dataesr/scientific-paragraphs-categorization huggingface dataset:
8% continue the education. Similarly to CSBD, in HC group most participants ended their education at a university level (77. 2%), only 12. 1% continue their education and 9. 1% had only primary or secondary level of education. Using methods of frequentist statistics, we did not find any significant differences between CSBD and HC in the behavioral dataset. However, analysis using Bayesian methods produced some noteworthy results, particularly in two tasks, for which the results were clear, and four others, for which evidence was found concerning half of the parameters. Interactions between groups and conditions were significant only in IDT and showed higher motivation to No group effects were found for any parameter; BFexclude statistics support the null hypothesis for risk and ambiguity aversion indicators Significant main effects of the type of condition (both risk aversion indicator and ambiguity aversion indicator were higher in trials with a win) Ambiguity aversion indicator  No statistically significant group effect, BF provide evidence for H0 concerning mean reaction time No main effects of condition and no significant interaction between group and condition in accuracy analyses; Bayes Factors support H0 (no main effect in 2 types of reward and 3 types of probability) Significant effect of magnitude in RT analysis (trials with large rewards were associated with reduced RTs relative to trials with small rewards) and significant effect of probability (reaction time decreased linearly with increasing probability) In RT analyses the only significant interaction was found between two types of reward and two groups (reaction times of CSBD patients were shorter in erotic reward trials, while those of HCs were shorter in monetary reward trials).
CONCLUSIONS The main aim of the study was to identify deficits in affective and cognitive systems among CSBD patients. To this end we used a dataset consisting of a set of questionnaires and eight computer-based behavioral tasks; we analyzed the data with the help of frequentist and Bayesian statistics. The dataset is unique in that it characterizes the CSBD sample utilizing a wide array of tasks, modified to include monetary and/or erotic context. Questionnaire data revealed significant group differences in punishment sensitivity, state and trait of anxiety, level of depression, and intensity of compulsivity and impulsivity symptoms. Compared to HC, CSBD patients scored significantly higher on all sub-scales. Behavioral data was not that clear. Frequentist statistical tests revealed no significant differences between the examined groups in all analyzed parameters, but Bayesian methods provided evidence of the absence of group differences only in half of the parameters across all the tasks (for details, see Table  5 , BFexclude and BFinclude values).
DISCUSSION The results of our analysis show clearly that it is considerably easier to find significant differences between CSBD patients and healthy controls in questionnaire data than in behavioral datasets. This may be due to the fact that patients have low self-esteem and therefore misjudge their own traits. This effect was noticed also in other studies, e. g. Antons & Brand, 2018; Reid et al. (2010 Reid et al. ( ), (2011a,b),b). It is also possible that questionnaires and tasks do not always measure overlapping constructs, e. g. , it is possible that none of the tasks measured anxiety or depression. Also, task-based vs. questionnaire-based measures are often uncorrelated, even when overlapping constructs are measured  (Pedroni et al. , 2017; Wennerhold, Friese, & Vazire, 2020). On the other hand, patterns of group differences in questionnaires were created on the basis of our clinical experience and previously published results  (Gola et al.
, 2017; Mechelmans, et al. , 2014; Odlaug, et al. , 2013; Reid, Carpenter, Spackman, & Willes, 2008; Schmidt et al. , 2017; Seok & Sohn, 2015; Voon et al. , 2014; Wordecha et al. , 2018) , while the collection of behavioral data was an exploratory study, designed to develop fresh insights in a hitherto little-studied field. The data collected from behavioral tasks were more mixed. Results from two tasks (Facial Discrimination Task (FDT) and Risk and Ambiguity Task (RAT)) (see Tables  5  and 6 ) could be clearly and unequivocally taken as evidence of the absence of differences between CSBD patients and HC in the domain of Negative Valence Systems, specifically in the processing of negative social stimuli, and an aversion to risk and ambiguous choice. The Facial Discrimination Task has never been used in a study of CSBD patients before.
