dataesr/scientific-paragraphs-categorization huggingface dataset:
In the 'Get_HQ_Genome_1. py' script, the function named 'getFastafromNuccore' downloads fasta files and transcribes the accession number of the downloaded fasta files in a tsv file. The function named 'Renamer' renames every fasta file as "ID_ Accession. fasta" and creates a folder with the same name to which it moves the fasta files. The function named "Filter1Genome" works only if the user chooses the "complete genome mode". The function named "Filter1Contig" works only if the user chooses the "contigs mode". These two functions copy the accession numbers of the fasta files in a tsv file named "Genome_HQ. tsv". Then, they count the number of contigs in every fasta file and report it in a second tsv file named "Genome_HQ_Filter1. tsv". If the "complete genome mode" is selected, it discards all fasta files with more than one contig.
Get_HQ_Genome2. py. The 'Get_HQ_Genome2. py' script runs after the Bash process queries the SeqSero, MLSTseeman and Quast tools. The function named 'ReadSeqSero' reads the results from the SeqSero2 tool and retrieves the accession numbers of the genomes and the serovar predictions, with the associated probabilities. Similarly, the function named 'ReadMLST' reads results from the MLSTseeman tool and stores accession numbers and MLST profiles. The function named 'ReadQuast' reads results from the Quast tool and retrieves length, the N50 value and the number of contigs of genomes. The function named 'MergeResult' merges all the information from the previous functions (ie, serovar predictions, MLST profiles, number of contigs, length, N50 and genome size) along with information from 'Genome_HQ_Fil-ter1.
tsv' (ie, produced by the 'Get_HQ_Genome_1. py' script) in a third tsv file named 'TableMerge. tsv'. The function named 'GetGBK' downloads the gbk (GenBank) files associated with fasta files. The function named 'Renamer2' moves the gbk files to the folder containing fasta files and renames them according to the fasta file names. The function named 'Filter2' generates a fourth tsv file called 'TableMergeFilter2. tsv' with the keys (ie, accession numbers) of all genomes that have a coverage higher than 50x (> 50x) based on gbk files and a length longer than 4 Mb (> 4 Mb). It also adds information on the sequencing technology used to this tsv file. The function named 'GetGFF' downloads gff files. The function named 'RenamerGFF_FASTAprot' renames gff files and protein fasta files. It moves them to the folder containing the fasta files.
Workflow application In this study, we report two application examples for SalmoDEST. In the first example, we evaluate the ability of SalmoDEST tool to download complete Salmonella genomes from the NCBI GenBank database and, in the second, its ability to download Salmonella genome contigs for strains isolated from bovine sources. Selection of complete genomes from a public database. Complete reference genomes are often required for assembly reference mapping or phylogenetic SNP analysis for the mapping step and the calculation of pairwise distance between genomes. Nevertheless, for a single laboratory it may be difficult to have a complete set of reference genomes, particularly considering that the genus Salmonella is separated into six subspecies and over 2000 serovars. 9 The SalmoDEST tool was tested to search, download and select all complete Salmonella reference genomes available in the GenBank database.
SalmoDEST applies a coverage filter set to a minimum of 50x. A second manual filter is based on serovar identification. SalmoDEST was used to compare the listed serovars with the serovars predicted by Seqsero2 in the TableMergeFilter2 tsv file. In this study, SalmoDEST was tested using the list of accession numbers obtained using the NCBI ' All Databases' query: 'Salmonella[title] AND Genome[title] AND Salmonella enterica[title] AND Genome Assembly and Annotation report[title]' (https://www. ncbi. nlm. nih. gov/genome/browse/#!/prokaryotes/152/) with the filter 'Complete' (on 24 June 2021). A list with 1648 accession numbers was retrieved, and after eliminating duplicates, 1048 unique accession numbers were found (Supplementary Table  S1 ). The SalmoDEST option for complete genome mode '-m g' was used.
