dataesr/scientific-paragraphs-categorization huggingface dataset:
5, B=0. 6, and statistically significant relationships with MIC ≥0. 4 (Malaspina) or MIC ≥0. 5 (TARA) were considered; significance was assessed using precomputed p-values  (38). Non-linear associations were defined as MIC-⍴ 2 >0. 2  (38). Secondly, we constructed association networks with the Malaspina dataset considering OTUs with >100 reads using SparCC  (117)  as implemented in FastSpar  (118). To determine correlations, FastSpar was run with 1,000 iterations, including 1,000 bootstraps to infer p-values. We used OTU associations with absolute correlation scores >0. 3 and p<0. 01. Networks were visualized with Cytoscape (119) and their properties determined using igraph  (120).
ACKNOWLEDGEMENTS We thank all scientists and crews from the Malaspina-2010 expedition. RL was supported by a  Ramón y Cajal fellowship  ( RYC-2013-12554 ,  MINECO, Spain). IMD was supported by an  ITN-SINGEK fellowship  ( ESR2-EU-H2020-MSCA-ITN-2015, Grant Agreement 675752 ) and  CRG  by a  Juan de la Cierva  ( IJCI-2015-23505 ,  MINECO, Spain)  fellowship. This work was supported by the projects Malaspina-2010 Expedition ( CSD2008-00077 ,  MINECO, Spain) ,  INTERACTOMICS  ( CTM2015-69936-P ,  MINECO, Spain) ,  REMEI  ( CTM2015-70340-P ,  MINECO , Spain) and MicroEcoSystems (240904, RCN, Norway). Bioinformatics analyses were performed at the MARBITS platform of the  Institut de Ciències del Mar (ICM ; http://marbits. icm. csic. es)as well as in MareNostrum ( Barcelona Supercomputing Center)  via grants obtained from the  Spanish Network of Supercomputing (RES)  to RL.
AE, Needham DM, & Fuhrman JA (2016) Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ Microbiol 18(5):1403-1414. 95. Logares R (2017) Workflow for Analysing MiSeq Amplicons based on Uparse v1. 5. (https://doi. org/10. 5281/zenodo. 259579). 96. Nikolenko SI, Korobeynikov AI, & Alekseyev MA (2013) BayesHammer: Bayesian clustering for error correction in single-cell sequencing. BMC Genomics 14 Suppl 1:S7. 97. Schirmer M, et al. (2015) Insight into biases and sequencing errors for amplicon sequencing with the Illumina MiSeq platform. Nucleic Acids Res 43(6):e37. 98. Zhang J, Kobert K, Flouri T, & Stamatakis A (2014) PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30(5):614-620.
99. Edgar RC (2010) Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26(19):2460-2461. 100. Edgar RC (2013) UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods 10(10):996-998. 101. Quast C, et al. (2013) The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res 41(Database issue):D590-596.
Disclosure statement We also used the data from the present experiment to support another study published in a Dutch journal [i. e. , Dijkstra, Verkoeijen, Van Kuijk, Yee Chow,  Bakker & Zwaan (2015). Leidt het lezen van literaire fictie tot meer empathie? Een replicatiestudie. De  Psycholoog, 50 (10), [10] [11] [12] [13] [14] [15] [16] [17] [18] [19] [20] [21]. In that study, we applied different exclusion criteria and we used a slightly different analytic approach than in the current study. These exclusion criteria are listed on the Open Science Framework page of the present experiment (https://osf. io/b64mj, see the Exlusion criteria clarification Word document). In the SPSS file on the Open Science Framework page named "De Psycholoog data", the filter variable indicates the cases we selected based on the exclusion criteria from the previously mentioned paper.
Participants and design For the present experiment, we sampled from the same population as  Kidd & Castano (2013)  in their experiments. Specifically, a total of 558 participants were recruited using Amazon's Mechanical Turk and they were rewarded $1. 50 for their participation. Participants were informed about the procedure, the required time investment, and the anonymity of the data analysis. However, we did not obtain written informed consent. The experimental procedures for the three stories in the literary fiction condition and the three stories the popular fiction condition were posted as separate Human Intelligence Tasks (HITS) on the Mechanical Turk platform. The description of the experimental procedure was the same for each text/HIT. Participants took part in one of the experimental conditions by selecting a HIT.
