dataesr/scientific-paragraphs-categorization huggingface dataset:
This open-source analysis platform "takes the analysis to the data, not the data to the analysis", providing researchers with the ability to remotely analyse data from multiple datasets without being able to access the data itself. 45, 46 oving the need to physically share data externally means participating cohorts bypass ethical concerns related to the protection of privacy and other issues that arise when participant data are being The heterogeneity of the psychological and cognitive measures available presents a potential limitation. Depending on the specific research question under investigation and measurement equivalence of constructs between different instruments, robust harmonisation  30, 32  of certain measures may not be possible or may be limited to a small number of cohorts. This reduces the sample size or the range of participant ages that are possible to include.
Within-country geographical bias of many of the cohorts may also present a weakness. Specifically, the urban-centric nature of many of the studies could mean that the generalizability of findings will be somewhat skewed, and the population-level inferences will need to take this bias into account. Furthermore, DNA methylation and brain imaging data are only available for less than 10% of total study participants. These smaller sample sizes may limit the number and strength of associations that can be found, as well as the distribution of participant ages and geographic and ethnic origins. However, the cohort studies are continuously expanding and adding new data on their participants, including phenotypic, genetic, epigenetic and biological data. The collaborative groundwork laid by LifeCycle will make it possible to continue building upon the analyses that have been performed, and help to mitigate some of the limitations that have been described.
Data Access LifeCycle has developed an application procedure for data use proposals as described by Jaddoe et al. 1  It should be noted that approvals for data use and associated fees remain under the purview of the participating cohorts. This is the case regardless of whether one applies through LifeCycle or directly to the cohort, and these practices may vary across cohorts. The project strives to conduct as many analyses as possible within DataSHIELD. DataSHIELD is freely available to download and use (http://www. datashield. ac. uk/). This enables external cohorts to collaborate with LifeCycle and perform co-analyses. For more information, please visit the official website for the LifeCycle Project (https://lifecycle-project. eu/), or refer to the consortium design paper.
This type of modeling is data-driven, meaning that it requires a large database of known solutions encompassing a broad range of possible scenarios. While the method is applicable for any region, we choose North Africa and the Arabian Peninsula for this study because desert dust and anthropogenic emissions in this area are highly uncertain. Therefore, chemistry transport simulations over this region are particularly imprecise and require correction. In addition, this region provides an interesting AOD training dataset, since clear sky conditions allow greater availability of satellite data over large areas with a wide range of AOD values. bian Peninsula for this study because desert dust and anthropogenic emissions in this area are highly uncertain. Therefore, chemistry transport simulations over this region are particularly imprecise and require correction.
In addition, this region provides an interesting AOD training dataset, since clear sky conditions allow greater availability of satellite data over large areas with a wide range of AOD values. In the following subsections, we describe the dataset used for the training (Sections 2. 1. 1 and 2. 1. 2), the data preparation (Section 2. 1. 3), and then the ML implementation for correcting AOD regional biases (Section 2. 2).
The Dark Target algorithm is suitable over ocean and dark land (e. g. , vegetation), while the Deep Blue algorithm covers the entire land area, including both dark and bright surfaces. This MODIS dataset is interpolated at the horizontal resolution of the CHIMERE model (0. 45° × 0. 45°) using the mean value method. All MODIS AODs mentioned hereafter refer to the observations from the AQUA platform at the wavelength of 550 nm, unless otherwise noted. The quality of the reference dataset for supervision is critical for accurate ML modeling. Therefore, we filter out low-quality observations by keeping only the retrievals with the highest quality assurance flag. We verify the quality of the MODIS AODs used here by comparing them (Figure  2a ) with daily averages of direct measurements of the AOD from ground-based sun photometers of the AErosol RObotic NETwork  [26].
