dataesr/scientific-paragraphs-categorization huggingface dataset:
90 and 0. 75 (at 550 nm) in the model. As mentioned in the introduction, the simulations using enhanced absorbing properties of BBAs are motivated by recent studies showing very low SSA for an aged BBA plume emitted from central Africa  (Zuidema et al. , 2018; Denjean et al. , 2020; Wu et al. , 2020). Global Fire Emissions Database version 4 (GFED) biomass-burning emissions  (van Marle et al. , 2017)  are prescribed in both models. The GFED is based on estimates of burned area, active fire detections, and plant productivity derived from MODIS. Carbon emission fluxes are converted to trace gas and aerosol emissions using species-specific emission factors based on  Andreae and Merlet (2001). Monthlymean GFED emissions are used in ALADIN, while RegCM is forced by daily mean emissions. In all experiments, the BBA emissions have been scaled up by a factor of 1.
Radiation and surface temperature data In order to evaluate the performance of both models, we use several data sets from ground-based measurements and satellite products. The Climatic Research Unit (CRU) of the University of East Anglia provides 2 m temperature and precipitation at a 0. 5 • × 0. 5 • resolution  (Harris et al. , 2013). It includes most of the land weather stations data around the world. In addition, we used the EUMETSAT CM-SAF Surface Solar Radiation Parameters (SARAH-2), which comprises five parameters related to https://doi. org/10. 5194/acp-20-13191-2020 Atmos. Chem. Phys. , 20, 13191-13216, 2020 surface solar irradiance, including surface incoming shortwave radiation (SIS). These are derived from the geostationary first-generation (Meteosat-MVIRI) and secondgeneration (Meteosat-SEVIRI) satellite sensors.
ERAI in general has been used in many climate studies in the past, including cloud studies (e. g. ,  Jiang et al. , 2011). Two different reanalysis products are used to evaluate aerosols. The ECMWF reanalysis of global atmospheric composition includes five main aerosol species. In this work, we use the recent CAMS-RA aerosol reanalysis  (Inness et al. , 2019)  for the total AOD. In addition, we use Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2), generated with version 5. 2. 0 of the Goddard Earth Observing System atmospheric model and data assimilation system  (Randles et al. , 2017). We rely on the AOD for the different species at 0. 5 • ×0. 625 • spatial resolution. In addition, and more specifically for the absorbing properties, we have used the recent MACv2 aerosol climatology in its second version  (Kinne, 2019) , which provides monthly global fields of optical properties at 1 • × 1 • spatial resolution, derived from a combination of observations (notably from the AERONET network) and model outputs.
Cloud and aerosol satellite data Spatiotemporally highly (0. 05 • ×0. 05 • ) resolved geostationary satellite observations are taken here from the CLoud property dAtAset based on SEVIRI edition 2 (CLAAS-2;  Benas et al. , 2017). The CLAAS-2 data set is based on mea-surements of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) and was generated and released by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF). CLAAS-2 includes a variety of cloud properties, including LWP, cloud optical depth and effective radius. The CLAAS-2 level 2 data are instantaneous data on native SEVIRI resolution with a temporal resolution of 15 min. For this study, the data are projected onto a regular latitude-longitude grid using the nearest-neighbor approach. It should be noted that Sc cloud retrievals could be affected by the presence of BBAs over the SEA.
Two MODIS-based products are also used. One, the Deep Blue ACAOD data set, was described initially by  Sayer et al. (2016)  and updated and evaluated against ORACLES field campaign data by  Sayer et al. (2019). In brief, this algorithm performs a multispectral weighted least-squares fit of measured reflectance in four bands across the visible spectral region to simultaneously retrieve ACAOD and COD. Finally, the MOD06ACAERO products are also used, which take reflectance observations at six MODIS spectral channels to simultaneously retrieve ACAOD, COD and the cloud effective radius of the underlying marine boundary layer clouds  (Meyer et al. , 2015). The main conceptual difference between these two MODIS data sets is that the former was designed primarily to extend AOD coverage into cloudy scenes, while the latter was designed to address known regional bi-ases in cloud property retrievals resulting from the BBA signal.
