dataesr/scientific-paragraphs-categorization huggingface dataset:
Technical infrastructure Our infrastructure is a Semantic PIMS (SPIMS)  (Abiteboul 2015)  in the cloud. In a previous publication  (Gilliot et al. , 2016) , we have presented a proof-of-concept prototype based on a Personal cloud infrastructure and standard interface implementation to collect data. Figure  1  shows the prototype architecture  (Gilliot et al. , 2016). Components of the personal cloud are highlighted in green. Learning components (including services) are highlighted in violet. In our architecture, e-Portfolio (1) is seen as an example of Learner Model. It enables data access according to lifelong personal goals. Implementing such Learner Model in a Personal Cloud provides personal data storage (2), enabling full data access to the learner and full duration control as well. Data are collected in two different ways: external learning achievements may be collected thanks a data transfer mechanism (3) from external servers, whether institutional or commercial, or learning traces thanks a learning streaming flow (4).
Susilo and Cats (2014) , for example, found that there is a systematic tendency to report higher satisfaction levels immediately after the completion of a public transport or cycling trip stage when compared with a retrospective satisfaction report. This is consistent with findings based on a similar question asked years after a trip occurred  (Pedersen et al. , 2011). Similarly private car travellers reported significantly lower travel satisfaction levels in retrospective reports. Therefore, from the above discussion it is deduced that the data collected by different survey methods may yield distinct answers. The fact that the method used can affect the quality of the responses and bring about some other bias (response rate, non-response bias, etc. ) associated with the characteristics of the data collection tool, makes it difficult to define the ground of truth.
The travel satisfaction measurement survey tools tested consisted of two types of smartphone applications (a satellite navigation app and a game app), an on-line survey, a paper-based semi-structured questionnaire, and a focus group. Each of the measurement tools comprised of a similar set of questions, but in different formats, aimed at exploring the pros and cons of each tool among different group of travellers. In the next section, we will describe the survey and the tool design and the data collection activities. The descriptive and correlation analyses of the collected datasets from 8 different cities and five FIA's national networks are then presented. This is followed by multivariate analyses which are employed to measure the impacts of different survey methods towards the overall travel satisfaction reported.
METPEX project and survey description This study was part of METPEX  FP7 EU project (www. metpex. eu, METPEX, 2012) , which aimed to develop a Pan-European standardised measurement tool to measure passenger experience across whole journeys, whilst taking into account wider human socio-economic, cultural, geographic and environmental factors. The early stage of the research comprised desktop research, stakeholder consultation and a small size experiment among approximately 550 respondents across 8 different European cities. The results were used to identify the variables that could be used to better monitor and evaluate the passenger experience during journeys by public and active forms of terrestrial transport, with special attention toward the needs of vulnerable groups, such as older people, lower income groups, rural dwellers, children and those with both physical and cognitive disabilities  (METPEX, 2013; Cats et al.
In total, 6,360 completed responses were collected during the survey period. After the data had been cleaned and double checked for consistency and reliability across different sections, the total number of valid samples was 5,275 (See As can be seen from Table  1 , the traditional on-line (64%) and the paper-and-pencil (19%) methods attracted the highest number of respondents (total 83%), whilst the more technologically driven methods; game app (8%) and SbNavi (5%), attracted the lowest response rates. This low response rate, however, varied in different countries, and it is important to note that the Game app contained a fewer number of questions, which were less complex and thus more user friendly, than the SbNavi app. This may be why the former attracted a higher number of respondents than the latter.
Tourists/visitors seemed to report a higher travel satisfaction, compared with other groups of travellers, except for tourists/visitors in Rome. and  Eluru, 2013)  to the whole dataset. The estimated indicators, however, show that it is not advised to apply a Generalized Ordered response model for our rich model specification and to the city-specific models, M1 and M2, since the sample for each city-specific models are not large enough. Therefore we only refer to the ordered logit regression model estimation results. In terms of survey methods, only Stockholm, Bucharest and Coventry had valid samples for all five survey methods (see Table  6 ). Overall, responses collected via the game app (and sbNavi in Stockholm) reported a significantly lower travel satisfaction than other survey methods.
