dataesr/scientific-paragraphs-categorization huggingface dataset:
Bioinformatics. Reference sequences were downloaded from GenBank using the eDirect API using the following search terms: Taxonomic lineages for each downloaded accession were retrieved using the R package taxonomizr  43. Reference sequence sets were then trimmed to the precise barcode region of interest for each marker using MetaCurator  44. The MetaCurator pipeline uses hidden Markov models, produced by HMMER3  45  , to identify and excise the amplicon region of interest from all available references, including long genomic contigs and whole plastid genomes. Additionally, using a VSEARCH-based method  46  , this procedure removes duplicate reference sequences using a taxonomically aware method wherein a duplicate is only removed from the data if it belongs to the same taxonomic lineage as the alternate duplicate.
Finally, the curated database was geographically filtered at the genus level using USDA PLANTS Database 17 , records for the states of Connecticut, Massachusetts, New Jersey, New York, Pennsylvania, and Rhode Island, plus the genera found in our nursery inventories (some of which lacked USDA records for our study region). The final ITS2 database contained 43,844 sequences representing 1,183 of of the 1,994 genera in the USDA plant records (59%), plus 211 of the 247 genera from our nursery inventories (85%). MiSeq paired-end reads were first mate-paired using PEAR  47. Then, using the-usearch_global function of the VSEARCH program  46  , mate-paired reads were queried against the reference libraries described above, and we selected the top hit (i. e. best percent identity) for each query sequence after constraining hits to a minimum of 75% identity and 80% query coverage.
05% of the total reads as likely false positives. Reference library curation and VSEARCH alignments were performed on high-performance computing clusters at the Penn State Institute for CyberSciences and the Ohio Supercomputer Center. Post-alignment data processing was performed in R  48  , and annotated scripts are available at https://github. com/sponslerdb/vsearchr. pollen microscopy. Thirty-one of our 47 samples were cross-validated by microscopy to provide a second line of evidence for inferring the role of ornamental plants in the honey bee pollen diet. Pollen microscopy was performed at the Paleoecology Laboratory, Climate Change Institute, University of Maine, Orono, Maine, as described in  38. Bulk corbicular pollen samples (0. 47-0. 85 g) were first granularized in 10% hydrochloric acid, washed in deionized water to remove excess organics, and then rinsed in glacial acetic acid to remove all water.
Samples were then acetolyzed following  49  , mounted, and viewed at 400x magnification on a Nikon Labophot-2 microscope (Nikon Corporation, Tokyo). Pollen grains were identified to the lowest possible taxonomic level using reference specimens collected from the study nurseries, previously collected reference material at the Climate Change Institute, published keys  [49] [50] [51]  , and images from the PalDat database (https://www. paldat. org/). The proportional abundance of each pollen type was estimated using the volumetric methods of  52. Data analysis. To simplify data visualization and focus interpretation on the most significant components of the honey bee pollen diet, we created subsets of our metabarcoding and microscopy data including only genera with a maximum proportional abundance within a site-date sample of at least 5%, hereafter termed "major genera".
These subsets of major genera were used to evaluate our central question of which ornamental plants were attractive to honey bees, as well as to evaluate the qualitative (presence/absence) agreement between metabarcoding and microscopy. For evaluating the quantitative consistency of metabarcoding and microscopy, we used the full datasets. For the subset of genera within each sample detected by both metabarcoding and microscopy, we used linear regression to evaluate the quantitative consistency of the two methods using square-root transformed to improve homogeneity of variance. All processing and analysis was done in R  48.
Acknowledgements Special thanks to the three Connecticut nurseries that allowed us to install hives and trap pollen. Richard Cowles ,  Alejandro Chiriboga ,  Mark Creighton ,  Benjamin Gluck ,  Allison Labelle ,  Annalise Kieley , and  Anna Radchenko  assisted with managing the honey bees, collecting pollen, and working with the nurseries. C. Praul  ( Penn State Genomics Core Facility ) advised Illumina library preparation. Computational work was supported by the  Ohio Supercomputer Center, Columbus, OH , and the  Penn State Institute for Cyber Science, University Park, PA. Funding for the pollen collection and initial analysis came from the  Connecticut Department of Energy and Environmental Protection , with additional funding for this study from  USDA-NIFA-SCRI  grant  2016-51181-235399 , " Protecting Pollinators with Economically Feasible and Environmentally Sound Ornamental Horticulture ".