Readers interested in investigating the Yoni dependent measures with the covariates from the RMET analysis, are referred to the dataset on the Open Science Framework page. The analysis showed that the mean (corrected) performance on the first-order Yoni tasks was higher (11. 71, 95% CI = 11. 66; 11. 77) than on the second-order Yoni tasks (10. 12, 95% CI = 9. 99; 10. 26), F(1, 389) = 14. 429, p <. 001, partial η 2 =. 036. In addition, when controlling for all other variables in the model, the ART-scores F(1, 389) = 8. 744, p =. 003, partial η 2 =. 022 were positively related to the overall Yoni scores. The interaction between Yoni order and the ART-scores was significant, F(1, 389) = 7. 934, p =. 005, partial η 2 =. 020. The effect of condition was significant with higher corrected mean overall Yoni scores in the literary fiction condition  (11.
, 2017). We searched for relevant articles using the databases Google Scholar and PsycINFO and the keywords "Kidd and Castano" and "replication". We selected only experimental studies that compared reading literary fiction to reading popular fiction and that used the RMET as outcome variable. Consequently, studies like  Black and Barnes (2015) ,  Pino and Mazza (2016)  and Experiment 1 of  Samur et al. (2017)  were excluded. The studies included in the meta-analysis can be found in Table  3. To obtain (1) a more precise estimate of the mean RMET difference between the literary-fiction condition and the popular fiction condition and (2) an indication of the variability in mean RMET differences between the six studies, we conducted a small-scale random-effects meta-analysis (see  Cumming, 2012).
Generation of urban patterns Implementation The model is implemented both in NetLogo  [29]  for exploration and visualization purposes, and in Scala for performance reasons and easy integration into OpenMole  [30] , which allows a transparent access to High Performance Computing environments. Computation of indicator values on geographical data is done in R using the raster package  [31]. Source code and results are available on the open repository of the project at https://github. com/JusteRaimbault/Density. Raw datasets for real indicator values and simulation results are available on Dataverse at http://dx. doi. org/10. 7910/DVN/WSUSBA.
Study design The current analysis used data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-InterAct case-cohort study  (12) , conducted in eight European countries. Briefly, a total of 12,403 T2D cases were ascertained and verified from among 340,234 participants of the EPIC cohort study with 3. 99 million person-years of follow-up (1991 to 2007). A subcohort was created by randomly selecting 16,835 individuals from those with available stored blood and buffy coat, stratified by center. A total of 16,154 participants remained in the subcohort after exclusion of those with prevalent diabetes (n = 548) and unknown diabetes status (n = 129). There were 778 overlapping incident T2D cases in the subcohort, which is a feature of the case-cohort design. For this analysis, we excluded individuals with no plasma samples available (n = 4969) or with hemolyzed samples (n = 32), or samples that were too low in volume for analysis (n = 49) or failed in biochemical analysis (n = 78).
Therefore, our final sample for analysis included 22,651 participants with measurement of at least one 25(OH)D metabolite (9671 T2D cases and 13,562 subcohort individuals, with 582 incident T2D cases in the subcohort)  (7). All participants provided written informed consent, and the study was approved by the local ethics committees in the participating centers and by the Internal Review Board of the International Agency for Research on Cancer. Ascertainment of incident T2D cases was conducted through a review of multiple sources of evidence, including self-report, linkage to primary care registers, secondary care registers, medication use (drug registers), hospital admissions, and mortality data  (12). Information from any follow-up visit or external evidence with a date later than the baseline visit was used.
The analysis of 25(OH)D 2 was not stratified by country owing to the small sample size within each country. All metabolites except for nonepimeric 25(OH)D 3 had skewed distributions and were log transformed. Estimated associations were combined across countries using random-effects metaanalysis. We used Prentice-weighted Cox regression to estimate countryspecific hazard ratios (HRs) for T2D comparing quintiles and per 1 SD (calculated from the subcohort) of each 25(OH)D variable, and then combined these HRs using random-effects meta-analysis. For 25(OH)D 2 , we estimated the HRs from models fit to the overall dataset owing to the small sample sizes with available data within each country. We also estimated the HR of T2D for 3-epi-25(OH)D 3 or 25(OH)D 2 below the LLQ compared with the lowest quintile of the exposure.
