dataesr/scientific-paragraphs-categorization huggingface dataset:
For comparison with the model output (which is provided at 550 nm), the measurements at 500 nm and 440 nm were used to derive the total AOD at 550 nm, using the provided AE data to make the wavelength adjustment (the 500 nm channel was preferred over the 440 nm channel). Similarly, the AOD f and AOD c data provided at 500 nm via the AERONET spectral deconvolution algorithm (SDA) product were shifted to 550 nm using the AE data. The SDA product  (O'Neill et al. , 2003)  computes AOD f and AOD c in an optical sense, based on the spectral curvature of the retrieved AODs in several wavelength channels and assuming bimodal aerosol size distributions. Thus, as pointed out by  O'Neill et al. (2003) , it does not correspond to a strict size cut at a certain radius, such as the R = 0. 6 µm established in the AERONET Inversion product  (Dubovik and King, 2000).
Surface in situ data Surface in situ measurements of the aerosol light scattering (SC) and absorption coefficients (AC) were accessed through the GAW-WDCA database EBAS (http://ebas. nilu. no/, last access: 21 December 2020). As with AERONET, only sites with elevations below 1000 m were considered. Annual mean values of scattering and absorption are shown in Fig. 1g , h. The in situ site density is highest in Europe, followed by North America, while other regions are poorly represented. The EBAS database also includes various observations of atmospheric chemical composition and physical parameters, although those were not used here. For both scattering and absorption variables, only level 2 data from the EBAS database were used (i. e. quality-controlled, hourly averaged, reported at standard temperature and pressure (STP); T std = 273.
For the in situ AC data used in this study, most of the measurements are performed at wavelengths other than 550 nm (see Sect. S1 in Supplement 2). These were converted to 550 nm assuming an absorption Ångström exponent (AAE) of 1 (i. e. a 1/λ dependence; e. g. Bond and Bergstrom, 2006). This is a fairly typical assumption when the spectral absorption is not measured. For about 50 % of the sites, absorption was measured at ∼ 530 nm, meaning that even if the true AAE had a value of 2, the wavelength-adjusted AC value would only be underestimated by ca. 4 %. For another 25 % of the sites, absorption was measured at ∼ 670 nm. For these sites, the impact of an incorrect AAE value is larger (ca. 26 % overestimation for an actual AAE of 2 and ca. 6 % for Table  1. Observations used in this study, including relevant meta data information.
ID: name of observation network. Source: data source or subset. Var: variable name. NMB: Normalised mean bias of satellite product at AERONET sites (monthly statistics). λ: wavelength used for analysis (may be different from measurement wavelength; for details, see text). Ver: data version. Lev: data level. Freq: original frequency of data used to derive monthly means. Res: resolution of gridded data product. Clim: use of a multi-annual climatology or not. No. st: number of stations/coordinates, with observations used. Date: retrieval date from respective database. See text in Sect. 2. 1 for additional quality control measures that have been applied to some of these data sets. AAE = 1. 25). The remaining 25 % of sites typically utilised wavelengths between these two values. Schmeisser et al.
Satellite data sets -introduction In addition to the ground-based observations, data from four different satellite data sets (MODIS Aqua & Terra, AATSR SU v4. 3 and a merged AOD satellite data set) were used to evaluate optical properties from the AP3 models. The four satellite data sets are introduced below. Even though the satellite observations usually come with larger uncertainties and may exhibit potential biases against ground-based column observations (e. g. Gupta et al. , 2018) , we believe that it is a valuable addition to not only evaluate models at ground sites but also incorporate satellite records for an assessment of model performance. The main advantage of satellite data is the spatial coverage relative to groundbased measurements. Satellites provide more coverage over land masses than AERONET, and in addition, they are the primary observational tool for column optical properties over oceans.
https://doi. org/10. 5194/acp-21-87-2021 Atmos. Chem. Phys. , 21, 87-128, 2021 Because of AERONET's reliability and data quality, it is generally accepted as the gold standard for column AOD measurements. Therefore, all four satellites used in this paper were evaluated against AERONET data, in order to establish relative biases and correlation coefficients. Details related to this satellite assessment are discussed in Supplement 2 and are briefly mentioned in the introduction sections for each individual satellite below. The results from this satellite assessment are also available online (see  Mortier et al. , 2020a) , allowing for an interactive exploration of the data and results (down to the station level), and they include many evaluation metrics (e. g. various biases, correlation coefficients, root mean square error (RMSE)).
