dataesr/scientific-paragraphs-categorization huggingface dataset:
g. web applications used on a desktop). In most cases, mobile apps have fewer features and thus a smaller set of interactions are expected to be learned and performed. Conversely, many desktop or web applications are much more feature-rich and seem to be designed under the assumption that the user will eventually spend some time 40 See LabCam app, http://labcam. mpdl. mpg. de/. learning the tool. This is congruent with the expectations that researchers had regarding the devices: for capturing data in the field, it was deemed sufficient to let the device acquire data automatically and to assign only basic keywords. The proper documentation of the field work would later be performed on a desktop computer. On the implementation side, this may mean having to develop different user interfaces for one single backend (the database): a finger-friendly mobile version with reduced functionality plus a desktop application providing the complete functionality.
A "one system does it all" approach was not feasible. Therefore the strategy was to give priority to common needs amongst the different user groups (e. g. note-taking). This allowed a variety of user scenarios and stories to be implemented but may not have addressed the specific needs of a specialized user group. Finally, besides user-centered design, CENDARI also highlighted the factors that may impact the adoption of a tool, such as data privacy. CENDARI's recommendation was to keep historians' notes private by default and tagged enti-ties46 public by default. This was seen as being helpful to "spread […] historical knowledge with little risk of disclosing historians' work"47. Another factor was user perception of the cost/benefit of structuring and enriching their research data. CENDARI's strategy was to demonstrate to users how their annotations can be effectively exploited, for example through visualization and faceted search.
Adhere to Standards Research data can come from various sources and in various formats. Tool development should therefore adhere to standards and openness. For example, preference should be given to a tool that exposes a well-described REST interface over a tool where a direct database is the only means of access. Developing test data sets and providing users with sample data to test and play with the tool should be common practice. When aggregating heterogeneous data, it is difficult to find a common relevant denominator to answer upcoming and as yet ill-defined research questions. The proper representation of such data is a challenging task. It is better to start with a minimal set of attributes and then to iterate as more is learned about each data type. It is preferable that the REST interfaces are designed more generic and the user interfaces more specific.
Claire, Melissa Terras, Isabel Galina, et al. : Evaluating Digital Humanities Resources: The LAIRAH Project Checklist and the Internet Shakespeare Editions Project. London, 2007. http://elpub. scix. net/data/works/att/144_elpub2007. content. pdf. It was often assumed that the resources created in digital humanities would be used by humanities scholars, who were not technically gifted or, perhaps, even luddites. Thus, there was little point asking them what they needed, because they would not know, or their opinion about how a resource functioned, because they would not care. It was also assumed that technical experts were the people who knew what digital resources should look like, what they should do and how they should work. If developers decided that a tool or resource was a good one, then their opinion was the one that counted, since they understood the details of programming, databases, XML and website building.
Bulatovic et al. 2016(a). 18 Green, Harriett E. : "Under the Workbench: An Analysis of the Use and Preservation of MONK Text Mining Research Software. " In: Literary and Linguistic Computing 29 (1) (2014): 23-40. Bereitgestellt von | Humboldt-Universität zu Berlin Angemeldet Heruntergeladen am | 23. 04. 19 12:25.
Identification and Sequence Analysis of MYB Proteins in Sesame The sesame genome sequence was downloaded from Sinbase (http://ocri-genomics. org/ Sinbase/)  [37]. The HMM profile of MYB DNA-binding domain (PF00249) was downloaded from Pfam protein families database (http://pfam. sanger. ac. uk/) and was exploited for the identification of MYB genes from Sesamum indicum L. genome, using HMMER3 tool of the software Unipro UGENE (Unipro, Novosibirsk, Russia)  [38]. To confirm the presence of MYB domain, the PROSITE (http://prosite. expasy. org/scanprosite/) and the SMART program (http://smart. embl-heidelberg. de/)  [39]  were used for further analysis. The isoelectric point and protein molecular weight of MYB proteins in sesame were obtained using ExPASy proteomics server (http://www. expasy.
Chromosomal Location and Gene Duplication of MYB Genes in Sesame The MYB genes were mapped onto the 16 linkage groups (LGs) of the sesame genome on the basis of their genomic location from Sinbase using the software MapChart 2. 3 (Wageningen UR, Wageningen, The Netherlands)  [40]. The tandemly repeated MYB genes were identified when two sesame MYB genes were separated by three or fewer genes  [41].
