dataesr/scientific-paragraphs-categorization huggingface dataset:
Introduction Steady-state formulations are applied to two simulations of rolling processes. Their results in terms of thermo-mechanical variables and product shape will be compared to nonsteady simulations using Forge ®  [24]  software as well as steady-state simulations using Lam3  [6] , a finite element software based on structured meshes, brick elements, streamline integration and using a dedicated pre-processor providing best possible initial shape for the mesh. For these computations, material consistency is supposed to be constant and tools are assumed to be perfectly rigid. Values for material behavior and friction are specified in (48). Free surface correction formulation LS_sl is used in these simulations. The Eq. (  38b ) is used to penalize any corrections along the material flow.
For the thick plate simulation, the steady-state version of Forge ® is 8 times faster than the incremental version (see Table  3 ). It can be noticed that it requires more iterations and more nodes than the Lam3 software, so making it less efficient. On the other hand, the software is stopped when the maximum number of 100 iterations is reached but the solution is not fully satisfactory. In this case, the speedup with respect to the incremental approach is even more impressive (see Table  4 ) because the domain length is much larger: the steady-state version is 21 times faster than the transient computation with Forge ®. V incremental = V final -V init V init.
To-date, the commonest way to deal with geographical information and processes still appears to consume local resources, i. e. locally stored data processed on a local desktop or server. The maturity and subsequent growing use of OGC standards to exchange data on the World Wide Web, enhanced in Europe by the INSPIRE Directive, is bound to change the way people (and among them research scientists, especially in environmental sciences) make use of, and manage, spatial data. A clever use of OGC standards can help scientists to better store, share and use data, in particular for modelling. We propose a framework for online processing by making an intensive use of OGC standards. We illustrate it using the Spatial Data Infrastructure (SDI) GéoSAS which is the SDI set up for researchers' needs in our department.
gov, 2009, building on the science. gov, 2002, experience). As a consequence, the Open Data (OD) movement now percolates widely. The three items above act at different levels to ease the access to data and knowledge: (1) OGC standards are on the technical side and set common ground for exchange of geospatial data and processes; (2) INSPIRE provides standards of a more organisational and operational nature, and sets rules for organisational interoperability; (3) OD in turn can be viewed as a "philosophical" standard, and sets general guidelines towards un-refrained openness, which for science also comes as an international recommendation  (OECD, 2007; Pilat and Fukasaku, 2007). These guidelines are therefore becoming widely accepted for data, metadata, and even related web-services, but few implementations are satisfactory as: (1) though OGC standards are quite easy to use separately, it still takes time to fully understand the logics and the best ways to have them interact.
(2) INSPIRE only applies to the European Union, though the underpinning principles would apply globally. Moreover it may be a painful process for many data providers to comply with the Directive. (3) When research institutes implementing a SDI already struggle with the techniques and laws, aiming to open data and science may come second or be seen as a source for extra technical/legal trouble. As a result, everything implemented tends to be a minima, e. g. merely complies with the compulsory requirements, leaving the rest unaddressed. Indeed, there is a cost to assimilate, a cost to implement, and an extra top-up cost as the time thus spent is not dedicated to pure science. But we posit there are also benefits to come resulting from the increased availability and accessibility of research data, processes and services (as OGC Web Services -OWS).
Organisational standards have their role and research institutes need to agree on common rules and behaviours. In the European Union INSPIRE sets the foundations for such an agreement. INSPIRE schematically states that publicly-funded data is public-sector and must be accessible to the public. This involves publishing the data and means to easily find and explore datasets, on the Web. Thus, datasets must be described by strictly formatted metadata. Metadata must be at the public's disposal in online catalogues reachable from a number of well known Web portals. Beyond the obligation to comply with technical guidance or organisational rules there must also be a will to extensively share (in a manner that is as versatile as possible) scientific outputs. This is a crucial point, as research scientists must wholeheartedly accept the handing of the results of their efforts to others with no guarantee of proper acknowledgement (it is expected that a wider diffusion increases the chances for improper use or citation: though scientists are accustomed to good practices this may not hold for the general public), nor a certitude that their output will be used the expected way.
