dataesr/scientific-paragraphs-categorization huggingface dataset:
Derived media and media accessibility We produced a set of derived materials, including pictures, movies and 3D objects. Cloud platforms for pictures and movies are very common nowadays and we will not go into much detail on the media accessibility for these derivatives. We used both the Zenodo and figshare platforms (http:// figshare. com) for pictures and for movies, the former for consistency and the latter because of its visually oriented interface and popularity. To regroup our productions on figshare, we created the collection (https://doi. org/10. 6084/ m9. figshare. c. 5101400) shown in  Fig. 4(b). We also experimented with tangible physical objects as a particularly accessible form for 3D models through 3D printing. Many generic 3D-printing databases exist, whereas the NIH 3D Print Exchange  (Coakley et al.
, 2014)  is a specific research initiative dedicated to bioscientific 3D prints. We deposited our models in this resource (Fig. 4c ). Such 3D models can also be disseminated and shared virtually, which is a less common process. We experimented with four platforms: Sketchfab (https:// sketchfab. com; Fig. 4d ), Google Poly (https://poly. google. com; Fig. 4e ), SketchBoxVR (http://sketchboxvr. com) and Trimble 3D Warehouse (https://3dwarehouse. sketchup. com). For the time being, we used the data-set, software, figure and video categories to reference all information on Zenodo. We have created a versioned catalog page of all of the outcomes of these experiments at https://github. com/bam93/fair_covid_ molvisexp/tree/master/overview. Once all of the functionalities described here have been stabilized in the UnityMol software, we will provide instructions on how to export to these platforms on the same catalog page.
Challenges Referencing our data sets on these platforms only represents the first, relatively easy step towards implementing the FAIR principles. One inherent challenge is that some of the elements we share require a specific program to view, for instance the command scripts that run with UnityMol. Such a dependence is difficult to reconcile with the FAIR principle, in particular in terms of interoperability and reusability. For interoperability, the developers of other visualization software  would need to implement a compatibility layer to interpret such command scripts and maintain them, which may not be a viable and easily adoptable generic solution. Reusability is hampered by the fact that software solutions are relatively short-lived and typically become obsolete within a few decades.
Some technical workarounds such as virtual machines may exist, but may themselves be subject to such obsolescence. Further issues may lie in the interoperability and reusability aspects for the (meta)data associated with the visual experiences. For example, metadata are needed to describe software version, script purpose, underlying data sets, dependencies and the variants of output that can be produced. There are definitely many ways to go about implementing the FAIR principles. In this early attempt at visual experiences, we first and foremost want to raise awareness about this as-yet overlooked category, but do not have the ambition to provide a full-fledged solution. Concerning the fact that our data should be findable, the choice to target COVID-19 makes this goal relatively easy to achieve as there are an abundance of hubs for increasing the findability of such data, as described briefly above (Table  1 ).
The accessible attribute should be taken care of by our choice of well established FAIR-compliant databases for most of the produced media. Interoperability is clearly an area for further improvement. In particular, we consider bridges between different visualization environments as an important way forward. Initiatives fostering universal protocols that can be interchanged, such as MolQL for the query language to define selections  (Rose et al. , 2018) , need to be extended to describe all ingredients needed for a visual experience, in particular the molecular scene representation. As a modest step in this direction, we have experimented with a PyMOL session interpreter for UnityMol (https://github. com/LBT-CNRS/PymolToUnityMol), so that PyMOL users can reuse their previously prepared scenes.
ebi. ac. uk/pdbe/covid-19 Ligand-binding sites, A knowledge-base portal with a useful data summary Portal protein-protein on structures, ligands, interactions, functional interaction residues annotations and similar proteins Coronavirus Structural Task https://github. com/thorn-lab/ Structures A github repository that makes it easy to download and Force coronavirus_structural_task_force keep an updated copy of the structural data provided SwissModel https://swissmodel. expasy. org/ Models, interactions The full SARS-CoV-2 proteome was modeled; dedicated repository/species/2697049 information on interactions is provided CASP_Commons https://predictioncenter. org/ Models Generation and evaluation of models of the most caspcommons/index. cgi challenging proteins and domains of the virus COVID Moonshot https://covid.