presented here is a product derived from quality-controlled in situ observations. Annual mean gridded nutrient fields for the period 1981-2017 and its sub-periods 1981-2004 and 2005-2017  on a horizontal 1/4 • × 1/4 • grid have been produced. The biogeochemical climatology is built on 19 depth levels and for the dissolved inorganic nutrients nitrate, phosphate and orthosilicate. To generate smooth and homogeneous interpolated fields, the method of the variational inverse model (VIM) was applied. A sensitivity analysis was carried out to assess the comparability of the data product with the observational data. The BGC-WMED was then compared to other available data products, i. e. , the MedBFM biogeochemical reanalysis of the Mediterranean Sea and the World Ocean Atlas 2018 (WOA18) (its biogeochemical part).
Data distribution The data distribution per year is shown in Fig. 2a. Most observations were collected between 1981 and 1995 and between 2004 and 2017, with a marked gap between 1997 and 2003. The measurement distribution differs from month to month (Fig. 2b ) and tends to be biased toward the warm season. Very few measurements were made during December-January-February, and June and July are the months with the highest number of available observations (> 7000). Consequently, the climatological product may be considered more representative of spring and summer conditions. Figure  3a  shows the regional distribution of nutrient measurements, while Fig. 3b  indicates the number of observations found in each depth range around the standard levels chosen for the vertical resolution of the climatology.
Data quality check Data were gathered from different data sources and different analytical methods (Table  A1. ); thus before merging them, observations were first checked for duplicates (the number of profiles listed in Table  2  refers to all data after removing duplicate measurements). The criterion to detect and remove duplicates is simple: observations collected during the same cruises extracted from the different sources were removed. Since profiles were measured during specific cruises (identified with a unique identification code) at specific times, data from duplicate cruises were removed. Then, data were converted to a common format (similar to the. csv CNR_DIN_WMED_20042017 data product;  Bel-gacem et al. , 2019). This recently released product contains measurements covering the WMED from 2004 to 2017.
The data of the CNR_DIN_WMED_20042017 product have undergone a rigorous quality control process that was focused on a primary quality check of the precision of the data and a secondary quality control targeting the accuracy of the data; details about the adjustments and the applied corrections can be found in  Belgacem et al. (2020). As detailed in Table  2 , we combined observations from reliable sources (covering the time period 1981-2017), which were quality controlled according to international recommendations before being published  (Maillard et al. , 2007; SeaDataNet Group, 2010). However, these historical data collections coming from sources different from the CNR_DIN_WMED_20042017 were subjected to a quality check before merging them to eliminate the effect of any aberrant observation.
The spatial correlation length scale (L c ) L c indicates the distance over which an observation affects its neighbors. The correlation length can be set by the user or computed using the data distribution. For the BGC-WMED, this parameter was optimized for the whole time span and at each depth layer. The correlation length has been evaluated by fitting the empirical kernel function to the correlation between data isotropy and homogeneity in correlations. The quality of the fit is dependent on the number of observations  (Troupin et al. , 2018). The analytical covariance model used in the fit is derived for an infinite domain  (Barth et al. , 2014). To assess the quality of the fit, the data covariance and the fitted covariance are plotted against the distance between data points (Fig. 5 ).
Comparisons are carried out between horizontal maps , as well as along a vertical longitudinal transect. In addition, following  Reale et al. (2020) , the first 150 m has been evaluated (Figs. 14  and 15 ) since this is a depth level with a representative number of in situ observations in all three products. The evaluation is based on the estimation of the horizontal average, the BGC-WMED, the MedBFM biogeochemical reanalysis and the WOA18 climatology by subregion. i. e. , a spatial subdivision made according to  Manca et al. (2004). Products have a different grid resolution; thus to compare them and combine variables on a compatible grid, the new BGC-WMED climatological data product (at 0. The regridding is computed at all depth levels of the different products using nearest-neighbor interpolation.
