dataesr/scientific-paragraphs-categorization huggingface dataset:
Several important caveats are worth highlighting. Even this large MRI dataset was biased towards European and North American populations and European ancestry groups within those populations. This bias is unfortunately common in many clinical and scientific references, including anthropometric growth charts and benchmark genetic datasets, representing an inequity that must be addressed by the global scientific community  68. In the particular case of brain charts, further increasing ethnic, socioeconomic and demographic diversity in MRI research will enable more population-representative normative trajectories  69, 70  that can be expected to improve the accuracy and strengthen the interpretation of centile scores in relation to appropriate norms  26. The available reference data were also not equally distributed across all ages-for example, foetal, neonatal and mid-adulthood (30-40 years of age) epochs were under-represented (Supplementary Information 17-19).
2 , Supplementary Information 8). As ongoing and future efforts provide increasing amounts of high-quality MRI data, we predict an iterative process of improved brain charts for an increasing number of multimodal  72  neuroimaging phenotypes. Such diversification will require the development, implementation and standardization of additional data quality control procedures  27  to underpin robust brain chart modelling. To facilitate further research using our reference charts, we have provided interactive tools to explore these statistical models and to derive normalized centile scores for new datasets across the lifespan at www. brainchart. io.
Model generation and specification To accurately and comprehensively establish standardized brain reference charts across the lifespan, it is crucial to leverage multiple independent and diverse datasets, especially those spanning prenatal and early postnatal life. Here we sought to chart normative brain development and ageing across the largest age-span and largest aggregated neuroimaging dataset to date using a robust and scalable methodological framework  2, 24. We used GAMLSS 2 to estimate cross-sectional normative age-related trends from 100 studies, comprising a reference dataset of more than 100,000 scans (see Supplementary Tables 1. 1-1. 7 for full demographic information and Supplementary Information 19 for dataset descriptions). We optimised GAMLSS model specification and parameterization to estimate non-linear normative growth curves, their confidence intervals and first derivatives, separately for males and females, allowing for random effects on the mean and higher order moments of the outcome distributions.
Defining developmental milestones GAMLSS modelling also allowed us to leverage the aggregated life-spanning neuroimaging dataset to derive developmental milestones (that is, peaks of trajectories) and compare them to existing literature. The cerebrum tissue classes from 100 studies (Fig. 1 , Supplementary Tables 1. 1-1. 7, Supplementary Information 18) showed clear, predominantly age-related trends, even prior to any modelling. Comparing these models with multiple non-MRI metrics of brain size demonstrated high correspondence across the lifespan (Supplementary Information 3). Peaks were determined based on the GAMLSS model output (50th centile) for each of the tissue classes and TCV, for both total tissue volumes and rates of change or growth (velocity). A similar series of methodological steps was performed for the set of extended global and regional cortical morphometric phenotypes (Fig.
2 , Supplementary Information 7, 8). To further contextualize the neuroimaging trajectories, diagnostic age ranges from previous literature  73, 76  (blue boxes in Fig. 3 ) were compared with empirical age ranges of patients with a given diagnosis across the aggregated neuroimaging dataset (black boxes in Fig. 3 ). Note that age of diagnosis is significantly later than age of symptom onset for many disorders  73. Developmental milestones were also compared to published work for brain resting metabolic rate  61  , from its minimum in infancy to its maximum in early childhood; anthropometric variables (height and weight), which reach a first peak in velocity during infancy and a second peak in velocity in adolescence  52  ; typical acquisition of the six gross motor capabilities  62  ; and pubertal age ranges as defined based on previous reports  51, 53.
Longitudinal stability To use centile scores in a diagnostically meaningful or predictive way, they need to be stable across multiple measuring points. To assess this intra-individual stability, we calculated the subject-specific IQR of centiles across timepoints for the datasets that included longitudinal scans (N = 9,306, 41 unique studies). Exploratory longitudinal clinical analyses were restricted to clinical groups that had at least 50 subjects with longitudinal data to allow for robust group-wise estimates of longitudinal variability. In addition, there was a subset of individuals with documented clinical progression over the course of longitudinal scans, for instance from mild cognitive impairment to Alzheimer's disease, where we expected an associated change in centile scored brain structure.
