dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2014) , the ureC database (ureolytic bacteria;  Jin et al. , 2017) , and AF-RefSeq (anaerobic fungi;  Paul et al. , 2018)  drastically improve our ability to monitor rumen microbial diversity. Irrespective, metataxonomic rDNA data have provided insights into the composition of the rumen microbiome under differing parameters, but these techniques are limited in terms of providing insight into microbial function. Nonetheless, due to their low cost, these techniques are the most published and will continue to be important in microbiome research for the near future. Software to predict microbial function from metataxonomic data, such as PICRUSt  (Langille et al. , 2013) , has been applied to many different ecosystems, including the rumen. Although this approach saves on the cost associated with more thorough and accurate shotgun metagenomic analysis, it has limitations in accurately represent microbiome function  (Wilkinson et al.
, 2018). The accuracy of PICRUSt prediction, originally intended for human microbiota data, has recently been tested for the rumen microbiome using datasets with 16S rDNA data and accompanying metagenomics or metatranscriptomic datasets  (Wilkinson et al. , 2018). The data shows poor correlation of predicted function with the actual function seen within the metagenomics/metatranscriptomic datasets  (Wilkinson et al. , 2018). Wilkinson et al. (2018) , developed CowPI an improved 16S rDNA inference platform for the rumen which is based on PICRUSt but uses the Hungate1000 genomes as the searchable genomes (http://www. cowpi. org/). Other platforms allowing inference of function from 16S rDNA data have also been developed, such as Tax4Fun  (Aßhauer et al. , 2015)  and PanFP  (Jun et al. , 2015) , and have been proposed to provide more accurate functional annotations than PICRUSt  (Koo et al.
Binning Genomes From Metagenomes Another major advancement in understanding the capacity of rumen bacteria has been our increased ability to bin genomes from metagenomes. Assembly binning refers to the construction of complete or near complete microbial genomes directly from metagenomic sequencing data, and was first achieved by  Tyson et al. (2004)  from an acidophilic biofilm. Hess et al. (2011)  were the first to apply this to ruminants, assembling 15 draft microbial genomes from the switchgrass associated microbiome of cattle. Subsequently,  Svartstrom et al. (2017)  assembled 99 microbial genomes from the moose rumen,  Stewart et al. (2018)  assembled 913 microbial genomes from the rumen of cattle, and  Parks et al. (2017)  assembled over 8,000 novel microbial genomes from 1,500 public datasets, some of which originated from the rumen.
Traditional metagenomic binning takes an in silico approach whereby metagenomic assembled contigs are clustered by base-composition and abundance across multiple datasetsthe hypothesis being that contigs from the same organism will follow a very similar abundance profile across multiple samples, and will have a roughly similar base composition. The success of such binning procedures is validated by investigating the number of single-copy core-genome genes within each bin, as implemented by software such as CheckM  (Parks et al. , 2015). More recently, physical methods of metagenomic binning such as the use of Hi-C have been published  (Beitel et al. , 2014). In Hi-C experiments, parts of the chromosome that are in contact with one another inside the cell are cross-linked using formaldehyde; cells are then lysed, the DNA is fragmented using a restriction enzyme, followed by random ligation, amplification and sequencing.
, 2018; O'callaghan et al. , 2018). Using a combination of several metabolomics platforms, the pioneer study by  Saleem et al. (2013)  demonstrated that the rumen metabolome is not as simple as previously anticipated. Indeed, 246 compounds were reported as part of the rumen metabolome, including: phospholipids, inorganic ions, gases, amino acids, dicarboxylic acids, fatty acids, volatile fatty acids, glycerides, carbohydrate and cholesterol esters  (Saleem et al. , 2013). As part of this study  Saleem et al. (2013)  also set up a searchable rumen metabolome database to improve metabolite assignments in the rumen (www. rumendb. ca). Recent studies have mainly explored how rumen metabolites are affected by different levels of roughages and concentrate. For example, there is alteration to organic acids, amino acids, amines, sugars and nucleosides/nucleotides when cows were fed diets low on concentrate (40%, DM basis) compared to high concentrate (70%, DM basis;  Zhang R.
