dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Records Format Organization The dataset follows the Brain Imaging Data Structure (BIDS, v. 1. 0. 1) organization and is available and updated on the OpenNeuro Data sharing platform (https://Openneuro. org). BIDS organization are used to facilitate data sharing and unify the majority of projects in the field by using simple folder structure, standardized file names accordingly to acquisition modality in NIfTI format (converted from DICOM) with data descriptions and metadata in JavaScript Object Notation (JSON) files  20. DICOM to NIfTI conversion was performed using dcm2bids v. 2. 1. 4, which uses dcm2niix v. 1. 0. 201  21. All subjects were anonymized using pydeface to remove facial features  22. Please find further details in the corresponding documentation (https://bids. neuroimaging.
Quality Control Quality assessment of the MRI dataset was run using image Quality Measures (MRIQC v. 0. 15: an automatic prediction of quality with visual reports of each sequence) from structural T1w and rs-fMRI images, available in OpenNeuro platform. This tool helps to extract and compute different metrics such as signal-to-noise ratio, a noise model fitting parameters, and spatial distribution of information. All of these can be exported to HTML and JSON reports  23. Please find further details in corresponding documentation (mriqc. readthedocs. io/).
In the same way, volumes (Fig. 1C ) were obtained as the count of all voxels that are in total of cortical gray matter and in subcortical gray matter, these metrics are commonly associated with alteration according to demographic or genetic variables and in several pathologies such as CUD  67. Signal in MRI refers to the mean voxel intensity in the image, in contrast to the random differences in voxel intensity which is considered as noise  68, 69. The ratio of the mean signal intensity and noise is defined as Signal-to-Noise ratio. SNR was extracted from MRIQC output for T1w and rs-fMRI images. Moreover, for DWI, SNR within the Corpus Callosum was calculated using Diffusion Imaging in Python (Dipy; https://dipy. org/documentation/) protocol estimation  70. We extracted the worst (Fig. 3B ) and best case (Fig.
Usage Notes The present MRI dataset is composed from patients with cocaine use disorder and healthy controls. Uses include scientific research and academic purposes. Researchers can use novel analysis, compare variance within techniques, for the pursuit of brain variability between these groups or use one of them for a larger population sample. All clinical and cognitive data are available in Zenodo and accompany each participant's MRI data. We recommend the use of framewise displacement (FD) and artifacts correction methods for preprocessing data due to high motion during scanning.
Acknowledgments We thank the people who helped this project in one way or another:  Alan Dávalos ,  Adedamola Falana ,  Francisco J. Pellicer Graham ,  Margarita López-Titla ,  Aline Leduc ,  Erik Morelos-Santana ,  Lya Paas ,  Daniela Casillas ,  Sarael Alcauter ,  Luis Concha  and  Bernd Foerster. We also thank  Rocio Estrada Ordoñez  and  Isabel Lizarindari Espinosa Luna  at the  Unidad de Atención Toxicológica Xochimilco  for all their help and effort. Finally, we thank the study participants for their cooperation and patience. Victor Issa-Garcia  and  Eduardo A. Garza-Villarreal  would like to thank  Dirección General de Calidad y Educación en Salud, Secretaría de Salud, México  for the scholarship support provided to Victor. This project was funded by  CONACYT-FOSISS  project No. 0201493  and  CONACYT-Cátedras  project No.
Many challenges prevail in cropland mapping over large areas, including dealing with massive volumes of datasets and computing capabilities. Accordingly, new opportunities have been opened at a breakneck pace with the launch of new satellites, the continuous improvements in data retrieval technology, and the upsurge of cloud computing solutions such as Google Earth Engine (GEE). Therefore, the present work is an attempt to automate the extraction of multi-year (2016-2020) cropland phenological metrics on GEE and use them as inputs with environmental covariates in a trained machine-learning model to generate high-resolution cropland and crop field-probabilities maps in Morocco. The comparison of our phenological retrievals against the MODIS phenology product shows very close agreement, implying that the suggested approach accurately captures crop phenology dynamics, which allows better cropland classification.
