dataesr/scientific-paragraphs-categorization huggingface dataset:
Numerical Model Simulation Data Numerical simulations can help to better understand the processes involved and to evaluate the ocean hydrodynamics when in situ observations are lacking. To cover the 2000-2018 period, two numerical simulations were used: the MARS3D-BACH (BiscAy Channel; modelled fields available from 2001 to 2012) and the MARS3D-MARC (Modelling and Analysis for Coastal Research; modelled fields available from 2010 to 2018) simulations. These realistic simulations are based on the primitive equation MARS3D model (3D hydrodynamical Model for Application at Regional Scale;  Lazure and Dumas, 2008) , developed by French Institute for Research and Exploitation of the Sea (IFREMER). The MARS3D-BACH and MARS3D-MARC simulations are thoroughly described in  Theetten et al. (2017) ;  Charria et al.
Data Analysis To identify low salinity extreme events that occurred in the Bay of Brest, we focused on the winter period (December to March) from 2000 to 2018. We developed a data processing chain that first identifies and filters tides. We then detected extreme low salinity events and analysed the relationship with precipitation, river runoffs and atmospheric weather regimes. The Bay of Brest is governed by a strong tidal regime with a tidal range of about 6 m. During the ebb tide, freshwater accumulates in the bay from river water flowing to the open ocean; seawater inflow from the Iroise Sea is observed during the flood tide. To detect a freshwater signature at the COAST-HF-Iroise buoy, several tidal cycles are necessary. The lag between river discharge and its detection at the sampling station is mainly modulated by the tidal amplitude and phase  (Frère et al.
Note that the 15th percentile showed the best detection rate (Table  2 ). At the COAST-HF-Smart buoy, the threshold was lower (equal to 23. 73) due to its location close to the Aulne river estuary. When analysing in situ measurements (Figure  2 ), we detected 46 (COAST-HF-Iroise) and 52 (COAST-HF-Smart) extreme low salinity events over the common sampling period  (2008) (2009) (2010) (2011) (2012) (2013) (2014) (2015) (2016) (2017) (2018). From 2000 to 2018, 72 events were reported at COAST-HF-Iroise, i. e. , an average occurrence of four events per winter. For both in situ datasets, we found a mean duration of the extreme low salinity events of about 2-3 days (Table  3 ), the maximum duration of an event being 27 days. The same number of events is not exactly similar due to differences in: amplitude, duration and lack of in situ data.
Then, in some cases, observed in situ events are not detected in modelled time series due to modelled less intense events. Modelled events can also be too short in the simulation to be identified as events. Missing observations can explain modelled detected events not observed. To explore the dynamics during periods with missing observations, model simulations are essential. We therefore compared in situ dataset between the COAST-HF-Iroise buoy and the MARS3D-BACH and MARS3D-MARC simulations. We reproduced the seasonal cycle well with the MARS3D-BACH simulation, with both and increase in salinity from March to September and a decrease during autumn and winter (Figure  2A ); we also obtained similar intensity signals. The MARS3D-MARC simulation also reproduced the salinity seasonal cycle well, but salinity was overestimated in summer (Figure  2A ), most likely because of the use of different forcing parameters in both simulations.
ACKNOWLEDGMENTS This study is part of the Contrat Plan Etat-Région ROEC partly supported by  European Regional Development Funds. In situ observations were provided by the  COAST-HF  (http://www. coast-hf. fr)and the SOMLIT (http://somlit. epoc. u-bordeaux1. fr) national observation network from the ILICO research infrastructure. Modelling experiments (BACH configuration) were performed with  GENCI  ( French Big National Equipment Intensive Computing ) computational resources administered at  CINES  ( National Computing Center for Higher Education ). Data processing and figures were partly carried out using the open-source Python library VACUMM (http://www. ifremer. fr/vacumm/) and the Geographical Information System (QGIShttp://www. qgis. org). Tide gauges data were provided by the  French Naval and Hydrographic Service (Shom).
