dataesr/scientific-paragraphs-categorization huggingface dataset:
We use the emcee package (Foreman-Mackey et al. 2013), a Python implementation of the Markov Chain Monte Carlo method, to explore the posterior probability distributions of our model parameters in order to derive the best-fitting model. The models are initiated near the optimal solutions indicated by previous test model iterations. We use 5000 steps, with the first 3500 being discarded based on the estimated auto-correlation lengths. For the runs we use 200 walkers and we verify upon completion that all chains have converged. Three distinct models were implemented in order to investigate the nature of the over-brightness in the south-east of the disc and its effect on model fitting. The external south-west compact source is marginally resolved and so treated as an azimuthally symmetric two dimensional Gaussian source and is included in all models.
CO Non-Detection The spectral setup of the ALMA observations was designed to allow a search for CO gas produced in collisions of planetesimals that are rich in volatiles via the J=3-2 emission line. After subtracting the continuum emission, visual inspection revealed no clear signal in both the dirty cube and a moment-0 map produced by summing pixels across the channels in the velocity range where gas is expected. To enhance the signal of potential CO in the system we also employed the spectro-spatial filtering approach as described in  Matrà et al. (2015)  under the assumption that any CO present would be co-located with the dust. In this method pixels are spectrally shifted within the data cube to account for the expected radial velocities from the Keplerian motion within the disc, here we assume a stellar mass for Fomalhaut C of 0.
The minimum grain size,  min , is an unknown here in the regime of stellar wind dominated grain removal and we do not have enough short wavelength data to retrieve an estimate from the flux density distribution as in  Matrà et al. (2019). The minimum grain size for TWA 7 was found to be 0. 1 µm and using this number as a fiducial value for Fomalhaut C we get   min = 6×10 -5  ⊕ Myr -1. For comparison the value is 3 × 10 -3  ⊕ Myr -1 for TWA 7. The CO+CO 2 mass (which photodissociates in time  phd ) is estimated as 𝑀 CO+CO 2 = 𝑡 phd 𝑓 𝐶𝑂+𝐶𝑂 2 1 -𝑓 𝐶𝑂+𝐶𝑂 2 𝑀 𝐷 min , (1) where  + 2 is the fraction of planetesimal mass in CO and CO 2 ice  (Matrà et al. 2017). Thus, if we assume the same CO excitation and lifetime for Fomalhaut C as for TWA 7, and that the observed CO flux is proportional to the CO mass, then it is only the difference in mass loss rates and planetesimal CO+CO 2 fraction that changes the observed CO flux.
The results are summarised in Table  2  and are highly consistent between the two PSFs. We find that a small pointing offset is favoured, but within the 1 absolute pointing uncertainty of 1. 12". The radius of the disc is not found to be well constrained, but are consistent with the resolved ALMA value. Smaller radii still fit the data well, implying the disc is either unresolved or not substantially resolved with Herschel; radii larger than ∼ 5 -6" do not fit the data well. Thus we conclude that there is not sufficient evidence to suggest that the grains probed by Herschel lie at significantly larger radii than the grains probed by ALMA. A flat background flux of ∼0. 05 mJy/arcsec -2 is fit by the model, but is not interpreted as significant evidence of a halo of small grains as large amounts of the Herschel map not associated with Fomalhaut C share this nonzero flux.
However, the feature was not detected anywhere in the 2018 field, showing it to be a spurious artifact in the 2014 data. No circumstellar nebulosity is detected in the STIS data with a 3 limited surface brightness of 3. 39 Jy arcseconds - 2  at 3" radius from the star (using a zero point of 1 DN/s/pixel = 4. 55x10 -7 Jy). The 7 ALMA compact source south-west of Fomalhaut C is detected in the 2014 observation (Figure  8  left). It may also be detected in the 2018 observation (Figure  8  middle), but its location is obscured by the telescope's diffraction spikes. The ALMA source is significantly detected in a combined image of all HST observations (Figure  8  right), showing that it is indeed real. These detections together demonstrate that the source does not share Fomalhaut C's proper motion and is a background object.
