dataesr/scientific-paragraphs-categorization huggingface dataset:
Interviews in particular could be expected to differ from the other types, due to differences between spoken and written language. The corpus contains 54 documents, totaling just over 44,000 tokens, as outlined in Table  1. Document structure is annotated using TEI XML labels, and each text is annotated with POS tags and lemmas, dependency and constituent syntax, entities (using a subset of categories from OntoNotes,  Hovy et al. 2006) , information status (the scheme in  Dipper et al. 2007) , coreference, 1 An anonymous reviewer has pointed out that many other covariates of genre could be subjected to a similar treatment, in the vein of Biber's multidimensional analysis (see Biber 2009 for an overview), such as tense and other grammatical features. We agree completely: we are only beginning to understand the components of genre variation and how it interacts with annotation quality.
2 The data is freely available under a CC license from http://corpling. uis. georgetown. edu/gum. We would like to thank the annotators, a current list of which is found at the same Web site. Rhetorical Structure Theory  (Mann & Thompson 1988) , and crucially, sentence type (see below). In this paper we will be concerned with: 1. POS tags -annotated manually using the extended Penn tag set used by the Tree-Tagger 3 (Schmid 1994) 2. Manually corrected Stanford Typed Dependencies (de Marneffe & Manning 2013) 3. Coreference annotation, including pronominal anaphora, lexical coreference and appositions (but not bridging, which is also annotated in the corpus). Finally, the sentence type annotation layer supplies a kind of rough speech act or sentence mood, using an extended form of the SPAAC annotation scheme  (Leech et al.
2003). The sentence types distinguished are given in Table  2. Given genre metadata and sentence type annotations in the corpus, we would like to know which is a better predictor of errors on each layer. 4  Our analyses of each data type will be addressed in separate experiments, similar in general configuration but adapted to the needs of the data type: POS tags in Section 4, dependencies in Section 5, and coreference in Section 6. 3 The tag set used by TreeTagger distinguishes forms of be (VB, 3 rd person present VBZ,. ) from have (VH, VHZ,. ) and other verbs (VV, VVZ,. ), as well as several punctuation tags and a special tag for that as a complementizer (IN/that). GUM also contains a second POS layer using the CLAWS5 tags (Garside & Smith 1997), which will not be evaluated here. 4 An anonymous reviewer has asked about the decision to include the sub type as distinct from decl: this type was already in the existing annotation of GUM and was not added for this study.
Experimental setup For each of the three tasks, POS tagging, dependency annotation, and coreference resolution, we first split the corpus into each of the four text types and collate responses from manual, automatic and gold annotation in GUM. Ordinarily, the manual annotation data released for a corpus is the same as the gold data -for this study we obtained uncorrected, single annotator versions of the data to approach annotation quality effects in an initial manually produced analysis. Since GUM is a 'class-sourced' corpus, the unadjudicated annotations always represent work from relatively inexperienced student annotators, which was subsequently corrected by an experienced instructor. These corrections will be considered the 'gold' data for our evaluation. 5 nce we have annotation graphs and labels from all three sources, we can easily compare manual and automatic annotation with the gold standard in each subcorpus.
Method For the evaluation, we compare data from the annotators, who received only brief training, to three popular taggers: TreeTagger (TT, Schmid 1994), the Stanford Tagger  (Toutanova et al. 2003)  and Spacy (https://spacy. io/). Double corrected gold data was available for only 38,022 tokens, which are evaluated below. Since GUM was annotated using the TreeTagger's extended tag set, the most comparable evaluation will be between TT and human annotators. How-ever, it is fairly straightforward to collapse the extended tag set into the more compact 36 tags used by the other taggers (VVZ and VHZ become VBZ, etc. ), so that results for those taggers can be evaluated as well (though with somewhat less potential for errors, especially for the tag IN/that). While our primary interest lies in gauging the relative influence of genre and sentence type, we would also like to consider some alternative explanations.
