dataesr/scientific-paragraphs-categorization huggingface dataset:
All trait information was available at the individual species level from published literature  [36, 37]. Traits were organized into binary dummy variables for testing directly the trait response to environmental condition (i. e. forest type); see S2 Table for a full list of the trait sub-categories, descriptions and data sources. In addition, we also calculated two taxonomic metrics used commonly in ecological studies: species richness that measures only species incident, and Shannon diversity index that also incorporates species abundance.
Phylogenetic diversity We obtained subsets of a global phylogeny  [38] , and subset to only include species recorded in our dataset. We downloaded 1000 phylogenies from the posterior distribution of the version of this phylogeny constrained to the Ericson All Species backbone, available from www. birdtree. org. We calculated phylogenetic diversity as the sum of branch lengths of the subset of the phylogeny containing all species in a community using the PD function in the 'picante' package  [39]. We calculated phylogenetic dispersion (D) using the phylo. d function in the 'caper' package  [40] ; D assesses phylogenetic signal in binary traits (such as community membership) against null models of Brownian motion trait evolution and phylogenetic randomness, and is scaled to be zero under Brownian motion trait evolution and one under phylogenetic randomness  [41].
Binary coding also allowed for directional correlations (positive or negative) between species traits and forest types. We applied permutation Model 1 (with 9999 permutations) to test the null hypothesis that R is not linked to Q, when examining links between the fixed table of species traits and the fixed table of site attributes, mediated by the observed abundance data of matrix L  [42]. We opted for Model 1 because it randomises presence-absence of individual species relative to site characteristics (permuting within each column of matrix L), without resampling the species-trait relationship (matrix Q) or the environment-site relationship (matrix R). This was the appropriate approach for our dataset as traits were determined from the literature and not by empirical sampling, and the environment attributes (forest type) were determined a priori  [42].
Trait-environment associations No single trait-forest type association correlated in a consistent direction across all three study regions (Table  1  and S3 Table). Again, results from France were intermediate between those in Ireland and Portugal. That is, response patterns were either shared with Ireland or with Portugal but never with both regions simultaneously. Where trait correlations were significant in both Ireland and Portugal, the sign (direction) of the correlation was always opposite (Table  1 ). The only consistent directional correlation for France and Portugal was for small/  medium clutch sizes. There were a greater number of significant trait correlations in France than in Ireland or Portugal, although sample size and hence statistical power was greater for the French dataset.
Soft tissue sarcoma samples and data sets We retrospectively gathered clinicopathological and gene expression data of clinical STS samples from 16 public data sets  (Baird et al. , 2005; Barretina et al. , 2010; Beck et al. , 2010; Cancer Genome Atlas Research Network, 2017; Chibon et al. , 2010; Detwiller et al. , 2005; Gibault et al. , 2011; Gobble et al. , 2011; Hajdu et al. , 2010; Henderson et al. , 2005; Nakayama et al. , 2007; Nielsen et al. , 2002; Renner et al. , 2013; Skubitz et al. , 2012; West et al. , 2005; Ylipaa et al. , 2011). Sets and raw data were collected from the National Center for Biotechnology Information (NCBI)/GenBank GEO and ArrayExpress databases and authors' web sites (Table  S1 ). The selection of data sets was based according to the availability of clinical and expression data, including PARP1 expression measurement.
Samples had been profiled using DNA microarrays or RNASeq. The pooled data set contained a total of 1464 clinical samples of primary STS, including 1432 primary STSs and 32 STS relapses. These relapse samples were included in order to compare the PARP1 mRNA expression level between primary tumors and relapse samples, since these later will be the first candidates to PARP inhibitors in their clinical development. We also collected PARP1 DNA copy number, DNA methylation, and DNA mutational data of 224 STS primary tumors profiled in the The Cancer Genome Atlas (TCGA) data set (Cancer Genome Atlas Research Network, 2017) using SNP array and whole-exome sequencing, respectively.
