dataesr/scientific-paragraphs-categorization huggingface dataset:
Package design lightr has been designed to provide two levels on the complexity / customability trade-off: • Spectral data and metadata for each file format are extracted using specialized parsers. Parsers are also aliased with many different names so that users can often use lr_pa rse_$extension() where $extension is the file extension of the file to parse. For convenience, we also provide a generic fallback, named lr_parse_generic() that works for many "simple" formats, often derived from csv or tsv. Specialized parsers should usually be preferred to lr_parse_generic() because lr_parse_generic() is not able to parse metadata. • Because spectrometers store each measurement in a separate file, the number of files for a single study can quickly increase. To ensure easy and efficient processing of those files, lightr also provides three high-levels functions that can recursively find files and process them with a parallelized loop using the parallel R package: lr_get_spec() and lr_get_metadata(), which import respectively spectral data and metadata as data.
Recommended workflow As mentioned earlier, proprietary spectrometry software can also export spectral data into a human-readable format (usually a kind of tabulation separated values, or tsv, with a complex header). lightr can read files generated by this export step. We however do not recommend you use the software's built-in export function, because it will apply possibly unwanted transformation to your data (interpolation and subsetting) and may discard important metadata. Instead, we recommend you keep the files in the proprietary format (such as Avantes ABS, ROH and TRM, or OceanOptics ProcSpec and jdx) and that you use lightr to convert them into your preferred file format (such as csv).
Usage and future directions lightr can serve as a basis for colour analysis R packages to deal with the file import step. Most of them can only read a limited variety of file formats currently. Future versions of pavo, for example, will include lightr as a dependency. Below is an illustration of a workflow where lightr is used to import the spectral data, which is then analysed with pavo: library(lightr) specs <-lr_get_spec(where = "yourfolder", ext = "ProcSpec") library(pavo) plot(specs, col = spec2rgb(specs)) The first column indicates the brightness (in % relative to a white reference), the second is the saturation (also called spectral purity) and the last contains the hue (in nm). lightr can also prove useful for developers of other programming languages, providing a free and open source template that can easily be translated to such other languages.
Of the 40 primer pairs, 28 had ambiguous banding patterns. The 12 markers that proved to be monolocus were analysed separately using labelled forward primers on an ABI 3130 xl Genetic Analyzer (Applied Biosystems, genomic platform of IMRB, Mondor Institute, Créteil). Fragment lengths were manually checked on chromatograms to detect inconsistencies and genotypes were scored against the GeneScan-500 Liz Size Standard (Applied Biosystems) using GeneMapper 5 software (Applied Biosystems). Of the 12 primer pairs, 4 were discarded because of ambiguous banding patterns or monomorphism. The eight microsatellite markers (Table  2  and Table  S1  of the supplementary data) showing both consistent amplification results and genetic variation among tested individuals were used to develop one multiplex set to be carried out in 12.
5 µl reactions using 10 ng of DNA and the Qiagen ® Multiplex Kit according to the manufacturer's protocol. In order to quantify genotyping errors, 23 individuals of the VLR2 population were genotyped twice (genotypes were produced from two different DNA extracts from the same individuals). Error rates were quantified by computing the mean error rate per allele (e a ) and the mean error rate per locus (e l ,  Pompanon et al. , 2005). Moreover, null allele frequencies were estimated using the software FreeNa  (Chapuis and Estoup, 2007)  with a number of replicates fixed to 1000. From the entire data set (153 individuals), the number of alleles, the observed and expected heterozygosities and the estimator of the inbreeding coefficient (F is ) were calculated using the program Genetix V. 4. 05  (Belkhir et al.
, 2004). The allelic richness standardized for sample size (A r N = 14) was computed using the program Fstat V. 2. 9. 3. 2  (Goudet, 2000). Exact test for genotypic disequilibrium and deviations from Hardy-Weinberg equilibrium were computed using Genepop V. 4. 4  (Rousset, 2008). In order to investigate the importance of genetic exchanges among populations, exact tests of allelic differentiation were carried out using Genepop V. 4. 4. To adjust for multiple comparisons, sequential Bonferroni correction was used. Moreover,  Weir and Cockerham's (1984)  estimator of the fixation index F st was calculated with Genepop V. 4. 4. The program BAPS V. 6  (Corander and Marttinen, 2006; Corander et al. , 2008)  was used to carry out an analysis of spatial clustering of groups. We ran 10 replicates for k = 6 where k is the maximum number of genetically divergent groups (populations).
