dataesr/scientific-paragraphs-categorization huggingface dataset:
A logit link was used in all models, making the CLMM model equivalent to a proportional odds mixed model. Visual cancellation priming task. For the visual cancellation control task, a generalized linear mixed model using a poisson distribution (for the count data) was run using the lme4 package  78  in R Studio (comparable to the cumulative link model but for count data). Only the participant random effect was included in the model as there was only one sheet after each given prime excerpt (with a total of three sheets for each prime condition). Testing and covariates. Likelihood ratio tests were used to compare models with and without different fixed effects using drop1 and add1 methods. Directions of fixed effects were investigated using emmeans  79. Significant effects that included covariates were investigated using emtrends to model the covariate data direction.
Effect sizes were calculated with the package effectsize 80 based on the z-ratio and sample size reported by emmeans. Cohen's d and Pearson's correlation coefficient (r) are reported. Cohen's d effect sizes were interpreted according to the guidelines in  81  and the effectsize package: d < 0. 2 = very small; 0. 2 <= d < 0. 5 = small; 0. 5 <= d < 0. 8 = medium; and d >= 0. 8 = large. For r, 0. 1 <= r < 0. 2 = small; 0. 2 <= r < 0. 3 = medium; 0. 3 <= r < 0. 4 = large; and r >= 0. 4 = very large  80. We additionally calculated the 95% confidence interval around the effect sizes to estimate confidence in our effect sizes  82. To analyze the effects of chronological age, reading age, digit span, and rhythm perception, each covariate was first scaled and normalized. As the rhythm perception task (BAT) and the digit span task were not age-normed, we extracted the residuals from a regression analysis that was run to predict BAT/digit span from chronological age and used these age-adjusted scores to measure the pure effect of these covariates.
ACKNOWLEDGEMENTS We thank  Olivia Boorom  for input on the sentence scoring system,  Youjia Wang  for help creating the EPrime experiment,  Navya Thakkar  for help with the cancellation task materials,  Alexandra Corneyllie  for help preparing the testing space,  Simone Dalla Bella  for sharing the BAT stimuli, and  Phillipe Lalitte  for creating the rhythmic primes. Research reported in this publication is supported by the  National Institutes of Health Common Fund  under award  DP2HD098859  through the  Office of Strategic Coordination/Office of the NIH Director , and the  National Institute On Deafness And Other Communication Disorders of the NIH  under Award  R01DC016977. The content is solely the responsibility of the authors and does not necessarily represent the official views of the  NIH.
Numerical simulations We present in Figure  1  a map of the positions of the third optimum O 3 in the phenotypic plane R 2 with a colorbar picturing the corresponding difference  (19). We recall that the difference λ 2 (O 1 , O 2 ) - λ 3 (O 1 , O 2 , O 3 ), with O 1 and O 2 as in λ 2 (O 1 , O 2 ) - λ 3 (O 1 , O 2 , O 3 ) measures the gain (or loss, if negative) in the chances of persistence, when the third host is added to the system. All Matlab source code used to generate the analyses performed here is available at https://doi. org/10. 17605/OSF. IO/QAV2M. This figure serves as an illustration of the different results of this section. In each figure, the set Λ δ , defined in  (22) , corresponds to the green regions. The last column illustrates the behaviour δ → +∞ of Proposition 4. 1. In particular, on the first line, for β = 1/2, there is a perfect match between the ball given in the asymptotic regime δ → +∞ and the numerical simulations.
* This work has received funding from the  French ANR RESISTE  ( ANR-18-CE45-0019 ) and  DEEV  ( ANR-20-CE40-0011-01 ) projects, from  Excellence Initiative of Aix-Marseille Université  -A* MIDEX , a  French "Investissements d'Avenir" program , and from the  région Normandie BIOMA-NORMAN  ( 21E04343 ) project. The authors acknowledge support of the  Institut Henri Poincaré (UAR 839 CNRS-Sorbonne Université)  and  LabEx CARMIN  ( ANR-10-LABX-59-01 ).