59|. The robustness of the resulting gene list was tested in the validation set of 1208 remaining samples (592 'PARP1-low' samples and 616 'PARP1-high' samples) by computing for each tumor a metagene-based prediction score defined by the difference between the 'metagene PARP1-up' (mean expression of all genes upregulated in the 'PARP1-high' class) and the 'metagene PARP1-low' (mean expression of all genes upregulated in the 'PARP1-low' class). This score was then compared between the 'PARP1-high' and 'PARP1low' samples. Ontology analysis of the resulting gene list was based on Gene ontology (GO) biological processes of the Database for Annotation, Visualization and Integrated Discovery (DAVID; http://david. abcc. ncifcrf. gov/).
Multivariate analysis incorporated all variables with a P-value inferior to 5% in univariate analysis. The likelihood ratio (LR) tests were used to assess the prognostic information provided beyond that of the CINSARC signature, assuming a chi-square distribution. Changes in the LR values (LR-ΔX 2 ) measured quantitatively the relative amount of information of one model compared with another. All statistical tests were two-sided at the 5% level of significance. Statistical analysis was done using the survival package (version 2. 30) in the R software (version 2. 15. 2) (R Foundation for Statistical Computing, Vienna, Austria). The paper was written in accordance with reporting recommendations for tumor marker prognostic studies (REMARK) criteria  (McShane et al. , 2005).
Of note, expression was similar in primary and secondary tumors. No PARP1 mutation was reported in those 244 primary tumors, nor in the 1215 metastatic samples of the GENIE AACR database (data not shown). The frequency of PARP1 amplification was also very low in the TCGA primary tumors (0. 8%) and the GENIE metastatic samples (0. 2%; data not shown). This wide range of expression values within the 1432 operated primary tumors provided opportunity to search for correlations with clinicopathological features. Our analysis was based on discrete values using the median PARP1 expression level across the 1432 samples as cut-off, but similar correlations were found with continuous values. An optimal expression cut-off able to stratify patients on MFS was measured by means of ROC analysis at 11. 62 in the learning set (N = 340 samples), very close to our median cut-off measured at 11.
Acknowledgements We thank  Institut Paoli-Calmettes ,  Inserm ,  Ligue Nationale contre le Cancer (label DB/FB) ,  Fondation Groupe EDF  for their support. This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Pr. Blay received research support from  Astra Zeneca. Dr. Mir  has acted as consultant for  Amgen, Astra-Zeneca, Bayer ,  Blueprint Medicines ,  Bristol Myers-Squibb, Eli-Lilly ,  Incyte ,  Ipsen ,  Lundbeck ,  MSD ,  Novartis ,  Pfizer ,  Roche ,  Servier ,  Vifor Pharma. Pr. Le Cesne  received fees from  Pharmamar, Lilly ,  Pfizer.
The aim of this study is to distinguish genuine cosmic voids, found in a galaxy catalog by the void finder ZOBOV-VIDE, from under-dense regions in a Poisson distribution of objects. For this purpose, we perform two multivariate analyses using the following physical void characteristics: volume, redshift, density contrast, minimum density, contrast significance and number of member galaxies of the void. The multivariate analyses are trained on a catalog of voids obtained from a random Poisson distribution of points, used as background, and a catalog of voids identified in a mock galaxy catalog, used as signal. The classifications are then applied to voids extracted from the Data Release 12 sample of Luminous Red Galaxies in the redshift range 0. 45 ≤ z ≤ 0. 7 from the Sloan Digital Sky Survey Baryon Oscillation Spectroscopic Survey (SDSS BOSS DR12 CMASS).
The void is then classified as 'spurious' or 'genuine' according to whether it is overdense or underdense compared with the mean density, respectively, at the circumcenter. However, voids identified in a Poisson distribution of particles are also underdense. Furthermore, some genuine voids can exhibit overdense cores, which can be used as an argument against this type classification scheme. In other analyses, the choice has been made to exclude voids with radii below twice the mean particle separation, this mean particle separation being calculated as a function of redshift  (Hamaus et al. , 2016 (Hamaus et al. , , 2017)). However, using the void catalogs described in section 4, a comparison between the abundance of voids as a function of radius from SDSS BOSS DR12 CMASS data with the abundance of voids found in a random galaxy catalog, shows that keeping only the voids with a radius greater than twice the mean particle separation strongly reduces the size of the sample (65% of voids kept) without removing all of the voids of the random sample (44% of random voids are not discarded).
