dataesr/scientific-paragraphs-categorization huggingface dataset:
The study of every particular environment calls upon peculiar requirements. Dairy products are no exception, as their bacterial communities are usually dominated by few phylogenetically highly related species, which are often difficult to discern, such as L. casei, L. paracasei and L. rhamnosus or S. thermophilus and S. salivarius. Particularly for S. thermophilus, which is a very important representative bacterium in dairy products, the official name still is S. salivarius subsp. thermophilus  (90). Although a separate full species status was proposed (91), persistent contention prevented a full ratification by the taxonomic committees Increasing sequence read lengths will make it possible to cover three HVRs or even the entire 16S, thus significantly improving taxonomic annotation accuracy at species rank by using a manually curated database like the DAIRYdb.
D R A F T Streptococcus spp. An automatic and reliable taxonomic annotation to the correct species is pivotal to further routine microbial diagnostics. While universal 16S databases, such as Silva, RDP and Greengenes cover a broad biodiversity allowing to capture the maximal biodiversity available in a system, the enormous number of sequences in those databases lead to conflicting taxonomy annotation at genus and species ranks and ambiguous annotations or blanks due to competing sequences increase accordingly  (69). Moreover, the size of the database can be a deterrent for researchers to improve the quality of taxonomic annotation of the sequences therein. Most of the detected OTUs in NGS analyses diverge from authoritative reference sequences from type strains either due to sequencing biases or missing cultivated representative strains.
Beside reference sequences, many environmental sequences are annotated by the universal databases Silva, RDP and Greengenes based on different taxonomic classification standards, e. g. , Bergey's Manual, the List of Prokaryotic Names with Standing Nomenclature (LPSN), International Nucleotide Sequence Database Collaboration These different curation strategies lead to annotation conflicts between the databases and disagreement between microbiome studies, which are not biologically explained, rather a consequence of the database used for annotation. Different to available universal databases, DAIRYdb achieved correct taxonomy annotation for ∼90% of species names on single HVRs and HVR pairs with sequences present in dairy samples  (70). In fact, the DAIRYdb significantly reduced conflicting miss-annotated sequences and facilitated manual curation, while covering the inspected biodiversity.
The better performance of the DAIRYdb over universal databases can be explained by the overall reduced number of sequences, only 10'290, with no conflicting taxonomy at all taxonomic ranks. Our results are in disagreement to the recommendation to use the largest and most diverse database possible for 16S classification  (96). On the opposite, manually curated 16S databases with authoritative fulllength 16S sequences dedicated to the studied environment enormously improve classification confidence to the species rank  (54, 68, 69). Reducing the number of representative sequences to a minimal number in the training set further diminishes the risk of highly similar sequences with conflicting taxonomy, thus lowering the performance of the database used for classification  (54, 68). A certainly valid argument against manually curated databases is their lack of reproducibility  (54).
However, annotation accuracy achieved with the DAIRYdb significantly outperformed all universal databases tested here, as well as the RDP trainset v16, which was shown to have the best performance among the universal databases  (54). The training sets of the universal databases contained sequences with missing taxonomic labels at uncertain classifications at any taxonomic rank with increasing number of blanks at species levels  (54). The consequences are numerous unclassified OTUs with no biological meaning. We therefore propose the manually curated DAIRYdb as a gold standard database for 16S microbiome studies on cheese and dairy products. The implementation of a curated database may lead to wider consensus and standardization processes reducing conflicts in literature due to the use of different universal databases integrated in different classification tools  (67, 97).
