dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements The authors acknowledge the technical assistance of  J. Matulla  and  S. Grund  and thank  K. Riedel ,  L. Westhoff  and  M. Bengtson  for discussions. This work was supported by the  EU  in the framework of the  Marie Curie ITN 'Symbiomics ' project (no. 264774 ) and by the  German Science Foundation  ( DFG  project no. MA6346/2-1 ). O. G. 's group was partially supported by the  French National Program   EC2CO   Biohefect/Ecodyn/Dril/MicrobiEn 'Fixation de l'azote chez les bivalves marins de la famille des Lucinidae: impact sur le fonctionnement de ces organismes et des écosystèmes tropicaux côtiers associés '.
thiotaurini do not have a homologue in the C. orbicularis symbiont's nif cluster). Sequence similarities are symbolized by red hues for direct comparisons and blue hues for reversed comparisons. Darker colours correspond to higher identities. For protein functions see Supplementary Table3. *Identified as a protein in this study. Sequence data and the BLAST comparison files were drawn with the R package genoPlotR (ref. 57) and edited in Inkscape.
Related Surveys There has been several prior surveys describing matrix reordering approaches. Liiv's overview  [Lii10]  summarizes the most common reordering methods, highlighting the historical developments in various affected domains. Similarly, Wilkinson and Friendly present an abstract of the history of cluster heatmaps  [WF09]. Also, Wilkinson describes in "Grammar of Graphics"  [Wil05]  the process of creating a range of visual base patterns (cf. Section 2. 3) and compares four reordering approaches with regards to their retrieval performance. Influential for our evaluation approach is the work of Mueller et al. , which focuses on interpreting and describing structures in matrix plots  [Mue04, MML07b, MML07a]. Although related, none of these works provide empirical evidence of the individual reordering performances with respect to (i) specific user tasks and (ii) the data set characteristics.
Implementations Most of the reordering algorithms are available in public libraries, although no library implements them all yet. The Reorder. js  [Fek15]  library provides many algorithms in JavaScript. The R packages 'corrplot'  [Wei13] , 'biclust'  [KL08]  and seriation  [HHB08]  provide a large number of seriation algorithms for tabular data. Several graph algorithms are available in the C++ Boost library  [SLL01].
While all algorithms share the same objective of deriving an appropriate matrix reordering, every algorithm itself comprises its own design considerations and decisions. Our classification of algorithms is tailored to the central goal of providing guidance on what algorithm to use depending on the dataset characteristics (e. g. , size and structure). During our research we examined different taxonomies and orthogonal dimensions to describe algorithms in a comprehensive way; the domain they were developed in, the mathematical background, and the kind of information used to determine the distances between vertices (rows and columns). However, we found that none of those taxonomies was expressive enough while remaining simple to classify algorithms. Overall, we derived orthogonal taxonomy families/groups wherever possible, but also use the concept of overlapping and meta families to stress the importance of shared concepts or to emphasize particular features.
Discussion The quality of Robinsonian approaches is essentially influenced by two choices: (i) the measure of distance (or similarity) and (ii) the enumeration approach. The goal of every R-matrix reordering approach is to optimize similarity between neighboring rows and columns. The direct outcome is that block patterns (P1 ) are made visible. Hence, Robinsonian approaches should be preferred if a dataset partitioning is expected for yet undetermined data subgroups. On the other hand, even if reordering is less strict than clustering, "analysis via clustering makes several a-priori assumptions that may not be perfectly adequate in all circumstances. First, clustering [. ] implicitly directs the analysis to a particular aspect of the system under study (e. g. , groups of patients or groups of coregulated genes).
Spectral Methods Computing the eigendecomposition of a matrix is an expensive operation. However, since the reordering algorithms will use only the few first or last eigenvectors, iterative methods can efficiently be used, in particular Power-Iteration  [HK02]  (see Algorithm 2). The Power-iteration method is efficient when the largest eigenvalues have different magnitudes. Otherwise, the system is illconditioned and convergence will be slower or not existent at all. Efficient preconditioning methods exist to address this problem, such as the LOBPCG method  [Kny01] , with implementations in many popular languages. To compute the eigenvectors with the smallest eigenvalues, a simple transformation is required. The matrix M = g • I -M has the same eigenvectors as a symmetric matrix M but with the eigenvalues in reverse order.