g. , acidosis? Given that preliminary data shows that these animals have a less diverse rumen microbiome? • How can we ensure comparability of data generated globally to form a consensus on best practices for achieving the global livestock challenges? are more relevant to the rumen environment  (Hart et al. , 2018). This, however, is only scratching the surface of what is possible. For example, online databases, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG;  Kanehisa et al. , 2017)  and BioCyc  (Caspi et al. , 2016) , which provide collections of characterized metabolic pathways and software tools for exploring them, have been used to provide a better understanding of rumen microbial activity in a variety of contexts  (Hess et al. , 2011; Shi et al. , 2014; Rubino et al. , 2017; Seshadri et al.
, 2018). However, many of these databases are targeted toward human pathogens or model aerobic organisms, such as Escherichia coli, and do not necessarily reflect the functions important to the facultative anaerobic lifestyle of rumen microbes. With nearly 500 genomes from rumen organisms currently available, an opportunity exists to generate rumenspecific databases of known and predicted metabolic functions, similar to INTERMINE  (Kalderimis et al. , 2014)  although this will likely require a parallel concerted effort to generate better phenotypic information for these cultured organisms in vitro and in vivo. The ultimate goal is to be able to combine information from all types of "omics" data, and provide a true "systems" overview of the rumen microbiome. Progress toward achieving this continues for a few other model microbiomes, where the goal has been to identify interactions in order to interpret biological data and ultimately model in silico how microbial communities behave  (Bittner et al.
ACKNOWLEDGMENTS SH, DM, MP, RM-T, SW, IT, HS, JE, SK, GA, and CC acknowledge the support of  ERA-net gas co-fund  for funding (Project name: RumenPredict). SH, HM and CC acknowledge support from  BBSRC  ( BBL/L026716/1  and  BBL/L026716/2 ) and a  British Council Newton Institutional Links funding  (Grant  172629373 ). IM acknowledges funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation program  (Grant  640384 ). JE acknowledges funding from an  EU   H2020 Marie Curie Fellowship  ( 706899 ). CC, AK-S, and EH were supported by the  Biotechnology and Biological Sciences Research Council  (Grants  BBS/OS/GC/000011B  and  BBS/E/W/0012843D ). CN and OM acknowledge the support of the  British Council Newton Institutional Links funding  (Grant  216425215 ).
3. 2. 13 Lysine biosynthesis; Peptidoglycan biosynthesis Roehe et al. , alanyl-D-glutamate-2,6- diaminopimelate ligase K02006 Obalt/nickel transport system - ABC transporters Roehe et al. , ATP-binding protein K02008 Cobalt/nickel transport system - ABC transporters Roehe et al. , permease protein K02030 Polar amino acid transport - - Li and Guan, system substrate-binding protein K02313 Chromosomal replication initiator - Two-component system; Cell cycle-Caulobacter Roehe et al. , protein K02343 DNA polymerae III gamma/tau EC:2. 7. 7. 7 Purine metabolism; Pyrimidine metabolism; Metabolic Roehe et al. , pathways; DNA replication; Mismatch repair; Homologous recombination K02377 GDP-L-fucose synthase EC:1. 1. 1. 271 Fructose and mannose metabolism; Amino sugar and Roehe et al. , nucleotide sugar metabolism; Metabolic pathways K02907 Large subunit ribosomal protein - Ribosome Roehe et al.
2. 1. 30 Phenylalanine metabolism; Microbial metabolism in Kamke et al. , 2016 diverse environments; Biofilm formation-Vibrio cholerae K01913 Trans-2-methyl-5-isopropylhexa- - - Kamke et al. , 2016 2,5-dienoate-CoA ligase K01959 Pyruvate carboxylase subunit A EC:6. 4. 1. 1 Citrate cycle (TCA cycle); Pyruvate metabolism; Carbon Auffret et al. , 2017b fixation pathways in prokaryotes; Metabolic pathways; Microbial metabolism in diverse environments; Carbon metabolism; Biosynthesis of amino acids K02117 V-type H+-transporting ATPase EC:3. 6. 3. 14 Oxidative phosphorylation; Metabolic pathways Wallace et al. , 2015; Auffret subunit A or et al. , 2017b EC:3. 6. 3. 15 K02118 V-type H+-transporting ATPase EC:3. 6. 3. 14 Oxidative phosphorylation; Metabolic pathways Wallace et al. , 2015; Auffret subunit B or et al.
