dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2012) : HadGEM2-ES, IPSL-CM5A-LR, MIROC5  (Sanderson et al. , 2015). Results from the most recent CMIP6 were not used as they did not include all three climatic scenarios selected above. Climatic data were downloaded at a 10 min resolution (~18. 5 km) and resampled at a 25 km resolution with the 'resample' function in the raster R package  (Hijmans & van Etten, 2012). Although at continental and global scales, climate is the major factor driving species distributions  (Pearson & Dawson, 2003) , primary terrain attributes, such as elevation and slope, can also influence the distribution and biology of Em, especially in heterogeneous montane zones such as the Alps  (Romig et al. , 2017). Hence, median elevation raster data at a 30 arc-second resolution were obtained from the Global Multi-resolution Terrain Elevation Data (GMTED) 2010 (United States Geological Survey-available at https://topot ools.
cr. usgs. gov). Slope, aspect, roughness, Topographic Position Index (TPI) and Terrain Ruggedness Index raster layers were then generated from median elevation raster data with the 'terrain' function of the raster R package  (Hijmans & van Etten, 2012)  in R 3. 6. 3 (R Core Team, 2020) and used to provide information on the geomorphology of the landscape, which served as a proxy for terrain complexity. Since correlation often occurs within or between bioclimatic and GMTED layers  (Cruz-Cardenas et al. , 2014; Merow et al. , 2013; Warren et al. , 2014) , we applied a principal component analysis (PCA) to the 19 climatic and five orographic variables to remove  SSP1 'Sustainability', SSP3 'Regional rivalry' and SSP5 'Fossil-fueled development'  (Di Marco et al. , 2019; Kim et al. , 2018). Hence, our projections described three SSP-RCP scenarios: SSP1-RCP2.
| E. multilocularis presence data The bibliographic search identified 1567 scientific papers, from which we obtained the coordinates of 1959 Em records in red fox in Europe and combine them with the 212 from the EWET database (for a total of 2171), spanning the period 1985-2020. After retaining a maximum of one record per grid cell and performing the spatial filtering, 449 occurrence points were obtained and used to build the Maxent model (Figure  1 ).
their rodent prey according to literature (see Romig et al. , 2017  for a review of the ecology of the parasite): (1) Open, which included the subcategories (a) Land under a rotation system, (b) Pastures, (c) Land principally occupied by agriculture, with significant areas of natural vegetation, (d) Natural grasslands, (e) Moors and heathlands; (2) Forest, which included (a) Broad-leaved forest, (b) Coniferous forest, (c) Mixed forest; (3) Urban areas, with subcategories (a) Continuous urban fabric, (b) Discontinuous urban fabric. To represent the future conditions for LULC variables, we used the GCAM dataset provided by Chen et al. (2020), covering three representative SSP scenarios:.
F Occurrence records of Echinococcus multilocularis in Europe, used to model its current and future probability of occurrence as a function of climatic, topographic and land-use/land-cover variables. Data from a bibliographic search were combined with data from the EmsB Website for Echinococcus Typing database (updated on 6 February 2020). Map lines delineate study areas and do not necessarily depict accepted national boundaries.
Acknowledgements The authors gratefully acknowledge the  French National Agency for Research (ANR) , grant number  ANR-17-CE07-0031 " for financial supports. A. N and J. N would like to thank  Dr. Hakim Amara  and  Dr. Riccardo Gatti  for insightful discussions regarding the mechanical properties of metallic nanoparticles and the role of dislocations in their plastic deformation. The authors would also like to thank the five reviewers for their valuable and profound comments. Keywords: 1,3-butadiene, environmental TEM, gold catalysis, in situ, reactivity.
Dispersal is an important behavioral process that plays a significant role in, among others, speciation, population viability, and individual fitness. Despite progress in avian dispersal research, there are still many knowledge gaps. For example, it is of interest to study how dispersal propensity relates to age-and/or sex-specific patterns. Here, we investigated the role of sex and life stage on natal (i. e. , movement from birth site to first breeding site) and breeding dispersal (i. e. , movement between sequential breeding sites) in the Kentish Plover (Charadrius alexandrinus) for dispersal events of more than 10 km. This small and inconspicuous wader is characterized by flexible mating behavior that includes monogamy, and serial polygynandry. Using a continent-wide dataset of ringing and re-encounter data throughout the species' range in Europe, we found that adult females generally dispersed further than adult males between seasons, but we detected no sex difference in natal dispersal distances and no general difference between natal and breeding dispersal distances.