D-Bact-1114-b-A-16 v6R 1099-1114 16 GGGTYKCGCTCGTTRY 50 mod. after (62) S-D-Bact-1114-a-A-16 1099F_v7f S-*-Univ-1099-a-S-16 v7F 1099-1114 16 RYAACGAGCGMRACCC 50 new primer S-*-Univ-1100-a-S-15 1200R_v7r S-D-Bact-1200-a-A-16 v7R 1185-1200 16 GAYTTGACRTCVTCCM 38 new primer 1185F_v8f S-D-Bact-1185-a-S-16 v8F 1185-1200 16 KGGABCACCGCYCGYC 63 new primer 1407R_v8r S-D-Bact-1407-a-A-16 v8R 1391-1407 16 GRCGRGCGGTGWGTRC 63 mod. after (65) S-D-Bact-1391-a-A-17 1391F_v9f S-D-Bact-1391-a-S-16 v9F 1391-1407 16 GYACWCACCGCYCGYC 63 new primer 1510R_v9r S-*-Univ-1510-b-A-19 v9R 1492-1510 19 GGNTACCTTGTTACGACTT 42 mod. after (66) S-*-Univ-1492-a-A-21 D R A F T manually curated databases are of paramount importance to improve reproducibility, speed during the bioinformatics process of microbial community studies and communication between researchers (54).
ACKNOWLEDGEMENTS Eric Dugat-Bony  ( INRA Grignon, GMPA ) improved the completeness of the DAIRYdb by providing some additional 16S sequences from his own studies related to cheese rind bacteria. We are grateful to the genotoul bioinformatics platform  Toulouse Midi-Pyrenees  ( Bioinfo Genotoul ) and  INRA   MIGALE bioinformatics platform  (http://migale. jouy. inra. fr)for providing computing and storage resources.
5 DFig. 5. 55 Fig. 5. Taxonomy annotation accuracy of the DAIRYdb on reads extracted with V-Xtractor. Single HVR V1-V9 were re-annotated using three different classification algorithms, Blast+, Metaxa2 or SINTAX, respectively. This figure shows the results with SINTAX (Analyses with Metaxa2 and Blast+ are shown in Additional File 2). Taxonomy annotation was bootstrapped 1000 times with a subset of 100 randomly selected sequences from the DAIRYdb and annotated with DAIRYdb (A), Greengenes (B), LTP (C), RDP (D) and Silva (E). Average performance of all HVR for each database (F) (accuracy = correctly annotated/total).
1 ). Survey data were used to estimate the initial sediment volume assuming a spanwise-uniform flat bed with a constant packed bed porosity of 45%. The ACVP was positioned at  = 2. 64 m (Figure  1 ) before starting the experiment. An experimental run was initiated by rapidly opening the sluice gate at the outlet of the channel, provoking a sudden water level drop that deactivated the circular overflowweir and transferred the entire flow discharge into the flume. The start time (i. e. ,  = 0 s) was defined for each run as the instant the water level -recorded by the ultrasonic sensordropped to 95% of its original level. After a period of transient flow of about 30 s, the fluid flow and the particle transport layer became fully developed with a normal flow depth of   = 0. 14 m (0. 13 m) and a mean bulk flow velocity of  = 0.
57 m/s (0. 46 m/s) for S3 (S1). After a duration of about two minutes, the sluice gate was closed to end the run and the original still water level was recovered. A post-experiment topographic survey was conducted by traversing the ACVP along the flume from B to A (see Figure  1a ). The topographic surveys are used to estimate the total transported volume of sediments for each run assuming spanwise topographic uniformity. The experimental protocol was repeated for  = 6 runs for the coarse particles (S3) and  = 3 runs for the fine particles (S1) which provided a sufficient amount of data to minimize the bias errors of the statistical flow quantities. For each run,  = 0 m was defined as the initial "no flow" bed elevation at  = 0s.
Acknowledgments: This research was funded by the  Région Rhône-Alpes  ( COOPERA project and Explora Pro  grant), the  French national program EC2CO-LEFE MODSED , the  European Community 's  Horizon 2020 Program  through the  Integrated Infrastructure Initiative HYDRALAB+ COMPLEX  (no. 654110 ) and the  French DGA  funded  ANR  ASTRID  Maturation  project  MESURE  (no. ANR-16-ASMA-0005-01. The authors are grateful for their support. The data used are available as supplementary material.
Enteric methane (CH 4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH 4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH 4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH 4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH 4 prediction accuracy.