Conclusions In this study, we investigated spatial variability in the inorganic nutrients in the WMED and presented a climatological field reconstruction of nitrate, phosphate and silicate using an important collection dataset spanning 1981 to 2017. The new BGC-WMED product is generated on 19 vertical levels on a grid with a 1/4 • spatial resolution. The new product represents very well the spatial patterns of nutrient distribution because of its higher spatial and temporal data coverage compared to the existing climatological   (1981-2004, in violet) and after (2005-2017 , in green) the WMT. products (see Table  1 ); it contributes to the understanding of the spatial variability in nutrients in the WMED. The novelty of the present work is the use of the variational analysis that takes into consideration physical and geographical boundaries, topography, and the resulting estimate of the associated error field.
Comparison with previously reported studies indicates that the BGC-WMED reproduces common features and agrees with previous records. The reference products of the WOA18 and MedBFM biogeochemical reanalysis tend to underestimate nutrient distribution in the region with respect to the new product. The new product captures the strong east-west nutrient gradient and vertical features. The results obtained do not include seasonal or annual analysis fields. However, the aggregated dataset here does show improvements in describing the spatial distribution of inorganic nutrients in the WMED. We acknowledge that computing a climatological mean over a time period is not enough to estimate and detect the trend driven by the climate shift WMT change. However, comparing climatologies based on the two time periods, 1981-2004 (pre-WMT) and 2005-2017 (post-WMT), has already produced important results.
Observational data The observational data used in this study are described in Sects. S1 and S2 in the Supplement and 5. 3 for heat, drought and the Fire Weather Index, respectively, including justifications for including or excluding certain datasets for certain research questions. For the global mean surface temperature (GMST) we use GISTEMP (Goddard Institute for Space Studies Surface Temperature Analysis) surface temperature  (Hansen et al. , 2010).
Finally, we used the coupled IPSL-CM6A-LR (Institut Pierre-Simon Laplace Climate Model) low-resolution CMIP6 ensemble. The GFDL-CM3 and MPI-ESM models that did not have daily data were not used for the extremeheat analysis. A list of these climate models and their properties is given in Table  1. For the FWI analysis, which requires daily data of relative humidity (RH), temperature, precipitation and wind speed, the list of models used is shortened to CanESM2, CESM1-CAM5, EC-Earth, IPSL-CM6A-LR and weather@home.
Therefore, in practice, an approximation with a GEV is not entirely unsuitable for temperature, but caution should be exercised. For the FWI and MSR, the order of operations does make a clear difference. Indeed, we find that the whole distribution is not described well by a GEV for one climate model used (CanESM2). For that model we take block maxima over five ensemble member blocks, effectively looking only at the most extreme events. For this part of the distribution the GEV fit agrees with the data points in the return time plot, as expected from taking block maxima. We evaluate all climate models on the fitting parameters by determining whether the model-derived parameters fall within the uncertainty range of observation-derived parameters. We allow for a mean bias correction; i. e. we only check the scale and shape parameters σ and ξ.
Observational analysis: return time and trend For the observational analysis we use the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5) dataset for 1979-January 2020  (Hersbach et al. , 2019). This reanalysis dataset is heavily constrained by observations and thus provides one of the best estimates of the actual state of the atmosphere for all the variables needed to compute the FWI over the study area. Other reanalyses did not yet include the full 2019/20 event at the time of the analysis. Figure  3  shows the time series of the highest 7 d mean FWI averaged over the study area. Both for the FWI7x-SM and MSR-SM the event is the highest over the 1979-2020 time period. Note that for the MSR-SM, the value is considerably more extreme than for the FWI7x-SM.
Note that all models severely underestimate the increased risk compared to ERA5, which has a lower bound of the PR of a factor of 4 relative to 1920 (extrapolated), above the upper end of the model average. Note that the ERA5 value is probably biased high, as the positive contribution of trend towards a drier climate over 1979-2019 is not present over 1900-2019; see Sects. S2 and 5. 6. For a future climate of 2 • C warming above pre-industrial levels we find that such events become about 8 times more likely in the models, with a lower bound of about 4 times more likely. Note that the estimate of future climate is only based on two climate models, CanESM2 and EC-Earth, due to the absence of future data for the others. For the MSR-SM the models on average show about a doubling of probability for the present climate relative to the early 20th century (Fig.