The intuitive idea is that a wellordered matrix plot-containing locally coherent regions of similar values (e. g. , black or white)-requires a minimum number of bits for encoding. In other words, the better a matrix plot can be compressed, the better is its reordering (under the assumption the data contains clusters, groupings, or other partitionings). For the Niermann's genetic algorithm he models permutations, the individuals of the algorithm, as arrays of ordering indices. Child individuals are created from consistently rearranging permutation subsequences in the parents (crossover) and mutations are implemented by reversing arbitrary subsequences in the permutation. After each round, the fitness of every offspring is evaluated. Less fit individuals are discarded, while more fit offsprings-in our case permutationssurvive and can reproduce.
Discussion Heuristic approaches transform the matrix reordering problem, such that specific assumptions are met. While problem simplification algorithms are usually fast, they suffer inherently from this restriction. If a dataset is not of the expected form, the results will be inappropriate for an analysis (A1 ). One other problem seems to be specific for problem space transformation: The algorithms are reported to converge slowly and are sensitive to parameter settings. Also it is questionable whether general settings can be derived or inherently depend on the structure and size of the data sets. Particularly, in these cases, it might be beneficial to (pre-)assess the matrix in terms of density, clusteredness, etc. Graph-based approaches share a commonality with heuristic methods: They transform the permutation problem into a related problem space, in this case graph enumeration.
Design and Setup Algorithms We selected 35 algorithms satisfying the following criteria: (i) well-known and used in practice, (ii) available and accessible implementation, and (iii) runtime complexity is reasonable given the tested datasets. For example, we tested the two Branch-and-Bound algorithms BBURCG and BBWRCG, (R seriation package  [BS05] ), but opted to remove them due to their long runtime. Our experiments reveal them being impractical for graphs larger than 30 nodes. Table  1  gives an overview of the selected algorithms, the group they belong to (Section 3), and the source of their implementation. Graphs To obtain a large and heterogeneous collection for graphs, we selected 150 graphs from three different sources and with varying characteristics (e. g. , size, density, cluster coefficient).
However, the R corrplot package is as fast as the fastest algorithms, which makes it unlikely that the used data structure (access times on the row-or column vectors and random cell access) has a significant impact on the overall calculation time. Runtime for large graphs (large) are still < 3000 msec. An exception are RSeriationBEA and RSeriationARSA with a runtime about 144, 000 msec and 24, 000 msec respectively, on the "c4y" real-world Integrated Circuit (IC) network with 1366 nodes and 2915 edges. Linear Arrangement Linear Arrangement (LA) is a loss function that refers to the minimum linear arrangement problem (e. g. , described in  [KCH02] ). As Figure  18  depicts, it allows assessing the visual quality of a matrix plot: The exemplified low LA scores refer to the block pattern (P1 and P2 ), while high scores prove to be valid indicators for noisy plots (A1 ).
We can broadly categorize interactive reordering techniques in two categories: interactive and semi-assisted (steering). Bertifier  [PDF14] , TableLens  [RC94]  and InfoZoom  [SBB96]  are examples of interactive techniques, providing a user interface in which users can manually reorder rows and columns. Compare to Bertin's initial physical device that enable users to move a single row or column at a time  [Ber81] , strategies used in these pieces of software provide grouping and aggregation mechanisms that enable to move sets or rows and columns at a time, decreasing the labor to reorder a matrix. In addition, Bertifier provides primitives to let users change the visual style of the resulting visual matrices, an important step towards communication of the results to an audience. On the other hand, MatrixExplorer [HF06], Select Objective Measures  [BW13] , or PermutMatrix  [CP05]  provide semiautomatic approaches and enable the user to steer or guide algorithms when reordering a matrix.
It seemed interesting to us to study the emotional influences of colors in the paradigms of video games taken as a whole context. We therefore selected 24 still images from video games categorized into different genres, using the Elverdam and Aarseth dynamic classification to ensure a wide range  [25] : Racing, FPS, RPG, Casual, Strategic, and Experimental. For each of these six themes, four still images from video games were selected. These four images were randomly selected in a real-time footage database comprising 20 frames. All these still images were produced by the studios that own them. The 120 preselected video games are from the video games database rewarded by the Academy of Interactive Arts & Sciences. We have calculated the RGB value of each still image by adding up the red, green, and blue values.
