dataesr/scientific-paragraphs-categorization huggingface dataset:
Data description The daily inflow counts dataset was collected between 01/01/2011 and 07/31/2020 for two public transport lines (RER A and metro 1). These data were provided by the Paris public transport operator (Régie Autonome des Transports Parisiens, RATP). Figure  2  presents the daily counts and shows that there are more incoming flows to the RER line than to the metro line (40% more crowded when averaged over all days in the dataset). We can already have an idea of the underlying structures that govern the ridership time series by considering Figure  2. The average daily inflows per year are given by Figure  3. For the RER A the figure shows a long-term trend of decreasing ridership from 2012 to 2014, then an increasing trend from 2015 to 2018, and a decrease in 2019 and 2020. For metro 1, on the other hand, there is a constant increase in ridership except for 2020.
These seasonal effects do not explain all the phenomena; the time series remain very noisy because they are subject to multiple external factors. Those listed in Table 1 are known  to have an impact on daily ridership and were included in the models as explanatory variables. Most of these factors have been divided between working days and non-working days to account for the calendar effect. By non-working days we mean weekend days as well as bank holidays. On these days, there is very little work activity as opposed to working days, which are weekdays without bank holidays. We also consider maintenance events, strike days, and the health crisis period with the days of lockdown and post-lockdown. Finally we take into account the days when the urban transit system was free: during pollution peaks or car-free days; very few validation data were reported for these specific days.
Application objectives To the best of our knowledge, no studies have yet been conducted on the decomposition of time series applied to transit station ridership data. Considering the long time horizon over which the dataset was collected, it is interesting to decompose the time series to identify the impact of each exogenous factor on known or specific mobility behaviors (e. g. transfers). The transport operator provided us with nine-year ticketing data from two transport lines in the multimodal transport hub: RER A and metro 1. These lines are competing because they serve several stations in common but they are also complementary: the RER line serves more distant suburbs than the metro 1 line but the latter has a denser network of stations within Paris. One of the lines is automated (metro line 1), the other (RER line) is with a driver.
Conclusion This paper has presented the analysis of ticketing data collected at several control points of a multimodal transportation hub, each serving access to a transport line, based on structural models of time series decomposition. We focused our analysis on daily ridership collected over nine years. The strength of such models lies in their explanatory capacity. They are capable of highlighting long-term phenomena on the ridership, such as the trend and seasonality (annual, weekly) and the impact of exogenous factors, whether anticipated, such as maintenance work or not, such as strikes or the Covid-19 health crisis. Through the regression coefficients of the decomposition model, we can quantify this impact separately for each factor, which is impossible with other learning-based models.
We sought to test the hypothesis that richness of estuarine red-listed fishes, which are supposedly vulnerable to anthropogenic environmental disturbance, is determined by watershed-scale land-use factors. For this purpose, we compiled fish eDNA metabarcoding data and socioecological and physicochemical data in 22 watersheds (i. e. , sites) in Japan and examined whether environmental parameters and land-use affect the ability of estuaries to harbor red-listed species.
0. 240  (Edger, 2010). The minimum threshold for inclusion of a species in a sample was 16 reads. Few fish species were detected in the negative control samples, albeit within a small number of reads. The number of reads corresponding to every fish species detected in the negative control was deducted from the number of reads of the respective fish species detected in each sample. Detailed procedures for the extraction and verification of the species are described in the report by  Ahn et al. (2020). Census, land-use, and physicochemical data sets of Japanese watersheds and rivers are publicly available online but are mostly in Japanese. The source code of the main tools, scripts, and algorithms used is available from https://github. com/edouardlavergne/eDNA-Japan.
Because we focused on Japan, we used the JMOE lists. Species that were detected using eDNA metabarcoding that were not included in the JMOE list were designated as least concerned (LC) so as to include all available species in the analyses. We refer to species on the JMOE list (i. e. , non-LC species) as red-listed species and to species belonging to LP, VU, EN, or CR categories as threatened species. We then combined presenceabsence data obtained from eDNA metabarcoding with the JMOE lists (Appendix S1) to generate a matrix indicating the number of species per category in each estuary. We used spp. with a genus name to mean more than one species was detected and all species in the genus were in the same threat category.
