dataesr/scientific-paragraphs-categorization huggingface dataset:
The coded data were subjected to quantitative and qualitative processing. Regarding the quantitative treatment, the frequency distribution was measured in absolute numbers and in percentages. This processing interprets participants' responses and organizes them into "repeated patterns"  (Willig 2013) , in order to assess the central tendency (mean value). To add nuance to our understanding of how emotions were labeled, the responses in each category were also subjected to a supplementary qualitative analysis for each of these categories, and patterns of responses were inductively identified. This enabled us to interpret the findings in a way that was not based solely on the use of prespecified variables.
| DATA DESCRIPTION We use three main sources of data. The first one is fiscal declarations (24-83 declarations) from the statistical service of the French Minister of Labour (DARES). It contains information about training for firms with at least 10 workers for a given year. In France, firms have to spend a minimum part of their payroll on training their employees. The 24-83 declarations are the tax forms they complete each year to report their provided training. We have information about the total duration of training provided and the number of workers trained. This database also includes firm training spending. However, according to  Carriou and Jeger (1997)  Our third source of data, FICUS/FARE, 5  contains fiscal information about balance sheets. We use value-added (in thousand euros) as a measure of firm output and both tangible and intangible fixed assets as a proxy for capital stock (in thousand euros).
This result is different from the interpretation of agglomeration from  Brunello and Gambarotto (2007) , who argue that firms are providing less training in denser areas because trained workers are more likely to be poached. However, this explanation is not compatible with returns to training being higher in denser local labour markets, and labour pooling seems to be a more plausible candidate to explain our effects. In our study, decomposing the effect of labour pooling between learning and matching effects is more complex, mainly due to the design of the DADS database which does not allow a longitudinal follow-up of workers, necessary to estimate a matching index between high-skilled workers and highquality firms  (Abowd et al. , 1999; Dauth et al. , 2018). However, the literature suggests that the effects of learning are mostly limited to the close neighbourhood, with a range of a few kilometres  (Andersson et al.
Because we only have information about formal training, we do not study the effect of informal training on firms' productivity. Informal training was not considered in the literature we compared our results to. Thus, it should not explain the difference between our results and those of previous studies. Potential biases from the lack of data about informal training are further discussed in the conclusion of the paper. Our second source of data is the Annual Social Data Declarations Database (DADS). It is a matched employer-employee data set produced by the French Institute for Statistics (INSEE). An observation represents an employee working for a given firm. If there are multiple periods of work for an employee in the same establishment and the same task in the same year, those are condensed into one record.
ACKNOWLEDGMENTS The authors thank  Cécile Détang-Dessendre ,  Salima Bouayad Agha ,  Danielle Galiano ,  Mathieu Bunel ,  Mathieu Sanch-Maritan ,  Sébastien Bourdin  and the two anonymous referees for their useful comments and suggestions. They also thank  Marco Mariani  for his discussion on an earlier version of the paper, alongside the participants of the special session 'Counterfactual methods for regional policy evaluation: Policies for regions and cities', at the ERSA 2019 (Lyon).
The exposome recognizes that individuals are exposed simultaneously to a multitude of different environmental factors and takes a holistic approach to the discovery of etiological factors for disease. However, challenges arise when trying to quantify the health effects of complex exposure mixtures. Analytical challenges include dealing with high dimensionality, studying the combined effects of these exposures and their interactions, integrating causal pathways, and integrating high-throughput omics layers. To tackle these challenges, the Barcelona Institute for Global Health (ISGlobal) held a data challenge event open to researchers from all over the world and from all expertises. Analysts had a chance to compete and apply state-of-the-art methods on a common partially simulated exposome dataset (based on real case data from the HELIX project) with multiple correlated exposure variables (P > 100 exposure variables) arising from general and personal environments at different time points, biological molecular data (multi-omics: DNA methylation, gene expression, proteins, metabolomics) and multiple clinical phenotypes in 1301 mother-child pairs.