Furthermore, females were the main group exhibiting 'long-distance' breeding dispersal, which we defined as dispersal greater than ≥ 108 km, i. e. , the upper 10% percentile of our dataset. The data set included dispersal of two females that first bred in the Mediterranean before being detected breeding at the North Sea in the subsequent year, having dispersed 1290 and 1704 km, respectively. These observations represent the longest breeding dispersal observed within the genus Charadrius. Our long-distance dispersal records are consistent with low genetic differentiation between mainland populations shown in previous work. The dispersal of the Kentish Plover is likely linked to its breeding behavior: polyandrous females exhibit extensive mate searching and habitat prospecting. We recommend that the dispersal traits of Kentish Plover be incorporated into the species' conservation and management planning to more accurately inform models of population connectivity and metapopulation dynamics.
Re-encounter data of previously marked birds enable more detailed investigations of dispersal, but previous studies in this species were restricted to local or regional scales (e. g. , Székely and Lessels 1993;  Foppen et al. 2006). To characterize the dispersal movements of Kentish Plovers in a more comprehensive manner, we utilized a European-wide bird ringing database provided by EURING. We predicted that natal and breeding dispersal distances would not differ in Kentish Plovers: dispersal should remain high across both life-history stages due to frequent re-mating in the species and the widely spaced breeding distribution in Europe leading to regular movements of individuals between breeding sites in search of new partners or higher quality habitat. Although both males and females can be serially polygamous, we predicted that breeding dispersal would be higher in females than in males because males acquire nesting territories and invest more in brood care than females, and serial polyandry is more common than serial polygyny in this species  (Lessells 1984; Amat et al.
Source of data and fundamental data categorization The primary source for our analyses was the 'EURING Data Bank'  (obtained 9/10/2019; du Feu et al. 2009 ), which contained 12,057 records of Kentish Plovers: 9651 re-encounters (i. e. , re-captures, re-sightings, and dead recoveries) of 2406 individually ringed birds. Of those, we excluded entries of individual plovers that were hand reared or moved before release (13 individuals) and imprecise entries. Imprecise entries were those without a precision of less than 1 week for the date (35 individuals), or had imprecise geographical locations with an uncertainty of more than 5 km (544 data points of 264 individuals). Finally, we excluded re-encounters of dead plovers except those that were found freshly dead (86 individuals). To separate dispersal from other movements, we classified the status of each ringing or re-encounter data point.
We defined 'certain breeding' as either being classified as 'nesting or breeding' (status 'N') or being caught at a nest (catching method 'N'). We assigned 'possible breeding' for all other data with status 'K' ('in colony') or birds being ringed or re-encountered during the core breeding period between May and June for European populations (see  Rittinghaus 1975). As the base data for natal dispersal, we took plovers being ringed as a chick (EURING code '1' for age), because those chicks certainly hatched close to their ringing sites. In total, 5428 ringing or re-encounter data of 1906 individuals applied to one of those definitions (i. e. , certainly breeding, possibly breeding or ringed as a chick). We aligned ringing and re-encounter data to identify data that fulfilled one of our breeding status criteria for both the ringing and re-encounter data of an individual.
As there is no general threshold for long-distance dispersal in animals and plants  (Nathan et al. 2003) , and it has, therefore, to be defined on a caseby-case basis, we classified movements equal or greater than 108 km as long-distance dispersal for our focal group of birds (between-season breeding dispersal) because this threshold represents the upper 10% percentile of our dataset (see Fig. 1b ). We did not use the threshold of 50 km used by  Stenzel et al. (1994)  for long-distance dispersal in the closely related Snowy Plover because their threshold was case specific to the US Pacific Coast and was not informed by a statistical distribution. We obtained distances between ringing and re-encounter locations directly from the EUR-ING database or measured in QGIS version 3. 16. 10-Hannover (QGIS Development Team 2020) using its 'Shape Tools' extension.
e. , those birds performed  Depending on region more than one dispersal movement). For consistency, we used the first observed dispersal movement after ringing for all individuals. Our data included between-season movements of seven birds back to the area around their ringing place (< 10 km) following within-season dispersal to another place. We refer to between-season dispersal distances ≥ 108 km as 'long-distance' breeding dispersal (i. e. , the upper 10% percentile of our dataset). We refer to within-season breeding dispersal for adult birds that were found at a second place within the same breeding season either in the year of ringing or in subsequent years. However, our sample size was low for this subset of our data (n = 20 males, 24 females, of which 6 females referred to certain dispersal).
