dataesr/scientific-paragraphs-categorization huggingface dataset:
Nonetheless, we decided to pool the data because part of this heterogeneity could be accounted for by differences in access to early diagnosis and ART and by the background cervical cancer risk in the studied populations, including the availability and uptake of cervical cancer screening. Moreover, we did not find substantial regional variations, differences between risk measures (RR, OR, or SIR), or changes in degrees of bias that would justify stratifying the RR for our estimates. Furthermore, 11 of the 24 included studies were registry linkage studies, providing SIRs as a measure of risk for which the comparison group was the general population (including women living with HIV). This could have led to an underestimation of risk in a high HIV prevalence setting, where women living with HIV would contribute to a considerable share of cases in the general population.
Acknowledgments We acknowledge the contribution of  Tomas Allen ,  Kavita U Kothari , and  Jose Luis Garnica Carreno  ( WHO, Geneva, Switzerland ) for creating and doing the literature search; and we thank  Mary Mahy  ( UNAIDS, Geneva, Switzerland ) for providing HIV estimates. The contents in this Article are the sole responsibility of the authors and do not necessarily represent the decisions, policy, or views of WHO, IARC,  USAID ,  PEPFAR , or the  US Government. Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps.
In order to enhance safety assessments of Sodium Fast Reactors (SFR), some scenarios involving transient Fluid-Structure Interactions (FSI) are investigated using numerical simulation tools. SFRs are indeed quite sensible to mechanical deformations regarding their nuclear power (see  [1]  for more details). The originality of the scenario presented in the paper is to consider sufficient large mechanical interactions involving a large pressure decrease in the fluid domain. This decrease leads to vaporization of the fluid and then to a different impact on the structures. By means of the open-source software Code Saturne developed by EDF [2], this scenario is investigated in 2D using a 3-equation model derived from the Navier-Stokes equations while an harmonic model is applied for the mechanical structures.
Phylogenetic reconstructions were then done using a Nectriaceae sequence dataset. Additionally, morphological observations as well as metabolite profiling were performed. Results clearly revealed the existence of four novel cheese-associated species, namely Bisifusarium allantoides, Bisifusarium penicilloides, Longinectria lagenoides, Longinectria verticilliforme, the two latter belonging to a novel genus (Longinectria gen. nov. ).
DNA extraction, PCR amplification and sequencing DNA extractions were performed on seven to ten-day monospore cultures grown on M2Lev medium using the FastDNA SPIN kit (MP Biomedicals, Fisher Scientific) according to manufacturer recommendations. Each DNA extract was diluted to 40 ng/µL and stored at -20°C until PCR amplification. Firstly, sequencing of the translation elongation factor 1-alpha (tef1) was used for species identification. Generated sequence data was compared to the GenBank database using the Basic Local Alignment Search Tool (BLAST). Then, a subset of thirteen representative isolates, including eleven unidentified ones, was selected and used for the phylogenetic study (Table  1 ). Secondly, their phylogenetic position was assessed using the ten loci and same primers as those described by  Lombard et al.
Dataset preparation Since a preliminary phylogenetic reconstruction performed using tef1 sequences nested all the isolates of interest within the IX to XII clades described in  Lombard et al. (2015)  (data not shown), for each of the ten analyzed loci, a first dataset, consisting of the sequences of the isolates of interest from our collection as well as 57 sequences corresponding to unique representative strains of each taxon from those four clades, was established (Table  S2 ). An additional reduced dataset that included only ITS, LSU, rpb1, rpb2, tef1 and tub2 genes sequences from the first dataset identified as pertaining to Bisifusarium, based on the phylogenetic reconstruction obtained with the full dataset in the present study, was used to confirm that the four loci missing for B. tonghuanum had no impact on the positioning of several isolates (despite our best efforts, additional sequence data could not be obtained due to the unavailability of any B.
