dataesr/scientific-paragraphs-categorization huggingface dataset:
The MERRA-2 data used in this study have been provided by the  Global Modeling and Assimilation Office (GMAO)  at  NASA Goddard Space Flight Center. This work was granted access to the HPC resources of TGCC under allocations  2019-A0010102201  and  2020-A0010102201  made by  GENCI. Financial support. This research has been supported by the  National Science Foundation (NSF)  (grant nos. 1552519  and  1856389 ) and the  Army Research Office  (cooperative agreement number  W911NF-20-2-0150 ). This research was further supported by the  University of California President's Postdoctoral Fellowship  awarded to  Adeyemi A. Adebiyi  and the  European Union 's  Horizon 2020 research and innovation program  under  Marie Skłodowska-Curie  grant agreement no. 708119  awarded to  Samuel Albani  and no.
Data availability. The data obtained in this paper are available at https://doi. org/10. 15144/S4WC77 (Kok et al. , 2021b). These data include gridded dust emissions, loading, (surface) concentration, wet and dry deposition, and DAOD fields that are resolved by season and particle size, including by particle bin and for PM 2. 5 , PM 10 , and PM 20 dust. All fields include 1 and 2 standard error uncertainty estimates. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/acp-21-8127-2021-supplement.
2017 , Kieslinger et al. 2018). The spatial and temporal scales, the resolution, and several other parameters should also be defined before using such informal datasets. The main trade-off is having increasing volumes and frequency of data, but having to characterize crowdsourced data, vs using standard monitoring stations that can lead to limited discrete records  (Davids et al. 2017). The data gathering, processing, production, and dissemination chain of crowdsourced data should follow quality standards, and these should be analysed and reported  (Antelio et al. 2012). Standardization procedures that actually characterize traditional monitoring networks require innovations to include and exploit the opportunity offered by such data. Standard metadata structures are required to accommodate the heterogeneity of specifications associated with crowdsourced data.
At the same time, a change of human behaviour as a reaction to the gathered observation or knowledge will be analysed as well. It is, thus, crucial to analyse interactions between the "human sensors" and "human behaviour" components to understand their combined effects on the data-information-knowledgebehaviour-action workflow. Nonetheless, while the randomness and subjectivity of human-derived observations and actions are valuable for citizen science projects in hydrology, obtaining different information from the same crowdsourcing methodology also represents a challenge, as hydrological data require consistency over space and time  (Strobl et al. 2019a). This thematic area is, thus, also suitable to investigate how the hydrological sciences should connect with knowledge, expertise, and scientific reasoning from other disciplines.
2015 , Shukla et al. 2019). Water information systems are generally equipped with GIS and web-GIS interfaces implementing geospatial data models representing morphometric, environmental, hydrological, and socio-economic parameters associated with river basins and networks  (Singh and Fiorentino 1996 , Maguire et al. 2005 , Whiteaker et al. 2006 , Silberbauer 2019). A number of regional and local water management agencies are investing relevant economic resources to extend their monitoring networks and develop ad hoc hardware and software for data gathering and sharing (e. g. web GIS, dashboards). Notably, examples exist of hydrological information systems covering large spatial scales and a wide variety of data sources  (Addor et al. 2020) , such as WMO Hydrological Observing System (WHOS), United States Geological Survey National Water Information System (USGS Nwis), Consortium of Universities for the Advancement of Hydrologic Science (CUAHSI) HydroShare, Water Information System for Europe (WISE), UNESCO-IHP Water Information Network System, World Resources Institute, and Global Runoff Data Centre (GRDC) databases.
Also, it is necessary to evaluate data models and fuzzy methods that can incorporate multiple datasets into modelling frameworks  (Malczewski 2006). Comparative assessments are fundamental to building knowledge from diverse citizen science models and outcomes. Additionally, heterogeneity, gaps, flexibility, interoperability, scalability, and data assimilation should be evaluated. Investigations on model calibration and validation procedures are also needed to accommodate the varying formats, uncertainties, and availability of crowdsourcing data. In turn, models optimized for crowdsourced data can be compared with models built with expert-sourced observed data, standard observations, or previously validated simulated variables. This theme seeks to define and develop procedures and guidelines -as set out in the proposed conceptual transdisciplinary framework -and establish synergies among, as well as take into account the opportunities and caveats characterized in, the previous six themes.