Total column AOD The simulated seasonal (JAS) mean AOD values (at 550 nm) are reported in  Fig. 3 (2008 Fig. 3 ( -2015 period) period) , along with the CAMS-RA and MERRA-2 reanalyses, and the MODIS Dark Target (Aqua and Terra) and MISR satellite AOD products. Concerning the satellite data (MODIS-Terra, MODIS-Aqua, MISR), comparisons indicate important differences, over both the ocean and the continent. In particular, large differences are found between MODIS and MISR AOD retrievals, with lower values associated with MISR at the regional scale. The latter is in a better agreement with the two RCMs, especially over the ocean. The difference obtained in this study between the two sensors are in line with the recent results obtained by  Sogacheva et al. (2020)  over SEA. For the current MISR standard product, this study indicates that AOD is systematically underestimated for AOD > ∼ 0.
Regarding the SDE of BBAs on low clouds, both models moderately increase LCF by ∼ 5 % over the Sc region but their impacts differ over the Gulf of Guinea. These differences in SDE are likely due to the ocean-atmosphere coupling in RegCM only where changes in SSTs increase lower troposphere stability and LCF over SEA. Two additional ALADIN simulations have been performed with different SSAs (0. 75 and 0. 90 at 550 nm) and indicate that the DRE and SDE are sensitive to the absorbing properties of smoke. Over central Africa, feedbacks between BBAs and low-cloud properties, and so the surface albedo, contribute, in addition to the intrinsic absorbing properties of smoke, to modulating the DRE at TOA. Over the Sc region, the positive DRE is significantly increased for lower SSA simulations, with moderate SDE changes on low clouds.
Acknowledgements. This work was supported by the  French National Research Agency  under grant agreement no. ANR-15-CE01-0014-01 ; the  French national program LEFE/INSU ; the  Programme national de Télédetection Spatiale  (PNTS, http://www. insu. cnrs. fr/pnts, last access: April  2020 ), grant no. PNTS-2016-14 ; the  French National Agency for Space Studies (CNES, AEROCLIM  and EECLAT projects); and the  South African National Research Foundation (NRF)  under grant  UID 105958. The research leading to these results has received funding from the  European Union 's  7th Framework Programme  ( FP7/2014-2018 ) under EUFAR2 contract no. 312609. For the RegCM simulations, granted access to the HPC resources of CALMIP supercomputing center under the allocation  2019-p19062. We acknowledge all the principal investigators of the AERONET and PIRATA stations.
We would like also to thank the  NASA Langley Research Center Atmospheric Science Data Center , the  National Center for Atmospheric Research and the Climate Research Unit  for providing the data sets used to evaluate our climate simulations. Financial support. This research has been supported by the  French National Research Agency  (grant no. ANR-15-CE01-0014-01 ), the  French national program LEFE/INSU , the  Programme national de Télédetection Spatiale  (grant no. PNTS-2016-14 ), the  French National Agency for Space Studies (CNES, AEROCLIM and EECLAT projects) , and the  South African National Research Foundation (NRF)  (grant no. UID 105958 ). The research leading to these results has received funding from the  European Union 's  7th Framework Programme  (grant no. FP7/2014-2018 ) under  EU-FAR2  (grant no.
Data availability. All the ALADIN and RegCM model outputs could be provided upon request. MODIS data are available from the NASA Level 1 and Atmosphere Archive and Distribution System Distributed Active Archive Center at https://ladsweb. modaps. eosdis. nasa. gov/ (last access: April 2020) (NASA's Goddard Space Flight Center, 2020a). MODIS abovecloud data products are available from the authors upon request. The CALIOP L2 data are available on NASA Earth Science Data: https://earthdata. nasa. gov/ (last access: April 2020) (NASA  Langley Research Center, 2020). ACAOD from POLDER (https://doi. org/10. 25326/82, ICARE, 2020) could be obtained at https://www. icare. univ-lille. fr/aero-ac/ (last access: April 2020) (ICARE, 2020). MACv2 datasets are accessible at ftp://ftp-projects. zmaw. de/aerocom/climatology/MACv2_2018/ (last access: April 2020) (Max Planck, 2020).