[15] [16] [17]  In particular, we focus on the fact that a shift in the power spectra has been observed in MS, whereby patients with MS show lower power in the alpha frequency band (8-13 Hz) and a shift in the peak frequency toward slower frequencies. [18] [19] [20]  Even during tasks, alpha rhythms are most prominently affected in MS. 21  In this paper, we hypothesize that the delays that are induced by the damage to the myelin in MS are responsible for the observed specific changes in power spectrum, which in turn would be better predictors of clinical disability (as opposed to the total lesion load per se). To test this hypothesis, we utilized a previously published dataset  12  , made of 18 patients with MS and 20 controls. All subjects underwent both a magnetoencephalography scan and an MRI scan, which included the estimation of the tractography.
This way, we have access to the individual connectomes and to the large-scale activity (i. e. , the source-reconstructed MEG signals) for each subject. Firstly, we tested in this dataset for the presence of the previously described shift in the alpha-frequency peak and the reduction of the alpha-band in the powerspectra in MS patients. Once this was confirmed, we used large-scale mechanistic brain modeling  16  to show that, given the individual structural connectomes, such changes on the overall power-spectra could be theoretically expected by higher conduction velocities. 22  The large-scale brain network modeling approach emphasizes the whole-network character of the changes in the organization of brain activities, which provides a link between the underlying physiological mechanisms and the clinical disability.
MRI acquisition and processing The data was processed as in ref. 12  In short, each MRI scan was performed immediately after the MEG recording on the same MRI scanner (1. corrected for head movements and eddy current distortions using the "eddy_correct" routine,  30  rotating the diffusion sensitizing gradient directions accordingly,  31  and a brain mask was obtained from the B0 images using the Brain Extraction Tool routine. 32  A diffusion-tensor model was fitted at each voxel, and fiber tracts were generated over the whole brain by deterministic tractography using the Fiber Assignment by Continuous Tracking (FACT) algorithm implemented in Diffusion Toolkit (angle threshold 45°, splinefiltered, masking by the FA maps thresholded at 0. 2). A cortical study-specific Region of Interest (ROI) datasets was obtained by masking the ROIs available in an MNI space-defined volumetric version of the Desikan-Killiany-Tourville (DKT) ROI atlas  33  using the GM tissue probability map available in SPM (thresholded at 0.
Power-spectra estimation Power spectral density (PSD) for each source-reconstructed time-series was estimated by the Welch method as implemented in MATLAB. 43  In short, each time-series is divided into the longest possible segments to obtain as close to but not exceed 8 segments with 50% overlap. 43  Each segment is windowed with a Hamming window. The modified periodograms are then averaged to obtain the PSD estimate. 43  The resulting PSD estimates were further grouped across ROIs to obtain a median PSD for each participant. The peak alpha frequency, peak alpha spectral density and total alpha power (area under the spectral density curve between 8-13 Hz) were estimated from the resulting median PSD. Differences between groups were tested using the Mann-Whitney U-test, and then Bonferroni corrected across the three parameters explored.
Prediction of clinical impairment via a multimodal linear model We then moved on to test the hypothesis that the inferred delays could improve the prediction of the individual clinical impairment as compared to only including the total volume load in the whole-brain model. To test this hypothesis, we built a multilinear model to predict the individual clinical disability (as estimated by the EDSS) based on the demographics, the clinical data and the total lesion load. Specifically, we considered the EDSS as a dependent variable, while age, education level, gender, disease duration and lesion load (i. e. , the total volume of the lesions) were considered as predictors. Multicollinearity was assessed through the variance inflation factor (VIF). 50, 51  Given the relatively low numerosity of our sample, we validated our model using the leave-one-out cross validation (LOOCV).
Importantly, while promising, our results need to be replicated in larger multimodal, multicentric cohorts, to check that they are not specific to this particular dataset. Although it will be important to check the feasibility of using EEG instead of MEG, as this would contribute to the widespread applicability of our approach, the pipeline we propose (in terms of the building of a subject-specific, large-scale brain model and the Bayesian model inversion to infer the individual delays) is available using the virtual brain (https://www. thevirtualbrain. org), a free, python-based tool designed for personalized largescale models. In conclusion, we propose a principled way to link the most relevant physiopathological feature in MS, namely the slower conduction velocities across the brain network induced by myelin damage, to a large-scale observable quantity, that is the modification of the frequency spectrum.
