dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding This study was supported by the  French National Research Agency CROpTAL project  to  NWGC  ( ANR-14-CE19-0002-04 ). JF is funded by a PhD grant ( BRUTAL  project) from the  French National Research Institute for Agriculture (INRAE)  and the  region Pays de la Loire, France. MR was funded by a PhD grant ( X-HOT  project) from  Angers-Loire Metropole, France. This work was performed in collaboration with the  GeT core facility, Toulouse, France  (http://get. genotoul. fr),and was supported by  France Génomique National  infrastructure, funded as part of " Investissement d'avenir" program  managed by  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ). The funding bodies did not participate in any of the study design, data collection and analysis, or writing the manuscript.
Availability of data and materials Whole genome assemblies generated for this study have been submitted to GenBank under accession numbers JAAIFG000000000 (BAT93) and JAAIFH000000000 (JaloEEP58). Sequences and annotations are available at https://bbric-pipelines. toulouse. inra. fr/myGenomeBrowser?public=1. The raw RNA-Seq data and metadata have been submitted to the Gene Expression Omnibus under the accession number GSE155080 (SRA: SRP273448).
Materials and Methods Sample A retrospective analysis was conducted in data collected consecutively from June 2012 to October 2012 in two different populations of patients with suspected OSA: a) 72 (37 males, 35 females) undergoing one night of type 1 recording, and b) 79 patients (51 males, 28 females) undergoing one night of type 3 recording, for a total 151 patients studied. Patients identified as having high risk for OSA based on clinical evaluation, which included: male sex, body mass index (BMI) >27, report of snoring or observed apnea, high blood pressure, sleepiness (Epworth >10) were oriented for an ambulatory type 3 recording. Non-obese patients with a suspicion of another sleep disorder or with atypical symptoms were directed to a type 1 study or PSG. Urgent high-risk patients were also oriented to a type 1 study for a split-night protocol with CPAP titration.
S9 ). A high n-n fraction (Fig. 6C ) therefore indicates that many points marked as low/high are indeed grouped by t-SNE and are therefore similar. The ranking (from low to high n-n fraction) reflects the potency of a given parameter to induce similar behavior between chromatin blobs with respect to all input features. S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 7 of 12 The relative frequency at which each parameter ranked first provides an intuitive feeling for the most "influential" parameters in the dataset (Fig. 6E ). The signal intensity plays a negligible role, suggesting that our data are free of potential artifacts related to the bare signal intensity. Furthermore, the blob area and the distance from the periphery likewise do not considerably shape chromatin blobs.
PALM imaging and PALM data analysis in fixed cells The same cell line (U2OS expressing H2B-PAtagRFP), as in live-cell imaging, was used for conventional PALM imaging. Before fixation, cells were washed with phosphate-buffered saline (PBS) (three times for 5 min each) and then fixed with 4% paraformaldehyde (Sigma-Aldrich) diluted in PBS for 15 min at room temperature. A movie of 8000 frames was acquired with an exposure time of 30 ms per frame (33. 3 frames/s). In comparison to Deep-PALM imaging, a relatively higher excitation laser of 561 nm (~60 W/cm 2 at the sample) was applied to photobleach H2B-PATagRFP and then combined with the 405 nm (~2. 5 W/cm 2 at the sample) for photoactivating the molecules. We used the same oblique illumination microscopy combined with TIRF system, as applied in live-cell imaging.
Deep-PALM analysis The CNN was trained using simulated data following Nehme et al. (15)  for three labeling densities (4, 6, and 9 emitters/m 2 per frame). Raw imaging data were checked for drift, as previously described  (12). The detected drift in raw images is in the range of <10 nm and therefore negligible. The accuracy of the trained net was evaluated by constructing ground truth images from the simulated emitter positions. The structural similarity index is computed to assess the similarity between reconstructed and ground truth images (36) SSIM = ∑ x,y (2  x  x + C 1 ) (2  xy + C 2 ) ─────────────── (  x 2 +  y 2 + C 1 ) (  x 2 +  y 2 + C 2 ) (1) where x and y are windows of the predicted and ground truth images, respectively,  and  denote their local means and SD, respectively, and  xy denotes their cross-variance.