Classification of voids with a Multivariate Data Analysis The two methods described here are multivariate classifications based on supervised training. Multivariate classification is a discriminant analysis to separate events into classes, based on differences between the distribution of variables, given as input. There are two steps to the analysis. In the first supervised learning step, the classification method must be trained on known samples which already provide the outcome: a sample made from a background process and a sample made from a signal process. Each of these samples is split into a training sample and a test sample. During this step the classifier computes optimal values of the weights in order to maximize signal to background separation. The result of the training is evaluated on the test sample.
Description As mentioned in the introduction, the VIDE algorithm can output a number of shallow voids due to shot noise. The multivariate data analysis, described here, was motivated by the need to know how many voids of this kind were in a void catalog used for cosmological analysis  (Hamaus et al. , 2017)  and by the wish to enhance signals with low signal to noise for future analysis. The data void catalog was extracted from the publicly available Data Release 12  (Reid et al. , 2015; Alam et al. , 2017)  catalog of Luminous Red Galaxies from the SDSS-III Baryon Oscillation Spectroscopic Survey  (Dawson et al. , 2012)  (BOSS), in the redshift range 0. 45 ≤ z ≤ 0. 7. To train and test the MVA we used a catalog of voids found in a mock galaxy catalog as signal and a sample of voids extracted from randomly distributed points with the same mean number density as in the mock as noise (which we shall refer to as Poisson noise voids).
We used the official BOSS DR12 mock and randoms, to replicate the data as closely as possible. The mock has been built from MultiDark PATCHY BOSS DR12 light-cones (with the cosmology: Ω m =0. 307, Ω Λ =0. 693, Ω b =0. 048, σ 8 =0. 829 and h=0. 678) and the randoms were generated to have the same angular and redshift selection function as the target galaxies of the survey  (Kitaura et al. , 2016b; Rodríguez-Torres et al. , 2016). The signal and background samples contain 5, 820 and 13, 607 voids respectively. The background sample is free of real cosmic voids. The signal sample, however, being the output of the VIDE algorithm, could contain a mixture of genuine and spurious voids. Since the background sample contains only spurious voids, the fact that the signal sample could contain some background voids makes the classification less efficient than if we were able to obtain a pure signal sample.
However, when we apply a cut on a classifier to purify the sample of voids from the mock, we will reduce the proportion of Poisson noise voids. Therefore the cosmic void efficiency is greater than the signal efficiency. The cut which gives the best signal efficiency with the highest background rejection is given by the highest value of the significance. The results are summarized in Tab. 1. The results obtained with the two methods are very similar: the signal efficiency is close to 96% with a background contamination of the order of 5%. The BOSS DR12 data void catalog on which we apply these MVA classifications contains 4, 455 voids. The distributions of the six input variables of this data sample are compared to those of the mock sample in Fig. 4. One can see that the characteristics of the voids from the mock show a high resemblance to, but do not exactly reproduce, those of the voids found in the data catalog.
Discussion and Conclusion We have performed two multivariate analyses, using the ROOT TMVA package. We have trained them on void catalogs obtained from a mock galaxy catalog (signal) and a random catalog (background). We have applied the resulting classifications to the SDSS BOSS DR12 CMASS void sample. We performed a multivariate analysis on void characteristics such as the normalized volume, the core density, the density contrast, the contrast significance of a void, the number of tracers defining a void, and the void redshift. We have shown that the SDSS BOSS DR12 CMASS void sample is nearly free of Poisson noise type voids. Indeed, when applying a cut corresponding to a MLP response ≥ 0. 45, the efficiency for the cosmic voids is greater than 93% in BOSS data with a spurious void contamination equal to 4.
