dataesr/scientific-paragraphs-categorization huggingface dataset:
Sensors, measurements, data storage, maintenance, and calibration Several types of sensors were installed to measure the downwelling solar radiation or the photosynthetic photon flux density (PPFD) received at the ground level on a horizontal plane in different spectral bands. All used sensors were placed at 2 m above ground. The database contains measurements from July 2014 to December 2016. Data loggers collected signals from the sensors each minute. The raw data were stored as 10 min averages to ease further numerical processing. At all three stations, there were sensors which measured global radiation in UVB, UVA, broadband, and the PPFD at various wavelength ranges within the PAR band: 400-700 nm, 510-700 nm, and 600-700 nm, and spectral bands around 660 and 730 nm (Table  3 ). The data were collected by Edgebox V12 data loggers from the EMS Brno company (http://www.
emsbrno. cz, last access: 19 April 2018). At all three stations, air temperature and relative air humidity were measured (Table  3 ), precipitation was measured by the tipping-bucket rain gauge only at S1 and S3. Table  S1  and Fig. S10  in the Supplement summarize the technical characteristics of the radiation sensors and their relative spectral responses. Maintenance of sensors was performed every month in winter, when heavy loads of dust particles in the atmosphere are frequent, and every two months during the rest of the year. The sensors were cleaned with pure ethanol and the silica gel for removing air humidity within the data logger was changed at each maintenance. The sensors were calibrated before the start of the measurements in 2014. EMS Brno recommends recalibration of their own sensors once every 5 years since all sensors are equipped with glass diffusers, which allow accurate measurements for a long time.
Measurements are controlled in three different procedures that occur (i) daily, (ii) once per month before sending to air pollutants database ISKO, and (iii) once per year. Air quality data measured by CHMI are not available for free usage. This is the reason why the database does not contain additional meteorological variables for the CHMI (station S3). This problem may have been overcome by using the PHI network but there was no PHI station near the CHMI station. Three additional pieces of information are included in the database. The first piece is the season when the measurement was done: spring for the term 21 March to 20 June, summer for the term 21 June to 22 September, autumn for the term 23 September to 20 December, and winter for the term 21 December to 20 March. This information was added for potential analyses of solar radiation during different yearly seasons.
The second piece is the prevailing cloudiness conditions -called "weather category" in the database -or each record with three categories: cloudy, partly cloudy, and sunny. This was determined by the analysis of the daily profile of the 10 min broadband irradiance as exemplified in Fig. 2 , which presents daily profiles for three different days in March 2015; sunny days offer a bell-shaped profile, cloudy days have flat, low profiles, and partly cloudy days exhibit large variability within the day. The information about prevailing cloudiness conditions was allotted to the whole day including night. The third piece is the air pollution category and is available only at S1 and S2. It was established on the basis of intervals of concentrations of PM 10 in the air at the measuring station of PHI following the five categories proposed by  Elshout et al.
Quality check applied to radiation data To the best knowledge of the authors, there has not been a published and accepted procedure for checking the quality of irradiances in the spectral bands of interest yet. In the absence of such a procedure we have adapted the approach of  Korany et al. (2016)  which applies to measurements of global and diffuse total irradiances. In our specific case, the quality check (QC) of  Korany et al. (2016)  consists of checking that each measurement E(t, λ) falls into the range [0. 03 E0(t, λ), E0(t, λ)], where t is the time of measurement, λ the wavelength, λ the spectral range, E(t, λ) the measurement, and E0(t, λ) the corresponding irradiance or PPFD at the top of the atmosphere (TOA). Given a typical spectrum E0(λ)  (Kurucz, 1992)  of the irradiance at TOA, the ratio of the irradiance at TOA for a given spectral band E0( λ) to the total irradiance at TOA E0 was computed for each spectral band (  culations with other reference spectra of irradiance at TOA found in LibRadtran software  (Mayer and Kylling, 2005).
Data availability One file containing 10 min averages of spectral irradiances, meteorological variables, and air quality observations was created for each of the three surface stations for each of the three years. These nine files can be downloaded from the PANGAEA website using a unique identifier (https://doi. org/10. 1594/PANGAEA. 879722). To avoid confusion, time stamps are given for both the beginning and the end of each 10 min averaging period. These files are self-explanatory and contain 40 columns for station S1, 29 columns for station S2, and 29 columns for station S3 with variables mentioned in Table  6. For each spectral band, there is one column with a quality flag, which gives information if the measured value of spectral irradiance passed the QC: "1" means that the measurement falls in the interval [0.
03 E0, E0] and "0" means that it is outside. All data including night measurements are present in the datasets. Figures depicting the influence of the horizon on the measuring stations are included in each dataset and in the Supplement. The dataset for the station S1 contains additional columns with meteorological data measured by PHI (Table  6 ). Data of air pollutants and meteorological variables measured by PHI had a native 1 h resolution, so they were copied to all six 10 min records of data measured by the University of Ostrava for each hour. These data can be connected with the dataset for station S2 because of the identical location of stations S1 and S2 (BGOU).