There were also significant privacy and data protection concerns among  EJTIR 17(3), 2017, pp. 384-410 408 Susilo et al. Findings from measuring door-to-door travellers' travel satisfaction with traditional and smartphone app survey methods in eight European cities potential respondents (e. g. in Rome) in terms of installing an app coming from an unknown source. The next step will involve further detailed examination of the impact of trip leg complexity, and an investigation of familiarity and uncertainty in relation to trip satisfaction. Structural equation modelling will be used to examine traveller satisfaction for each trip leg, the nature of the trip purpose and also the experience of access and egress during trip legs. Further analyses on the focus groups results, especially among various groups of travellers with special needs, will also be a future step of this study.
Acknowledgements This study was conducted as part of the  METPEX  (MEasurement Tool to determine the quality of the Passenger EXperience) project (www. metpex. eu), which was funded under the  European Union 's  Seventh Framework Programme for research, technological development and demonstration  under grant agreement no  314354. An earlier version of this paper was presented at the 14th International Conference on Travel  Behaviour Research (IATBR) , London, UK, 2015. The authors thank  Chengxi Liu 's help in estimating the Generalized Ordered Response model as an alternative model to explore the behaviours.
Extracellular capsules constitute the outermost layer of many bacteria, are major virulence factors, and affect antimicrobial therapies. They have been used as epidemiological markers and recently became vaccination targets. Despite the efforts to biochemically serotype capsules in a few model pathogens, little is known of their taxonomic and environmental distribution. We developed, validated, and made available a computational tool, CapsuleFinder, to identify capsules in genomes. The analysis of over 2500 prokaryotic genomes, accessible in a database, revealed that ca. 50% of them-including Archaea-encode a capsule. The Wzx/Wzy-dependent capsular group was by far the most abundant. Surprisingly, a fifth of the genomes encode more than one capsule system-often from different groups-and their non-random co-occurrence suggests the existence of negative and positive epistatic interactions.
The resulting computational tool-CapsuleFinder-uses as input the protein sequences of a genome, searches for the components of capsule systems using the HMM profiles and then delimits the systems based on the information provided in the models. There is no curated database with information on the organisms encoding and/or lacking capsule systems. The literature rarely mentions the absence (or presence) of a capsule for non-pathogens. Nevertheless, we sought to validate CapsuleFinder by comparing its results with those mentioned in two lists of some of the best-studied encapsulated Prokaryotes  [19, 35]. We successfully identified capsules in all 11 species that were reported as encapsulated and for which a complete genome sequence was available. To validate a broader set of systems, we randomly picked 100 species from our complete genome database.
Abundance and phylogenetic distribution of capsule systems We detected 2182 capsule systems in 1304 out of the 2643 genomes (Fig  2 ). The complete list of genomes and capsule systems is available in S1 Dataset. Around half (49%) of the genomes, representing 52% of the species, encoded at least one capsule system. Group I capsules were the most frequent, representing ca. 70% of the total. ABC-dependent and synthase-dependent capsules were less frequent (nearly 10% each), and subgroup CPS3 capsules were the most frequent among the latter. Group IV capsules (8. 8%), and PGA proteic capsules were rarer (3. 1%) (Fig  2.
and S1 Dataset). We investigated the presence of capsule systems in all major taxonomic divisions of Bacteria and Archaea (Fig  2 ). The highly abundant Group I capsule was detected in all bacterial phyla represented by more than 20 available complete genomes (except Spirochaetes and Tenericutes). PGA capsules, even if rare overall, were also present in most phyla. They were particularly abundant in Synergistetes, Planctomycetes, Bacillales and Fusobacteria (Fig  2 ). On the other hand, Group IV capsules were almost exclusively identified in γ-Proteobacteria and some subgroups were only identified in the taxa in which they were first described, e. g. , all Group IV_f capsules were identified in Francisella spp. (Fig 2). We identified at least five out of the eight capsule groups in α-and γ-Proteobacteria and in Actinobacteria.