Results We recruited  = 85 participants on the web by the use of social networks. They have answered the semantic subjective questionnaire via their web browser (Google Forms). We have temporarily kept the user's IP to avoid bounced questionnaires; all the IP addresses have been erased after this verification. Regarding the time taken to answer the questionnaire (generally more than 25 minutes), we argue that this delay helped avoid made-up answers. The group was composed of 31 females and 54 males, with an average age of 32 years. The participants answered the semantic subjective questionnaire after observing each of the 24 video game frames randomly presented (Figure  3 ). This method allowed us to collect data in a single online database. Since a difference of emotional sensitivity in video games has been shown to correlate to the participant's level of knowledge of the media  [40] , we only asked participants playing video games for more than 2 hours per week to participate in the experiment.
Study sites characteristics and climate The two studied sites, Fontainebleau (FON; 48 • 23 N, 2 • 40 E) and Angoulême (ANG; 45 • 44 N, 0 • 18 E; Fig. 1 ) are about 300 km away from each other and lie at a similar low altitude: FON 144 m a. s. l. , and ANG 110 m a. s. l. The soil at FON is about 1. 5 m deep and the texture is dominated by sand mixed with loam and clay. ANG is located in a hilly karstic landscape with a cambisol layer of 0. 3 to 0. 6 m depth. Monthly meteorological data (average temperature, maximum temperature, and precipitation, 1901-2009) were obtained from the CRU TS3. 10 data set  (Harris et al. , 2013). Time series were extracted for the grid cells containing the study sites. Both sites are characterized by a temperate oceanic climate. The average annual temperature is 11.
5 • C at FON and 11. 9 • C at ANG, the average summer (JJA) temperature is 18. 8 • C at both sites. Average annual precipitation sums range from 600 mm (FON) to 770 mm (ANG), while average summer precipitation at both sites is about 160 mm. FON has remarkably drier winters than ANG (140 and 210 mm respectively). The standardized precipitation evapotranspiration index  (SPEI; 1901 -2001; Beguería et al. , 2010; Vicente-Serrano et al. , 2010)  for the site grid cells was obtained from http://sac. csic. es/spei/database. html at different temporal resolutions. The index calculation is based on CRU data. We selected this drought index for reconstruction because it yielded consistently higher correlations with δ 18 O c compared to other drought indices. The SPEI includes potential evapotranspiration, and it might therefore be more representative of the influence on tree physiological processes than e.
, 1992) , and the site hydrology influences the δ 18 O  (Labuhn et al. , 2014). However, site effects can also influence the δ 18 O of trees within a species. We therefore consider that the site-related uncertainty is more important than the uncertainty due to the ambiguity in species determination. Tree ring width was measured under a binocular microscope using a LINTAB measuring table (Rinntech) with a precision of 0. 01 mm, and cores were crossdated. The crossdating was verified visually and statistically with the program COFECHA  (Holmes, 1983; Grissino-Mayer, 2001).
Acknowledgements. The authors thank the  Office National des Forêts (ONF) Centre Ouest  and the  ONF Fontainebleau  for permission to take samples in the forests. For access to the historic buildings and permission to sample, they thank  Droguet ,  José Gomez de Soto ,  la duchesse de Matossan , the  mairie de Poullignac , and the  Archives départementales de la Charente/architectes des bâtiments de France. They also thank  Vincent Bernard ,  Nathalie Bréda ,  François Geremia ,  Marie-Thérèse Guillemin  and  George Noël Lambert  for technical assistance with sampling and dating, as well as two anonymous reviewers whose comments helped to improve the manuscript. This research was supported by the  PALEOMEX-ISOMEX program  of the  CNRS-INSU , and the  EU  project  ISONET  ( EVK2-CT-2002-00147 ).
A wide range of instrument platforms including full-sized aircraft, remotely piloted aircraft systems (RPASs), remote sensing instruments, radiosoundings, tethered balloons, surface flux stations and various meteorological towers were deployed over different types of surface  (Lothon et al. , 2014). During this campaign, 12 fair-weather days were extensively documented by intensive observing periods (IOPs). These days corresponded mainly to high-pressure fair-weather situations. In this study, we take advantage of the large dataset provided by this campaign to evaluate the vertical structure of the boundary layer and its diurnal evolution as represented in NWP models. Here, we also focus on the mesoscale variability that can occur in the area and how this impacts the observations locally as well as how this is reproduced by the model.
