dataesr/scientific-paragraphs-categorization huggingface dataset:
24 × 10 -5 substitutions per site per year (95% HPD: 3. 93 × 10 -5 , 1. 32 × 10 -4 ). Indeed, of the branches within the presented maximum clade credibility tree, this branch had the lowest median rate across the posterior distribution of trees. Using BEAST, we reconstructed the sequence of the most recent common ancestor of the viruses sequenced from the second outbreak. This ancestral sequence displayed only 7 nucleotide substitutions (of which 6 were synonymous or in the untranslated regions [UTRs] of the viral genome) compared to BTV-8 FRA2007-3673 , the genetically closest virus within the dataset, which was collected in France in August 2007  (Fig 4A). In comparison, BTV-8 FRA2007-3673 displayed 23 nucleotide substitutions (of which 16 were synonymous or in the UTR) compared to the first sequence available from the first outbreak and collected in August 2006 in the Netherlands (BTV-8 NET2006-04 ) (Fig  4A ).
The number of mutations of BTV-8 FRA2007-3673 compared to the BTV-8 sequences in the dataset in 2006 (n = 23) ranges between 15 and 23, while those compared to the virus sequences collected in 2008 (n = 37) varies between 2 and 56. Hence, sequence variation between BTV-8 samples collected only a year apart during the first outbreak is, in general, far higher than that between the ancestor of the re-emerged BTV-8 strain and its closest relative in the first outbreak.
"Frozen evolution": Clock-like evolution of BTV-8 during, but not in between, outbreaks Next, we included the reconstructed ancestral sequence in our dataset and re-estimated the ML tree to get measures of genetic distance from the root of the tree. Consistent with clocklike evolution, the genetic distance between virus sample and the root increased linearly with time during both the first (TempEst: slope = 7. 2708 × 10 -4 subs/site/year, r 2 = 0. 8113) and the second outbreak (TempEst: slope = 6. 9291 × 10 -4 , r 2 = 0. 9605). There was no evidence that the evolutionary rate of the virus differed between the two outbreaks (p-value for Date:Outbreak interaction = 0. 194). However, there is a clear discontinuity in the accumulation of mutations between the two outbreaks, consistent with a period during which clock-like evolution had essentially ceased.
Discussion Diseases of livestock can be exceedingly interesting models to study virus emergence, given that harmonised international surveillance systems and regulatory frameworks provide opportunities to access field samples with associated metadata across national borders. Here, the BTV-8 European outbreaks provided us with the opportunity to investigate the mechanisms surrounding arbovirus emergence based on a uniquely rich dataset. Our results indicate that the re-emergence of BTV-8 in France in 2015 was caused by a virus that exhibits a lack of evolutionary changes since the first outbreak. This is inconsistent with the prevalent view of undetected low-level circulation of the virus in wild or domestic ruminants between 2010 and 2015, and instead points to another mechanism of emergence.
The lack of divergence is also illustrated by the fact that the reconstructed ancestor of the BTV-8 outbreak has only 7 mutations separating it from its closest relative in the analysed dataset, a French sample collected in August 2007 (BTV-8 FRA2007-3673 ), despite putatively having been replicating for at least half a decade after that sample's collection. In comparison, BTV-8 FRA2007-3673 showed 23 mutations compared to the genome of the earliest BTV-8 sample obtained from the Netherlands in August 2006, only a year earlier. The corresponding rate of evolution estimated for the emerging branch was almost an order of magnitude slower than the mean clock rate, highlighting it as exceptionally low (Fig  3 ). Moreover, we hypothesise that some or all of the estimated seven mutations on this branch might have accumulated during the first outbreak, given that the emerging branch connects to an internal node in the timescaled phylogeny with a date of early 2007, at the height of the first outbreak.
Samples Blood samples from animals infected with BTV-8 were received from ten European countries during the bluetongue outbreaks from 2006 to 2018. In some instances, samples analysed were viruses isolated in tissue culture from blood of infected animals. S1 Table provides the metadata related to the dataset used in this study. These include virus strain names, animal species of origin, geographical location, and date of sampling. In addition, metadata include whether the viral genome sequence was obtained directly from clinical material (blood) or from an isolate in tissue culture, sequencing methods, and GenBank accession numbers.