Acknowledgments Funding The work was supported by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No. 945539  (SGA3);  Human Brain Project and Virtual Brain Cloud  No. 826421 ;  Ministero Sviluppo Economico ;  Contratto di sviluppo industriale "Farmaceutica e Diagnostica " ( CDS 000606 ) and " NextGenerationEU " (project  IR0000011 ,  EBRAINS-Italy ). AP & AB were supported by  NBRC  core funds. AB was supported by  Ministry of Youth Affairs and Sports, Government of India , Award ID: F. NO. K-15015/42/2018/SP-V ,  NBRC Flagship program ,  Department of Biotechnology, Government of India , Award ID:  BT/MED-III/NBRC/Flagship/Flagship2019.
Data availability The MEG data and the reconstructed avalanches are available upon request to the corresponding author (Pierpaolo Sorrentino), conditional on appropriate ethics approval at the local site. In case data are requested, the corresponding author will request an amendment to the local ethical committee. Conditional to approval, the data will be made available. The Matlab code is available at https://github. com/Ziaeemehr/virtual_multiple_sclerosis_patient.
Also included are three additional stations located in the Maldives Archipelago situated in the Indian Ocean. A secondary goal is to assess whether our findings are in agreement with similar published works regarding the range of values for each indicator and the variability of these indicators between sites. The article is organized as follows: Sect. 2 presents the measuring stations, their instrumentation, and the check of the plausibility of the measurements. It also presents the estimates provided by the McClear service. Section 3 describes the selection of clear-sky conditions from measurements, the selection of periods of data from stations, and the methodology of comparisons between McClear estimates and groundbased measurements. The results of comparisons are given and discussed in Sect.
A few stations comprise two class-A pyranometers for G. In these cases, we have arbitrarily selected the time series measured by the pyranometers labeled number 1 by the operator in the description of the station. It could have been possible to compare the two datasets but we had no means to decide which one to keep as we did not have all necessary information on the day-to-day operation of each instrument. This could be best done by the site operator. In other cases, stations are equipped with two instruments measuring G. In these cases, we kept the dataset acquired with the most precise instrument, e. g. , class-A instrument. At most stations supported by the World Bank Group, the diffuse component D is provided by either rotating shadowband irradiometers, which measure G and D almost simultaneously on a horizontal plane every 1 min, or Delta-T SPN1 pyranometers, which comprise seven thermopiles and measure G and D simultaneously.
(2016)  made a detailed analysis of the uncertainty of measurements acquired by a very well-maintained instru- Circles and squares denote stations equipped with rotating shadowband irradiometers and with Delta-T SPN1 instruments, respectively. Red means an elevation less than 900 m. Orange means an elevation between 900 and 1200 m, brown an elevation between 1300 and 1600 m, and white an elevation greater than 1700 m. Numbers refer to the rank of the station in Table  1. The orographic basemap is under the public domain and is from the Etopo1 dataset from the National Oceanic and Atmospheric Administration of the United States of America. ment at the Plataforma Solar de Almería (PSA) station in Spain. They found that the effects of the correction functions, including the spectral irradiance errors, are significant and wrote that uncertainties for corrected 1 min data are estimated to be 2.
+100 (1) 0. 03 E0 ≤ D ≤ min 0. 8 E0 N , 0. 95 E0 N (cos (θ S )) 1. 2 +50 (2) 0 ≤ B N ≤ E0 N (3) The tests based on extremely rare limits are as follows. 0. 03 E0 ≤ G ≤ 1. 2 E0 N (cos (θ S ) ) 1. 2 + 50 (4) 0. 03 E0 ≤ D ≤ 0. 75 E0 N (cos (θ S ) ) 1. 2 + 30 (5) 0 ≤ B N ≤ 0. 95 E0 N (cos (θ S ) ) 1. 2 + 10 (6) The tests on consistency between independent measurements are only applied if G > 50 W m -2. If G and D are given by two independent instruments, the test is as follows. D ≤ 1. 1 G (7) If G, D, and B N are given by three independent instruments, the test is as follows. 0. 92 ≤ (D + B)/G ≤ 1. 08 if θ S ≤ 75 • (8) 0. 85 ≤ (D + B)/G ≤ 1. 15 if θ S > 75 • (9) Suspicious or erroneous measurements were flagged and then removed from the dataset. Then, time series of the retained measurements were plotted together with the corresponding irradiances at the top of the atmosphere, and a visual check was performed to detect and scrutinize outliers that are possibly rejected.