By 2030, more than 80% of Europe's population will live in an urban environment. The urban exposome, consisting of factors such as where we live and work, where and what we eat, our social network, and what chemical and physical hazards we are exposed to, provides important targets to improve population health. The EXPANSE (EXposome Powered tools for healthy living in urbAN SEttings) project will study the impact of the urban exposome on the major contributors to Europe's burden of disease: Cardio-Metabolic and Pulmonary Disease. EXPANSE will address one of the most pertinent questions for urban planners, policy makers, and European citizens: "How to maximize one's health in a modern urban environment?" EXPANSE will take the next step in exposome research by (1) bringing together exposome and health data of more than 55 million adult Europeans and OMICS information for more than 2 million Europeans; (2) perform personalized exposome assessment for 5,000 individuals in five urban regions; (3) applying ultra-high-resolution mass-spectrometry to screen for chemicals in 10,000 blood samples; (4) evaluating the evolution of the exposome and health through the life course; and (5) evaluating the impact of changes in the urban exposome on the burden of cardiometabolic and pulmonary disease.
Approach EXPANSE brings together large datasets on urban populations, including cohorts based on administrative data (covering more than 55 million individuals) and individual adult and matured birth cohorts (covering more than 2 million individuals) with highly personalized data prospectively collected in EXPANSE's Urban Laboratories. EXPANSE increases the coverage of the External exposome by extending the number of exposures that are included and by refining the resolution with which these exposures are assessed in space and time, collectively contributing to the number of data elements (e. g. , environmental stressors, exposome factors, and molecular markers) that is collected per individual. As such all datasets used in EXPANSE can be placed on a two-dimensional spectrum formed by the number of individuals and number of data elements (Figure  2 ).
Through the integration of datasets that have a different balance in number of individuals and number of data elements (e. g. , administrative cohorts versus urban laboratories), we strive towards replication of results across study designs, studies, and molecular (OMICS) levels. This triangulation approach will allow interrogating mediating (molecular) pathways from the external exposome to preclinical and clinical morbidity, as well as mortality. By including both adult and matured birth cohorts (in which participants have reached adulthood), we will be able to apply accelerated longitudinal design and trajectory methods  16  to study the exposome across the life course (i. e. , from birth to adult health). The combination of large data sets and novel technologies (such as tracking) and OMICS approaches requires ethical reflection from the onset of the project.
To fully understand the impact of the urban exposome on the burden of CMPD, we will move away from one exposure and one disease at a time toward the exploration of combinations of determinants and mediators over the full life span, accounting for variations in these factors over time. All used data sources (administrative, adult, and child cohorts) encompass multiple health endpoints allowing broadening of the disease scope of EXPANSE over time. To answer the three main questions defined within EXPANSE, we developed five major focus areas: (1) measure the external urban exposome; (2) measure the internal urban exposome; (3) link the urban exposome to CMPD; (4) evaluate intervention strategies to prevent CMPD; and (5) develop and apply novel enabling analytical methods.
One of the crucial aims for measuring the external exposome is to develop harmonized exposure datasets across the EU. For a number of stressors, these are already available, but often at a too coarse scale to capture intraurban variability, at a too low temporal resolution to capture temporal variation from the early 1990s, or covering only a fraction of the urban European population. 26 hile increasing both the spatial and temporal resolution of the external exposome, we will aim to go beyond the State of the Art in modeling a number of important stressors. For example, currently ultrafine particles (UFP) models exist for a number of cities/areas in Europe,  27, 28  but transferability of these models to other cities or areas in Europe is challenging. 29 To fill this gap,  we will develop models to predict UFP concentrations across the EU combining measurement data (routine monitoring sites, past monitoring campaigns, and mobile monitoring data) with a number of predictor variables depicting UFP source activity.