Country A for HepB, Country B for measles and Country C for YF, with a focus on the years 2000-2017. The countries were chosen as they included examples of relevant vaccination activities over the time period. Standardised demographic data (live births per year, death rates) are based on the 2017 United Nations World Population Prospects (UNWPP) [19]. Immunisation coverage from 1980 to 2016 corresponds to the WHO/UNICEF Estimates of National Immunization Coverage (WUENIC) as published in July 2017 [13] , and 2017-2030 coverage corresponds to Gavi's October 2017 Operational Forecast (OP) [14, 15]. Using each of the vaccine impact methods, we calculate the number of deaths averted due to vaccination over the years 2000-2017. Though we only show this for 2000-2017, we are taking into account the impact of vaccination activities from 2000 to 2030 as we follow individuals through their lives to capture later deaths averted.
Funding We thank Gavi, the Vaccine Alliance and the Bill & Melinda Gates Foundation for funding VIMC (BMGF grant number: OPP1157270 / INV-009125). Under the grant conditions of the Foundation, a Creative Commons Attribution 4. 0 Generic License has already been assigned to the Author Accepted Manuscript version that might arise from this submission. SEL, XL, JT, KW, TG, NMF, KAMG also acknowledge funding from the MRC Centre for Global Infectious Disease Analysis (reference MR/R015600/1), which is jointly funded by the UK Medical Research Council (MRC) and the UK Foreign, Commonwealth & Development Office (FCDO), under the MRC/FCDO Concordat agreement and is also part of the EDCTP2 programme supported by the European Union; and acknowledge funding by Community Jameel.
Acknowledgements This project was supported by the  Australian Research Council , grants  FT180100606  to L. M. ,  DP180100048  to L. M. and K. J. M. and  DE190100042  to K. M. G. P. C. T. acknowledges funding from the  UK Natural Environment Council  (grant:  NE/V001620/1 ). A. G. acknowledges funding from the  French Research National Agency  (grant  ANR-18-BELM-0001-06 ). Experiments were performed on UNSW HPC Katana https://doi. org/10. 26190/669X-A286. This project was facilitated by the activities of the PAGES QUIGS working group. The authors thank the reviewers for their constructive comments, which helped improve the manuscript. This is LSCE publication number 7733.
The key idea in these works is to train a neural network on sequences of consecutive rain radar images in order to predict the rainfall at a subsequent time. Although rain nowcasting based on deep learning is widely used, it is driven by observed radar or satellite images. In this work, we propose an algorithm merging meteorological forecasts with observed radar data to improve these predictions. Météo-France (the French national weather service) recently released MeteoNet  [18] , a database that provides a large number of meteorological parameters on the French territory. The data available are as diverse as rainfalls (acquired by Doppler radars of the Météo-France network), the outcomes of two meteorological models (high-scale ARPEGE and finer-scale AROME), topographical masks, and so on.
The outcomes of the weather forecast model AROME include hourly forecasts of wind velocity, considering that advection is a prominent factor in precipitations evolution we chose to include wind as a significant additional predictor. The forecasts of the neural network are based on a set of parameters weighting the features of their inputs. A training procedure adjusts the network's parameters to emphasize the weights on the features significant for the network's predictions. The deep learning model used in this work is a shallow U-Net architecture  [17]  known for its skill in image processing  [19]. Moreover, this architecture is flexible enough to easily add relevant inputs, which is an interesting property for data fusion. Two networks were trained on the data of MeteoNet restricted to the region of Brest in France.
