dataesr/scientific-paragraphs-categorization huggingface dataset:
Higher resolution was achieved using Bayesian clustering analysis in Structure. Using method of Puechmaille  [33] , the highest support was obtained for K = 6 (S2 Fig) where mean membership coefficient for each cluster differentiated all breeds. Considering each individual separately, some degree of shared ancestry between CF and BWPGCA and between DD and GWP is visible (Fig  4 ). On the other hand, method of Evanno  [32]  supported K = 2 as the best number of clusters, where the first group consisted of CF and BWPGCA and the second group consisted of the remainder of the studied breeds (S2 Fig). The first group represented approximately a half of the dataset which might bias the analysis.
Acknowledgments We would like to thank to all the owners from all over the world who provided a sample of their dog. Also, we thank to the  Czech Club of Česky ´Fousek Breeders  that agreed to submit samples of CFs for the realization of this study. We thank the  Bohemian Wirehaired Pointing Griffon Club of America  and  Cornell Veterinary Biobank  who helped us to obtain samples from the US. We thank also  Magdalena S  ˇetlı ´kova,  Milena Jindřichova , and Jiřı ´Černy ´for their help with the sample collection in the Czech Republic and the Netherlands and help in the lab. We thank the  Česky ´Fousek Vereniging Nederland  for letting us collect samples at their Club dogshow. Thanks to  Mr. Lloyd Birch ,  Sara Krs ˇı  ´kovaPes ˇatova,  Iveta Dočkalova ,  Ines Oebel ,  Rem DeJong , and  Amy Caswell  for giving permission to use photos of their dogs (Fig 1).
Environmental variables We considered four bioclimatic variables known to be strong drivers of community assembly in mountain systems  (Körner, 2003) : soil temperature, solar radiation, growing season length (GSL) and number of frost days. Growing season length and annual number of frost days were derived from Landsat 7 and 8 imageries  (Carlson, Choler, Renaud, Dedieu, & Thuiller, 2015). Solar radiations were calculated with the area solar radiation tool in ArcGIS (version 10. 2, 2013; Redlands, CA, USA) using a 2-m LiDAR digital elevation model and the clear sky model set to a sky size of 2800 pixels  (Carlson et al. , 2015). Soil temperature was measured in the field. To capture small scale variation, we added local topographic variation using elevation, slope, the topographic wetness index (TWI) and the topographic position index (TPI) with a 50cm resolution Digital Elevation Model derived from the airborne LiDAR data acquired the year of sampling.
Sampling To get an estimate of the diversity found in each of the sampled plot, we analyzed soils samples through DNA metabarcoding. In each plot, we collected 21 soil samples (10 cm depth, 5 cm diameter) to account for small-scale heterogeneity (ten samples on each diagonal and a supplementary central one). Soil biodiversity was measured with four different DNA markers. Two universal markers, one amplifying all Eukaryotes (v6-v7 region of the 18S rRNA gene) and one amplifying all Bacteria (v5-v6 region of the 16S rRNA gene), were used to obtain a general overview of the soil multi-trophic composition of the plots. We also used two additional markers focused on fungi (Internal Transcribed Spacer 1) and vascular plants (Chloroplast trnL-P6 loop). Molecular analyses and data curation are presented in  Ohlmann et al.
We further used the FUNguild database  (Nguyen et al. , 2016)  distinguish symbiotic (i. e. mycorrhizal), saprophytic and pathogenic fungi. Finally, the diversity of soil micro-and mesofauna regulates the activity and abundance of soil microbes and associated nutrient recycling by shredding/grazing leaf litter or feeding directly on microbes  (Crowther et al. , 2015; Pulleman et al. , 2012). They should thus shape the multi-trophic β-diversity and have a determinant impact on multiple ecosystem functions. We thus extracted from the Eukaryotes-marker, all MOTUs assigned to nematodes, springtails, and oribatid mites which are the most abundant belowground multicellular organisms  (Wall et al. 2012). We further distinguished bacterivore from herbivore/fungivore nematodes using the NEMguild database  (Nguyen et al.