The development of UFP models will be based on approaches described by Apte,  30  Messier 31 and van Nunen,  27  for the first time extending this to a European-wide scale. UFP was chosen as first priority for this exercise, due to the expertise available within the EXPANSE consortium, and the fact that such data is obtained by mobile sensors. Lessons learned from the use of data from multiple mobile sensors will be applied in the development of other exposome factors in a later stage of the project. Individual exposures can be improved by combining classical field-based modeling (e. g. , land-use regression and dispersion) with agent-based modeling, which incorporates behavior and time activity patterns. The latter addresses the geographical uncertainty problem as people are not static but move in space and time.
Measure the internal exposome One of the biggest challenges of internal exposome research is its complexity and the plethora of environmental components, their metabolites, and their induced molecular signatures, which complicate the quest to associate features of the internal exposome with specific characteristics of the external exposome. In the last 10 years, a considerable progress in untargeted liquid-(LC-) and gas (GC-) chromatography with high-resolution mass spectrometry (HRMS) shows it is possible to routinely detect 1000s of metabolites and chemicals in a relatively small volume of blood (or other biological material). While traditional metabolomic approaches focus on using untargeted LC-HRMS to measure endogenous metabolites, systematic optimization of LC-HRMS and GC-HRMS platforms combined with innovative new data extraction approaches now enable detection of exposure-related chemicals present at very low concentrations.
Application to human populations allows detection and characterization of a large range of exogenously derived small chemicals that include pharmaceuticals, pesticides, plasticizers, flame retardants, preservatives, dietary compounds, and microbial metabolites that provide a measure of chemical internal dose. Combined with endogenous metabolites also detected using these approaches, characterization of the internal exposome provides a central measure linking exposure to internal dose, biological response, and metabolic alterations consistent with disease pathobiology. Integrating measurements of the internal exposome with exposures faces several major challenges. Despite the rapid development of metabolite databases such as the Human Metabolite Database (HMDB) over the last 10 years,  32  a large fraction of chemical signals detected by LC-HRMS and GC-HRMS in human biospecimens do not match any of the known entries commonly present in chemical databases.
34 he EXPANSE project will address these challenges and analyze the exposome in 10,000 blood samples from various adult and matured birth cohorts through Europe by both LC-and GC-HRMS. The resulting database will provide the largest characterization of the human internal exposome to-date, and will be a key and lasting resource for understanding the distribution and magnitude of environmental exposures and associated effects in the EU. Annotation of the exposome will include in-house chemical libraries for over 1000 chemicals, including pollutants, contaminants or food compounds, and expert knowledge on the metabolism of xenobiotics, while databases (such as the recently developed CECscreen database  35  ) will be used to assign annotations of 1,000s of additional exposures. The evaluation of the value of these compounds as biomarkers of exposure will rely on literature data curated by IARC in the Exposome-Explorer database,  36  on analyses of repeated samples, and on measured correlations with the External exposome.
Ethics-by-design In exposome research, large amounts of data on the person and their environment are collected and combined. These data collections raise ethical questions that need to be identified and addressed to ensure ethically sound and responsible research and innovation. At the same time, there is a critical need to evaluate to which extent "society" will appreciate the dissemination of tools developed by projects such as EXPANSE. EXPANSE therefore will employ ethics parallel research, which is an approach for the early ethical guidance of novel (bio)medical research and innovation. 17 We will identify and evaluate the ethical challenges raised by EXPANSE using an interdisciplinary approach that deploys empirical and normative methods as well as public and patient involvement (PPI) elements.
The inclusion of a diverse group of citizens in the early phases of exposome research will enable us to understand and map how they anticipate exposome research will affect their lives and realities as well as their hopes and fears. In addition to the user stakeholder panel, we will set up citizen panels to empirically collect what citizens, researchers, and beyond perceive as the main ethical dimensions of this research. We will focus on what comprises, in their views, responsible research and data stewardship and what kind of ethical norms this would entail. This, in combination with conceptual clarification and normative analysis, will allow us to provide normative guidance for the design and goals of exposome research, the influence on our conceptions of health and illness, appropriate data governance as well as the interpretation of exposome data for health care allocation and public health policy.