The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16. 6%, 14. 7%, and 19. 8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH 4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction.
An empirical prediction approach requires fewer inputs and can generally be implemented by a much wider audience including scientists and policy makers. There are over 40 empirical prediction equations for enteric CH 4 production of lactating dairy cows in the literature  (Appuhamy, France, & Kebreab, 2016). The majority of these models were based on measurements from relatively small numbers of animals in the same geographic region, which may limit their application in other regions. Therefore, a more comprehensive database needs to be collated to develop enteric CH 4 production prediction models at both global and regional scales. Furthermore, the performance of global models in each geographic region should be evaluated and compared with regional-specific prediction models. The CH 4 conversion factor (Y m ) was introduced by the Intergovernmental Panel on Climate Change (IPCC) to indicate the proportion of the animal's gross energy intake (GEI) converted to enteric CH 4 energy, and it is widely used for national GHG emission inventories and global research on mitigation strategies.
This is because more complex models may contain predictor variables that are expensive and not easy to obtain and thereby not applicable, especially in developing countries. Therefore, a categorization of model types which reflect different types and levels of data availability (e. g. , diet composition, milk production and composition, and animal characteristics) needs to be conducted. Evaluation of model performance across various categories can be useful for different groups (e. g. , researchers, regulators etc. ). The objectives of this study were to: (1) collate a global database of enteric CH 4 production in individual lactating dairy cows; (2) determine the availability of key variables for predicting enteric CH 4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity [g/kg energy corrected milk (ECM)] and their respective relationships; (3) develop intercontinental and regional-specific prediction equations for CH 4 production, yield, and intensity using a large individual cow database and cross-validate their performance; and (4) assess the trade-off between the availability of on-farm variable inputs and prediction accuracy of enteric CH 4 production, yield, and intensity in lactating dairy cows.
| Database The "GLOBAL NETWORK" project (Global Network for the Develop- Energy corrected milk (3. 5% fat) was calculated based on an equation derived from  Tyrrell and Reid (1965) : ECM (kg/day) = 12. 95 9 fat yield (kg/day) + 7. 65 9 true protein yield (kg/day; i. e. , crude protein 9 0. 93) + 0. 327 9 milk yield (kg/day). The majority of studies had measured GE. If the feed ingredients and proportions in the diets were known, the GE was calculated from book values (about 6%). Methane yield (CH 4 production divide by DMI) and intensity  (CH 4  production divide by ECM) were calculated for all records. The majority of the studies in the database had investigated the impact of diet composition on enteric CH 4 production. However, about 20% of the studies tested the effect of feed additives or pure nutrient supplementation, so data from these studies were either completely excluded or only the control treatments were retained.
These feed additives included nitrate  (Olijhoek et al. , 2016) , 3nitrooxypropanol  (Hristov et al. , 2015) , and intragastric infusion of acetate, propionate, glucose, and cis-or trans-fatty acids. Measurements of enteric CH 4 production were conducted using various approaches although the observations from a given research group were usually measured using the same approach. To ensure data quality, only enteric CH 4 measurements from respiration chambers, the GreenFeed system (C-Lock Inc. , Rapid City, SD), and sulfur hexafluoride (SF 6 ) tracer technique were retained for the analysis. The variable selection and model evaluation approaches required complete data for all predictor and response variables. Therefore, records missing any predictor or response variable information were removed before being screened for outliers.
