dataesr/scientific-paragraphs-categorization huggingface dataset:
In this study there were 24 MDES probes in the scanning experiment, yielding a total of 1438 observations for Experiment 1, and 30. 7 on average in each session in the behavioural laboratory, yielding a total of 4482 observations in Experiment 2. We applied principal component analysis separately to the MDES data recorded in each dataset, in both cases identifying an off-task dimension (low-component loadings ontask, high loadings on episodic and social content). These components are presented as wordclouds in Fig. 1  and are highly similar across datasets (r(11) = 0. 882, p < 0. 001). Individual variation in off-task thinking was correlated across settings (0-back r = 0. 475, p = 0. 002; 1-back r = 0. 389, p = 0. 014) and more common in the 0-back task in both experiments (scanner: t (59) = 5.
997, p < 0. 001, lab: t(145) = 7. 120, p < 0. 001). In Experiment 1, neural data was acquired while participants performed this task and greater activity in superior parietal, sensorimotor, and mid-cingulate cortex was observed during 1-back blocks. Activity was greater in medial prefrontal, cingulate, and temporal cortex in 0-back blocks (Fig. 1  and Supplementary Table  2 ), replicating previous studies  23, 24. fMRI analysis to find regions related to context regulation. Having established patterns of off-task thinking using MDES, we next examined the neural associations with these patterns of thinking. In particular, we focused on how individuals prioritise this information when external demands are low, and how they prioritise on-task thinking when task demands are increased. We examined associations between momentary changes in off-task thinking and associated patterns of neural activity in both tasks (see Methods).
First, using the data we collected in Experiment 1 we extracted the relationship between brain activity in the same area of DLPFC and the other components of thought (Detail, Modality, and Emotion) to see if this region played a role in taskdependent regulation of these. We subtracted the relationship to each component in the 0-back from that in the 1-back, and used the effect seen for off-task thought in Experiment 1 (Cohen's d = 0. 48) to define the size of the effect we were interested in. We performed equivalence tests  34  to see if the relationship between the task and thoughts for any other component could be dismissed as null. These were all significant (Detail: t(59) = 1. 978, p = 0. 026; Modality: t(59) = 2. 614, p = 0. 006; Emotion: t(59) = 3. 300, p = 0. 001), suggesting these effects are equivalent to zero and can be rejected as null effects (see Supplementary Fig.
Methods Subject details. See Table  1  for a full description of the sample in both experiments. In Experiment 1, 63 participants took part in the online task-based fMRI study. After excluding participants (see Method Details) 60 participants (37 females, mean age = 20. 17 years, S. D = 2. 22 years) remained for data analysis. Thirty-four participants from this sample were scanned for the data used in Sormaz et al. 22. A group of 157 young adults were recruited for the resting-state fMRI and laboratory part of this study. After excluding participants (see Method Details) 146 participants remained for data analysis (89 females, mean age = 20. 21 years, S. D = 2. 49 years). These data have been used before by Turnbull et al. 20. Of these participants, 39 also participated in Experiment 1. All participants were native English speakers, with normal/corrected vision, and no history of psychiatric or neurological illness.
362, p < Thirty-nine participants performed both the resting state and task-based portions of this study. Cortical thickness analysis was performed in the same group as the resting-state analysis, but four participants were excluded as their structural data did not pass quality control 0. 001) in the easier 0-back task. This effect was not found in either during fMRI scanning (accuracy: t(59) = 0. 369, p = 0. 714, rt: t(59) = 0. 052, p = 0. 958, see Supplementary Fig. 1 ). Resting-state (Experiment 2). In the scanner, participants completed a 9-min eyes-open resting-state scan during which there was a fixation cross on-screen. Participants were instructed to look at the fixation cross and try to stay awake. fMRI acquisition. All MRI scanning was carried out at the York Neuroimaging Centre.
The scanning parameters were identical for both the resting state and online task-based scans. Structural and functional data were acquired using a 3T GE HDx Excite MRI scanner with an eight-channel phased array head coil tuned to 127. 4 MHz. Structural MRI acquisition was based on a T1-weighted three-dimensional (3D) fast spoiled gradient echo sequence (TR = 7. 8 s, TE = minimum full, flip angle = 20°, matrix size = 256 × 256, 176 slices, voxel size = 1. 13 × 1. 13 × 1 mm). Functional data were recorded using single-shot two-dimensional gradient echo planar imaging (TR = 3 s, TE = minimum full, flip angle = 90°, matrix size = 64 × 64, 60 slices, voxel size = 3 mm isotropic, 180 volumes). A FLAIR scan with the same orientation as the functional scans was collected to improve coregistration between scans.