ACKNOWLEDGEMENTS PFCC is supported by the  University of Warwick. GMK is supported by the  Royal Society as a Royal Society University  Research Fellow. GD acknowledges support from  NSF   AST-1518332  and  NASA NNX15AC89G  and  NNX15AD95G/NExSS. CJC acknowledges support the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Sklodowska-Curie  grant agreement No  823823  (DUSTBUSTERS) and  STFC  Consolidated Grant  ST/S000623/1. This paper makes use of the following ALMA data: ADS/JAO. ALMA# 2017. 1. 00561. S. ALMA is a partnership of  ESO  (representing its member states),  NSF (USA)  and  NINS (Japan) , together with  NRC (Canada) ,  MOST  and  ASIAA (Taiwan) , and  KASI (Republic of Korea) , in cooperation with the  Republic of Chile. The Joint ALMA Observatory is operated by  ESO ,  AUI/NRAO  and  NAOJ.
DATA AVAILABILITY The data underlying this article are available in http://almascience. nrao. edu/aq/, https://archive. stsci. edu/index. html, and http://archive. eso. org/cms. html and can be accessed with ALMA project ID: 2017. 1. 00561. S; HST proposal IDs: HST-GO-13725 and HST-GO-15172; and ESO program IDs: 096. C-0388 and 097. C-0394.
Geodetic Data We map the crustal deformation over the Hengill area by using a combination of GNSS and InSAR methods (GNSS and InSAR Data). The GNSS and InSAR data sets are complimentary: while the InSAR data allow for excellent ground coverage, the campaign GNSS data capture better 3D motion and the continuous GNSS network (cGNSS), with six stations in the Hengill area (Figure  1 ), helps constrain the start and end times of the 2017-2018 uplift episode. To simplify our study of the transient ground deformation between 2017 and 2018, we calculate the change in deformation relative to previous years. In practical terms, this means we subtract the pre-inflation velocity field from the data covering the inflation period. This approach allows us to cancel out what we assume to be steady background deformation (i.
GNSS We analyse of a total of 62 GNSS time series, composed of data from seven continuous sites (HVER, GFEL, OLKE, HLID, NVEL, HUSM and SELF; see detrended time series in Figure  3 ) and 55 campaign sites located within approximately 20 km of the main study area (Figure  1 ). Campaign GNSS data are typically acquired annually in early summer. Each benchmark is usually measured for at least 48 h, in order to obtain accurate position; however, a subset of data from  2009, 2012, 2016, and 2018  were measured for a shorter time. The campaign and continuous GNSS data between 2009 and 2019 are processed using the GAMIT/ GLOBK suite of programs  (version 10. 6, Herring et al. , 2015)  to estimate daily positions in the ITRF14 reference frame  (Altamimi et al. , 2016). We solved for satellite orbits and Earth rotation parameters, estimating atmospheric zenith delay every 2 hours.
We then delete outlier data, defined as points with position offset by more than 15 mm in the horizontal components (for cGNSS 10 mm) and more than 20 mm in the vertical components relative to the other positions recorded within the same campaign. We also remove data points with standard deviation above 10 and 30 mm in the horizontal and vertical components, respectively. We further remove data that are clearly affected by post-seismic motion following the 2008 SISZ earthquakes (Figure  1 ) or local transient motions in the Húsmúli area (e. g. 2011 uplift;  Juncu et al. , 2020) , whose characterizations are outside the scope of this study. For cGNSS stations we estimate and remove seasonal motion (annual and biannual) in each component. This is done using a least-square fit of cyclic components  (Blewitt and Lavallée, 2002) , using time series between 2012 and 2017, or shorter if the data of a station do not cover this time span (e.
g. NVEL, Figure  3 ). We use the Tsview module from the GGMatlab toolbox (version 2. 02;  Herring, 2003; Herring et al. , 2015)  to estimate the displacements between 2017 and 2018 for the horizontal components of each of our cleaned campaign and continuous GNSS time series. We found that the vertical component was too noisy to allow for reliable displacement estimates. For stations with more than 30 data points, such as cGNSS and most frequently observed campaign stations, the displacement uncertainty was estimated with the Real Sigma option  (Herring, 2003) ; otherwise, the uncertainty estimate assumes uncorrelated (white) noise. The velocity of each station is calculated, allowing for an offset (i. e. , the displacement we are after) in the time series at the time of the uplift. Our estimated horizontal displacements range from ∼0-15 mm (Supplementary Material) and show an overall outward radial pattern from the Ölkelduháls region (Figure  4 ).
