dataesr/scientific-paragraphs-categorization huggingface dataset:
This is expected, because there are always subtle physical effects that were missed, or shortcomings of the adopted models, or hidden inaccuracies of the statistical processing. All this may lead to a systematically wrong uncertainties in the derived data. The same phenomenon was known long ago in the precision Doppler data  (Wright 2005; Baluev 2009 ), and a generally similar effect should be expected in TTVs. Then the TTV data released by different groups may have quite a different level of hidden noise. We therefore caution the reader against simplistic joining of TTV data coming from different sources. Such a merging should be made in an adaptive manner instead, taking into account possibly different relative weights of heterogeneous subsets. One way of such an adaptive treatment is demonstrated below for the WASP-4 case.
Analysis of the TTV We processed our TTV data in the homogeneous manner, using the same protocol for each target. For the first step, we tested the existence of a possible long-term nonlinear trend in the TTV time series, expressing it as a quadratic model: TTV(n) = T0 + P (n -n0) + 1 2 dP dn (n -n0) 2 , ( 5 ) where n is the transit count (or epoch), P is the orbital period, and dP/dn is the small quadratic coefficient. Defining a temporal variable t = nP , we can alternatively rewrite (5) as: TTV(t) = T0 + (t -t0) + 1 2 Ṗ P (t -t0) 2 = T0 + (t -t0) - (t -t0) 2 2T d. (6) In this model the quantity T d = -P/ Ṗ represents a characteristic time of period decay (the time when the apparent period would turn zero if it decreased linearly). Since it has an intuitive interpretation, we often use this quantity below as a reference fit parameter (rather than the quadratic coefficient itself).
However, we emphasize that multiple physical phenomena may be approximated by mathematically the same formula (5): tidal orbital decay, tidal apsidal drift, or even non-tidal effect of a perturbation from a distant companion (causing the TTV via the light-travel effect). The TTV residuals themselves are plotted in the onlineonly Fig. 6  and 7 for all our targets. They correspond to a linear TTV ephemeris and are given separately for Stage 4 (all data) and Stage 5 (HQ data). We were able to easily confirm the TTV trend of WASP-12  (Maciejewski et al. 2016 ) at this step. This case is discussed in details below in a separate section. The TESS timing data  (Bouma et al. 2019 ) claimed that a similar TTV trend may exist in WASP-4, but our data do not confirm such a trend. The detailed analysis of this target is discussed below in a separate section.
2010). These additional companions would impose a variable light travel delay effect on the inner tight subsystem, causing therefore a TTV. However, this type of TTV is not detectable in HAT-P-13 due to the small magnitude (e. g. ∼ 7 sec from HAT-P-13 c). In this work we did not investigate the TTVs possibly coming from gravitational perturbations of the planet b orbital motion. For WASP-4 HQ data we find that multiple peaks rise above the two-sigma significance level in the short-period range. However, these peaks look more like noise rather than a systematic variation. Moreover, they disappear if we remove just a single timing measurement, namely the one derived from the lightcurve by Sanchis-Ojeda et al. (  2011 ), dated by 02 Aug 2009. We believe that this lightcurve could be affected by a subtle residual systematic effect or by a spot-transit event, even though it was not classified as an outlier and looks visually reasonable.
For these alternative fits we only consider the best fitting quadratic TTV ephemeris (5). Our approach may inspire statistical correlations between different transit times  (Baluev et al. 2015) , but they mostly appeared negligible. Only for GJ 436 and WASP-6 some pairs of transits generated a large correlation of up to 0. 68. This is because now we included several partial transits in the analysis. Nonetheless, on average the effect of correlations becomes negligible, so we decided to keep such transits particularly since we have rather little transits data for these two targets. We notice that for Kelt-1 the impact parameter estimation b = 0. 05 ± 0. 64 is a formal and non-informative value, since the parameter b becomes severely nonlinear and hence non-Gaussian whenever it becomes smaller than the uncertainty.