The Stanford tagger model (english-bidirectional-distsim) did not distinguish tags for opening and closing quotation marks, which slightly boosts its accuracy. is only indicated for deviations of 2% accuracy from the mean or more. Despite their significance, some of these are however based on very little data and should be interpreted with caution. Gerunds, which are usually headings as in (1), are significantly worse for manual annotation, and infinitives as in (2) are worst for automatic tagging, but these are based on only 219 and 147 tokens respectively, so that results should be taken with a grain of salt despite their significance. (1) Hiring/VVG employees/NNS (2) How/WRB to/TO Grow/VV Basil/NN Though the data is limited, the fact that these are mostly headings means it is possible that capitalization is causing problems in mistagging common nouns as proper nouns, which manual annotation is less susceptible to.
8% of the data -a very significant proportional deviation from the average of 2. 3%, but still modest in absolute terms. tic parsing. Parsing is not only well known to be affected by genre and domain  (Lease & Charniak 2005 , Khan et al. 2013) , as well as sentence length  (Ravi et al. 2008 ), but it is also directly related to sentence type, since the unit of annotation is the sentence, and local problems in a parse can disrupt accuracy throughout each clause. Unlike POS tagging, dependency annotations in GUM represent manually corrected output from the Stanford Parser (see Chen & Manning 2014; V3. 5 was used). While the entire corpus was corrected by student annotators, only 4,872 tokens were corrected a second time by an experienced instructor. Although this is a small dataset, we choose to use it rather than the whole corpus both because it is more reliable, and because this allows us to evaluate human errors in the initial correction.
It should be noted that the genres surveyed here are not very distant: We are certain that adding Computer Mediated Communication (e. g. Twitter data) as a further text type would radically alter our results. However, given the scope of differences in annotation quality across sentence types, we would also expect to see strong effects of sentence type within and across more disparate genres, such as CMC data of various kinds. A practical implication of this study is that it may be worth redoubling annotation quality control on sentence types known to be problematic for a certain task. As we have seen, these can vary between manual and automatic annotation, the automatic tool used, and the task itself. It is also clear that, as noted by  Silveira et al. (2014) , we are in great need of more diverse annotated datasets, and especially ones containing underrepresented sentence types, such as imperatives, questions and non-canonical sentences.
Image: Mathias Krumbholz. (7) Tuesday, September 1, 2015 (8) Beauveria bassiana on a cicada in Bolivia. (9) Clothing supply closet Imperatives were not a strong predictor of error; this is surprising given Silvera et al. (2014)'s characterization of imperatives being an essential difference between newswire and non-newswire text. While lacking an overt subject, imperatives were largely syntactically conventional. Omitting the subject relation did not create difficulties for the parser or annotators. Domain adaptation in coreference resolution has been discussed often, both in the context of multiple text types within standard reference corpora (e. g. conversation, newswire and Web subcorpora in datasets such as the ACE corpus, see Yang.
( 6 ) 6 Image: Mathias Krumbholz. (7) Tuesday, September 1, 2015 (8) Beauveria bassiana on a cicada in Bolivia. (9) Clothing supply closet Imperatives were not a strong predictor of error; this is surprising given Silvera et al. (2014)'s characterization of imperatives being an essential difference between newswire and non-newswire text. While lacking an overt subject, imperatives were largely syntactically conventional. Omitting the subject relation did not create difficulties for the parser or annotators. Domain adaptation in coreference resolution has been discussed often, both in the context of multiple text types within standard reference corpora (e. g. conversation, newswire and Web subcorpora in datasets such as the ACE corpus, see Yang.
Results Previous analyses have revealed similarities in the patterns of gene co-expression between E. coli and B. subtilis  [45]  despite their substantial evolutionary divergence (E. coli is a Gram-negative proteobacteria, B. subtilis is a Gram-positive firmicute). In this context, we first quantify the extent to which known regulatory mechanisms can explain gene co-expression in these two organisms by analyzing publicly available micro-array datasets. For E. coli, we use a compendium of micro-array data collected by different laboratories and normalized uniformly using a quantile normalization procedure  [8] ; this dataset covers 4320 genes (NC_000913 genome in NCBI reference) in 466 different conditions. For B. subtilis, we use the 22-base high-resolution micro-array dataset produced by the BaSysBio consortium, which covers 4162 genes (NC_000964 genome) in 104 different conditions  [9] ; for consistency, we first normalized this dataset using the same quantile normalization procedure as the E.