Acknowledgements M. C. C. thanks  Yann Coadou  ( CPPM ) for fruitful discussions on the MVA analyses, the TMVA tool and its utilization. A. P. and A. J. H. acknowledge financial support of the  OCEVU LABEX  (Grant No. ANR-11-LABX-0060 ) and the A* MIDEX  project (Grant No. ANR-11-IDEX-0001-02 ) funded by the  Investissements d'Avenir french government program  managed by the  ANR. This work also acknowledges support from the  ANR  eBOSS project (under reference  ANR-16-CE31-0021 ) of the  French National Research Agency. N. H. acknowledges support from the  DFG cluster of excellence 'Origin and Structure of the Universe'  and the  Trans-Regional Collaborative Research Center TRR 33 'The Dark Universe ' of the DFG. Funding for SDSS-III has been provided by the  Alfred P. Sloan Foundation , the Participating Institutions, the  National Science Foundation , and the  U.
Data treatment: Adaptation and automatization The phase transitions are detected using the time-eigenfunctions for each droplet (see more details in  [2] ). A transition is associated to an abrupt change in the signal trend. This change can be detected using the Matlab function findchangepts described in  [12]. It allows finding in a signal, the locations where the deviation according to a chosen statistical parameter (mean, root-mean-square, standard deviation and linear) is maximal. Looking at the third eigenfunction of a droplet (see Figure  4a ), one can rather easily identify transitions via the abrupt changes in the linearity of the signal. However, the noise in the raw signal makes the automation of the transition detection process unreliable and requires therefore a filtering step.
Given the shape of the signal and after reviewing the Fourier transform of the signal it is concluded that an adequately designed lowpass filter can filter this higher frequency noise and provide the filtered eigenfunction as seen in Figure  4b. The Matlab function can now be used to find the location of the most abrupt changes in the linearity of the signal. Given that noise still remains in the right part of the filtered signal, this process is performed iteratively. First, the liquid-solid transition constituting the liquidus line is identified, then, transitions occurring at temperatures below this line are sought after (namely metatectic, peritectic or eutectic transitions). We see in Figure  5  that this method is successful in detecting a solid-liquid transition temperature at 63. 1°C for this given sample.
Differential Scanning Calorimetry (DSC) For the DSC analysis, a DSC 131 supplied by Setaram is used at ambient atmosphere and the same 1°C/min ramp as for the IRT experiments is applied to validate the previously obtained data. A 10% increment is chosen for the compositions of validation points to limit the time spent on this procedure. It can indeed be very time-consuming if accurate results and/or several compositions are required. Moreover, two cycles are usually necessary to ensure the contact between the material and the crucible required for a homogeneous heating. The phase transitions are generally related to the heat flux evolution as a function of temperature. A peak appears when a specific process such as melting or crystallization occurs. The transition temperatures are extracted from the thermograms using the software SETSOFT2000.
InSight's SEIS: Introduction and High Level Science Objectives The InSight mission will deploy the first complete geophysical observatory on Mars following in the footsteps of the Apollo Lunar Surface Experiments Package (ALSEP) deployed on the Moon during the Apollo program (e. g. Latham et al. 1969 Latham et al. , 1970;; Bates et al. 1979 ) It will thus provide the first ground truth constraints on interior structure of the planet. The InSight spacecraft was launched on May 5, 2018 and landed on Mars on November 26, 2018 in Elysium Planitia  (Golombek et al. 2017). The three primary scientific investigations are the Seismic Experiment for Interior Structure (SEIS), the Heat Flow and Physical Properties Package (HP 3 ,  Spohn et al. 2018 ), a self-hammering mole that deploys a tether with temperature sensors to a depth of 3-5 m and the Rotation and Interior Structure Experiment  (RISE; Folkner et al.
2002; Dehant et al. 2004 ). We will describe only those directly related to the conception of InSight and SEIS. The collapse of NetLander marked indeed the end of near terms perspectives for a Mars Seismic Network mission and implied focus on a single-station seismic pathfinder mission  (Banerdt and Lognonné 2003; Lognonné and Banerdt 2003). The first attempts were made jointly in the USA and in Europe, respectively with the GEMS (Geophysical and Environment Monitoring Station) NASA Mars Scout Program proposal (based on a Mars Pathfinderlike lander;  Banerdt and Smrekar 2007)  and with the Humboldt package  (Lognonné et al. 2006; Biele et al. 2007 , Mimoun et al. 2008 ) onboard the ExoMars lander and later, the MarsTwin proposal to ESA  (Dehant et al. 2010). Although none of these projects were selected, they all paved the way for the InSight/SEIS design  (Lognonné et al.