With this model we obtain T d = 3. 55±0. 31 Myr (3. 60±0. 31 Myr from only HQ TTVs), practically the same values. The best fitting TTV jitter for our data is estimated to be 20. 8 ± 2. 5 sec (18. 9 ± 2. 5 sec for the HQ subsample). Therefore, this result is practically modelinvariant and thus very trustable. As such, the tidal quality factor remains at Q * ∼ 2 • 10 5 , the value from  (Patra et al. 2017). We did not include secondary eclipses in our analysis, and did not use some transit timings published without lightcurves that could be reprocessed. From only the transit timing data, we did not obtain any qualitatively new result for WASP-12, but RV data brought a significant additional information about the nature of this TTV trend (see Section 6).
WASP-4: yet another TTV trend? We suspected the nonlinear trend in WASP-4, analogous to the WASP-12 one, right after the new EXPANSION lightcurves from 2017 observing season were processed. The magnitude of the trend corresponded to T d ∼ 10 Myr (surprisingly close to what was recently claimed by  Bouma et al. 2019). However, that time the trend interpretation depended on just a few data points from 2017. To confirm or retract the trend hypothesis we initiated in 2018 a prioritized observing campaign of WASP-4 within the EXPANSION project. By the end of 2018 we acquired 10 new transit lightcurves. Table  3  shows the observation log, including the EX-PANSION data, as well as a few older lightcurves found in the ETD and AXA databases, and also 6 archival lightcurves from the TRAPPIST-South telescope.
This table does not include data taken from the literature (29 lightcurves). The total number of WASP-4 lightcurves reprocessed in this work was 66 (plus one outlier not included in the final analysis). The trend information mainly comes from 14 observations made in 2017-2018. Among them 10 were taken by P. Evans with a 36 cm Planewave CDK telescope equipped by a SBIG STT 1603-3 CCD and hosted at El Sauce Observatory, Chile. This is a good quality equipment at a good site, and the corresponding TTV measurements appeared in turn quite competitive with even TESS ones (which were released later). Our new data did not confirm the trend: the updated TTV time series became consistent with strictly linear ephemeris, so we decided that our trend hypothesis was wrong. But  Bouma et al. (2019)  reported a detection of this trend based on the new TESS transit data, obtained practically simultaneously with our observations in the EXPAN-SION network.
To shed more light on this apparent controversy, we then performed additional analysis, including the TTV data published in the literature without lightcurves and the new TESS timings. This includes very accurate transit times derived from the transmission spectroscopy by  Huitson et al. (2017) , transit times by  Hoyer et al. (2013) , by  Wilson et al. (2008)  and two early WASP timings given in  (Gillon et al. 2009a ). We did not use the HST spectral observations from  Ranjan et al. (2014) : these data might be inaccurate because the spectra were partly overexposed and hence the flux measurements are likely not very reliable. The full TTV time series is shown in Fig. 3. Now, with the new TESS transit times added, the quadratic term of the trend indeed appears significant, according to our analysis.
However, we obtain a smaller magnitude and significance than  Bouma et al. (2019)  reported. The trend is still not detectable with the use of only the homogeneously derived portion of TTV data from this work. That is, the information about the trend comes mainly from the third-party observations rather than from our data release. By inspecting Fig. 3  we may suspect that the trend depends primarily on just the four high-accuracy timings provided by  Huitson et al. (2017). The TESS timing does not in fact contradict anything and visually they are in a satisfactory agreement with what was obtained in the EXPANSION project in 2018. However, justifying the trend detection based on just four data points, even apparently accurate ones, might be quite dangerous. Looking into the details of the  (Huitson et al.
Significant red noise was also detected in the WASP-52 transmission spectroscopy lightcurve from  (Chen et al. 2017) , though not detected in the HD 189733 data by  Kasper et al. (2019). The latter, however, revealed the anomalous transit time discussed above. A public release of the  Huitson et al. (2017)  lightcurves is not available, so we did not reanalyse them in our pipeline. We therefore decided to investigate this issue using a different approach. As it was explained above, formally declared TTV uncertainties never appear entirely accurate: the actual scatter of TTV residuals may be systematically different (usually larger). However, different teams may process data quite differently, and hence each team might have its own bias in the reported TTV uncertainties. Therefore, different portions of such a heterogeneous TTV compilation may need to be weighted differently to balance this effect.