Watershed variables The total watershed area, human population (number of inhabitants), and density data from 2015 were compiled for each watershed based on publicly available data (Appendix S2). Landuse data from 2014 (i. e. , geographic area of rice cultivation, agriculture not used for rice cultivation, forest, abandoned land, urbanization, rivers and lakes, and golf courses) were compiled for each watershed also based on publicly available information (Appendix S3). Paddy fields are usually covered by water and dense rice crops and are very different from agricultural lands not used to grow rice, which are relatively dry (i. e. , not inundated with water). We used agriculture to refer only to agriculture that was not rice. Urban area was the summed area covered by buildings, roads, and railways.
A coastal and river artificialization index (CRAI) was calculated based on the percentage of artificial revetments (e. g. , concrete-sealed banks and tetrapods) along coastlines and riverbanks, determined using Google Earth Pro (version 7. 3. 2. 5776 [Map data: Google, Digital Globe]). The CRAI was based on a total of 13 km of coastline and riverbank: 3 km of coastline (i. e. , 1. 5 km on both sides of the river mouth) and a 10 km of the riverbank (i. e. , 5 km upstream on both sides of the river). Physicochemical data were measured or compiled from several publicly available databases (Appendix S4). The water surface temperature (ST) and salinity were measured in the same sampling bucket immediately after water collection for eDNA analysis, from both high and low tides, with a handheld waterquality analyzer (LAQUA act ES-71, HORIBA, Kyoto).
Because there was overall no significant difference in ST and salinity between high and low tides apart from three sites with large variations in salinity between tides (Appendix S5), the average ST and salinity between tides were used for later analyses. Data regarding river length, river discharge, surface water dissolved oxygen (DO s ), bottom water dissolved oxygen (DO b ), pH, total nitrogen (TN), and suspended solid (SS) were also obtained from publicly available national or regional databases (Appendix S4). We selected data collected from the most downstream observatory station in each river and calculated a 5-year average from 2014 to 2018 for June-August. We selected the minimum DO b values for the summer season (June-September) from the most recent year for which data were available (from 2016 to 2018).
ACKNOWLEDGMENTS Funding for this research was provided by the  Nippon Foundation  (to Y. Y. ) in the framework of the  Link Again Program. All concerned prefectures are gratefully acknowledged for providing us with environmental data of their respective waters. We also thank  M. Miya  for providing technological advice on the eDNA approach,  T. Ise  and  Y. Oba  for supporting water sampling, and  R. Masuda  for his useful comments on our research.
The accuracy of theoretical mass, radius and effective temperature values for M-dwarf stars is an active topic of debate. Differences between observed and theoretical values have raised the possibility that current theoretical stellar structure and evolution models are inaccurate towards the low-mass end of the main sequence. To explore this issue we use the CHEOPS satellite to obtain high-precision light curves of eclipsing binaries with low mass stellar companions. We use these light curves combined with the spectroscopic orbit for the solar-type companion to measure the mass, radius and effective temperature of the M-dwarf star. Here we present the analysis of three eclipsing binaries. We use the pycheops data analysis software to fit the observed transit and eclipse events of each system.
The observed and processed data are made available on the Data Analysis Center for Exoplanets (DACE) web platform 1. We downloaded our data from DACE using pycheops 2  , a module developed for the analysis of data from the CHEOPS mission  (Maxted et al. 2021). We fitted the light curves from all four apertures and found that different choice of aperture radius has a negligible impact on the results. Therefore, for EBLM J1741+31 and EBLM J1934-42, we selected the aperture radius that gave the minimum median absolute deviation (MAD) of the point-to-point differences in the light curve of the eclipse visit. We then used the chosen aperture type for the respective transit visits. For EBLM J2046+06 this criterion resulted in slightly different aperture radii for the two visits from the preferred OPTIMAL aperture (25.