Their inputs were sequences of rain radar images and wind forecasts five minutes apart over an hour, and their targets were rain radar images at the horizon of 30 min for the first neural network and 1 h for the second. An accurate regression of rainfall is an ill-posed problem, mainly due to issues of an imbalanced dataset, heavily skewed towards null and small values. We chose to transform the problem into a classification problem, similarly to the work in  [16]. This approach is relevant given the potential uses of rain nowcasting, especially in predicting flash flooding, in aviation and agriculture, where the exact measurement of rain is not as important as the reaching of a threshold  [1]. We split the rain data into several classes depending on its precipitation rate. A major issue faced during the training is rain scarcity.
Given that an overwhelming number of images corresponds to a clear sky, the training dataset is imbalanced in favor of null rainfalls which makes it quite difficult for a neural network to extract significant features during training. We present a method of data over-sampling to address this issue. We compared our model to the persistence model which consists of taking the last rain radar image of an input sequence as the prediction (though simplistic, this model is frequently used in rain nowcasting  [11, 12, 16] ) and to an operational and optical flow-based rain nowcasting system  [20]. We also compare the neural network merging radar image and wind forecast to a similar neural network trained using only radar rain images as inputs.
01 degrees spatially and 5 min temporally. We define CRF i,j,k as the cumulative rainfall between times t k-1 and t k at longitude lon i and latitude lat j. We define U i,j,k and V i,j,k , respectively, as the horizontal (East to West) and vertical (South to North) components of the wind velocity vector at t k , longitude lon i , and latitude lat j. Finally, we define M i,j,k = (CRF i,j,k , U i,j,k , V i,j,k ) as the vector stacking all data. Given a sequence of MeteoNet data M (k-1,k-s) = (M i,j,k-1 , • • • , M i,j,k-s ) where s ∈ N is the length of the sequence, the target of our study would ideally be to forecast the rainfall at a subsequent time CRF i,j,k+p where p ∈ N is the lead time step. A pixel belongs to a class if the rainfall accumulated between the time t k-1 and the time t k is greater than the threshold associated with this class.
Presentation of the Meteonet Rain Radar Images and Definition of the Study Area The rain data in the northwest part of France provided by MeteoNet is the cumulative rainfall over time steps of 5 min. The acquisition of the data is made using Météo-France Doppler radar network: each radar scans the sky to build a 3D reflectivity map, the different maps are then checked by Météo-France to remove meteorological artifacts and to obtain MeteoNet rainfall data. The spatial resolution of the data is 0. 01 degrees (roughly 1 km × 1. 2 km). More information can be found in  [22]  about the Météo-France radar network and in  [23]  about the measurement of rainfall. The data presented in MeteoNet are images of size 565 × 784 pixels, each pixel's value being the CRF over 5 min. These images are often referred to as rainfall maps in this paper (see Figure  1  for an example).
Definition and Distribution of Rainfall Classes in the Training Base Similarly to the work in  [16] , the three classes are defined using the following thresholds: L 1 = 0. 1 mm/h, L 2 = 1 mm/h, and L 3 = 2. 5 mm/h. In the following, we define three classifiers (N L = 3) associated with these thresholds. Classifier m (m ∈ {1, 2, 3}) aims at estimating the probability of each pixel of an image to belong to the class C m , with C m = {CRF i,j,k ≥ L m }. The definition of the classes is summarized in the first three columns of Table  1. Note that the scale is in millimeters of rain per hour, whereas MeteoNet provides the cumulative rainfall over 5 min, and therefore a factor of 1/12 is applied. Now, we further detail the distribution of the classes across the database by assessing, for each class, the proportion of CRF i,j,k exceeding its threshold.
2% of the total proportion of the dataset, more than 30% of rainfall maps contain at least one pixel belonging to this class (see the last column of Table  1 ). It is therefore likely that the data of this class form small patches distributed across the images. This phenomenon and rain scarcity are major problems in rain nowcasting; because of them, the adjustment of the weights of the neural network during the training phase is unstable for the classes of heavy rain. Therefore, higher classes are more difficult to predict. This problem is tackled in Section 4. 1. 1.