We also performed an interaction analysis between season and nonepimeric 25(OH)D 3 on T2D, as well as an analysis stratified by season (in model 4) to investigate whether the association between nonepimeric 25(OH)D 3 and incident T2D was consistent across seasons. We investigated the impact of missing covariate data on the results of both the linear and weighted Cox regression analyses using multiple imputation. Analysis was performed in each of 10 imputed datasets that were created using all the covariates from model 4, with the event variable and Nelson-Aalen estimate of cumulative hazard accounting for the case-cohort design; estimates from each imputation were combined using Rubin's rules  (25). To explore the shape of the association between 25(OH) D metabolites and incident T2D, we applied a multivariable random-effects meta-analysis to combine the estimated country-specific association of plasma 25(OH)D metabolites with incident T2D (based on model 4 above) using a restricted cubic spline  (26, 27) ; for 25(OH)D 2 , the model was fit to the overall dataset rather than by country.
Acknowledgments We thank all EPIC participants and staff for their contribution to the study. We thank  Nicola Kerrison  ( MRC Epidemiology Unit, University of Cambridge ) for managing the data for the InterAct project. We thank the technical and functional operational teams of the  MRC Epidemiology Unit and laboratory team at Vitas AS (Oslo, Norway)  for vitamin D metabolite measurements. We thank staff from the  EPIC-CVD  and  EPIC-InterAct Coordinating Centers  for carrying out sample preparation and data-handling work, particularly  Sarah Spackman  (EPIC-CVD Data Manager), and  Cambridge Genomic Services  for genotyping. Financial Support: The  InterAct  project was funded by the  European Union   Sixth Framework Program  (Grant  LSHM_ CT_2006_037197 ). Biomarker measurements for vitamin D metabolites were funded jointly by the  MRC Cambridge Initiative  (Grants  RG71466  and  SJAH/004 ), and by the  EPIC-CVD  project.
G. F. is supported by  Agence Nationale de la Recherche  via " Investissement d'Avenir"  Grant  ANR-10-COHO-0006  and by the  IDEX Paris Saclay Nutriperso Project. This  project has received funding from the  European Union 's  Horizon 2020 research and innovation program  under  Marie Sklodowska-Curie Grant   701708.
Acknowledgements This work was supported by the  European Research Council , the  European Social Fund , the  Istituto Nazionale di Fisica Nucleare , the  Austrian Ministry for Science, Research and Economy , the  German Research Foundation the Research Council of Norway , the  Bergen Research Foundation , the  Russian Ministry of Education and Science , the  Russian Academy of Sciences  and the  John Templeton Foundation  and a  Marie Sklodowska-Curie Innovative Training Network Fellowship  of the  European Commission 's  Horizon 2020 Programme  under contract number  721559 AVA.
Unless otherwise stated all measurements are in micrometers. Minimum and maximum values are reported followed by the mean and number of observations in parentheses when applicable. Conventions for dilepidid descriptions follow  Bona (1994). All material is deposited in the Invertebrates Department of the Museum of Natural History of Geneva (MHNG). The nomenclature of birds follows the latest available edition of Avibase  (Lepage 2018). Partial COI (cox1) sequences were generated according to the methodology described in  Scholz et al. (2013). These sequences are deposited in Genbank under accession numbers MH663460 to MH663465 (data by A. Waeschenbach, Natural History Museum London, UK). Collections acronyms: MHNG-PLAT: Muséum d'Histoire Naturelle de Genève (Invertebrates Department, Platyhelminthes Collection), USNM: United States National Museum (Cestode Collection).
Oncospheres ovoid, 29-37 × 19-25 (33 × 21. 5, n = 21). Embryonic hooks 11-13 long (Fig. 10 ). Remarks. The new species is the type species of the newly erected monotypic genus Huinaylepis gen. n. In 2005, the late Prof. Franco V. Bona (Torino) left his tapeworm collection to the MHNG. Among this rich material we found 35 slides of worms collected in Aphrastura spinicaudata and Pygarrhichas albogularis. All material was collected in the Valdivia Forest (about 250 km N of our study area) on 19-20 January 1985. On his accompanying notes, Bona noticed the interest of this material and indicated that it belonged to a new genus on some slides. Most of Bona's specimens are juveniles, a few are mature and only a couple of proglottides are pregravid. We have examined all these slides and this materiel fully corresponds to Huinaylepis elegans sp.
Xolmis pyrope(Gmelin, 1789)  (Passeriformes, Tyrannidae), Diucon, Fire-eyed Diucon. Prevalence: 1/1. Intensity: 3 specimens. Site of infection: Small intestine. Locality: HSFS, Comau Fjord, Los Lagos region, Chile, -42. 39, -72. 42. Altitude 10-30 m. Date: 1. 12. 2008. Specimens studied: MHNG-PLAT-87930. Hologenophore (genseq-2 COI): MHNG-PLAT-87930 [CHIL-028/C2]. Partial COI sequence, Genbank MH663465.