This network enforces standardization of instrumentation, calibration, and processing to ensure the best quality. The comparison (Figure  2a ) shows that the AOD observations from MODIS for the year 2021 correlate well with the measurements from the eight AERONET stations (the location of each station in Figure  3 ). The Pearson correlation coefficient (r) is 0. 88 and the root mean squared error (RMSE) is quite low (0. 13). The AERONET data being used for the comparison are of version 3, level 1. 5. We convert the AOD from 500 nm to 550 nm using the 440-675 nm Angstrom exponent provided by the same station. In the following subsections, we describe the dataset used for the training (Sections 2. 1. 1 and 2. 1. 2), the data preparation (Section 2. 1. 3), and then the ML implementation for correcting AOD regional biases (Section 2.
The Dark Target algorithm is suitable over ocean and dark land (e. g. , vegetation), while the Deep Blue algorithm covers the entire land area, including both dark and bright surfaces. This MODIS dataset is interpolated at the horizontal resolution of the CHIMERE model (0. 45 • × 0. 45 • ) using the mean value method. All MODIS AODs mentioned hereafter refer to the observations from the AQUA platform at the wavelength of 550 nm, unless otherwise noted. The quality of the reference dataset for supervision is critical for accurate ML modeling. Therefore, we filter out low-quality observations by keeping only the retrievals with the highest quality assurance flag. We verify the quality of the MODIS AODs used here by comparing them (Figure  2a ) with daily averages of direct measurements of the AOD from ground-based sun photometers of the AErosol RObotic NETwork  [26].
An emission inventory derived for 2015 from the Emissions Database for Global Atmospheric Research EDGARv5. 0  [40]  is used for anthropogenic gaseous and particulate matter emissions. The dust emission scheme implemented in CHIMERE requires the knowledge of soil properties and wind conditions. This module allows for the calculation of dust aerosol emissions and their size distribution, by modeling the processes of sandblasting and saltation  [41, 42]. It takes into account the uplift of both silty and sandy soils, which are emitted from the northern Algerian region and the Sahara, respectively  [43]. It should be noted that non-negligible uncertainties remain in the modeling of Saharan dust emission and transport  [44]. An evaluation of the accuracy of the AOD simulated by CHIMERE with respect to AERONET reference measurements is shown in Figure  2b.
Dataset Preparation We chose the days used for training following the common convention in the field of machine learning, i. e. , dividing the whole 12 months of 2021 into two parts: 66% is used in the training and validation phase, and the remaining 33% is reserved for testing the performance of the models. Adequate size and diversity of the training data are important for a good generalization of an ML model. In order to avoid the bias caused by seasonal variability, we take two-thirds of each month in 2021 to build the models. Specifically, we use consecutive days from the 1st to the 20th of each month for the training and validation stages, making 240 days from 2021. We call this dataset D Train and it consists of about 1. 4 million cloud-free ground pixels. According to several tests performed during the design of the ML models, this training data sampling strategy allows for good performance at any season and at diverse pollution levels, from low background pollution to intense dust outbreaks.
Even though D Train is made of sequences of shuffled pixels of consecutive images, the correlation between a training subset and a validation subset is limited due to the decorrelation time scale of the atmospheric processes. This is a key point to ensure that the test dataset is independent and that the computed scores are robust to evaluate the generalization ability of the ML model. The period from the 21st to the end of each month are reserved to test the ML models, which amounts to 122 days not used in the training process. We call this dataset D Test and it consists of about 0. 7 million ground pixels of the CHIMERE simulation. We had to exclude the days 23, 24, and 25 of September 2021 due to the unavailability of MODIS observations. Besides the CHIMERE-simulated AOD, which we will refer to as the "RAW AOD", additional atmospheric composition variables from CHIMERE are used as inputs of the ML models.
Bias Correction ML Models Construction The fitting problem we are trying to solve is overdetermined and the high dimensional input variables are not independent. We implement several machine learning approaches, tree based, a neural network model, and the more classical linear regression model. In the following sections, we describe how the four models are built (Sections 2. 2. 1-2. 2. 4) using the Python programming language version 3. 8 [48] and the web-based interactive computing environment Jupyter notebook  [49]. While training is partially performed on a Quadro P620 GPU, inference runs only on a single thread of a CPU (i9 2. 30 GHz). The time required for training and inference of the ML models is an important indicator of the usability of the developed method. The training phases are relatively short; in fact, the multiple linear regression takes a few seconds, and the training of the other ML models does not exceed one hour each (not counting the hyperparameter search step, which is much longer).