Statistical Analyses To address our first two objectives, we performed a set of path analyses using the lavaan package in R (Figure  2 ,  R Core Team, 2018; Rosseel, 2012). All variables were first centered and normalized. First, we built four path analyses independently for each of the three single ecosystem functions and multifunctionality to determine the effects of climatic and soil properties turnover and of total multi-trophic β-diversity. Second, we built four path analyses independently for each of the three single ecosystem functions and multifunctionality to determine the effects of environmental turnover (turnover of climate and soil properties) and of vertical β-diversity. Finally, we built 36 independent path analyses independently to assess the effect of each of the 9 trophic groups on each of the three single ecosystem functions and multifunctionality while including in each model the effects of the two sets of variables (climate and soil properties) for environmental turnover.
Acknowledgements We thank the large team of researchers and students that helped collect the data. The research received funding from the  French Agence Nationale de la Recherche (ANR)  through the  GlobNets  ( ANR-16-CE02-0009 ) project, and from " Investissement d'Avenir " grants managed by the  ANR  ( Trajectories :  ANR-15-IDEX-02 ; Montane: OSUG@2020:  ANR-10-LAB-56 ).
Educational activities to increase data mobilisation by taxonomists, data users, and/or wider audiences Many scientists and the general public around the world are unaware of the presence and advantages of large-scale open-access databases. Even if scientists are familiar with these facilities, data preparation and submission can be complex for contributors unfamiliar with data publication protocols. These issues are more pronounced for scientists in developing countries or non-native English speakers. As a result of these perceived and real data publication obstacles, significant biodiversity and biogeography knowledge remains in personal databases and non-digital archives. These logistical hurdles and data ownership perceptions frequently stand in the way of data publication. To expose researchers to these resources, initiatives like OceanTeacher Global Academy (OTGA) (https://classroom.
Promoting synergy of biodiversity research efforts via increased collaboration at all levels In order to predict and discover biodiversity on a global scale, collaborative approaches among institutions and nations are necessary. Guralnick, Hill & Lane (2007)  proposed a framework to use online databases and tools to improve and standardize geographic data, and to validate and highlight taxonomic data and misidentifications. They also suggested that a global infrastructure for web-based tools would enhance the quality of visualizing and standardizing raw biodiversity data and lead to a higher degree of collaboration and accessibility of knowledge  (Guralnick, Hill & Lane, 2007). The decade long Census of Marine Life was the largest global collaboration amongst marine biologists  (O'Dor et al.
Thus, sharing of taxonomic expertise can alleviate funding deficits, allow the transfer of knowledge, and lead to international partnerships. The number of marine species formally described each year has never been greater, and aside from naming these species, more work is required to understand their life histories and ecology, biogeography, and evolution  (Appeltans et al. , 2012). Costello, Vanhoorne & Appeltans (2015)  recommended the use of collaborative online databases, taxonomic effort improved and increased through communication, easier access to specimens, engagement of non-specialists, and international collaboration  (Costello, Vanhoorne & Appeltans, 2015). Further,  Costello et al. (2013b)  advocated abandoning ''data-sharing'' and instead suggested requiring data publication within a journal or to online infrastructures such as OBIS, WoRMS, and/or the Global Biodiversity Information Facility (GBIF)  (Costello et al.
Moreover, DNA is often only useful if the species has already been formally described, specimens from which DNA has been sampled have been correctly identified, and the DNA sequence(s) published in an open access database. Other tools that can help in species identification for conservation and resource management include interactive keys. Unfortunately, there are only such keys for a few marine taxa, and these mostly concern higher taxonomic levels (family-level or higher)  (Dallwitz, 1997; Nimbs, 2017). Along with Aichi Target 19, the exchange of knowledge between experienced scientists and young researchers as well as the use of different identification tools (e. g. , interactive keys, barcode databases) will help in biodiversity recognition. These targets need to be prioritised by governments and funding agencies as they are fundamental to quality assurance in other areas of biology, ecology, and resource management.