Summary and perspectives A homogeneous database of 10 min measurements of downward solar irradiance in several ultraviolet and visible bands has been assembled. Currently, the data cover the period from July 2014 to December 2016 and originate from three ground stations representing two locations in the Ostrava city (Czech Republic) with different air pollution conditions. These quality-checked data are available online at https://doi. org/10. 1594/PANGAEA. 879722. The data provide an insight into the spatial and temporal variability in solar radiation in spectral bands 280-315 nm (UVB), 315-380 nm (UVA), 400-700 nm (PAR), 510-700 nm, 600-700 nm, 610-680 nm (660 nm), 690-760 nm (730 nm), and 400-1100 nm (broadband). These spectral intervals were selected according to their significance for the plant physiology and development.
One of the goals is to characterize the influence of atmospheric pollution on the spectral composition of incident solar radiation with a focus on analyses of differences between measured values. For example, analyses of relationships between some spectral ratios (e. g. UVB/PAR, UVA/PAR, blue/PAR, blue/red, etc. ) and concentrations of air pollutants are planned. Based on preliminary results, changes in spectral composition of incident solar radiation induced by individual air pollutants can be specific and they could differently influence physiological processes in plants. Thus, the understanding of the radiation environment is crucial in relation to vegetation and its ecological services in cities. The measurements of solar irradiation will continue and new data can be added to this database later, thus longer and detailed studies of time series will become available for this location of an industrial city in central Europe.
The Supplement related to this article is available online at https://doi. org/10. 5194/essd-10-837-2018-supplement. Author contributions. Lucien Wald suggested possibilities for the data quality check. Philippe Blanc determined a shading effect on measuring stations according to combinations of solar zenithal and elevation angles and horizon. Lucien Wald, Philippe Blanc, Martin Navrátil, and Vladimír Špunda participated in the manuscript preparation. Marie Opálková did the operation and maintenance of the sensors and designed the data set, made a data quality check, and prepared the manuscript with contributions from all co-authors.
Acknowledgements. This work was financially supported by  COST   CZ LD14005 ,  Institutional Research Support grants  ( SGS06/UVAFM/2016  and  SGS05/UVAFM/2017 ) from the  University of Ostrava , by  EU structural funding Operational Programme Research and Development for Innovation , project no. CZ. 1. 05/2. 1. 00/19. 0388 ,and by the  Ministry of Education, Youth and Sports of the Czech Republic  in the " National Feasibility Program I", project LO1208  " TEWEP ". We thank the  Public Health Institute Ostrava and EMS Brno  for the cooperation. We thank  Amelie Driemel  for her valuable comments. We thank  Vladimír Bradáč  for his English language assistance. We thank the anonymous reviewers for their helpful comments and suggestions. Edited by:  Kirsten Elger Reviewed  by: two anonymous referees.
statistical analyses There was no primary study hypothesis with statistical inference for the small registry study. All p-values were presented for informational purposes. According to the prespecified analysis plan, continuous data were summarized using mean and SD, or medians and quartiles in the presence of nonnormality. Categorical data were tabulated with counts and percentages. All the available data were summarized with no imputation for missing data. Final analyses were conducted using SAS version 9. 3 (SAS Institute Inc. , Cary, NC, USA) by an independent statistical group (NAMSA, Minneapolis, MN, USA).
Acknowledgments We would like to thank the investigators, coordinators, and patients who participated in this study and acknowledge the late  Chris T Bollinger  ( Stellenbosch University, Cape Town, South Africa ) who assisted with study design. This study was funded by  Nuvaira, Inc. (Minneapolis, MN, USA). Part of this study was presented in abstract form at the  ERS Congress 2015 (Amsterdam , the Netherlands). The clinical trial registration number is ClinicalTrials. gov, NCT01716598.
3 ) to the 'slightly drier' scenario (Ac85). This implies that scenarios of climate change may be wetter on a yearly temporal scale due to spring or winter rain, but drier summers may still promote higher fire activity. The model projected an overall reduction in annual tree cohort growth, although there was high variation, dependent on the species, climate, and soils (see growth related parameters in Tables  S4  and S5 ). Overall, and particularly for the drier portions of the study area, summer growth is projected to decline; this finding is consistent Dominance shift from CON to SCH was calculated when SCH dominance was persistent for more than 30 years See Table  1  for climate change scenario acronyms. Maps were created using raster package v 2. 3. 40 in R 3. 3. 0 (https://www. r-project.
Input geophysical data. Weather data were extracted from the United States Geological Service data portal (http://cida. usgs. gov/gdp/; June 2016). We used historical weather data for baseline conditions for the period 1949-2010  70  and bias-corrected constructed analogs v2 of daily weather for climate change projections. We chose four GCM (global circulation models) -RCP (representative concentration pathways) scenarios that portray the breadth of climate change conditions predicted for the study area (ACCESS 8. 5 (Ac85, hotter and drier); CanESM2 (Ca85, hotter and wetter); CNRM-CM5 4. 5 (Cn45, slightly hotter and slightly wetter), MIROC5 2. 6 (Mi26, slightly hotter and slightly drier; Table  1 , Supplementary Fig. S1 ). These scenarios span the range of predicted projected changes in average annual temperature and precipitation.