Additional data from color-ringing projects We used own color-ringing data that are not yet included in the EURING dataset, to enlarge the sample size for betweenseason long-distance breeding dispersal (≥ 108 km). We color-ringed Kentish Plovers at the Tagus estuary in Portugal (n = 135 males, 161 females; ringing period 2007-2021), Brittany, NW France (n = 95 males, 191 females; ringing period 2008-2018), the Marker Wadden in The Netherlands (n = 7 males, 17 females; ringing period 2020-2021), and Schleswig-Holstein, Germany (n = 92 males, 127 females; ringing period 2009-2021). Of those, 12 plovers (2 males and 10 females) were found ≥ 108 km to have dispersed in later breeding seasons until 2022.
Data analyses All analyses were performed in R (version 4. 1. 1, R Core Team 2021). We checked the fit of the data with and without transformation in competing models using the fitdistrplus package  (Dutang 2015)  and diagnostic plots in R. We then chose appropriate linear or generalized linear models, or non-parametric tests for hypothesis testing. We performed a linear model with log-transformed dispersal distances to evaluate sex-and stage-specific differences. As the observed dispersal distances were strongly skewed and the fit of tested generalized linear models was poor, we used the nonparametric Kruskal-Wallis test to examine differences in breeding dispersal distances between the sexes because of its robustness (i. e. , making no assumptions about the distribution of the data). To compare sex-specific proportions of long-distance breeding dispersal, we used Fisher's exact test.
Medium-to long-distance natal vs. breeding dispersal distances in the first year after ringing Median natal dispersal distance > 10 km of Kentish Plovers in the EURING dataset was 18 km (interquartile range (IQR) = 16-84 km, maximum = 201 km; n = 13) for males and 21 km (IQR = 16-53 km, maximum = 449 km; n = 21) for females (Fig. 1a ). Median breeding dispersal distance > 10 km was 14 km (IQR = 12-17. 5 km, maximum = 66 km; n = 6) for males and 22 km (IQR = 16-76 km, maximum = 1,704 km; n = 33) for females (Fig. 1a ). We found no statistically clear effect of sex (F 1,70 = 0. 841, p = 0. 36, 95% CI = -0. 436-0. 161 for males), stage (F 1,70 = 0. 073, p = 0. 79, 95% CI = -0. 299-0. 227 for natal dispersal) or the interaction of sex and stage (F 1,69 = 2. 068, p = 0. 15, 95% CI = -0. 170-1. 048 for natal dispersal of males) on dispersal distance.
Sex differences in between The proportion of long-distance breeding dispersal (≥ 108 km) in relation to all observed movements > 10 km between seasons tended to be lower in adult males than in adult females: only 1 of 33 adult males (3. 0%) compared to 11 of 87 adult females (12. 6%) moved ≥ 108 km, even though the relationship was not significant (Fisher test, p = 0. 18). Similarly, the proportion of males moving ≥ 108 km in relation to total ringing numbers with given sex (0. 2%, 1 of 403) was about an order of magnitude lower than in females (1. 7%, 11 of 656; Fisher's exact test: p = 0. 04). In our additional data not included in the EURING dataset, the proportion of colorringed males with long-distance breeding dispersal (0. 6%, 2 of 329) was also lower than the proportion of females (2.
2015) , because of limitations of the EURING dataset (see Methods). Consequently, our results are meaningful with regard to medium-to long-distance dispersal. This could explain why local-scale dispersal distances of Kentish Plovers studied by  Foppen et al. (2006)  in the Dutch Wadden Sea were further for juveniles than for adults, which is in contrast to our results. A general limitation of large-scale ringing data is that we might have not detected all dispersal movements of an individual, potentially leading to false categorizations (e. g. , mixing of early breeding and natal dispersal or within-season and between-season breeding dispersal). Moreover, an observed dispersal movement might be the product of several successive movements. However, as we found no relationship between the time gap and between-season breeding dispersal distance (see Fig.