Alignment and phylogenetic reconstruction Sequences of the ten loci: acl1, act, cmdA, his3, ITS, LSU, rpb1, rpb2, tef1 and tub2 were aligned independently using MAAFT v7. 450  (Kuraku et al. 2013 , Katoh et al. 2019)  with the Auto algorithm as implemented in Geneious prime v2020. 0. 3. Introns and exons were separated in each dataset. Alignment of the exonic region was ascertained by checking the deduced translated aminoacyl alignment and alignment of the intronic regions was edited using Gblocks b0. 91b (http://molevol. cmima. csic. es/castresana/Gblocks_server. html) with all the options enabled to allow less stringent selection, discard highly ambiguous regions and ensure reproducibility. Since phylogenetic incongruence between the different genes was not detected (P = 0. 001) by performing an Incongruence Length Difference (ILD) test on 1000 replicates as implemented in PAUP 4.
0a166  (Swofford 2003) , the whole dataset was used after concatenation of the ten individual alignments. 025 *For some isolates, in order to obtain amplicons with rpb1 primers, the MgCl2 concentration was adjusted to 3. 5 mm Phylogenetic analyses were based on Maximum Likelihood (ML) and Bayesian Inference (BI). For ML analysis, the best partition scheme and evolution models were assessed using PartitionFinder2 v2. 1. 1 using the "greedy" algorithm and selecting the best models for each partition using the corrected Akaike information criterion (AICc), restricting the search among the models available in RAxML2 v8. 2. 4. 1  (Stamatakis 2014 ) and excluding the models with proportion of invariable (P-Invar) site estimates since it can cause analysis problems according to the RAxML manual  (Yang 2006 , Stamatakis 2016 ).
The ML analysis was performed using RAxML2 v8. 2. 4. 1 on the ABiMS platform (http://abims. sb-roscoff. fr/), using the default algorithm (-f d) and the GTRGAMMA model. The robustness of the analysis was indirectly evaluated by bootstrap support among 1000 replicates (-N 1000) and a majority-rule consensus tree was further built (-J MR). For BI analysis, the best models (among the namedExtended models of the bModelTest add on in Bayesian Evolutionary Analysis Utility -BEAUti2-) were estimated for each intronic and exonic regions of each gene (a total of 24 partitions) simultaneously with the BI analysis using the Bayesian Evolutionary Analysis Sampling Trees (BEAST)2 v2. 6. 2 package. The BI analysis was performed using the Monte Carlo Markov Chain method using the BEAST2 v2. 6. 2 package by performing eight independent repetitions of 100,000,000 generations each, with sampling a tree at every 1,000 generations and a burn-in was fixed at 10% (discard the first 10% trees).
For both analyses, Allantonectria miltina (CBS 121121) was selected as an outgroup. Convergence of the independent BI analyses was checked using Tracer v1. 7. 1. software  (Rambaut et al. 2018 ) and a consensus tree was obtained using Logcombiner and Treeannotator. The two phylogenetic trees obtained with the ML and BI methods were compared using FigTree v1. 4. 4  (Rambaut 2006 ) (http://tree. bio. ed. ac. uk/). A tree based on the topology of ML tree with nodes with bootstrap support < 60% collapsed using Tree Collapser CL 4 (Hodcroft), and annotated with bootstrap and posterior probability node supports was built. The number of informative sites was calculated using the web server DIVEIN  (Deng et al. 2010 ). The exact same set of methods was applied to analyze the reduced dataset (six loci) focused on Bisifusarium isolates.
Theses plates were incubated at 25°C, in the dark, for eight weeks. In parallel, for isolates identified as new type strains, five µL of an inoculum concentrated at 10 6 conidia/mL were also inoculated on PDA medium (two biological and two technical replicates) and incubated at 15 and 25°C in darkness. Colony diameters were monitored for 40 and 21 days, respectively. Growth rates fitting each kinetic were estimated by the modified Gompertz equation  (Zwietering et al. 1990 ) and using the MATLAB software (MATLAB. 9. 5. 0. 944444. (R2018b), Natick, Massachusetts: The MathWorks Inc. ). Two supplementary technical replicates were also deposited and incubated at 5, 10, 20, 30 and 37°C in darkness, then colony diameters were monitored during 14 days to determine growth limits as a function of temperature.