Regarding the inference time, all approaches perform the daily correction of the AOD in less than half a second (Table  1 ), which allows for a potential use of these models in real-time applications. Multiple linear regression modeling is widely used in several fields because of its simplicity and its ease of use and interpretation. While it is more appropriate when the variables are independent, we tested it here to serve as a performance baseline and compare it to the three other more sophisticated ML models. Training is performed on half of randomly sampled D Train data to keep the comparison fair with respect to the other ML models, which also use sampled data during a tuning phase. If F is the function that maps the input features X to the output denoted by y, our goal is to find a function G that approximates F using a set of known solutions {X i ,y i } i N.
Feed-Forward Neural Networks (NN) Neural network regression is a modeling technique that is increasingly being used in many fields, thanks to the advancement of big data and parallelized hardware. The principle is to use perceptrons that are stacked in layers to approximate a non-linear relationship  [51] , where the weight of each perceptron is learned from a reference dataset by gradient backpropagation  [47, 52, 53]. The technique can model non-linear relationships by using some activation functions  [54]. Optimizing multi-dimensional functions with neural networks is difficult because of the proliferation of saddle points  [55] , but stochastic gradient descent algorithms are good at finding the global minimum  [56] [57] [58]. Training of the NN AOD bias corrector is performed using the Tensorflow library v2.
Random Forest (RF) A random forest regressor  [66]  is an ensemble model that is widely used in both classification and regression problems. Each member of the ensemble, called a tree  [67] , is constructed using different randomized parameters. The final prediction is the average of the predictions of all the trees (forest). The model is tuned for three key hyperparameters: number of estimators (trees), minimum samples in a leaf, and maximum number of features. The tuning is performed using the brute-force search method with two cross-fold validations on 10,000 randomly selected ground pixel data from D Train. Using only a subset of the available training dataset speeds up the search process and saves memory. We find out that the best performing RF candidate has a maximum of 20 features, a minimum of 4 samples in leaf, and 100 estimators.
Gradient Boosting (XGB) A gradient boosting regressor  [68, 69]  is also a tree-based modeling technique, but it constructs trees successively in such a way that each tree minimizes the residuals of the preceding trees. The final estimate is the sum of predictions of all the trees. We use the Tree-based Pipeline Optimization Tool (TPOT) library v0. 11. 7. It is a genetic programming-based automated ML system that optimizes a set of feature preprocessors and ML models to build a better data processing pipeline for supervised tasks  [70]. We let TPOT determine the best pipeline for correcting the CHIMERE AOD using 10,000 randomly selected data from D Train. The number of generations is set empirically to 20 and the population size is set to 20, because further increasing these numbers slows down the search process.
The best pipeline suggested by TPOT added the zeros and non-zero counts to the input features, and uses the XGBoost model (XGB)  [69] , which is a variation of the gradient boost model that uses L1 and L2 regularization. The XGB post-processor consists of 100 estimators, with a maximum depth of 9 and a minimum child weight of 17 (see the documentation [71] for more details on the meaning of these hyperparameters). The best pipeline proposed by TPOT is retrained using half of the D Train dataset and is randomly sampled.
Gradient Boosting (XGB) A gradient boosting regressor  [68, 69]  is also a tree-based modeling technique, but it constructs trees successively in such a way that each tree minimizes the residuals of the preceding trees. The final estimate is the sum of predictions of all the trees. We use the Tree-based Pipeline Optimization Tool (TPOT) library v0. 11. 7. It is a genetic programming-based automated ML system that optimizes a set of feature preprocessors and ML models to build a better data processing pipeline for supervised tasks  [70]. We let TPOT determine the best pipeline for correcting the CHIMERE AOD using 10,000 randomly selected data from DTrain. The number of generations is set empirically to 20 and the population size is set to 20, because further increasing these numbers slows down the search process.