BUILDING ON GEORCHESTRA geOrchestra is a free and open source (FOSS), modular and interoperable Spatial Data Architecture (SDA) primarily developed in 2009 by Camptocamp for the Council of Brittany to meet the requirements of  INSPIRE (geOrchestra, 2009). geOrchestra features a security proxy and a single-sign-on authentication system, and consists of independent and interoperable modules, which can be selectively deployed: (1) a metadata catalogue: GeoNetwork; (2) a map and features server: GeoServer; (3) a map tile server: GeoWebCache; (4) a set of advanced web map viewers (swiewer, mapfishapp…); (5) a data extractor; and (6) several other admin-oriented modules (user and group manager, log parser) plus a data access list manager. A number of add-ons to these components are also available, contributed by the communities involved (e.
g. GeoServer add-ons), or by the geOrchestra community itself. These components interact through OGC interfaces and standards, or REST APIs where no OGC standard applies. To further develop and keep geOrchestra thriving, a dynamic community of users and developers is dedicated to the project, and can rely on the communities around the specific modules. A number of geOrchestra-based SDIs are now run by local and national governments, research institutes and teams, and in academia. SDA geOrchestra uses OGC standard web services to discover (CSW), view (WMS, WMTS), symbolize (SLD), query and download (WFS), edit (WFS-T), and even process (WPS) geographical features. It also provides powerful web applications making use of, and combining, these services. It operates perfectly with GIS desktop software such as QGIS, GvSIG, and ArcGIS.
Openness The code is made public from the early stages of development to favour exchange and widen participation. This is important to have developers as well as the wider community adhere to the project, have their say, and contribute, hence foster on-going development, securing geOrchestra as a long-term solution. Interoperability This mostly relies on compliance to standards, whether OGC or INSPIRE. OGC technical standards apply to services, whereas INSPIRE sets guidelines to properly publish spatial data, with associated metadata sharing a common structure. They can be exchanged ("harvested") between SDI's catalogues. Modularity geOrchestra is deliberately kept as a set of weakly coupled modules exchanging through OGC standards/REST APIs; depending on needs, one or more components can be skipped or replaced with others fulfilling the same role, as long as they comply with the standards.
GéoSAS Following early investigations  (CSEB, 2008) , the SAS research department became in 2010 the second organisation after GéoBretagne (2009) to run a geOrchestra SDI (the first one for scientific purposes), under the name GéoSAS. Rather than deploying the whole architecture at once we started making use of the different components gradually, starting with GeoServer, GeoWebCache and mapfishapp, then GeoNetwork. The central authentication service was deployed only to restrict access to data layers where privacy was at stake. We did not deploy a data extractor for the following reasons: (1) download is possible directly from the viewer, and (2) we consider that best practices should expunge the widespread habit of systematically downloading (and hence producing duplicates) data, as an SDI is meant to persistently give access to it.
It was applied to environmental research observations on agro-hydrological systems AgrHyS. All these elements were integrated into GéoSAS. Applications are also being diversified with the development of WPS, and add-ons for: (1) soil types database exploration add-on, project Sols de Bretagne (SdB, 2013); (2) a remote-sensing WPS and add-on to detect and monitor invasive aquatic and amphibious vegetal species based on imagery and on-site knowledge. All the developments we have made (proof-of-concept an production alike) are either directly publicly shared or included in FOSS, with possible contributions back from the community to help improve the piece of software. This is an important element that helped and helps turning the efforts of a very few people (our small team) into valuable outputs without much funding.