InSAR Data We use all available Sentinel-1 (S1; C-band sensor) and TerraSAR-X (TSX; X-band sensor) data over the Hengill area, which consist of three different tracks from the S1 constellation and data from a single track of the TSX satellite (Table  1 ). Snow-coverage limits useful images to the summer months, from early June until late September of each year. The relatively short and small-amplitude uplift signal of interest (Figure  5 ) is partly overprinted by other ongoing processes, such as plate motion and deformation in the geothermal production areas. We thus perform a two-fold time series analysis for each of the tracks. First, we estimate the mean velocity between 2015 and 2017 (time period prior to the uplift episode). Second, we estimate the mean velocity between 2017 and 2018 using the same methodology as the analysis for 2015-2017.
Sentinel-1 Our S1 InSAR data set is generated from images acquired in interferometric wide-swath mode provided by the European Space Agency (ESA), from June 2015 to September 2018. Three tracks, two ascending (T16, T118) and one descending (T155), cover our area of interest. Interferograms were generated using the InSAR Scientific Computing Environment software (ISCE, version 2. 2. 0;  Rosen et al. , 2012) , all referred to a single image in 2017 for each track (Table  1 ). The topsApp function of the software initially uses the single look complex bursts from the Terrain Observation with Progressive Scanning SAR (TOPSAR) acquisition of the S1 constellation. The bursts are merged before coarse and fine co-registration and formation of filtered wrapped and unwrapped interferograms. The interferograms generated for each track are then divided in two time spans for the generation of our time series; 2015-2017 and 2017-2018, and cropped to our common area of interest.
TerraSAR-X To complement our ground deformation analysis of this uplift episode, we use a third independent data set which is our longterm time series  (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018) (2019)  of SAR images from the high resolution X-band TSX satellite from the German Aerospace Center (DLR). TSX images from the ascending track T41 between 2015 and 2018 are limited to 2-4 StripMap SAR acquisitions per year. We adopt a different methodology than described in InSAR Data to exploit the higher signal-to-noise ratio scatterers from the reduced number of images at our disposal for our time series analysis. We thus use the Small-Baseline time series analysis available from the Stanford Method for Persistent Scatterers software  (StaMPS, version 3. 3b1; Hooper, 2008; Hooper et al.
, 2012)  to generate short-temporal baseline interferograms. In this methodology, interferograms, referenced to a single image (September 2017, Table  1 ), are generated from single-look complex images generated using the DORIS software  (Kampes et al. , 2003). The small-baseline processing then generates interferograms which minimize the perpendicular baseline and the time between acquisitions. This method maximizes the correlation in the interferogram stack and therefore allows to find more permament scatterers. They are used for time series estimations on this track between 2015 and 2018. The StaMPS processing further allows estimation and removal of errors, for example orbital ramps and spatially correlated look angle errors to limit the propagation of these errors to our final InSAR time series.
Physically, a volume cannot simultaneously inflate and deflate. It is thus necessary to isolate the transient displacements associated with the uplift in our geodetic data sets. In our prior approach (Geodetic Data), the data used to correct for background deformation contain longer term subsidence signal. If we assume that the subsidence was not on-going during the period of uplift, then we need to evaluate the time-span of the uplift in our 2017-2018 data (using cGNSS time series, see Uplift Time Span). The estimated time-span of uplift is then used to correct our geodetic displacements for the time-span in which the area did not subside in 2017-2018 (5 months; from approximately November 2017 until March 2018). We calculate the ground motion using the source of contraction (model of  Juncu et al.
e. prior linear trend, uplift motions and subsequent linear trend) to the north, east and vertical components of the selected and detrended cGNSS data. We use a custom optimization algorithm that includes the individual uncertainties of our data points in our likelihood function which is then used by the algorithm to retrieve the optimal breakpoints of the piecewise function. Additional details on the method are in the Supplementary Material. The vertical component has larger uncertainties and is more affected by variable seasonal signals than the horizontal motions observed by GNSS. Therefore, we estimate the time interval of uplift using only the horizontal displacements. The optimisation results show that the uplift lasted for 5 months.