Finally, after serovar prediction and genome length verifications, 1040 genomes were retained and downloaded. Four tsv output files were produced, including the final TableMergeFilter2 tsv file (Supplementary Table  S2 ). Selection of contig genomes from public database. 1] [12] [13]  Obtaining genomes from laboratories around the world is therefore a major advantage. Here, we tested the ability of the SalmoDEST tool to obtain Salmonella genomes from strains isolated from bovine sources worldwide. The SalmoDEST tool was tested using the list of assembly accession numbers obtained using the NCBI 'All Databases' query: 'Salmonella[title] AND Genome[title] AND Salmonella enterica[title] AND Genome Assembly and Annotation report[title]' (https://www. ncbi. nlm. nih. gov/ genome/browse/#!/prokaryotes/152/) with the following filters: 'Contig' AND 'Bovine' AND 'bovine' (on 24 June 2021), 89 unique accession numbers were found (Supplementary Table  S3 ).
6 Good coverage prevents poor MLST, cgMLST and, antigenic data and contributes to the correct listing of the serovar. 6, 26, 27 rors in serotyping Salmonella genomes from GenBank have already revealed errors in the serovar listed in their metadata. In 2016, Yoshiba et al carried out in silico serovar prediction on over 4,291 genomes extracted from GenBank, and revealed that 3. 5% gave incorrect serovar predictions and that 1. 8% had missing or ambiguous metadata, making it impossible to ascertain the listed phenotypic serovar. 26 For this reason, we integrated the Bash process in the SalmoDEST tool to query the SeqSero2 6 and MLSTseeman 7 tools. SeqSero is a Web-based tool developed by the Centres for Disease Control and Prevention (CDC) in Atlanta, GA (US) for determining Salmonella serotypes using the rfb region and the fliC and fljB alleles.
Conclusion SalmoDEST is a handy and easy-to-use tool that can be routinely used in public health, food safety and research laboratories to extract complete Salmonella reference genomes of high quality from GenBank. It can also be used to download contig genomes from a list of assembly IDs. A coverage of 50x, as well as correct Salmonella genome size and serovar and MLST type prediction, are used as quality controls for both genome modes (ie, complete and contig genomes search and download). Moreover, SalmoDEST screens downloaded genomes for contamination by using the SeqSero2 tool for serovar prediction.
genomes in the input txt file, SalmoDEST excluded one genome (CP060132. 1) for incorrect serovar prediction and seven others (OU015718. 1, OU015719. 1, OU015720. 1, OU015717. 1, LR792437. 1, LR792391. 1 and LN868943. 1) due to low genome length (genome lengths of < 4 Mb, comprised between 277 503 and 3 746 274 bases). We obtained 16 genomes of S. enterica subsp. salamae, 10 S. enterica subsp. arizonae, 13 S. enterica subsp. diarizonae, 10 S. enterica subsp. houtenae and 991 S. enterica subsp. enterica, representing 135 serovars with different antigenic formulas. No S. enterica subsp. indica genomes with a coverage higher than 50x were found. Four serovars were overrepresented (ie, more than 50 complete genomes) in the GenBank database and in our results: S. Typhi (ie, responsible for human typhoid fever with 124 genomes/1040), S.
Supplementary material such as pictures was sometimes shared by the interviewees (see Appendix C). Diaries are seldom used in marketing research despite their ability to capture rich insights into processes, settings, and consumers  (Patterson, 2005). Because diaries enable the timely collection of data about a person's most recent experience at regular intervals over a period  (Hyers, 2018) , they are suitable to explore a repetitive, daily life experience. To complement interview data, a qualitative diary was implemented online to capture perceptions of users' most recent interactions with the sanitation system and collection service. All customers were invited by email to complete the diary every fortnight. As shown in Table  2 , 12 diary entries were collected from 36 participants over an 18-month period.
These data provide a longitudinal view of the customer experience as it was lived  (Bolger et al. , 2003)  and illuminate how perceptions and judgments about it changed or not over time  (Patterson, 2005). Transcribed interview and diary data were analyzed through thematic content analysis using a combination of deductive coding generated from theoretical concepts from the customer experience literature (De Keyser et al. , 2020) and inductive coding generated from an open reading of the material. First, we followed inductive logic procedures, which involved "organizing the data into 1st-and 2nd-order categories to facilitate their later assembly into a more structured form"  (Gioia et al. , 2013, p. 20) , underpinning an insightful conceptual understanding of the phenomenon that was grounded in empirical data.