Data pre-processing: online task-based fMRI (Experiment 1). Two participants were excluded for falling asleep. Task-based functional and structural data were pre-processed and analysed using FMRIB's Software Library (FSL version 4. 1, http://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FEAT/). Individual FLAIR and T1-weighted structural brain images were extracted using BET (Brain Extraction Tool). The functional data were pre-processed and analysed using the FMRI Expert Analysis Tool (FEAT). The individual subject analysis first involved motion correction using MCFLIRT and slice-timing correction using Fourier space timeseries phaseshifting. After coregistration to the structural images, individual functional images were linearly registered to the MNI-152 template using FMRIB's Linear Image Registration Tool (FLIRT).
Functional images were spatial smoothed using a Gaussian kernel of FWHM 6 mm, underwent grand-mean intensity normalisation of the entire four-dimensional (4D) dataset by a single multiplicative factor, and both highpass temporal filtering (Gaussian-weighted least-squares straight line fitting, with sigma = 100 s); and Gaussian lowpass temporal filtering, with sigma = 2. 8 s. An additional participant was excluded for having relative motion > 0. 2 mm in >50% of runs (three participants total excluded). Data pre-processing: resting-state fMRI (Experiment 2). Pre-processing of the resting-state fMRI data was carried out using the SPM software package (SPM Version 12. 0, http://www. fil. ion. ucl. ac. uk/spm/) based on the MATLAB platform (Version 16. a, https://uk. mathworks. com/products/matlab.
009 and 0. 08 Hz to focus on low-frequency fluctuations  61. Principal component analysis. Behavioural analyses were carried out in SPSS (Version 24. 0, 2016). The scores from the 13 experience sampling questions were entered into a PCA to describe the underlying structure of the participants' responses. Following prior studies  30, 62  we concatenated the responses of each participant in each task into a single matrix and employed a PCA with varimax rotation. Four components were selected based on the inflection point in the scree plot (see Supplementary Fig. 3 ). These were defined as Task-relatedness, Detail, Modality, and Emotion of thought based on their question loadings. These loadings for both the scanner and laboratory components can be seen in Supplementary Fig. 2. Several analyses were performed to assess the similarity between PCA analyses in the laboratory and in the scanner.
In these analyses we followed best practice as described by Eklund and colleagues  63. Specifically, we used FLAME, as implemented in FSL, applied a cluster-forming threshold of Z = 3. 1, and corrected these at p < 0. 05 (corrected for family-wise error rate using random field theory). Average motion was included at the group level to additionally control for effects relating to this nuisance variable. This model was repeated for each component as a follow-up analysis (see below). Brain figures were made using BrainNet Viewer 64 , plots made using matplotlib in Python (version 3. 6. 5). Meta-analytic decoding used Neurosynth  25  to find terms most commonly associated with our neural maps in the literature. This platform collects and synthesises results from many different research studies, and identifies the terms associated most often with each region of the brain.
To do this, the 17 Yeo network masks  33  were binarised and merged into a single 4D nifti file. In order to reduce statistical bias from the region itself, the corresponding region (left DLPFC) was masked out of this nifti. These networks were entered into a dual regression that extracted the timeseries from within each Yeo network and subsequently regressed these against each subjects 4D dataset within the left DLPFC. Thus, for each network, each voxel in the left DLPFC was given a value for how much it represented the Yeo network in question, to make up 17 different components within this region that each represented one of the Yeo networks. These maps were again merged into a single file and entered into the first step of a second dual regression in order to extract the timeseries of each Yeo 17 echo or component.
This was entered into a MANOVA, with the off-task scores in each task as the dependent variables, as well as age and gender as covariates of no interest. All data were mean centred before this analysis. Overlaps were calculated and displayed with Brodmann and Glasser 68 labels using Connectome Workbench, with labels being acquired from the BALSA database  69. Reporting summary. Further information on research design is available in the Nature Research Reporting Summary linked to this article.