Outliers in the database were screened using the interquartile range (IQR) method  (Zwillinger & Kokoska, 2000)  based on CH 4 yield and intensity records for each region. In this study, a factor of 1. 5 for extremes was used in constructing markers to identify outliers, as shown in the Equations (i-iii): IQR ¼ Third Quartile (Q3) À First Quartile (Q1); (i) Lower Fence ¼ Q1 À ðIQR Â 1:5Þ; (ii) Upper Fence ¼ Q3 þ ðIQR Â 1:5Þ: (iii) As a result, a refined complete data set (n = 2,566), containing Unlike variable selection and model evaluation approaches, the construction of equations does not require data that contain a complete set of all predictor variables. Therefore, to be able to maximize the amount of data useful to construct prediction equations, subsets of data that contain complete information of CH 4 production and of the selected corresponding predictor variables were screened for outliers using the IQR method stated above and then used for the construction of equations.
| Database A general description of the data set collated and summary statistics of dietary composition, intake, milk production and composition, BW  the United States were soybean meal, ground corn, and canola meal. In the EU experimental diets, the major forage sources were grass and corn silages, whereas the most frequently used concentrate sources were soybean meal, barley, and wheat. production and models based on these variables were of comparable accuracy with negligible bias. Adding dietary NDF to DMI (Equation  3 ) performed slightly better than one-variable models or adding dietary EE (Equation  4 , Table  2 ). Although dietary compositions were available for selection in diet based category, only dietary NDF and EE concentrations along with DMI were selected as predictor variables (Equation 5, Table  2 ), which performed slightly better than those that used only DMI and EE but slightly worse than using DMI and NDF.
| Regional models (EU) Models developed on the EU database and model evaluations are presented in Table  3. The internal EU model evaluations based on EU observations and model comparisons across different categories followed a trend similar to the intercontinental prediction models. Adding NDF to DMI improved model accuracy compared to using either DMI or GEI alone or adding EE to DMI (Table  3 ). A model with dietary concentrations without DMI did not perform as well as models in previous categories. Models using ECM and milk composition performed better than those using MY only. When all predictors were available for selection, DMI, dietary EE, dietary NDF, MF, and BW were selected and had a similar performance (RMSPE = 14. 6%, Equation  23 ) as the DMI + NDF. Once again, if DMI was taken out, prediction accuracy became worse (RMSPE = 15.
8%, Equation  24 ). Similar to RMSPE, evaluation through CCC and MAE also indicated that models using DMI + NDF and all variables had better prediction accuracy compared to the other models (CCC = 0. 72 and 0. 72, respectively; Table  3 ) and (MAE = 44. 9 and 44. 5 g/day, respectively; Figure  2 ). In addition, the intercontinental and EU models had similar overall performance for predicting enteric CH 4 production of EU cows (mean = 0. 73 and 0. 72, respectively). However, systematic biases were proportionally smaller for EU models compared to intercontinental models (4% vs. 8%, respectively). Furthermore, all categories of models based on the EU database had smaller RSR (mean = 0. 72) when used to predict CH 4 production in EU cows compared to prediction for US cows (mean = 0. 80). There was significant MB when EU models were evaluated against US data (Table  3 ).
| Models for methane yield Intercontinental CH 4 yield (g/kg DMI) prediction models of various complexity levels and with evaluations based on different datasets are shown in Table  5. Results for the regional based models of CH 4 yield are given in Tables  S1  and S2  for EU and US, respectively. In both intercontinental and regional models, we observed positive   42 ; Table  5 ). The CCC and MAE analyses also confirmed that it was the best performing model (Tables  5, S1 , and S2). Such findings were also observed in EU and US regional models (Equations 58 and 65, RMSPE = 14. 8% and 18. 6%, respectively; Tables  S1  and S2 , respectively). Using milk components as model variables resulted in the second-best model in all regions. Furthermore, the intercontinental models had a similar RSR while predicting EU or US observations (mean = 0.
| Models for methane intensity Intercontinental CH 4 intensity (g/kg ECM) prediction models of various complexity levels and with model evaluations based on different datasets are shown in Table  6 , and results for the regional models for EU, and the US are shown in Tables  S3  and S4 , respectively. We consistently observed negative relationships between GEI, DMI, and dietary EE concentration with CH 4 intensity, and positive relationships between MP, BW and dietary NDF concentration with CH 4 intensity. However, models that were based on GEI, DMI, or dietary composition did not predict CH 4 intensity well. Substantial improvement in prediction accuracy was observed when milk component and animal variables were included in the model. Similar to CH 4 production and yield models, intercontinental models performed well for both EU and US cows (Figures  S4-S6 ).