Responses to the 21 items of the PVQ (see Appendix A) were aggregated for the 10 human values and mean-centered according to  Schwartz's (2003b Schwartz's ( , 2012) )  procedure to reflect value priorities. As shown in Fig. 3 , users of dry sanitation scored high on security, tradition, achievement, and stimulation values (above 3) and low on universalism, benevolence, self-direction, conformity, power, and hedonism values (below 3). Furthermore, the survey respondents' values were compared to the European Social Survey Round 9 Data (2018), released in 2021. Independent sample t-tests were performed. The results indicate that the two datasets were significantly different on 6 of the 10 basic values: the dry sanitation sample endorsed the values of tradition, security, power, and benevolence more than, and the values of achievement and conformity less than, the general French population.
Acknowledgements I am grateful to the people of Membalong who agreed to share their knowledge of bees and rafter making. I thank  Jane MacAvock ,  Doyle McKey ,  Douglas Nakashima  and  Nathalie Wirja  for their comments and help with the English and Indonesian translations, and  Clément Vuillier  for the drawings, as well as  Soesilawati Hadisoesilo  for her initial inspiration and  Meggi Rhomadona  and  Stéphane Villière  for their enthusiasm and friendship in the field. This work was funded by the  French National Museum of Natural History (MNHN) , the  French National Center for Scientific Research (CNRS)  and the  Japan Society for the Promotion of Science (JSPS). The paper benefited from constructive suggestions by two anonymous reviewers.
Crop residue and soil N 2 O emissions database We collected data on N 2 O emissions from field experiments with crop residue retention treatments across the world. We defined crop residues as any constituent of a crop (above-and below-ground) returned to the soil. These may originate from cash crops, cover crops, green manures, grassland or others. Depending on the crop, the residues may include below-ground and above-ground biomass, except for root crops for which it refers to above-ground biomass only since most of the roots are harvested. The crop residues treated through biogas fermentation, composting and other methods were not included. Another difference with respect to previous meta-analyses is that we included grassland termination data in our database, because temporary grasslands make up a substantial share of arable land in Europe, especially in the Scandinavian countries and in some regions with high livestock densities, e.
g. the Netherlands, Belgium and Bretagne, France  (Lesschen et al. , 2014). Peer-reviewed research articles published before 1 January 2021 were searched in Google Scholar and Web of Science with the following keywords: "residue" OR "straw" OR "stubble" AND "nitrous oxide" OR "N 2 O" OR "greenhouse gas" OR "GHG" OR "emission factor". The initial search resulted in 337 publications. We also included results from two unpublished studies conducted by authors of this meta-analysis  (Bleken et al. , n. d. ; Ernfors et al. , n. d. ). All publications were screened and included in the database if they met the following criteria: (i) the experimental design had to be sufficiently detailed regarding the treatments and recent history; (ii) included treatment replicates; and (iii) were conducted under realistic field conditions.
The length of the studies ranged from 5 to 575 days. To account for potential biases due to the inclusion of shortterm studies (<40 days), we ran an initial sensitivity analysis excluding short-term studies and it did not affect our main results. For each study in our dataset, we tabulated details on the site and experimental conditions. When data were only presented graphically, WebPlotDigitizer was used to extract data points. If relevant information was not reported, the authors were contacted to supply the missing information.
Data aggregation We identified 21 factors as potential drivers of N 2 O emissions from crop residues (Table  1 ). These factors related to crop type, residue type, residue properties, soil properties, and climatic conditions, and included both categorical and continuous factors. A detailed data overview is presented in a publicly available database accompanying this publication  (Rittl et al. , n. d. ). For crop type, we distinguished between cereal (mature harvested cereals and rapeseed), rice, cover crop (including immature cereals, grasses, clovers, Brassica spp. cultivated as cover crops or one-year green manures), grassland, grain legumes, vegetables, and sugar cane, and double cropping (combination of two species at different biomass ratios). For soil properties, soil texture was divided into clay (clay, sandy clay, silt clay), loam (loam, clay loam, silty clay loam, silt loam, sandy loam, sandy clay loam, fine silt, silt), and sand (sand, loamy sand, fine sand)  (Quemada et al.
