dataesr/scientific-paragraphs-categorization huggingface dataset:
Groundwater flow model The MODFLOW software suite is used to solve the depth-integrated groundwater flow equation under steady state conditions (Eq. (  1 )) using a three-dimensional finite difference approach  (Harbaugh, 2005; Niswonger et al. , 2011) : 𝜕 𝜕𝑥 [ℎ 𝐾 𝑥𝑥 𝜕ℎ 𝜕𝑥 ] + 𝜕 𝜕𝑦 [ℎ 𝐾 𝑦𝑦 𝜕ℎ 𝜕𝑦 ] + 𝜕 𝜕𝑥 [ℎ 𝐾 𝑧𝑧 𝜕ℎ 𝜕𝑧 ] = 𝑊 (1) where ℎ [L] is the hydraulic head,  [L T -1 ] is the hydraulic conductivity along the , , and  coordinate axes,  [T -1 ] is the volumetric flux per unit volume from the porous medium. We use the FloPy Python package  (Bakker et al. , 2016)  to set and handle simulations. To reduce uncertainties linked to potential flow across topographic boundaries, a buffer zone is added to the topographical catchment boundaries, increasing the modelled domain area by 10 %. The model domain is discretized using the regular mesh of the DEM.
We have systematically tested the method using six hydrographic networks issued by different global, national, and local databases (Figure  7b1 ). These products display largely evolving densities coming from the origin, nature, and scale of observations (Appendix A). For   = 75 m, the criterion   (Eq. (  4 )) remains smaller than 2 for cases B to F qualifying them to estimate   (Figure  7b2 ). For case A only, the global-scale product HydroRIVERS  (Lehner et al. , 2013)  locates rivers away from the topographic valleys of the DEM issuing a   value more than ten times larger than. For cases B to F, the hydrographic network is well captured. Values of   vary over one order of magnitude from 7 x 10 -6 to 6 x 10 -5 m s -1. decreases with more extended and denser hydrographic networks. Indeed, dense river networks are generated by low hydraulic conductivities enabling high water table and headwater streams to develop upwards in the catchment (Figure  7b , E and F).
The nearest downslope flowpath distances (  ̅̅̅̅̅ and   ̅̅̅̅̅ ) improve the Euclidean distance of the cell-by-cell and cellby-neighborhood analysis  (Franks et al. , 1998; Güntner et al. , 2004 ) by constraining the observed-to-simulated and simulatedto-observed stream networks to the topographical structures. The high sensitivity of the computed / with both the density and spatial extent of the observed stream network highlights the requirements to use high-quality observed stream products and, when necessary, to improve them. River maps available on national to global databases are often incomplete compared to local databases compiled by stakeholders on direct field observations leading to an overestimation of the effective hydraulic conductivities. Moreover, the results showed that the resolution of DEM should not be much higher than the resolution of the hydrographic network to ensure the consistency of the observed stream network with the DEM.
Acknowledgments This work was supported by " Eaux et Territoires " research chaire driven by the  Fondation Rennes 1. Clément Roques  acknowledges financial supports from the  Rennes Métropole research chair "Ressource en Eau du Futur"  and the  European  project  WATERLINE , project id  CHIST-ERA-19-CES-006. Alexandre Guavain  acknowledges funding from the  RIVAGES Normands 2100  project. The investigations benefited from the support of OZCAR, H+. https://doi. org/10. 5194/hess-2022-175Preprint. Discussion started: 16 May 2022 c Author(s)  2022. CC BY 4. 0 License.
Digital Tools for Marking Places Other exciting tools exist, such as map programs that allow one to mark places digitally with pins on a map. There are different programs available. For example, the map function of Padlet software is straightforward (https://de. padlet. com/) (accessed on 20 November 2021). Padlet is not a geographic tool but software used to create a digital pinboard for collaborative work  [35]. To date, it has mainly been used in disciplines other than geography  [35, 36]. An accurate map program is the software uMap (https://umap. openstreetmap. fr/de/) (version 1. 2. 3; France). uMap is open-source software that lets a user create a map relatively quickly.