The observed unusual pattern in our study is possibly a result of methodological differences. Dispersal distances up to 10 km, which might comprise most dispersal movements, are missing in our analyses leading to a strong bias toward longer dispersal distances. Studies on local populations usually only anecdotally detect long-distance dispersal. In contrast, the EURING dataset covered a large geographical range allowing us to examine the differences between demographic classes and sexes with a focus toward the upper range of realized dispersal movements. Thereby, the known maximum breeding dispersal distance of the 1 3 Kentish Plover increased tenfold from 170 km (Székely and Lessels 1993) to 1704 km. The observed long-distance dispersal in Kentish Plovers includes the farthest recorded breeding dispersal movements within the genus Charadrius (see Table  1 ).
's 2011 parcellation). The results, in Figure  3e , revealed differences in the functional networks of these DMN regions that extended to visual cortex. Semantic decision regions showed stronger connectivity to lateral visual regions along with lateral temporal cortex, inferior frontal gyrus, angular gyrus and dorsomedial prefrontal cortex. Spatial decision regions were more connected to medial visual regions, ventro-medial temporal regions, medial parietal cortex, ventral parts of medial prefrontal cortex, motor cortex and dorsal parts of lateral occipital cortex. Cognitive decoding of these connectivity maps using Neurosynth (Figure  3 , panel f) revealed that the semantic decision network was associated with terms related to language, semantic processing and reading, while the spatial context decision network was associated with navigation, episodic and autobiographical memory.
91, p=. 004) and their interaction (F(1,26)=24. 65, p<. 001). Post-hoc comparisons showed a stronger response to spatial context decisions, relative to semantic decisions, in the visual ROI sensitive to scenes (t(26)=3. 63, p<. 001), but not in the object-selective region (t(26)=1. 88 p=. 071) falling with the semantic pathway (Figure  3 , panel d). S3. Panel e: Brain maps depicting the seeds and intrinsic connectivity results for the semantic and spatial context decision regions. Panel f: Word clouds depicting the cognitive decoding of unthresholded connectivity maps for semantic and spatial context decision seeds using Neurosynth (bigger words reflect stronger correlation of the functional maps with the terms); the colour-code follows that of the brain maps. Panel g: Brain maps showing the overlap of these intrinsic connectivity maps with the visual network from the 7-network parcellation from  Yeo et al.
Pathways analysis The analysis above identified regions of visual cortex showing a differential response to semantic and spatial context probes, related to category effects for objects versus scenes. We also found distinct DMN subnetworks, which supported semantic and spatial context decisions respectively. Next, we consider if these effects are linked: Do FT-DMN regions have stronger connectivity to object perception areas of visual cortex, while MT-DMN regions connect to scene perception regions? Using resting-state data from Study 3, we performed a series of Seed-to-ROI analyses to answer this question. We seeded the visual regions in Figure  2e  (i. e. , probe responses marked by Yeo et al. 's visual networks) and extracted their intrinsic connectivity to DMN, using ROIs differentially activated by semantic and spatial context decisions (corresponding to the seeds in Figure  3 , panel e).
We examined structural connectivity in a subset of the HCP dataset (n = 164), asking if object probe visual regions showed a greater proportion of white matter tracts terminating in semantic DMN regions, and if scene probe visual regions showed stronger structural connectivity to spatial context DMN regions (Figure  4 , panel h). A 2x2 repeated-measures ANOVA with visual regions as seeds and DMN regions as ROIs revealed a significant main effect of seed (F(1,163)=5. 13, p=. 025), ROI (F(1,163)=82. 46, p<. 001) and their interaction (F(1,163)=664. 57, p<. 001). Post-hoc comparisons confirmed stronger structural connectivity from the semantic probe visual regions to the semantic decision DMN regions; likewise, the spatial context probe visual regions showed stronger structural connectivity to the spatial context decision DMN regions (semantic probe visual: t(163)=8.