, 2013). Soil pH values measured in CaCl 2 were converted to H 2 O-based values using the equation proposed by  Ros et al. (2020). Soil pH values in our dataset ranged from 4. 3 to 9. 3, and were grouped into acidic (<6), neutral (6-7), and alkaline (>7) soils. We converted organic matter content to organic C content using the standard conversion factor of 0. 58  (Pribyl, 2010). We calculated the Aridity Index (AI) as the ratio of mean annual precipitation to mean annual potential evapotranspiration (UNEP). The long-term mean annual precipitation was extracted from the publications, and the long-term mean annual potential evapotranspiration for each site was extracted from the Global Aridity Index geodatabase (https://cgiarcsi. community/data/global-aridity-and-pet-database/). We used the classification of wet (AI >1) and dry land (AI <1) utilized by IPCC (2019) to differentiate N 2 O emission factors from N sources (including organic amendments such as crop residues).
Crop residue quality characteristics and categorical groups From each study in our dataset, we extracted information on the chemical characteristics of crop residues. We calculated the N returned with crop residues from the residue N concentration and the dry matter (DM) added. When C and N added were reported, we calculated crop residue C:N ratio. When these data were not provided, mean values were taken from the crop residue quality dataset provided by  Thiébeau et al. (2021)  for the same species or group of species. Residue concentration of soluble fraction (neutral detergent soluble, referred to as soluble NDS fraction), hemicellulose, cellulose, and lignin (according to  Van Soest (1963) ) were tabulated when these were reported. When these data were not available, which was the case for the vast majority of studies included in the database (>90%), we used crop residue quality data from  Thiébeau et al.
(2021). Previous research has shown that, on a global scale, most of the variation in plant characteristics is represented by species identity  (Kattge et al. , 2011). Accordingly, the use of a global dataset for residue biochemical characteristics was deemed appropriate for our global meta-analysis. Hemicellulose, cellulose and lignin relative contents (% total DM), indicative of the composition of the insoluble residue fraction (i. e. the plant cell walls), were used, the difference between their sum and 100%, being the soluble NDS fraction. The lignocellulose index (LCI) [lignin: (lignin + cellulose + hemicellulose)] was used as a criteria to express the recalcitrance of the plant cell wall  (Herman et al. , 2008). Water soluble C (WSC) determined after water extraction, was expressed as a percentage of total C.
Crop C:N ratio was divided into three categories (<20, 20-60, >60). We used two criteria to categorize crop residues: according to maturity stage, and to the type of residue generated. Crop residues were classified as mature or immature, based on the stage of physiological maturity of the plant at which the residue were generated  (Sylvester-Bradley et al. , 2015). This could be by cultivation practice (harvesting of annual crops at the end of the cycle, harvesting of root or vegetable crops, mechanical destruction, grassland mowing or grassland renewal) or possibly naturally (senescence), irrespective of the plant type. Incorporation of immature residues into the soil happens during: (i) destruction of green cover crops or   (Sylvester-Bradley et al. , 2015). In our database, the classification of immature residues refers to cover crops, vegetable residues and grassland renewal.
Dataset syntheses The database included studies from 69 sites in 20 countries. Europe contributed 41% of the comparisons (n = 148). For Europe, 32% of the data were from residues classified as cover crop; 80% as green plant biomass; 60% of the N 2 O comparisons were from experiments where fertilizer was applied to either the growing crop generating the residue, or at some point following residue return. Regarding climate, 63% of our comparisons originated from field sites in dry climates (AI <1), and 37% from sites in wet climates (AI >1). The range of each continuous factor is shown in Table  1.