coli dataset, even though this has no incidence on the results. For each dataset, we quantify the level of co-expression between two genes by the Pearson correlation coefficient of their transcriptomic profile (Materials and methods) and display the results in the form of a heat-map (panels B and F of  Fig 1). To assess the role of operons and of TF and SF binding sites, we rely on public databases. For E. coli, we use the RegulonDB database  [46]  (last update: 02/05/2015). For B. subtilis, we respectively identify operons, TFs and SFs binding sites using the biocyc database (biocyc. org), the DBTBS database  [47]  (last update: 02/05/2015) and the comprehensive database provided by the BaSysBio consortium  [9]. For the latter, we consider that an operon is directly regulated by a SF if its promoter region (up to 500 bases) contain at least one binding site of this SF (Table  S2  in SOM of  [9] ).
coli, our analysis is limited to pairs of genes separated by more than 100 bp). Two types of TUs were in fact defined in  [9] : "short TUs", which are minimal TUs found in most conditions, and "long TUs", which are maximal TUs found in at least one condition. Here, we find a more significant overlap with the long TUs (S11 Fig), in agreement with the fact that small TUs correspond more often to single operons and therefore are not accounted for in our analysis of successive operons. Evidence from RNA-seq data that transcriptional read-through is wide-spread inside the synteny segments of E. coli. For pairs of consecutive co-directional operons, we measure the correlation between the transcription of the first gene g in the downstream operon and the sense or antisense transcription of the upstream inter-operonic sequence I (small drawing on top).
Micro-array data and transcriptional co-expression analyses Our analysis of co-expression in E. coli is based on transcription profiles generated from the M 3D database, which concerns the expression of 4320 genes across 466 conditions normalized altogether using a quantile normalization procedure  [8]. Our analysis in B. subtilis is based on the dataset produced by the BaSysBio consortium using 22 bases tiling resolution micro-arrays and concerns the expression of 4162 genes in 104 different conditions (for a total of 269 different experiments given the various replicates)  [9] ; for consistency with E. coli, we quantile normalized the data. For each dataset, given a gene i in condition s, we define its expression level, or activity a si , as the average of the values associated with the micro-array probes overlapping with the gene-a quantity already computed in the original data of B.
Construction of synteny segments Genomes. The synteny segments are defined from a systematic comparison of the relative positions of orthologous genes across multiple genomes. We downloaded all the complete and COG-annotated bacterial genomes available in the NCBI databases as of March 2015 (ftp. ncbi. nih. gov), representing a data-set of 1445 genomes. COGs are Clusters of Orthologous Genes  [50] , which we use to map the genes to orthology classes. COGs are defined on the principle that any group of at least three genes from distant genomes that are more similar in sequence to each other than to any other genes from the same genomes should belong to the same COG  [50]. As a result, a genome may contain one, several or no gene associated with any given COG, and a gene may be associated with one or no COG.
These genes were taken from a list of genes shown to reflect phylogenetic distances between bacterial strains  [91] , with the additional constraint that they comprise a single copy in most of the genomes of our dataset. We aligned the amino sequences of these genes with MAFFT  [92]  and defined the similarity between any two genes by their fraction of common amino acids in the resulting multiple sequence alignment, excluding positions with gaps in the two genes. The evolutionary similarity S gh between two strains g and h was obtained by averaging these similarities over the representative genes, taking only into account those genes present in single copy in the two strains. We then defined an evolutionary distance between strains as D gh = 1 -S gh. We checked that this procedure yields a robust estimation of evolutionary distance by repeating the analysis with subsets of only 5 of the 10 genes and verifying that it leads to equivalent results (S14 Fig).
This index is computed for every genome. We also compute for every genome a co-directionality index defined as the difference of the ratio of co-directional over divergent or convergent orientations: CoDI = Codir(in)/[Conv(in) + Dir(in)] -Codir(out)/[Conv(out) + Dir(out)]. Transcriptional read-through analysis. To analyze transcription in non-coding, interoperon regions of E. coli, we use RNA-seq data from  [24] , which we retrieved in the form of. sra files. RNA reads were mapped to the genome of E. coli K12 MG1655 using bowtie2. The number of reads per bp was then computed as the genomic coverage of the data (using genomeCov-erageBed and the flags "-d -split"), with the final expression levels equal to the log-value of the mean number of reads found in the regions of interest. We considered datasets for which more than * 90% of the reads were uniquely mapped.