Cloud products have been obtained from the CM_SAF dataset (https://www. cmsaf. eu/EN/Products/AvailableProducts/Climate_ Data_recordsClimate_Data_Records_node. html, last access: April 2020) (EUMETSAT, 2020). Shortwave radiation PIRATA data are available at https://www. pmel. noaa. gov/tao/drupal/disdel/ (last access: April 2020) (Global Tropical Moored Buoy Array,  2020). Further information can be found on the CRU website for the CRU temperature and precipitation data set (http://www. cru. uea. ac. uk/data, last access: April 2020) (Climatic Research Unit, 2020), on the AERONET website for AERONET AOD data (https://aeronet. gsfc. nasa. gov/ (last access: April 2020) (NASA's Goddard Space Flight Center, 2020b). Supplement. The supplement related to this article is available online at: https://doi.
Setting and study design We conducted a cross-sectional analysis using data from an online experiment that was originally pre-registered in the Open Science Framework (https://osf. io/mhty3). The experiment had been designed by FE prior to the outbreak as part of a research programme on the relationships between subjective identity and various economic preferences in young adults. After the lockdown and confinement policy measures enacted by the French government on March 16 th , we (FE and PYG) decided to add a specific COVID-19 module that was inserted in the post-experiment questionnaire at a distance from the experimental treatments and measures. The questionnaire was developed using Qualtrics online survey services. Qualtrics distributed the survey between April 10 th and April 20 th.
Therefore, we defined the relative closeness of individuals with their relatives to their closeness with their other acquaintances by dividing the former score by the latter. This ratio measures the relative 'family orientation' of individuals. We also computed the average of all ratings to control for inter-individual variability in response styles (see Appendix A. 8). The trust, reciprocity and family-orientation variables were all standardised (z-score) by their gender-specific means and standard deviations. The data and codes used for producing the results are available at https://osf. io/gesyt/.
Acknowledgements The authors are grateful to all participants for providing the data used in this study. The present study was supported by a research grant from  AXA Research  (AXA Award 2015 -FE). We also acknowledge administrative support from the  Paris School Economics  via the  Agence Nationale de la Recherche EUR  grant  ANR-17-EURE-0001. The funders had no role in the design or conduct of the study; the collection, management, analysis, or interpretation of the data; or the preparation, review, or approval of this manuscript.
In the present study, we provide a comprehensive analysis and a multi-dimensional dataset of semantic transparency measures for 1,810 German compound words. Compound words are considered semantically transparent when the contribution of the constituents' meaning to the compound meaning is clear (as in airport), but the degree of semantic transparency varies between compounds (compare strawberry or sandman). Our dataset includes both compositional and relatedness-based semantic transparency measures, also differentiated by constituents. The measures are obtained from a computational and fully implemented semantic model based on distributional semantics. We validate the measures using data from four behavioral experiments: Explicit transparency ratings, two different lexical decision tasks using different nonwords, and an eye-tracking study.
Semantic transparency effects in German compounds: A large dataset and multiple-task investigation Many languages make use of compound words such as airport or smartphone in order to express new concepts and meanings  (Thagard, 1984). Indeed, compounding is a very prevalent diachronic phenomenon in different languages  (Libben, 2014) , and is assumed to be central in the development of complex expressions, compositionality and lexical productivity in general  (Bauer, 2009; Jackendoff, 2002). In a complex expression such as a compound, the meanings of the constituents are typically informative about the meaning of the expression as a whole: An airport shares some semantics with both air and with port. In Germanic languages such as English or German, the rightmost (last) constituent generally serves as the head  (Williams, 1981)  which defines the semantic category of the word, while the first constituent serves as a modifier further specifying the meaning -an airport is a kind of port dedicated to travel by air.
