dataesr/scientific-paragraphs-categorization huggingface dataset:
Second, considering larger sample sizes (i. e. species with a large number of populations) should help reduce the effect of sampling noise and yield more robust estimates of parameters. Indeed, although mixed-effect models made it possible to unravel undetected patterns for all taxonomic groups, relationships were still uncertain for fish and mammals, the groups with the lowest sample sizes, precluding us from drawing any firm conclusions for these groups. Third, the ACH should be tested using data that reflect abundance (and presumably fitness) at the population level and not at particular size or age classes. For instance, for trees, we found strong signals for unexpected relationships under a number of settings. While these results can be explained by the sampling protocol or by a weak relationship between fitness and abundance  (McGill, 2012) , they may also be explained by the fact that inferences were based on the abundance of seedlings, which can rather be regarded as a demographic parameter (e.
For plants, although considering abundance at the population level could be more appropriate, a more robust assessment of the ACH would likely be obtained using a measure of biomass density, which has proved to be a good proxy of individual fitness (e. g. the average diameter at breast height within a given area;  Younginger et al. , 2017). Fourth, abundance data should be collected over the whole species range  (Dallas, Pironon, et al. , 2020; Knouft, 2018; Soberón et al. , 2018). This was not the case for any of the datasets considered in this study. We attempted to cope with this problem by estimating species envelopes based on IUCN range maps instead of the pool of populations for which abundance data were available but these range maps are not ideal  (Herkt et al. , 2017). Fifth, a robust estimation of species envelopes in both spaces is paramount since all inferences are based on this assessment.
| A multi-setting inference framework to test the ACH? We considered various settings to provide a comparative overview of their impact on the estimated distance-abundance relationship. For most taxonomic groups, mixed-effect models displayed a similar explanatory power, despite providing varying support for the ACH, thus suggesting that standard evaluation metrics (R² and Bayesian p-values in our case) can hardly discriminate between robust and bad settings. This similar performance arose because all models performed well in capturing among-species variations in the distance-abundance relationship (i. e. most of the variance was captured by the random effects), a pattern that was expected given the variations observed in previous studies (e. g. Dallas et al. , 2017). This does not mean that differences between models cannot emerge with other datasets (e.
g. the tree dataset) or with other measures of model performance [e. g. Akaike's information criterion (AIC), Bayesian information criterion (BIC)]. In theory, this would make it possible to run various models with distances computed under different settings and to select the model that best fits the data  (Bolker et al. , 2009). Using this approach, only settings supported by the theory and based on robust estimators (e. g. KDE instead of CH) should be considered. Otherwise, there is a risk of false inference because (a) multiple-testing could increase the probability of finding spurious relationships (type-I error rate) and (b) bad settings (or bad predictors; see e. g. Fourcade et al. , 2018)  can sometimes fit the data just as well or even better than more robust settings, for example because the data or the performance measures are inappropriate.
lated the sum of squared standardized Pearson residuals for both the observed data and a replicated dataset derived from model estimates. From this χ² discrepancy metric, we computed the Byesian p-value, which quantifies the proportion of posterior samples in which the distance of observed data to the model is greater than the distance of replicated data to the model. Values close to. 5 suggest a good model fit, whereas values close to 0 or 1 indicate a lack of fit. These Bayesian p-values provide information about the plausibility of a model given the data. To obtain information regarding the proportion of variance explained we computed the marginal (variance explained by fixed effects only) and the conditional (variance explained by fixed and random effects) R² of each model (Nakagawa & Schielzeth, 2013).
Violin plots showing the probability density of Spearman's correlation coefficients computed between distance and population density for mammals, fish, birds [both eBird and North American Breeding Bird Survey (BBS) data] and trees under the 12 different settings. The horizontal dashed lines point to the zero value. Note that the coefficients are expected to be positive when considering the distance to margins but negative for the two other distance metrics (Mahalanobis and Euclidean). For each setting and taxonomic group, we tested whether the average value differed from zero using Wilcoxon tests (ns: p >. 05. *p <. 05. **p <. 01. ***p <. 001. ****p <. 0001). CH, convex hull; KDE, kernel density estimator; MVE, minimum volume ellipsoid I G U R E 3 Barplots showing the proportion of expected (hatched areas), unexpected (crosshatched areas) and undetermined (blanked areas) relationships globally (a), in each space (b), for both envelopes in each space (c), for the three measures of distance in each space (d) and for each dataset (e).