5 and 26. 0 pixels), so we used the DEFAULT aperture instead. The TESS survey is split into overlapping 90 • × 24 • degree sky sectors over both northern and southern hemispheres with each sector being observed for approximately one month. EBLM J1741+31 (TIC 18319090) was observed in Sectors 25 and 26 of the survey as part of the Guest Investigator programs G022156 and G022253, with 2-minute cadence data made available. EBLM J1934-42 (TIC 143291764) was observed in Sectors 13 and 27 of the survey as part of the Guest Investigator programs G011278 and G03216, with 2minute cadence data made available. Data was reduced by the Science Processing Operations Center Pipeline (SPOC;  Jenkins et al. 2016)  and made available from the Mikulski Archive for Space Telescopes (MAST) 3  web service. We used the Pre-search Data Conditioned Simple Aperture Photometry (PDCSAP) flux data for our analysis.
Any cadences in the light curve with severe quality issues were ignored using the "default" bitmask 175  (Tenenbaum & Jenkins 2018). The TESS light curve of EBLM J1741+31 shows a smooth variation with an amplitude ∼ 0. 2% in the flux between the transits. To remove this variability we divided the light curve by a low-order polynomial fitted by least-squares to the data between the transits. EBLM J1934-42 shows variability in the TESS light curve with an amplitude of about 1% on timescales of a few days. This may be due to moderate stellar activity modulated by stellar rotation. To remove this low-frequency noise we fit the data between the transits with a Gaussian process (GP) calculated using the celerite (Foreman-Mackey et al. 2017) software package. The kernel of the GP is the stochastically-driven damped simple harmonic oscillator function defined by Foreman-Mackey et al.
For EBLM J2046+06 there were 22 individual observations, with SNR varying between 50-80. The combined spectra has a total SNR ∼300. We used the ARES code 4   (Sousa et al. 2007 (Sousa et al. , 2015) )  to measure equivalent widths (EW) of iron lines measured using the list of lines presented in  Sousa et al. (2008). A minimization process assuming ionization and excitation equilibrium is used to find convergence for the best set of spectroscopic parameters. In this process we use a grid of Kurucz model atmospheres  (Kurucz 1993 ) and the radiative transfer code MOOG  (Sneden 1973). The radii of the three targets was determined using an adapted infrared flux method (IRFM;  Blackwell & Shallis 1977)  via relationships between the bolometric flux, the stellar angular diameter, the effective temperature, and the parallax, recently detailed in  Schanche et al.
(2021). The masses obtained are given in Table  2. The semi-amplitude of the primary star's spectroscopic orbit, , is required for the calculations of secondary star's mass. For EBLM J1934-42 and EBLM J2046+06 we used values of  from the Binaries Escorted By Orbiting Planets survey (BEBOP,  Martin et al. 2019). For J1741-31 we calculated  from a fit to radial velocity data from the SOPHIE high-resolution échelle spectrograph  (Perruchot et al. 2008 ) mounted on the 193cm telescope at the Observatoire de Haute-Provence (France). Twenty measurements were collected between the dates of 2019-02-24 and 2020-09-03 with a typical exposure time of 1800s leading to a mean uncertainty of 13. 7 m s -1. These were obtained as part of a Large Programme aiming to detect circumbinary planets (e. g. Martin et al.
2019). The 20 spectra were obtained in High-Efficiency mode, where the resolution is reduced to 40,000 for a 2. 5× gain in throughput over the High-Resolution mode of 75,000. All observations were performed with a fibre on the science target and a fibre on the sky. The latter is used to remove background contamination originating from the Moon. All science and sky spectra were reduced using the SOPHIE Data Reduction Software (DRS) and cross-correlated with a G2 mask to obtain radial-velocities. These methods are described in  Baranne et al. (1996), and Courcol et al. (2015) , and have been shown to produce precisions and accuracies of a few meters per seconds (e. g. Bouchy et al. 2013; Hara et al. 2020) , well below what we typically obtained on this system. We used the module ellc (Maxted 2016) to model radial velocity.
ANALYSIS We analyse the CHEOPS light curves for each star in two steps. In the first step, we analyse each CHEOPS visit in order to determine initial values for our model parameters, and to determine which nuisance parameters must be included in the model to deal with instrumental noise. In the second step, we analyse all the data for each star in a single Markov chain Monte Carlo (MCMC) analysis to obtain our final results. These results are then compared to a MCMC analysis of TESS data when available. The output from the light curve analysis is then combined with as estimate of for the mass of the primary star and  to determine the mass and radius of the M-dwarf. The depth of the secondary eclipse is used together with model spectral energy distributions to estimate the effective temperature of the M-dwarf.