A 1-μg aliquot of the pooled library was enriched using a SeqCap EZ Developer Probe (Roche/NimbleGen) (see below), according to the manufacturer's protocol. After a 14-cycle post-enrichment PCR amplification, the cleaned PCR products were pooled and were sequenced with a 151-base paired-end reads on a NexSeq500/550 cartridge (Illumina, San Diego, CA). Probes were designed using all BTV sequences available on NCBI Genbank, RefSeq, DNA Data Bank of Japan (DDBJ), and EMBL EBI databases (as accessed by October 2016). The resulting NimbleGen biotinylated soluble capture probe library ("BTV-Cap") contains a probe set of more than 500,000 probes, designed to minimise capture of Culicoides sonorensis, Bos taurus, Ovis aries, Capra hircus, and Mesocricetus auratus genomes.
Consensus calling All consensus calling was performed on a cluster running Ubuntu v. 14. 04. 5 LTS. In all cases, BAM and SAM files were handled using samtools v. 1. 3  [70]. The R packages ggplot2 v. 3. 2. 1  [71] , seqinr v. 3. 6-1  [72] , stringr v. 1. 4. 0  [73] , and vcfR v. 1. 8. 0  [74]  were all used in scripts at various points in the following section. Paired-end raw reads were trimmed with Trim Galore! v. 0. 4. 0 (http://www. bioinformatics. babraham. ac. uk/projects/trim_galore/) with a quality cutoff of 30. Any reads below 50 bp were discarded. Following this, any reads that were unpaired were discarded if they were under 100 bp. Overlapping reads were combined using FLASH v. 1. 2. 11 [75]. Reads were mapped using bowtie2 v. 2. 3. 4. 2  [76]  to a reference database containing all the segments of all the described strains of BTV in order to manually check for mixed infections.
Reads were allowed to have as many valid maps as could be found. Mapping statistics were generated with weeSAM v. 1. 5 (https://github. com/centre-for-virus-research/weeSAM) and transcripts per million for each target were then generated using eXpress v. 1. 5. 1  [77]. Separately, for the consensus generation, reads were mapped using Tanoti v. 9 July 2018 (https:// github. com/vbsreenu/Tanoti/tree/master/src) against a reference BTV-8 genome from the European BTV-8 outbreak (GenBank accession numbers: JX680447-JX680456). Different software was used for the quality control and consensus building steps, as bowtie2 generates metadata required for the downstream quality control steps that Tanoti does not. Variants from the BTV-8 reference were then called from the Tanoti alignment with lofreq � v.
2. 1. 2 [78] , with the minimum coverage of the filtering step set to 5 and all other parameters at their default values. Any variants from the reference with an allele frequency of greater than 0. 5 were replaced into their positions in the reference to build a new reference sequence. Reads were then remapped to this new reference. This process was then repeated either 5 times or until the reference generated after the process was identical to the reference at the start of the last round of mapping. Reads were then mapped again against this new reference, and ambiguities were called. A base was called unambiguously if the allele frequency of the dominant allele was greater than 0. 75; otherwise, the base was called ambiguously over all alleles with a frequency of greater than 0. 05 using a bespoke script in R (S1 Data).
As such, the counts of infected premises from 2009 and 2018 are not comparable to previous years as they exclude France, where the virus was widespread. Data are available from the WAHIS database at https://www. oie. int/wahis_2/public/wahid. php/Wahidhome/Home. BTV-8, bluetongue virus serotype 8. Map adapted from tiles by Stamen Design, under Creative Commons (CC BY 3. 0) using data by OpenStreetMap, under the Open Database Licence. https://doi. org/10. 1371/journal. pbio. 3000673. g001.