Main strengths of the project EXPANSE will span the evidence space from mega-sized administrative cohorts, comprising the health data of more than 55 million European citizens, large individual adult and matured birth cohorts (>2 million European citizens), and Urban laboratories (5000 individuals). The large size of the cohorts, the use of different study designs, a life course approach from childhood to adulthood, and evidence across different biological layers allow for a pluralistic approach in terms of statistical analysis, and where possible replicating results in different populations allowing an improved causal interpretation of statistical associations. EXPANSE will expand on the number of factors currently measured as part of the urban exposome; make the urban exposome surfaces time-varying and more spatially refined; and incorporate time-activity patterns in estimating individualized urban exposome features.
As a key component of the Earth system, roots play a key role in linking Earth's lithosphere, hydrosphere, biosphere and atmosphere. Here we combine 10 307 field measurements of forest root biomass worldwide with global observations of forest structure, climatic conditions, topography, land management and soil characteristics to derive a spatially explicit global high-resolution (∼ 1 km) root biomass dataset, including fine and coarse roots. In total, 142 ± 25 (95 % CI) Pg of live dry-matter biomass is stored belowground, representing a global average root : shoot biomass ratio of 0. 25 ± 0. 10. Earlier studies  (Jackson et al. , 1997; Robinson, 2007; Saugier et al. , 2001)  are 44 %-226 % larger than our estimations of the total root biomass in tropical, temperate and boreal forests. The total global forest root biomass from a recent estimate (Spawn  et al.
Overview We use a new approach to upscale root biomass of trees at the global scale (Supplement, Fig. S1 ) based on machine learning algorithms trained by a large dataset of in situ measurements (n = 10 307) of root and shoot biomass for individual woody plants (see Methods, "Field measurements"; Supplement), covering 465 species across 10 biomes defined by The Nature Conservancy  (Olson and Dinerstein, 2002)  (Supplement, Fig. S2 ). We compared the results of allometric upscaling to those of three machine learning techniques (the random forest, artificial neural networks and multiple adaptive regression splines), through a pool of 47 predictor variables that include shoot biomass and other vegetation, edaphic, topographic, anthropogenic and climatic variables (Supplement, Table  S1 ). After comparing the results of all three machine learning techniques and the allometric upscaling, we chose the random forest (RF) model because it performed best on cross-validation samples (see section "Building predictive models" below and Supplement, Table  S8 ), and we only used the RF for subsequent mapping and analysis.
Field measurements Our dataset  (Huang et al. , 2020)  was compiled from the literature and existing forest biomass structure or allometry databases  (Falster et al. , 2015; Ledo et al. , 2018; Schepaschenko et al. , 2018 Schepaschenko et al. , , 2017)). We included studies and databases that reported georeferenced location, root biomass and shoot biomass. Note here that we focus on studies that measured biomass of individual trees instead of stand-level biomass. We included studies that measured biomass directly, instead of those that estimated it indirectly via allometric equations. For example, the study of  Poorter et al. (2015)  is not included due to lack of georeferenced location and the study of  Iversen et al. (2017)  is not used as we also need measurements of other plant compartments like shoot biomass.
Repeated entries from existing databases were removed. One of the databases  (Falster et al. , 2015)  reported data on woody plants which also include shrub species. We kept the shrub data partly because the remote sensing products we used to generate our root map do not clearly separate trees from shrubs. Around 82 % of the extracted entries also recorded plant height and management status. Height was identified as an important predictor in our model assessment, and entries were discarded when height was missing (18 % of data). As woody plant age was reported in 19 % of the entries only, the values of this variable were determined from another source of information, i. e. , from a composite global map introduced in the next section. Species names were systematically reported, but biotic, climatic, topographic and soil information was missing for a substantial proportion of entries, and values of these variables were thus extracted from independent observation-driven global maps as explained in the next section.