16S rRNA Read Filtering The raw sequences, after Q-score filtering performed by Genome Quebec (reads with Q-score higher than 25 were kept), were processed and analyzed with QIIME v1. 9. 1 (Quantitative Insights Into Microbial Ecology)  (Caporaso et al. , 2010) , according to  Legrand et al. (2018)  and  Cobo-Díaz et al. (2019). For 16S rRNA amplicons, the forward (R1) and reverse (R2) pairedend sequences were joined using multiple_join_paired_ends. py, followed by multiple_split_libraries_fastq. py for demultiplexing. Final length of joined sequences was approximately 460 bp. Chimera and suspicious candidates sequences (representing 12. 3% of total sequences) were removed using UCHIME algorithm  (Edgar et al. , 2011)  implemented in vsearch v1. 1. 3 1 against the ChimeraSlayer database  (Haas et al.
, 2011). Pick open strategy was used to cluster the sequences into Operational Taxonomic Units (OTUs) at 97% similarity cut-off using pick_open_reference_otus. py and GreenGenes v13_8 database  (McDonald et al. , 2012). Taxonomic assignment was performed using UCLUST algorithm  (Edgar, 2010)  against GreenGenes v13_8 database preclustered at 97% similarity cutoff  (McDonald et al. , 2012). Up to 4295 chloroplast, mitochondria and unassigned OTUs were discarded for further analysis. To minimize the inflation of rare OTUs, only OTUs with sequence count greater than 10 were included  (Brown et al. , 2015; Oliver et al. , 2015). 1 https://github. com/torognes/vsearch.
ITS Read Filtering The raw sequences were processed and analyzed with QIIME v1. 9. 1 (Quantitative Insights Into Microbial Ecology)  (Caporaso et al. , 2010) , according to  Legrand et al. (2018)  and  Cobo-Díaz et al. (2019). The forward and reverse files were merged independently, using multiple_split_libraries_fastq. py. ITS1 and ITS2 regions were first extracted separately from forward and reverse fasta files respectively, using ITSx v1. 0. 11 (Bengtsson-Palme et al. , 2013)  before being concatenated in a new file. A chimera filtering, allowing to discard 7. 8% of total sequences, was made on concatenated file using the UCHIME algorithm  (Edgar et al. , 2011)   The ITS1-ITS2 concatenated file of non-chimeric sequences was used for OTU picking running the QIIME script pick_open_reference_otus.
py, with BLAST  (Altschul et al. , 1990)  as taxonomic assignment method and a modified version of UNITE plus INSD non-redundant ITS database version 7. 1  (Kõljalg et al. , 2013). The modified version consisted in concatenating ITS1 and ITS2 regions after extracting them using ITSx software. To minimize the inflation of rare OTUs, only OTUs with sequence count greater than 10 were included  (Brown et al. , 2015; Oliver et al. , 2015).
Alpha and Beta-Diversity Analysis Metabarcoding datasets obtained after filtering (V3-V4 region of 16S rRNA, ITS1-ITS2 concatenated regions) were processed equally. A single rarefaction, based on the sample with the lowest number of reads (that is, 36,900 and 15,943 for 16S rRNA and ITS sequences, respectively), was used for alpha-diversity analysis using single_rarefaction. py QIIME script. Good's coverage, OTUs richness (observed otus, Chao1 index), diversity (Shannon and Simpson indices) and evenness (dominance and equitability) were calculated with alpha_diversity. py QIIME script. The statistical software R version 3. 5. 0 was used to perform one-way ANOVA with Tukey HSD post hoc test, for statistical analysis. The level of significance was set at α = 0. 05. Bacterial functions were predicted by FAPROTAX  (Louca et al.
, 2016)  while fungal ecological guilds were predicted by FUNGuild v1. 0  (Nguyen et al. , 2016)  as either pathotrophs, saprotrophs, symbiotrophs or 2 of them or all three of them. Corresponding OTU table was used for prediction of bacterial function or fungal ecological guilds as input file. Heatmaps were made using pheatmap R package with the default parameters. Relative abundance data were z-scored normalized by row. The barplots were obtained using vegan and RAM R-package using OTU relative abundance table  (version 1. 2. 1. 7, Chen et al. , 2016). Principal Coordinates Analysis (PCoA) based on Bray-Curtis distance were performed in Calypso webtool  (Zakrzewski et al. , 2016)  after a Hellinger-transformation of the OTU relative abundance tables and removal of OTUs with less than 0. 01% of abundance.