Acknowledgments: We acknowledge support from the  Pôle Scientifique de Modélisation Numérique ,  ENS de Lyon  for providing computational resources. We thank  B. Zhang  ( Massachusetts Institute of Technology ) for providing data of simulated chromosomes and  S. Kocanova  ( LBME, CBI-CNRS; University of Toulouse ) for providing PALM videos for fixed cells. We thank  H. Babcock  ( Harvard University ),  A. Seeber  ( Harvard University ), and  M. Tamm  ( Moscow State University ) for valuable feedback on the manuscript. Funding: This publication is based upon work from  COST Action   CA18127 , supported by  COST (European Cooperation in Science and Technology). This work is supported by  Agence Nationale de la Recherche.
dimension 1 Fig. 6. 16 Fig. 6. Chromatin feature extraction. (A) The six-dimensional parameter space is input to the t-SNE algorithm and projected to two dimensions. (B) The two-dimensional embedding of an exemplary dataset is shown and colored according to the magnitude of each input feature (blue to red; the parameter average is shown in beige). (C) Points below the first (blue) and above the third (red) quartile points of the corresponding parameter are marked, and the parameters are ranked according to the fraction of nearest neighbors that fall in one of the marked regions. (D) Data points marked below the first or above the third quartile points are labeled according to the feature in which they were marked. Priority is given to the feature with the higher n-n fraction if necessary. (E) t-SNE analysis is carried out for each nucleus over the whole time series, and it is counted how often a parameter ranked first.
The haptophyte genus Pseudohaptolina (formerly Chrysochromulina clade B1-3) currently harbors two species: Pseudohaptolina arctica and Pseudohaptolina sorokinii. In addition, Chrysochromulina birgeri is expected to belong to this genus due to its morphological similarity to P. sorokinii, but has not yet been genetically characterized. A strain belonging to Pseudohaptolina was brought into culture from Arctic waters, characterized by 18S and 28S rRNA gene sequencing as well as optical and transmission electron microscopy, and deposited in the Roscoff Culture Collection with the code RCC5270. Molecular and morphological data from RCC5270 were compared with those from previously described Pseudohaptolina and Pseudohaptolina-like species. Strain RCC5270 showed strong phylogenetic affinity to P.
, 2005). The 28S rRNA gene was amplified and sequenced using primers D1R (5'-ACCCGCTGAATTTAAGCATA-3') and D3Ca (5'-ACGAACGATTTGCACGTCAG-3')  (Lenaers et al. , 1989). Sequencing was performed at Macrogen Europe (https://dna. macrogen-europe. com). Consensus sequences were generated using de novo assembly in Geneious R 10  (Kearse et al. , 2012). The RCC5270 18S and 28S rRNA gene sequences were deposited in GenBank under accession numbers MT311519 and MT311520, respectively. For phylogenies, sequences from strain RCC5270 were aligned to closely related Haptophyta sequences from Genbank using the Muscle plugin in Geneious R 10 (  Kearse et al. , 2012). Samples for transmission electron microscopy (TEM) were prepared as whole mounts fixed with osmium vapor following  Eikrem (1996)  with slight modifications (cooling of all equipment).
Observations were made using a Jeol JEM-2010 FEG at the Imaging Core Facility at the Station Biologique de Roscoff, France. The size of more than 100 scales from RCC5270 and RCC5268 was measured from TEM micrographs using the imaging software ImageJ (https://imagej. nih. gov/ij/). Representative images are available at http://www. roscoff-culture-collection. org/rcc-strain-details/5270. In order to determine the oceanic distribution of the species correspond-ing to RCC5270, we examined a large set of publicly available metabarcode datasets (Table  1 ) covering the V4 and V9 region of the 18S rRNA gene. Twenty-one oceanic 18S rRNA metabarcode datasets were downloaded and reprocessed with the dada2 R package  (Callahan et al. , 2016)  following the standard operating procedure https://benjjneb.
github. io/dada2/tutorial. html in order to produce amplicon single variants (ASVs). The taxonomy of each ASV was assigned using the dada2 assignTaxonomy function against version 4. 12 of the PR 2 database  (Guillou et al. , 2013)  available at https://github. com/pr2database/pr2database/releases/tag/v4. 12. 0. Twenty datasets corresponded to the V4 of the 18S rRNA gene, and one to the V9 region (Tara Oceans). ASVs with a 100% match to the sequence of RCC5270 were selected and the number of reads in each sample determined using the R library dplyr. Maps and figures were drawn using the R libraries ggplot2, sf and cowplot.