org/10. 5281/zenodo. 4725985, https://tedana. readthedocs. io/) to combine the images  (Kundu et al. , 2012; Kundu et al. , 2013; Posse et al. , 1999). Before images were combined, some pre-processing was performed. FSL_anat (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/fsl_anat) was used to process the anatomical images, including re-orientation to standard (MNI) space (fslreorient2std), automatic cropping (robustfov), bias-field correction (RF/B1 -inhomogeneitycorrection, using FAST), linear and non-linear registration to standard-space (using FLIRT and FNIRT), brain extraction (using FNIRT, BET), tissue-type and subcortical structure segmentation (using FAST). The multi-echo data were pre-processed using AFNI (https://afni. nimh. nih. gov/), including despiking (3dDespike), slice timing correction (3dTshift; heptic interpolation), and motion correction of all echoes aligned to the first echo (with a cubic interpolation; 3dvolreg was applied to the first echo to realign all images to the first volume; these transformation parameters were then applied to echoes 2 and 3).
The pre-processing script is available at OSF (https://osf. io/sh79m/). Task-based fMRI Data Analysis: Further pre-processing of the functional and structural data was carried out using FSL version 6. 0 (FMRIB's Software Library, www. fmrib. ox. ac. uk/fsl;  Jenkinson et al. , 2002; Smith et al. , 2004; Woolrich et al. , 2009). Functional data were pre-processed using FSL's FMRI Expert Analysis Tool (FEAT). The TEDANA outputs (denoised optimally combined timeseries) registered to the participants' native space were submitted to FSL's FEAT. The first volume of each functional scan was deleted to negate T1 saturation effects. Pre-processing included high-pass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma = 50s), linear coregistration to the corresponding T1-weighted image followed by linear co-registration to MNI152 2mm standard space  (Jenkinson and Smith, 2001) , which was then further refined using FSL's FNIRT nonlinear registration  (Andersson et al.
, 2007a; Andersson et al. , 2007b)  with 10mm warp resolution, spatial smoothing using a Gaussian kernel with full-width-half-maximum (FWHM) of 5 mm, and grand-mean intensity normalisation of the entire 4D dataset by a single multiplicative factor. Task GLM: Second and group-level analyses were also conducted using FSL's FEAT version 6. Preprocessed time series data were modelled using a general linear model in FSL, using FILM correcting for local autocorrelation  (Woolrich et al. , 2001). We used an event-related design. Our aim was twofold: (1) to characterise differential activation between the semantic and spatial context tasks at each phase of the trials, and (2) to document any potential differences of activation in response to MCB and SCB trials in probe and decision phases, in each task.
Fifteen different moving objects were selected that minimized any suggestion of animacy of the object itself or of a hidden actor pushing the object (these included mobiles, windup toys, toy planes and tractors, balls rolling down sloped inclines). Within each block, stimuli were randomly selected from within the entire set for that stimulus category (faces, bodies, scenes, objects, scrambled objects). Study 2: Procedure and Data Acquisition Functional data were acquired over 6 block-design functional runs lasting 234 seconds each. Each functional run contained three 18-second rest blocks, at the beginning, middle, and end of the run, during which a series of six uniform color fields were presented for three seconds. Participants were instructed to watch the movies but were not asked to perform any overt task.
Imaging data were acquired using a 3T Siemens Magnetom Prisma MRI scanner (Siemens Healthcare, Erlangen, Germany) at the University of York. Functional images were acquired with a twenty-channel phased array head coil and a gradient-echo EPI sequence (38 interleaved slices, repetition time (TR) = 3 sec, echo time (TE) = 30ms, flip angle = 90%; voxel size 3mm isotropic; matrix size = 128 x 128) providing whole brain coverage. Slices were aligned with the anterior to posterior commissure line. Structural images were acquired using the same head coil and a high-resolution T-1 weighted 3D fast spoilt gradient (SPGR) sequence (176 interleaved slices, repetition time (TR) = 7. 8 sec, echo time (TE) = 3ms, flip angle = 20 degrees; voxel size 1mm isotropic; matrix size = 256 x 256). Study 2: Imaging Analysis Functional MRI data were analyzed using AFNI (http://afni.