Presentation of the Meteonet Wind Data MeteoNet provides the weather forecasts produced by two Meteo-France weather forecast models: AROME and ARPEGE. Because it provides a better precision in both time and space, only AROME data were used. From 2016 to 2018, AROME was run every day at midnight, forecasting wind velocity and direction for every hour of the day. The wind-related data available are U component (wind velocity vector component from west to east in m/s) and V component (wind velocity vector components from south to north in m/s). These forecasts are made with a spatial resolution of 0. 025 degrees (≈1 km) at 10 m above the ground. The data presented in MeteoNet is equivalent to images of size 227 × 315 pixels, each pixel's value is the wind velocity at a given time. In the following, those images are referred to as wind maps (see Figure  2  for an example).
Definition of the Datasets The whole database is split into training, validation, and test sets. The years 2016 and 2017 are used for the training set. For the year 2018, one week out of two is used in the validation set and the other one in the test set. Before splitting, one hour of data is removed at each cut between two consecutive weeks to prevent data leakage  [24]. The splitting process is done on the whole year to assess the seasonal effects of the prediction in both validation and test sets. The training set is used to optimize the so-called trainable parameter of the neural network (see Section 4. 3), the validation set is used to tune the hyperparameters (see Table  2 ), and the test set is finally used to estimate the final scores presented in Section 5. The inputs of the network are sequences of MeteoNet images: 12 images collected five minutes apart over an hour are concatenated to create an input.
Each input corresponds to a distinct hour: there is no overlapping between the different inputs but note that overlapping can be an option to increase the size of the training set even if it can result in overfitting. It has not been used here as the actual training set contains 16,837 sequences which are considered to be large enough. The validation set contains 4293 sequences and the test set contains 4150 sequences. The main issue in rain nowcasting is to tackle rain scarcity that causes imbalanced classes. Indeed, in the training base 92. 6% of the pixels does not have rain at all (see Table  1 ). Therefore, the last class is the most underrepresented in the dataset and thus it will be the most difficult to predict. An oversampling procedure is thus proposed to balance this underrepresentation.
Data Normalization Data used as an input to train and validate the neural network are first normalized. The normalization procedure for the rain is the following. After computing the maximum cumulative rainfall over the training dataset, max(CRF), the following transformation is applied to each data: CRF i,j,k ← log(1 + CRF i,j,k ) log(1 + max(CRF)) (2) This invertible normalization function brings the dataset into the [0, 1] range while spreading out the values closest to 0. As for wind data, considering that U and V follow a Gaussian distribution, with µ and σ, respectively, the mean and the standard deviation of wind over the overall training set, we apply U i,j,k ← U i,j,k -µ U σ U (3) V i,j,k ← V i,j,k -µ V σ V (4).
Network Training We call θ the vector of length N θ containing the trainable parameters (also name weights) that are to be determined through the training procedure. The training process consists of splitting the training dataset into several batches, inputting successively the batches into the network, calculating the distance between the predictions and the targets via a loss function, and finally, based on the calculated loss, updating the network weights using an optimization algorithm. The training procedure is repeated during several epochs (one epoch being achieved when the entire training set has gone through the network) and aims at minimizing the loss function. For a given input sequence M (k-1,k-s) , we define the binary cross-entropy loss function  [29]  Loss comparing the output P m i,j,k to its target T m i,j,k : Loss(θ) = - 1 N L N L ∑ m=1 T m i,j,k log(P m i,j,k ) + (1 -T m i,j,k ) log(1 -P m i,j,k ) (6) This loss is averaged across the batch, then a regularization term is added: Regularization(θ) = δ N θ N θ ∑ l=1 θ 2 l ( 7 ) The loss function minimizes the discrepancy between the targeted value and the predicted value, and the second term is a square regularization (also called Tikhonov or 2 -regularization) aiming at preventing overfitting and distributing the weights more evenly.