Mean ± standard deviation of 3 values are reported (except for GW/NS ITS dataset for which there is one value). Different letters indicate significant differences between treatments (ANOVA, Tukey HSD test, p < 0. 05). was also observed with biofiltration but to a lower extent. Significant predicted functional differences were also detected after biofiltration, including a decrease in nitrification, ureolysis, aromatic compound degradation, photoheterotrophy and an increase in photoautotrophy and predicted functions related to nitrogen metabolism (including nitrate reduction, nitrogen-, nitrate-and nitrite respiration, nitrite-, nitrate-and nitrous-oxide denitrification and denitrification) (Figure  4B ).
PCoA were generated by pooling all samples (A,D) and by separating RW, GW/S, and GW/NS into one subgroup (B,E) and ABF with BBF in another subgroup (C,F). The relative abundance of each genus was Hellinger-transformed. The plots were obtained using Calypso webtool. Dot with the same color represents the 3 replicates of each water sample, except for GW/NS which had only one replicate for ITS dataset. Water sample codes can be found in Figure  1  legend or Table  1. Fusarium, Monographella, Zymoseptoria, Phoma, Magnaporthe, and Colletotrichum fungal genera, all of which include important phytopathogen species responsible for important economic loss including in greenhouses  (Dean et al. , 2012). Oomycetes, including Pythium and Saprolegnia, were not detected by metabarcoding simply because primer ITS1F that was used here is not a suitable forward primer for the successful amplification of oomycetes  (Manter and Vivanco, 2007) , with a high number of mismatches when blasted against the NCBI nt database for oomycota.
The value of each of the 3 replicates (water sample code followed by A, B, or C) is reported for each water sample, except for GW/NS which had only one replicate for ITS dataset. Water sample codes can be found in Figure  1  legend or Table  1. of microbial communities, as well as in terms of bacterial function, than with the water samples directly collected from the ground (GW/NS). This indicates that a common suite of taxa and functions may underpin the microbiota associated with open-storage conditions, independently of the origin of water (rainwater or groundwater) or site. Significant differences in terms of bacterial taxa abundances were also found between storage conditions. Differences in the composition of microbial communities implies differences in functional characteristics. Because DNA was sequenced (and not RNA or proteins), we are rather looking at the potential functions than the actual functions of the microbiota  (Korbel et al.
, 2005; Nie et al. , 2019). Since the latter process is not yet included in FAPROTAX database, we manually looked for anamox taxa in our OTU table and retrieved OTUs assigned to FIGURE 4 | Mean relative abundance in percentage of total reads (square-root transformed) of bacterial functions assigned by FAPROTAX and significantly different from at least one other sample. Functional abundances across water samples before entering the greenhouse (A) and before and after biofiltration (B) were compared using Wilcoxon-rank test and Deseq2 test, respectively. The average value of the 3 replicates is reported for each water sample. Water sample codes can be found in Figure  1  legend or Table  1. the Brocadiaceae family only in one replicate of the non-stored groundwater sample (GW/NS, 216 reads).
, 2020). Furthermore, CspA1 is known to contribute to P. plecoglossicida virulence in a temperature-specific manner via regulation of sigX expression  (Huang et al. , 2020). In previous studies from our laboratory, we found that the dksA gene from P. plecoglossicida was highly expressed during host infection (data deposited in GenBank SRA database under accession numbers SRP114910 and SRP115064). The dksA gene encodes an RNA polymerasebinding transcription factor  (Kamarthapu et al. , 2016). The gene has a variety of important functions, such as regulating rRNA promoter activity  (Paul et al. , 2004) , HFQ gene expression and virulence of Shigella flexneri  (Sharma & Payne, 2006) , growth rate of Escherichia coli  (Mallik et al. , 2006) , and assembly of flagella  (Dalebroux et al. , 2010).
qRT-PCR qRT-PCR was performed using a QuantStudio 6 Flex Real-Time PCR System (Life Technologies, USA). All primer sequences were designed using Primer Premier 5. 0 (Supplementary Table  S2 ). Bacterial gene expression was normalized using 16S rDNA, L. crocea gene expression was normalized using β-actin, and the relative level of gene expression was calculated using the 2 -ΔΔCt method. To assess the pathogen load of P. plecoglossicida in the infected spleens, the gyrB gene copy number was used to assess the number of P. plecoglossicida. Dual RNA-seq and transcriptome data analysis Library preparation and sequencing: Sequencing experiments were carried out using an Illumina Truseq TM RNA Sample Prep Kit (Illumina, USA). Total RNA was extracted from tissue samples using TRIzol® reagent, with concentration and purity measured using a Nanodrop 2000, RNA integrity detected by agarose gel electrophoresis, and RIN values determined by an Agilent 2 100.