Credible intervals are omitted because nonindependence of points would have made conventional estimate of standard errors invalid. When the day was unknown, the date was fixed to the 16th of the month, and when the month was unknown, the date was fixed to the midpoint of the year. The inferred age of the ancestor of the second outbreak is shown as a square, with a 95% HPD error. The dashed line indicates that the inferred ancestor of the second outbreak has a degree of divergence that is equivalent to a virus from the first outbreak circulating in 2008. Mutations can be derived from the raw sequence data deposited on GenBank under the IDs listed in S1 Table. Distances to the root can be extracted from the tree in S3 Data as MlincludingAncestor. tree, and dates are available in dateswithuncertainty.
This work was supported by the  European Union   H2020  (https://ec. europa. eu/programmes/horizon2020/en)  PALE-Blu grant  (project no. 727393-2  to PPCM, SZ, BH, GS, PAvR, RB, and MP) and by the  Wellcome Trust  (https:// wellcome. ac. uk/) ( 092806/B/10/Z ;  206369/Z/17/Z  approximately 2008. for pathogen surveillance programmes in livestock and illustrates the power of genomic epidemiology to identify pathways of infectious disease emergence. to MP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
INTRODUCTION Traditional data management has long been adopted by many researchers involved in Digital Humanities (DH). However, it requires a substantial investment in data modeling, including, at the physical level, technologies such as relational and semi-structured Database Management Systems (DBMSs), various data formats, e. g. , XML and JSON for semi-structured data, RDF for linked data, and query languages such as SQL and XQuery. This investment in computer science and the fact that initial data are inevitably transformed are presumably impediments to the adoption of DBMSs and related digital tools for DH. Moreover, most source information exploited by humanities and social sciences comes in textual format. Again, such textual documents are difficult to manage without substantial transformations: digitization, encoding and tagging, e.
g. , via the Text Encoding Initiative (TEI), and even lowercasing, stemming, lemmatization, stopword removal or normalization when it comes to text mining and natural language processing. Another important methodological issue is the black box effect that occurs when resorting to computer scientists only "as a service". How can DH researchers work without mastering the whole process? Furthermore, designing and managing such processes also lead to research issues for computer scientists. To leverage the above-mentioned issues, we propose the use of data lakes, a concept introduced by Dixon in 2010 as a solution to data siloing and big data variety problems  [2]. Even if data exploited by DH are not always big data in terms of volume, they can bear considerable variety, i. e. , including structured and semi-structured data, as well as unstructured data such as texts, various types of images, sounds and videos.
Traditional data management tends to manage such heterogeneity with different systems, thus separating data into so-called silos. A data lake is a scalable storage and analysis system for data of any type, retained in their native format and used mainly (but not only) by data specialists (statisticians, data scientists or analysts) for knowledge extraction  [10]. One of the main advantages of data lakes is that data are stored in their initial form, and are thus recognizable by their producers, such as DH researchers. A data lake does not propose a new data model nor new data formats for data archiving. Moreover, when data are transformed for processing, the data lineage is stored as metadata, thus enforcing traceability. However, a drawback is that unprepared data are difficult to process and require data specialists who can program.
Bretez/STRATEGE Bretez  [7]  is a multidisciplinary project aiming at a visual and sonorous restitution of the XVIII th -century Paris. It is also an exploratory project constituted of successive, interlinked modules that are (and must be) interoperable and open. The historical urban restitution is achieved with video game engines that bear their own respective characteristics, of course related to gaming. Yet, here, they are used for specific management and traceability needs. Moreover, Bretez' documentation is a voluminous corpus of heterogeneous and multimedia data. Within project Bretez, the "Traceability and information management system for multimedia data" (STRATEGE) aims at designing, storing, querying and analyzing all the project's data. To master data heterogeneity, manage data quality and volume, warrant data interoperability and an efficient access while keeping data in their Figure  2 : CODAL example screenshot  [9]  original form so that they remain usable reference for the project's researchers, we resort to a data lake.