The algorithm subsamples adequately the steep gradient between the main broad inflation signal and local subsidence signal in Húsmúli; however, we note that data points are also increased in areas of higher noise (e. g. topography-related noise in eastern Hengill). As this noise is different between the four tracks used for inversion (i. e. T155, T16, T118 and T41), we minimize any potential biases introduced in our inversion. To quantify the limits of spatial correlation and subsequent weight of our individual SAR tracks, we use the bootstrapping approach of  Bekaert et al. (2015)  to estimate the nugget, range and sill parameters, which are then used to form the variance-covariance matrix. The posterior probability density function (PDF) is then formulated using the residuals of InSAR and GNSS (difference between the predicted and observed displacements) and their respective weights.
DATA AVAILABILITY STATEMENT Initial InSAR mean LOS velocities grids and GNSS displacements are detailed in Supplementary Material and are available on request to the authors. Original Sentinel-1 images are available on the Copernicus and ESA platform. Seismic data sets used in the study can be requested from the Icelandic Meteorological Office or downloaded from https://skjalftalisa. vedur. is. Borehole data sets used in the study can be requested from Reykjavík Energy.
ACKNOWLEDGMENTS We are very thankful to  Thorsteinn Jónsson ,  Sveinbjörn Steinthórsson  and students for assistance in the GNSS data collection over the years. We acknowledge  UNAVCO  and  National Land Survey of Iceland  for sharing their GNSS equipments. We are grateful to the  National Land Survey of Iceland and Iceland GeoSurvey  for sharing of their GNSS campaign data sets. We extend the gratitude to  Benedikt G. Ófeigsson and  Icelandic Meteorological Office  for the maintenance of continuous GNSS stations. We are thankful to the  Copernicus and European Space Agency  for the free access data sets of Sentinel-1. We fully acknowledge the satellite data sets provided by the  German Aerospace Center  used in the study: the TanDEM-X DEM (project  IDEM_GEOL0123 ) and TerraSAR-X images provided through the  Icelandic Volcanoes Supersite  project (http://geo-gsnl.
4% had myeloproliferative neoplasia, and 35. 7% were candidates for an allograft for acute leukemia. 94% were having their rst transplant. The ethical committee Sud-Est III (IRB 2017-026 B) approved the study. Informed written consent was obtained from each participant. The data that support the ndings of this study are openly available in gshare at https://doi. org/10. 6084/m9. gshare. 12382916. v2 Procedure. All participants were informed of the study during the pre-graft interview and read an information note. They had 15 days to decide whether they would participate or not. Then, they lled out an informed consent form and completed a rst questionnaire assessing mental health (i. e. anxiety, depression and happiness), psychological dispositions (i. e. optimism, extraversion, experiential avoidance, acceptance, and dispositional mindfulness) and sociodemographic variables, 20 days (mean = 19.
6) before their hospitalization for an allograft (Time 0). All scales used were previously validated measures. A second questionnaire was given at the start of hospitalization and had to be completed during the rst week from the day after the allograft intervention (Time 1). This second questionnaire evaluated ve dimensions of post-traumatic growth to constitute the measure of actual growth. Finally, participants were invited to complete a third questionnaire at ve months after their entrance to hospital during a follow-up date (Time 2). This third questionnaire measured the same ve domains of growth that constitute the measure of actual growth, a scale of perceived growth (PTGI) and a measure of Post-Traumatic Stress Disorder (PTSD). The relevant medical data were extracted from the ProMISe (Project Manager Internet Server).
Statistical Analyses. Concerning the actual growth, we compared the scores obtained at the 5-month follow-up with the scores assessed before HSCT. For each comparison, we reported the p-value and BF 10 (i. e. the extent to which the data support H1). Substantial support for H 1 was provided by a BF > 3 (BF > 10 was judged strong; > 30 very strong and > 100 decisive). Support for H 0 was provided by a BF < 1. The reliable change index (RCI) was computed for each dimension of actual growth  [36]. All relations between variables were assessed using Pearson r. Finally, we tested the Moderating Effect of Experiential Avoidance on the Relationship between Post-Traumatic Stress Disorder (PTSD) and Perceived/Actual Growth using a moderation analysis. First, we centered all the variables on the grand mean.