Results and Discussion The 18S rRNA gene sequence from RCC5270 was compared with similar sequences in GenBank including those from previously described Pseudohaptolina species. The best match of the sequence was to the two P. sorokinii 18S rRNA sequences in GenBank (KF684962 and KU589286), both linked to its original description, although only KF684962 is cited in the text of the original description. The 18S rRNA gene sequence of strain RCC5270 differs from sequence KF684962 by five base pairs (four substitutions and one deletion) in a 1,655 bp alignment and by only one base pair deletion when compared to KU589286 (1,213 bp alignment). The divergences from KF684962 seem to originate from sequencing errors in the P. sorokinii description, since they occur in well conserved posi-tions (Figure  1 ) and when there is a base variation within these positions in related haptophytes, they do not match with those in the P.
sorokinii sequence (Figure  1 ). Furthermore, the two sequences linked to the original description of P. sorokinii do not share the same substitutions. The 28S rRNA gene sequence from RCC5270 has a six base pair difference to the only P. sorokinii 28S rRNA sequence available in GenBank (KU589284), which did not originate from the same isolate used for the description of P. sorokinii, and is not mentioned in  Orlova et al. (2016). Both RCC5270 28S rRNA and KU589284 best hits in GenBank correspond to the environmental clone KU898784 from a sea ice sample in the Barrow Sea  (Hassett et al. , 2017) , with 100% and 98% similarity, respectively. The shape, size and ornamentation of the organic body scales are taxonomically important characters in Haptophyta, and usually more than one type of body scale occurs per species.
, 1990; Edvardsen and Vaulot, 1996). The metabarcode datasets used to determine the oceanic distribution of RCC5270 correspond to more than 2,200 samples included in large scale surveys such as Ocean Sampling Day (OSD) and the Tara Oceans and Malaspina expeditions that sampled a wide range of coastal and oceanic waters as well as more limited studies from polar waters and the Baltic Sea. We did not retrieve any V9 metabarcodes identical to the RCC5270 sequence. We did, however, retrieve six V4 metabarcodes (ASVs) that were 100% identical to the RCC5270 sequence (Figure  S1 ). In contrast, no exact match was found to either KF684962 or KU589286 P. sorokinii in any of these datasets, which further corroborates the assumption that the mismatch between 18S rRNA P. sorokinii and RCC5270 sequences are due to sequencing errors.
The RCC5270 metabarcodes were only observed in the Arctic Ocean and in the Baltic Sea from ice and water samples as well from algal aggregates collected from the deep-sea floor (Figure  5A-B ). Metabarcodes identical to the sequence of RCC5270 were particularly abundant in three datasets (Table  1 ) from the Polarstern expedition in the Central Arctic Ocean  (Rapp et al. , 2018) , from the Nares strait, the northernmost outflow gateway of Baffin Bay  (Kalenitchenko et al. , 2019)  and from the Gulf of Finland (Baltic Sea)  (Enberg et al. , 2018). At the latter location, which corresponds to the region from which C. birgeri was initially described, metabarcodes identical to the RCC5270 sequence first appeared in February in the ice where they peaked in early March and then increased massively in the water column one month later, representing up to 70% of the metabarcodes at the time the ice melted in mid-April (Figure  5C ).
sorokinii. Table  1. Datasets considered for metabarcode analysis. These 21 datasets correspond to the V4 (20) and V9 (1) regions of the 18S rRNA gene. All datasets have been processed with the dada2 software  (Callahan et al. , 2016)  to extract ASV (amplicon single variants) and assigned using the PR2 database  (Guillou et al. , 2013). 2 0 1 2 -1 0 -0 1 2 0 1 2 -1 1 -0 1 2 0 1 2 -1 2 -0 1 2 0 1 3 -0 1 -0 1 2 0 1 3 -0 2 -0 1 2 0 1 3 -0 3 -0 1 2 0 1 3 -0 4 -0 1 2 0 1 3 -0 5 -0 1 0. 25% rRNA metabarcodes 100% identical to RCC5270 sequence have been detected in public sequence datasets (see Table  1 ). Color corresponds to substrate. The location of samples where these metabarcodes have not been detected are marked by grey crosses. B) Zoom on the North Pole region. C) Maximum fraction of RCC5270 metabarcodes (excluding Metazoa) as a function of date in the Gulf of Finland (Baltic Sea) in ice and water  (Enberg et al.
Funding information Financial support for this work was provided by the  Green Edge project  ( ANR-14-CE01-0017 ,  Fondation Total) , the  ANR PhytoPol  ( ANR-15-CE02-0007 ) and  TaxMArc (Research Council of Norway ,  268286/E40 ). ALS was supported by  FONDECYT grant PiSCOSouth  ( N1171802 ). CGR was supported by the  FONDECYT  project  3190827.