STRATEGE is in its first stages: we catalogued all existing data, which included a database, textual documents, sounds, images and a 3D Unity 7  (a game engine) model, and more. The database is particularly interesting, for it contains both data and metadata. While retaining it, we also restructured it so as to allow its metadata to interoperate with specific data lake metadata. In short, there are "business" metadata and technical metadata. The remaining tasks include fully designing and integrating the metadata system, on the basis of MEDAL  [10] ; make the data from the Unity model accessible into the lake; formalize analysis needs; and design tools that must jointly handle textual, visual and audio content, as well as the heterogeneity of data sources. Such software tools must be accessible to all researchers involved in the Bretez project.
With such data handy, the objective is to cross-analyze the terms and expressions found in textual resources with structured, qualitative and quantitative data, in order to discover new insights regarding how companies communicate vs. their actual customer relationship management strategy (for COREL) and how digitization and servicization impact economic performance (for AURA PMI). The challenges here are to: (1) leverage metadata that allow querying the whole corpus; (2) jointly analyze structured and unstructured data; (3) allow management science researchers to perform analyses by themselves. To complete these tasks for the COREL project, we designed a metadata system that prefigured MEDAL  [10]  and proposed the lightweight COREL Data Lake architecture (CODAL)  [9] , which is composed of: (1) a storage layer that notably includes Elasticsearch 9 for indexing textual contents; (2) a metadata layer leveraging and extending the Metadata Encoding & Transmission Standard (METS)  [11] , stored in the BaseX XML DBMS 10 ; (3) an analysis layer, i.
e. , an intuitive Web-based graphical interface that allows management science researchers to perform analyses in autonomy, thus enforcing the "industrialization" of CODAL. The analysis layer features three kinds of analyses: (1) data exploration akin to On-Line Analytical Processing (OLAP)  [1] ; (2) proximity analyses such as similarity (what documents are similar or different  [8] ) and centrality (to identify the documents bearing a specific or common vocabulary, hinting at its importance  [3] ) analyses; (3) custom highlights of the context of terms and, optionally, their synonyms, in textual documents. All three types of analyses come with various dataviz (Figure  2 ). The AURA PMI Data Lake (AUDAL) is currently being developed, and builds upon CODAL. Its metadata system will notably be a substantial evolution of MEDAL supported by the Neo4J 11 graph DBMS.
CONCLUSION In all four data lake projects summarized in Section 2, we use different versions of the MEDAL metadata system, which is designed to be generic. However, although MEDAL is quite flexible, we do not believe in a single model for data lakes. There are indeed significant differences in data in only four projects, in terms of volume, variety and velocity, which imply different architectures and technologies. Thus, we think that much needed methodological tools for data lakes should be instantiated for each project rather than applied "as is". Furthermore, the software layer we add to "industrialize" our data lakes might become yet another black box, while there is a strong stake for researchers in humanities and social sciences involved in DH projects not to be dispossessed of data by an analysis layer that would adopt a "click and go" approach.
Data are indeed often partly constructed by said researchers themselves as a product of scientific work that takes time, thus giving a significant value to datasets. 9 https://www. elastic. co/elasticsearch/ 10 http://basex. org 11 https://neo4j. com In consequence, we take great care of accompanying DH users in their appropriation of our analysis tools, not only by training, but especially by interweaving research methodologies from computer science and other disciplines by design, in close collaboration with partner researchers. Moreover, the possibility of having both access to the raw data and the entire possible processing chain is necessary, because black boxes are seldom compatible with a sound methodological approach aiming at producing scientific knowledge. Data lakes precisely allow this much needed transparency.
Exclusions from the study were carried out a posteriori when the adolescents presented simultaneous participation in another clinical trial; age <12•5 or ≥17•5 years; and/or acute infection within 1 week before inclusion. Those adolescents who completed the FCP questionnaire were included (n 2954) for factor analysis. To estimate the IRT parameters, a random sample of 2000 adolescents was selected in accordance to the maximum number of respondents for analyses using the GGUM 2004 software. No differences were observed between those who participated in the current analyses and those who did not, except for their age; adolescents who were included in the current analyses are older than the whole sample (online Supplementary Table  S1 ). The IRT scores could only be estimated for 2000 adolescents.