The 658 bp fragment of the COI gene proposed as a standard DNA barcode for animals  (Hebert et al. , 2003)  was sequenced using LCO1490F and HCO2192R primers  (Folmer et al. , 1994)  for six individuals used for the genomic library (DNA of 3 individuals could not be amplified) and for a sub-sample of each study population (54 individuals, details in Table  1 , GenBank Accession Numbers MH684623 -MH684682). DNA sequencing was performed in both directions by Eurofins Genomics company and sequences were manually aligned using the BioEdit program  (Hall, 1999). Taxonomy of the samples was checked using the identification engine of BOLD (Barcode of Life Data Systemshttps//www. boldsystems. org/). Using the software MEGA6, we constructed a phylogenetic tree by neighbour joining with p-distance and 1000 bootstraps.
To root the tree, we used a sequence of Lumbricus herculeus (GenBank Accession Number MH638308). Moreover, as  Porco et al. (2018)  revealed the existence of three lineages within the morphospecies L. castaneus, one sequence per lineage was added to the dataset (GenBank Accession Numbers FJ937284, MF121706, GU206163). The neighbor-joining phylogenetic tree revealed that all the study specimens belong to the L1 lineage  (Fig S1, supplementary data). Crossamplification of the new L. castaneus microsatellites across mitochondrial lineages could thus not be tested.
Acknowledgements This work was funded by the  French National Research Agency (ANR)  as a part of the project  Ecoville  No. ANR-14-CE22-0021 , by the town council of Paris through the  Paris2030 calls for projects (Soil biodiversity in green spaces of Paris  project) and by the  Ile de France region  through the  "Partenariats institutions-citoyens pour la recherche et l'innovation" (Picri) calls for projects (ReFUJ project ). The development of the microsatellite-enriched genomic library was funded by the  "Institut de Recherches Pluridisciplinaires en Sciences de l'Environnement" (IREPSE, Lille 1 University ).
993 +0. 046 -0. 043 , together with Ω m = 0. 330 ± 0. 012, Ω b = 0. 0505 ± 0. 002, S 8 ≡ σ 8 Ω m /0. 3 = 0. 863 ± 0. 016, and h = 0. 661 ± 0. 012. For the same combination of datasets, but now considering a ΛCDM model with massive neutrinos and the same scale cut, we find: Ω m = 0. 328 ± 0. 009, Ω b = 0. 05017 +0. 0009 -0. 0008 , S 8 = 0. 862 ± 0. 017, and h = 0. 663 +0. 006 -0. 007 , and a 95% credible interval (CI) upper limit of m ν < 0. 14 eV for a normal hierarchy. These results are competitive if not better than standard analyses with the same dataset, and demonstrate this should be a method of choice for future surveys, opening the door for their full exploitation in cross-correlations probes.
These mocks were also Poisson sampled to reproduce noise properties and radial and angular selection effects. The data covariance matrix was produced as follows: 1. Produce a spline, S( ), using the Ŝ∆ measurements (Figure  3 ) and a Gaussian filter to smooth the measurements. 2. Deconvolve the mixing matrix R from the splines to obtain Cij ( ) = R -1 Sij ( ). (51) 3. Linearly extrapolate the splines to max = 8192 (necessary to allow FLASK to create high resolution HEALPix maps). 4. For each tomographic redshift bin, produce FLASK partial sky galaxy number count mocks with N side = max = 2048. 8. Degrade the mocks to N side = 512 to match the N side used when analysing the data. 6. Produce up-weighted galaxy overdensity maps using the pixel completeness factor (as described in Section 2. 2.
This effect has been detected and corrected for in several previous analyses with a range of datasets  (Blake et al. 2007; Ross et al. 2011; Thomas et al. 2011b; Leistedt et al. 2013; Leistedt & Peiris 2014; Ho et al. 2012; Elsner et al. 2016 Elsner et al. , 2017;; Doux et al. 2017). In this section, we present the analysis performed on the data to ensure that the measured power spectra are not significantly dominated by any known observational systematic effects. We consider a systematic to have a significant effect on the observed power spectra if the cross-power spectra between them deviates from zero, with a deviation that is bigger than both the data variance and the cross-power spectra variance. We describe the systematic effects considered in our analyses, describe our methods for map creation and cross-spectrum measurement, and give some representative results.