Phylogenetic Analysis and Functional Classification of MYB Proteins in Sesame To characterize the evolutionary features of MYB proteins, we extracted all of the predicted MYB protein sequences from Sinbase (http://ocri-genomics. org/Sinbase/). According to the numbers of the conserved domains, we classified the sesame MYB genes into four subfamilies: R1-(single domain), R1R2-(2 domains), R1R2R3-(3 domains), and Atypical MYB (4 or more domains)  [2]. Multiple alignments of their conserved domains were conducted using ClustalW program that was embedded in the MEGA6. 0 software (Temple University, Philadelphia, PA, USA)  [42] , with a gap open penalty of 10 and gap extension penalty of 0. 2. Thereafter, a Neighbor-Joining (NJ) tree was constructed with a 1000 bootstrap value. The tree was loaded along with the information of the gene CDS organization into the Gene Structure Display Server (GSDS2.
0) (http://gsds. cbi. pku. edu. cn/)  [43]  to construct the exon-intron map. Moreover, a Neighbour-Joining (NJ) tree was constructed from ClustalW aligned full-length of 259 R2R3-MYB proteins (125 MYB of Arabidopsis and 134 R2R3 MYB proteins for sesame) using MEGA6. 0 software (Temple University, Philadelphia, PA, USA)  [42]  with the settings described above. Functional classification of the R2R3-MYB genes in sesame was performed according to their phylogenetic relationships with the corresponding Arabidopsis AtMYB genes. To identify the orthologous genes of the sesame MYBs, we conducted a BLASTp search of their protein sequences against the protein database of Arabidopsis in TAIR. Hits with E-value ≤ 1 e-40 and at least 70% similarity were considered significant  [44]. The relationships between the orthologous genes of sesame and Arabidopsis MYBs were illustrated using Circos package  [45].
Analysis of the Expression Patterns of Sesame MYB Genes in Different Tissues, under Drought and Waterlogging Stresses Using RNA-seq Data To analyze the expression patterns of sesame MYB genes in various sesame tissues, we used RNA-seq data from root, stem seed, capsule, and leaf developed by our group and accessible from sesameFG (http://www. ncgr. ac. cn/SesameFG;  [46] ). Moreover, we investigated the expression levels of sesame MYB genes in responses to the two major abiotic stresses: waterlogging and drought. Transcriptomic data from a tolerant cultivar (Zhongzhi No. 13) under 3, 9, and 15 h of waterlogging stress were retrieved from GeneBank with the SRA accession number SRR2886790  [30]. Concerning the drought stress, the RNA-seq data of a drought tolerant genotype (ZZM0635) submitted to 3 (d 1 ), 7 (d 2 ) and 11 (d 3 ) days of drought periods  [47, 48]  were downloaded from http://www.
RNA Isolation and Quantitative RT-PCR Total RNAs from various tissues samples were extracted with Easy Spin RNA kit (Aidlab, Beijing, China). The quantity and quality of RNA samples were assessed by agarose gel electrophoresis and the spectrophotometer (Unico SpectroQuest Model SQ2800) measurement of the A260/A280 ratio. For cDNA synthesis, 1. 5 µg of RNA was reverse transcribed using the Superscript III reverse transcription kit (Invitrogen, Carlsbad, CA, USA), according to the manufacturer's instructions. Gene-specific primers were designed by using Primer5. 0  [50]. We designed primers to amplify 29 SIMYB genes using Primer Premier 5. 0 software (Premier Biosoft International, Palo Alto, CA, USA) (Table  S5 ). The qRT-PCR was conducted on Roche Lightcyler ® 480 instrument using SYBR Green Master Mix (Vazyme), according to the manufacturer's protocol.
For all of the genes tested, qRT-PCR results confirmed well the RNA-seq expression profiles, proving the accuracy of the RNA-seq dataset that was used in this study (Figure  4d ). Genes 2017, 8, 362 8 of 18 expression (Table  S4 ). A total of 268 SIMYB genes were expressed (RPKM > 1) in the different tissues, although their transcript abundance was highly varied according to the tissues. Of these expressed genes, 173 SIMYBs (~65%) were commonly detected in all of the tissues, suggesting that they regulate sesame development and growth (Figure  4a ). We found some highly expressed genes (SIMYB69, SIMYB106, SIMYB171, SIMYB175, SIMYB245, etc. ) in the tissues, some low expressed genes (SIMYB84, SIMYB134, etc. ), and also found some tissue-specific SIMYBs. For instance, the gene SIMYB73 was only expressed in the stem; two genes, including SIMYB72 and SIMYB200 were exclusively expressed in the sesame root; one gene in the leaf (SIMYB56), three genes (SIMYB74, SIMYB242, and SIMYB249) were expressed in the seed and one gene (SIMYB19) was exclusively expressed in the capsule (Figure  4b ,c; Table  S4 ).