A single database requires a total RNA of 1 μg, concentration of ≥50 ng/μL, and OD260/280 of between 1. 8 and 2. 2. Eukaryotic and prokaryotic mRNAs were simultaneously obtained by removing rRNA, and fragmentation was carried out by adding a fragmentation buffer. After reverse-synthesizing the cDNA, an end repair mix was added to make it blunt-ended, and then poly(A) was added to the 3' end for ligation of the Y-shaped linker. Sequencing was performed on an Illumina HiSeq4000 sequencing platform from Majorbio Biotech Co. , Ltd. (China). The RNA sequence data were deposited in the GenBank SRA database under accession Nos. PRJNA607373 and SRP176599. The basis of transcriptome data analysis is highquality sequencing. Analysis showed that the distribution of the A/T/G/C base content was uniform (Supplementary Figure  S2 ), the base mass distribution of the sequence data met the requirements of subsequent analysis (Supplementary Table  S3 ), and the repeatability between the three repeated samples was good (Supplementary Figure  S1 ).
Processing and mapping of reads: Trimming and quality control of raw Illumina reads were performed using Sickle (https://github. com/najoshi/sickle) and SeqPrep (https:// github. com/jstjohn/SeqPrep) with default settings. For RNAseq, clean data were mapped to the genome of P. plecoglossicida strain NB2011 (NCBI RefSeq accession number: NZ_ASJX00000000. 1) using Bowtie2  (Langmead & Salzberg, 2012). Clean data were mapped to the genome of L. crocea (NCBI RefSeq accession number: GCF_000972845. 2) using Hisat2  (Kim et al. , 2015). Differentially expressed mRNAs (DEMs) and enrichment analysis: Differential expression was determined using edgeR, which performs differential expression calculations based on mRNA read count data and a negative binomial distribution model  (Anders & Huber, 2010; Robinson et al.
, 2010). The screening criteria for significant DEMs were: FDR<0. 05 and |log 2 FC|>=1. The DEMs were then subjected to enrichment analysis by hypergeometric distribution testing using Goatools (https://github. com/tanghaibao/goatools) and KOBAS (http://kobas. cbi. pku. edu. cn/home. do) (Xie et al. , 2011).
Analysis and verification of transcriptome data of P. plecoglossicida We used edgeR software to calculate gene expression levels. The screening criteria for significant differentially expressed genes (DEGs) were FDR<0. 05 and |log 2 FC|>=1. From the constructed volcano map, a total of 4 988 P. plecoglossicida mRNAs were obtained from the transcriptome of the L. crocea spleens infected with the dksA-RNAi strain. Compared with the wild-type strain, we identified 145 differentially expressed P. plecoglossicida mRNAs in the dksA-RNAi strain-infected spleens, 24 of which were up-regulated and 121 of which were down-regulated (Figure  3A ). The heat map (Figure  3B ) shows pathogenic genes whose differential expression in the three samples exhibited >2-fold change by transcriptome sequencing. The most variable up-regulated gene was L321_RS17380 (log 2 FC=5.
Analysis and verification of transcriptome data of infected L. crocea The software used for gene expression calculation and the screening criteria for significant DEGs were the same as those used for the pathogen. From the constructed volcanic map, 27 520 L. crocea mRNAs were obtained from the spleen transcriptome of L. crocea infected with the dksA-RNAi strain. Compared with the wild-type-infected L. crocea, we identified 970 differentially expressed P. plecoglossicida mRNAs in the Zoological Research 41(4): 410-422, 2020 413 dksA-RNAi strain-infected spleens, 366 of which were upregulated and 604 of which were down-regulated (Figure  5A ). The heat map (Figure  5B ) shows the expression levels of the top 50 up-regulated and down-regulated genes in the three samples of each group. The greatest fold-change in the upregulated genes was for LOC104939330 (log 2 FC=8.