Creating Virtual Field Trips Another possible use of digital tools is to create VFTs. This is especially important for pre-service or in-service teachers, who can make these field trips either for students or with students. Various tools are available for this purpose. In addition to creating regular websites, one can use software that allows a relatively well-structured and straightforward presentation, such as ArcGIS Storymaps and Google Earth  [28]. In addition to the above variants (see Section 2. 3. ), with ArcGIS Storymaps locations can be marked on a map and linked with different text, pictures, or videos to create a tour. Kerski [27]  provides an excellent introduction to ArcGIS Storymaps and its possible applications.
Organization of the Seminar The seminar was built on different principles, as follows. A blended learning approach: First, the seminar has a blended learning approach. Some activities were planned in real places. Real field trips concerning sustainability were designed in Hamburg and Paris-HafenCity as an example of urban development; Inselparkquartier as an example of sustainable housing; Science City Bahrenfeld as an example of a new science district; the Paris Rive Gauche district as a place of urban renewal to meet the challenges of densification in Paris; and Ecoquartier Bois Badeau (Brétigny-sur-Orge) as an example of urban development following the principles of sustainable development. Real field trips were used to collect data (images, videos) for the creation of Storymaps. Some activities occurred in a virtual learning environment (online conference, virtual workshop, online student presentation).
Data Generation (Questionaire) An online questionnaire was conducted as part of this project. The questionnaire was developed based on existing literature on field trip education. The development of the closed-question items on geographical working methods was based on Lößner's items  [45]. The questionnaire consisted of four closed questions (research questions 1 and 2) and three open questions (research questions 3 and 5). A five-point Likert scale was used for the closed-question items, ranging from "strongly disagree" to "strongly agree. " The open-question items could be filled in freely by the participants. Quotation marks indicate the direct responses of the students presented in the results. The questionnaire was used after the seminar.
Data Generation (Questionaire) An online questionnaire was conducted as part of this project. The questionnaire was developed based on existing literature on field trip education. The development of the closed-question items on geographical working methods was based on Lößner's items  [45]. The questionnaire consisted of four closed questions (research questions 1 and 2) and three open questions (research questions 3 and 5). A five-point Likert scale was used for the closed-question items, ranging from "strongly disagree" to "strongly agree". The openquestion items could be filled in freely by the participants. Quotation marks indicate the direct responses of the students presented in the results. The questionnaire was used after the seminar.
These emergent findings suggest that conservatism has direct and indirect associations with compliance with preventative measures through trust in science and risk perceptions. Here, we investigated whether, among Americans, conservatism is indirectly associated with concern about contracting the coronavirus (i. e. , personal threat perceptions) and preventative behaviors (e. g. , social distancing) through (dis)trust in scientific information about the coronavirus pandemic, using larger, more representative samples than prior work. We further investigated whether these patterns generalized beyond the United States, a context in which scientific trust in general, and about the coronavirus pandemic in particular, is highly polarized [see  15  ], using a large cross-national dataset.
The data presented in Studies 1a-1b utilize public, representative samples from the ANES 2020 Social Media Study before and after the 2020 Presidential election. In Studies 2a-2b, we present parallel data from two distinct representative samples of Americans during the same time period, allowing for replication of effects between different research teams as well as across time periods. In Study 3, we analyzed data from a previously collected (May-July 2020) large, three-wave, cross-sectional study of attitudes and behaviors in the coronavirus pandemic from 21 countries to evaluate how present such relationships are across the world.
Studies 1a-1b We made use of publicly available data from large, representative samples of the American population from the American National Election Studies (ANES). Specifically, data for Studies 1a-1b were obtained from the ANES Social Media Study, conducted between before (Study 1a) and after (Study 1b) the 2020 U. S. presidential election. All data and materials for this study can be found here: https:// elect ionst udies. org/ data-center/ 2020social-media-study/. With these data, we again examined associations between conservatism, concern about contracting the coronavirus, and trust in science and scientific information. Owing to the vastly different set of variables available in this data, we examined trust in a specific organization dispensing scientific and medical information in Studies 1a-1b, specifically, the Center for Disease Control (CDC).