Cheese sampling and analysis Among the 56 cheeses sampled to collect B. domesticum isolates, a large number of fungal isolates pertaining to different genera such as Geotrichum, Mucor, Penicillium or Scopulariopsis were observed (data not shown) along with the typical B. domesticum morphotypes (n = 9, one isolate per cheese). Interestingly, other isolates (n = 10, one macroscopically different isolate per cheese) exhibited Fusarium-like morphotypes. Identifications based on translation elongation factor 1-alpha sequencing confirmed that the isolates exhibiting the typical B. domesticum morphotypes indeed belonged to the latter species, while the sequences obtained for the Fusarium-like isolates could not be identified at the species level. Based on the observed blastn scores (closest identification to already described Nectriaceae species estimated to 83.
Phylogenetic analyses A phylogenetic reconstruction (Fig. 1 ) was performed using a concatenated non-redundant dataset of ten loci: acl1, act, cmdA, his3, ITS, LSU, rpb1, rpb2, tef1 and tub2 sequences of 64 strains (Table  1 , Supplementary Table  2 ) belonging to 19 genera (18 based on  Lombard et al. nomenclature (2015)  and a new genus proposed in this study), as well as A. miltina CBS 121121 as an outgroup. The multi-gene alignment length was 5134 nucleotides, including few gaps and missing data (acl1 487 bp, act 536 bp, cmdA 413 bp, his3 319 bp, ITS 533 bp, LSU 758 bp, rpb1 656 bp, rpb2 780 bp, tef1 351 bp and tub2 301bp). Moreover, this alignment contained 2226 informative sites (2122 without gaps). After alignment using the ten loci dataset, some of the unidentified isolates presented high identity levels between them, thus forming four distinct genetic groups, namely (i) UBOCC-A-120021, UBOCC-A-120034 and VTT-D-041022 (maximum divergence of 10/6441 nucleotides over the whole raw dataset), (ii) UBOCC-A-120035, UBOCC-A-120036 and UBOCC-A-120037 (maximum divergence of 2/6173 nucleotides), (iii) ESE-00140, UBOCC-A-120038, UBOCC-A-120039 and UBOCC-A-120041 (maximum divergence of 6/6187 nucleotides) and (iv) UBOCC-A-120043.
Therefore, a second phylogenetic reconstruction was performed using a concatenated non-redundant dataset of the six common loci (ITS, LSU, rpb1, rpb2, tef1 and tub2) of the ten species belonging to the Bisifusarium genus (the eight already described and the two additional ones based on this study) and F. venenatum CBS 458. 93 T as an outgroup. This alignment length was 2854 nucleotides long, including few gaps. It contained 400 informative sites (391 without gaps). The resulting ML and BI analyses were highly concordant (data not shown) and the position of UBOCC-A-120021 and UBOCC-A-120036 in the resulting phylogenetic tree (Fig. S1 ) was concordant with the one observed for the ten loci-based analysis. Figs 2, 3 MycoBank number: MB840947 Etymology -From latin "allantois" referring to the allantoid-shape of the conidia.
(2015), namely acl1, act, cmdA, his3, ITS, LSU, rpb1, rpb2, tef1  and tub2, from thirteen isolates, eleven corresponding to unidentified isolates with a Fusarium-like morphology and the two others where F. babinda and F. solani s. l. isolates. Finally, B. biseptatum CBS 110311 T , missing from  Lombard et al. 2015 analysis, and B. dimerum MNHN-RF-05625 T , as an internal control, were also analysed. The obtained sequences were compared with the corresponding sequences from 57 strains issued from the dataset analyzed by the latter authors and supplemented with B. tonghuanum CGMCC 3. 17369 T  (Sun et al. 2017 ) sequences (6/10 loci available). A complementary analysis was then performed with the sequences of six loci, namely ITS, LSU, rpb1, rpb2, tef1 and tub2, from the ten isolates identified as pertaining to the Bisifusarium genus.
It is noteworthy that the genus Bisifusarium might actually include a larger number of species than the ten Bisifusarium spp. presented in this study. Indeed, among the five strains assigned to unnamed Bisifusarium species in  Sun et al. (2017) , a preliminary Maximum Likelihood analysis was carried out on a reduced dataset comprising the only sequences available for four loci for these strains (ITS, LSU, tef1 and tub2). The results suggested that these strains might pertain to new Bisifusarium species (data not shown). As for the other isolates, the position of the genetic cluster (iii) consisting of UBOCC-A-120038, UBOCC-A-120039, UBOCC-A-120041 and ESE-00140, on the one hand, and (iv) UBOCC-A-120043, on the other hand, that formed two branches within an independent sister clade of a clade grouping Albonectria, Fusarium, Geejayessia, Cyanonectria and Neocosmospora genera (as described by  Lombard et al.