Acknowledgements We are very grateful to  Vreni Haüssermann ,  Günter ("Fossi") Försterra  and  Rheinhardt Fitzek  for their support and dedication and for their very efficient help with administrative matters. We also thank  Joshua Biro ,  Hannes Imhoff ,  Michelle Marcotte , and all the  Huinay Station  staff for their help, as well as  Janik Pralong (Geneva)  for specimen preparation. We enjoyed the support of  Daniel Gonzalez Acuña ,  Karen Ardiles Vilegas  and  Tomás Torres  in the field and in the lab. Veronica Ivanov  ( Buenos Aires ) provided some rare literature. The late  Franco V. Bona  (Torino) entrusted us with his exceptional collection, we hope to make good use of it. We are especially thankful to  Andrea Waeschenbach (London)  for sharing unpublished sequence data. Finally we are indebted to  John Hollier  ( Geneva ) for editing our English.
Host: Xolmis pyrope(Gmelin, 1789)  (Passeriformes, Tyrannidae), Diucon, Fire-eyed Diucon. Prevalence: 1/1. Intensity: 3 specimens. Site of infection: Small intestine. Locality: HSFS, Comau Fjord, Los Lagos region, Chile, -42. 39, -72. 42. Altitude 10-30 m. Date: 1. 12. 2008. Specimens studied: MHNG-PLAT-87930. Hologenophore (genseq-2 COI): MHNG-PLAT-87930 [CHIL-028/C2]. Partial COI sequence, Genbank MH663465.
The study is focussed on two populations selected by colour, red and blue, and further selected by luminosity to form volume-limited samples. In Sect. 2 we describe the VIPERS dataset and the methodology used for sample selection. In Sect. 3 we describe the construction of the mock catalogues that we used for building covariance matrices and testing the accuracy of the estimators. The computation of the correlation function statistics is described in Sect. 4. In Sect. 5 we present the redshift-space distortion models that we use to fit the data in Sects. 6 and 7. The results and final conclusions are given in Sects. 8 and 9. Throughout the work we adopted the standard flat ΛCDM cosmological model with parameters (Ω b , Ω m , h, n s , σ 8 ) = (0. 045, 0. 30, 0. 7, 0. 96, 0. 80).
The typical redshift error for the sample of reliable redshifts is σ z = 0. 00054(1 + z), which corresponds to an error on a galaxy peculiar velocity at any redshift of 163 km s -1. These and other details are given in the PDR-2 release paper  (Scodeggio et al. 2017). A discussion of the data reduction and management infrastructure was presented in  Garilli et al. (2014) , while a complete description of the survey design and target selection was given in  Guzzo et al. (2014). The dataset used in this paper is an early version of the PDR-2 data, from which it differs by a few hundred redshifts revised during the very last period before the release. In total it includes 89 022 objects with measured redshifts. As in all statistical analyses of the VIPERS data, only measurements with quality flags 2 to 9 inclusive are used, corresponding to a sample with a redshift confirmation rate of 96.
Mock construction The mocks were constructed from the Big MultiDark Planck (BigMDPL,  Prada et al. 2012 ) dark matter N-body simulation using HOD prescriptions to populate dark matter haloes with galaxies. The HOD parameters were calibrated using luminosity-dependent clustering measurements from the preliminary data release of VIPERS. The detailed procedure is described in de la  Torre et al. (2013 Torre et al. ( , 2017)). The simulations were carried out in the flat ΛCDM cosmological model with parameters: (Ω m , Ω b , h, n s , σ 8 ) = (0. 307, 0. 048, 0. 678, 0. 96, 0. 823). Since the resolution is not sufficient to match the typical halo masses probed by VIPERS, low mass haloes were added following the recipe proposed in de la Torre & Peacock (2013). Central galaxies were placed at the halo centre with no peculiar velocities in the rest frame of the hosting halo.