However, those weights are not known to us a priori, so they need to be estimated from the TTV data 'on-the-fly', e. g. based on the actually observed scatter of the TTV residuals in each homogeneous portion. We therefore separated all our TTV data into the following four more or less homogeneous classes: (i) the 'main' subset including transit timings derived in this work and three old timings given in  (Gillon et al. 2009a ) without public lightcurve data; (ii) the rich TTV subset by  Hoyer et al. (2013) ; (iii) the four high-accuracy timings by  Huitson et al. (2017) ; and (iv) the TESS timings from  (Bouma et al. 2019). All these data sets should have an independently fittable noise parameter. This noise was modelled by one of two models discussed in  (Baluev 2015) , namely by (i) the multiplicative model, or (ii) the so-called regularized model.
These 'noise models' represent a parametrized model for the variance of each TTV measurement, in which a single free parameter regulates the weight of the corresponding TTV data set as a whole. Since this approach involves a separate and largely independent treatment of each TTV data set, we call this as 'separated' model of the TTV data. It can be fitted by using the maximum-likelihood method, as discussed in  (Baluev 2009). In such a way the relative weighting of different TTV subsets is determined adaptively and basically tied to the corresponding TTV residuals RMS. For a comparison, we also analysed the TTV data plainly merged into a single time series without any relative weighting. This analysis was also performed for the same two noise models, multiplicative and regularized ones. The TTV trend itself was always modelled by the quadratic function (  5 ) with three free coefficients.
As we expected, it appeared that the magnitude of the quadratic term and especially its derived uncertainty is sensitive to the choice of the noise model. In the case of a 'separated' model the trend uncertainty gets increased. Therefore, by allowing some TTV data to be actually less accurate than stated, the significance of the trend may reduce. For example, it may reduce if the four  Huitson et al. (2017)  transit times are less accurate than formally stated. And because of the small number of these data (just the four), their RMS does not constrain the noise level well, so this level can be varied relatively freely. In Fig. 4 , we demonstrate this effect in the shape of the likelihood function L. For this goal we consider the loglikelihood-ratio statistic Z determined in accordance with All TTV data except for  Huitson et al.
(2017)  merged, multiplicative merged, regularized separated, multiplicative separated, regularized Figure  4. Logarithm of the likelihood ratio statistic for WASP-4, Z(q), as a function of q = T -1 d. Three graphs correspond to different compilations of TTV data (homogeneous from this work / all / all but  Huitson et al. (2017) ). The curves within each graph correspond to different models of the TTV noise (merged/separated and multiplicative/regularized). In each graph a set of the significance threshold levels is also shown, corresponding to the frequentist χ 2 test or to the Bayesian information criterion (BIC). See text for more details. (Baluev 2009). We compute (i) the global maximum of the likelihood function Lmax with respect to all the noise parameters and all three TTV trend coefficients, and (ii) the value L ′ max (q) maximized with respect to all parameters except for the quadratic coefficient dP/dn = P 2 T -1 d = qP 2 , where q = T -1 d was fixed prior to the fit.
how much q = 0 is consistent with the data, with the adopted TTV noise model). In Fig. 4 , we plot this statistic Z(q) for three TTV data compilations, including (i) only the homogeneous data from this work, (ii) all TTV data, (iii) all TTV data excluding  (Huitson et al. 2017) , and for all our noise models, including (i) the plain merging of heterogeneous datasets and (ii) adaptive merging of heterogeneous datasets with individually fittable noise parameters. For each of these model layouts we adopt either a multiplicative or regularized noise model, defined in  (Baluev 2015). As we can see, the shape of the likelihood function may change a lot depending on the model and TTV data involved. We can draw the following conclusions: (i) Our homogeneously derived TTV data do not support the existence of any quadratic trend.
These data are consistent with a linear ephemeris below 1-sigma level. (ii) Simultaneously, the value of T d = 9. 2 Myr from  (Bouma et al. 2019 ) seems too poorly consistent with our homogeneous TTV subsample, at the level above 3-sigma in terms of the χ 2 test or with ∆BIC ∼ 4 -6 (depending on the model). We believe this may appear, at least in some part, because  Bouma et al. (2019)  did not take into account the heterogeneous nature of the TTV data, merging them into a single time series. (iii) Joining our data with the remaining third-party TTV measurements allows us to refine the localization of the parameter q greatly and even suggests that this q can be significantly non-zero. However, the significance of this conclusion, as well as possible confidence ranges for q appear very model dependent.
