dataesr/scientific-paragraphs-categorization huggingface dataset:
Variables were added to the model based on their pairwise R 2 with the residuals of the current model, and only if their addition decreased the Akaike Information Criterion (AIC;  Hosmer et al. , 2013). To develop a more reliable estimate of GPP using solely remote sensing data sources, a simple decision tree model was developed where SIF was considered as a predictor of GPP along with other remote-sensing variables. These predictors included top-surface soil moisture estimates, LAI, PRI, fPAR, NIR, LE, and APAR calculated using MODIS products, which was calculated following Eq. 5, but with PAR retrieved from MODIS instead of PAR observations at the EC-tower location. Table  3  shows the source of each of the remote sensing product, in addition to their spatial resolution. For each variable, grids were aggregate consistent with a typical Eddy-Covariance footprint (1.
, 2019) , but not necessarily for the instantaneous photochemical activity  (Magney et al. , 2020). Site-level analysis shows limited relationship between SIF and GPP at midday, half-hourly resolution using both linear and asymptotic fits, while it is expected to have a hyperbolic correlation at this time of day. This can be due to light saturation that dominates midday photosynthesis under most conditions  (Zhang et al. , 2016a). Thus, TROPOMI-sampled points at solar noon would lie mostly on the asymptotic end of the GPP vs. SIF hypothetical non-linear curve  (Damm et al. , 2015). In order  to get the hypothetical non-linear correlation between GPP and SIF, data points with minimal light saturation are needed to complement the hyperbolic shape of the curve, i. e. , morning SIF data should be available, which is not the case with TROPOMI, or midday data with less stress conditions.
DATA AVAILABILITY STATEMENT Observations of carbon fluxes and other site-level meteorological variables are available through Amerilfux (https://ameriflux. lbl. gov/data/aboutdata/) and European Fluxes Database Cluster (EFDC) (http://www. europe-fluxdata. eu/home/data/ data-policy). All Site IDs and DOIs for all sites used are listed in Table  S1. 1 in Supplementary Material 1. MODIS datasets MCD15A2H v006, MCD18A2 v006, MOD13Q1 v006, MOD16A2 v006, and MYDOCGA v006 provide LAI and fPAR, PAR, PRI, NIR, LE, and PRI data and are available through NASA's EarthData (https://search. earthdata. nasa. gov/search). SMAP soil moisture data are available through the National Snow and Ice Data Center (NSIDC) (https://nsidc. org/data/smap). SIF data from the TROPOMI satellite are available through (http://www.
FUNDING Funding for this study and for Ameriflux core sites was provided by the United States Department of Energy's Office of Science through the Ameriflux Management Project. MU was partially supported by the Arctic Challenge for Sustainability II (ArCS II; JPMXD1420318865). LŠ was supported by the Ministry of Education, Youth and Sports of CR within Mobility CzechGlobe2 (CZ. 02. 2. 69/0. 0/0. 0/18_053/0016924). Site-level data are provided by AmeriFlux and the European Fluxes Database Cluster. The National Ecological Observatory Network (NEON) is sponsored by the National Science Foundation and operated under cooperative agreement by Battelle Memorial Institute.
Overview of SRs evaluating maintenance pharmacotherapy for paediatric asthma Eligibility criteria Eligible studies will comprise SRs and meta-analyses of controlled clinical trials or of real-life studies evaluating maintenance treatments that are broadly used in clinical practice for asthma or recurrent wheeze in children Open access and adolescents, aged up to 18 years. More specifically, we will include SRs comparing any combination of ICS, LABA, LAMA, LTRA, biological therapies (namely omalizumab, mepolizumab, reslizumab, benralizumab or dupilumab), or placebo as monotherapy or add-on maintenance therapy for paediatric asthma. We will accept SRs and meta-analyses evaluating any molecule of the abovementioned categories, administered at any dose and for a duration of at least 6 weeks. SRs comparing asthma maintenance treatment both in children and adults will be included provided that paediatric data are presented separately.