Improvement and standardization of genetic, genomic, and other "omics" tools to aid in discovery, assessment, description, and cataloging of biodiversity Countless studies have used genomic tools to study marine biodiversity, connectivity, and functional diversity  (Carvalho et al. , 2010). While data sharing, standardized sampling, metadata collection, and sequencing protocols still require significant standardization, the use of repositories such as GenBank, Dryad, and Sequence Read Archive have made published data much more accessible. We expect this trend to continue as new tools, such as the Genomic Observatories Metadata database (GeOMe), streamline sequencing data and metadata submission  (Deck et al. , 2017). The classification and phylogeny of eukaryotic organisms benefits from the use of genetic markers that help delineate species in combination with morphological, ecological, and/or physiological information  (Hebert, Ratnasingham & De Waard, 2003; Palumbi, 2003).
, 2017). In order to obtain robust and reproducible metabarcoding results, critical methodological aspects remain to be improved  (Dickie et al. , 2018; Goldberg et al. , 2016; Nichols et al. , 2018). Studies are needed to address the effects of alternative protocols on sampling, molecular, and bioinformatic processing level in order to develop standardized and reliable techniques for applying these new methods. Furthermore, because large amounts of marine organisms have not been genetically characterized, integrative approaches should be supported in order to fill database gaps. The continued improvement and standardization of genetic, genomic, and other ''-omics'' tools (e. g. , proteomics, transcriptomics) will continue to be valuable components in the discovery of new marine prokaryotes and eukaryotes, as well as in monitoring biodiversity, thereby contributing to Aichi Target 19.
ACKNOWLEDGEMENTS We would like to thank  David Beauchesne ,  Rémi M. Daigle ,  Jésica Goldsmit ,  Philippe Archambault ,  Anna Metaxas , and  Paul Snelgrove  for their thoughts and leadership of the mentorship program organizing committee and workshop facilitators. Special thanks also to  Rémi M. Daigle  and  Jésica Goldsmit  for reviewing the current paper. Comments from  Martin Thiel  and  Naomi Kingston  greatly improved an earlier version of this paper.
Acknowledgements B. S. F. gratefully acknowledges support from the  Deutsche Forschungsgemeinschaft (DFG)  under grant numbers  FL 834/1-1 ,  FL 834/2-2 ,  FL 834/5-1 , and  FL 834/7-1. All authors wish to thank  Yuta Nishina  for assistance with the DWCNTs used in this work.
595 The permeability was examined for single phase flows through di↵erent porous materials. These materials include magnetite sintering  [5] , copper sintering  [24]  and natural rocks  [25]. The predicted and measured e↵ective permeabilities are depicted in Fig. 9. The liquid perme-600 ability K l was estimated with equation 20 for R ⇤ = 1. The median radii of open pores level R i were estimated for each experiment. When is not provided in the data base, it is set equal to 0. 4. The SF predictions are, overall, in good agreement with the experimental data. These 605 results also confirm that the lognormal distribution is a suitable choice for depicting a large range of porous media. w ) as well as data , thickness l dep , radius R 1 of CRUD database provides ho layers of deposit. As a result, ried out for no more than two 620 validation reported here rest predicted and measured over h f ouled defined in equation 1.
Three sub-cooling conditions database: T sub = 10K, 18 625 heat transfer regime examin The dry-out regime, which ra (Table  2 ). This behavior is a good indicator of the qualitative trend of the SF methodology. Note that fouling deposits of porosity greater than 0. 7 are rarely observed in SGs. Their mesh structure is too fragile to stick to the tube surface. As a result, they are washed away by the bulk fluid stream. The permeability was examined for single phase flows through different porous materials. These materials include magnetite sintering  [5] , copper sintering  [24]  and natural rocks  [25]. The predicted and measured effective permeabilities are depicted in Fig. 9. The liquid permeability K l was estimated with equation 20 for R * = ∞. The median radii of open pores level R i were estimated for each experiment.
9. The liquid permeh equation 20 for R ⇤ = 1. res level R i were estimated is not provided in the data he SF predictions are, overhe experimental data. These lognormal distribution is a a large range of porous me- B3  [5]  B4  [5]  B4  (7)   [5]  TS1  [24]  TS2  [24]  TS3  [24]  TS4  [24]  TS5  [24]  FB  [25]  AC  [25]  y=x Figure  9 : Predicted and measured permeability  [5] [24]  [25]  properties of nuclear fuel channels (fluid pressure, cladding temperature T w , fluid temperature T 1 , mass flow M 1 , 615 heat flux q 00 w ) as well as data on fouling deposits (porosity , thickness l dep , radius R 1 of open pores of scale 1). The CRUD database provides however data for at most two layers of deposit. As a result, the SF simulations were carried out for no more than two layers of deposit.