MODIS data Daily gridded level 3 AOD data from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been used from both satellite platforms (Terra and Aqua) for evaluation of the models. The merged land and ocean global product (named AOD_550_Dark_Target_Deep_Blue_Combined_Mean in the product files) of the recent collection 6. 1 was used. This is an updated and improved version of collection 6 (e. g. Levy et al. , 2013; Sayer et al. , 2014). For changes between both data sets, see  Hubanks (2017). Details about the MODIS data sets used are provided in Table  1. Compared to AERONET, both Aqua and Terra exhibit positive AOD biases, suggesting an overestimation of ca. +9 % and +17 %, respectively, at AERONET sites and for the year 2010 (for details, see Supplement 2). The larger overestimate for Terra is in agreement with the findings from  Hsu et al.
AATSR SU v4. 3 data The AATSR SU v4. 3 data set provides gridded AOD and associated parameters from the Advanced Along Track Scanning Radiometer (AATSR) instrument series, developed by Swansea University (SU) under the ESA Aerosol Climate Change Initiative (CCI). The AATSR instrument on EN-VISAT covers the period 2002-2012, and in this study, data from 2010 are used. The instrument's conical scan provides two near-simultaneous views of the surface, at solar reflective wavelengths from 555 nm to 1. 6 µm. Over land, the algorithm uses the dual-view capability of the instrument to allow estimation without a priori assumptions on surface spectral reflectance  (North, 2002; Bevan et al. , 2012). Over ocean, the algorithm uses a simple model of ocean surface reflectance including wind speed and pigment dependency at both nadir and along-track view angles.
The most recent version AATSR SU V4. 3  (North and Heckel, 2017)  advances on previous versions by improved surface modelling and shows reduced positive bias over bright surfaces. Retrieval uncertainty and comparison with sun photometer observations show highest accuracy retrieval over ocean and darker surfaces, with higher uncertainty over bright surfaces (e. g. desert, snow) and for large zenith angles  (Popp et al. , 2016). This study uses the level 3 output, which is provided at daily and monthly 1 • × 1 • resolution, intended for climate model comparison. Specifically, AATSR SU values for AE and total, fine, and coarse AODs are used. The AE calculation is only performed for 0. 05 < AOD < 1. 5 due to increased retrieval uncertainty of AE at low and high AODs. In comparison with AERONET, the AATSR data exhibit an AOD bias of ∼ -4 %, suggesting a slight underestimation of AOD at AERONET sites, in contrast to the two MODIS products used (see Table  1 ).
Merged satellite AOD data The MERGED-FMI data set, developed by the Finnish Meteorological Institute, includes gridded level 3 monthly AOD products merged from 12 available satellite products  (Sogacheva et al. , 2020). It should be noted that MODIS and AATSR products are considered inside this MERGED-FMI data set. It is available for the period 1995-2017; however, here only 2010 data are used. Compared to AERONET measurements from 2010, this merged satellite product has shown excellent performance with the highest correlation (R = 0. 89) among the four satellites used and only a slight underestimation of AOD (bias of -5. 4 %) at AERONET sites (see Supplement 2 and  Mortier et al. , 2020a). The merging method is based on the results of the evaluation of the individual satellite AOD products against AERONET.
Models This study uses output from 14 models that are participating in the AeroCom AP3-CTRL experiment. Details on the Ae-roCom phase III experiments can be found on the AeroCom wiki page (AeroCom wiki, 2020). The wiki also includes information on how to access the model data from the different AeroCom phases and experiments, which are stored in the AeroCom database. Note that the database location and information about it might change in the future; the intention is however to keep updated information available via the website: https://aerocom. met. no (last access: 14 September 2020). Table  2  provides an overview of the models used in this paper. For the AP3-CTRL experiment, modellers were asked to submit simulations of at least the years 2010 and 1850, with 2010 meteorology and prescribed (observed) sea surface temperature and sea ice concentrations, and using emission inventories from CMIP6  (Eyring et al.