Sociodemographic characteristics Socioeconomic status was evaluated with a self-reported questionnaire that collected data on living conditions, family structure, employment status and parental occupation, and educational level  (18). The Family Affluence Scale index was calculated based on this questionnaire and used as an indicator of adolescents' material affluence. The final score ranged from 0 to 8 and was dichotomised into 'low familial wealth' (0-4) and 'high familial wealth' (5-8). Maternal education levels were categorised in 'primary education', 'lower secondary education', 'higher secondary education' and 'university degree'  (19).
Limitations and strengths Limitations of this study include the inherent limitation of the GGUM 2004 software, regarding the maximum number of respondents allowed for analysis, which is 2000. In addition, the cross-sectional nature of our study cannot be used to establish causation between food intakes and/or nutritional biomarkers with motivation influencing food choices or vice versa. The study has also several strengths. The estimation of item parameters and scores presented low standard errors. The use of IRT analysis allowed including new items in SOFIE based on the items' parameters calculated in the present analyses. GGUM may provide new insights to evaluate motivation influencing food choices, and SOFIE can be presented as a new approach to evaluate health motivation.
Acknowledgements Financial support: This work was supported by the  Exterior Sandwich Doctorate Program for the Coordination of Improvement of Higher Level Personnel (CAPES -Coordenação de Aperfeiçoamento de Pessoal de Nível Superior)  (grant number  88881. 132853/2016-01 ). The HELENA study took place with the financial support of the  European Community Sixth RTD Framework Programme  (contract  FOOD-CT-2005-007034 ). Conflict of interest: None. Authorship: S. S. S. and C. J. analysed the data and wrote the first draft with contributions from L. A. M. All authors reviewed and commented on subsequent drafts of the manuscript. Ethics of human subject participation: This study was conducted according to the guidelines laid down in the  Declaration of Helsinki , and all procedures involving study participants were approved by the responsible institution in each of the participating countries.
The NetLogo Design Pattern is used to efficiently integrate the spatial aspects of the ABM. The paper highlights benefits offered by this software technique of engineering to design modular, extensible and scalable ABM/LUCC computer models. We present preliminary work aiming to better model the dynamical complex system of ABM/LUCC. Preliminary results show that the MR POTATOHEAD modeling process facilitates both interdisciplinary dialogs and computer model formulation. This conceptual template approach facilitates interdisciplinary communication and interaction between economists and computer scientists.
This trend is no less evident even in the smaller sub-field of agent-based land market models (ABM/LMM), where even collaborators sharing similar disciplinary backgrounds have developed divergent models of almost identical systems. Concurrently, agent-based modeling of complex systems has acquired increasing rigor and formal methods  (Wilensky and Rand, 2015; Railsback and Grimm, 2019) , and can be now considered as a science in its own right, providing today a plethora of concepts, formalisms and methods aiming at considerably streamling the modeler's job. The conceptual design pattern and conceptual modeling framework MR POTATOHEAD presented in this paper follow this trend, striving to bring harmonized formalization and standardization to the cacophony of ABM/LUCC and ABM/LMM. The need for systematize the information on ABM for bridging the gap between social scientists and computer scientist has led to attempts to develop a common language, e.
, 2010)  and UML  (Ambler, 2005). These forms were found to be too generic, and to lack sufficient detail, to ensure full model transparency and replicability. Further, they could not be used to show how different model implementations were instances of a more general meta-model. Most significantly from the perspective of this paper, there is not a clear and direct linkage between the structure of a protocol such as ODD and the structure of a software design pattern (discuss below), which is one of the issues and challenges of ODD  (Grimm et al. , 2020). MR POTATOHEAD has been demonstrated to overcome some of these deficits. The CDP has been used to describe five different models  (Parker et al. , 2008a) , to compare ABM/LUCC of frontier regions  (Parker et al. , 2008b) , and to develop an interdisciplinary survey of the behaviour of residential land owners.