Acknowledgements Two anonymous reviewers provided ideas and comments that greatly helped to improve this paper. The IAHS CANDHY Working Group is continuously welcoming new members, citizens and hydrologists, interested in joining efforts and contributing to the CANDHY mission. To become a CANDHY friend -a member of the working group -and stay up to date, please visit the webpage https://iahs. info/Commissions-W-Groups/Working-Groups/ Candhy. do.
Generation of transcriptomic sequence data The algal strains used, sequencing statistics, and accession numbers are listed in Table  S1. Gametophytes were grown at 14°C and 2-3 µE m -2 sec -1 from daylight type fluorescent lamps for 14:10 (light:dark) cycles for several weeks. Vegetative gametophytes were then transferred to conditions to induce fertility for one week (20 µE m -2 sec -1 ), and were collected before sexual structures were visible. RNA-seq analysis was carried out to compare the relative abundances of gene transcripts in the different samples. For each sample, total RNA was extracted from 2 independent bulks of app. 1000 male individuals and 2 bulks of 1000 female individuals (two biological replicates for each sex) using the Qiagen Mini kit (http://www. qiagen. com) as previously described  (Lipinska et al.
Accepted Article This article is protected by copyright. All rights reserved 2015b;  Arun et al. , 2019). RNA quality and quantity were assessed using an Agilent 2100 bioanalyzer, associated with an RNA 6000 Nano kit. For each replicate, the RNA was quantified and cDNA was synthesized using an oligo-dT primer. The cDNA was fragmented, cloned, and sequenced by Fasteris (CH-1228 Plan-les-Ouates, Switzerland) using an Illumina Hi-seq 2000 set to generate 150-bp single-end reads. Data quality was assessed using the FastQC  (Wingett et al. 2018). Reads were trimmed and filtered using Trimmomatic (B  (Bolger et al. , 2014)  with average quality > 28, a quality threshold of 24 (base calling) and a minimal size of 60 bp. Filtered reads were mapped to the M. pyrifera haploid genome  (Lipinska et al.
, 2019)  using TopHat2  (Kim et al. , 2013)  with the Bowtie2 aligner  (Langmead & Salzberg, 2012). More than 70% of the sequencing reads for each library could be mapped to the genome (Table  S1 ). The mapped sequencing data were then processed with FeatureCounts  (Liao et al. , 2014)  to obtain counts for sequencing reads mapped to exons and counts by gene. Transcript abundances, measured as transcript per million (TPM) were strongly correlated between biological replicates of each sample (Figure  S5 ). Expression values were represented as transcript per million (TPM); any genes with TPM value above the 5 th percentile is considered as expressed. This resulted in a total of 19208 genes with expression in at least one of the three strains.
Identification of sex-biased genes The filtering steps described above yielded a set of expressed genes in the transcriptome that were then classified based on their sex-expression patterns. Differential expression analysis was performed with the DESeq2 package (Love et al. 2014) (Bioconductor). Genes were considered to be male-biased or female-biased if they exhibited at least a twofold difference in expression between generations with a false discovery rate (FDR) of < 0. 05. Sex-biased genes were defined as sex-specific when the TPM was below the fifth percentile for one of the sexes. Full lists of sexbiased genes can be found in Table  S3. Note that sex-linked genes  (Lipinska et al. , 2017)  were removed from the analysis.
Acknowledgements We thank  Dominique Marie  for help with the flow cytometry analysis, the  Institut Français de Bioinformatique  and the  Roscoff Analysis  and uting and data storage resources. This work was supported by the  CNRS, Sorbonne Université  and  ERC  grants to  S. M. C. (grant agreement  638240  and  864038 ). Bioinformatics for Marine Science platform ABiMS  (http://abims. sb-roscoff. fr)for providing comp.
5-cm length were washed three times with molecular ethanol and rinsed several times with sterile milliQ water before being placed in the aquariums, with the exception of the "control aquarium, " which only contained the circulating seawater. Pieces were sampled after 7 (D7), 15 (D15), 30 (D30), and 40 days (D40). Aquariums were kept in the dark to avoid UV-driven degradation of the materials. Throughout the incubation, seawater temperature (between 12. 5 and 13. 5 • C) and salinity (38. 5) were monitored in the aquariums and were similar to the data found at the SOLA observatory in the Banyuls Bay during this period 1.