Data sharing and out-of-sample estimation We have provided an interactive tool (www. brainchart. io) and made our code and models openly available (https://github. com/brainchart/Lifespan). The tool allows the user to visualize the underlying demographics of the primary studies and to explore the normative brain charts in a much more detailed fashion than static images allow. It also provides the opportunity for interactive exploration of case-control differences in centile scores across many diagnostic categories that is beyond the scope of this paper. Perhaps most significantly, the brain chart interactive tool includes an out-of-sample estimator of model parameters for new MRI data that enables the user to compute centile scores for their own datasets without the computational or data-sharing hurdles involved in adding that data to the reference dataset used to estimate normative charts (Fig.
5 ). Bias and reliability of out-of-sample centile scoring was extensively assessed and endorsed by resampling and cross-validation studies for 'new' studies comprising at least 100 scans. Although already based on the largest and most comprehensive neuroimaging dataset to date, and supporting analyses of out-of-sample data, these normative brain charts will continue to be updated as additional data are made available for aggregation with the reference dataset. See Supplementary Information 1. 8, 4 for further details about out-of-sample estimation.
Data availability Model parameters and out-of-sample centile scores are available at www. brainchart. io and on https://github. com/brainchart/Lifespan. Summary statistics are available in the Supplementary Tables (Supplementary Tables  1 2 3 4 5 6 7 8 ). Links to open datasets are also listed on https://github. com/brainchart/Lifespan. Availability of other MRI datasets aggregated here is through application procedures individually managed at the discretion of each primary study, with additional information provided in Supplementary Table  1. 1 and Supplementary Information 19.
In the parameter selection, GENOA first assigns the error tolerance defined as the largest acceptable error induced by each change in the mechanism (see Sect. 2. 4), and then employs one of the reduction strategies along with its required parameters (see Sect. 2. 1). Afterward, in the reduction cycle, GENOA searches for potential reductions according to the selected reduction strategy. The new mechanism with the first found reduction is then simulated over the conditions from the training dataset (a limited set of conditions used through all the reduction processes, Sect. 2. 2. 1) or from the pre-testing dataset (a more extensive set of conditions used only at the end of the reduction process see Sect. 2. 2. 2). The simulated total SOA concentrations are then compared to those simulated with the reference mechanism, where the differences are used to evaluate the potential reduction (see Sect.
2. 4). In case the SOA differences are under the pre-defined error tolerances, the mechanism with the current reduction is accepted and serves as the basis for the next search for reduction. If the reduction is refused, the following reduction attempt starts with the previously validated mechanism. Once no more reduction is found, the current reduction cycle ends. The next step is either selecting the subsequent error tolerance and/or reduction strategy in the next parameter selection or terminating the GENOA training section. Finally, the performance evaluation of the final reduced mechanism is evaluated under a variety of environmental conditions denoted as the testing dataset (see  Sect. 2. 2. 3). The 0D aerosol model SSH-aerosol is used to simulate SOA concentration and composition, which is required in all the GENOA sections (e.
Datasets of atmospheric conditions applied to reduction All the atmospheric conditions applied to the reduction are extracted from a 3D simulation spanning the latitudes from 32 • N to 79 • N and the longitudes 17 • W to 39. 8 • E over continental Europe in a one-year period (  2015  monthly average of hourly meteorological data (e. g. , temperature, relative humidity), and hourly concentrations of oxidants and inorganic species were extracted from each location. The coordinates and time of each condition are also provided to calculate the solar zenith angle. Because the reduction focuses on the impact on SOA variation, and because no inorganic reactions are considered in the reduced chemical mechanism, the oxidant, radical and inorganic concentrations are fixed as the hourly background profiles obtained from the CHIMERE data.
The concentration of the CHIMERE surrogate for sesquiterpene (denoted C SQT ) is used to estimate the SQT concentration. For the purpose of calculating reduction parameters (e. g. , weighting ratio f w , branching ratio B) and evaluating the reduced mechanisms, a dataset of representative physio-chemical conditions extracted from CHIMERE simulation results is employed in GENOA. Depending on their usages, three groups of conditions are defined: the training dataset, the pre-testing dataset, and the testing dataset.