TESS light curve analysis We have compared our results using CHEOPS data to a similar analysis of the TESS light curves for EBLM J1741+31 and EBLM J1934-42. For EBLM J1741+31 we used data from TESS sectors 25 and 26 covering 5 transits. For EBLM J1934-42 we used data from sectors 13 and 27 covering 6 transits. Sampling of the posterior probability distribution of our model parameters was again performed using emcee. Gaussian priors were set on   and   using the same spectroscopically derived values as in the CHEOPS fit. Gaussian priors were also set on ℎ 1 and ℎ 2 using the stellar parameters given in Section 2 and assuming the same offset, but using the TESS passband to interpolate our values. For EBLM J1741+31 a prior on eclipse depth  was again set using the predicted flux ratio of the target, adjusting to  Ic magnitudes from  Pecaut & Mamajek (2013)  due to the different passband of TESS.
ACKNOWLEDGEMENTS CHEOPS is an ESA mission in partnership with Switzerland with important contributions to the payload and the ground segment from  Austria, Belgium, France, Germany, Hungary, Italy, Portugal, Spain, Sweden , and the  United Kingdom. The CHEOPS Consortium would like to gratefully acknowledge the support received by all the agencies, offices, universities, and industries involved. Their flexibility and willingness to explore new approaches were essential to the success of this mission. This publication makes use of The Data & Analysis Center for Exoplanets (DACE), which is a facility based at the  University of Geneva (CH)  dedicated to extrasolar planets data visualisation, exchange and analysis. DACE is a platform of the  Swiss National Centre of Competence in Research (NCCR) PlanetS , federating the Swiss expertise in Exoplanet research.
The DACE platform is available at https://dace. unige. ch. MIS thanks  Giovanni Bruno  for his comments on the draft of this manuscript. MIS and PFLM are supported by the  UK Science and Technology Facilities Council (STFC)  grant numbers  ST/M001040/1  and  ST/T506175/1. This research is supported from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement n  • 803193/BEBOP ), by a  Leverhulme Trust   Research Project Grant  (n •  RPG-2018-418 ), and by observations obtained at the  Observatoire de Haute-Provence (CNRS), France (PI Santerne ). S. G. S. acknowledge support from  FCT  through  FCT  contract nr. CEECIND/00826/2018  and  POPH/FSE (EC). IB, AS and TL acknowledge financial support from the  French Programme National de Planétologie (PNP, INSU).
Support for this work was provided by  NASA  through the  NASA Hubble Fellowship  grant # HST-HF2-51464  awarded by the  Space Telescope Science Institute , which is operated by the  Association of Universities for Research in Astronomy, Inc. , for  NASA , under contract  NAS5-26555. S. S. has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  833925 , project  STAREX ). V. V. G. is an F. R. S-FNRS Research Associate. ACC and TW acknowledge support from  STFC  consolidated grant number  ST/M001296/1. YA and MJH acknowledge the support of the  Swiss National Fund  under grant  200020_172746. We acknowledge support from the  Spanish Ministry of Science and Innovation  and the  European Regional Development Fund  through grants  ESP2016-80435 MDM-2017-0737  Unidad de Excelencia "María de Maeztu"-Centro de Astrobiología (INTA-CSIC) , as well as the support of the  Generalitat de Catalunya/CERCA  programme.
O. D. S. D. is supported in the form of work contract ( DL 57/2016/CP1364/CT0004 ) funded by national funds through FCT. B. -O. D. acknowledges support from the  Swiss National Science Foundation  ( PP00P2 -190080 ). This project has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (project F A ; grant agreement No  724427 ). This project has been carried out in the frame of the  National Centre for Competence in Research PlanetS  supported by the  Swiss National Science Foundation (SNSF). MF and CMP gratefully acknowledge the support of the  Swedish National Space Agency  ( DNR 65/19 ,  174/18 ). DG gratefully acknowledges financial support from the  CRT foundation  under Grant No. 2018. 2323"Gaseous or rocky?Unveiling the nature of small worlds".