Biotic samples were treated in triplicates, and a fourth one was killed with trichloroacetic acid (TCA 50%) and served as a control of the reaction. Abiotic controls (consisting of plastic sticks of the eight material types incubated in sterile seawater) were analyzed following the same protocol. The final leucine concentrations were 36 nmol L -1 ( 3 H-leucine at 4 nmol L -1 ) during the colonization phase and 150 nmol L -1 after the transfer to minimum medium ( 3 H-leucine at 1 nmol L -1 ). The saturation of the leucine incorporation was checked during the biodegradation phase, and a higher final leucine concentration was required in order to avoid leucine being limiting (data not shown), explaining why the final leucine concentrations differed between the colonization and degradation phases.
Briefly, the forward and reverse reads were assembled; and sequences were clustered using the SWARM algorithm  (Mahé et al. , 2014)  and an aggregation distance of 3. Then, chimeras were removed using the FROGS Vsearch tool  (Rognes et al. , 2016 ). An additional filter step was used on the abundance at a 5 × 10 -5 threshold to select the most relevant operational taxonomic unit (OTU) sequences. The taxonomic affiliation was assigned by standard nucleotid BLAST  (Camacho et al. , 2009)  using the SILVA 132 database 16S  (Quast et al. , 2013). Chloroplast, mitochondrial, and eukaryotic sequences were removed. All the 16S rRNA data are available in the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) repository (accession number PRJNA649857).
Statistical Analysis In order to allow normalization and comparisons of the data, bacterial sequences of each sample were randomly resampled in the OTU file based on the sample with the fewest sequences (n = 9,698). These data sets were used to conduct all the analyses performed using the Rstudio software 2. The α-diversity indices (Chao1, Shannon, evenness, and Simpson) and the β-diversity analysis (the Bray-Curtis similarity) were calculated with the phyloseq package  (McMurdie and Holmes, 2013). Differences between material types were tested using an ANOVA test and confirmed by a post hoc Tukey test. Results were considered to be significant if the p value was less than 0. 05. Dendrogram was obtained based on the dissimilarity matrix of the Bray-Curtis and applying the Ward algorithm  (Ward, 1963).
FUNDING This project was funded by the  Ministry of Ecological and Solidarity Transition of the French Republic  (project  BIOTIGE ). This work was also supported by the European project  JRA-ASSEMBLE+ , the  French National Research Agency  (project  ANR-OXOMAR ), the  Tara Ocean Foundation  (project  Microplastic   2019 ), and the  CNRS  (project  PEPS ' I-PHABIO ).
Biomasses were obtained after calcination in the oven at 450 • C for 4 h. Physicochemical parameters (water temperature, salinity and pH) were also measured in situ with a HANNA portable multiparameter. Each sample of the macrofauna was accompanied by an additional sediment sample to determine their precise granulometry, carbon content and total organic matter (TOM). Grain size was measured using a laser particle size analyzer (Malvern Mastersizer 2000©) after preparing the sediments in a sodium hexametaphosphate solution  [26]. The grain size distribution was then treated with the Gradistat © Excel package  [27]. Mean, sorting, skewness, kurtosis, decile statistics (including the median used to characterize the sediment type: d50) and clay/silt/sand composition were calculated to precise the textural group of each sample  [28, 29].
RDA is a constrained ordinate used to identify the linear combinations of predictor variables that explain the greatest variation in the species/abundance matrix, i. e. , it shows the pattern of species/abundance (response) data as constrained by the predictor variables  [33]. Spatial distribution and biodiversity were described by univariate analyses based on the following parameters: abundance (N, the number of individuals per m 2 ), species richness (S), Shannon-Wiener diversity index (H )  [34]  and Pielou's evenness index (J )  [35]. All these analyses were performed with PRIMER v6. 0 software  [36, 37]. The (S) (log 2 S) and (H ) indices of the assemblages were plotted together on a twodimensional graphical representation in the Diversity Model (DIMO) considered as a synthetic tool  [38].
The crystals were thereafter immersed in a decanted inoculum solution used for MFC studies, which contained 50% v/v of wastewater in Na 2 HPO 4 /NaH 2 PO 4 buffer solution, as described in the experimental section. Sodium acetate was added as a nutrient to a 0. 012 M concentration and the frequency of the crystal was thereafter monitored over 5 consecutive days to investigate the rate of biofilm development under conditions similar to those experienced in the early stages of MFC startup. Figure  5a  shows changes in resonant frequency observed for PhOLac and PhOMan functionalized carbon crystals immersed in the inoculum. The data clearly show that greater changes in frequency are observed in the case of PhOMan functionalized carbon; for these surfaces, the frequency shows a progressive decrease over the 5 days, whereas for PhOLac surfaces, the frequency shows slow oscillations of ca.