Training dataset The training dataset is the set of conditions used to initialize the reduction parameters, estimate the reference concentrations, as well as to evaluate the mechanism at each potential reduction. For a mechanism containing over 1000 reactions and 500 species, a complete reduction may require more than 10 000 SOA simulations to evaluate all the reduction attempts. To reduce the number of simulations and the computational cost, a limited number of conditions can be evaluated at each reduction attempt. For the reduction of BCARY degradation, a training dataset of eight conditions is selected, which contains six chemistryrelevant conditions and two additional meteorological conditions. The geographic and meteorological information of each condition is described in Table  5 , where the conditions cover a broad range in time (summer and winter conditions), temperatures ranging from 260 K to 302 K, and relative humidity from 39 % to 89 %.
Pre-testing dataset The pre-testing dataset contains a greater number of conditions than the training dataset, providing a more accurate estimation of the reduction mechanism on SOA formation. After the mechanism has been significantly reduced, the pre-testing dataset is included along with the training dataset in order to evaluate the reduction attempts at the late-stage reduction. At this point of reduction, a slight change in the mechanism significantly impacts the SOA concentrations; therefore, merely evaluating reduction based on the training dataset may not be adequate. Meanwhile, the size of the mechanism has already been significantly reduced, which makes the evaluation of each reduction attempt on the pre-testing dataset less computationally expensive. For the application to BCARY, a pre-testing dataset with 150 atmospheric conditions is selected from the testing dataset, among which 50 conditions for each level (low, medium, and high) of SQT emissions.
Testing dataset The final reduced mechanism, obtained from training, is eventually evaluated with a large number of atmospheric conditions in the testing section. This set of conditions for the final evaluation is referred to as the testing dataset. Among all datasets, the results on the testing dataset are most likely to reflect the performance of the reduced mechanism for 3D modelling. In the BCARY reduction, the testing dataset is selected based on the concentrations of the CHIMERE sesquiterpene surrogate. Its maximum hourly concentration C SQT in ppb is used to exclude conditions with negligible SQT concentration. A testing dataset within a total of 12 159 conditions is applied (see Sect. 3. 2), including all conditions (2 159 conditions) with high SQT concentration (C SQT ≥ 0. 1 ppb), 5 000 random-select ones with medium SQT concentration (C SQT between 0.
Settings for SOA simulations The chemical composition and time variation of SOA due to gas-phase chemistry and condensation/evaporation are simulated using the 0D aerosol module SSH-aerosol  (Sartelet et al. , 2020). As detailed in  Couvidat and Sartelet (2015) , the gas/particle partitioning is estimated with Raoult's law (for the partitioning between the gas phase and the organic phase) and Henry's law (for the partitioning between the gas phase and the aqueous phase). Therefore, some properties of the condensable compounds, such as the saturation vapor pressure P sat and the decomposition in functional groups, are crucial for the modelling. For BCARY derived organics, P sat is calculated using UManSysProp  (Topping et al. , 2016)  with the method of  Myrdal and Yalkowsky (1997)  combined with the boiling point estimation from  Joback and Reid (1987) , and the vapor pressure is computed using the method of  Myrdal and Yalkowsky (1997).
Settings for evaluation For the different datasets, the performance of the reduced mechanism on SOA concentrations is evaluated using the fractional mean error (FME) computed with Eq. 1, where C val,i and C ref,i denote the SOA mass concentration at time step i simulated with the reduced and the reference mechanisms, respectively. To address the difference of performance of the reduced mechanisms at the early stage of the simulations (first day) and at the later stage, the error of one simulation is defined as the larger FME between the FME of the first simulation day 1 and the FME from day 2 to 5, and it is compared to the error tolerance specified in training (Eq. 1). For the evaluation on the training dataset, two errors are estimated compared to the previously verified reduced mechanism with a tolerance denoted pre , and the MCM mechanism with a tolerance denoted ref.