DATA AVA I L A B I L I T Y S TAT E M E N T All the data used in this study have already been published [see  Dallas et al. (2017)  and  Osorio-Olvera et al. (2020)  for abundance data] or are freely available to download on the web (climatic data can be downloaded at https://www. world clim. org/data/world clim21. html; range maps for trees can be downloaded at https://www. fs. fed. us/ nrs/atlas/ littl efia/ and range maps for mammals, birds and fish can be downloaded at https://www. iucnr edlist. org/resou rces/spati al-datadownload). Nonetheless, to ensure reproducibility, all these data have been compiled into one unique folder available to download on Dryad (https://doi. org/10. 5061/dryad. bzkh1 8993). The R code, the JAGS model and the functions used to draw inferences from these data are available to download on GitHub (https://github.
F Violin plots showing the probability density of Spearman's correlation coefficients computed between distance and population density for mammals, fish, birds [both eBird and North American Breeding Bird Survey (BBS) data] and trees under the 12 different settings. The horizontal dashed lines point to the zero value. Note that the coefficients are expected to be positive when considering the distance to margins but negative for the two other distance metrics (Mahalanobis and Euclidean). For each setting and taxonomic group, we tested whether the average value differed from zero using Wilcoxon tests (ns: p >. 05. *p <. 05. **p <. 01. ***p <. 001. ****p <. 0001). CH, convex hull; KDE, kernel density estimator; MVE, minimum volume ellipsoid I G U R E 3 Barplots showing the proportion of expected (hatched areas), unexpected (crosshatched areas) and undetermined (blanked areas) relationships globally (a), in each space (b), for both envelopes in each space (c), for the three measures of distance in each space (d) and for each dataset (e).
Blood samples A total number of 11,972 horse sera samples, collected from 1994 to 2013, were analyzed for EIA using agar gel immunodiffusion test (AGID) as described by the World Organization of Animal Health (OIE) chapter 2. 5. 6. The Fig. 2  The evolutionary history was inferred by using the Maximum Likelihood method based on the Tamura-Nei model. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The analysis involved 24 EIAV full genome sequences. All positions containing gaps and missing data were eliminated. There were a total of 7269 positions in the final dataset. Evolutionary analyses were conducted in MEGA7 samples of horses analysed were from two different regions of Serbia southern Backa and Srem. Sera samples from 316 horses, held in the Province of Vojvodina in Serbia, were collected between 2013 and 2014.
Genomic DNA from collected spleen was extracted as previously described  [4]. DNA libraries were prepared using the Sure-Select XT Library preparation kit for Illumina Multiplexed sequencing Kit (Agilent Technologies, Santa Clara, CA, USA) as described previously  [9]  and subjected to multiplex deep sequencing on the MiSeq platform using the MiSeq Reagent Kit V2 (Illumina). Fastq format files from each viral genome were trimmed and paired reads were merged using the Geneious Prime sequencing data analysis software (Biomatters LTD, Auckland, New Zealand)  [15]. Contigs were obtained by alignment to the reference genome EIAV F2 (GenBank accession number: JX480631. 1). Phylogenetic reconstruction was carried out using the MEGA software version 7  [16]. Nucleotide and amino acid identities were calculated using MegAlign from DNA star software.