In this study, we aimed to assess the quality of the DEMs retrieved from three different sources with temporally consistent in-situ measurements at the glacier surface. We generated and assessed the accuracy and precision of a photogrammetric DEM based on aerial photographs from 2011  [3]  over Hansbreen, southern Spitsbergen. Furthermore, we evaluated the accuracy and precision of DEMs generated from VHR images: two WorldView-2 and two Pléiades satellite stereo pairs over Hansbreen and Hornbreen glaciers. Two methods of DEM generation were verified: DEMs generated without any ground control point (GCP), and DEMs based on one GCP-enhanced rational polynomial coefficient (RPC). Finally, we tested the accuracy of the ArcticDEM Strip product  [15]  for a selected study area. The DEMs from different sources were validated against field data (precise GPS positioning of mass balance stakes and/or points along GPR -ground penetrating radar profiles).
All accuracy assessments were conducted only over glacierized areas of Hansbreen (all types of DEMs) and Hornbreen (DEMs from VHR). Using the DEMs generated in this study we estimated geometry changes of Hansbreen and Hornbreen, two tidewater glaciers in southern Spitsbergen. We also aimed to close the mass budget of Hansbreen, using in situ data from the continuous monitoring of the glacier mass balance, and to compare the total mass balance derived using the geodetic and mass budget methods.
Datasets and Processing In this study, we generated DEMs from different sources. Furthermore, we validated these DEMs, as well as individual strips of the ArcticDEM with in-situ data. In the end, we estimated geometry changes of Hansbreen and Hornbreen and compared the geodetic and climatic mass balance of Hansbreen. Figure  2  illustrates the flowchart of the data processing and result analysis. The main reason for selecting these glaciers was that they were the subject of ongoing field programs (e. g. ,  [24, 25, [27] [28] [29] [30]  and World Glacier Monitoring Service-https://wgms. ch/). The proximity of the Polish Polar Station Hornsund enabled wide-ranging field studies (mass balance, GPR and GPS measurements) to be carried out, which could be further used to investigate the accuracy and application of DEMs.
DEM from Aerial Photographs The aerial photo DEM was generated from 39 aerial photographs acquired by the Norwegian Polar Institute in 2011. The pixel size of the photographs equaled 0. 5 m, and the average flight height was 7400 m a. s. l. Due to cloudiness over some areas, two sets of images were used: from 25. 07. 2011 and from 18. 08. 2011. To take into account surface elevation changes of the glacier between both acquisitions, the aerial photo DEM over the Hansbreen area from August was elevationally adjusted to July by removing the mean elevation difference estimated from the mass balance stake measurements. The aerotriangulation (sigma0 = 5. 7 µm) and DEM generation were carried out in INFO MATCH AT and INFO MATCH T software by MGGPAero (https://mggpaero. com). The 9 x,y,z GCPs measured with dGPS (differential Global Positioning System) and 20 z-points were used for aero-triangulation of the photographs.
Glacier DEMs from WorldView-2 and Pléiades were processed using the Rational Function Model (RFM) developed in the OrthoEngine module of PCI Geomatica 2016. The RFM model, defined as a ratio of two cubic polynomials, is currently the most popular generic sensor model used in the field  [36] [37] [38] [39] [40]. Both types of stereo pairs (WorldView-2 and Pléiades) contain the RPC files, which are used to define the initial functions for transforming the sensor geometry to image geometry  [14]. This allowed us to generate DEMs without any GCP (0 GCP DEM). Since biases or errors still exist in the RPCs, the results can be improved with a polynomial adjustment and several accurate GCPs  [34]. In our study, to improve image orientation, additional DEMs based on one GCP for each stereo pair (1 GCP DEM) were also created.
ArcticDEM The ArcticDEM is an open product provided by the Polar Geospatial Center  [15]. It is an NGA-NSF (National Geospatial-Intelligence Agency and National Science Foundation) public-private initiative to automatically produce a high-resolution, high-quality, digital surface model of the Arctic using optical stereo imagery, high-performance computing, and open source photogrammetry software. ArcticDEM data is constructed from in-track and cross-track high-resolution (~0. 5 m) imagery acquired mainly from the panchromatic bands of the WorldView-1, WorldView-2, and WorldView-3 satellites (https://www. pgc. umn. edu/data/arcticdem/). In the study, we assessed the accuracy of five Strip ArcticDEM products (Table  2 ) provided at 2 m spatial resolution (v3. 0 Pan-Arctic, RELEASE 7), as ~17 km wide and ~110 km long strips.
