be in terms of presence or absence of amplification for each pair of primers. The design of this PCR method can in some particular cases lead to misleading results (in vitro as well as in silico), for example when a SNP at a particular position prevents primer annealing or in cases of recombination or horizontal transfers, including insertion sequences (IS), between genomes  [18]. To address these cases, we added a phylogroup determination step based on a genome-clustering tool called Mash  [19].
Mash allows us to approximate a pairwise mutation distance between the query genome and a manually curated database representing as much as possible the Escherichia sp. /phylogroup genomic diversity. We use a Mash2. 0 screen command to estimate the nearest genome in an Escherichia sp. manually curated genomic database. What we term the 'Mash group' is the species/ phylogroup of the nearest genome in the database (Fig. 1 ). This information is then aggregated in the form of an HTML report easily interpretable for the user.
This in silico method allows a given strain sequence to be assigned to E. albertii, E. fergusonii, Escherichia clades I-V, E. coli sensu stricto as well as to the seven main E. coli phylogroups. We propose the ClermonTyper as a userfriendly and very efficient tool for Escherichia species/phylogroup identification and provide it freely available to the scientific community. produced through any kind of sequencing method. The general quality of the sequence and its assembly is crucial for accurate results. This file path is passed to the script through the ÀÀfasta argument. The user can optionally input a name for the analysis using the ÀÀname argument. The results are stored in a directory named after the ÀÀname argument value (created inside the working directory). In the absence of a value, the results can be found in a directory named 'analysis' followed by the current date as year, month, day and time: analysis_YYYY-MM-DD_hhmmss, e.
Outputs ClermonTyping outputs multiple files, and we chose not to delete any intermediates that could be of interest for some users. The output directory will contain the following (in the case of no ÀÀname given): db/; a directory containing the BLAST database -analysis_YYYY-MM-DD_hhmmss. html; this file is the main report and contains most of the information pertaining to the analysis; it is also the form in which the online ClermonTyping will return its result, directly in the user's web browser -fasta_file_name_mash_screen. tab; a tsv table written by Mash and described here  [20]  (one for each FASTA in the input query) ClermonTyping also copies the query FASTA file into the output directory.
In silico PCR assays In order to mimic the behaviour of the in vitro PCR methods, the genomic FASTA file is first converted to a BLAST formatted database using the makeblastdb tool. A set of 30 primers (15 primer pairs described in Table  S1 ) is then fed to the BLAST N algorithm using the 90 % identity threshold and a word size of six  [17]. The xml formatted BLAST report obtained is processed by a python script that translates it into a PCR result. Three conditions are required for a target to be considered a valid amplicon:. forward and reverse primers must match on the same contig. the amplicon size must not differ from its expected size by more than 20 % (allowing for small indels in the target) Fig. 1. ClermonTyping method flowchart. The algorithm takes a FASTA file as input and determines the species/plylogroup in two distinct ways: by Mash and in silico PCR.
The Mash database The Mash database was created using the mash -sketch option and comprises 83 strains manually curated to represent much of the diversity of E. clades, E. fergusonii and E. albertii as well as representatives of E. coli phylogroups (Fig. 3 ). We chose not to include an extensive number of strains to keep it lightweight and very fast to interrogate. These strains were then included as part of our testing dataset and will be discussed further below.
Final report The generation of the final report comprises a few validation steps that will allow printing of warning messages that are easily interpretable for the user. First, for each FASTA in the query, we analyse the Mash output and check that only one species/phylogroup stands out. Otherwise an output warning message is created indicating that the input FASTA file might contain multiple genomes. If Mash simply fails to find a close match in its database the most probable hypothesis is that the query FASTA is only a partial genome. As stated earlier, a mutation affecting the binding of one of the primers would lead to an incorrect species/phylogroup assignment. Mash is insensitive to these mutations and in the case of a discrepancy between the in silico PCR assay result and the Mash phylogroup/species the user will be warned and should be able to make a decision regarding the data.
Performance assessment On well-characterized strains To demonstrate the accuracy of ClermonTyping, we gathered a test dataset of 334 well-characterized strains representative of the E. albertii, E. fergusonii, Escherichia clade and E. coli phylogroup diversity, encompassing 230 strains from three archetypal collections (ECOR, IAI and NILS)  [21] [22] [23]  as well as 104 archetypal strains  [24]  from which the complete genomes were available. For all these strains (except SMS-3-5 for which the DNA was not readily available), the species as well as the phylogroup assignment have been determined in vitro in our laboratory by the various Clermont PCR assays (Table  S2 ). The sequence type according to the Warwick and Pasteur schemes are indicated when available  [9, 10]. Lastly, we undertook a Mash assignment of the strains (Table  S2 ).