14, 15 re, we built on the assumption that the first early N1 component is generated via a direct cortico-cortical pathway between the stimulation and the recording site, mainly because we have previously shown that the peak delay of the N1 component linearly increases with distance, which suggests direct cortico-cortical pathways, assuming constant propagation speed. 15 In addition, we did not primarily consider indirect cortico-subcortico-cortical pathways that would rather be implicated in the later N2 component. 16 To characterize the direct connection underlying the N1 component, we applied the DCM approach to all significant responses available from the F-TRACT database that occurred during the first 80 ms following the stimulation (774 445 CCEPs). Based on local neural mass models embedded in a global model of effective connectivity,  17, 18  DCM implements an efficient Bayesian inversion to estimate the parameters of a generative neuronal model, including neuronal delays, which minimize the error prediction of the model.
Processing and selection of CCEPs The multicenter F-TRACT database (f-tract. eu) comprises CCEPs induced by low frequency bipolar stimulations. Extensive details on the anatomical and functional preprocessing steps used to compute the CCEPs are available in two previous studies. 14, 15 In brief, with regard to anatomy, electrode and contact positions extracted from the post-implantation Computed Tomography (CT) scan were first co-registered to the pre-implantation 3D T1 MRI of the patient and their tissue classification was determined according to a gray/white matter segmentation of the MRI. Then, spatial normalization with DARTEL -as implemented in SPM12 (www. fil. ion. ucl. ac. uk/spm) -projected the contacts of each patient in the MNI referential. In addition, in order to formally cluster, summarize, and present the extracted CCEPs characteristics at the group level, we computed parcel labels with respect to a series of neuroanatomical atlases for each contact.
The full neuroanatomical preparation of the data was performed with the IntranatElectrodes software. 19 e slow early components of CCEPs, which are only considered here, are thought to be less affected by the pathology than the late ones, even for stimulating or recording electrode contacts located in either the epileptogenic or the propagation zone. 20, 21 However, to minimize a potential bias in our findings due to epileptogenic processes, we removed from our analysis the data recorded by electrode contacts suspected to present a pathological activity. To do so in an unsupervised manner, we computed the interictal spiking rate of each contact from prestimulation period recordings using Delphos software (Detector of Electro Physiological Oscillations and Spikes)  22, 23. Given that no direct relationship is commonly accepted in SEEG between epileptogenicity and interictal spiking rate, we defined a rejection threshold on the interictal spiking rate from the results of a retrospective study.
24 In this study, the data from 100 patients with focal epilepsy and implanted with ECoG and SEEG electrodes were collected. The authors reviewed recorded seizures and epileptiform spikes and concluded that the majority of electrodes were located in non-epileptic tissue (82%) and that sites with pathological activity were clustered in a few electrode contacts. Based on these observations, we decided to keep a similar proportion (80%) of electrode contacts for analyses, which corresponded to a maximum interictal spiking rate of 8. 4 spikes per minute (the estimation and the thresholding of the spiking rates across contacts and CCEPs are detailed in Supplementary Fig. 2 ). Following this initial selection step, stimulation runs (series of maximum 40 single electrical pulses at low frequency, i.
Technical implementation The technical implementation made use of the generic code of DCM as provided in the official SPM software (SPM12, version r6732, fil. ion. ucl. ac. uk/spm). In particular, the definition of the ERP neural mass model was left unchanged (Supplementary Part 1 and Supplementary Fig. 1 ). Yet, some adjustments were necessary. First, the model was equipped with a numerical integration scheme based on Runge-Kutta techniques to generate neural time series using an accurate integration of the system of delay differential equations describing brain dynamics. 28 cond, the DCM priors of the neuronal model parameters were modified from their default values, to account for the specific context of DES, which induced faster evoked responses than usually observed from EEG during standard cognitive tasks.
Distance between contacts To assess the axonal conduction velocities, distances between stimulating and recording contacts were measured along the white matter fibers supposedly connecting them. Because the diffusion tensor imaging data were not available for the patients of F-TRACT, we used the ARCHI database built from 79 healthy subjects, aged between 18 and 40, at the Neurospin center. 34 We registered each patient anatomy on the ARCHI DTI atlas and computed the distance as the average path length of the database bundles running close (within 5 mm) to both contacts.