To further validate the gene expression profile from RNA-seq data, we performed qRT-PCR analysis with ten selected genes, including six genes with high or low expression levels from the group I and one tissue-specific gene each from the groups XXII, XXIII, XIV, and XXV. For all of the genes tested, qRT-PCR results confirmed well the RNA-seq expression profiles, proving the accuracy of the RNA-seq dataset that was used in this study (Figure  4d ). S4 ).
Expression Profiles of SIMYB Genes in Response to Drought Stress Previously developed RNA-seq data under drought stress  [42, 43]  allowed for us to analyze the expression profiles of SIMYBs under this important abiotic stress. First, we retrieved all SIMYBs from the DEGs at d 1 , d 2 , and d 3 when compared to control (period prior to water stress application). A total of 46 SIMYBs were found as DEGs across the different drought application periods, and their expression profiles were subsequently summarized in the heatmap represented in the Figure  5b. The results showed that SIMYBs regulating drought responses could be assembled into four clusters: The first group (I) represented five genes constitutively up-regulated under drought stress with a striking induction at d 3. The group II gathered six genes with increased activity under severe stresses (d 2 and d 3 ).
Results: The 'VARCall' workflow produced a robust phylogenetic inference confirming that the monophyletic clade S. Dublin diverged from the polyphyletic clade S. Enteritidis which includes the divergent clades S. Pullorum and S. Gallinarum (i). The scripts 'phyloFixedVar' and 'FixedVar' detected non-synonymous and non-homoplastic fixed variants supporting the phylogenetic reconstruction (ii). The scripts 'GetGOxML' and 'EveryGO' identified representative metabolic pathways related to host adaptation using the first geneontology enrichment analysis based on bacterial coregenome variants (iii).
Covering Eukaryotic and Prokaryotic organisms  [11] , the gene-ontology enrichment analysis can be applied on lists of annotated genes or variants  [12]. Based on several annotation databases  [13] , such as Gene Ontology (GO)  [14]  where the GO-terms form a directed acyclic graph (DAG) organized as three independent ontologies of GO-terms, namely biological process (BP), molecular function (MF), cellular component (CC), different kinds of gene-ontology enrichment analyses must be distinguished. The enrichment likelihood may indeed be calculated using Chi-square, Fisher's exact test, Binomial probability based on preselected interesting gene lists (i. e. a singular enrichment analysis), entire genes with associated experimental values (i. e. a gene set enrichment analysis)  [10] , or using hypergeometric distribution based on the approaches term-for-term (i.
Gallinarum  [25] ), the partial losses of ssf and saf operons are highly specific to avian adaptation of S. Gallinarum and S. Pullorum  [22]. Here, we propose a novel approach coupling identification of clade specific SNPs and InDels  [1, 2]  and associated gene-ontology enrichment analyses  [14, 15]  in order to link evolutionary history and biological function. By targeting the coregenome, we propose an additional and complementary method to analyses focusing on the accessory genome only. We tested its performance by studying the host-adaptation processes at the coregenome scale on a dataset constituted by Langridge et al. [22]  in order to describe at the accessory genome scale the host adaptations of Salmonella serovars: Enteritidis (i. e. multihosts), Dublin (i. e. mammalian-hosts), Gallinarum (i.
Robustness of the variant dataset After trimming and mapping of paired-end reads, the previously published genome dataset (Additional file 1) displayed satisfactory depth (i. e. 175X) and breadth (i. e. 99 ± 1%) coverage (Additional file 2) against the reference genome S. Enteritidis (strain P125109)  [32]. This depth coverage (i. e. >30X) is sufficient to accurately detect InDels with GATK  [32] , and this breadth coverage is quantitatively in accordance with results obtained by mapping of S. Typhimurium reads against the reference LT2 genome (i. e. 95% mean, 91% min. , 98% max. )  [24]. The set of sequencing data corresponding to 59 Salmonella genomes was analyzed through the variant calling workflow 'VARCall' where high-confidence variants were retained  [33]. We identified 14,086 coregenome variants among the four serovars corresponding to 12,929 SNPs and 1157 InDels.