, 2016) , when possible. Details concerning the anthropogenic and biomass burning emissions are given in the Community Emissions Data System (CEDS;  Hoesly et al. , 2018)  and in biomass burning emissions for CMIP6 (BB4CMIP;  van Marle et al. , 2017). In this paper, only the 2010 model output is used. The year 2010 was chosen as a reference year by the AeroCom consortium and is used throughout many phase II and III experiments for the inter-comparability of different experiments and model generations. The AeroCom phase I simulations (e. g. Dentener et al. , 2006; Kinne et al. , 2006; Schulz et al. , 2006; Textor et al. , 2006)  used the year 2000 as a reference year. One of the main reasons to update the reference year from 2000 to 2010 was that many more observations became available between 2000 and 2010 and also to account for changes in the present-day climate, for instance, due to changing emissions and composition (e.
Data processing and statistics Most of the analysis in this study was performed with the software pyaerocom (Zenodo: https://doi. org/10. 5281/zenodo. 4362479, Gliß et al. , 2020). pyaerocom is an open-source Python software project that is being developed and maintained for the AeroCom initiative, at the Norwegian Meteorological Institute. It provides tools for the harmonisation and co-location of model and observation data and dedicated algorithms for the assessment of model performance at all scales. Evaluation results from different AeroCom experiments are uploaded to a dedicated website that allows exploration of the model and observation data and evaluation metrics. The website includes interactive visualisations of performance charts (e. g. biases, correlation coefficients), scatter plots, bias maps, and individual station and regional time series data, for all models and observation variables, as well as bar charts summarising regional statistics.
AeroCom ensemble mean and median For all variables investigated in this paper, the monthly Ae-roCom ensemble mean (ENS-MEAN) and median (ENS-MED) fields were computed and have been made available in the AeroCom database, for future reference. This was done in order to enable an assessment of the AP3 model ensemble, which we consider to represent the most likely modelling output of the state-of-the-art aerosol model versions participating in the AP3-CTRL exercise. The ensemble fields were computed in a latitudelongitude resolution of 2 • ×3 • , which corresponds to the lowest available model resolution (i. e. of models EC-Earth and TM5; see Table  2 ). Model fields were all re-gridded to this resolution before the ensemble mean and median were computed. In this paper, only the output from the median model is used.
However, the impact of these uncertainties on total AOD is small as NO 3 contributes a small fraction to the total AOD (< 5 % based on the median results of this study). The quality of the constructed AeroCom median ensemble model is a very solid reference for the parameters investigated. In terms of correlation, only in a few instances do individual models outperform it; however no single model is better than the ensemble model for all parameters. This paper and its associated supplements, the AeroCom database, and web interfaces are available as reference for further investigations, in particular for developing recommendations for global aerosol modelling. Model diagnostics are comprehensive but clearly not enough to understand all aspects of how models simulate the path from emissions to optical properties.
is developed by a consortium composed of ETH Zurich, Max-Planck-Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute, and the Leibniz Institute for Tropospheric Research and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich. Peter North and Andreas Heckel are supported by the ESA Aerosol Climate Change Initiative (Aerosol CCI). Hitoshi Matsui acknowledges funding from the Ministry of Education, Culture, Sports, Science, and Technology and the Japan Society for the Promotion of Science (MEXT/JSPS) KAKENHI (grant no. JP17H04709). Gunnar Myhre has received support from project SUPER (grant no. 250573), funded by the Research Council of Norway. Huisheng Bian acknowledges funding from NASA's Atmospheric Composition Modeling and Analysis Program (ACMAP), contract no.
Financial support. This research has been supported by the Research Council of Norway (EVA (grant no. 229771), INES (grant no. 270061), and KeyClim (grant no. 295046)) and the Horizon 2020 project CRESCENDO (grant no. 641816). High performance computing and storage resources were provided by the Norwegian Infrastructure for Computational Science (through projects NN2345K, NN9560K, NS2345K, and NS9560K). Please also note further funding sources in the Acknowledgements.