Supplementary Materials: The following are available online. Figure  S1 : additional AFM images; Figure  S2 : additional QCM results; Figure  S3 : additional SEM of biofilms; Figure  S4 : potential @1kΩ vs. time for all MFC devices; Table  S1 : peak power densities at day 6; Figures  S5-S7 : cyclic voltammograms and power output curves for all MFC devices tested at selected time intervals. Funding: This project has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 799175 (HiBriCarbon). The results of this publication reflect only the authors' views and the Commission is not responsible for any use that may be made of the information it contains. This publication has also emanated from research conducted with the financial support of Science Foundation Ireland under Grant No.
, 2018). OMI pixels are at relatively coarse resolution (13 km × 24 km at nadir), and there is substantial data loss after 2007 due to the so-called row anomaly  (Schenkeveld et al. , 2017; Torres et al. , 2018). The recently launched (October 2017) TROPOMI instrument on the Sentinel-5P satellite has the same spatial coverage as pre-row-anomaly OMI (swath width of 2600 km) but with a finer nadir pixel resolution of 7. 2 km × 3. 5 km (along track × across track) until 5 August 2019, further refined thereafter to 5. 6 km × 3. 5 km  (Argyrouli et al. , 2019). This offers better cloud-resolving capability and greater data pixel density than OMI with potential to retrieve finer-resolution NO 2 in the UT. Here we refine and test the cloud-slicing retrieval using synthetic partial NO 2 columns from the GEOS-Chem CTM before retrieving UT NO 2 from TROPOMI partial NO 2 columns with cloud information from two distinct TROPOMI cloud products.
Application of cloud-slicing to TROPOMI follows evaluation of TROPOMI total, stratospheric, and tropospheric columns with ground-based measurements of NO 2 from Pandora and multi-axis differential optical absorption spectroscopy (MAX-DOAS) at freetropospheric monitoring sites. We also evaluate TROPOMI UT NO 2 with the OMI UT NO 2 product. 2 Cloud-slicing of GEOS-Chem synthetic partial columns Targeting cloudy scenes could yield representation errors in NO 2 mixing ratios in the UT, due to the influence of clouds on NO x photochemistry  (Holmes et al. , 2019) , large enhancements in NO x from lightning and convective uplift of surface pollution that accompany cloud formation  (Price and Rind, 1992; Bertram et al. , 2007) , and low sampling frequency due to strict data filtering  (Choi et al.
75-60 ) global GEOS-Chem simulation. The model is driven with NASA GEOS-FP assimilated meteorology and includes comprehensive emission inventories from anthropogenic and natural sources. These include local emissions of NO x in the UT from lightning as described by  Murray et al. (2012)  and from aircraft using the Aviation Emissions Inventory Code (AEIC) inventory detailed in  Stettler et al. (2011). The model is simulated in boreal summer (June-August) when variability in UT NO x in all nested domains is dominated by lightning  (Marais et al. , 2018). The model is sampled daily at 12:00-15:00 local time (LT) to be consistent with the TROPOMI overpass time (13:30 LT). Two years  (2016 and 2017)  are simulated to increase data density. The model years predate TROPOMI, but this has no bearing on assessment of the cloud-slicing technique.
The cloud-slicing approach we apply to synthetic partial columns above synthetic clouds to estimate seasonal means of UT NO 2 is the same as will be applied to TROPOMI, so model variables are only used if these are also available in or can be derived from publicly available TROPOMI data products. GEOS-Chem daily partial NO 2 column densities (stratosphere + partial troposphere) and the corresponding GEOS-FP cloud-top pressures at 450-180 hPa and 0. 25 • × 0. 3125 • are gathered into grid squares of the target resolution of 4 • × 5 •. Any 4 • × 5 • grids of gathered pixels with nonuniform overlying stratospheric column NO 2 are discarded, as diagnosed with a strict stratospheric column NO 2 relative standard deviation of 0. 02. Variability in stratospheric NO 2 is mostly from oxidation of nitrous oxide (N 2 O) in the midstratosphere  (Crutzen, 1970).