Preparing predictor variables We used 47 predictors that broadly cover five categories: vegetative, edaphic, climatic, topographic and anthropogenic (Supplement, Table  S1 ). These variables are chosen due to their relevance to root dynamics based on field studies and their availability at the global scale. We take the management status (managed or not) as the anthropogenic predictor. All references are given in the Supplement, Table  S1. For each variable, we collected multiple datasets whenever possible for uncertainty quantification. For the "central" estimate, we stick to only one set of these predictors considering the quality and coverage of the databases. We favored the database that is known to have higher-quality data or covered more predictors that are relevant to our study for consistency.
For example, we used the GSDE soil database  (Wang et al. , 2014)  for the central estimate instead of SoilGrids 2. 0  (Poggio et al. , 2021)  as the latter does not have the whole set of soil property variables needed in this study. As in situ field measurements of aboveground biomass (AGB) do not offer a full global coverage, gridded shoot biomass data were derived from satellite AGB products to predict root biomass at the global scale with a 1 km by 1 km spatial resolution. The gridded global shoot biomass dataset used in our study has been extensively calibrated with in situ observations and is currently the most reliable source of information on shoot biomass offering global coverage  (Santoro et al. , 2021 (Santoro et al. , , 2018;; Spawn et al. , 2020). To derive the shoot or AGB per tree (in units of weight per tree) to generate spatially explicit global root biomass, we combined the GlobBiomass AGB satellite data product  (Santoro et al.
, 2021 (Santoro et al. , , 2018) )  (in units of weight per unit area) with a tree density map (number of trees per unit area)  (Crowther et al. , 2015b). The GlobBiomass dataset was based on multiple remote sensing products (radar, optical, lidar) and a large pool of in situ observations of forest variables  (San-toro et al. , 2015 (San-toro et al. , , 2021;; Santoro, 2018). The original Glob-Biomass AGB map was generated at a 100 m spatial resolution; for this study, the map was averaged into a 1 km pixel by considering only those pixels that were labeled as forest  (Santoro, 2018). A pixel was labeled as forest when the tree canopy density was larger than 15 % according to the dataset of  Hansen et al. (2013; hereafter Hansen2013)  averaged at 100 m. The 1 km resolution global tree density map was constructed through upscaling 429 775 ground-based tree density measurements with a predictive regression model for forests in each biome  (Crowther et al.
, 2015b). The forest canopy height map took advantage of the Geoscience Laser Altimeter System (GLAS) aboard ICESat (Ice, Cloud, and land Elevation Satellite)  (Simard et al. , 2011). Forest definitions are slightly different among these three maps. Forest area of the tree density map was based on a global consensus land cover dataset that merged four land cover products  (Tuanmu and Jetz, 2014). Crowther et al. (2015b)  showed the total tree count from a tree density map based on the Tuanmu and Jetz (2014) land cover is the same as from the Hansen2013 land cover product. The canopy height map used the GlobCover land cover map  (Hagolle et al. , 2005)  as a reference to define forest land. We approximated the missing values in tree density and height (due to mismatches in forest cover) by the mean of a 5 × 5 window that is centered on the corresponding pixel.
With a combination of learning trees (models), RF generally increases the overall prediction performance and reduces over-fitting. An ANN computes through an interconnected group of nodes, inspired by a simplification of neurons in a brain. A MARS is a non-parametric regression method that builds multiple linear regression models across a range of predictors. Tree shoot biomass from the in situ observation data spans a wider range than shoot biomass per plant derived from global maps (1 × 10 -7 to 8800 vs. 7. 9 × 10 -5 to 933 kg/plant). To reduce potential mapping errors, we selected training samples with shoot biomass between 5×10 -5 and 1000 kg/plant. The medians and means of shoot biomass, root biomass and R : S from the selected training samples are similar to those from the entire database.