, 2017b EC:3. 6. 3. 15 K02319 DNA polymerase EC:2. 7. 7. 7 Pyrimidine metabolism Shi et al. , 2014) K02319 DNA polymerase I EC:2. 7. 7. 7 Metabolic pathways; Purine metabolism; Pyrimidine Kamke et al. , 2016 metabolism; DNA replication (Continued).
Gene ontology Gene Ontology (GO) was designed to describe the roles of genes in any biological organism  [16]. GO is organized according to three distinct categories, also known as ontologies, namely, Molecular Function, Cellular Component, and Biological Process. GO contains more than 44,000 terms connected through different kinds of relations: hierarchical (namely is_a and part_of) and transversal (e. g. , regulates and more recently starts_with). As the relations between terms are oriented, GO ontologies are directed acyclic graphs (DAGs). Of all GO terms, nearly 11,000 terms are molecular functions, nearly 4,000 terms are cellular components, and nearly 30,000 terms are biological processes. GO is the most popular knowledge source used by the biology community to annotate genes. The GO Annotation (GOA) database has been developed for this purpose, specifically to associate GO terms to a gene in a given organism to describe its biological roles  [17].
Thus, the average silhouette width (ASW) of the clusters for a given K is given by: ASW K ¼ P i2n s i n ð8Þ where n refers to the total number of terms. Then, considering the results obtained for each set of K clusters, the optimal partition (i. e. , the K value) can be deduced from the highest ASW score. The ASW score is then computed from the two sets of modules to analyze its distribution. To guide the interpretation of the ASW score distributions, a score below 0. 25 or above 0. 50 might correspond to an artificial or real structure within the data  [40] ).
Experimental data We concentrated our study on two human data experimental datasets to illustrate our analytic framework. The Chaussabel and Baldwin dataset comprises 260 genes sets called modules that were computed according to their expression profile achieved from one to fifteen experimental protocols using a coexpressed network approach  [3]. Each of these protocols corresponds to a study of a disease with the aim of identifying genes that trigger an immune response. More precisely, the modules that gather genes with a similar behavior within the fifteen experimental protocols may be related to the immune response in general. In contrast, modules of genes that show a similar behavior in a limited number of protocols may be expressed specifically in some diseases. The BTM (blood transcriptional modules) dataset comprises 346 modules and aims to characterize innate and adaptive immune responses in vaccination studies  [15].
Clustering of annotation terms using computed semantic similarity matrices 4. 2. 1 Clustering partition fitness. Each module in both datasets has been used to retrieve annotation terms related to the genes within the modules. Then, the semantic similarity between each pair of annotation terms was computed. To derive a quality score for the clusters of terms, we analyzed the CCC value. As shown in Fig  5 , we observe that the CCC scores given by the SLHM, CLHM and ALHM methods follow the same trend as they tend to increase or decrease according to the semantic similarity measures. However, there is a significant difference in the CCC score dispersion among the clustering methods. We observe that the CCC scores are noticeably lower with the SLHM method (e. g. , the CCC median scores of the LC and Zhou measures are below 0.
50). The observation of such low varying degrees of quality could be expected for SLHM because this clustering method is known to often perform consecutive additions of terms and is more often used to reveal gradients in a dataset. The dispersion of scores according to the modules and semantic similarity is less important for the other two clustering methods, and in both cases, the CCC median scores are the highest for the same https://doi. org/10. 1371/journal. pone. 0208037. g005 measures, i. e. , DF and Nunivers. Notably, the Nunivers measure has the greatest CCC score with a very small dispersion according to the modules. In contrast, the measures that provide the most significant CCC score dispersion are not systematically the same according to CLHM and ALHM. While the best clustering quality score was obtained by using the ALHM method, the following question emerges: for a given semantic similarity measure and a given module, do we retrieve the same partitions of terms regardless of the clustering method used? Thus, how similar are the partitions given by both clustering methods for a given module and a given semantic similarity measure? If the partitions are highly similar, this information could be consider reliability and robustness criteria for the semantic similarity measure due to the little influence of the clustering methods.