Ever more detailed attribution statements are desired by society, and scientific progress in the modelling of extreme events is therefore needed. Data availability. Almost all data used in the analyses can be downloaded from the KNMI Climate Explorer at https://climexp. knmi. nl/bushfires_timeseries. cgi (last access: 7 March 2021)  (van Oldenborgh, 2021). This also contains the scripts that we used to do all the extreme value fits. These can also be performed using the graphical user interface of the Climate Explorer. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/nhess-21-941-2021-supplement. Author contributions. GJvO and FELO started the attribution study, and GJvO led it. FELO wrote and improved much of the text. FK performed the calculations with the FWI and wrote much of the analysis of it.
SL provided local information and helped write the article. NJL did the initial version of the heat wave analysis. FL provided the large ensemble data, analysed it and helped revise the article. KRS supported the statistical analyses. KH, SL, DW and SS provided the weather@home data. JA, RPS and MKvA helped with the event definition and wrote the "Vulnerability and exposure" section. SYP did the initial drought analysis, and RV provided the IPSL data and helped write the article. Competing interests. The authors declare that they have no conflict of interest. computing time to https://www. climateprediction. net/ (last access: 7 March 2021) and weather@home. Financial support. Geert Jan van Oldenborgh was supported by the ERA4CS projects SERV_FORFIRE and EUPHEME (grant no. 690462). The contribution of Folmer Krikken was supported by the Belmont Forum project PREREAL  (grant no.
Acknowledgements. We thank  Pandora Hope ,  Andrew Dowdy  and  Mitchell Black  of the  Bureau of Meteorology and Sarah Perkins-Kirkpatrick of the University of New South Wales  for substantial contributions to the article. We acknowledge the use of data and imagery from  LANCE FIRMS  operated by  NASA's Earth Science Data and Information System (ESDIS)  with funding provided by  NASA Headquarters. We would like to thank all the volunteers who have donated their.
shinyapps. io/optimia/. The Web application allowed model simulation outputs to be adapted to other LBMs identified by Vietnamese veterinary services as at high risk of H7N9 introduction but which were not described in this paper for clarity reasons. Such Web application applied in LBMs could help in reducing zoonotic and pandemic risks posed by similar emerging AIV. Data accessibility. A Web application was developed using the shiny package and made publicly available with a graphical user interface in html format at: https://envt-inra. shinyapps. io/optimia/. The Web application allowed to provide model simulation outputs adapted to other LBMs identified by Vietnamese veterinary services as at high risk of H7N9 introduction but which were not described in this paper for clarity reasons. The data are provided in the electronic supplementary material.
Acknowledgements. The authors are very grateful to all the LBM managers, staff and traders who responded to the questionnaire. They also gratefully acknowledge representatives from District Veterinary Station (DVS) and  Sub-Department of Animal Health (SDAH)  for facilitating the interviews and providing detailed information on the surveillance and sampling strategies of LP H7N9 in Vietnam. They are also very grateful to the  Epidec group (ENVT-INRAE, France)  for providing advice on the Web application and to  Guillaume Fournié  ( RVC, UK ) and  Benjamin Roche  ( IRD, France ) for providing useful comments on this research study.
Evidence before this study To identify previous research on lesion location and surgical outcome in FCD, we searched PubMed using the terms "focal cortical dysplasia" OR "FCD", AND "location" OR "lobe", AND "outcome". The search was for full manuscripts from database inception until 24 November 2020 without language restrictions and yielded 307 results. Only five papers had sample sizes of over 200 patients with focal cortical dysplasia. All five studies found that temporal resections are associated with better postsurgical seizure freedom. However, four of these studies limit lesion localisation to temporal and extra-temporal, with none describing lesion location beyond coarse lobar categorisations.