Here we consider the combinations f σ 8 and bσ 8 once the input power spectra P δδ , P δθ and P θθ are renormalised by σ 2 8. The input model for the linear matter power spectrum P lin δδ was obtained using the 'code for anisotropies in the microwave background' (CAMB)  (Lewis et al. 2000)  which was then combined with the HALOFIT tool  (Takahashi et al. 2012 ) to predict the non-linear matter power spectrum P δδ at the effective redshift of the galaxy sample. The density-velocity divergence cross power spectrum P δθ and the velocity divergence-velocity divergence auto power spectrum P θθ were obtained using the new fitting formulae calibrated on a large set of N-body simulations with various cosmologies (DEMNUni:  Carbone et al. 2016)  which are described in a companion work  (Bel et al. 2017, in preparation) , P δθ (k) = P lin δδ (k) P δδ (k) exp - k k cut δθ 1/2 , ( 24a ) P θθ (k) = P lin δδ (k) exp - k k cut θθ.
Data covariance matrix The clustering measurements in configuration space show a strong bin-to-bin correlation that needs to be taken into account when comparing data with theoretical models. This is quantified by means of the covariance matrix and its off-diagonal terms. To estimate the covariance matrix of the monopole and quadrupole moments of the auto-and cross-correlation functions we have used the 153 VIPERS mock catalogues. The covariance of a given quantity y in two different measurement bins i and j is estimated as where N s is the number of mocks, y k i is the measurement in bin i drawn from the k-th mock while y i denotes the mean of y i among its N s measurements. We fitted both the monopole and quadrupole of the two-point correlation function; thus, we also computed the cross-covariance of the multipoles.
13. Top panel: same as in Fig. 10  but now using the volumelimited samples in fully VIPERS-like mocks. Filled points result from using the Scoccimarro model while the empty markers show the growth rate estimates adopting the dispersion model. Both models adopt a second Gaussian damping factor to mimic the effect of spectroscopic redshift errors in the galaxy redshifts (see Sect. 7. 3). The shaded regions are the 5% and 1% intervals around the unbiased case. Bottom panel: relative statistical uncertainties expected for a single VIPERS-like realisation. For clarity, only the Scoccimarro model case is shown. luminosity cut there is closer to the original flux cut of the parent sample. We then made the assumption that this interpretation applies equally well to the VIPERS data, which is reasonable given that the HOD fit used in the mocks was calibrated using the VIPERS dataset.
Test on full VIPERS-like mocks Before proceeding to the analysis of the luminous galaxy samples from VIPERS, we performed a final test of the modelling when all observational effects are included (masks, TSR and redshift measurement errors), analysing the fully realistic VIPERSlike mocks. Following de la Torre et al. (  2017 ) and  Pezzotta et al. (2017) , redshift errors were accounted for in the RSD modelling through an extra Gaussian damping factor, whose dispersion is fixed to the known rms value of the VIPERS redshift errors. In the mocks, this value is σ z = 4. 7 × 10 -4 (1 + z), corresponding to the first estimate from the PDR-1  (Guzzo et al. 2014) , while when fitting the data we used the most updated estimate from PDR-2 that is σ z = 5. 4 × 10 -4 (1 + z)  (Scodeggio et al. 2017).
This combination is clearly affected by the poor performances of the red population. The interest in adding this combination was clearly stimulated by the hope to reduce statistical errors. This is in fact the case, as shown the bottom panel of the same figure, where statistical errors expected for a single VIPERS-like realisation using the Scoccimarro model are shown. However, the gain is marginal when compared to the increase in the systematic bias with respect to the use of blue galaxies alone. Based on these results, in the application to the real VIPERS data we will therefore not use red galaxy data (auto-and crosscorrelation) as a contribution to our main conclusions regarding f σ 8. Nevertheless, we will report the measurements for the VIPERS real red galaxy data as it is interesting to see how the consistency of the growth-rate measurements compares with what is seen in the mocks, and discuss this comparison in Appendix E.
Results on the VIPERS data Our detailed mock experiments suggest that an analysis of the blue luminous galaxy sample using the Scoccimarro RSD model should allow us to obtain unbiased measurements of f σ 8 from the data, down to s = 5 h -1 Mpc (Fig. 13 ). But although the mock samples are realistic in many respects, they cannot be expected to match all aspects of the real VIPERS sample. We therefore performed an additional robustness test to check the stability of the values of f σ 8 measured from the data as a function of s min , as was done with the mocks. The results are shown in Fig. 14. The recovered values of f σ 8 are very stable, down to the minimum scales considered, with some oscillation around s min = 4 h -1 Mpc, which we know coincides with the scale where the quadrupole changes sign and is intrinsically difficult to fit.