Acknowledgments We thank all people who kindly sent us the various cheeses analyzed in this study (Pr. Luca Cocolin ,  Pr. Jérôme Mounier ,  Dr. Guillaume Dubrulle ,  MR Sten Marc ) as well as those who have provided isolates from their research studies ( Dr. Miguel Alvarez ,  Dr. Baltasar Mayo  and  Franck Deniel ). Otherwise, we would like to thank  Mr. Philippe Elies  of the PIMMS platform at the  Université de Bretagne Occidentale  for electronic microscopy acquisitions and  Maud Etienne Duplessis  for her help regarding the new species names. We would also like to thank  Erwan Corre  for his help for developing bioinformatics workflows ( ABiMS Platform ,  Station Biologique de Roscoff ). Finally, we are also grateful to  Pr. Pedro W. Crous ,  Dr. Lorenzo Lombard  and  Dr. Marcelo Sandoval-Denis  for helpful discussions and for providing the sequences of recently described new Nectriaceae genera.
Availability of data and material The sequences generated and used in this phylogenetic analysis were deposited in GenBank under accession numbers detailed in this paper and the multi-gene phylogenetic analyses were deposited in TreeBASE. The holotype strains were made available at the UBOCC (Université de Bretagne Occidentale, France) and CBS-KNAW (Westerdijk Fungal Biodiversity Institute, The Netherland) culture collections.
The lack of standards for the capitalisation of this data leads to difficulties in replicating results, a lack of transparency and efficiency of the arbitrations made during these very dynamic projects, and a loss of resources during the data understanding and qualification phases of subsequent Data Science projects (portfolio management). These limitations are critical in the context of increasing European regulation and growing acculturation of business decision-makers to algorithms. Both trends require a shared and facilitated data understanding that goes beyond technical and mathematical measures. This paper proposes a standard documentation framework, called Databook. It has been conceived for Data Science projects in which algorithms are designed. The emergence of the Databook is guided by (1) the theoretical and practical limitations of standard Data Science processes.
This gap has been (2) compensated in the field by a Databook prototype with a unique structure. The prototype was (3) tested and confirmed as efficient for several purposes and stakeholders: this paper proposes its evolution to the first standard algorithm design documentation framework. first Data Science projects, did not generate the expected value. The relevance of the CRISP_DM framework was confirmed, as were its expected limitations. The iterative nature of the main tasks was revealed as a regular and beneficial overlap between the six phases. The framework also found to be too focused on the algorithmic model, with a risk of uprooting the project results from the practitioner's activity  (Nesvijevskaia 2017). It explains potential project failure in terms of result exploitation. Indeed, the process delays the anticipation of the usage, data inclusion/exclusion criteria but also the co-construction of the results restitution.
). In the field, this complexity and high uncertainty require successive arbitrations implying a diversity of stakeholders. To achieve efficient arbitrations, heterogeneous stakeholders urgently need a common intelligible framework and semantics of raw and processed data at each stage of the project. However, reference documentation outputs are little mentioned in research work on data process, lacking anthropocentric anchoring. The principle of the first Databook, as a dynamic documentation device, was guided by this urgent need and by a broader interdisciplinary approach to data quality. It had to take into account both computational (Berti-Equille 2012; Wang 1998) and cognitive  (Arruabarrena et al. 2019; Broudoux & Scopsi 2011; Cottin & Nesme 2017; Odeh & Chartron 2016)  aspects of transforming data into useful information by reducing uncertainties  (Mayère 1990 ) in a given economic context  (Doucet 2010 ).
The Databook: prototype structure The Databook prototype is a generic documentation output specific to Data Science projects. It describes all the algorithm components and the decisions that occurred during the algorithm design. Hence, its structure reproduces the critical path outputs documentation and encapsulates the dynamic links between the algorithm, its usages and its design process punctuated by the mediation milestones. This structure is materialised in a single, common and shareable Excel file: each spreadsheet of this file represents a module. All the prototype modules are listed in Figure  2  and classified following the documentation categories listed in section 2. These modules can be supplemented with less specific documentation objects in different formats, such as PowerPoint reports, Data visualisation interfaces or tools required by the usage or by the project management.