Even if InDels are considered more challenging to be called accurately than SNPs  [34] , the ratio of 0. 9 InDels/10 SNPs identified by the 'VARCall' workflow on the current Salmonella dataset falls inside the boundaries of 0. 5-2 InDels / 10 SNPs which have been previously observed in other prokaryotic species (e. g. Blochmannia vafer  [35] , Streptococcus  [36] , Salmonella Typhimurium  [24] ) and eukaryotic studies (e. g. Rice  [37] , e. g. Human  [38] ). We found that 12,031 variants  (11, 285  SNPs and 746 InDels) were intragenic, corresponding to 85% of the 14,086 coregenome variants  (12, 929  SNPs and 1157 InDels). As the coding regions represent about 88% of the S. Enteritidis chromosome (strain P125109, accession NC_011294. 1) [25] , intra and intergenic regions of the chromosome seem to be similarly impacted by variants.
Accumulation of coregenome recombination events in the ancient branches Following lateral transfer of genetic material, recombination events are a common modality of evolution of bacterial genomes  [44]. In Salmonella, the PAIs called SPIs have long been recognized to play a determinant role in the virulence properties of the pathogen  [45]. These regions are likely acquired by lateral transfer and can be excised from the chromosome by site-specific recombination events  [46]. We performed a specific recombination analysis on the whole dataset to identify branch specific recombination events that could explain some characteristics of the corresponding isolates. Detecting high densities of SNPs (Fig. 3 ), we identified 112 recombination events associated to 12 nodes of the phylogenetic inference (Additional files 4 and 5).
Phylogenetically relevant fixed variants With the general aim to link functional information to the genetic variations identified by the 'VARCall' workflow, we retrieved the SNPs and InDels which are specific and sensitive (i. e. fixed variants) either of branches in the phylogenetic tree (i. e. 'PhyloFixedVar') or between groups defined by the user (i. e. 'FixedVar'). The 'phyloFixedVar' and 'FixedVar' scripts retrieve SNPs and InDels and distinguish intragenic from intergenic variants, as well as homoplastic and non-homoplastic fixed variants at each branch of the phylogenetic tree. Additional information related to impact on protein translation was associated to each fixed variant (e. g. synonymous, nonsynonymous, missense, frameshift). According to a study on S. Typhimurium estimating non-synonymous SNPs at 44%  [24] , most of the branches presented significantly more synonymous fixed variants that non-synonymous fixed variants (Fig.
Modification in Pullorum The identification of variants, detection of fixed variants, assignment of GO-terms to variants, and gene-ontology enrichment analysis were performed with the scripts 'VARCall' , 'phyloFixedVar' , 'GetGOxML' , and 'EveryGO' , respectively. The variant annotation was performed with SnpEff against reference genome S. Enteritidis (strain P125109, accession NC_011294. 1). The p-values of hypergeometric tests were adjusted by Bonferroni correction. The lowest corrected p-values representing GO-terms highly impacted by fixed variants (i. e. < 5. 0×10 -2 ), the highest GO-levels presenting the most accurate GO-terms (i. e. ≥ 5), and the highest number of hits representing relevant GO-terms quantitatively (i. e. ≥ 4) are presented impacting SPI-1, SPI-2, SPI-5 and SPI-12 (Additional file 9).
Because of our objective to detect clade specific traits related to host adaptation, we decided to exclude the homoplastic variants in the analyses of the S. Dublin, S. Enteritidis, S. Gallinarum and S. Pullorum genomes. Considering all leaves of the phylogenetic inference, the non-homoplastic fixed variants represented 65% of all fixed variants. It should however be mentioned that it is fully possible to use the scripts 'phyloFixedVar' , 'FixedVar' , 'GetGOxML' and 'EveryGO' to select homoplastic variants for studies that would focus on coevolution  [51]. The gene-ontology enrichment analysis was applied to synonymous and non-synonymous fixed variants because synonymous variants may be involved in regulation of gene expression or level of protein synthesis even if they do not impact the protein sequence  [52].
In order to browse easily between tree branches, genes and their annotations, the xml file produced by the scripts 'phyloFixedVar' or 'FixedVar' centralizes the annotations of variants including the homoplastic variants for all the combinations of genomes present in the phylogenetic inference. The polyvalent scripts 'phyloFix-edVar' , 'FixedVar' , 'GetGOxML' and 'EveryGO' were developed to perform the first gene-ontology enrichment analysis based on bacterial coregenome variants and can be applied to all kind of bacterial genome collection.