Its variability is dominated by solar insolation and stratospheric circulation but is also influenced by upwelling from the troposphere and downwelling from the mesosphere  (Randall et al. , 1998; Gruzdev and Elokhov, 2011). GEOS-FP thermal tropopause heights are used to determine the vertical extent of the stratosphere in the model. As many as 256 0. 25 • × 0. 3125 • partial columns can be gathered in a 4 • × 5 • grid, so we increase the number of possible cloud-sliced NO 2 retrievals by subdividing clusters of at least 100 partial NO 2 columns into clusters of at least 40. A threshold of 80 instead of 100 yields similar seasonal mean UT NO 2. Subdividing the clusters of partial columns doubles the number of cloud-sliced NO 2 data used to obtain multiyear seasonal means. Additional filtering is applied to clusters to remove extreme NO 2 partial columns (partial columns falling outside the 10th to 90th percentile range) that have a large influence on regression of NO 2 partial columns against cloud-top pressures, clusters with fewer than 10 partial columns after screening for extreme values, and clusters that do not extend across a sufficiently wide altitude range (cloud-top pressure range ≤ 140 hPa and standard deviation ≤ 30 hPa).
The two datasets are spatially consistent (R = 0. 64) and exhibit similar variance (slope = 1. 1±0. 1). The cloud-sliced UT NO 2 has a small positive offset in background UT NO 2 (intercept = 2. 3 ± 1. 2 pptv). On average, cloud-sliced UT NO 2 is 17 % more than the true cloudy UT NO 2 , but this depends on the spatial resolution of the retrieved cloud-sliced product. Regression slopes increase from 0. 87±0. 03 for cloud-sliced UT NO 2 obtained at 2 • × 2. 5 • to 1. 4 ± 0. 2 at 8 • × 10 • , and the cloud-sliced UT NO 2 is 4. 1 % less than the true cloudy UT NO 2 at 2 • × 2. 5 • and 37 % more at 8 • × 10 •. The increasingly positive bias with spatial resolution is because a greater relative proportion of clusters with NO 2 vertical gradients ≥ 0. 33 pptv hPa -1 are retained at coarser resolution.
, 2011; Silvern et al. , 2018). Strict filtering applied to cloud-slicing removes most scenes where the increase in NO 2 with altitude exceeds 33 pptv across 280-180 hPa, based on the synthetic experiment with GEOS-Chem. The influence of more than a 10-year gap between the OMI and TROPOMI UT NO 2 datasets on the comparison is challenging to quantify, due to paucity of routine measurements of NO 2 in the UT. The contribution of changes in lightning activity should be small, as interannual variability is small (< 5 %), and there is no discernible trend in the long-term record of satellite observations of lightning flashes  (Schumann and Huntrieser, 2007). According to most climate models, there is an increase in lightning flashes due to global warming  (Romps et al. , 2014; Finney et al. , 2016) , though almost all of these models use the same positive relationship between lightning flashes and cloud-top heights first proposed by  Price and Rind (1992).
Acknowledgements. The authors are grateful to  Piet Stammes  and colleagues for retrieval of the FRESCO-S cloud product and  Diego Loyola  and colleagues for retrieval of the OCRA and ROCINN-CAL cloud product. The PGN is a bilateral project supported with funding from  NASA  and  ESA. The Pandora measurements in Altzomoni were possible thanks to technical assistance from  Alejandro Bezanilla. Financial support. This research has been supported by the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (through the  Starting Grant  awarded to  Eloise A. Marais ,  UpTrop  (grant no. 851854 )). The Pandora measurements in Altzomoni were possible thanks to financial support of  Conacyt-AEM  (grant no. 275239 ). Robert G. Ryan  has received financial support from the  Australian Research Council Centre of Excellence for Climate Extremes  and the  Albert Shimmins Memorial Fund  through the  University of Melbourne.
Code availability. Python code used to process TROPOMI data is available at https://doi. org/10. 5281/zenodo. 4058442 (Marais and Roberts, 2020). Data availability. Data can be requested from Eloise A. Marais for TROPOMI UT NO 2 , Sungyeon Choi for OMI UT NO 2 , Monica Navarro-Comas for MAX-DOAS slant columns, and Robert G. Ryan for MAX-DOAS vertical columns.
https://doi. org/10. 5194/nhess-2021-169 Preprint. Discussion started: 18 June 2021 c Author(s) 2021. CC BY 4. 0 License. List of the historical records -or their origin -used to provide information about flood events 559 and impacts collected in the HIFAVa database. The date mentions the year of publication for books 560 and period covered by the newspapers.