Data analysis. All data analysis in Studies 1a-1b was conducted using SAS 9. 4  53. As the data collected in Studies 1a-1b were non-probability samples, with the aim of matching the population of the United States, ANES recommends the use of sampling weights for analyses with these data (see https:// elect ionst udies. org/ data-center/ 2020-social-media-study/). Thus, all analytical procedures accounted for the sampling weights provided by ANES. For preliminary analyses, we used the proc corr command (accounting for sample weights) to probe (weighted) correlations between conservatism, confidence in the CDC, and concern about contracting Indirect effect test. For both the pre and post-election surveys, we conducted tests for indirect effects of conservatism on concern through a proxy for trust in scientific institutions, here confidence in the CDC, controlling for age, binarized gender (male = 1, female = -1), income, and education level.
Studies 2a-2b We collected data in two nationally representative samples, before and after the 2020 U. S. presidential election. Given our ability to control the survey instruments, we conceptually replicated our findings with more comprehensive and detailed measures of trust in science and concern for COVID-19. Further, we also expanded on our findings by investigating the additional outcome of compliance to COVID-19 recommendations. Informed consent was obtained by the researchers for all participant data used in Studies 2a-2b.
Data analysis. All data analysis for Studies 2a-2b was conducted using SAS 9. 4  53. Unlike Studies 1a-1b, the samples we collected here were not recruited using non-probability methods, and thus weighting was not necessary. Similar to Studies 1a-1b, however, preliminary analyses involved computing zero-order correlations between conservatism, trust in science, concern about contracting the coronavirus, and coronavirus compliance using the proc corr command. Sequential indirect effects were modeled using Hayes's PROCESS macro, version 3, for SAS 9. 4  55  with 10,000 bootstrap samples, as this was possible given the non-necessity of weighting samples in these studies (see https:// proce ssmac ro. org/ faq. html).
Methods Participants. We recruited data across 21 countries and special administrative regions, recruiting a total of 25,159 participants. These included: Australia, Canada, China, Spain, France, Germany, Hong Kong, Hungary, Indonesia, the Republic of Ireland, Israel, Italy, Korea, Malaysia, the Netherlands, the Philippines, Poland, Serbia, Turkey, the United Kingdom, and the United States. In China and Hong Kong no measure of political ideology was included in the survey because of risks to participants, and thus participants from these countries were excluded from the current investigation, leaving us with a total of 19 countries (N = 20,580; Table  S2  for country specific Ns and descriptive statistics). Participants were recruited across three cross-sectional waves: Wave 1: May 4th, 2020-May 21st, 2020; Wave 2: June 15th, 2020-June 23rd, 2020; Wave 3: July 20th, 2020-July 28th, 2020, via CloudResearch  58, 59.
Samples sizes were determined based on an a priori power analysis detect interactions between time-point comparisons and cross-country comparisons for a small-to-medium effect size (Cohen's f = 0. 160; see  60  ). Thus, we aimed to recruit at least 300 participants per wave in each country. To approach a more representative sample from each country, data was collected to fill known representative percentages for a variety of demographic characteristics, such as level of education, race/ethnicity, urbanization, religion, age, Table  1. Bivariate correlations for Study 2a (below the diagonal) and Study 2b (above the diagonal). All coefficients are higher than. 10 are significant at p < 0. 001, all coefficients below. 10 are significant at p <. 01. gender, income. These levels were established through census-level data of each country population.
In the U. S we aimed to recruit truly representative samples, via the same demographic characteristics as above but with a larger number of participants (N = 1200) per wave. While these analyses were not pre-registered, the sampling method was pre-determined for all samples (see https:// osf. io/ g29z4/). All research conducted in this study received approval from the University of Massachusetts Amherst IRB (IRB Protocol #2063) and complied with all APA guidelines and policies for human subjects research. Informed consent was obtained by the researchers for all participant data used in Study 3.
Data analysis. For preliminary analyses, we estimated correlations within each country for each wave between political ideology and: (1) trust in science, (2) concerns about contracting COVID-19, (3) compliance with COVID-19 regulations, (  4 ) support for lockdown restrictions to prevent the spread of the virus using the proc corr command in SAS 9. 4  53. These correlations were used in order to estimate mean correlation effect sizes ("meta-correlations") across all three cross-sectional waves in each country using Goh and colleagues' (2016) methodology and publicly available calculation spreadsheets (see https:// osf. io/ 8yubf/). These publicly available materials detail the exact mathematical procedures used to compute and test the significance of meta-correlations. Thus, our meta-correlations were computed by inputting our data into these Microsoft Excel spreadsheets.