Capsules are rare in obligatory and frequent in facultative pathogens The observation that multiple capsules are more frequently observed in commensals, mutualists or environmental bacteria seems at odds with the hypothesis of a tight association between capsules and pathogenesis. We classified bacterial species according to the degree of host-association they commonly exhibit (S1 Dataset, see Methods for criteria and  [45, 46] ) and found that the probability of encoding a capsule depends on the lifestyle of the bacteria (Fig 5A ), even when accounting for genome size (S9 Table ). We then first tested whether free living species were more likely to code capsules than pathogens. We found that, indeed, capsules were slightly rarer in pathogenic species as opposed to free living species  (Fig 5A and S4 Fig).
The lower frequency of capsules in pathogens remains qualitatively similar when commensals and mutualists (or both) are grouped together with free living species. Additionally, we observed no difference in genome size between pathogenic bacteria encoding a capsule system and the others, suggesting that the association between the presence of capsule and pathogenesis is independent of genome size. Many of the pathogenic bacteria in our dataset are facultative or opportunistic. These bacteria typically have environmental reservoirs and larger genomes than obligatory symbionts (pathogens or mutualists)  [47, 48]. We observed that many facultative pathogens encode capsules, in contrast to most obligate pathogens, independently of the differences in genome size between the groups  (Fig 5B and S1 Dataset).
C sp + bacteria are over-represented across different environments We analyzed microbiome data to confirm that capsule systems are frequent in environmental bacteria and in facultative pathogens (that often have environmental reservoirs). Unfortunately, loci encoding capsule systems are too long and complex to be identifiable in the sequences of metagenomes. To circumvent this difficulty, we identified the presence of the species for which we had at least one complete genome in a large number of publicly available metagenomics datasets (16S rRNA). We used this information to quantify the abundance of each species and, using the species' complete genomes as a proxy, to predict the presence of capsules in these environments. Specifically, we searched for the presence of C sp + in 16S datasets from four classes and numerous sub-classes of environments (Fig  6A ).
This allowed both the qualitative and quantitative identification of bacterial species in 6700 environmental 16S datasets (S8 Table, see Methods). We computed the abundance of C sp + relative to C sp -species in the 16S datasets in qualitative (number of species) and quantitative (number of 16S sequences) ways (see Methods). The percentage of C sp + was similar in the 16S (53% out of 1197 bacterial species) and in the genome (52%) datasets. C sp + were more frequently present Capsules allow Prokaryotes to withstand a series of stresses, from environmental disruptions to protozoa grazing, and are expected to be associated with broader environmental ranges. Indeed, C sp + species were present in significantly more environmental subclasses than C sp -(Fig  6B ). Importantly, the number of environmental subclasses for a given species increased with its average number of capsules per genome (Partial Spearman test, P < 0.
C sp + in the human microbiome The vast majority of previous studies focused on capsules of bacterial pathogens. To disentangle the relation between capsule and pathogenesis, we analyzed the presence of C sp + species in human-associated datasets. We first checked that we were able to identify well-known pathogens in the host-associated environments. Indeed, we detected pathogens with Group I capsules, such as K. pneumoniae and S. pneumoniae, as well as pathogens with ABC capsule systems, namely Neisseria meningitidis, in samples of the human microbiome, and sometimes also in other environments (S10 Table ). The total abundance of species encoding capsules within the human host varied between body locations (Fig 6C ), and was higher overall than within the complete genome database (57%, binomial test, P = 0.
Unraveling the repertoire of capsules of Prokaryotes Capsules play important roles in bacterial virulence, but their study has been hampered by the lack of computational tools to identify them in genomes. Our tool, CapsuleFinder, identifies the eight major groups and subgroups of capsule systems in bacterial and archaeal genomes and is thus complementary to software designed to analyze very specific capsule systems, e. g. , the recently released Blast-based tool to identify capsular serotypes in Klebsiella spp. (Kaptive,  [50] ). The models in CapsuleFinder can be modified to either increase specificity (obtain systems closer to the experimental models) or sensitivity (to detect more distantly related systems). This can be done by changing the number, type and genetic organization of the components that are required to identify a system.