Acknowledgements. Data providers from all observation networks and satellite products and all people involved in the production of these products are highly acknowledged for sharing and submitting their data and for support. Jonas Gliß  wishes to thank  Oskar Landgren  and  Hanna Svennevik  for providing data and code used in this study and  Stefan Kinne  for helpful comments via personal communication. Toshihiko Takemura  was supported by the supercomputer system of the  National Institute for Environmental Studies, Japan, Japan Society for the Promotion of Science (JSPS) KAKENHI  ( JP19H05669 ), and  Environment Research and Technology Development Fund  ( JPMEERF20202F01 ) of the  Environmental Restoration and Conservation Agency, Japan. Twan van Noije and  Philippe Le Sager  acknowledge funding from the  European Union 's  Horizon 2020 Research and Innovation programme , project  CRESCENDO (Coordinated Research in Earth Systems and Climate: Experiments, Knowledge, Dissemination and Outreach ), under grant agreement no.
641816. David Neubauer  acknowledges funding from the  European Union 's  Horizon 2020 Research and Innovation programme , project  FORCeS , under grant agreement no. 821205. Susanne E. Bauer  and  Kostas Tsigaridis  acknowledge funding from  NASA's Atmospheric Composition Modeling and Analysis Program (ACMAP) , contract number  NNX15AE36G. Paul Ginoux  acknowledges partial funding from  NASA's Earth Surface Mineral Dust Source Investigation (EMIT) , program number  NNH12ZDA0060-EVI4. They also thank  Jingbo Yu  for running the GISS model. Resources supporting this work were provided by the  NASA   High-End Computing (HEC) Program  through the  NASA Center for Climate Simulation (NCCS)  at  Goddard Space Flight Center. Harri Kokkola  acknowledges the  Academy of Finland  Projects  317390  and  308292.
Code and data availability. Most of the data analysis was performed using the open-source software pyaerocom (version 0. 10. 0, https://doi. org/10. 5281/zenodo. 4362479, Gliß et al. , 2020). All additional analysis scripts are stored in a private GitHub repository and can be provided upon request. All data used in this study are stored on servers of the Norwegian Meteorological Institute and can be provided upon request. The GAW data used are accessible through the EBAS database. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-87-2021-supplement.
NIRS Phenotyping The NIRS technique can be used to analyse coffee products as it can extract considerable information on the biochemical composition of the product. In a previous study  (Posada et al. , 2009) , we showed that NIRS-based inter-variety relationships were stable across environments. In the present study, we tested whether the Euclidean distances between the NIRS signatures of the genotypes were the same under shade and in full sun. NIR reflectance spectra were collected using a scanning monochromator NIR systems spectrophotometer (model 6500, Perstorp Analytical Inc. , 1201 Tech Road Silver Spring, MD, USA) controlled by NIRS2 (4. 0) software (Intrasoft Intl. , LLC, RD109, Sellers Lane, Port Matilda, PA, USA). The analyses were performed on 50 gm aliquots of green coffee after grinding to <0.
Heritability Estimations Analyses of variance of the full factorial mating design using a two-way cross-classification model were carried out according to the Henderson III procedure for shade and sun conditions. The R package lme4 was used  (Bates et al. , 2015). We used the following random effects model: Y ijk = µ + M i + F j + (MF) ij + E ijk where µ is the general mean, Mi is the random effect of the i th female, Fj is the random effect of the j th male, (MF) ij is the female x male interaction, and E ijk is the within-family deviation of the k th individual within the(ij) th female x male combination. We estimated the variance components (including maternal and paternal variance) and narrow-sense heritability for the main traits (yield, final cupping score, bean size). The estimated phenotypic variance is free from any genetic model and is given by: VP = σ 2 M + σ 2 F + σ 2 (MF) + σ 2 E As usual, the estimates of narrow-sense and broad-sense heritability are given by:.