, 2017). So far, quality assurance frameworks have been developed for selected ECVs, not including soil moisture (e. g. ,  Peng et al. , 2017). The guidelines developed by QA4EO and QA4ECV are currently embraced by the Copernicus Climate Change Service (C3S; https://climate. copernicus. eu/; last access: 1 July 2019) in order to build quality assured, fully traceable Climate Data Records. In 2018, the Quality Assurance for Soil Moisture project (QA4SM; https://qa4sm. eodc. eu/; last access: 1 July 2019) was launched, specifically to create an online validation tool that employs a communityagreed validation protocol (which we aim to provide with this paper) for automatically and regularly generating soil moisture product validation reports, thereby addressing the CEOS validation framework requirements (see Fig.
Dense networks To meet the requirements on fiducial reference data  (Fox, 2010) , the SMAP Calibration and Validation (Cal/Val) Team defined certain criteria for dense measuring networks, so-called core validation sites, ensuring that they provide a traceable representation of footprint-scale soil moisture and therefore allow for a reliable assessment of satellite soil moisture data quality. Currently, 18 densely stationed and thoroughly calibrated in situ measurement sites fulfil these requirements  (Jackson et al. , 2012; Colliander et al. , 2017a) , operated by independent SMAP Cal/Val partners. These SMAP Cal/Val partners have a diverse heritage. Some networks were originally deployed for Cal/Val of the AMSR-E product  (Martínez-Fernández and Ceballos, 2005; Jackson et al. , 2010) , SMOS  (Bircher et al.
, 2012; Smith et al. , 2012; Djamai et al. , 2015) , or SMAP  (Caldwell et al. , 2019) , while others evolved from hydrologic monitoring networks  (Bogena et al. , 2018)  or from some other purpose such as aircraft validation projects like AIRMOSS  (Moghaddam et al. , 2010). During the SMAP project, several networks were selected as potential candidate sites for Cal/Val activities. The candidate networks whose accuracy versus physically collected volumetric soil moisture was already demonstrated and documented in a traceable manner, were promoted to core validation sites. To date, these sites are considered to provide the best possible ground reference data for satellite footprintscale soil moisture dynamics  (Colliander et al. , 2017a; Chen et al. , 2019).
, 2013) , and distributes them on a cost-free basis in a common format (http://ismn. geo. tuwien. ac. at/; last access: 1 July 2019). The ISMN was established by ESA in the framework of SMOS Cal/Val activities. Currently, it contains data from more than 2400 stations worldwide, operated across 59 different measurement networks (see Fig. 2 ) including historical networks that are no longer operational. In addition to soil moisture, many networks provide measurements of other variables such as precipitation or temperature as well as ancillary information such as soil texture or land cover. Note, however, that sensor technologies and data quality vary greatly across networks and measurement stations  (Dorigo et al. , 2011b; Babaeian et al. , 2019).
If not accessible via open repositories (in particular software specifically designed for that study), we recommend to make source code publicly available, for example on GitHub (https://github. com/; last access: 1 July 2019). A list of some current publicly available software that is specifically aimed at, or closely related to soil moisture validation is provided in Table  3. An online validation tool that is built around these software packages and follows the good practice guidelines presented in this paper is provided by the Quality Assurance Framework for Soil Moisture (QA4SM; https://qa4sm. eodc. eu/;last access: 1 July 2019). Note that the re-distribution of in situ measurements (see the third point above) may be particularly problematic as many networks do not operate for free. Requiring networks to freely distribute their data will likely decrease the number of datasets available for validation activities, which may ultimately hamper the evolution of satellite soil moisture products and downstream products derived thereof.
A. 1. Data sets and study area Select validation examples are shown for soil moisture retrievals from the Advanced SCATterometer (ASCAT;  Naeimi et al. , 2009) , the Soil Moisture and Ocean Salinity (SMOS) mission  (Kerr et al. , 2010) , and the Soil Moisture Active Passive (SMAP) mission  (Entekhabi et al. , 2010a). Reference data used are coarse-resolution model estimates from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2;  Gelaro et al. , 2017). This analysis is performed over the Contiguous United States (CONUS) using data from the beginning of 2015 through the end of 2018. ASCAT data used are the EUMETSAT H SAF H113 data record and its extension H114, which are Level 2 (L2) soil moisture products that have been retrieved from inter-calibrated backscatter measurements from identical ASCAT instruments onboard the MetOp-A and MetOp-B satellites using the TU Wien WAter Retrieval Package (WARP) algorithm  (Wagner et al.