In addition, we have put one more constraint on measurements. Since the lowest values can be noise and are therefore insignificant in a validation process, any measurement should be greater than a minimum significant value. If it was not, the measurement was removed from the dataset. The thresholds were selected in such a way such that there is a 99. 7 % chance that the actual irradiances G, D, and B N are significantly different from 0 and that they can be used for the comparison. Based on the uncertainty of goodquality measurements of B N as reported by the WMO (2018), the threshold was set to 1. 5 times the minimum uncertainty, i. e. , 26 W m 2 for B N. The WMO document does not give any minimum uncertainty for G or D, and the thresholds were arbitrarily set to 30 W m -2 for both.
Methodology of validation The validation was performed by computing the differences between the McClear estimates and the measurements for coincident instants and location, for each year, and for the ensemble of years. The differences were summarized by their mean, known as the bias or mean bias error, their standard deviation, and the root mean square error (RMSE). Relative biases, relative standard deviations, and relative RMSEs at each station were expressed with respect to the means of the measurements for the corresponding period at this station. The Pearson correlation coefficients, slopes, and offsets of the least-squares fitting lines were computed as were the ratios of estimates to measurements and the ratios of variances of estimates to those of measurements. Several graphs were also drawn such as 2D histograms of measurements and estimates, histograms of each dataset, and histograms of differences as well as box plots of ratios and differences as a function of θ S , total column contents in ozone and water vapor, and optical depth of aerosols at 550 nm.
These operations were performed for G, B N , KT, and KT BN at each station for the whole dataset and also for subsets of data built for different years, different classes of θ S , different classes of readings from CAMS, namely optical depth of aerosols at 500 nm as well as total column contents in water vapor and ozone, and different classes of ground albedo read from McClear outputs. Graphs were drawn to assess the influence of these quantities. Results were analyzed as a function of the station, latitude, climate, elevation, year, means of irradiance or clearness index, variances of the measurements, instruments, and even operators to evidence possible trends. Since correlation coefficients close to 1 mean that measurements and estimates vary similarly in time and slopes close to 1 mean that the amplitudes of the variations are similar, expectations are correlation coefficients and slopes of the fitting lines both close to 1.
McClear uses the description of the aerosol properties taken from the database OPAC in libRadtran, namely sea salt, dust, organic matter, black carbon, and sulfate aerosol species. This description may be too coarse to precisely describe the aerosol properties and their influence on the SSI. The mapping between CAMS and OPAC species adopted in McClear may also account for some of the error. McClear also uses prescribed vertical profiles of aerosol loads instead of actual ones, and this may lead to differences of a few percent in irradiance at the surface  (Fountoulakis et al. , 2022). The parameters fiso, fvol, and fgeo  (Schaaf et al. , 2002 ) that describe the bidirectional reflectance distribution functions (BRDFs) of the ground are values averaged over several years for each month taken from  Blanc et al.
Acknowledgements. The authors thank all operators of the ground stations whose valuable measurements they use in their work as well as the  Alfred Wegener Institute , SAURAN, and the  World Bank Group  for hosting and maintaining the websites from which measurements can be collected. Financial support. The research leading to these results has partly received funding from the  Copernicus Atmosphere Monitoring Service , a program being operated by the  European Centre for Medium-Range Weather Forecasts (ECMWF)  on behalf of the  European Union. Review statement. This paper was edited by  Udo Friess  and reviewed by  Brighton Mabasa  and one anonymous referee.
Code availability. The various codes used for the comparison and plots implement well-known equations for computation of differences and statistics as well as well-known libraries in MATLAB and Python and offer no specificities. Data availability. All data used in this research can be freely accessed through several public sources. The BSRN data are the LR0100 product. They can be accessed freely upon registration at https://bsrn. awi. de (last access: 23 August 2022; Alfred-Wegener-Institute, 2022). SAURAN offers free access to its measurements via the website at https://sauran. ac. za/ (last access: 12 August 2022; Sauran, 2022). The World Bank and the International Finance Corporation, collectively the World Bank Group, have developed an open data platform -energydata. info -that provides access to a large number of datasets of solar radiation at the surface.