Mapping of axonal conduction velocities For each estimated CCEP and given the white matter path length between the stimulating and the recording contacts computed from the ARCHI database  34  (see Group level analysis), we produced two measures of conduction velocity, first based on N1 peak latency and second based on estimated axonal conduction delay. These velocity estimates were then extended at the group level using the same parcellation scheme as previously described. The distributions across parcels pairs of the Lausanne2008-60 parcellation scheme were computed for N1 peak latencies (median: 37. 0 ms, Fig. 5A ), axonal conduction delays (median: 10. 2 ms, Fig. 5B ), distances between contacts (median: 42. 9 mm), and for the conduction velocities derived from the N1 peak latencies (median: 1.
Mapping of synaptic time constants Whereas the axonal conduction delays were estimated for each parcel pair, the synaptic time constants were considered as intrinsic neuronal properties of each parcel and were computed from all the CCEPs having their recording contact in that parcel, whatever the stimulated parcel. We assigned the median value of the synaptic time constants to each parcel for which a minimum of 5 significant CCEPs were accurately fitted by the model. The number of data was sufficient to provide an estimation for almost all parcels in each hemisphere, using the functional HCP-MMP1  33  parcellation scheme (360 parcels, see Group level analysis). Considering the whole group of patients, the distributions exhibited globally shorter excitatory (median: 5. 8 ms, Fig. 6A ) than inhibitory (median: 7.
Moreover, within the younger group (<15 y. o. ), axonal delays were significantly increased (Table  1 ). This result indicates that the dynamics of CCEP are sufficiently sensitive to indirectly track brain maturation mechanisms, like the degree of myelination at the origin of axonal velocity changes. In this regard, the ambition of the present study was very limited and the simple comparison between two age ranges was used as an internal validation step. Another limitation of the present analysis is that we used structural connectivity pathways obtained in adults (the ARCHI database was built with subjects aged between 18 and 40) to estimate axonal conduction velocity at all ages. More sophisticated analyses should be considered in the future to evaluate better the interest of CCEPs for addressing developmental questions.
Effect of the contact tissue classification To maximise the biological plausibility of the model, we could have selected only CCEPs for which both stimulating and recording contacts were located in the gray matter, because when contacts are located in white matter, the direct stimulation of axons can make difficult to clearly distinguish between the stimulation and the recording sites. 39 However, we did not consider this option in the main report, because it would have dramatically decreased the number of available data as 39% of contacts of the F-TRACT database are located in white matter; we preferred to prioritise statistical robustness of reported effects. In the Supplementary Materials, we elaborate further on this issue and provide additional data on the influence of tissue classification (gray or white matter) of CCEPs contacts on the estimations of neuronal parameters.
It is important to underline that, here, although different neural models could have been suitably used,  46  we assumed a simple but robust 3-population, convolution based, neural mass model. 17 Our aim was to adopt a compromise between biological realism and computational efficiency. However, given the uniqueness of the F-TRACT database, such modelling issues as the development and comparison of neural model architectures will undoubtedly be the matter of dedicated studies in the future.
Because the observed scatter is consistently higher than the theoretically predicted scatter independent from the kind of XCH 4 or tropospheric partial column satellite data product and also independent from the reference data (AirCore or TCCON), it is very reasonable to assume that close to ground we underestimate the temporal collocation uncertainty covariance (S ∆t ) and the Similar to the TCCON comparisons, the AirCore study suggests a systematic bias between the TROPOMI and MUSICA IASI XCH 4 products of slightly above 1%, which seems to be mainly due to a positive bias of about 2% in the MUSICA IASI tropospheric data product. In the UTLS we observe no significant bias in the MUSICA IASI data.