Users can also add novel models and protein profiles to improve the tool, e. g. , to account for novel experimental data. If enough experimental serotype data is available for a given species, then the models can be specified in order to infer a putative serotype for the strains. The construction of our models was based on previous experimental studies restricted to a relatively small number of model organisms from Proteobacteria and Firmicutes. Capsules, like many extracellular structures  [51] , are subject to rapid evolution and reorganization via recombination, complicating their detect from a small number of taxonomically restricted reference systems. In spite of this, we were able to identify them in many phyla of Prokaryoteseven in Archaea-with few putative false positives. Hence, we expect to have identified the majority of capsules of known groups in the complete genome database.
Given our results in the phyla with higher representation in the database, capsules might occur across all prokaryote phyla. Capsule-like structures have been described in Archaea  [38] [39] [40] , where a previous bioinformatic study revealed the presence of proteins similar to those involved in the synthesis of the PGA capsule in one species  [29]. We identified PGA capsule systems and also Syn_CPS3 systems in many genomes of Archaea. These two groups of systems have few components and we couldn't find data suggesting that they allow extensive serotypic variation. However, the lack of more complex capsule groups, should be subject to caution owing to the lack of experimental data. Furthermore, our tools to identify capsules were based on bacterial systems. Alternatively, the peculiarities of the cellular envelope of Archaea may explain the absence of certain capsule groups in the phyla.
Data Genomes. We analyzed 2786 chromosomes and 2087 plasmids of 2484 bacterial and 159 archaeal fully sequenced genomes from NCBI RefSeq (ftp://ftp. ncbi. nih. gov/genomes/, downloaded in November 2013). This accounts for 1440 different species of which 140 are Archaea. The details are listed in S1 Dataset. Metagenome samples. We downloaded 6743 metagenome samples (16S rRNA assembled reads) and associated metadata from MG-RAST (http://metagenomics. anl. gov/, last accessed on  March, 2016). The identification of capsules was performed at the genome level whereas metagenome and life-style analyses were performed at the species level. Analysis at the species level required a classification of species into those encoding capsules (C sp + ) and those lacking them (C sp -). In the vast majority of cases, the different strains of a species had the same group of capsules.
Whenever possible, the information related to pathogenesis was retrieved from the Bergey's Manual of Systematic Bacteriology  [73]  to maximise the homogeneity of the criteria. When needed, we used primary literature (references in S1 Dataset). We did not class some species because information was lacking or was ambiguous. When the classification was based on little information we added a comment in the table with appropriate references. Bacteria were classed as facultative or opportunistic pathogens when there were several reports showing their role in host infection, but they are also commonly able to proliferate as commensals or as free-living bacteria. Most nosocomial pathogens fall in this category. Bacterial species were classed as obligate pathogens when they are known to require infection to proliferate significantly in the host.
The class "other animals" corresponds to bacteria that grow on animals, but not usually found in mammals. The class "plants" corresponds to bacteria usually associated with plants, like rhizobia or phytopathogens. The class "other" corresponds to bacteria associated with Eukaryotes (typically with protozoa, like amoeba). This classification is hierarchical (humans > other mammals > other animals > plants > other), i. e. , a bacteria present in hosts of all these groups is classed in the top group (humans). The details of the classification are listed in S1 Dataset. The histograms with the distribution of the different classes are in S7 Fig. From  this table we used two sets of categories. The "Pathogen" category includes the facultative, opportunistic pathogens and the "No Pathogen" includes the free-living but not the mutualists.