In the present study, DMI was not considered as a predictor for CH 4 yield, and MY or ECM was not used for prediction of CH 4 intensity because these variables already have However, negative relationships between CH 4 yield and MY (or ECM), and between CH 4 intensity and DMI were observed because of the overall positive relationship between MY and DMI, which is in close agreement with previous reports (e. g. ,  Johnson & Johnson, 1995; Moe & Tyrrell, 1979; Warner et al. , 2017). In agreement with  Kebreab et al. (2008)  and  Appuhamy et al. (2016) ,  IPCC (2006) , the Tier 2 model overpredicted CH 4 production of US cows in our database by 22%, whereas it performed well on EU cows mainly because the Y m in IPCC Tier 2 (6. 5%) was similar to the average Y m of EU cows (6. 4%) in our database.
There was a moderate SB of the IPCC (2006) Tier 2 model for EU cows probably due to the absence of an intercept in the IPCC model. The Y m for US cows in our database (5. 4%) was close to that reported by  Kebreab et al. (2008)  and  Appuhamy et al. (2016)  for US cows. This illustrates that it is important to either use a regional model or intercontinental model that was developed using representative samples from each region. Furthermore, the GEI-based models appear to perform better and are associated with small systematic biases when they include an intercept term as for the GEI_C models developed in the present study. Dietary NDF concentration was selected previously as the key dietary variable to predict enteric CH 4 production of dairy cows across regions  (Moe & Tyrrell, 1979; Nielsen et al.
Although overall predictive performance was similar, it should be noted that actual predictions based on the model derived from intercontinental data may differ from the model based on regional-specific data, because the slopes of the variables included in these models differ. Finally, it is important to note that the majority of data used in this study was from temperate regions and there is a scarcity of dairy cow data from tropical regions, which differ in breeds and the quality of forage fed. Many developing countries are in the tropical regions, and milk production rather than GHG emissions is still the top priority in those countries  (FAO, 2016). Therefore, further research on determinants and predictors of CH 4 emission applicable to animals in the tropics is warranted. In addition, spatial auto correlations should also be considered to incorporate the effects of environmental factors once a broader database becomes available in the future.
In summary, our analysis based on a relatively large dataset from the GLOBAL NETWORK project, indicated that the ability to predict enteric CH 4 production increases with increasing model complexity. As observed previously, DMI is the key factor for enteric CH 4 production prediction. Although complex models that use DMI, NDF, EE, MF, and BW had the best performance for predicting CH 4 production, models requiring only DMI or DMI + NDF had the second best predictive ability and offer an alternative to complex models. Milk production and composition variables are key factors to predict CH 4 yield, whereas milk composition and animal variables are key factors to predict CH 4 intensity. Model evaluation specific to individual regions compared with that of intercontinental based models suggests that enteric CH 4 production, yield, and intensity can be accurately predicted from both intercontinental models and regionalspecific models with similar performance.
ment and Maintenance of Nutrition-Related Strategies for Mitigation of Methane and Nitrous Oxide Emissions from Ruminant Livestock; 2014-2018) is an international collaborative initiative of animal scientists from all continents, except Africa (http://animalscience. psu. ed u/fnn; accessed May 16, 2017). The dairy CH 4 database, developed in the frame of the "GLOBAL NETWORK" project, contains 5,233 individual dairy cow records from 154 published and unpublished studies conducted from 1962 to 2016 by researchers and research institutes from 15 countries in Europe (EU; n = 3,015 from 82 studies), the United States of America (US; n = 1,916 from 64 studies), Chile (CL; n = 108 from 3 studies), Australia (AU; n = 64 from 1 study), and New Zealand (NZ; n = 130 from 4 studies). The database includes records of enteric CH 4 production along with corresponding DMI, dietary concentration of gross energy (GE), crude protein (CP), ether extract (EE), neutral detergent fiber (NDF), and ash.