Most of the methods presented included feature selection or feature reduction to deal with the high dimensionality of the exposome dataset. Several approaches explicitly searched for combined effects of exposures and/or their interactions using linear index models or response surface methods, including Bayesian methods. Other methods dealt with the multi-omics dataset in mediation analyses using multiple-step approaches. Here we discuss features of the statistical models used and provide the data and codes used, so that analysts have examples of implementation and can learn how to use these methods. Overall, the exposome data challenge presented a unique opportunity for researchers from different disciplines to create and share state-of-the-art analytical methods, setting a new standard for open science in the exposome and environmental health field.
g. LASSO, elastic-net, adaptive elastic-net), dimension reduction techniques (e. g. principal component, partial least square analysis) or bayesian model averaging (BMA), Bayesian kernel machine regression (BKMR), etc. )  (Stafoggia et al. , 2017 , Lazarevic et al. , 2019). A series of limitations of these approaches have been previously identified such as the lack of model selection stability (shrinkage methods), lack of interpretability of the latent variables (dimension reduction) and computational inefficiency (Bayesian models). In addition, they are rarely applied in the context of large (>100 variables) and heterogenous exposome data (omics, categorical/continuous variables). To address the numerous challenges that come with the analysis of newly available exposome data and to promote interdisciplinary collaboration between researchers from around the world, ISGlobal hosted a 3-day online data challenge in April 2021 entitled "the Exposome Data Challenge Event".
This is the first data challenge organised in this field, and still a rarity in the academic sphere. Data challenges, hackathons or crowdsourcing events were initially used for software development in the 2000's but are now additionally used in healthcare research to accelerate innovation and peer-reviewing, structure learning, test reproducibility of results, and enable wide participation. Briefly, these events allow participants, usually organized in teams, to respond in a short time frame (1-3 months) to common biological questions using a specifically provided dataset. The Exposome Data Challenge Event was inspired by previous events such as the National Institute of Environmental Health Sciences (NIEHS) workshop for assessing Health Effects of Environmental Chemical Mixtures in Epidemiology  (Taylor et al.
, 2016)  organised in 2015 or the DREAM challenge annual series  (Ellrott et al. , 2019)  during which biological data sets are released to the international community to build computational models that address specific biological questions. This data challenge was particularly motivated by the need to address interpersonal interactions constrained by the COVID-19 pandemic and provide a platform to researchers from various genders, backgrounds, and career stages. It was built upon a well-established dataset from the HELIX cohorts which measured the early life exposome  (Maitre et al. , 2018; Vrijheid et al. , 2014). Exposome-health and exposome-omics associations in this dataset have been previously well described within the HELIX consortium. However, the consortium was ready to open and brainstorm with a wider scientific community about unresolved challenges of exposome cohort data such as non-linear combined effects of exposures, causality and omics integration, repeated time points and multi-cohort design.
Our main objective was to promote innovative statistical, data science, or other quantitative approaches to study the health effects of complex multi-dimensional exposures and high throughput omics measurements in this unique exposome dataset. At this stage, exposome studies are often framed without an a priori hypothesis, with an explorative approach, therefore we formulated the challenges around the data analysis and not specific research questions, ensuring the generalizability of the outputs. In addition, we wanted to deliver for the community a common public training dataset for exposome studies, programming code clearinghouse and open collaborations for future projects. This event gathered a widely diverse scientific audience of participants, including environmental epidemiologists, biostatisticians and computational scientists, to discuss state-of-the-art statistical methods for studying exposome-health associations.
The participants were offered an opportunity to test their statistical methods of choice addressing one or several key challenges: a) the high dimensionality of the data, b) combined effect of exposure or mixtures, c) the omics data integration and the d) causal structure in the exposome. Participants were encouraged to accommodate in their approaches some of the particularities of the data (e. g. multi-cohort, count responses, categorical and continuous exposure variables, exposures measured at two time points, etc). Visualization of the results was also a key point across all the challenges listed above. This report outlines the approaches presented at the event, which represent useful computational, conceptual, and statistical models for analyzing high dimensional exposome datasets, including omics and health outcome associations.
Event organisation First, participants were invited to submit an abstract describing their team, the challenge(s) and the method they would apply on a common partially simulated exposome dataset (based on real case data from the HELIX project). The planning committee selected a total of 25 abstracts out of 39 based on method clarity, novelty, relevance for the exposome field and challenges presented. Second, the selected participants were invited to apply their method on the dataset during a month leading to the event. Third, they presented at the event their method's statistical background, type of research question(s) it best addressed, and their results. At the end of the event, the committee and the audience voted for the best presentations based on clarity, novelty and relevance. Finally, the participants made their code available on the github account  (Gonzalez, 2021)  of the event and videos of the presentation are available on the youtube channel of the ATHLETE project.