Conclusions The BLLAST field campaign gathered a large dataset, in particular high-frequency observations of the vertical structure of the boundary layer and observations of the turbulent kinetic energy; this enabled us to extensively evaluate three numerical weather prediction models. In summary, all models reproduced the temporal variability observed among the dif- ferent IOPs in terms of variations of the cloud amount (clear vs. partly cloudy conditions), maximum height of the boundary layer and variations of temperature. This is also a necessary first step if we want to use such models further to derive the large-scale fields, e. g. large-scale advection which are needed for smaller scale modelling studies. For instance, during the hot period, models and observations produced lower sensible heat fluxes, higher temperature, stronger winds and weaker TKE than during the other days.
Molecular detection of phytopathogenic agents on seed samples Seed transmission of phytopathogenic agents was also monitored by quantitative PCR. The number of copies of the predicted gene XC_1533 encoding a hypothetical protein was used as an estimator of the number of Xcc 8004 cells per seed samples  (Rijlaarsdam et al. , 2004). A portion of this gene was amplified with the primer set Zup4F/Zup4R (Table  S6 ). Data normalization between seed samples was performed with a portion of the 16S rRNA gene, using the primers 926F/1062R (Table  S1 ). All reactions were performed in 25 µl qPCR reaction using 12. 5 µl of SYBR Green Master Mix (MESA BLUE qPCR MasterMix Plus for SYBR Assay; Eurogentec, Cologne, Germany), 2 µl of DNA (10 ng µl -1 ) and 0. 5 µL of each primer (10 µM). Amplification conditions were 5 min at 95 • C, followed by 40 two-step cycles of 95 • C (15s) and 60 • C (60s).
The number of copies of the gene AbDhn1 encoding a dehydrin-like protein was used as an estimator of the number of Abra43 cells  (Pochon et al. , 2013)  using the primer set AbraDHN1-Tq-F/AbraDHN1-Tq-R and a TaqMan MGB probe (Table  S1 ). Reactions were conducted using StepOnePlus TM qPCR system (Applied Biosystems). qPCR reaction were performed in 20 µl using 10 µl of Master Mix, 2 µl of TaqMan probe (250 nM), 2 µl of each primer (0. 3 µM and 0. 9 µM for the forward and the reverse primer respectively) and 2 µl of DNA (10 ng µl -1 ). Amplification conditions were 10 min at 95 • C, followed by 40 two-step cycles of 95 • C (15s) and 60 • C (60s). Data normalization between seed samples was performed with a portion of the β-actin gene, using the primers ACT 512-F/ACT 783-R (Table  S6 ) and amplification conditions described previously  (Carbone & Kohn, 1999).
31. 2  (Schloss et al. , 2009)  using standard operating procedure  (Kozich et al. , 2013)  described earlier  (Barret et al. , 2015). Briefly,16S rRNA gene and gyrB sequences were aligned against the 16S rRNA gene SILVA alignment and a gyrB reference alignment, respectively. Chimeric sequences were detected with UCHIME  (Edgar et al. , 2011)  and subsequently removed from the dataset. Moreover, gyrB sequences containing stop codon were discarded. Taxonomic affiliation of 16S rRNA gene and gyrB sequences was performed with a Bayesian classifier  (Wang et al. , 2007)  (80% bootstrap confidence score) against the 16S rRNA gene training set (v9) of the Ribosomal Database Project  (Cole et al. , 2009)  or against an in-house gyrB database created with sequences retrieved from the IMG database  (Markowitz et al.
, 2012; Barret et al. , 2015). Unclassified sequences (0. 001% of the 16S rRNA gene sequences) or sequences belonging to Archaea (0. 002%), chloroplasts (0. 9%) or mitochondria (0. 004%) were discarded. Sequences were divided into groups according to their taxonomic rank (level of order) and then assigned to operational taxonomic units (OTUs) at 97% identity cutoff for 16S rRNA gene and 98% identity for gyrB sequences. The variable ITS1 regions of ITS sequences were extracted with the Perl-based software ITSx  (Bengtsson-Palme et al. , 2013). Then sequences were clustered at a 97% identity cut-off using Uclust  (Edgar, 2010)  and taxonomic affiliation was performed with a Bayesian classifier  (Wang et al. , 2007)  (80% bootstrap confidence score) against the UNITE database  (Abarenkov et al.