Participants Patients over age 3 were included if they had a 3D preoperative T1-weighted MRI brain scan (1•5T or 3T) and a radiological diagnosis of FCD or were MRI-negative with histopathological confirmation of FCD. Participants were excluded if they had previous surgeries, large structural abnormalities in addition to the FCD or T1 scans with gadolinium-enhancement. Centres and patients were given pseudo-anonymised ID codes. 3. Site-level data collection and post-processing MRI data collection and post-processing T1-weighted MRI scans were collected at the 20 participating centres for all participants and cortical surfaces were reconstructed using FreeSurfer. 16 Detailed protocols for structural MRI post-processing were adapted from openly available ENIGMA-epilepsy protocols. 9 FCD lesion masking FCD lesions were delineated on the T1-weighted MRI scans at each site according to our lesion masking protocol.
Participant demographics The following data were collected for all patients: age at preoperative scan, sex, age of epilepsy onset, duration of epilepsy (time from age of epilepsy onset to age at preoperative scan), ever reported MRI-negative and histopathological diagnosis (ILAE three-tiered classification system), 2 seizure-freedom (Engel class I) and follow-up time in operated patients. Participant demographic information and lesion masks were shared with the study coordinators for multi-centre analysis. At the point of retrieval by the study coordinators, all participant data were deidentified, as keys linking pseudo-anonymised IDs to patient identifiable information were stored securely by individual sites.
Factors associated with lesion distribution We trained logistic regression models to test for associations between lesion location and clinical data (Supplementary Figure  1 ). The model was fitted to assess which preoperative factors are associated with the presence or absence of lesions at a particular vertex (a point on the cortical surface). The following preoperative factors were included: age of epilepsy onset, sex, ever reported MRI-negative, duration of epilepsy and lesion hemisphere. Regression coefficients were tested for significance against those calculated on 1000 randomly permuted cohorts. Factors were deemed significant if their coefficient was less than 2•5% or greater than 97•5% of the coefficients from permuted cohorts. Approximately 5% of vertices would be significant by chance.
Participant demographics Data was collected from 580 FCD patients. Demographic information is available in Table  1. Histopathological diagnosis was available in 380 patients (66%, Table  2 ). Post-surgical outcome data was available in 319 patients (75% of operated patients); 69% were seizure free (Engel class 1) at last follow-up (median follow up = 2 years). Seizure freedom rates across histopathological subtypes are presented in Table  2. Although FCD Type I had a lower mean seizure freedom (63%) than FCD Type IIA (70%) and FCD Type IIB (69%), there was no significant difference in outcome according to histopathological subtype.
3, 4 Additionally, these maps extend previous understanding, demonstrating "hot-spots" in the superior frontal sulcus driven by FCD Type IIB, and a cluster of temporal pole lesions in all FCD subtypes. Surface and volumetric atlases of FCD lesion locations offer a resource for guiding the clinical search for FCDs and research into the underlying neurobiology of malformations of cortical development. Our unique dataset linking individual clinical and demographic data with lesion location uncovered relationships between age of epilepsy onset, duration of epilepsy and lesion location. Lesions in primary sensory, motor and visual areas were associated with younger epilepsy onset, while lesions in higher-order association cortex were associated with later epilepsy onset. This relationship may reflect differing developmental trajectories of these areas,  22, 23  with seizures initiating as a result of the development of particular cortical properties.
26 esions in visual, motor and premotor areas were associated with low likelihoods of seizure freedom. The most likely reason is that these lesions were in eloquent cortex or close to white matter tracts such as the optic radiation or corticospinal tracts, and as such neurosurgeons may have been intentionally cautious with their resections to minimise the risk of deficits such as hemiparesis or hemianopia. 27 Additionally, the medial occipital and cingulate areas are located on the medial wall of the cortex, restricting ease of surgical access. Both factors could contribute to higher rates of incomplete resections of the dysplastic tissue known to be associated with worse postsurgical outcome. 11 ne strength of our study is the inclusion of operated and non-operated patients. This helped to minimise ascertainment bias in the dataset, as the lesional distribution of non-operated patients captured more lesions in eloquent cortex (e.