Data The exposome data provided for this challenge came from the HELIX subcohort database and were partially simulated. The HELIX study  (Maitre et al. , 2018; Vrijheid et al. , 2014)  represents a collaborative project across six established and ongoing longitudinal populationbased birth cohort studies in six European countries (France, Greece, Lithuania, Norway, Spain, and the United Kingdom). From the 31 mother-child pairs included in the cohorts, a subcohort of 1301 mother-child pairs were followed up with measurements of biomarkers, omics signatures and health outcomes at 6-11 years of age. The data provided for the challenge came from this subsample, but it was partially simulated to respond to policies of data anonymization for privacy protection in the cohorts. In detail, for the set of health outcomes and exposures, we conducted the following process: for each participant, a total of 50 random variables (different for each subject) were converted into missing values and then imputed (in successive rounds of variables at a time) with the method of chained equations.
Thus, for each participant, some of the values in the provided dataset were real and some were simulated, in a way that precluded knowing what is real data and what is simulated. Omics data instead were kept intact but annotations of genes and metabolites were not provided. We provided an imputed dataset in which all missing values in the original data were imputed by the chained equations method. Exposure data were transformed (e. g. logarithmic, square root, tertiles) to achieve symmetric distributions with a homogenous range of values (Annex 1). The original raw data are available on request subject to ethical and legislative review. The "HELIX Data External Data Request Procedures" are available with the data inventory in this website: https://www. projecthelix. eu/data-inventory. The datasets are available in the github repository of the challenge event and transcriptomic and Epigenomics through a FigShare account: https://figshare.
com/account/home#/projects/98813. An overview of available data is shown in Fig. 1  and the complete codebook description is available in Annex 1. It includes more than two hundred environmental exposure variables, 13 covariates, six health outcomes [body mass index (BMI), asthma, birth weight, neurobehaviour, intelligence quotient (IQ)], and omics data (serum metabolome, urine metabolome, proteins, gene expression, methylation). Exposure, covariate and health outcome datasets contain both continuous and categorical values; health outcome data additionally included count data. Exposure and omic datasets both included highly correlated features (correlation > 0. 8).
Challenges The main challenges that were addressed during the workshop were as follows: (1) The high dimensionality of exposure data and, more precisely, methods to reduce it while minimizing the loss of useful information regarding health associations and combined effects. The exposome dataset available during the challenge consisted of a large number of environmental exposure variables and a multiomics dataset (approximately 0. 5 M features), with a high inter-variable correlation, but a small sample size (N = 1301), typical of exposome studies. (2) The combined effect of exposure or mixtures. Researchers are interested in studying individual and combined effects of a large number of exposures together accounting for their potential interactions. Effects of environmental exposures may be small when taken individually, but their aggregation may lead to a significant alteration of the health outcome of interest, leading to cocktail effects that researchers want to investigate.
(3) Omics data integration. Omics data may be used, in addition to exposure data, in order to provide causal inference on the link between exposome and health. The challenge here was to incorporate one or several of the different omic layers available, with the purpose of finding patterns that can explain variations in one or more health outcomes and analysing how the exposome and the omes interact with regard to these outcomes. The method used in this context must be able to maximise omics data predictive power with very high dimensional data and small sample size (N≪P). (4) Causal structures in exposure data. This challenge included: 1) how to incorporate a priori hypothesized causal relationships between the different exposures and one health outcome into the analysis, 2) the comparison of this a priori approach with agnostic analyses that would perform variable selection treating all exposures in the same way, 3) how one can answer a large number of causal questions referring to different exposures using causal inference techniques for high-dimensional data, 4) the incorporation of mediation analysis and high-dimensional mediation analysis.
Results In this section, we summarise the statistical methods used by the participants in the Exposome Data Challenge listed in Table  1. All the codes developed by the participants to perform their analyses are available in the GitHub repository of the event. Briefly, most presentations focused on one health outcome out of the six available, mainly child BMI, and five used multiple outcomes. Seven presentations included categorical outcomes (i. e. asthma, BMI and birth weight < 2500gr), the rest focused on continuous outcomes only. Less than half of the methods included categorical exposure variables, focusing mainly Fig. 1. Overview of the data available during the challenge. The data were based on the HELIX project which collected exposome, omics and health data from six mother-child cohorts across Europe in 1301 participants  (Maitre et al.
, 2021). In the application, they included several indices according to exposure families, and each exposure family could have two indices if the family contained both exposures that were positively and negatively correlated with the outcome. They conducted analyses separately by cohort and included serum metabolomics data as an additional group of exposures. Midya (2) et al. presented the application of LASSO-type Bayesian Weighted Quantile Sum Regression (LBWQSR), which is similar to BGIR, but introducing LASSO and Elastic net penalties to prevent overfitting  (Xu and Ghosh, 2015). In the group of response surface methods, Chattopadhyay (5) et al. presented a method to search for two-way non-linear interactions. Twodimensional splines were used to capture the shape of the association for all pairs of continuous exposures.