Model evaluation has been carried out by comparing numerical results of single-scattering albedo (SSA), aerosol optical depth (AOD), and absorption aerosol optical depth (AAOD) to data provided by Aerosol Robotic Network (AERONET) stations, at the local scale, and by different satellite products, at the global scale. The implementation of BrC and its bleaching parameterization has resulted in an improvement of the estimation of the total SSA and AAOD at 350 and 440 nm. This improvement is observed at both the local scale, for several locations of AERONET stations, and the regional scale, over regions of Africa (AFR) and South America (AME), where large quantities of biomass burning aerosols are emitted. The annual global BrC effective radiative forcing (all-sky conditions) has been calculated in terms of both aerosol-radiation interactions (ERF ari , 0.
Brown carbon implementation A new prognostic aerosol species has been added to the TAC-TIC aerosol scheme: the BrC. The first step of this implementation, which allows for a good representation of the spatial and temporal variability of the BrC absorption, consisted in separating BrC from OA according to their sources. To do this, OA emissions were separated into three sources: BB, fossil fuel (FF), and BF as presented in Fig. 2. Included in the model BB emissions, provided over the 2000-2014 period, are those described in van Marle et al. (  2017 ) with the GFED4s (Global Fire Emissions Database) as an anchor data set. Then, BF emissions mainly come from the residential, industry, and energy sectors, and FF emissions are those of the CEDS (Community Emissions Data System) inventory released for CMIP6  (Hoesly et al.
Reference data sets In this study, different data sets have been used to evaluate the ability of the ARPEGE-Climat model to reproduce the total absorption aerosol optical depth (AAOD), single-scattering albedo (SSA), and aerosol optical depth (AOD). First, given their spatial and temporal scales, four satellite products have been used to provide estimates of the total AAOD and SSA (at 350 and/or 440 nm) and of the total AOD (at 550 nm). -PARASOL-GRASP (2006-2012, 1 • resolution,  Chen et al. , 2020)  for the AAOD and SSA at 440 nm and the AOD at 550 nm. This satellite product, initially developed by the science team at LOA (Laboratoire d'Optique Atmosphérique, Lille, France), is obtained by the Generalized Retrieval of Atmosphere and Surface Properties (GRASP) algorithm from POLDER-PARASOL observations.
The AAOD uncertainty is estimated at about 0. 003. -FMI_SAT (1995-2017, 1 • resolution,  Sogacheva et al. , 2020)  for the AOD at 550 nm. FMI_SAT is the name given to the merged AOD product presented in  Sogacheva et al. (2020). This product, which provides AOD monthly data, was built from 12 individual satellite products and evaluated with the AERONET ground network. It provides AOD data with an uncertainty reaching 0. 006 on average and up to 0. 05 in regions with high AOD  (Sogacheva et al. , 2020). Second, we considered AERONET (from 2000 to 2020 depending on the station,  Holben et al. , 1998)  data. This network consists of globally distributed ground-based Sun photometers which provide local column-integrated aerosol properties at different solar wavelengths, including 440 nm. The column extinction Ångström exponent can be directly calculated from the wavelength-dependent AOD measurements  (Eck et al.
, 1999). In this study, monthly average data (version 3, level 1. 5 with an automated cloud screen- ing,  Smirnov et al. , 2000)  have been used. A complete description of the version-3 AERONET product is available in  Sinyuk et al. (2020). Unlike level 2. 0 (quality-assured data), level 1. 5 reports 440 nm AAOD and SSA for all AODs, including AOD lower than 0. 4  (Lacagnina et al. , 2015). For comparison to our model results, AAOD and SSA data at 440 nm were directly used. It should be noted that 440 nm is the shortest available wavelength in AERONET data. We calculated AOD at 550 nm using the Ångström coefficients provided. AERONET uncertainties have been reported in several papers. Eck et al. (1999)  and  Kinne et al. (2013)  indicate that the AOD and AAOD uncertainties are approximately 0.