Thus, a reliable cropland monitoring system requires satellite datasets with both high spatial and temporal resolutions  [4]. For a long time, agricultural research has traditionally relied on satellite data with high temporal frequency and coarse spatial resolution, particularly over large areas. For example, numerous global land cover products have been developed at moderate resolution such as GLC2000  [5] , GlobCover  [6] , GLC-SHARE  [7] , and MODIS Land Cover  [8]. These products are extremely useful for gaining a preliminary understanding of the spatial production patterns and characteristics of agricultural croplands, such as crop dominance and intensities  [9]. However, due to their relatively coarse resolution and considerably low accuracies, these kinds of products are far from satisfactory for describing and capturing the process of land use and land cover changes and consequently capturing the cropland information, especially at the field scale  [10, 11].
For this purpose, a variety of approaches and models may be used to extract socalled phenological metrics. TIMESAT software, for example, allows for the production of smoother vegetation indices curve for each year of study by employing three filtering functions: Savitzky-Golay, Double Logistic, and Asymmetric Gaussian  [32]. In addition, the software allows for the extraction of thirteen phenological metrics from vegetation indices, like the beginning, peak, and length of the growing season, by common extraction methods, such as the simplest threshold method, which assumes that the phenological stage starts when the smoothed vegetation index values reach a specific value  [33, 34]. One important shortcoming of such research tools is that users lack the ability to migrate them to a cloud computing application for rapid phenological metrics extraction and large-scale applications such as at the national scale.
Software Tools and Processing Platforms Because of the large study area, any type of remote sensing process was never going to be simple as it involves massively huge data volumes with highly variable compute and storage workloads (running into tera-to peta-bytes). This makes data processing highly difficult or impractical in regular systems. Therefore, a cloud computing platform for image processing is needed to handle efficiently the storage and exploitation of such large datasets. Google Earth Engine (GEE) has enabled us to overcome these constraints gradually by allowing petabyte-and planetary scale exploration and analysis of geospatial data. GEE provides public access to the most freely accessible satellite datasets, including the entire Landsat archives from 4 to 8, Sentinel missions, MODIS, and many other openly available weather and climate datasets, as well as digital elevation models.
For the spatial and temporal manipulation of these datasets, GEE provides both JavaScript and Python application programming interfaces (API), which allow us to easily develop algorithms that work in parallel on the Google data computer facilities. Another open-source software tool that has been utilized in our work is the Collect Earth Online (CEO), which is a costeffective tool that promotes consistency in locating, interpreting, and labeling reference data plots with high-resolution imagery (0. 5 m, WorldView) for use in classifying and monitoring land cover/land-use change  [39].
Data Preparation Satellite Data The primary datasets for this study came from the twin Sentinel-2A and Sentinel-2B satellites developed by the European Commission and the European Space Agency for global land observation within the Copernicus Program. Both satellites allow the availability of free multispectral imagery at high spatial resolution (10-60 m), with a revisiting cycle of up to 5 days. Two spectral bands were used in the current study: band 4 (665 nm) and band 8 (842 nm), with a spatial resolution of 10 m. Overall, 56,258 Sentinel-2 scenes were acquired over the study area for the observation period from 2016 to 2020. This database was used to derive the cropland extent for a typical agricultural season that was regarded as starting on 1 September and ending on 31 August of the following year.
These comprise 19 bioclimatic variables originally derived from long-term normal monthly climate data on temperature and rainfall collected from weather stations for the period of 1950-2000 and with a spatial resolution of 1 km 2. Lastly, two single layers of forest cover and water provided by GEE were employed to mask forest and water bodies and thus prevent class confusion and minimize processing time. Water surfaces, such as rivers and lakes, were masked out using the 30-m water body mask from  [49] , while forest was masked out using the Global PALSAR-2/PALSAR 30 m binary forest/non-forest map (JAXA FNF, Tokyo Japan)  [50]. Before analysis, all auxiliary variables were reprojected and resampled to 10 m to match the spatial resolution of Sentinel-2 using the nearest neighbor interpolation in GEE platform.