Quality of ArcticDEM Strips Table  4  summarizes the results of the evaluation of the quality of the Strip DEM product of ArcticDEM with mass balance stakes. Generally, the product after applying offset provided in metadata has good accuracy (median < 4 m) and precision (NMAD < 1 m) over the glacierized area. However, one product, the DEM from 21. 08. 2015, exhibits a gross height error (median over -67 m). The WV-2 stereo pair used for this particular DEM is the same as the one we used to generate the surface model over Hansbreen (Table  1 ). From detailed analyses we concluded that the offset provided in metadata with Strip ArcticDEM was wrong. But the vertical accuracy of ArcticDEM strips without applying the coefficient was still very low (the median was c. -46. 5 m) while precision increased (NMAD equaled 0.
Quality Measures of DEMs The quality of the DEMs was characterized by different statistical measures: (i) median, to evaluate the vertical accuracy of the DEMs, and (ii) standard deviation (SD) and normalised median absolute deviation (NMAD) to characterize their vertical precision, following Berthier et al. [12]  and Hobi and Ginzler  [44]. Additionally, maximal error is shown here. The NMAD is a measure of the dispersion of the data that is less sensitive to outliers than the standard deviation  [45]  and is defined as: NMAD = 1. 4826 • median (|∆h j -m∆h|) (3) where ∆h j denotes the individual errors and m∆h is the median of the errors. We assessed here the vertical quality of the DEMs without any horizontal and vertical DEM shifting. Assessment of the horizontal DEM accuracy was not possible given the lack of a GCP measured with decimal accuracy on the stable terrain and recognizable on satellite images.
DEM from Pléiades and WorldView-2 The quality of DEMs generated using one GCP and without GCPs was examined based on two reference datasets for Hansbreen (stake position measurements and GPR/GPS data) and only GPR/GPS data for Hornbreen (Figures  4  and 5 , Table  3 ). The quality of 0 GCP DEM and 1 GCP DEM generated from WorldView-2 stereo pairs over both glaciers is comparable (Table  3 ). Median and maximal errors estimated for mass balance stakes on Hansbreen are generally better in the case of the DEM generated without using a GCP, while precision (NMAD and SD) is better for 1 GCP DEM. Statistical measures for GPR/GPS data on both glaciers show a slightly better precision of 1 GCP DEM (e. g. , NMAD below 0. 5 for Hansbreen and 0. 7 m for Hornbreen), but accuracy (i. e. The median of the elevation differences) for Hansbreen is better for 1 GCP DEM, whilst for Hornbreen for 0 GCP DEM.
DEM Co-Registration The difference between the two DEMs computed on the ice-free areas is presented in Table  5. We also estimated the residual of the triangulated shift vectors for Hansbreen (between 0. 27 m and 0. 58 m in the X,Y,Z directions), which is the remaining un-removed shift between the three datasets and thus represents the internal consistency of the datasets  [10]. For all DEM differences (only one is shown in Figure  6 ), there is a strong relationship between the dispersion of the residuals (NMAD) and the slope of the terrain, a feature observed in earlier studies using VHR stereo-images  [23, 53]. The precision is high and more or less constant for slopes below 40 to 50 • and then the NMAD increase drastically. If we restrict the analysis to a range of slope similar to the one found on the glacier surface (<10 • ) we find dispersion of the elevation difference (last column of Table  5 ) which is in agreement with the results of the evaluation off the glacier.
It suggests that the stable terrain can be used to assess the precision of the elevation difference on glaciers,  [21]  if the difference of slopes is accounted for. Table  5. Horizontal and vertical shifts of the slave DEM in relation to the master DEM calculated during the co-registration process over the stable terrain. For Hansbreen, we also provide the residual of the triangulation which quantifies the consistency of our 3D co-registration. We also provide the NMAD of the elevation difference over the stable terrain with a slope of less than 10 The difference between the two DEMs computed on the ice-free areas is presented in Table  5. We also estimated the residual of the triangulated shift vectors for Hansbreen (between 0. 27 m and 0. 58 m in the X,Y,Z directions), which is the remaining un-removed shift between the three datasets and thus represents the internal consistency of the datasets  [10].