2013 ). The idea is to treat the systematic maps in the same way as the data in order to apply the statistical estimators consistently. Therefore, we degrade the high-resolution maps to the data resolution (N side = 512) and up-weight the maps according to the pixel completeness mask that takes into account the holes in the footprint (see Section 2. 2. 1); we then perform a cut in pixel completeness C pix = 0. 8 in the maps. This up-weighting is due to the pixelisation of the degraded mask, as explained in Sec-. An example of the systematics analysis described in Section 5. 2. Here, we show the cross-power spectra between the 18 systematics overdensity maps produced in 5. 1, and LOWZ-3(CMASS-9) tomographic bins in blue dots (red squares). The error-bars were obtained by cross-correlating the δ S y s maps with the FLASK mocks produced in Section 4.
3. We cross-correlated all 13 tomographic redshift bins with all 18 systematic maps, resulting in a total of 234 crosspower spectra. Figure  7  shows an example for LOWZ-3 and CMASS-9 bins and cross-power spectra for all systematics. From all of these measurements, the majority are consistent with the variance of the data measured from the log-normal simulation (Section 4. 3), which lead us to be confident in using the full shape of our measured C s. Note, however, that a few of the large scale measurements (low-) in CMASS sample have a small excess in cross-power spectra with stellar overdensity. The first point on the cross-power spectra between some of the systematic maps and most BOSS bins is clearly more than one sigma away from the data's variance. Due to this excess in correlation with stellar overdensity and the level of cosmic variance on the first -band, we decided to exclude this first point from our cosmological analysis (see Section 6 for details on the range used).
COSMOLOGICAL ANALYSIS In this section, we present the cosmological implications from the measured angular power spectra of BOSS galaxies for flat ΛCDM, wCDM, and ΛCDM with m ν models. Table  2. Maximum multipole considered in the cosmological analysis for each tomographic redshift bin. All the samples start at mi n = 13. 5 and have a bandwidth of ∆ = 8. When considering the cross-power spectra between bins, the lower ma x is used. The 5% ma x column corresponds to a k ma x 0. 07h Mpc -1 , and the ). All analyses considered in this section use the auto-power spectra and the cross-power spectra from adjacent tomographic bins using the measurements presented in Section 3. Cross-power spectra between distant bins are not a part of our final BOSS-C data vector.
Finally, to perform consistency checks between BOSS DR12 and the external datasets described in Section 6. 3, we used the Bayes factor. The Bayes factor for the consistency of two datasets (A and B) is given by: R A, B = P( ì A, ì B|M) P( ì A|M)P( ì B|M) (64) or, for three datasets (A, B, and C): R A, B,C = P( ì A, ì B, ì C|M) P( ì A|M)P( ì B|M)P( ì C|M) ( 65 ) where M is the model, P( ì A, ì B|M) is the evidence when the model is fitted to both datasets simultaneously and P( ì A|M)P( ì B|M) is the product of the evidences when the model is fitted to each dataset individually. Since PLINY is a nested sampler, all our cosmological estimations lead to values for the evidences of each model, dataset, and combination of datasets. We are aware that this method has received some criticism as the value of R A, B depends crucially on the prior volume chosen for the sampling.
Although this is true, choosing priors which are physically motivated should lead to values of R A,B which can be reflective of the real consistency between datasets A and B. However, a host of new methods have been proposed in the literature which we have not fully investigated in this paper, but which we intend to implement in further analysis with this dataset. These methods involve a range of overlap integrals between the separate posteriors and can be better suited to determine the consistency between two or more datasets. We refer the reader to these methods in  Charnock et al. (2017a) ;  Raveri & Hu (2018) ;  Feeney et al. (2018)  In order to perform a robust analysis, we implemented the three likelihoods mentioned above: Gaussian, Gaussian plus the Hartlap correction, and the SH16 t-distribution likelihood.
Controlled cosmology pipeline test For this test, we generated theory auto-and cross-C s to mimic the BOSS dataset using a Planck-like cosmology: h = 0. 6725, Ω b = 0. 0492, Ω m = 0. 314, w 0 = -1. 0, S 8 = 0. 830, n s = 0. 96575. We simulated these fiducial power spectra using the BOSS redshift distribution n(z) from Figure  1. We chose the nuisance parameters to match the best fit values obtained in Section 6. 4 from the combination of the entire cosmological dataset available to us. Using BOSS masks as input, we created FLASK mocks like described in Section 4. 3: generating the mocks at higher resolution, degrading them, and creating galaxy overdensity maps. We applied the PCL estimator on the 13 overdensity maps, calculating the autoand cross-power spectra as described for the data in Section 3.