The best pipeline suggested by TPOT added the zeros and non-zero counts to the input features, and uses the XGBoost model (XGB)  [69] , which is a variation of the gradient boost model that uses L1 and L2 regularization. The XGB post-processor consists of 100 estimators, with a maximum depth of 9 and a minimum child weight of 17 (see the documentation [71] for more details on the meaning of these hyperparameters). The best pipeline proposed by TPOT is retrained using half of the DTrain dataset and is randomly sampled.
b. Statistical analysis on the testing dataset For the entire DTest dataset in 2021, we compare the estimates of the raw AOD CHI-MERE simulation and the inferences from the ML models with respect to the MODIS observations that are not used in the training phase (Table  1 ). The table shows that the ML models successfully correct the raw CHIMERE AODs in a statistically consistent and stable manner throughout the year 2021. They all show a comparable improvement in AOD correction, with both larger correlation coefficients r with MODIS AOD (r between 0. 62 and 0. 71) and smaller RMSE (from 0. 19 to 0. 21) and MAE. The improvement in AOD estimation is clearly evident when comparing to the raw CHIMERE AODs performance (r value of 0. 56 and RMSE of 0. 65). We note that the XGB and RF are the best-performing bias correctors in terms of the correlation coefficient (0.
b. Statistical analysis on the testing dataset For the entire D Test dataset in 2021, we compare the estimates of the raw AOD CHIMERE simulation and the inferences from the ML models with respect to the MODIS observations that are not used in the training phase (Table  1 ). The table shows that the ML models successfully correct the raw CHIMERE AODs in a statistically consistent and stable manner throughout the year 2021. They all show a comparable improvement in AOD correction, with both larger correlation coefficients r with MODIS AOD (r between 0. 62 and 0. 71) and smaller RMSE (from 0. 19 to 0. 21) and MAE. The improvement in AOD estimation is clearly evident when comparing to the raw CHIMERE AODs performance (r value of 0. 56 and RMSE of 0. 65). We note that the XGB and RF are the best-performing bias correctors in terms of the correlation coefficient (0.
For low AOD levels (below 1), the models mostly cor AOD of CHIMERE to an acceptable degree (yellow colors), with a slightly insu correction for higher AOD levels. The MLR model (Figure  10a ) infers negative A some pixels. This is an artifact (see also Figure  4c ) that is associated with the use tive regression coefficients, which improves the overall performance (r) of th model. In these terms, the best performing ML model is the XGB (Figure  10d ) with fit slope of 0. 52, followed by the RF model. Figure  11  shows an evaluation of the performance of the four ML-based bias correctors in terms of the median bias of the AOD correction over the entire test dataset, D Test. The median bias does not exceed 0. 3 in most of the domain, and it is significantly smaller than that of the raw CHIMERE simulations (Figure  3 ).
In these terms, the best performing ML model is the XGB (Figure  10d ) wit fit slope of 0. 52, followed by the RF model. Figure  11  shows an evaluation of the performance of the four ML-based bias correctors in terms of the median bias of the AOD correction over the entire test dataset, DTest. The median bias does not exceed 0. 3 in most of the domain, and it is significantly smaller than that of the raw CHIMERE simulations (Figure  3 ). Similar biases are seen for the four correctors, with some positive and some negative biases for several regions characterized by high annual mean AOD values (Supplementary Material, Figure  S1b ), and they are smaller in absolute terms for RF and XGB, especially over the Arabian Peninsula and the southwestern part of the Sahara. A common behavior of all the ML models is that they strongly underestimate the AOD over a zone (10° to 20°E, 14° to 20°N) corresponding to the Bodélé Depression (Figure  11 ).
c. Prediction of bias corrected AODs at the different daytimes The AOD correction models are trained using MODIS AQUA satellite observations, which has an overpass time of (13:30 LT). It is necessary to ensure that the models are temporally stable and usable outside the AQUA overpass time. For this purpose, we correct the raw CHIMERE AOD outputs at 10:00 LT using the RF model, and then compare the inferred AODs to MODIS observations from the Terra satellite, which has a local overpass time of 10:30 LT. Thus, the daytime of the training database (13:30 LT) is different from that of the test (10:30 LT). We chose only the RF model to perform the upcoming analysis because the statistical performance of the other ML models is similar. The comparison shows that improvement in correlation and residuals is comparable to the corrected AODs at 13:00 LT.