Data analysis We used the log response ratio (LnRR) as effect size, which is a common metric in meta-analyses  (Hedges et al. , 1999; Osenberg et al. , 1999). We performed a weighted mixed-effects meta-analysis, using the rma. mv function of the 'metafor' package  (Viechtbauer, 2010) , including "study" as a random effect because several studies contributed more than one effect size. Effect sizes from individual comparisons were weighted by the inverse of their variance. Missing variances were estimated using the average coefficient of variation across the dataset  (van Groenigen et al. , 2017). We used a Wald test to evaluate statistical differences between subgroups within categories at p-value < 0. 05. We used a random-meta-forest approach to identify the most important predictors of crop residue effects on N 2 O emissions.
This approach is based on the machine learning random forest algorithm. This approach incorporates the variance and weight of each study as in classic meta-analysis, is robust to overfitting, allows for numerous predictors, and accounts for nonlinear relationships  (Terrer et al. , 2019 (Terrer et al. , , 2021 ). Among the 21 potential factors, we discarded those based on expert knowledge to avoid possible artifacts associated with arbitrary category definitions (i. e. crop type, residue type, maturity class) (Table  1 ), and those with more than 30% missing data (soil texture, clay, annual temperature, soil bulk density, soil total N, and ND15). The final dataset consisted of 267 observations with information on 12 predictors, which are indicated in Table  1. Using 'metaforest' (Van Lissa, 2017), we performed variable pre-selection with 10,000 iterations and 100 replications, using a recursive algorithm in the preselect function from metafor.
Predictors with negative variable importance were dropped using the preselect_vars function. Predictors with positive predictive performance were then used to optimize the model. Parameters of the meta-forest model were optimized using the train function from the caret package  (Kuhn, 2008) , and calculated 10-fold cross validated R 2 with 75% of the data used as training data and 25% as validation data. We generated partial dependence plots to depict the association of each selected predictor with the effect size, while accounting for the average effect of all other predictors. To check the robustness of our random-meta-forest approach, we ran another mixed-effects meta-regression model selection procedure with the six most important predictors identified by random forest analysis. The model selection was based on maximum likelihood estimation, which was carried out with the 'glmulti' package in R  (Calcagno and de Mazancourt, 2010).
When the interaction term was non-significant, we proceeded with the analyses using the complete dataset; when the interaction was significant, we conducted separate analyses for studies with and without fertilizer application. We fitted linear and quadratic meta-regressions to identify the best model describing the relationships between the most important predictors and LnRR, based on the Akaike information criterion (AIC); the model with lower AIC was retained. Principal component analyses (PCA) were performed with R (prcomp available in the base package, and the 'ggbiplot' package;  Vu, 2011)  to visualize the main biochemical characteristics of mature and immature residues, and of the main crop type.
Acknowledgments This study was supported by the  FACCE ERA-GAS Cofund (European Research Areafor mitigation of Greenhouse gases from Agri-and Silviculture ; id number:  696356 ), project  ResidueGas  (https://projects. au. dk/ residuegas/). The Danish funding was received from  Innovation Fund Denmark  (project  7108-00005B ). The French funding was received from the  Agence Nationale de la Recherche  ( ANR-17-EGAS-0003 ) and the  National Institute for Food, Agriculture and the Environment (INRAE). The authors acknowledge funding in the UK from the  Department for Environment, Food and Rural Affairs (Defra)  and the  Scottish Government's Strategic Research Programme. The authors acknowledge funding in Norway from the  Research Council of Norway  (Project No. :  276389 ). We would like to thank the researchers who were contacted and kindly supplied any missing information:  Liz Baggs ,  Søren O.
This entire process is complicated by the fact that intracellular viruses are neglected, by the difficulty to simultaneously amplify the different types of viral genomes and by the lack of targeted conserved viral elements. Furthermore, research in the human gut virome is further retarded by the limited viral databases and bioinformatic tools  [22]. More recently, several emerging methods have been employed in virome isolation, purification, and quantification, each of which has its peculiar advantage and limitations. For example, the traditional sampling method, e. g. , using a 2-micron filter, can be biased towards isolating the most abundant virome species in the compartment of interest. Similarly, the caesium chloride (CsCl) gradient ultracentrifugation purification technique can be biased towards species with atypical buoyancy and specific phage type depending on how the method has been performed, although this technique yields a very pure virome isolate.