3389/fmicb. 2018. 01035. V4 17 water Arctic Ocean Survey -2005-2011 Arctic Ocean PRJNA243055 10. 1128/AEM. 02737-14 V4 Chukchi Sea -ICESCAPE -2010 Arctic Ocean PRJNA217438 10. 1128/AEM. 02737-14 V4 65898 water Nares Strait -2014 Arctic Ocean PRJEB24314 10. 3389/fmars. 2019. 00479 V4 Oslo fjord -2009-2011 Atlantic Ocean PRJNA497792 10. 1111/jeu. 12700 V4 127118 first year ice, water Gulf of Finland -2012-2013 Baltic Sea PRJEB21047 10. 3354/meps12645 V4 Gdansk Gulf -2012 Baltic Sea PRJEB23971 10. 1002/lno. 11177 V4 Blanes Time Series -2004-2013 Mediterranean Sea PRJEB23788 10. 1111/mec. 14929 V4 Bay of Naples -2011 Mediterranean Sea PRJEB24595 10. 1093/femsec/fiw200 V4 Ocean Sampling Day 2014 V4 LGC Ocean survey PRJEB8682 10. 1186/s13742-015-0066-5 V4 Ocean Sampling Day 2015 V4 Ocean survey https://github.
com/MicroB3-IS/osd-analysis/wiki/Guide-to-OSD-2015-data 10. 1186/s13742-015-0066-5 V4 Ocean Sampling Day 2014 V4 LW Ocean survey https://github. com/MicroB3-IS/osd-analysis/wiki/Guide-to-OSD-2014-data 10. 1186/s13742-015-0066-5 V4 Malaspina expedition -vertical profiles -2010-2011 Ocean survey PRJEB23771 10. 1038/s41396-019-0506-9 V4 Malaspina expedition -surface -2010-2011 Ocean survey PRJEB23913 V4 Fieldes Bay, Antarctic -2013 Southern Ocean PRJNA254097 10. 1007/s00300-015-1815-8 V9 Tara Oceans -2009-2012 Ocean survey PRJEB6610 10. 1126/science. 1261605 5 6 9 37 38 39 40 41 42 20 19 43 36 49 1 2 3 34 35 11 15.
We combine an empirical analysis with arguments from the management literature, as well as from large auditing firms and think tanks, to show that there is support for businesses' underlying belief of a positive association between diversity and firm competitiveness. Using a novel and detailed dataset covering 3,800 large corporations from member countries of the Organisation for Economics Co-operation and Development (OECD), which allows us to document the extent to which large publicly traded firms and other large organizations have implemented policies in favour of gender diversity, we address three important issues. First, we look at the determinants of the percentage of women in firms, as a function of diversity policy implementation. Second, to understand whether diversity is good for productivity, we explicitly look at the relationship between performance and firm diversity.
Descriptive Analysis of Policies and Gender Diversity As the push for gender diversity in corporate governance has increased, some organizations have emerged to keep track of the progress firms have been making towards gender equality in leadership positions. Vigeo Eiris, a company related to Moody's that provides ratings on organizations' environmental, social and governance worldwide, collects data on nearly 4,000 firms to document the extent to which large publicly traded firms and other large organizations have implemented policies in favour of gender diversity, as well as the percentage of women on boards, in the C-suite, and in management positions. These data are used by organizations that promote gender diversity in leadership and in governance, such as the LeaderXXchange. In what follows, we use this novel dataset, which also contains the name of firms, sector, and country information, to study the relationships between the types of policies implemented and the percentage of women in leadership positions, as well as firm performance.
For instance, large firms from countries that have implemented gender quotas policies show the highest representation of women. Norway and France, which arguably apply the most constraining quotas (and enforcing a threshold of 40% of women), have the highest percentages of women on boards in their largest corporations (41. 8% for Norway, and 41. 4% for France). Cultural differences and gender norms seem to also influence the extent to which firms increase the share of women in leadership positions. For instance, Asian firms in the dataset (Japan and South Korea) have the lowest share of women in all types of leadership positions (2. 9% and 3. 4% of women in the C-suite in the Japanese and Korean firms, respectively). Table  2  presents the use of different types of policies that firms have adopted, by country.