Moreover, if the RV data belonging to the same instrument contained in-transit pieces, we separated these portions of the data from each other and treated them as individual RV data sets. This might make the model more adequate, because e. g. the scatter of the RV residuals within each such short run covering just a few hours is significantly smaller than for the entire dataset covering years. This is essentially the impact of red noise in the RV data. Also, the red noise may result in a small individual offset of each in-transit run. For some targets we also found several compact series of out-of-transit runs covering just a single night. Such portions of the RV data were treated separately too, with an individual offset and individual noise parameters. After performing a preliminary fit of the joint transit+RV model described above, we run the red-noise detection algorithm described above for transit data, but now we extended it to all the RV data sets as well.
We found only the following targets convincingly demonstrating significant nonzero values: Corot-2 (µ for the HARPS RV data), HD 189733 (ν for Keck/HIRES and SO-PHIE, and µ for HARPS, HARPSN, and Keck/HIRES), WASP-5 (µ for HARPS), and possibly GJ 436 (ν for GJ 436 HARPS, HARPSN, Keck/HIRES). In theory, ν should usually be zero, since most our RV data were derived with TERRA, which is a kind of a spectrum modelling method (Anglada-Escudé & Butler 2012). A nonzero ν may appear only if the RV data were derived by the cross-correlation technique, and simultaneously the spectral lines have some asymmetry on average  (Baluev & Shaidulin 2015). Since this is not the case for the most targets, a few significantly nonzero ν estimates may indicate that the adopted limbdarkening coefficients ARV and BRV are inaccurate for the relevant RV dataset.
In such a case, the value of v sin i and λ may involve an additional bias which is difficult to estimate without a better guess for ARV and BRV. Even though we did not detect here very many occurrences of significantly nonzero RM correction coefficients, it is still important to preserve them in the RV model as free parameters, in order to have a more realistic (increased) uncertainty in v sin i and λ, as well as in other RV-derived parameters, like e. g. the RV trends. Now let us consider the putative RV trends (radial accelerations). They are modelled by a linear function RV = c ′ 0 + c1(t -t0), (9) where c ′ 0 is a dataset-specific RV offset, and c1 is the trend coefficient (common for all datasets). If not an instrumental effect or some long-term astrophysical variation, this trend can be explained through a gravitational effect from a distant unseen companion.
ACKNOWLEDGEMENTS Organization of the  EXPANSION  project ( E. N. Sokov ,  I. A. Sokova ), programming and statistical analysis (R. V. Baluev), and collection of literature data (V. Sh. Shaidulin ), apart from the observations, were supported by the  Russian Science Foundation  grant  19-72-10023. Students of  Horten Upper Secondary School (Horten Videregående Skole )  H. Fjeldstad ,  S. Herstad ,  D. N. Lønvik ,  I. Oknes , and  W. Zhao  are acknowledged for taking part in observing WASP-12 b on the  Nordic Optical Telescope. Özgür Baştürk thanks The Scientific and Technological Research Council of Turkey (T ÜB İTAK)  for their support through the research grant  118F042  and for a partial support in using T100 telescope with the project No. 17BT100-1196. E. Pakštienė  acknowledges support from the  Research Council of Lithuania (LMTLT)  through grant  9.