Distribution of residuals For a dataset with uncorrelated errors (diagonal covariance matrix), the vector of normalised residuals is given by: R = Ξ -1 (D -T( ì θ)) ( 66 ) where Ξ is a diagonal matrix containing the square roots of the variances, D is the data vector and T( ì θ) is the theory vector for a given parameter vector ì θ. If T( ì θ) represents the true model, and the true errors are known, the residuals are by definition given by a Gaussian with µ = 0 and σ = 1  (Andrae et al. 2010). On the other hand, if the errors are estimated from the data, the residuals are given by a Student's t-distribution. This distribution approaches a Gaussian with increasing number of data points. If this distribution shows a significant deviation from a Gaussian, the model is ruled out. If it follows a Gaussian distribution, either we found the true model, or the current data is not enough to distinguish between the model we found and the true model  (Andrae et al.
This configuration is commonly referred to as Planck TT,TE,EE+lowP. Plik also introduces 94 additional nuisance parameters. In order to reduce this large parameter space, we use the lite version of the data offered by the Planck Collaboration. This lite version allows us to compute a nuisance marginalized CMB likelihood. The only CMB nuisance parameter left is the Planck absolute calibration parameter (y cal ). We sample this parameter in all the runs that include Planck data. The Planck likelihood codes were added to UCLPI and all the Planck results presented have been obtained using this pipeline. We show in the cosmological contours and in table 4 that this pipeline reproduces the cosmological results found by the Planck Collaboration  (Planck Collaboration et al. 2016c). The SN data from JLA was added to the UCLPI pipeline through the likelihood code provided by the JLA Collaboration  (Betoule et al.
2014). This likelihood code needs the luminosity distances to the 740 Supernovae in the sample and 4 nuisance parameters (α, β, M B , ∆ M ) described in  Betoule et al. (2014). The luminosity distances are calculated by CLASS  (Blas et al. 2011 ) using the redshifts of the supernovae within a given cosmology (set by the sampled cosmological parameters). We sample the absolute magnitude at peak brightness (M B ) as part of our analysis, and keep the other 3 nuisance parameters fixed to their best-fit values found by  Betoule et al. (2014)  since these have a small impact in the cosmological parameters when combined with the BOSS dataset. We also implemented a BAO likelihood in order to compare our results with the official BOSS Consensus BAO results from  Alam et al. (2016). This measurements use 3 redshift bins with z eff = 0.
3. Finally, as described in the previous section, we also varied the BOSS, Planck and JLA nuisance parameters. For this analysis, we used the 5% max cuts (see table  2 ). We checked the consistency of the datasets by running the same analysis for these probes alone and combined (Planck, JLA, and Planck plus JLA) and calculating respective Bayes factors for these runs. The Bayes factor, Equation (65), for combinations of the considered datasets indicates consistency between all three probes:   2017 ). Figure  13  shows the Ω m × S 8 2D plane for this analysis and comparisons with Planck and the BOSS full-shape post-reconstruction from  Alam et al. (2016). Despite the Bayes factors showing no significant reason to be concerned about the compatibility of these datasets we see an interesting trend in this figure insofar as a tension and the BOSS dataset in this paper preferring a smaller S 8 than the Planck analysis.
The blue contours where estimated from the BOSS C s data alone using the SH16 likelihood; the green contours are a combination the BOSS likelihood and JLA data (see Section 6. 3); the red contours are the Planck high-TT, TE, EE and low-P likelihood results (see Section 6. 3); finally, the purple contours are a combination of the three probes: BOSS C , JLA and Planck (also high-TT, TE, EE and low-P). Note that none of the results here use Planck Lensing data. from this analysis with a χ 2 r ed = 1. 08, which also indicates reliability and robustness of the analysis performed. Even though we do not show the results in this work, we performed a cosmological analysis using a ΛCDM with a fixed zero neutrino mass, m ν = 0 eV. We compared it with the model used in this section, ΛCDM with m ν fixed to 0.
Auto-and cross-angular power spectra for the 13 tomographic redshift bins considered for the BOSS DR12 samples: LOWZ (sample 1) and CMASS (sample 2). The shaded blue regions show the scales considered in the cosmological parameter estimation in Section 6. The data points are the pseudo-C estimates, described in Section 3, for LOWZ and CMASS. The solid blue lines, generated with UCLCL, reflect the best fit auto-and cross-power spectra for the ΛCDM model estimated in Section 6. 4. Finally, the black dashed lines show both shot noise and sampled shot noise (for bins 11 and 12). The overall reduced χ 2 for this fit is χ 2 r e d ≈ 1. 08, where the number of data points is 441 and the total number of sampled parameters is 33 -5 cosmological parameters and 28 nuisance parameters. The title on each individual plot reflects the bins i & j for each C i j , the χ 2 per data point (χ 2 /N d ), and the number of data points for that individual angular power spectrum, N d.