Search strategy and study selection The electronic databases of Medline/PubMed and Cochrane Library will be systematically searched, using appropriate controlled vocabulary and free search terms to identify relevant SRs (terms describing: childhood asthma, LABA, LAMA, LTRA, ICS, biologics, SRs, detailed search strategy is available in online supplemental appendix). Databases will be searched from 2006 onwards. Titles and abstracts of all identified manuscripts, and the full texts of potentially relevant manuscripts, will be screened by two investigators independently. We will report the reasons of exclusion of studies that will be excluded after full-text review. Disagreement will be resolved through discussion or adjudication by a third investigator, when necessary.
Eligibility criteria For each meta-analysis, eligible studies will comprise controlled clinical trials and observational comparative effectiveness studies comparing the index medication with placebo, no treatment or any active control, as an add-on treatment for acute severe asthma attacks. Index medication will be defined based on the pharmacological action, meaning that molecules targeting the same pharmacological target (eg, salbutamol and terbutaline, both being SABA) will be grouped. Only studies evaluating the management of acute severe asthma attacks, defined as those requiring a hospital admission or emergency presentation, in children and adolescents, aged between 1 and 18 years of age will be included. Studies evaluating both children and adults will be included, provided that paediatric data are reported separately or that we will be able to access these data after requesting them from the investigators.
Data abstraction The full study reference, study identifiers, details on the study design, eligibility criteria, predefined outcomes and potential confounding factors that were considered by the investigators, number and baseline characteristics of participants will be extracted by one investigator and will be cross-checked for validity by a second extractor. Details on the outcomes of interest from all included studies will be extracted by two investigators independently. Conflicts will be resolved through discussion and when needed adjudication by a third investigator.
Data synthesis Data from controlled clinical trials or observational studies will be analysed separately. In addition, studies evaluating different comparators, will be analysed separately. If different doses of the index medication or comparator are evaluated across the included studies, we will consider grouping studies using similar doses, providing that their results are not significantly dissimilar. For every analysis, I 2 statistic will be used to assess statistical heterogeneity. Substantial heterogeneity (I 2 >50%) will be explored using prespecified subgroup analyses (details in the next section). We will not perform metaanalyses in cases of considerable unresolved heterogeneity (I 2 >75%). When it is considered meaningful, meta-analyses will be performed using the random-effects model, because we anticipate significant heterogeneity in our data.
Results will be presented in the form of relative risk (95% CI) for dichotomous data, mean difference (95% CI) for continuous data and (HR, 95% CI) for time to event data. Meta-analyses will be performed using Review Manager V. 5 (RevMan, http:// community. cochrane. org/ tools/ review-production-tools/ revman-5) and R statistics V. 3. 4. 3 or newer (R Foundation for Statistical Computing, Vienna, Austria). For dichotomous outcomes, the unit of analysis will preferably be participants, rather than events (ie, number of participants admitted to the intensive care unit, rather than number of admissions per participants).
Sensitivity and subgroup analyses In sensitivity analyses for all comparisons, we will (1) use fixed effects models, (2) only include studies with low risk of bias, (3) exclude studies reporting limited adherence to the study drugs (<80%) and (  4 ) evaluate separately studies assessing different doses of the index medication, which we may pool in the main analysis. Subgroup analyses according to participants' age, asthma phenotypes or, possibly, acute attack phenotypes will also by conducted, depending on data availability. In an additional subgroup analysis, we will evaluate separately trials utilising exploratory versus pragmatic study designs.
Certainty of the body of evidence Certainty of the body of evidence, for every comparison will be evaluated using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology. 28 GRADE assesses the certainty in a body of evidence as high, moderate, low or very low after considering the methodological quality of the included studies, imprecision, inconsistency, indirectness, publication bias, the magnitude of effect, dose response and confounders likely to minimise the effect. All decisions to upgrade or downgrade the quality of evidence will be transparent and justified in evidence profile and summary of evidence tables, in accordance with GRADE guidance. GRADEPro Software (2014; www. gradepro. org) will be used for the development of these tables. We will use GRADE methodology to assess the risk of bias associated with missing participant outcome data across the body of the available evidence.