Identification of capsule systems We used MacSyFinder to search for capsule systems  [33]. This program takes as input a proteome, a set of hidden Markov models (HMM) protein profiles (one for each component of the system, see below), and models describing the number of components and their genetic organization (Fig  1 ). MacSyFinder identifies the individual components of each capsule system using hmmsearch from the HMMER package v3. 1b2  [86]. A component was retained for further analysis when its alignment covered more than 50% of the length of the profile and obtained an e-value smaller than 0. 001.
Definition of HMM protein profiles We used 58 different HMM protein profiles in our searches (S1 Table ), 31 retrieved from the PFAM 28. 0 database (http://pfam. xfam. org, [87] , last accessed November 2015) and 27 built in this study. Each protein profile was constructed as follows (except when explicitly stated otherwise). We started from a well-described and experimentally-validated component of a system and used BLASTP v 2. 2. 28  [88]  (default settings, -v 4000, e-value < 10 -4 ) to search for homologs among complete genomes. To reduce the redundancy of the dataset (i. e. , to remove very closely related proteins), we performed an all-against-all BLASTP v 2. 2. 28 analysis and clustered the proteins with at least 80% sequence similarity using SiLiX v1. 2. 9 (http://lbbe. univ-lyon1.
fr/SiLiX, default settings)  [89]. We selected the longest sequence from each family as a representative. The set of representative sequences was then used to produce a multiple alignment with MAFFT v7. 215 using the L-INS-i option and 1000 cycles of iterative refinement  [90]. The alignment was manually trimmed to remove poorly aligned regions at the extremities, using SEAVIEW  [91]. The HMM profile was then built from the trimmed alignment using hmmbuild (defaults parameters) from the HMMER package v3. 1b2  [86].
Model validation We validated the method to identify capsule systems using two published lists of capsulated bacterial pathogens  [19, 35]. Since these lists were very short, and not necessarily meant to be exhaustive, we made a complementary validation on a random set of species from our dataset. We used the R function sample to randomly draw 100 species from a curated list of 1241 species in our database (this list did not include genomes for which a genus but not a species was defined, such as Glacieola sp. ). We identified capsule systems in 40 of the 100 species. We then sought to confirm the presence of capsule in the latter (they include 52. 5% of free-living, 30% facultative pathogens, 12. 5% commensals and 5% of mutualists) by analyzing the primary scientific literature. For those species for which we did not detect a capsule system, we did not seek further validation as negative results are not systematically reported.
Construction of phylogenetic trees To control for phylogenetic independence of data at the genome-level, we aligned the 16S rRNA using secondary structure models with the program SSU_Align v0. 1  [94]  of 2440 bacterial genomes. The alignment was trimmed with trimAl v1. 4  [95]  using the option -noallgaps to delete only the gap positions but not the regions that are poorly conserved. The 16S rRNA phylogenetic tree was infered using IQTREE v. 1. 4. 3  [96]  under the GTR+I+G4 model with the options-wbtl (to conserve all optimal trees and their branch lengths), and-bb 1000 to run the ultrafast bootstrap option with 1000 replicates. Two hundred and eleven genomes from our database were excluded from the final phylogenetic tree because identical 16S sequences were already present in the multiple alignment.
Controls for phylogenetic dependence and genome size All phylogenetic corrections were done using the 16S rRNA tree of Bacteria. We restricted our phylogenetic controls to Bacteria, because the inclusion of Archaea reduced very much the phylogenetic signal (resulting from a shorter multiple alignment) and clumped together many species' 16S sequences. The presence of phylogenetic signal in the evolution of traits was estimated with Pagel's lambda using the phylosig function of the phytools package v. 0. 5-20 for R  [42]  and the aforementioned 16S rRNA phylogenetic tree. To estimate the phylogenetic signal across capsule groups, instead of using the 16S rRNA tree, we built new trees comprising only the 16S rRNA sequences of the genomes for which we detected the given capsule groups. To control for the effect of the uncertainty in phylogenetic inference on the key positive results, we produced 1000 bootstrap trees (options -wbtl -bb 1000 in IQTREE) and randomly selected 100 of those trees.