2011) , which was proposed in 2010 to the NASA Discovery Program of the GEMS proposal  (Banerdt et al. 2011). The latter was finally selected and renamed InSight in 2012. Thus some 40 years after the Viking landings, InSight will explore a virgin planet for seismology, armed only with a sparse set of a-priori constraints on the internal structure derived from other types of orbital or rover investigations and measurements on Martian meteorites.
org/10. 5281/zenodo. 1478804 are primarily due to the trade-off between the core radius, the spherically averaged thickness of the crust and of their corresponding densities. Seismological constraints on the depth of the Moho and hopefully on the core radius, will be extremely useful to resolve these ambiguities. Waiting for these data, the mean density and moment of inertia already provide rather precise values for the gradients of pressure and adiabatic increase of temperature inside the mantle (0. 12 K/km). In the mantle, relatively shallow variation arises primarily due to a wide range of possible thermal profiles  (Plesa et al. 2016 ). In the bulk of the mantle, however, velocity and density variations between possible models are smaller. For example, when a suite of models was calculated in the study of  Panning et al.
Calibration is made by a 1000 s long sweep (top panel of Fig. 83 ) applied to the calibration coil of the VBB, which is defined as the digital input to the Digital-to-Analog converter, sampled at 20 sps (samples per second). This wave form is stored in the EPROM of the EBOX and can be modified by upload and command. Both the POS and VEL outputs of the VBB were digitized and recorded in the Ebox: the POS signal at 1 sps and the VEL signal at 100 sps. The FORTRAN program CALEX  (Wielandt and Forbriger 2016) , which uses impulse invariant recursive filters  (Schuessler 1981)  to model the digital output of a system with a rational transfer function was used on the processed data, decimated to the same acquisition rate as the calibration waveform (20 sps) and detrended for POS output when necessary.
The MSDS is responsible for the management of the raw, calibrated data and reduced products generated by the SEIS instrument (VBB and SP), the SEIS Flight Software and the APSS instrument (magnetometer, pressure, wind and temperature) in the same format as SEIS. In addition, the MSDS collects and archives housekeeping data. The data flow is described in Fig. 119  Data will be collected, archived and distributed in the standard exchange format defined by the International Federation of Digital Seismograph Networks (FDSN, http://fdsn. org): dataless SEED or stationXML for the metadata and miniSEED for the waveforms. Data will be automatically collected by the MSDS from SISMOC after being converted to miniSEED format and following the notification of the new or updated available data. Then, MSDS checks integrity and format of the data; ingests new or updated data making them automatically available.
Users will access all available data provided by the MSDS through the standard FDSN Web Services station in stationXML format and dataselect in miniSEED format (https:// www. fdsn. org/webservices/), as well as the dataless SEED through http protocol. The SEIS Science Team will be able to access all the data by the authenticated Web Service during proprietary periods, while these data will be open after. Web services available in MSDS will be used by the SEIS Data Portal to provide an interactive and guided access to the user, both through the public and the restricted access SEIS Portal sections. Users will be notified of new or updated data by a RSS notification available through the SEIS Data Portal when registered on the RSS feed.
Marsquake Service The MarsQuake Service (MQS) is the operational service for the SEIS instrument responsible for delivering catalogues of seismicity, one of the primary science products of the In-Sight mission. In this role, throughout the course of the mission, the MQS is responsible for prompt and routine detection and characterization of seismic events according to currently preferred sets of Martian interior models; assembling events into catalogues; disseminating event and catalogue information to the Mars Structural Service (MSS), scientists and public via the InSight portal; and reviewing catalogues following model updates. At the end of the mission, the MQS will deliver a final catalogue version to the Planetary Data System (PDS). A detailed discussion on the methods used to detect, locate and characterize seismic events is described in  Khan et al.