The Present Dataset and Related Work In the present study, we provide and validate a dataset of six different semantic transparency measures (represented by the links in Figure  1 ) for a large set of German compounds. For English, different datasets containing human ratings of semantic transparency are available:  Juhasz, Lai, and Woodcock (2015)  provide ratings for 629 English compounds, where participants had to indicate whether the compound meaning was transparently related to its constituents (i. e. , one single variable per compound). Kim, Yap, and Goh (2018)  collected ratings for the transparency of both modifier and head for a set of 2,861 English compounds (i. e. , two transparency variables per compound). And in a very recent study,  Gagné, Spalding, and Schmidtke (in press ) provide a large database of 8,376 English compounds, including head and modifier relatedness measures as well as ratings for the extent to which the constituents retain their meaning in the compound, and to which the meaning of the compound is predictable from its parts.
Furthermore,  Günther and Marelli (2018a)  provide a dataset of the semantic transparency measures displayed in Figure  1  -head and modifier relatedness, head and modifier composition, and compound compositionality -for 1,865 English compounds as a Supplemental Material to their study, available at http://dx. doi. org/10. 1037/xlm0000677. supp. However, comparable resources are not readily available for German, even though German is ideally suited for the investigation of semantic effects in compound representation and processing due to its rich and synchronically extremely productive compounding system  (Smolka & Libben, 2017). Additionally, the word formation of compounds in German is extremely consistent: As in English, the head of German compounds is always the rightmost (last) constituent  (Williams, 1981).
Differently from English, with its open forms such as tree house, German compounds are consistently written as a single string (Baumhaus). Accordingly, many psycholinguistic studies on semantic transparency are being conducted using German compounds (e. g. ,  Böhl, 2007; Bronk et al. , 2013; Dohmes, Zwitserlood, & Bölte, 2004; Gumnior, Bölte, & Zwitserlood, 2006; Koester, Gunter, & Wagner, 2007; Lorenz, Heide, & Burchert, 2014; Lorenz & Zwitserlood, 2014; Sahel, Nottbusch, Grimm, & Weingarten, 2008; Smolka & Libben, 2017). The most comprehensive German resource to date has been the GhoSt-NN dataset (Schulte im Walde, Hätty, Bott, & Khvtisavrishvili, 2016), which includes constituent-wise ratings of compositionality for 868 German compounds (i. e. , two transparency variables for each compound, either related to its modifier or head), collected from and averaged over 5 to 13 human expert raters (linguists or computational linguists).
However, the instruction given to the participants was to provide these ratings on a scale ranging from "definitely semantically opaque" to "definitely semantically transparent", which can be interpreted as evaluating the relatedness between the compound and each of its constituents, amounting to the modifier and head relatedness measures described in Figure  1  (compare Appendix A for an analysis of the GhoSt-NN dataset). Importantly, the GhoSt-NN dataset provides only two constituent-wise transparency measures, and does therefore not explicitly distinguish between constituent composition and constituent relatedness as separate constructs (while the description of the dataset suggests the former, the concrete instruction suggests the latter; compare also Appendix A). Furthermore, the GhoSt-NN dataset does not consider other transparency measures such as constituent similarity or compound compositionality.
, 2018; Libben et al. , 2003). Secondly, it allows to measure semantic transparency as a graded variable, beyond a simple and coarse distinction between transparent and opaque compounds  (Marelli & Luzzatti, 2012). Thirdly, the framework can easily be generalized to obtain semantic transparency measures for large sets of compounds (see the large dataset provided in the present article, containing these measures for a total of over 42,000 German compounds). We validate these measures using data from four behavioral experiments by Böhl SEMANTIC TRANSPARENCY OF GERMAN COMPOUNDS 10 (2007): Explicit transparency ratings, two different lexical decision tasks using different nonwords, and an eye-tracking study using the boundary technique. We show that different semantic effects emerge in different behavioral tasks, demonstrating the importance of a comprehensive model of semantic transparency that is able to capture the different aspects of the construct.