10GHz, and a GPU PNY Tesla P100 (12GB). For the implementation details, please refer to the code available online: some demonstration code to train the network, the weights, and an example of usage is available on the GitLab repository ( https://github. com/VincentBouget/rain-nowcasting-with-fusion-of-rainfall-and-wind-data-article) and archived in Zenodo (https://zenodo. org/record/4284847).
On the other hand, we can define the precision and the recall: Precision m = ∑ i,j,k TP m i,j,k ∑ i,j,k TP m i,j,k + FP m i,j,k (10) Recall m = ∑ i,j,k TP m i,j,k ∑ i,j,k TP m i,j,k + FN m i,j,k (11) (12) Note that, in theory, if the classifier is predicting 0 for all the data (i. e. , no rain), the Precision is not defined because its denominator is null. Nevertheless, as the simulation is done over all the samples of the validation or test dataset, this situation hardly occurs in practice. Based on those definitions, the F1-score, F1 m for the classifier m, can be defined as the harmonic mean between the precision and the recall as F1 m = 2 Precision m × Recall m Precision m + Recall m (13) Precision, Recall, and F1-score range from 0 to 1, where 0 is the worst possible classification and 1 is a perfect classifier.
Tuning the Oversampling Parameter The percentage of oversampling η defined in Section 4. 1. 1 is an important parameter as it will highly modify the training database; therefore, its impact will now be investigated. Several runs with different values of η have been performed and the F1-score calculated on the validation set are reported in Figures  10  and 11. It appears on those figures that the oversampling procedure has three main advantages: the F1-score converges faster, the results are higher for all classes, and most of all it stabilizes the training process. On raw data (without oversampling) the learning procedure is very unstable, and thus the results will poorly generalize. The oversampling procedure tackles this important issue. Based on this, an oversampling percentage of 90% (η = 0.
Conclusion and Future Work This work aims at studying the impact of merging rain radar images with wind forecasts to predict rainfalls in a near future. With a few meteorological parameters used as inputs, our model can forecast rainfalls with satisfactory results, outperforming the results obtained using only the radar image (without using the wind velocity). The problem is transformed into a classification problem by defining classes corresponding to an increasing quantity of rainfall. To overcome the imbalanced distribution of these classes, we perform an oversampling of the highest class which is less frequent in the database. The F1-score calculated on the highest class for forecasts at a horizon time of 30 min is 45%, our model has been compared to a basic persistence model and an approach based on optical flow and outperformed both.
Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants' demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT ), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT + SLP ), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6. 2-81.
Introduction Large consortia, such as Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA)  ( Thompson et al. , 2020 ) , Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE)  ( Hofer et al. , 2020 ) , and others have aggregated neuroimaging data acquired on many different scanners and recruited subjects at many different sites to conduct meta-and mega-analyses. By applying standardized analysis pipelines to extremely large datasets of thousands or tens of thousands of samples, consortia improve reliability, enhance reproducibility of results, amass sufficient statistical power to detect relatively small effect sizes, and support the ability to divide samples while retaining the power to delineate subsample (e. g. , male vs female or young vs old) and interaction effects.
, 2020 ). The challenge of post hoc combination of datasets stems partly from a lack of a priori harmonization of MRI acquisition sequences. Prospective data collection by consortia such as NCANDA  ( Brown et al. , 2015 ) , ABCD  ( Volkow et al. , 2018 ) , TRACK-TBI  ( Hicks et al. , 2013 ) , and others have prescribed harmonized acquisition parameters at study outset with the expectation of superior performance and obviating the need for post-acquisition harmonization. However, even prospective standardization and prescription of acquisition parameters results in significant variance attributed to sites for relatively short scan duration (e. g. , 5 min) that can be reduced significantly by increasing scan duration (e. g. , 25 min)  ( Noble et al. , 2017 ). It remains unclear whether further post hoc harmonization of these datasets may improve sensitivity and power of analyses.