01. Concerning the SSA,  Dubovik et al. (2000)  report an uncertainty of 0. 03. However, it should be noted that larger SSA uncertainties are expected at low AODs  (Lacagnina et al. , 2015). In order to have the most accurate comparison to the model, we decided to keep only AERONET monthly data with at least eight daily values to derive the mean of each month, and for a given month we keep only the stations with at least three monthly values over the 2000-2020 period. In the end, we selected 18 stations: 6 in Africa, 3 in South America, and 9 in Europe and Asia, as shown in Fig. 1  (see also Table  1 ). It can be noted that a global representation of the spectral aerosol absorption in the UV-to-visible wavelength range (340-670 nm) based on a synergy of ground measurements (AERONET AOD) and satellite observations (near-UV OMI radiances and visible MODIS (Moderate-Resolution Imaging Spectroradiometer) radiances) is presented in  Kayetha et al.
It is interesting to note here that the different satellite products (see Table  2  for reference data set details) are not always very consistent with the AERONET data, both in annual cycle and in annual average. Over regions with high biomass burning activity such as southern Africa and South America, Figs. 3  and 4  indicate a decrease (increase) in SSA (AAOD) for simulations including BrC at all AERONET stations, particularly during the JAS period. These figures also show that these changes are even more pronounced with BRC_NOBL. Conversely, Fig. A1  shows a very small BrC impact on total aerosol AOD; indeed, the three simulations show similar values at all AERONET stations. In more detail, Fig. 3    South America than NOBRC SSA. The bleaching parameterization allows us, at some AERONET stations, to better represent the observations (e.
g. , HESS, Windpoort, or Skukuza). However, at other AERONET stations (e. g. , Lubango, As-cension_Island, and Mongu Inn), the BRC_NOBL simulation seems to show results in better agreement with the observations. At Windpoort or Skukuza, Fig. 3  shows a SSA decrease of about 0. 05 with the BRC simulation during the summer period, which is consistent with the SSA decreases shown by all observation data sets. At Mongu Inn, there is a stronger SSA decrease of 0. 08 associated with an AAOD increase of 0. 06 over the JAS period between the NOBRC and BRC_NOBL simulations. BRC_NOBL is therefore the closest to the AERONET observations. As for the SSA, the BrC implementation allows us to simulate AAOD close to all observations. Indeed, Fig. 4  shows that AAOD is systematically underestimated with the NOBRC simulation, both over African and South American stations.
3  for details) and reference data sets (PARASOL-GRASP, OMI-OMAERUVd, MACv2; see Table  2  for details). Uncertainty of the AERONET observations appears shaded. improvement thanks to the BrC parameterization, and this betterment is even clearer when the BrC bleaching parameterization is also taken into account. For clarity reasons, only the NOBRC and BRC simulations will therefore be presented in the remainder of this study.
A3  and A4 ). Both on annual average or for the JAS season, the OMI-OMAERUVd product shows higher SSA (at least +0. 04) than those observed by PARASOL-GRASP and MACv2 over AFR and AME. Previous studies have already shown differences, sometimes systematic, between AERONET and satellite data sets and also among satellite products  (Schutgens et al. , 2020 (Schutgens et al. , , 2021)). The NOBRC SSA during JAS, with averaged values of 0. 925 ± 0. 001 and 0. 930 ± 0. 001 at, respectively, 350 and 440 nm over AFR, is overestimated com-pared to all reference data sets. Table  5  also shows similar results over the AME region. The BRC simulation, characterized by an SSA of 0. 889 ± 0. 002 (0. 906 ± 0. 002) at 350 nm (440 nm) over AFR during this season, is therefore more consistent with the reference data sets, with SSA close to those measured by OMI-OMAERUVd (0.
2  and 3  for details) averaged over the year and over the months of July, August, and September (JAS). Black dots in the model plots show common locations between the NOBRC and BRC simulations where the SSA is inside the range covered by MACv2 and OMI-OMAERUVd (reference data set uncertainties included; AERONET uncertainties are used when data are not available). Changes in these locations between the NOBRC and BRC simulations are highlighted by white hatching. Shaded areas indicate a missing value.