DISCUSSION We report on the methodology of a series of planned systematic evidence updates, aiming to evaluate maintenance management of childhood asthma, and the treatment of acute severe asthma attacks. Their design is informed by preliminary searches and the anticipated data availability. These SRs will be conducted by the PeARL group and will be used to inform clinical recommendations and future research needs. The need for high-quality evidence updates and clinical practice guidelines to improve the management of asthma in children is more urgent now, given the pressure that the unfolding COVID-19 pandemic pose on the healthcare systems, forcing us to reconsider our daily clinical practice. 30 31 ajor strengths of our evidence update series are the inclusion of a wide evidence base, including data from RCTs and real-life comparative studies, the prospective design and strong methodology.
This wide approach would aid the prioritisation of interventions to be further validated in future clinical research studies. Next, meta-analyses of individual pharmacological interventions will be conducted to further assess the safety and clinical effectiveness of treatments for acute severe asthma attacks that will appear efficacious in our broad SR. In contrast to most previous meta-analyses, that may have been conducted, we will include both controlled clinical trials and observational comparative effectiveness studies. Due to limitations that have already been discussed, few controlled clinical trials are conducted in children. 3] [34] [35]  We believe that by incorporating data from observational studies we may be able to conclude more robust results. While observational studies are at a higher risk of bias, we will carefully evaluate this risk using the newly developed, thorough RELEVANT tool and we will discuss potential implications on our findings.
Acknowledgements AGM was supported by the  National Institute of Health Research Manchester Biomedical Research Centre (NIHR Manchester BRC ). We thank  Mrs Maria Kritikou  for excellent administrative support of the study Contributors Study conception: AGM and NGP. Study design: AGM, MM and NGP. Preparation of the manuscript: AGM. Critical revision and final approval of the manuscript: AGM, MM, CB, GSA, AC, AD, FMD, OK, CM, ANG, WP, DP, AS, IOA, LB, AB, AB, MB, JAC-R, GDC, TC, ZD, WF, TF, JEG, JG, GH, EMH, DI, TJ, AK, RFL, PNLS, MJM, GAM, PM, MM, MM-A, KN, EP, HP, PMCP, PP, GR, IT, ST, VS, TAW, GWKW, PX, HJZ and NGP. Funding This work was supported by the  Respiratory Effectiveness Group (REG). REG has received support from  AstraZeneca ,  Novartis  and  Sanofi  for continued work on PeARL.
2 and S11, Supplement). Furthermore, we also contrast the interannual anomalies in temperature and salinity in the northern AS in the model to those from WOD2018 observations at different depths  (Figs. S12 and S13, Supplement). This comparison reveals that in the northern AS the simulated and observed trends in upper-ocean temperature agree well. Indeed, the linear trends in temperature amount to +0. 11 • C per decade at the surface (+0. 12 • C per decade at 100 m) in the model and +0. 11 • C per decade at the surface (+0. 14 • C per decade at 100 m) in the WOD2018 dataset  (Boyer et al. , 2019 ) (all trends are statistically significant at both depths (p < 0. 01)). In contrast, temperature trends at 200 m are very weak and statistically non-significant in both the model and WOD2018. For salinity, both the model and the WOD2018 observations suggest a slight increase in the upper-ocean salinity over the study period (Fig.
Acknowledgements. Support for this research has come from the  Center for Prototype Climate Modeling (CPCM)  of the  New York University Abu Dhabi (NYUAD) Research Institute. Computations were performed at the  NYUAD High-Performance Computing (HPC) cluster, Dalma. We thank the  NYUAD HPC team  for technical support. The authors are also grateful to  Matthieu Lengaigne  and the two anonymous reviewers for their constructive comments that helped improve the paper. Review statement. This paper was edited by  Marilaure Grégoire  and reviewed by  Matthieu Lengaigne  and two anonymous referees.