The information is collected by self-identification either with an open question about one's ethnicity or by ticking a box (or several in the case of multiple choices) in a list of categories. None of these questions explicitly mention race: for example, the categories in the UK census refer to "White," "black British," or "Asian British" among other items, but the question itself is called the "ethnic group question. " In the rest of Europe, place of birth and nationality of the parents would be used as proxies for ethnicity in a limited number of countries: Scandinavia, the Netherlands, and Belgium to name a few. Data on second generations can be found in France, Germany, and Switzerland among others in specialized surveys with limitations in size and scope. Moreover, the succession of generations since the arrival of the first migrants will fade groups into invisibility by the third generation.
Genetic analyses Two individuals were removed because of missing data for two loci. Loci were tested for null allele frequencies with Brookfield's methods  (Brookfield, 1996 ) using Microchecker v2. 2. 3 (Van Oosterhout et al. , 2004). The GCPM2995 marker presented a high null-allele frequency (Table  1 ) and was therefore removed from the dataset. To estimate the discrimination power of the dataset, we calculated the probability of sampling two different genotypes with the same multilocus SSR phenotype (probability of identity) with one to seven markers using GenAIEx v6. 5  (Peakall and Smouse, 2012). This probability decreased from 9. 9 e10-2 to 4. 6 e10-3 with one marker (depending on the marker), and reached a plateau around 3 e-07 with five markers suggesting a high discrimination power of our seven markers combination (see supplementary material Fig  S1 ).
Genetic diversity As high levels of clonality can have a significant impact on SGS because some multilocus genotypes can be overrepresented over small distances  (Chenault et al. , 2011) , we kept only one ramet per multilocus genotype when two or more ramets were present, resulting in a dataset of 280 multilocus genotypes. We estimated allele frequencies, total and effective number of alleles, expected and observed heterozygosities and inbreeding coefficient using SPAGeDi 1. 5 software  (Hardy and Vekemans, 2002). Genetic structure was analysed using the STRUCTURE 2. 3. 4 software  (Pritchard et al. , 2000) , with 20 runs, a burnin period of 100 000 iterations followed by 200 000 additional MCMC iterations, with an admixture model, uncorrelated allele frequencies and recessive alleles. The best number of clusters (K) was estimated using the ΔK method of  Evanno et al.
Spatial genetic structure SGS was assessed for the whole population and within sampling groups (FSGS) for each age cohort using SPAGeDi 1. 5 software  (Hardy and Vekemans, 2002). Loiselle multilocus kinship coefficients, Rij  (Loiselle et al. , 1995) , were calculated between each pair of individuals. Because Rij is a kinship coefficient relative to the population mean, negative values can result, meaning that two individuals are less related on average than randomly selected individuals from the population  (Hardy, 2003). Kinship coefficient values were averaged within distance classes (d), giving F(d) and plotted against geographical distances. Ten classes were formed manually. Significance was tested by running permutations of the spatial position of individuals 10,000 times, yielding a 95% confidence interval for F(d) for each distance class.
Sibship structure was analysed using the software COLONY  (Wang, 2004; Wang and Santure, 2009; Jones and Wang, 2010). For each pair of samples, COLONY computes the likelihood of assignment into half sibs (one parent in common), full sibs (same mother and father) or clones. We ran COLONY using the dataset containing one ramet per multilocus genotype (280 samples). In order to find the best assignment with the maximum likelihood, we chose the "Long run", "Full likelihood" and "High precision" computing options, together with no update of allele frequencies, no sibship size prior, dioecious species with male and female polygamy, and no known parental genotypes or sibships.
Clonality Using the complete dataset, we estimated clonality as: 1 -( 𝐺-1 𝑁-1 ), with N, the number of samples and G, the number of multilocus genotypes  (Arnaud-Haond et al. , 2007). The location and elevation (over a range of a few meters) of sampled saplings were plotted on a digital elevation model (DEM) of the Allier River using QGIS© v2. 18. 21 (QGIS Geographic Information System. Open Source Geospatial Foundation Project. http://qgis. org) Statistical analyses were performed using R©v3. 5. 1. (R Development Core Team, 2005). To test for differences in the spatial elevational and age distributions of replicated genotypes, we conducted non-parametric test (normality of the data were tested using Shapiro-Wilk). We conducted Wilcoxon test to test for differences in the distribution of elevation values of replicated and non-replicated genotypes.