The other measurements used in this work originate from this resource (https: //energydata. info/dataset, last access: 8 August 2021; energydata, 2021). The McClear outputs and inputs from CAMS can be freely accessed upon registration at the CAMS Radiation Service (http:// www. soda-pro. com/web-services/radiation/cams-mcclear, last access: 30 July 2022; ECMWF, 2022). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/amt-16-2001-2023-supplement.
(2019). Both works used the same dataset of measurements from 11 BSRN stations filtered for clear- sky instants. For the purpose of the comparison with BSRN measurements, Gschwind et al. (2019) computed two spe- cific BSRN-like v1/v2 and v3 versions of McClear tailored to the BSRN spectral range. Hence, the results of the BSRNlike v1/v2 in Tables5 and 6inGschwind et al. (2019;Tables 7 and 8 for B) may be compared to those of the original v1 in Table2inLefèvre et al. (2013)(Table.
6 projections. Similarly, CMIP6 models cover the historical period 1979-2014 and the future scenarios (SSP126, SSP245 and SSP585) for 2015-2100, including 18 GCMs for maximum temperature, 16 GCMs for minimum temperature and 19 GCMs for precipitation. At the time of the crop simulations, only five CMIP6 models were available with the daily fields of global radiation and wind speed required for crop modeling. The data were first rescaled at 0. 5˚spatial horizontal resolution and then bias corrected using the CDF-t method, following the protocol described by Famien et al. [14] , using the EWEMBI forcing data as the reference dataset. This bias-correction method is widely used in Africa and globally both as a statistical downscaling model and as a bias-correction method  [20] [21] [22]. We considered three different time periods: the reference period  for CMIP5 and CMIP6) and future horizons in the short term (2035-2064) and long term (2065-2094) future horizons.
Soil data Soil data were obtained from the Harmonized World Soil Database (HWSD; https://www. fao. org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v12/en/). The original data had a resolution of 30 arcseconds by 30 arcseconds. Physical and chemical characteristics of topsoil (0-30 cm) and subsoil (30-100 cm) were selected for clay, silt, sand, bulk density, organic carbon, and available water capacity. Data were aggregated to the 0. 5h orizontal resolution grid of the climate data by selecting the soil class with the largest area in each grid cell. The parameters such as soil water at field capacity, wilting point and saturation were calculated using the Pedotransfer functions (https://cran. r-project. org/web/packages/ medfate/index. html), and the Van Genuchten parameters were determined from texture class.
Defrance et al. [37]  compared simulations of climate change impacts on crop yields of millet, sorghum and maize in West Africa and found that maize is much more sensitive to water stress variations than millet and sorghum. A total of eight different varieties (Mais90, Mais120, Mil90, Mil120, MilPP, Sor90, Sor120, SorPP) were simulated under current fertilizer use (N limitation) and intensified fertilizer (no NPK limitation). Crop growth parameters were derived from  [33] , who used experimental datasets of local varieties in Africa for calibration. Since these crops are typically grown without irrigation, simulations were conducted under rainfed conditions. Simulations were run for each year from 1975 to 2094, and each emission scenario and simulated crop yields were then averaged over the three time horizons: the baseline  and the two future periods 2035-2064 and 2065-2094.
We did not consider any varietal adaptation between baseline or climate change scenarios. Rising atmospheric [CO 2 ] has the potential to increase crop water productivity (ratio of crop yield to total crop water use) by enhancing photosynthesis and reducing leaf-level transpiration, but the amplitude of this effect is still uncertain in crop models  [11]. Therefore, we replicated the simulations under ambient historical [CO 2 ] (no change in CO 2 concentration) and elevated [CO 2 ]. There was no effect of elevated [CO 2 ] on radiation use efficiency (RUE) for all crops since maize, pearl millet and sorghum are C 4 crops. The CO 2 concentration for each period and each scenario is shown in S2 Table. To evaluate the performance of the crop model in simulating historical yield anomalies, we conducted an additional simulation using the reanalyzed EWEMBI datasets as climate inputs over the 1979-2013 period.