Variations induced by the clustering methods. We used the Z-index to compare and decipher the similarities and differences between both partitions given by the CLHM and ALHM methods (Fig  6 ). Based on the assumption that a pertinent semantic similarity measure should provide the same clusters regardless of the adopted clustering approach, we assume that the smaller the Z-index score, the greater the similarity between two distinct clustering method partitions. Thus, most semantic similarity measures give similar results with Z-index scores smaller than 0. 25, except for the Ganesan and PS measures considering both datasets (LC and Zhou were also below 0. 25 when computed using the Chaussabel and Baldwin dataset). As an intermediate conclusion, based on this analysis, we can highlight five semantic similarity measures (LC, Zhou, Resnik, Lin, Nunivers, DF and AIC) for which the choice of CLHM or ALHM does not show a significant impact regardless of the dataset.
4. 2. 3 Variations induced by the semantic similarity measures. Finally, we analyzed the impact of each semantic similarity measure according to the specific features that may be good markers providing relevant information to domain experts. A-Identifying and filtering representative terms First, after applying the representative algorithm, we analyzed the quality of the clusters of terms using the ASW score. This score was computed for each module in both datasets. As shown in  Fig 7,  we observe that the ASW score is below 0. 25 for the PS and Resnik measures and between 0. 26-0. 50 for the other measures. Thus, no semantic similarity measure managed to compute a global partition with a structuring score above 0. 50, which is the threshold used to separate the unstructured from structured clusters.
Of particular interest, their respective box-plots given for the three other quartiles show that only a few terms with pertinent levels of information were retained. In contrast, Resnik, Nunivers, DF and AIC provided the best results with a larger number of synthetic terms having IC GOu scores between the first and third quartiles regardless of the dataset. The other measures are more sensitive to the type of experimental data (e. g. , Ganesan shows interesting results for the Chaussabel and Baldwin dataset, whereas it performs poorly with the BTM). Finally, the same analysis was performed using the results given by the DAVID tool  [5]. Thus, we applied the enrichment method and only retained the resulting terms that annotate at least three genes. The decrease in terms from Q 0 to Q 3 is less pronounced than all semantic similarity measures, corresponding to a higher number of more important terms with a high IC GOu score.
The second line in each figure highlights the related gene coverage according to the same quartiles before and after applying the pipeline. The first observation considers the Q 0 boxplots in which all semantic similarity measures nearly achieve a 100% coverage. However, in the other box-plots, the LC and Zhou measures show singular box-plot profiles compared to the other measures. Indeed, most genes are related to synthetic terms with the weakest IC GOu scores (i. e. , within the first quartile of values). Thus, these two measures positively retained a small number of terms (from Q 1 to Q 3 ) with the disadvantage of a low level of details. In contrast, Resnik, Lin, Nunivers, DF and AIC have better IC GOu scores considering the percentages of the Q 1 to Q 3 categories of terms regardless of the dataset.
Among these measures, we focus on Nunivers and DF because most specific terms (within the last quartile of the IC GOu score) still annotate more than 25% of the genes. From this perspective, the other measures appear to be sensitive to the type of dataset. Finally, considering DAVID, we observe that its coverage of genes is lower than that of most semantic similarity measures. However, notably, DAVID performs better with the BTM, while simultaneously, it does not provide a gene coverage above 60% for half of the modules.