This decision is a qualification traced through a status of the data object resulting from its judgement based on different criteria. These metadata (criteria and statuses) are also usually organised thematically or/and hierarchically: the choice of this structure remains specific to each project and will be presented in section 2. 2. The matrix representation of the structured list of homogeneous items associated with metadata fits perfectly formats such as Excel. For instance, the Module 2 (source data) aims the qualification of all the data that must be explored: an illustration of this module completed in a real-world project is presented in Appendices in Figure  9. Another illustration can be found in Figure  10  for the Module 3 (model structure), with the qualification of all new generated variables and their selection in a context of multiple algorithm development.
Documentation of usages and knowledge The last deployment phase is often restricted in the literature to the usages directly aimed by the project. However, the Databook framework includes the documentation of both direct usages (technical and operational aspects) and knowledge generated throughout the project. It splits knowledge into two types: knowledge that can be potentially transformed into a business lever (indirect usage) and knowledge that can be useful for further Data Science projects (data project experience). Direct usages are immediately operational levers which have been decided upon for deployment. Each direct usage is associated with deployment actions that can be described in terms of purpose, modus operandi, associated version of the solution to deploy, expected benefits, deadlines, responsibilities, key indicators to monitor and so on.
Documentation of the milestones of the project The milestones are usually the tip of the iceberg for the project management and provide essential elements for arbitrations throughout the project. As project decision facilitators, these milestones are usually more convenient to present with storytelling components, including texts, graphs and other project management best-in-class practices. However, they also necessarily include elements that must be fed with structured data and metadata issued from the other modules presented above. This category includes three modules graded A, B and C. The Module A is a project roadmap with project advancement statistics based on statuses of elements qualified at each phase. It is illustrated in Appendices (see Figure  12 ). Each time the Databook is versioned, the Module A represents a photography of the version.
Core structure synthesis Each of the ten modules remains adaptable to the complexity of different projects thanks to a flexible database composed of custom lists, data objects, elements and associated metadata. The complete Databook core structure is presented below in Figure  3 , with an illustration of the most common documented elements observed in the field for each module. Experience return B Summary, description of the used modules and of their purposes, reading grid Work remaining to be done for the project and synthesis of statuses at a given date for modules 1 to 6a.
Metadata structure As presented above, each data object is described in terms of criteria and qualified with a status during the project. This documentation treatment is recorded through metadata. But, unlike the analytical treatments carried on the data throughout the critical path realization, the metadata treatment does not follow a sequential dynamic. Indeed, as seen in Figure  1 , documentation can occur before the analytical work to anticipate it, during or after critical path completion. This dynamic is closely linked to the nature of the uncertainties reduced at each stage of the project: the skills required to anticipate the risks of each phase and to carry out the associated treatments are usually the same. The next sections reproduce the standard process and concentrate on the skills associated to each phase.
Data understanding While source data understanding is part of the critical path, it does not stop here. All data objects produced during the project need semantics, units, names and other metadata that will make sense to all stakeholders in various communities. As observed in the field, this is one of the most used metadata in the Databook, facilitating co-construction and appropriation of all intermediate outputs. The level of detail can vary from a name of a data element to a definition or an in-depth explanation of its generation process. It must be aligned with the level of maturity of the stakeholders, their usual vocabulary, language, shortcuts and so on. If the Databook is to be shared outside the project team, these semantics can be completed with translations, comments and other facilitators.
The best practice is to imagine the performance indicators first and then derive the statistical evaluation criteria from the business criteria, even for exploratory projects aimed at generating original knowledge. In this case, the initial performance indicators are specified as they are developed. However, this type of documentation applies not only to analytical results but also to all the other data objects. For instance, interpretability can be judged for source data or newly generated variables in terms of consistency between the definition and the perceived meaning. Value calculation rules and orders of magnitude can also be anticipated since the beginning and progressively controlled. For instance, the control of the consistence of a customer database used in the project needs a comparison between the volume of the database lines and the number of customers usually measured by the company in existing reporting.