M. G. is an F. R. S. -FNRS Senior Research Associate. MH acknowledges support from  ANID -Millennium Science Initiative   -ICN12 009. SH gratefully acknowledges CNES funding through the grant  837319. KGI is the  ESA   CHEOPS  Project Scientist and is responsible for the  ESA CHEOPS Guest Observers Programme. She does not participate in, or contribute to, the definition of the  Guaranteed Time Programme  of the CHEOPS mission through which observations described in this paper have been taken, nor to any aspect of target selection for the programme. This work was granted access to the HPC resources of MesoPSL financed by the  Region Ile de France  and the project  Equip@Meso  (reference  ANR-10-EQPX-29-01 ) of the programme Investissements d'Avenir supervised by the  Agence Nationale pour la Recherche.
, 2019). Paraburkholderia phytofirmans strain PsJN, a well-known PGPR, has been isolated from surface-sterilized onion roots  (Nowak, 1998). PsJN is able to colonize a large variety of plants, such as tomato, potato and grapevine. In addition, this bacterium improves the tolerance against biotic stress  (Miotto-Vilanova et al. , 2016). According to previous results, PsJN has no antagonistic effect against D. teres in barley  (Backes et al. , 2020). Therefore, this bacterium was used as a bacterization control in this study. Unlike PsJN, another bacterium, belonging to the genus Paraburkholderia was able to limit significantly the development of D. teres. This is a new species of Burkholderia after a comparison against all genomes of typical strains available in the Microbial Genomes Atlas (MiGA) webserver and TYGS database.
Statistics was performed on log2 transformed data using a one-way ANOVA with a Tukey's post-hoc test after having checked the normal distribution of the data and homogeneity with a Shapiro-Wilk's and a Levene's tests, respectively, using IBM SPSS Statistics V19 (IBM SPSS, Chicago, IL, USA). A hierarchical clustering using uncentered absolute correlation and complete linkage was used to generate groups with similar expression patterns in Cluster 3. 0 (correlation coefficient threshold = 0. 83). The heat map was visualized with Java TreeView available at http://jtreeview. sourceforge. net/. The principal component analysis (PCA) was performed with Clust-Vis (available online at https://biit. cs. ut. ee/clustvis/). To visualize the expression pattern of genes related to the phenylpropanoid and cell wall biosynthetic pathways, the barley eFP Browser (http://bar.
utoronto. ca /efpbarley/cgi-bin/efpWeb. cgi) was queried. To obtain the contig number necessary as input in the eFP Browser, the FASTA sequence of the gene was blasted on PLEXdb (Plant Expression Database; http ://www. plexdb. org). The expression levels of the genes are shown as a heatmap with yellow and red colors indicating low and high expression, respectively, in various tissues.
Data processing and statistical analysis Data were processed using Progenesis QI (v 2. 3, Nonlinear Dynamics, Waters, Newcastle, UK). Briefly, the software automatically extracts features (combinations of isotopic peaks and adducts' ions at a given retention time) between 0 and 50 min and aligns them. A normalization to all compounds was carried out. For each comparison of modalities two by two, only features with MS2 data and both foldchange above 1. 5 and p-value below 0. 05 in Progenesis's ANOVA were kept for the identification step, which consisted in the use of Metascope, ChemSpider and National Institute of Standards and Technology (NIST) plugins, for the search in an in-house database, in CHEBI 3-star, in MassBank for the first plugin, in PubChem for the second and in NIST MS/MS database for the third.
A manual review of the output data was then carried out, in combination with the software PeakView (v 1. 2, SCIEX, Framingham, MA), LipidMaps (http://www. lipidmaps. org/ ), Metlin (https://metlin. scripps. edu/index. php), MassBank of North America (MoNA) (https://mona. fiehnlab. ucdavis. edu/) and PubChem databases (https://pubchem. ncbi. nlm. nih. gov), as well as literature data for structure elucidation. To reduce the number of compounds to be identified and further sharpen the analysis, different comparisons were made according to several variables. Indeed, comparisons 1 to 6 were focused on time as variable (12 hpi vs 48 hpi), while comparisons from 15 to 20 were dedicated to the pathogen's absence or presence as variable. Finally, only comparisons from 7 to 14, with the presence of either bacteria B25 or PsJN as variable, were further investigated for this study, since we were interested in knowing the effects on barley metabolism of these bacteria used as a biocontrol agent.