Among the four ML-based bias correctors, the best results are found for the RF regressor, which gives a spatially smooth AOD in good agreement with MODIS AODs. The other bias correctors used here (MLR, XGB, and NN) also improve the accuracy of the AOD, but show spatial artifacts, some of them related to overfitting. The AOD correction was tested for a different time of the day than the one used for the training. The comparison of the CHIMERE AOD correction at 10:00 LT with that of MODIS Terra, which has a close overpass time (10:30 LT), shows that the RF model also provides good results even for this time of the day, which is different from that of the training dataset. This is a step toward validating the AOD correction for any daytime application of the CHIMERE simulation. Evaluation of the daily consistency of the corrected AOD shows that the overestimation of peaks and the underestimation of background values at a given location (shown for Cairo) have been successfully improved, for both desert dust and anthropogenic aerosol loads.
Acknowledgments: We thank the  DIM Qi 2 of Ile -de-France region  for funding this study. We are grateful to the  CNES  ( SURVEYPOLLUTION/TOSCA  project), and to  ARIA Technologies  for the financial support. We acknowledge the free use of NASA's Earth  Observing System Data and Information System (EOSDIS ). We also acknowledge the free use of the open-source Python software and libraries. We thank the developers of the CHIMERE chemistry-transport model from LMD and  LISA laboratories. We also thank the staff of the (8) AERONET stations used in this work for establishing and maintaining the sites. Finally, we thank the anonymous reviewers for their constructive comments and suggestions, which helped to improve the quality of this manuscript.
Funding: This work is funded by the  Region Ile-de-France  in the framework of the  Domaine d'Intérêt Majeur Réseau de recherche Qualité de l'air en Ile-de-France (DIM QI2)  through the program  Paris   Region PhD  (PRPHD) and  ARIA Technologies , and is supported by the  Centre National d'Études Spatiales (CNES)  through the  SURVEYPOLLUTION  project from  TOSCA  ( Terre Ocean Surface Continental et Atmosphère ), the  Centre National de Recherche Scientifique-Institute National de Sciences de l'Univers (CNRS-INSU) , and the  Université Paris Est Créteil (UPEC).
A resource of ancestral genomes for evolutionary genomics To facilitate the investigation of chromosomal and local genome dynamics across evolution, we developed an extensive resource of ancestral genome reconstructions that spans large portions of the eukaryotic tree of life. This resource is based on an algorithm named AGORA, which computes highly contiguous, near-exhaustive reconstructions of the ancestral gene order at every bifurcation in the species tree, based on gene order information in the extant species of the clade (Fig. 1a ). While AGORA can be installed as a standalone package for tailored research applications, we routinely precompute and release the complete set of ancestral vertebrate genomes for every update of the Ensembl database and for a broad selection of plant and fungi clades as part of the Genomicus synteny database  36.
At the time of submission, Genomicus contains a total of 624 ancestral genomes readily available for download across the vertebrates, plants, metazoa, protists and fungi databases (Supplementary Data 1). These ancestral genomes can be explored and manipulated using the different utilities of the Genomicus web server  36  to perform karyotype comparisons, extraction and evolutionary tracing of conserved synteny blocks (Fig. 1b ), and local gene-gene synteny visualization across ancestral and extant species (Fig. 1c ). A partial draft version of AGORA, combined with extensive manual curation, has previously been used to reconstruct the Brassicacea  37  and Amniota  38  ancestors, illustrating several of these applications.