Our results are averaged over 7 different conditions corresponding to the following GEO Accession Number: GSM1104381 (sgrS-with vector), GSM1104384 (sgrS-with sgrS+ plasmid), GSM1104387 (WT in LB +αMG), GSM1104401 (WT in defined medium with glycerol +αMG), GSM1104402 (WT in defined medium with glycerol -αMG), GSM1104405 (sgrS-in defined medium with glycerol +αMG) and GSM1104408 (sgrS-in defined medium with glycerol -αMG). Analyzing inter-operonic transcription also requires identifying transcription start sites (TSS). We retrieved TSS datasets from the most recent update of RegulonDB (Morett dataset  [46] ) and from the recent dataset of Palsson's group  [5]. We combined these two datasets into a single list of TSSs, and considered operons for which the first gene had an associated TSS in the immediate upstream interoperonic region.
In both cases, only genes belonging to different operons are considered (operon map from  [25]  for M. pneumoniae and from the ProOpDB database  [95]  for D. dadantii). Co-expression levels are computed from rRNA normalized RNA-seq data obtained in 151 different conditions for M. pneumoniae  [25]  and from rRNA normalized micro-array data obtained in 32 different conditions for D. dadantii  [96]. Although global levels of co-expression differ between strains (see  [25]  for a detailed analysis of co-expression properties in M. pneumoniae), a systematic enhancement of co-expression is observed inside synteny segments, which is nearly independent of the distance separating the genes. Co-expression between E. coli genes in different operons that are not regulated by any TF and that do not share the same SF (gray squares).
Acknowledgments This work was conducted as part of the research project " Addressing biological organization in the post-genomic era " which is supported by the  International Blaise Pascal Chairs, Region Ile de France. Additional support was provided by Award Number  R01ES08314 (P. I. AMS ) from the  U. S. National Institute of Environmental Health Sciences. The funders had no role in the study design, data collection and analysis, decision to publish, or 7 Darwin's theory of evolution underwent changes, a major one as the "modern synthesis" in the 20th century and it is now undergoing major critical reconstruction (Noble et al. , 2014).
, 2013). The study is based on a new and unique database including SBR measurements at three microwave frequencies  (37, 19 and 10. 67 GHz)  in boreal and subarctic environments. The study assesses a wide range of contributions that could lead to uncertainties in ground-based microwave snow emission modeling: snow grains, snow density, soil roughness, ice lenses (ILs) and vegetation. More specifically, the objectives of the study are   (Latifovic et al. , 2004). 1. to validate the snow emission modeling, including recent improvements accounting for ice lenses  (Montpetit et al. , 2013)  and snow density in the 367-550 kg m -3 range  (Dierking et al. , 2012) ; 2. to evaluate the different contributions to modeling uncertainty (snow grains, snow density, ice lenses, soil and vegetation measurements); 3.
Ice lenses The microwave signal is very sensitive to ice lens formation within a snowpack at H-pol  (Montpetit et al. , 2013; Rees et al. , 2010; Lemmetyinen et al. , 2010). To simulate the ice lenses present in this study's database (see Tables  1 2 3 4 5 ) using DMRT-ML, snow layers with a high density of 900 kg m -3 close to the density of pure ice (917 kg m -3 ) and a null snow grain size were integrated into the snowpack input file where ice lenses were observed. The value of 900 kg m -3 was chosen because only pure ice lenses were observed. To keep the same total snow depth, the adjoining layers were adjusted by removing 0. 5 cm of the layer above and below the ice layer. However, an analysis of the effect of ice lens density on T B simulations will be conducted in Sect. 3. 2. 4. Because coherency  (Mätzler, 1987)  is neglected in DMRT-ML, the ice lens thickness has a negligible effect on simulated T B.