CONCLUSION In this paper we have expressed the needs (generic and specific) of a department in environmental science in terms of SDI. Such an SDI in itself must serve a double purpose: to publish and share data with the science community and to present output to the wider public. We stated that it is also convenient to publish and share processes and models. geOrchestra was then introduced and we explained why it is especially convenient and fulfils our needs. We also explored the various ways a geOrchestra-based SDI can be further tuned for specific scientific needs (online processing, charting, etc. ) and showed the importance of openness, modularity and interoperability, for SDIs, individually and collectively. We clearly support the maximum of interoperability and openness, together with a modular architecture, as it induces best practices to foster advances in knowledge sharing and discovery, and ultimately advertise and advance science.
Methods In the phantom, differences in lesion size due to differences in ambient temperature are revealed by visual inspection. A simple temperature scale will correspond to concentric circles of varying radii printed on the phantom. The generated lesion size matches the radius of the corresponding circle at each ambient temperature, giving a visual check as to whether or not the transducer's power output has changed. A Matlab code has been developed to numerically solve the bio-heat equation in finite differences time domain with a generation term dependent on the induced pressure field. The code generates plots with a color map corresponding to the thermochromic material's color changing properties, and the acoustic absorber's acoustic attenuation and thermal diffusivity properties. The attenuation coefficient of the material, provided by Precision Acoustics Ltd, was 28.
RNA-seq evidence for riboswitches and new cis-regulatory sequences We combined different approaches to identify riboswitches and cis-regulatory sequences. First, long 5′UTRs (>75 nt) were screened for similarity to known cisregulatory sequences in the Rfam database  [42]  and for the presence of predicted rho-independent terminators (Additional file 7). This in silico analysis was then confronted to experimental evidence: premature termination of transcription and production of a sRNA deduced from RNA-seq data. Altogether, 28 regulatory regions belonging to known Rfam families were detected (Table  1 , Additional file 7), among which 24 were previously predicted through a computational analysis of the genome of S. agalactiae strain 2603 V/R  [43]. For 19 of them, a non-coding RNA was detected together with a longer transcript encompassing the downstream CDS.
RNA-seq data analysis Reads generated from strand-specific RNA-seq experiments were aligned to the genome of strain NEM316. The number of reads mapping to each predicted CDS was determined by Rsamtools (version 1. 13. 35), GenomicRanges (version 1. 13. 39) and GenomicFeatures (version 1. 12. 3) in R 3. 0. 1. The gene expression values were quantified in terms of reads per million (RPKM) defined as the total number of reads mapping to the feature divided by feature length (in kbp) normalized by the total number of reads (in millions)  [68]. For differential expression analysis, normalization and statistical analyses were performed using the EdgeR Software (version 3. 2. 4) [65] , p-values were adjusted for multiple testing using the false discovery rate controlling procedure  [66].
Determination of operon structure and mapping of transcript 3′ ends To map transcript 3′ ends, the coverage per nucleotide was determined along both strands of the genome sequence by using the SAMtools  [69]  (version 0. 1. 12a) and variations in coverage were calculated with a custom R script. A more-than-five-fold decrease in coverage among two intergenic positions distant by less than 10 nucleotides was considered as indicative of a major transcript 3′ end. Only positions covered by at least five reads were taken into consideration. When several clustered positions fulfilled this condition, only the most downstream was considered as the mRNA end. Termination was considered to be complete when a region not covered by any read was found immediately after the transcript end. Identification of transcript ends was performed using six RNA-seq libraries and only transcript ends identified from at least two RNA-seq experiments and mapping in an intergenic region were considered.
The list of the transcript ends was compared with the positions of potential terminators as described by de Hoon et al. [38]. Supplementary terminators were searched with ARNold (http://rna. igmors. u-psud. fr/ toolbox/arnold/) or TranstermHP (http://transterm. cbcb. umd. edu/). Two consecutive genes in the same orientation were considered as belonging to different transcription units (TU) if transcription of the first gene ends with a 100% efficient termination site or if a primary TSS was detected upstream the second gene. Accordingly, TU were classified into one of three categories: 1) monocistronic TU; 2) simple operons, composed of several genes preceded by a primary TSS and separated from the next TU by a 100% efficient termination site and/or a primary TSS; 3) composite operons preceded by a primary TSS and characterized by the presence of at least one internal TSS and/or one inefficient terminator leading to variations in gene expression levels along the operon.