Also, the 620 validation reported here rests on the comparison of the predicted and measured overall heat transfer coe cient h f ouled defined in equation 1. Three sub-cooling conditions are available in the CRUD database: T sub = 10K, 18K and 55K. Moreover, the 625 heat transfer regime examined here is nucleate boiling. The dry-out regime, which rarely occurs in SG, is ignored (Table  2 ). Fig. 10  shows that the agreement between the predicted and the measured heat transfer coe cients is very good.
Validation of the overall heat transfer model The heat transfer model was validated with experimental data extracted from the Chalk-River Unidentified Deposit (CRUD) experimental database of EPRI  [26]. This database comprises data for large scale thermal-hydraulic properties 470 of nuclear fuel channels (fluid pressure, cladding temperature T w , fluid temperature T ∞ , mass flow M ∞ , heat flux q w ) as well as data on fouling deposits (porosity φ, thickness l dep , radius R 1 of open pores of scale 1). The CRUD database provides however data for at most two layers of deposit. As a result, the SF simulations were carried out for no more than two layers of deposit. 475 Also, the validation reported here rests on the comparison of the predicted and measured overall heat transfer coefficient h f ouled defined in equation 1.
Acknowledgements This work has benefited from th 795 tricité de France (EDF) and of  Recherche Technologie  (ANRT the financial support of the  N neering Research Council of Ca  Acknowledgements This work has benefited from the financial support of Elec-795 tricité de France (EDF) and of the  Association Nationale Recherche Technologie (ANRT). M. Lacroix acknowledges the financial support of the  Natural Sciences and Engineering Research Council of Canada (NSERC). Acknowledgements This work has benefited from the financial support of  Electricité de France (EDF)  and of the  Association Nationale Recherche Technologie (ANRT). M. Lacroix acknowledges the financial support of the  Natural Sciences and Engi-610 neering Research Council of Canada (NSERC).
DISCUSSION Guyader et al. (8)  showed that combining dietary strategies acting theoretically on hydrogen production (lipids) and consumption (nitrate) can have an additive effect on methane reduction. In a second study, they confirmed the antimethanogenic potential of nitrate supplementation but observed no effect of tea saponin on methane production  (13). These studies were conducted simultaneously; cows were selected at random from the same experimental herd and were randomly allocated to a study. Given the consistency of results for methane production and fermentation patterns reported in the two articles of Guyader et al. (8, 13) , we decided to analyze the rumen microbiota from both studies at the same time (from DNA extraction up to statistical tests). Although linseed and nitrate have a medium to high potential methane-mitigating effect (the effect of saponins being less reproductive)  (20) , microbial data are scarce and inconsistent between studies.
This could be explained by different methodologies for rumen sample collection, conservation, and nucleic acid extraction, as well as on how data were obtained and analyzed  (21, 22). Thus, second, we compiled the microbial data in order to get insight into the mode of action of nitrate on the rumen microbial ecosystem. To this aim, we first checked that the microbiota of the two groups of cows was comparable; hence, we performed a detailed analysis of microbial community structure and composition in rumen contents sampled during the period when CTL diet was fed to each animal. No major differences in bacterial communities were observed, except a nonsignificant shift in the Bacteroidales/Clostridiales ratio, which is known to vary widely across individual animals  (23). However, we observed numerical differences in the relative abundance of Entodinium (60% in study 1 versus 46% in study 2), which is consistent with enumeration results reported previously (5.
The libraries were sequenced on a 250-paired-end MiSeq run and generated 8,249,698 raw reads for bacterial 16S rRNA genes, 1,778,521 for archaeal 16S rRNA genes, 836,803 for ITS1, and 2,245,531 for eukaryotic 18S rDNA (Table  S6 ). Data were analyzed on an in-house Galaxy-based graphic user interface for QIIME  (46) , PIPITS  (47) , and IM Tornado (48) (Table  S5 ). All pipelines included a quality control step, removing sequences with Phred scores of Ͻ33 and trimming based on expected amplicon lengths, as well as merging paired reads, chimera search, and removal and OTU picking (Table  S6 ). Merging paired-end archaeal 16S rRNA gene reads was performed by mothur's (49) make. contigs command before input in the QIIME pipeline. Taxonomic classification for Bacteria and Protozoa was based on the SILVA v123 database  (50) , for Archaea on RIM-DB  (51) , and for fungi on the UNITE database  (52).