Comparison to MODIS Collection 6 MCD12Q2 Data Based on our extracted vegetation phenology metrics, we performed a number of analyses to visually assess whether the resulting spatial patterns are smoother or whether there are any undesirable artifacts and patches, as well as comparing the resulted phenological metrics with the MODIS Collection 6 MCD12Q2 phenology product. As described before, those data are currently the only operational global land cover dynamics products.
Comparison to MODIS Collection 6 MCD12Q2 Data Based on our extracted vegetation phenology metrics, we performed a number of analyses to visually assess whether the resulting spatial patterns are smoother or whether there are any undesirable artifacts and patches, as well as comparing the resulted phenological metrics with the MODIS Collection 6 MCD12Q2 phenology product. As described before, those data are currently the only operational global land cover dynamics products that include the timing of vegetation phenology at global scales at a 500 m spatial resolution and annual time step. In order not to inflate the number of explanatory pheno-variables, only three main metrics that relate well to biomass and seasonal timing productivity (Midgreenup, Midgreendown, and SINTG) were selected to be the basis for data validation in this study.
Given that MCD12Q2 products were only available at yearly intervals from 2001 to 2018, we selected the season 2017-2018 to serve as the basis for phenology retrievals validation. The three Sentinel-2 derived phenological metrics for the same corresponding period were resampled to 500 m resolution to more closely fit MCD12Q2 spatial resolution for the purpose of statistically comparing our findings with MODIS's phenological metrics. This aggregation was accomplished by taking the average Sentinel-2-derived phenological data of all 10 m resolution pixels for which the center is located within the geometry of a 500 m Modis grid cell. Furthermore, we assessed the relationship between resampled Sentinel-2 and MCD12Q2 metrics based on Pearson correlation coefficients (r) and the root mean squared error (RMSE).
Topographically, the timing of SOS50 values in higher elevation mountain areas were later (DOY > 200) than in lower elevation, flat areas with DOY ~100 (Figure  7 ). In addition to topography, this difference is most likely controlled by climatic conditions (temperature and precipitation), as can be deduced from the grey areas where no SOS data is detected; those areas are either too cold and frequently frozen to support sufficient crop growth (high elevations) or too dry to accommodate crop production without irrigation (the desert regions in the south and east of the country). Geographic variation in mid-green-down timing (EOS50) is similar to SOS50 estimates, with the same relationship to climate forcing and associated environmental variables geographically and temporally across the studied years.
The cropland seems to be concentrated near the coast and in the uplands, where precipitation is higher. However, some oasis-based cereal production and date harvesting can be identified just in the middle of the desert where precipitation is scarce. The eight zooms provided show croplands in great detail at a fine spatial resolution (1 pixel = 0. 01 ha) over different landscapes, ranging from intensive large fields in the western part of the country to fields under pivot irrigation in the central plains and oasis areas in the desert that often surround a water source. Table  2  reports the classification accuracy values and the confusion matrix derived from the analysis of the validation dataset. The overall accuracy of the cropland classification was found to be 97. 86% with a Kappa coefficient of 0.
By using all phenological metrics coupled with ancillary data, we were able to map the cropland distribution within the nominal year of 2019-2020 with an accuracy of 97. 86% using the RF classifier. These findings clearly imply a high degree of confidence in identifying croplands versus non-croplands across large areas such as Morocco, which supports evidence from previous observations such as the GFSAD30AFCE dataset achieved relatively higher mapping accuracy in Africa because it took into consideration phenological information  [19]. The results also proved the robustness of RF implemented in GEE to obtain accurate cropland extent over a very large area. The RF classifier was chosen because it needs few parameters to tune, gives more accurate results than most of the state-of-the-art methods, is computationally efficient in handling large-scale data, and generates a probability for each label  [60, 65].