Read dataset With the objective to validate the method of phylogenetic inference described in detail below, and to perform the first gene-ontology enrichment analysis based on bacterial coregenome variants, a previously published read dataset was used  [22]. This collection of reads is made of 59 genomes sequences of Salmonella strains and was constituted by Langridge et al. [22]  in order to describe host adaptation at the accessory gene scale of Salmonella serovars S. Enteritidis (i. e. multi-hosts), S. Dublin (i. e. mammalian-hosts), S. Gallinarum (i. e. avian hosts) and S. Pullorum (i. e. avian-hosts) (Additional file 1). While Langridge et al. proposed manual workflows mainly focusing on the accessory genome, we provide in the present manuscript automated workflows aiming to perform the first geneontology enrichment analysis based on bacterial coregenome variants.
Variant calling analysis (i. e. the 'VARCall' workflow) A driver script called 'VARCall' invokes 'BAMmaker' , 'VCFmaker_SNP' , 'VCFmaker_INDEL' , and 'SNP-INDEL_merge' , successively (Fig. 7 ). The script 'BAMmaker' allows trimming of single-and paired-end reads with Trimmomatic (i. e. quality score of 25 and minimal length of 20 bp)  [75] , read alignment against a reference genome with BWA  [76] , read sorting with Samtools  [77] , as well as duplication removal and realignment around InDels with the Genome Analysis Toolkit (GATK)  [2] , successively. Following an approved framework for variant discovery  [78] , the scripts 'VCFma-ker_SNP' and 'VCFmaker_INDEL' call and filter variants (i. e. SNPs and InDels) according to GATK best practices  [2]  in order to retain high-confidence variants  [33].
After variant combination prioritizing SNPs with GATK  [2]  and SNPs/InDels flagging with SnpSift  [79] , the variants presenting allele frequencies equal to one across the dataset of genomes (i. e. variants specific to the reference genome), as well as the variants presenting missing genotypes in at least one genome, are removed from the dataset of variants (i. e. vcf file). The 'VARCall' workflow produces four output files: matrices of pairwise distances ('VCFtoMATRIX), a report about breadth and depth coverages (i. e. script 'reportMaker'), and files of concatenated variants (i. e. script 'VCFtofasta') and pseudogenomes (i. e. 'VCFtoseudogenomes') to build fast or slow phylogenetic inferences, respectively (Fig. 7 ). The pseudogenomes correspond to the reference genome where the genotypes of detected variants are replaced in each genome of the dataset.
Finally, the variants are annotated with SnpEff (version 4. 1 g without variants from intron, UTR-5' , UTR-3′, upstream regions, and downstream regions)  [80]  based on the reference genome annotation from NCBI (i. e. gbk file). Independently of the 'VARCall' workflow, the density of SNPs were computed with the 'vcf-subset' module and the 'SNPdensity' argument of VCFtools  [81]. We develop currently a new version of the 'VARCall' workflow (i. e. iVARCall2) based on the HaplotypeCaller algorism (GATK) in order to call SNPs and InDels at the same time by local de novo assembly for each genome which would give us the opportunity to compute a faster phylogenetic inference based on variants independently called for each genome (HaplotypeCaller) rather than to perform time consuming variant calling across reference genome alignments (UnifiedGenotyper)  [82].
The positions of recombination events detected by ClonalFra-meML were also removed from the vcf file with a script 'Clonal_VCFilter' in order to compute phylogenetic inference based on pseudogenomes excluding variants linked to recombination events. Finally, the best-scoring ML trees were graphically represented with iTOL viewer  [84]. The comparison of the tree topologies was performed using the cophylo function of 'phytools' R package  [85]. In order to support results of phylogenetic inference, genetic structure analyses were also performed with BAPS based on SNPs  [41].
Fixed and homoplastic variants A script called 'phyloFixedVar' uses the annotated vcf file from the variant calling analysis and the nwk file from a binary tree. This script was developed in order to detect sensitive and specific variants at each branch of the phylogenetic inference. The variants are defined with a biological point of view either as fixed (i. e. variants with sensitive and specific genotypes at intermediate branches) or transient (i. e. variants with sensitive and specific genotypes at final leaves). The script 'phyloFixedVar' (a) identifies all comparisons of genome groups referring to all nodes, (b) selects and specific variants, and (c) defines if these variants are homoplastic (i. e. convergence of genotypes between independent phylogenetic clades), successively. More precisely, right child leaves are listed with corresponding leaves of left child at each node and reversely, then both comparisons (i.