661 ± 0. 012. ( 76 ) This value is lower than the quoted Planck value alone; this puts further tension in this measurements if compared to the Hubble constant result from Cepheid Variables  (Riess et al. 2016 (Riess et al. , 2018)). As the model in this section is different from the previous section, we performed an evidence analysis using the Bayes factor, Equation (65), in order to be sure that our measurements can be combined with the the external data described in Section 6. 3. The following measurements indicate that such combinations are consistent for a wCDM model: R wCD M BOSS+JLA 2 × 10 2 (77) R wCD M BOSS+PLANCK 4 × 10 3 (78) R wCD M PLANCK+JLA 2 (79) R wCD M BOSS+PLANCK+JLA 3 × 10 5. (80) Finally, we used the ratio of the evidences, the Bayes factor, to perform a model selection between wCDM and ΛCDM using the final dataset combination.
Assuming ì D to be the combination of data vectors for all the datasets, the Bayes factor between the two models is R w,Λ = P( ì D BOSS+Planck+JLA |wCDM) P( ì D BOSS+Planck+JLA |ΛCDM) = 0. 67 (81) 6. 6 Flat ΛCDM + m ν Constraints For the last model considered in this work, we assume a flat ΛCDM with variable neutrino masses, varying the sum of the species' masses, m ν. In the previous sections, we fixed the sum of neutrino masses to m ν = 0. 06 eV due to results from neutrino oscillation experiments for the lower bound of the normal neutrino mass ordering  (Hannestad 2003; Lesgourgues & Pastor 2006; Hannestad & Schwetz 2016). In this section, we considered one of the two different scenarios regarding different neutrino hierarchies, the normal hierarchy. To approximate the normal hierarchy, one can approximate the two lower masses to be zero and m ν for one remaining massive species.
We do not investigate details of how the prior on the hierarchy or on the absolute mass changes this result and we leave this to a future analysis. We fix N e f f = 3. 046 by changing the values of massive neutrinos and ultra-relativist particles for the case considered, i. e. , N ν = 1 and N ur = 2. 0328. We perform an analysis using the same -range as in the previous sections, 5% max from table 2. A summary of the marginalised 1D credible intervals from the cosmological es- The blue contours where estimated from the BOSS C s data alone using the SH16 likelihood; green contours are a combination the BOSS likelihood and JLA data; red contours are the Planck high-TT, TE, EE and low-P likelihood results; finally, the purple contours are a combination of the three probes: BOSS C , JLA and Planck (also high-TT, TE, EE and low-P).
The apparent cuts in the Planck alone contours are due to the prior in h. Note, again, that none of the results here use Planck Lensing data. timation made with this cut can be found in the third part of table  4  showing the one sigma intervals for the ΛCDM parameters plus the 95% upper limit for m ν. The 1D and 2D marginalised credible intervals for this analysis can be found in Figure  19. When considering an approximation for the normal hierarchy, for a combination of BOSS C s, Planck CMB data, and supernovae data from JLA, the 95% upper limit for sum of neutrino masses is: m ν < 0. 14 eV (BOSS + Planck + JLA -5% ma x cut). ( 82 ) From Figure  20  and even more so from Figure  21 , one can notice that we are not so far from excluding zero total neutrino mass using cosmological data alone.
As the power of such datasets increase we should be able, using the correct analysis and tools, to measure and detect neutrino masses independently from atmospheric experiments. We then proceed to check the consistency of datasets by using the evidence of each cosmological parameter estimation for these model to calculate the Bayes factor (Equation  65 ): R ΛCD M+ m ν 5% BOSS+JLA 1 × 10 2 (83) R ΛCD M+ m ν 5% BOSS+PLANCK 4 × 10 2 (84) R ΛCD M+ m ν PLANCK+JLA 40 (85) R ΛCD M+ m ν 5% BOSS+PLANCK+JLA 3 × 10 2. ( 86 ) These values, again, indicate the consistency of datasets for the considered model. For the final analysis in this work, we extended the scales considered for the 10% max cuts (see table 2 for details). This allow us to access smaller scales that are ΛCDM Cosmology with m ν when using scales up to k ma x ≈ 0.