This approach combines the dimension reduction and interpretability of linear index models (such as WQSR and BGIR) and flexible exposure-response modelling of response surface methods (such as BKMR and PIMGP). With BMIM, the original exposures are reduced to a set of indices, as in BGIR. The approach simultaneously estimates the index component weights (with variable selection) and a potentially complex, high-dimensional exposure-response relationship between the indices and health outcome. Thus, it allows non-linear effects of the indices and interactions between indices. This presentation won one of two exposome data challenge prizes.
Multi-stage approaches for combined effects Multi-stage modelling approaches were applied to model patterns in the exposome data prior to examining associations with the outcome. For example,  Pearce et al. (12)  linked the exposome to birth weight by applying the framework defined as Exposure Continuum Mapping (ECM). An exposure continuum map is a spatially organized map of exposome features that places similar exposure profiles close to each other and different ones are further apart. It is built in two steps: first they build a low dimensional (2D) representation of the data using Kohonen self-organising maps  (Kohonen, 1982)  in order to identify exposure profiles. Then, using information from this organised map, they used a Generalized Additive Model (GAM) to build a 3D exposure-response function that allows examination of a total mixture effect.
Studies using omics data to improve inference on the link between exposome and health Several studies used one or more omics datasets as intermediate layers and conducted some kind of mediation or meet-in-the-middle analyses. Wang (Ziyue) et al. (  10 ) studied the link between the exposome and asthma using transcriptome as an intermediate layer. In particular, they used a combination of 1) differential gene expression and gene set enrichment for asthma, 2) exposure selection in a model for asthma via elastic net and calculation of exposure risk scores, and 3) high-dimensional mediation analysis. Shen et al. (  18 ) also conducted mediation analysis, in this case using several omics datasets (transcriptome, proteome, serum/urine metabolome) as potential mediators in the relationship between the exposome and several health outcomes (IQ, behavior, BMI).
All models were fitted with linear mixed models and they used BH correction to correct for multiple comparisons. This presentation received the committee prize for integrating all the high dimensional omics and multiple outcomes while using informative visualisation of the results. Wang (Congrong) et al. (  19 ) conducted a causal mediation analysis using multi-omics layers (transcriptome, proteome, serum/urine metabolome) as potential mediators of the relationship between the exposome and BMI. In this case, they used multi-omics factor analysis to reduce dimensionality of the omics layers and factor analysis to reduce the dimensionality of the exposome. Yu et al. (21)  presented Gatekeepers  (Yu et al. , 2021)  a new theory to assess exposure-metabolites associations. They identified some Gatekeepers in the data using the Pearson correlation and then studied their associations with asthma.
Gatekeepers are metabolites associated with both exposures and other metabolites. They are presented as the bridge between the exposome and the metabolome. Brunius et al. (  17 ) also implemented a meet-in-the middle approach in which they used the proteome, serum metabolites, urine metabolites, gene expression and methylation as middle layers between the exposome and health outcomes. In their analytical pipeline, Omics Modules for Exposome Health Associations (OMEXA)  (Shi et al. , 2019) , they used machine learning (MUVR Multivariate Methods with Unbiased Variable Selection, a predictive machine learning algorithm using an embedded recursive feature elimination mechanism within a repeated double cross validation procedure) to select the exposure variables related to the phenotypes available and partial correlation to further refine the selected list of exposures, adjusting for covariates.
They inferred groups (clusters) of participants with specific exposomic profiles for which those differences were the highest. The underlying method used was a recent implementation (https://github. com/teff-pac kage/teff) of random causal forests  (Wager and Athey, 2017). They also looked for omics markers (transcriptomic and methylomic) that were associated with differences between the exposome clusters. This presentation won the popular vote of the challenge.
Results on chemical pollutants and zBMI In this section, we summarise the results obtained by different participating groups that addressed a similar research question, namely the relationship between chemical pollutants and zBMI. This is done for illustrative purposes and to provide some idea on how similar are the results when different methods are applied to the same dataset. The results of such comparison are of course dependent on the data and methods used and cannot be generalised, but they can give an idea of what could happen in a real research setting if multiple statistical analyses are applied. We also note that the dataset used was partially simulated, and that the analyses did not necessarily adjust for all necessary confounders. Therefore, subject-matter associations reported in this section should not be interpreted, and we just restrict the focus on the reproducibility of associations under the different analyses.