Code availability. The ROMS model code is provided by https:// www. croco-ocean. org (last access: 1 January 2018). Data availability. Drifter climatology data can be downloaded from the Global Drifter Program at https://www. aoml. noaa. gov/phod/gdp/mean_velocity. php (last access: 1 August 2021). ECMWF ERA-Interim data are available online at https://www. ecmwf. int/en/forecasts/datasets/reanalysis-datasets/era-interim (last access: 1 August 2021). ECMWF ERA5 SST can be downloaded from https://cds. climate. copernicus. eu/cdsapp# !/dataset/satellite-sea-surface-temperature?tab=form (last access: 1 August 2021). SeaWiFS and other sensor-merged satellite chlorophyll data were provided by the Copernicus Marine Environment Monitoring Service (CMEMS) and can be obtained from https://resources.
marine. copernicus. eu/product-detail/OCEANCOLOUR_GLO_CHL_L4_REP_ OBSERVATIONS_009_082/INFORMATION (last access: 1 August 2021). HadISST data can be downloaded from https://www. metoffice. gov. uk/hadobs/hadisst/data/download. html (last access: 1 August 2021). JRA-55 data can be accessed at https://rda. ucar. edu/datasets/ds628. 1/#!access (last access: 1 August 2021). NCEP-2 data are available online at https: //psl. noaa. gov/data/gridded/data. ncep. reanalysis2. gaussian. html (last access: 1 August 2021). NOAA OISST can be obtained from https://www. ncei. noaa. gov/thredds/catalog/OisstBase/ NetCDF/V2. 1/AVHRR/catalog. html (last access: 1 August 2021 ). ORAS5 data can be downloaded from https: //www. ecmwf. int/en/forecasts/dataset/ocean-reanalysis-system-5 (last access: 1 August 2021).
SODA reanalysis data can be downloaded from http://apdrc. soest. hawaii. edu/datadoc/soda_2. 2. 4. php (last access: 1 August 2021). WOA13 data can be accessed at https://www. nodc. noaa. gov/OC5/woa13/ (last access: 1 August 2021). WOA18 data can be downloaded from https: //www. nodc. noaa. gov/OC5/SELECT/woaselect/woaselect. html (last access: 1 August 2021). Finally, WOD18 data can be obtained from https://www. ncei. noaa. gov/access/ world-ocean-database-select/dbsearch. html (last access: 1 August 2021). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/bg-18-5831-2021-supplement. Author contributions. ZL conceived and designed the study. MM and MAA performed the simulations and carried out the model output analysis. ZL wrote the manuscript with contributions from all co-authors.
The availability of crop type reference datasets for satellite image classification is very limited for complex agricultural systems as observed in developing and emerging countries. Indeed, agricultural land use is very dynamic, agricultural censuses are often poorly georeferenced and crop types are difficult to interpret directly from satellite imagery. In this paper, we present a database made of 24 datasets collected in a standardized manner over nine sites within the framework of the international JECAM (Joint Experiment for Crop Assessment and Monitoring) initiative; the sites were spread over seven countries of the tropical belt, and the number of data collection years depended on the site (from 1 to 7 years between 2013 and 2020). These qualitycontrolled datasets are distinguished by in situ data collected at the field scale by local experts, with precise geographic coordinates, and following a common protocol.
Altogether, the datasets completed 27 074 polygons (20 257 crops and 6817 noncrops, ranging from 748 plots in 2013 (one site visited) to 5515 in 2015 (six sites visited)) documented by detailed keywords. These datasets can be used to produce and validate agricultural land use maps in the tropics. They can also be used to assess the performances and robustness of classification methods of cropland and crop types/practices in a large range of tropical farming systems.
Introduction Land use and land cover (LULC), and their changes, are key pieces of information for studying and monitoring carbon and water cycles, threats to biodiversity, and for establishing land use planning and public policies. In particular, accurate mapping of cropland and associated cropping practices is of primary importance for food security, agricultural and environmental monitoring and land management. However, cropland and crop-type mapping using Earth observation data is still challenging as it requires large sets of training and validation data, especially as the land use (field limits and content) generally changes annually, even seasonally. Large datasets on cropping practices are available in the Global North, mainly thanks to agricultural policies that support annual censuses and that provide tools for the digitization at field level using very high resolution remote sensing imagery (e.
g. , the Land Parcel Identification System designed to implement the common agricultural policy in the European Union or the Cropland Data Layer of the National Agricultural Statistic Services of the United States Department of Agriculture). Such datasets provide a very large number of annotated surface samples reporting yearly crop types, which can often easily be integrated in reference datasets for land cover mapping systems at the cost of a relatively simple "cleansing and harmonization" procedure  (Inglada et al. , 2017). Despite the fact that the declarative nature of such annotations makes them error prone, such "noise" is typically compensated by the large number of available crop type samples. Arguably, no such large-scale database currently exists in most of the developing and emerging countries.