Acknowledgements We are especially grateful to  Alexandre Almeida  for critical reading of the manuscript. We would like to thank  Laurence Ma  for performing the Illumina sequencing and  B. Jagla  for his help in bioinformatics analysis. This work was supported by the  French National Research Agency  (Grant  ANR-13-PRTS-0006-04 ) and by the  Labex IBEID. Sequencing was performed at the  Pasteur Genopole , a member of  France Génomique  ( ANR10-IBNS-09-08 ).
Background: In ecology and forensics, some population assignment techniques use molecular markers to assign individuals to known groups. However, assigning individuals to known populations can be difficult if the level of genetic differentiation among populations is small. Most assignment studies handle independent markers, often by pruning markers in Linkage Disequilibrium (LD), ignoring the information contained in the correlation among markers due to LD. Results: To improve the accuracy of population assignment, we present an algorithm, implemented in the HaploPOP software, that combines markers into haplotypes, without requiring independence. The algorithm is based on the Gain of Informativeness for Assignment that provides a measure to decide if a pair of markers should be combined into haplotypes, or not, in order to improve assignment.
Background Molecular markers provide powerful approaches in forensic science and ecology to assign individuals into predefined populations  [1, 2]. With the advent of new sequencing technologies, the number of available molecular markers in different species is rapidly increasing. At the same time, dense datasets tend to contain increasingly correlated markers because Single Nucleotide Polymorphisms (SNPs) that are physically close on a chromosome, often are in Linkage Disequilibrium (LD). Such correlations are usually perceived as a nuisance factor in statistical analyses since it violates a common assumption of *Correspondence: duforetn@berkeley. edu; mattias. jakobsson@ebc. uu. se † Equal contributors 1 Univ. Grenoble Alpes, TIMC-IMAG, F-38000 Grenoble, France 4 Department of Evolutionary Biology, Evolutionary Biology Centre, Uppsala University, Uppsala, Sweden Full list of author information is available at the end of the article independence among markers.
This statistical nuisance can be overcome by pruning SNPs using for example the software PLINK  [3]. However much information may be lost because of the pruning process. Another approach is to explicitly model the correlation between markers to control for LD  [4] [5] [6] , or to include the pruning process in the statistical analysis  [7]. In addition, it has been shown that it can be useful to combine correlated markers into haplotypes to augment the information about population structure at a finer scale  [8]. Such an approach is valuable for assignment methods when the level of genetic differentiation among groups is low  [9]. Gattepaille and Jakobsson  [10]  introduced the Gain of Informativeness for Assignment (GIA), which is a statistic measuring the gain in information for population assignment by combining two markers into haplotypes.
Maximizing the informativeness for assignment We assume that genotype data are available for n individuals at l molecular markers (M 1 ,. M l ). We also assume that the dataset has been phased, where all individuals have been phased together in one go to avoid introducing any haplotype difference due to phasing (note that there may still be switch errors from the phasing, but these should affect all individuals similarly). The approach implemented in the software HaploPOP builds a set of haplotypes that increases the total informativeness for assignment contained in the genotype data. To find the optimal haplotype set 0 , we address the maximization problem ⎧ ⎨ ⎩ 0 = arg max H∈ IA(H) ∈ Part(M 1. M l ) (3) where Part(M 1. M l ) is the set of all possible partitions of the l markers. The number of partitions in a dataset of l markers is given by Bell's number  [16].