Significance was considered at a P value of Յ0. 05. Trends were discussed at 0. 05 Ͻ P Յ 0. 1. Least-square means are reported throughout. OTUs with fewer than 3 sequences were withdrawn from further analysis. OTU tables were imported in R and rarefied to minimize the variations created by different sample depths of subsampling. Further analysis was performed using the vegan R package  (55). Alpha diversity values for all microbial communities were obtained using various diversity indices (Shannon and Simpson diversity indices, richness, and evenness) and analyzed by ANOVA for the effect of contrasts and the interactions described above. NMDS was used to ordinate microbial libraries (4 cows and 4 experimental periods per study and per microbial group). We used the betadisper function to check the homogeneity of group dispersions before performing a PERMANOVA analysis via the Adonis function of vegan.
The multipatt function from R package indicspecies  (56)  was used to find indicator OTUs using a 5% significance level for selecting indicators in cows fed linseed, tea saponin, and nitrate. The speciessite group association parameter was IndVal. g. Correlation analyses between microbial populations and some fermentation parameters (methane, hydrogen, and VFA ratio) were performed in R. Only microbial groups that represented more than 1% (average of all samples) of the total community within each of the three microbial groups (bacteria, archaea, or protozoa) and that were detected in at least 50% of rumen samples were included in the analysis. Spearman's rank correlations and P values were calculated by the above-described contrasts and plotted using the packages hmisc  (57)  and corrplot  (58).
ACKNOWLEDGMENTS J. G. was the recipient of an  INRA-Région Auvergne Ph. D. scholarship. C. S. acknowledges receipt of a postdoctoral fellowship from  Fundación Alfonso Martín Escudero (Madrid, Spain). We also thank  NEOVIA  for financial support. We thank the  INRA personnel of the Experimental Unit Herbipôle  ( L. Mouly ,  D. Roux ,  S. Rudel , and  V. Tate ) for taking care of the animals and of the  UMR Herbivores  ( L. Genestoux  and  D. Graviou ) for their help in sampling and performing laboratory analysis. We are grateful to the  INRA MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computational resources. We thank  David Marsh  for checking and amending our English. We are grateful to  Richard Dewhurst  for constructive criticism of the manuscript. Authors made the following contributions: conception or design of the work, M.
Relative levels calculated for three independent experiments after normalization to tubulin are shown. g DNA combing analysis of replication fork speed in sh-Ctrl, sh-CLSPN, and sh-TIM HCT116 cells collected in early S and late S. Median lengths of CldU tracks were determined for three independent experiments and indicated in red. ****p < 0. 0001, ***p < 0. 001, ns nonsignificant. Mann-Whitney rank sum test higher in cancer cell lines relative to immortalized fibroblasts, which is consistent with earlier studies  63, 64. Moreover, we observed a tight correlation between the abundance of Claspin, Timeless, and CHK1 proteins the proteomic landscape of 50 colon cancer cell lines  43. Together, these data indicate that the downstream components of the ATR-CHK1 pathway define a functional module that is specifically enhanced in cancer cells.
Gene-expression values were divided into two equal groups by taking the median value. Univariate analysis was performed using the Log-rank test. Two-sided p values of less than 0. 05 were considered statistically significant. Statistical analyses were performed using STATA 12. 0 software. Immunohistochemistry. Paraffin-embedded specimens were obtained from 14 breast cancer patients for immunohistochemical analysis. The procedures of immunostaining were carried out with standard protocols by the PETRA-AMMICa Facility (Gustave Roussy Institute, France). Briefly, 3 μm paraffin tissue slides were deparaffinized after antigen retrieval and quenching of endogenous biotin, sections were incubated at room temperature for 60 min with a rabbit anti-Timeless antibody (1/100, ab109512, Abcam). Antibody binding was detected using the EnVision Flex Dako kit (Agilent) and counterstaining was performed with Mayer's Hemalun solution.