Results We analyzed the data running the same statistical analyses than in Guida et al. 13. Concerning accuracy, the overall score was 0. 94 (SD = 0. 23), and the average correct recognition score for "yes" answers was 0. 94 (SD = 0. 23). This result is almost identical to Guida et al. 13  's results (0. 95 and 0. 94 respectively). There was no speed accuracy trade-off: the correlation between RTs and errors over the 10 cells of the design was r = 0. 39, p = 0. 27. Concerning reaction times (RT; raw data are available from OSF via the following link: https://osf. io/d9cp5/), the distribution skewness was dealt with by an inverse transform, as suggested by Brysbaert and Stevens,  38  statistical analyses were performed by fitting linear mixed-effects model to inverse RTs, where both participants and items were entered as random effects.
This is repeated 5000 times. Subsequently we established confidence intervals based on the range in which mid 80% of the bootstrap slopes were located (we used the same confidence level than van Dijck et al. 47  to allow direct comparison with this study). In the last step, we checked whether such a confidence interval for a given participant contains zero (no evidence for reliable effect) or not (evidence for a reliable effect; see Ref. 45 for a detailed description of the method applied to SNARC data; the R scripts are available at https://osf. io/n7szg/). Among our 29 participants (this bootstrapping technique necessitates to drop participants that have no valid data in one condition i. e. , item position × hand combinations, which was the case for one participant), 31% revealed reliable spatialization effect compared to 28% for the 29 participants in the Guida et al.
This provides some clues on how our thoughts can be organized. Although culture clearly shapes the way we organize our thoughts,  36  our result shows that we are nonetheless able to reverse them, with the caveat provided by the literature that this could be detrimental in terms of cognitive resources and mnemonic performance. Future research will certainly need to clarify this point. Note: All models included participants as random effects. "Hand" stands for "Hand of response" and "Position" for "Position in the sequence". Models were fitted using a restricted maximum likelihood technique. The R2STATS 92 package was used, which is based on the lme4 package 93 in R language. 94 Initially, a model that only included random factors (items and participants) was applied to the data (Model 0). This original model was then enriched by sequentially adding the control variable (Trial number), and then the two predictors, Hand and Position (taken as numeric) and their interaction.
Data availability. The WRF simulations used in this study can be obtained from the MISTRAL database website (registration required) at https://doi. org/10. 14768/MISTRALS-HYMEX. 1503 (Da Silva, 2019)  or upon request to the authors. Author contributions. PD, SM, and NDS designed the numerical experiments. NDS and SM performed the simulations. NDS prepared the manuscript with contributions from all co-authors.
0 data, based on 20th century reanalysis using the National Centers for Environmental Prediction (NCEP) land-atmosphere model and downscaled to a 0. 5 • , 3-hourly resolution covering the period 1901 to 2007 (Table  S1 ). This is then upscaled to 1 • resolution and interpolated to a 30 min time step to comply with the time step of ORCHIDEE's surface water and energy balance calculation period. Precipitation was partitioned into rainfall and snowfall, and a correction for wind-induced undercatch was also applied. These are described in greater detail in  Guimberteau et al. (2018). Over the simulation period with this dataset, the Lena basin experiences a mean thaw period warming of 1. 8 • C, while atmospheric CO 2 concentrations increase by 85. 6 ppm. The GSWP3 dataset was chosen due to its relative performance in simulating the inter-annual variability and seasonality of pan-Arctic riverine discharge in ORCHIDEE-MICT  (Guimberteau et al.
1 ). We evaluate at the basin scale, because the isolation of a single geographic unit allows for a more refined analysis of simulated variables than doing the same over the whole pan-Arctic region, much of which remains poorly accounted for in empirical databases and literature. The literature studies used in this evaluation are summarised in Table  S2. In order to derive an understanding of the environmental drivers of carbon cycling in the Lena watershed and analyse the model sensitivity to the corresponding forcing data, alternative simulations were run with constant climate and CO 2 conditions (Tables  1  and S1 ). Thus a factorial simulation was devised, consisting of two factors and three simulations whose inputs were identical except for the investigated factor (Table  1 ).
3c ) and may point to an issue in how ice is represented in the model, such as the fact that solid ice inclusions in the soil column are not represented, or the possibility that much slower groundwater dynamics than those represented in the model are feeding discharge. In addition to this, the presence of a dam on the Vilyuy tributary of the Lena has been shown to reduce main stem winter low-flow rates by up to 90 %  (Ye et al. , 2003) , similar to the discrepancy of our low-flow rates: given that our model only simulates "natural" hydrological flows and thus does not include dams, we expect that this effect is also at play. Causal factors for the apparently poor performance of the hydrological module include poor model representations (or lack thereof), climatological dataset choices, and deficiencies in evaluation datasets themselves, and these are covered in detail in Sect.