Conclusions The study generated a yearly cropland distribution at a 10-m resolution over very large fragmented and complex areas (e. g. , Morocco) using multi-year (2016-2020) phenological information, random forest machine learning algorithms, a large volume of reference training, and validation datasets from multiple sources by utilizing the big-data management and processing power of the Google Earth Engine cloud-computing platform. These methods and approaches demonstrated the ability to map croplands and their probabilities at a large big-country scale using petabyte volumes of big data. The resulting 10-m derived cropland extent products for the nominal season 2019-2020 had overall accuracies of over 97%. For the remaining years, a method for crop probabilities estimation was developed and evaluated based on the trained random forest model.
Acknowledgments: The authors would like to thank the  Google Earth Engine team  for providing cloud-computing resources and  European Space Agency (ESA)  for valuable open-access Sentinel data. In addition, the authors would like to thank  FAO  and  NASA  for providing access to Collect Earth Online platform. The authors would also like to thank the anonymous reviewers for their very competent comments and helpful suggestions. The authors acknowledge the financial support of the  National Centre for Scientific and Technical Research (CNRST)  under the  Excellence Research Scholarships Program.
The number of proximity events per user per day is shown in Figure  1. Contact Network. Standard Bluetooth technology can detect devices as far as 5-10 meters away  14. However, since SARS-CoV-2 is generally only spread when individuals are near one another, we focused on close proximity interactions, excluding data with an RSSI value less than -75 dBm. Previous studies have shown this RSSI threshold roughly corresponds to one meter  14. We generated separate contact networks for each day of the simulation. For each day, we computed the number of close proximity interactions (i. e. RSSI ≥ -75 dBm) between every pair of participants. Because devices were configured to scan every five minutes, a unique entry is recorded for every five minute time interval where a ping occurred. As such, the number of close proximity interactions between a pair of students is equivalent to the number of five minute intervals where a ping occurred.
All new individuals inherit the value at the neutral locus  from their parent with no mutation. (3) All adults die; juveniles then become adults. The model was written in Netlogo  (Wilensky, 1999) , version 6. 1. 1, and set up using the nlrx R package  (Salecker, Sciaini, Meyer, & Wiegand, 2019). We tested 5 scenarios (× 2 possible values of ; see below and Fig. 2 ). The "reference" scenario had no Allee effects, no density-dependent dispersal (  = 0) and a dispersal rate  0 set to 0. 2, a biologically "typical" rate according to experimental (e. g. Fronhofer et al. , 2018)  or natural observations  (Marjamäki, Contasti, Coulson, & McLoughlin, 2013; Stevens et al. , 2013) , spanning many taxa. Three other scenarios, which we expected to lead to pushed expansions, each differed from the reference by one parameter: either a weak Allee effect was present ( = 0.
Statistical analyses All data (experimental and simulated) were analyzed in a Bayesian framework, using R, version 4. 0. 3 (R Core Team, 2020) and the brms package version 2. 14. 4  (Bürkner, 2017)  as frontends to the Stan language (RStan version 2. 19. 3; Carpenter et al. , 2017; Stan Development Team, 2018). We used (non)linear multilevel/mixed models. The model descriptions below are summaries aiming for clarity rather than completeness; formal and complete write-ups for all statistical models are given in Supplementary Material 5. We use general-purpose "weakly informative" priors based on McElreath (2020) for all parameters except one where there is strong theoretical prior knowledge (see Supplementary Material 5). For each model, we ran four chains with enough iterations to ensure effective sizes were satisfactory for all fixed effect, random effect and distributional parameters (both bulk-and tail-ESS sensu Vehtari, Gelman, Simpson, Carpenter, & Bürkner, 2020 > 1000).