As previously mentioned, the annotation of biological data is still an open question in scientific fields  [41, 42]. Thus, we analyzed two large gene sets and specifically focused on human data, but the interpretation of biological experiments can change with the evolution of GO according to various organisms. Thus, the observations reported by Tomczak et al. [41]  and Haynes et al. [42]  regarding the strong annotation bias in the GO annotations in which more than half of the annotations are related to approximately one fifth of the human genes have strongly guided the choice of the datasets. Therefore, human data present competitive advantages for using very well-studied genes that are very well annotated in addition to a larger set of poorly studied genes. As enrichment methods seem to tend to focus on the well-known subpart, it is truly relevant to analyze how the integration of less known data (with still evolving knowledge) can affect the interpretation of biological data.
While these measures are often combined with enrichment methods, their a priori use may widely impact the interpretation of biological datasets. To investigate these challenges, we developed a large-scale approach that uses semantic similarity measures within a robust interpretive analytic framework. We chose to use a straightforward set of nine measures covering various features and explored their pitfalls by examining criteria that may be good markers providing relevant information to domain experts. Thus, we analyzed the semantic similarity measures in terms of their capacity to synthesize information and provide the best trade-off for retaining detailed information. Therefore, the main finding was that using Gene Ontology to annotate gene sets, we obtained better results with the node-based measures that used the terms' characteristics than with measures based on edges that link these terms.
This paper aims to present a wide dataset of ground penetrating radar (GPR) profiles recorded on a full-size geophysical test site, in Nantes (France). The geophysical test site was conceived to reproduce objects and obstacles commonly met in the urban subsurface, in a completely controlled environment; since the design phase, the site was especially adapted to the context of radar-based techniques. After a detailed description of the test site and its building process, the GPR profiles included in the dataset are presented and commented on. Overall, 67 profiles were recorded along eleven parallel lines crossing the test site in the transverse direction; three pulsed radar systems were used to perform the measurements, manufactured by different producers and equipped with various antennas having central frequencies from 200 MHz to 900 MHz.
An archive containing all profiles (raw data) is enclosed to this paper as supplementary material. This dataset is the core part of the Open Database of Radargrams initiative of COST (European Cooperation in Science and Technology) Action TU1208 "Civil engineering applications of Ground Penetrating Radar". The idea beyond such initiative is to share with the scientific community a selection of interesting and reliable GPR responses, to enable an effective benchmark for direct and inverse electromagnetic approaches, imaging methods and signal processing algorithms. We hope that the dataset presented in this paper will be enriched by the contributions of further users in the future, who will visit the test site and acquire new data with their GPR systems. Moreover, we hope that the dataset will be made alive by researchers who will perform advanced analyses of the profiles, measure the electromagnetic characteristics of the host materials, contribute with synthetic radargrams obtained by modeling the site with electromagnetic simulators, and more in general share results achieved by applying their techniques on the available profiles.
Several targets are embedded in the test site and accurately geo-referenced. They are representative of objects that can be generally met in trenchless works-such as pipes, cables, stones of various size, and masonry. Precautions were taken to prevent water inflows, to avoid any bias in GPR measurements due to environmental changes. To ensure optimum data acquisition, the site is easily accessible and far from noise sources such as electrical installations and trees. The dataset subject of this paper includes 67 radargrams recorded by using three commercial pulsed GPR systems equipped with various antennas working on different ranges of the electromagnetic spectrum, and in particular with central frequencies from 200 MHz to 900 MHz. Measurements were performed along 11 parallel lines, crossing the test site in the transverse direction.
The dataset is the core part of the Open Database of Radargrams initiative of COST (European Cooperation in Science and Technology) Action TU1208 "Civil engineering applications of Ground Penetrating Radar"  [4, 5] , which consists in realizing and making available an open collection of interesting GPR responses (experimental and synthetic) to enable a very effective benchmark for forward and inverse electromagnetic scattering methods imaging techniques and signal processing algorithms. Actually, numerous studies in the literature present and sometimes compare analytical and/or numerical methods for the analysis and interpretation of GPR data; but, each approach is generally considered individually in a specific context and compared with a limited number of other methods. The availability of a reference set of GPR profiles is a turning point in this field of research, because it facilitates a wider, more effective and rigorous comparison of different techniques.