As for the evaluation on the pre-testing dataset, one error compared to the MCM mechanism is calculated, with an error tolerance denoted pre-testing. F M E i1,i2 = 2 i=1 n abs(C val,i -C ref,i ) n i=1 n (C val,i + C ref,i ). (1) In order to begin with a conservative BCARY reduction, the initial values of pre and ref are both set to 1 %. The values of these error tolerances are then increased to larger values, reflecting the looser criteria used throughout the reducing. For every 1 % increase in ref , pre is stepped up by 1 % from 1 % to the value of ref. The maximum values for both ref and pre are set to 10 %. When ref reaches 3 %, the mechanism is expected to be largely reduced. From then, the evaluation under the pre-testing dataset is considered to be added to the reduction. This means that all subsequent reductions are evaluated using both the training and pre-testing datasets.
-Bypassing the evaluation of aerosol-oriented reductions on the training dataset, when applied to lumping, replacing, and jumping. As a result, the aerosol-oriented reduction is evaluated only on the pre-testing dataset to avoid being rejected under some of the extreme conditions in the training dataset (which are less representative of average atmospheric conditions than the conditions of the pre-testing dataset). -Applying an additional type of removing: removing elementary-like reaction. The additional reduction strategy of removing elementary-like reaction is targeted to reaction with multiple products. After rewriting the reaction into a set of elementary-like reactions, each with one oxidation product, GENOA investigates the possibility to remove the elementary-like reactions one by one.
The semi-explicit SQT-SOA mechanism "Rdc. " presented in this section is trained from MCM with GENOA. Detailed descriptions of the building process and its chemical scheme are provided in Sect. -Late stage I, from the 75 th to the 107 th reduction step, where the number of condensables decrease to 7 and no more reduction can be found within ref ≤ 10 % and pre ≤ 10 %. On the 75 th reduction step, the size of the mechanism drops to 68 reactions and 41 species. From there, the reduction is trained on the pre-testing dataset if the condensable species are removed with lumping, replacing, or jumping. For reduction with other types of reduction strategies, it is first trained on the training dataset and then on the pre-testing datasets. From all reduced mechanisms with seven condensable species, GENOA selected the one with the minimum average errors on pre-testing dataset (2.
44 %) to start the next stage. -Late stage II, from the 108 th to the 113 rd reduction step. At this stage, the reduction strategy of removing elementarylike reactions is applied to the training. All reductions that reduce the condensables are evacuated exclusively on the pre-testing dataset. From the 108 th reduction step to the end of the training, the size of the reduced mechanism was reduced from 37 reactions and 19 species to 23 reactions and 15 species, among which the number of condensable species is reduced from 7 to 6. The average (maximum) error of the final reduced mechanism "Rdc. " is 2. 65 % (17. 00 %) under the pre-testing dataset compared to MCM. a fraction of the original number (of reactions or species) that is reduced by the strategy. b columns from left to right are the number (and fraction) of reduced chemical reactions, reduced total gas-phase species, and reduced gas-phase species that can condense on the particle phase, compared to MCM with 1 241 reactions and 493 species (356 condensables).
Reproduction of the SOA concentrations During the testing procedure, the "Rdc. " mechanism is evaluated at 12 159 locations, with two different starting times (0 h and 12 h). Compared to MCM, "Rdc. " presents a high level of accuracy with an average error of 2. 66 % and a maximum error of 17. 29 %. The monthly distribution of the number of the testing conditions as well as the testing errors are described in Fig. 5. The error is lower than 10 % for more than 99 % of the simulations. The summer conditions, between June and September, covering more than half of the testing conditions (63 %, 15 294 conditions), result in an average error of 2. 37 % and a 3 rd quartile error of 2. 85 %. As compared to summer conditions, testing results in winter conditions from October to January (19 % of the testing dataset, 4 570 conditions) display slightly higher uncertainty, with an average error of 3.
Reproduction of the SOA composition The SOA concentrations and chemical composition simulated with the "Rdc. " mechanism and with MCM are compared in this section. The temporal profiles of the total SOA concentrations on an average of the pre-testing dataset and non-ideal conditions are displayed in Fig. 7. Throughout the entire 5-day simulation period, there is excellent agreement between hourly SOA concentrations simulated with MCM and those obtained from the "Rdc. " mechanism. The SOA concentration builds up rapidly in the first few hours, where the results of the "Rdc. " mechanism present relatively larger fluctuations (The maximum FB of 3. 74 % is observed at 1 h on the average pre-testing results). mechanism (black, plain) mechanisms under the non-ideal conditions. The average (blue, plain) and maximum FB (blue, shadow) between MCM and the "Rdc.