The arrival of Sentinel-1 and Sentinel-2 satellite image time series, the emergence of new classification algorithms in the domain of machine learning and artificial intelligence, and easy access to preprocessed images and image processing tools on web platforms have democratized image processing and opened up new avenues for LULC mapping over large areas. Following this trend, large benchmark datasets acquired using annotation tools of satellite images all over the world have multiplied to train algorithms and validate remote-sensing-derived products  (Long et al. , 2020). However, these datasets have a broad LULC nomenclature, and agricultural land use is often reduced to a single class due to difficulties in discriminating cropping practices from satellite images. The main data sources currently available for agricultural land use mapping in southern countries are listed below.
At a global and continental scale, initiatives that freely distribute land cover reference datasets exist (see review by  Tsendbazar et al. , 2015). The GOFC-GOLD (Global Observation for Forest and Land Cover Dynamics; see http:// www. gofcgold. wur. nl/sites/gofcgold_refdataportal. php, last access: 15 December 2021, for further details and access to data) regroups and consolidates existing reference datasets used for the validation of legacy global land cover products (prior to 2015) at moderate spatial resolution (300 m-1 km) such as GLC 2000 and GlobCover 2005. All referenced databases are provided at global scale, ranging from a few hundreds to around 2000 samples each. Except for Glob-Cover 2005, which contains a "rainfed cropland" class, other referred land cover nomenclatures only contain a single cropland class, sometimes referred to as "cultivated".
Other data collection experiences reached a sensibly higher number of samples through the use of crowdsourcing campaigns, a notable example being the LULC reference dataset presented in  Fritz et al. (2017)  and its companion work from Laso  Bayas et al. (2017b) ; thanks to the Geo-Wiki tool providing an easy-to-use interface for the photointerpretation of very high spatial resolution (VHSR) satellite images, it was possible to collect up to 150 000 samples of different LULC classes. This includes over 36 000 cropland locations, distributed over contrasted areas in terms of cropland density. As in the previous case, a single cropland class is referenced in the nomenclature, alone or mixed with natural vegetation ("mosaic" class). Although crowdsourcing proves to be a valuable strategy to collect reference cropland data at larger scales, it still remains unsuited when precise information has to be collected, both spatially (resolution, plot boundaries, etc.
) and in terms of crop type nomenclatures. As a matter of fact, most of the crowdsourcing initiatives are based on visual image interpretation, which prevents the precise localization and identification of cropping practices. Shifting to a crowdsourced field strategy will not be suitable as well, both because of the very specific agronomic and GIS (geographic information system) competence needed and the limited accessibility to cultivated areas in tropical countries. Recently, the LandCoverNet dataset was released for the African continent  (Alemohammad et al. , 2020) , with the specific aim to foster the use of recent machine-learning and deep-learning approaches for automatic land cover classification. Here, samples are provided in the form of densely annotated image chips (256 × 256 pixels at 20 m resolution) accompanied by the corresponding Sentinel-2 observations over the reference year (2018).
A total number of 1980 fully annotated chips, accounting for more than 30 million labeled pixels, are provided, spanning 66 tiles of Sentinel-2 over the entire African continent. Although such a dataset could allow for a finer spatial validation of LULC products at high resolution, it still provides a single "cultivated land" class, making it unsuitable for the assessment of LULC products specifically conceived for the monitoring of agricultural systems. These data are used to validate global  (Hoskins et al. , 2016)  or national cropland maps  (Laso Bayas et al. , 2017a)  as the nomenclature used for labeling the classes does not specify the crop type. At a national scale, ground campaigns, such as those carried out as part of the Sen2Agri project in South Africa and Mali, collected data on the main crop types  (Defourny et al.