Analysis of similarity (ANOSIM) was used to assess the effects of the different conditions on the microbial community structure. Moreover, canonical analysis of principal coordinates (CAP) was conducted to measure the relative influence of (i) the phytopathogenic agent, (ii) the harvesting year and (iii) the harvesting method on microbial β-diversity. CAP analyses were performed with the function capscale of vegan. Correlation between aOTUs were calculated with Sparse Correlation for Compositional data algorithm  (Friedman & Alm, 2012)  implemented in Mothur. Statistical significance of the inferred correlations was assessed with a bootstrap procedure (100 replications). Only correlations with pseudo p-value ≤ 0. 001 were represented in the network using the R package qgraph  (Epskamp et al.
, 2012). Changes in relative abundance of aOTUs between the different experimental conditions (C, X and A) were assessed with LEfSE  (Segata et al. , 2011). aOTUs were defined as significantly enriched or depleted in one treatment at a P value ≤ 0. 05 and a LDA score >2. All sequences have been deposited in the ENA database under the accession number PRJEB9588.
Correlation between microbial taxa within the seed microbiota In order to predict microbial interactions within seed-associated assemblages, we explored positive and negative associations between entities of these assemblages by generating correlations networks with SparCC  (Friedman & Alm, 2012). Considering only inferred correlations with pseudo p-values ≤ 0. 001, we identified 15 and 100 nodes in control samples with 16S and gyrB aOTUs, respectively (Fig. 4 , Fig. S6  and Table  S6 ). These nodes were sharing a total of 52 edges with 16S aOTUs and 206 edges with gyrB aOTUs. The other bacterial correlations networks generated with samples from A and X plots were not different from the network inferred with samples from control plots (Fig. 4 , Fig. S6  and Table  S6 ). Indeed all bacterial networks were split into multiples small modules with low connectivity between nodes.
infectoria) for X plots. On the contrary, Ab Abra43 strongly impacted the topology of the network with a decrease of nodes, edges and median number of connectivity (Fig. 4  and Table  S6 ). Although OTU0449 and OTU0536 are still part of the fungal network in samples from A plots, these entities are not hubs anymore and shared 2 and 5 connections with other nodes, respectively. Based on gyrB and 16S rRNA gene datasets, we were not able to detect inferred correlations between the bacterial aOTU affiliated to Xcc and other entities of seedassociated bacterial assemblages. Analysis of inferred correlation between Ab (OTU0446) and other fungal aOTUs revealed positive correlations with 7 entities that belong exclusively to Alternaria. Four of these aOTUs were already highlighted by hierarchical clustering (Fig.
2014). In total, we collected 29 SCN1A mutations in DS patients erroneously reported as mutation-negative. This illustrates that the frequency of SCN1A mutations in DS is still underestimated and higher than the reported 80%. The identification of an SCN1A mutation in 32% (10/31) of DS patients from our "SCN1A-negative" EuroEPI-NOMICS-RES consortium study clearly shows that DS is even more genetically homogenous than previously anticipated. That 13 of the 16 participating centers contributed false-negative cases indicates that missing SCN1A mutations occurs regularly. However, the exact frequency could not be determined as in the prospective EuroEPI- NOMICS-RES study. Given the variability in data storage procedures of the different genetic centers involved in this study, we were unable to retrieve information on the total number of SCN1A-negative patients that underwent a genetic screening with a second technology.
Acknowledgments We thank the families and patients for their participation in this study. This study was funded by the following sources:  Assistance Publique des Hôpitaux de Paris  ( AP-HP ),  Dravet Syndrome UK , the  Katy Baggott Foundation , the  Epilepsy Society , the  International Coordination Action (ICA , grant  G0E8614N ), and  EpiPGX (European Union   7th Framework Programme Grant 279062 ). This work was partly undertaken at  UCLH/UCL , which received a proportion of funding from the  UK Department of Health's NIHR Biomedical Research Centres funding scheme. This work was supported by  Folkh€ alsan Research Foundation  (A-KA, A-EL). Within the  Eurocores program  of the  European Science Foundation P. D. J. ( G. A. 136. 11. N and  FWO/ESF-ECRP)  and  I. H. ( HE5415/ 3-1 ) received financial support within the  EuroEPI-NOMICS-RES network.