Because ρ snow is relatively low in boreal regions due to weakening of the wind by trees, we also evaluated this approximation using a tundra data set to increase the number of high-density snow layers for the specific validation of the bridging. The database acquired at the Churchill Northern Studies Center (58 • 44 N, 93 • 49 W)  (Roy et al. , 2013; Derksen et al. , 2012)  from the winter 2010 campaign is composed of 13 sites with at least one layer in the bridging range.
Bridging To test the bridging parameterization (see Sect. 2. 2. 2), we used 13 tundra sites from the Churchill tundra database  (Roy et al. , 2013) , 4 from Umiujaq and 2 from the James Bay snow pits. In each case, at least one snow layer with a snow density higher than 367 kg m -3 (ice fraction of 0. 4:  Dierking et al. , 2012)  is used. For each of the 19 sites studied, simulations at 37 GHz (the most sensitive frequency to snow) with and without the bridging implementation were conducted (all input parameters kept the same). The bridging has a relatively modest impact on simulations with an improvement in the RMSEs of between 2 and 4 K at tundra sites (Umiujaq and James Bay). The greatest improvements are found for deep drifted tundra snow pits where there is a very thick wind slab www.
Discussion/conclusion This study presents a comprehensive analysis of the geophysical factors contributing to uncertainty in DMRT-ML for snow-covered surfaces in boreal forest, subarctic and arctic environments. A unique in situ database, including key information on the snowpack temporal winter evolution, allowed the assessment of the impact of spatial variability of (1) soil emission, (2) errors in snow grains and (3) density measurements, (4) ice lenses and (5) vegetation emission reflected from the surface on DMRT-ML simulations. The implementation in DMRT-ML of the bridging aiming at filling the gap between low and high snow density ranges where the theory is invalid has been tested. Bridging leads to a small improvement for tundra snow where wind slabs are present. These improvements are modest and could compensate for the measurement uncertainties or other limitations related to the use of the model such as stickiness and grain size distribution  (Roy et al.
Acknowledgements. The authors would like to thank the  National Sciences and Engineering Research Council of Canada (NSERC)  and  Environment Canada  for their financial support. The French collaboration was supported by the  Programme de développement de  partenariats stratégiques en matière d'enseignement et de recherche (FRQNT; Conseil franco-québécois de la coopération universitaire); the authors would like to thank  Patrick Cliche ,  Serge Langlois ,  Nathalie Thériault ,  Caroline Dolant  and  Bruno-Charles Busseau  ( Université de Sherbrooke ),  Éric Lefebvre  ( Laboratoire de glaciologie et géophysique de l'environnement de Grenoble ),  Bernard Lesaffre  ( CNRM-GAME, MétéoFrance-CNRS ) and  Florent Dominé  and  Mathieu Barrère  ( Takuvik and Université Laval ) for their contributions to the field work to obtain these measurements.
Datasets derived from measurements at Sodankylä, Finland, for driving and evaluating snow models are presented. This is the first time that such complete datasets have been made available for a site in the Arctic. The continuous October 2007-September 2014 driving data comprise all of the meteorological variables required as inputs for physically based snow models at hourly intervals: incoming solar and longwave radiation, snowfall and rainfall rates, air temperature, humidity, wind speed and atmospheric pressure. Two versions of the driving data are provided: one using radiation and wind speed measurements made above the height of the trees around the clearing where the evaluation data were measured and one with adjustments for the influence of the trees on conditions close to the ground.
Introduction Many studies have used meteorological data to drive snow models and meteorological or hydrological data to evaluate model performance at instrumented sites. These studies have often only used limited periods of driving data (e. g. two win-ters for several sites in  Essery et al. (2009) ) or limited evaluation data (e. g. infrequent manual measurements of snow mass in  Slater et al. (2001) ). Recently, valuable datasets have been published with multiple years of driving data and multiple sources of evaluation data for several snow research sites: Reynolds Mountain East in the Owyhee Mountains of Idaho  (Reba et al. , 2011) , Col de Porte in the Chartreuse Mountains of France  (Morin et al. , 2012) , the Senator Beck Basin in the San Juan Mountains of Colorado  (Landry et al. , 2014) , Snoqualmie Pass in the Cascade Range of Washington  (Wayand et al.