Quality of the ArcticDEM Strips According to the provider, the absolute accuracy of the Strip DEM product of ArcticDEM generated without GCPs is approximately 4 m in the horizontal and vertical planes (https://www. vedur. is/media/frettir/ArcticDEM_Documentation_and_User-Guidance_Rel4. pdf). The ICESat altimetry offsets provided within the metadata can be used to improve the product. These ICESat data points are filtered to exclude points in areas of high relief and over hydrographic features. But the documentation does not specify the accuracy of Strip DEM after applying the ICESat altimetry offsets and whether the points over glacierized area are excluded. ArcticDEM data is generated by applying stereo auto-correlation techniques using the SETSM software developed by Noh and Howat  [22].
In the case of Pléiades stereo pairs, using just one GCP considerably improved the DEM quality, compared to 0 GCP DEM. Considering all results, the use of 1 GCP generally improved VHR DEM accuracy. Generally, we reached an overall accuracy of 0. 5-0. 7 m for the surface elevation change. The tested methods show acceptable accuracy of DEMs generated from VHR images in PCI Geomatica for estimation of the mass balance budget of tidewater glaciers in Svalbard in the two-year period. Regarding the freely available ArcticDEM, the Strip product should be validated with in situ or other data before use.
Acknowledgments: WorldView-2 Ortho Ready 2A stereo pairs were acquired by the  ESA  (Project no. C1P. 34101 ). Pléiades stereo pairs used in the study were provided in the frame of the  Pléiades Glacier Observatory (PGO) program of the French Space Agency (CNES). ArcticDEM: DEMs freely provided by the  Polar Geospatial Center  under NSF-OPP awards 1043681, 1559691, and  1542736. The authors would like to thank  Dariusz Puczko  for assistance with the fieldwork in 2011 and to anonymous reviewers for use ful comments that helped to improve the manuscript.
Each master layout comprises hundreds of telescopes distributed over an area of about 6 km 2 and are designed to contain numerous possible CTA layouts of equivalent cost. To identify the optimal arrangement, these plausible layouts are extracted, analysed and their performances are compared with respect to each other. For each MC production, telescope models were sequentially improved, becoming more realistic in each iteration thanks to the increasing input coming from the prototype telescopes. Air showers initiated by gamma rays, cosmic-ray nuclei and electrons are simulated using the CORSIKA package  [28]. The telescope response is simulated using sim_telarray  [11] , used by the HEGRA and H. E. S. S.
Antiretroviral Therapy Cohort Collaboration data The ART-CC combines data from HIV cohorts in Europe and North America. Eligible patients are HIV-1 positive, aged at least 16 years, and started ARTon at least three drugs without having previously taken antiretrovirals. All contributing cohorts have been approved to use their data for research by institutional review boards or ethics committees. The cohorts use standardized methods of data collection, with follow-up visits scheduled at least every 6 months. The dataset is described in detail elsewhere (www. bristol. ac. uk/art-cc/)  [10]. Only European ART-CC cohorts were included in this study as the North American cohorts within NA-ACCORD are more representative of the US and Canadian populations of PLHIV than those in the ART-CC  [11]. The European cohorts included in this study were those representative of national populations of PLHIV in their countries because of their nationwide geographical coverage and because they contain a high percentage of the PLHIV on ART in that country, for example, AHIVCOS contains over 85% of Austria's PLHIV on ART  [12].
The countries and cohorts analysed were Austria (AHIVCOS), Denmark (DHK), France (ANRS C03 Aquitaine cohort, ANRS CO4 French Hospital Database on HIV FHDH), Italy (ICONA), Netherlands (ATHENA), Spain (Co-RIS, PISCIS, VACH), and Switzerland (SHCS). The ART-CC 2015 data update was used (ART-CC receives data from its cohorts in cycles, e. g. the previous update was in 2013). The latest ART start date in this analysis was 9 January 2015 and the latest follow-up date used was 31 December 2015. Follow-up started when PLHIV started ART and ended at the earliest of death, loss to follow-up (LTFU), or administrative censoring (a cohort-specific database closing date). If a patient's last clinical observation was more than a year before the cohort-specific database close date and they were not known to have died, then they were considered lost to follow-up.