Window size A key parameter of the method is the window size. This parameter is important for both speed of the algorithm and level of informativeness of the haplotypes. The choice of window size governs the number of operation performed by the algorithm. In the case of a fixed window of S markers, the number of windows is n window = l/S, and the cost C of the algorithm in number of operations is C(n window , S, n, K) = O(n window × (2nS 2 K + S 3 )) (4) where K is the number of populations in the data. The algorithm scales very well for genome wide datasets, since for a given window size S, the cost of the algorithm is proportional to the number of markers l in the data. The term proportional to S 3 corresponds to the iterative maximum search in all possible pairs of the window. The term S 3 is an upper bound for a search that is done in the worst case S times in a matrix of size S × S or less.
Split-validation To validate the gain in assignment accuracy provided by the constructed haplotypes, we implement a splitvalidation technique  [14]. For each population, we randomly split the set of individuals into two subsets consisting of the training subset used to learn the haplotypes and the validation subset used to compute assignment accuracy. It is important that the division between validation and training set is done after phasing. Phasing performed on the two datasets separately could introduce haplotypic differences and weaken the informativeness for assignment of the haplotypes built by the algorithm. To assign individuals to populations, we use Principal Component Analysis (PCA) as implemented in the software EIGENSOFT  [12]. For each of the constructed haplotype-loci, we enumerate all haplotype-alleles present in the dataset.
Application to simulated data We evaluated the assignment approach and the HaploPOP software for simulated data generated by the software ms  [17]. We simulated 200 kb sequences from a 3-population divergence model. We set the effective population sizes of all populations to N e = 1, 000, the mutation rate to μ = 0. 012, so that θ = 48, and we considered a sample of 100 individuals in each population. The population divergence between population 2 and population 3 was set to occur at T 1 = 0. 025 coalescent time units (or 100 generations) in the past and the population divergence between population 2 and population 1, was set to occur at T 2 = 0. 05 coalescent time units (or 200 generations) in the past. We generated four datasets for a hypothetical 200 kb region with effective recombination rates of ρ = 30, 60, 120, and 240, and replicated this procedure 10 times for each value of the recombination rate.
Application on human data We investigate to what extent constructing haplotypes with HaploPOP improves population assignment of respectively 133 and 125 self-reported Spanish and Portuguese individuals from the POPRES dataset, which contains 447,245 SNPs  [15]. We first phased the data using fastPhase  [10, 18]. No pruning of SNPs were performed because our aim is to capture the information for assignment for all markers, including markers in LD. All markers were therefore retained and used to build haplotypes. Considering the first two PCs based on all SNP-genotype data, we found that the two populations cannot be distinguished (Fig. 2 ). A thorough PCA exploration of all European individuals of the POPRES collection was further unable to distinguish between Spanish and Portuguese individuals  [19].
Conclusions In this article, we present a new algorithm that uses the GIA statistic to construct haplotypes with a windowbased approach. The algorithm is implemented in the command-line software HaploPOP. The software allows users to apply a 2-step procedure. First, HaploPOP constructs haplotypes that are informative about population assignment from a training set of individuals. Second, HaploPOP recodes the genotype data to haplotypes. These new haplotype data can then be used to assign unknown individuals to candidate populations or investigate fine-scale population structure using e. g. PCA. We have shown how constructing haplotypes with HaploPOP can substantially reduce mis-assignment of individuals to candidate populations. For SNP data simulated in a 3population divergence model, the assignment-error was reduced by 20 % to 70 %.
Acknowledgements This work was supported by a grant from the  Swedish Foundation for International Cooperation in Research and Higher Education (STINT)  awarded to  Mattias Jakobsson  and  Michael Blum. A grant from the  French  national research agency provided support to  Michael Blum  and  Nicolas Duforet-Frebourg  ( DATGEN  project,  ANR-2010-JCJC-1607-01 ) and a grant from the  Swedish  research council provided support to  Mattias Jakobsson. The POPRES data were obtained from dbGaP (accession number phs000145. v1. p1).