" mechanism is also presented. The average SOA concentrations per volatility class on the pre-testing dataset at two simulation times (8 h and 72 h) are listed in Table  7. At both 8 h and 72 h, the "Rdc. " mechanism accurately reproduces the total SOA mass with a relative difference lower than 0. 1 % compared to MCM. An accumulation of the SOA mass into the ELVOC class is observed (51 % of the total SOA mass at 8h and 66 % at 72h), with both the MCM and the "Rdc" mechanisms. The aging of SOA produces compounds of low and extremely low volatility. Regarding the volatility classes, the "Rdc. " mechanism tends to slightly overestimate the SOA resulting from ELVOCs and underestimate the SOA resulting from LVOCs, especially at 72 h. This suggests that ageing leads to "Rdc. " condensables of slightly lower volatility than the MCM ones.
However, the differences are low, up to 0. 4 µg m -3 difference (10 %) at 72 h. The average SOA composition per functional group simulated on the pre-testing dataset at 72 h is displayed in Fig. 8. No significant change in the functional group distributions is found between 8 h and 72 h of oxidation. The alkyl (RC) and carbonyl groups (RCO) contribute the most to the SOA mass, by more than 1 µg m -3 , whereas the other functional groups contribute by less than 1 µg m -3. Overall, the "Rdc. " mechanism satisfactorily reproduces the composition of the MCM-simulated SOA composition for most functional groups, except for nitrogen-containing groups. In comparison to MCM, only two condensable species containing nitrogen are retained in the "Rdc. " mechanism: NBCOOH and C131PAN, leading to an overestimation of the nitrate mass of the organic nitrate group (0.
Sensitivity on environmental parameters The sensitivities of the "Rdc. " mechanism to temperature, relative humidity (RH), and SOA mass conditions are investigated with the pre-testing dataset. The default value of BCARY concentration is 5 µg m -3 , and the default RH and T are set to constant 50 % and 298 K, respectively. As presented in Fig. 10 , the SOA yields simulated by the "Rdc. " mechanism with different environmental parameters show a remarkable resemblance with the SOA yields simulated by MCM. Under 10 µg m -3 , the simulated SOA yields are not affected by the SOA mass loading. This result is consistent with the large contribution of ELVOC reported in Table  7. A discrepancy of 25 % in the average SOA yield at 1 h with an SOA mass loading of 10 3 µg m -3 at 1 h and a discrepancy of 8 % at 72 h with an SOA mass loading of 10 -3 µg m -3 are observed.
Acknowledgements. This work was financially supported by  INERIS  and  DIM QI 2  ( Air Quality Research Network  on air quality in the  Île-de-France  region). The authors would like to thank  Youngseob Kim  from  CEREA  for his help in using the SSH-aerosol model. 29 https://doi. org/10. 5194/egusphere-2022-245 Preprint. Discussion started: 10 June 2022 c Author(s) 2022. CC BY 4. 0 License.
Code and data availability. The source code for GENOA v1. 0 is hosted on GitHub at https://github. com/tool-genoa/GENOA/tree/v1. 0 (last access: 25 April 2022). The associated Zenodo DOI is https://doi. org/10. 5281/zenodo. 6482978. The dataset we used to run the BCARY MCM reduction is publicly available online on Zenodo: https://doi. org/10. 5281/zenodo. 6483088.
https://doi. org/10. 5194/egusphere-2022-245 Preprint. Discussion started: 10 June 2022 c Author(s) 2022. CC BY 4. 0 License. The six chemistry-relevant conditions, which are named after the dominant oxidants (OH, O 3 and NO 3 ), focus on the influences of chemical regimes on SOA formation under either a high NO X regime (represented by high NO concentrations) or a low NO X regime (represented by high HO 2 concentrations). The two additional conditions included on the training dataset to improve the reduction, are referred to as ADD1 and ADD2.