Genetic diversity, experimental data When expected heterozygosity  (Nei, 1973)   is used as a measure of genetic diversity, theory predicts the genetic diversity both in core patches and at the edge of a range expansion should decay exponentially with time   (Birzu et al. , 2018; Hallatschek & Nelson, 2008) : 𝐻 𝑡 = 𝐻 0 × 𝑒 -𝜆 [𝑔] ×𝑡 , where  0 is the initial heterozygosity, and   [𝑔]  the rate of decay of genetic diversity. The decay rate  [] is proportional to the inverse of the effective population size   , with the exact relationship depending on ploidy (see e. g. Coop, 2020). For experimental data, we used this equation directly in a non-linear model to estimate whether the dynamics of genetic diversity varied between our two treatments and between core and edge patches. Multilocus expected heterozygosity was first calculated for each location (core/edge) × time combination using microsatellite data and the adegenet package  (Jombart, 2008).
Acknowledgements We thank participants to the 2019 conference of the  British Ecological Society  for their helpful comments, as well as  Géraldine Groussier  for letting us use her picture of Trichogramma brassicae. Version 4 of this preprint has been peer-reviewed and recommended by  Peer Community In Evolutionary Biology  (https://doi. org/10. 24072/pci. evolbiol. 100118).
Funding This work was funded by the  French Agence Nationale de la Recherche  ( TriPTIC ,  ANR-14-CE18-0002 ;  PushToiDeLa ,  ANR-18-CE32-0008 ), and received funding from the  European Union Seventh Framework Programme FP7  (grant agreement  FP7-IAPP # 324475 "COLBICS" ).
Naturalistic stimuli, such as movies, activate a substantial portion of the human brain, invoking a response shared across individuals. Encoding models that predict the neural response to a given stimulus can be very useful for studying brain function. However, existing neural encoding models focus on limited aspects of naturalistic stimuli, ignoring the complex and dynamic interactions of modalities in this inherently context-rich paradigm. Using movie watching data from the Human Connectome Project (HCP, N = 158) database, we build group-level models of neural activity that incorporate several inductive biases about information processing in the brain, including hierarchical processing, assimilation over longer timescales and multi-sensory auditory-visual interactions. We demonstrate how incorporating this joint information leads to remarkable prediction performance across large areas of the cortex, well beyond the visual and auditory cortices into multi-sensory sites and frontal cortex.
Dataset We study high-resolution 7T fMRI data of 158 individuals from the Human Connectome Project movie-watching protocol comprising 4 audio-visual movie scans  (15, 16). The movies represent a diverse collection, ranging from short snippets of Hollywood movies to independent vimeo clips. All fMRI data was preprocessed following the HCP pipeline, which includes motion and distortion correction, high-pass filtering, head motion effect regression using Friston 24-parameter model, automatic removal of artifactual timeseries identified with Independent Component Analysis (ICA) as well as nonlinear registration to the MNI template space  (16). Complete data acquisition and preprocessing details are described elsewhere  (15, 16). Finally, whole-brain fMRI volumes of size 113x136x113 are used as the prediction target of all proposed encoding models.
We further arrange the last frame of every second in a 20-second clip into a sequence of 20 images, denoted by   S3. s v t = {x v i } t i=t We employ state-of-the-art pre-trained ResNet-50  (20)  and VGG-ish  (17)  architectures in the pyramid network to extract multi-scale features from images and audio spectrograms, respectively. The base architectures were selected because pre-trained weights of these networks optimized for behaviorally relevant tasks (recognition) on large datasets, namely Imagenet  (21)  and Youtube-8M  (22) , were publicly available. Resnet-50 was trained on image classification with 1000 classes, while the VGG-ish network was pre-trained on audio event recognition with ∼30K categories. Further, due to computational and memory budget, the Resnet-50 was frozen during training across all models.
Interestingly, performance in auditory regions is much closer to the noise ceiling than visual regions. Understanding audition and vision in the same space further allows us to appreciate the differences between these modalities. While this may suggest that audition is perhaps a simpler modality to model, the differences could also result from a bias of the dataset. A more diverse sampling of acoustic stimuli in the training set could allow the model to generalize better in auditory regions. Furthermore, in contrast to auditory stimulation where all subjects hear the same sounds, visual stimulation can elicit highly varied responses dependent on gaze location. This variability could plausibly make group-level visual encoding a more difficult task.