Word Material The word material employed in this study consists of 1,810 German compound words, obtained from a large-scale study by  Böhl (2007) , also described in  Brysbaert et al. (2011). These compounds were originally collected from the German CELEX database  (Baayen, Piepenbrock, & Gulikers, 1995). For 344 additional compounds used in the original study, semantic measures could not be obtained due to the frequency restrictions described below. Thus, while the item material of all the experimental studies described here consisted of the same 2,154 compounds, all our analyses are based on the same subset of 1,810 compounds.
The semantic space is available online at https://sites. google. com/site/fritzgntr/software-resources/semantic_spaces (Günther, Dudschig, & Kaup, 2015) , labeled dewac_cbow. With this model, we obtained distributional vectors representing the constituent meanings as well as the whole-word compound meanings for the 1,810 compounds in our word material. Using the DISSECT toolkit  (Dinu, Pham, & Baroni, 2013) , we then computed the following measures on the basis of these vectors: • modifier relatedness: the cosine similarity between the modifier vector and the whole-word compound vector, measuring the degree of semantic relatedness between the modifier and the whole-word compound meaning, conceptualized as distinct, separate entries in semantic memory (see  Schmidtke et al. , 2018). • head relatedness: the cosine similarity between the head vector and the whole-word compound vector, analogously to modifier relatedness • constituent similarity: the cosine similarity between the modifier vector and the head vector, measuring the semantic coherence between the compound constituents  (Lynott & Ramscar, 2001).
2 Examples for English compounds are provided by  Günther and Marelli (2018a). The free parameters of this model, the weight matrices M and H, are obtained in a regression-based training process: With a given training set consisting of compounds and their constituents, M and H are estimated in a least-square regression procedure aimed at optimally predicting the whole-word compound meanings for these compounds (derived from the source corpus) from their constituent meanings u and v, following Equation 1. In our study, this training set consisted of 42,285 compounds for which constituent and whole-word compound vectors were available in our semantic space. This training set was collected from the word material used in the present study  (Böhl, 2007; Brysbaert et al. , 2011) , the GhoSt-NN dataset (Schulte im  Walde et al.
3 The dataset containing all the described measures for the 1,810 compounds of our word material is provided at https://doi. org/10. 6084/m9. figshare. 8295101. Due to the limited availability of experimental data, we only validate the semantic measures for the 1,810 compounds in our word material. However, in principle, our model architecture allows us to compute semantic measures for all compounds included in the training set. We provide the semantic measures for the remaining 40,475 at the same online repository, with the explicit cautionary note that they are not empirically validated. Lexical Variables. In addition to the semantic measures described here, we also considered several lexical variables as a non-semantic baseline in our subsequent analyses. We opted to include the most prominent variables considered in the literature on compound processing (length, frequencies, family sizes), which are often associated to morphological and lexical rather than semantic processes and representations (e.
Validation In the following sections, we validate our measures using four behavioral datasets presented in and collected by  Böhl (2007) : Explicit ratings on semantic transparency, two different lexical decision tasks using different sets of nonwords, and an eye-tracking study using the boundary technique, in which participants have to give semantic relatedness judgments after reading the target compound  (Beauvillain, 1996; Rayner, 1975). In the present study, we present brief summaries of the experimental methodology; detailed descriptions including participants, material, and procedure are provided by  Böhl (2007). We investigated whether semantic effects can be detected in all four paradigms, using the semantic transparency measures presented here. We further investigated whether different semantic effects emerge in different experimental paradigms, adopting the detailed, explicit and fully implemented model of semantic transparency postulated by Günther and Marelli (2018a).
Then, in a first screening step, we tested which of the semantic transparency measures significantly improves these baseline models in a likelihood-ratio test. This analysis was conducted to identify which semantic effects can be expected in the different paradigms when they are tested in isolation. Finally, we conducted a stepwise forward model analysis to identify the set of parameters that optimally predicts each dataset. In each step, we tested separately for each semantic parameter not yet included in the model whether it significantly improved the current model, as indicated by a likelihood-ratio test. In cases where more than one possible candidate significantly improved the current model, we selected and included the best-predicting parameter (for which the resulting model has the lowest AIC value  Akaike, 1998).