, 2020). On the other hand, the sensory quality of the Castillo pure lines appeared to be quite good: one of the lines (CU1842) had a final score over 84 both in full sun and under shade. There was no mention of off-type flavors during the tasting (data not shown), which confirmed that breeders have been able to circumvent the negative effects of introgression of chromosome fragments that have been reported in some Arabica varieties derived from the Timor hybrid  (Bertrand et al. , 2003). However, we noted that the selection resulted in a sensory quality inferior to that of most of the Ethiopian accessions. Commercial varieties produced and released in Colombia to date have been selected mainly for their high yield and resistance to leaf rust, while cup quality has been evaluated by discarding progenies whose cup quality differs from that of Typica, Bourbon and Caturra.
FUNDING This research was funded by  CENICAFE, MINCIENCIAS, National Agricultural Science, Technology and Innovation Program , Agreement RC-0148-2013, code  2251-569-33518  and by the  BREEDCAFS  project (EUROPEAN  COMMISSION  under the  Horizon 2020-Research and Innovation Programme ,  H2020-SFS-2016-2 , Grant Agreement Number:  727934 ).
The characteristics of these participants are presented according to the region of recruitment in Table  1. Each 120-minute session was video-recorded and conducted according to standard procedures for focus groups. The first and last authors (CB and PG) designed an interview guide that included the key topics to be investigated, after a review of the literature and consultation with the project team. The guide focused on three main topics: (1) concerns, beliefs and attitudes regarding diet and nutrition during pregnancy, (2) nutrition-related information-seeking behavior, and (3) expectations with respect to tailored dietary advice during pregnancy. The data relative to topics (1) and (2) have already been presented elsewhere  [9] , so only the data regarding topic (3) are considered in the present study.
Data Collection in the 6-Week Online Longitudinal Study We designed a 6-week online longitudinal study to evaluate the acceptability of the dietary changes (and their associated types) identified during a simulation study as being the most efficient  [28]  (Figure  1 ). Seventeen thousand, two hundred and forty-four women aged between 18 and 44 years, living in mainland France, and members of an online panel operated by a generalist market research company (QualiQuanti, Paris, France) were contacted by email to ask if they were pregnant and were willing to participate in the study. To register, the women had to sign the consent form electronically and answer a questionnaire on their sociodemographic profile to assess their eligibility. The non-inclusion criteria were as follows: not pregnant, more than six months pregnant (i.
Analysis of Qualitative Data All focus group discussions were transcribed in full by the moderator (CB). As no previous study had been performed on the expectations of pregnant French women relative to tailored dietary advice, we did not declare any pre-determined theory before data collection. An inductive thematic approach, adapted from the grounded theory, was therefore implemented to analyze the data. This approach involves familiarization with the data, an open-coding process, and data interpretation in themes derived from identified codes  [37, 38]. The transcripts were double-coded independently by the same two researchers (CB and GLG) using Nvivo 11 Pro for Windows (QSR International Pty Ltd. , Victoria, Australia). Discrepancies between the two researchers regarding the coded categories were identified through the software and resolved through discussion; the final codebook was then defined.
The coded data were then grouped into two major themes and their sub-themes (Supplementary Material Table  S1 ). 1 Values are mean ± SD. 2 Values correspond to the percentage of participants presenting the characteristic described in the first column followed by the associated number of participants in parentheses. 3 Socio-professional categories were derived from occupations. "Farmer, craftsperson, storekeeper", "Professional, executive", and "Intermediate profession" belong to the high socio-professional category, "Employee", "Manual worker" and "Student" belong to the low socio-professional category, and "Unemployed" belongs to the inactive socio-professional category. 4 No significant difference between groups for age as tested with ANOVA, p > 0. 05. 5 No significant difference between groups regarding the distribution of participants, as tested with Fisher's exact tests, p > 0.
Nevertheless, in light of the knowledge that this specific period is generally accompanied by a rise in nutrition awareness  [7] , this may have been less important here than during another period of life. Finally, dietary behavior might change over time and is known to be influenced by socio-cultural characteristics. In this study, data were collected in 2015 in pregnant French women; thus, it might be that some nutrition policies in France had contributed to modifications in the dietary behaviors of pregnant women. However, to our knowledge, no study was published regarding this issue. Thus, it might be interesting to conduct further studies in pregnant French women to update our findings. Furthermore, it might be interesting to replicate this study in other countries with different socio-cultural contexts to know if our findings could be generalized beyond pregnant French women.