Individuals of these populations exhibited varying phenotypic changes, when grown at sub-optimal temperature (22 vs. 15 • C). These phenotypic variations under controlled conditions reflected intraspecific morphological variations. This study could bring new information regarding the west European population structure of A. thaliana and its phenotypic variations at different temperatures. The integrative analysis combining genetic, phenotypic variation and environmental datasets is used to analyze the acclimation of population in response to temperature changes. Regarding their geographical proximity and environmental diversity, these populations represent a tool of choice for studying plant response to temperature variation.
thaliana analyzed in this study. Geolocalisation and altitude were determined during the sampling. We used the average of the monthly data to calculate the annual minimum, mean and maximum temperatures and annual precipitation obtained from the WorldClim project  (Hijmans et al. , 2005; www. worldclim. org). Annual UV radiation was obtained from the Photovoltaic Geographical Information System of the European Community  (Huld et al. , 2012). Climate PC1 values are the position of populations on the first principal component of the PCA of bioclimatic variables that allows to classify populations in function of their environmental conditions (Supplementary Figure  S1 ). lab under the same growth conditions  (Duruflé et al. , 2017). Field-collected seeds from about 10 individuals per sampled locations (Table  2 ) were reproduced one time in a growth chamber to obtain homogeneous batches of seeds and to reduce maternal effects, before phenotyping.
Climatic Data Climatic variables were obtained from WorldClim dataset 1  (Hijmans et al. , 2005). The values used are the mean of 30 years (1960 to 1990) with a resolution of ca. 1 km 2 per grid cell. Solar radiation reading were obtained from the Photovoltaic Geographical Information System of the European Communities  (Huld et al. , 2012).
Paired end sequencing and manual editing using BioEdit  (Hall, 2011 ) allowed a good sequencing quality. The five sequences obtained were concatenated and used for genetic and phylogenetic analysis. In order to confirm that a subset of populations was homogeneous (Camu, Gava, Grip, Hern, Hosp, Jaco, Pont, Prad and Roch), PCR amplification of 3 ETS 45S rRNA genes was done using primers flanking the 3 ETS variable region. The size of the product depends on the number of repeats and corresponds to variant of 45S rRNA genes  (Pontvianne et al. , 2010). The QGIS software 2. 18 3  was used to georeference the A. thaliana populations on a map and to display associated pie charts illustrating the proportion of 45S rDNA genotypes identified for each population.
Genetic Analysis The sequences of 30 Pyrenean populations, the 3 out-group (Lant, Col and Sha) and 22 accessions available from the 1001 Genomes Project (11 in South France and 11 in North Spain the most closely to the Pyrenees mountains) were analyzed (Supplementary Tables  S1, S2 ). Tajima's D neutrality test was performed using DnaSP v5 software  (Librado and Rozas, 2009 ). Tajima's D was calculated using the concatenated sequence of the 5 CIII Prx loci. It allows to compare the total number of segregating sites (polymorphisms) to the average pair-wise differences between sequences  (Tajima, 1989). This test distinguishes DNA sequences evolving under selective pressures from those evolving neutrally. For Col, Sha and the 22 French/Spanish accessions from the 1001 Genomes Project, only one individual was used to minimize the impact of the frequencies of these new haplotypes but also because no information regarding the genetic diversity of these accessions was available.
Phylogenetic Analysis by Maximum Likelihood Method The analysis involved 375 nucleotide sequences (from 341 natural individuals from Pyrenean populations, 10 individuals from Lant, the 2 references Col and Sha and 22 accessions from the 1001 Genomes Project). Phylogenies can be inferred separately from each gene or directly from concatenated supergene with various advantages  (Gadagkar et al. , 2005; Kubatko and Degnan, 2007). The concatenation approach has been chosen, and the 5 CIII Prx sequences were assembled for a total of 4074 positions in the final dataset. After alignment, all positions containing gaps and missing data were eliminated. A phylogenetic tree was inferred with concatenated sequences by using the maximum likelihood method based on the Tamura-Nei model  (Tamura and Nei, 1993).
Statistical Analysis Most data analyses were performed with R software (version 3. 2. 3). Student's statistical tests for two samples were carried out in order to determine the temperature effects on the populations. In addition, Duncan's multiple range tests were performed with the package agricolae 4  to allow a better visualization of the different sets when they correspond to averages. Kruskal-Wallis and Wilcoxon non-parametric tests were carried out to compare groups. To investigate the underlying variation between population data (phenotypic), Principal Component Analysis (PCA) with multilevel function was used with the mixOmics package  (Lê Cao et al. , 2011)  available in CRAN 5. This combination of multilevel and multivariate methods allowed distinguishing variations between populations and between temperatures.