Here we follow this approach and use the mean of the Jungfraujoch and Schauinsland CH 4 mixing ratio -whenever identified as a common signal -as a validation reference for the remote sensing data in South-western Germany and Northern Switzerland (indicated by the grey circle in Fig. 6 ). We assume that the signals obtained from this GAW data filtering are well representative for the tropospheric partial column averaged mixing ratios (surface -6 km a. s. l. ) and compare these data directly to different satellite products as a fully independent data set: we do not adjust the data to a common a priori data usage as in Sects. 3. 1 and Sect. 3. 2, because the in-situ data represent absolute measurements and do not depend on any a priori information. Furthermore, we do not adjust sensitivities as in Sect.
3. 2 (see Eq. (  7 )), which means that we validate here also the sensitivities of the products. Figure  12  shows the comparison with the different satellite products. Concerning the comparison with TROPOMI XCH 4 data we observe a very large systematic difference and very low values for R 2 (Fig. 12a  and d ). This indicates that the total column (XCH 4 ) signals are not a good proxy for lower tropospheric CH 4 signals, instead the former are strongly affected by signals in the UTLS, where CH 4 values are strongly affected by shifts of the tropopause height. For the MUSICA IASI tropospheric partial column averaged mixing ratio product (Fig. 12b  and e ) we observe a smaller mean difference than for the TROPOMI XCH 4 comparison, but at the same time an increased 1σ standard deviation (scatter) around the mean.
Global validity of the observed bias The AirCore comparison of Sect. 3. 2 indicates to a positive bias of about +1% for the combined tropospheric CH 4 data product and suggests no significant bias for the combined UTLS data product; however, the study is limited to a middle and high  northern latitude site, respectively. Nevertheless, in this section we argue that it is reasonable to assume similar biases also for other locations. According to Eq. (  4 ) a varying error in the a priori state together with a poor sensitivity (i. e. an averaging kernel being very different from an identity matrix) can cause a varying bias. If the error in the a priori state is latitudinal dependent the bias will also be latitudinal dependent. Furthermore, a systematic error source (like an error in a spectroscopic parameter) can have a variable impact on the remote sensing product, if the sensitivity is variable.
3  and 4 ), we expect -as for XCH 4 -a good latitudinal consistency, i. e. a bias at low and/or southern latitudes that is similar to the bias of about +1% as observed at middle and high northern latitudes. In summary, by reducing the systematic inconsistency between the TROPOMI and MUSICA IASI XCH 4 data we might be able to further improve the quality of the combined data product. Assuming that this inconsistency is mainly due to a positive bias in the lower/middle tropospheric MUSICA IASI CH 4 data (see Sect. 3. 2), follow-up studies can focus on removing the MUSICA IASI bias (for instance, by an approach as discussed in Sect. 3. 4 of  Kulawik et al. , 2021)  and in documenting the impact of the bias removal on the quality of the combined data product. However, such studies are currently compromised by the lack of high quality CH 4 profile data at low latitudes over land for the period after October 2017 (period with operating TROPOMI and IASI instruments).
We present a method for a synergetic use of TROPOMI total column and IASI vertical profile retrieval products. The method is based on simple linear algebra calculations, i. e. the execution of computationally expensive dedicated combined retrievals is not needed. Nevertheless, it approximates closely to a dedicated combined optimal estimation retrieval using the combined TROPOMI and IASI measurements (see Appendix A2). We apply the method to CH 4 data. By providing a compilation with all important equations we support the application of this method to other data products. We theoretically examine the sensitivity, vertical resolution, and errors of the individual TROPOMI and IASI products and of the combined product. The TROPOMI product consists of reliable total column CH 4 data, but does not offer information on the vertical distribution.
The IASI product offers some information on the vertical distribution and has best sensitivity in the UTLS region, but lacks sensitivity in the lower troposphere, i. e. it is not well sensitive to the total column. We show that the combined product combines both strengths: it is a reliable reference for the total column and also for the UTLS partial column. In addition, we found as a clear synergetic effect that the combined product is also a reliable reference for the tropospheric partial column. We generate the combined CH 4 product for the time period between November 2017 and December 2019 and compare the individual and combined products to reference data of TCCON, AirCore and GAW. TCCON data are available for the different latitudes in the northern and southern hemisphere and offer good references for XCH 4.