Soil characteristics were obtained through the web soil survey (http://websoilsurvey. sc. egov. usda. gov/App/ HomePage. htm) from the STATSGO2 database. We used the measures of soil organic matter content and physical characteristics of soils that drive water balance dynamics and biogeochemistry in the model (e. g. drainage class, soil texture). Nitrogen (N) deposition was obtained from the National Atmospheric Deposition Program database (http://nadp. sws. uiuc. edu/). In order to harmonize different scales and data types among physical inputs, we parsed the study area into ecoregions with similar environmental conditions  71. We grouped different cells in environmental space according to their soil and climate characteristics through cluster analysis, using the clara function in the 'cluster' package (v 2.
Comparison of miRNA detection in biological samples using the different protocols We next wished to determine how the various protocols would perform with biological samples. Our experimental strategy was to compare these protocols for the detection of either human miRNAs, that are unmodified, or Arabidopsis miRNAs, that are known to contain a 2'OMe modification. We assessed the protocols for three different aspects: (1) the percentage of sequencing reads corresponding to miRNAs, (2) the formation of side products, and (3) the number of different (i. e. unique) miRNAs detected. The libraries were sequenced using Illumina technology and the reads were trimmed for adapter sequences and mapped to human or Arabidopsis miRNA databases (miRBase; see Methods for details). Adapter trimming removed all sequences with inserts shorter than 10 nt.
For Arabidopsis, the TS1 protocol detected 142 known miRNAs, which represents ~33% of the total number of miRNAs registered in miRBase (427). As for the human miRNAs, protocols TS2 and TS4 performed less well, but in contrast TS3 performed better than TS1, with 201 miRNAs detected. Another difference was that for Arabidopsis the best results were obtained with protocol TS6 (217 miRNAs identified). Both in the TruSeq as well as in the NEXTflex kit, MRL adapter performed better than HD adapters (compare TS5 with TS6, and Nf2 with Nf6). The SMARTer protocol detected 161 miRNAs, slightly better than TS1. To validate these observations, the same experiment was done with Brassica napus (oilseed rape) RNA (except that protocols TS2, 3, 4, and S were not included). Very similar results were obtained as for Arabidopsis, underscoring the reproducibility of the data (Fig.
4 ). It should be noted that the numbers of miRNAs detected are without any threshold of read number per miRNA, and many of the miRNAs detected are covered by only one single read. We also examined the numbers of human or Arabidopsis miRNAs detected that have enough coverage to allow accurate detection of expression changes. We determined the numbers of miRNAs detected at thresholds of 10, 20, or 30 reads per miRNA (Additional file 8: Figure  S8 ). The numbers of detected miRNAs significantly decreased for all protocols, but the differences among the protocols did not change. To further substantiate the observations made with the plant miRNAs, we studied the representation of piR-NAs, which are 2' OMe modified  [1] , in the human libraries. The reads with 19-24 nt inserts were mapped to the piRNA database (see Methods) and the proportion of mapped reads and the numbers of different piRNAs detected with each protocol were determined (Additional file 9: Figure  S9 ).
Next-generation sequencing and bioinformatics analyses All sequencing was done using Illumina platforms, either the MiSeq or the NextSeq500 instrument. For each protocol, several independent libraries were prepared; the mean values are represented in the Figures. Cutadapt  [27]  version 1. 14 was used to remove standard Illumina and MRL adapter sequences. The minimum overlap between the read and the adapter was set to 4 nucleotides, and reads shorter than 10 nucleotides were discarded. Python scripts were used to trim HD adapters and also the random 3-base sequences upstream of the small RNA inserts for the SMARTer protocol. The microRNA databases were downloaded from http://www. mirbase. org/ftp. shtml (download date July 16, 2015); we only used the mature sequences of Arabidopsis thaliana, Homo sapiens and Brassica napus.
For piRNA detection, the reads of the human libraries were mapped to piR-Base (http://www. regulatoryrna. org/database/piRNA/; downloaded March 2, 2017). The sampling of the reads was done using seqtk, version 1. 0-r31 (H. Li, https:// github. com/lh3/seqtk/). Only the reads with a length between 19 and 24 nt were kept using python scripts. Mapping to the databases was performed with Bow-tie2, version 2. 1. 0, [23]  allowing no mismatches. Python scripts were used to count the number of miRNAs detected. For the annotation of the short side products (10-18 nt inserts) obtained with the SMARTer libraries from human or Arabidopsis RNA, the obtained sequences were trimmed for adapter sequences and were mapped to various databases. For human, the sequences were mapped to the database of small human non-coding RNAs (DASHR; downloaded on October 5, 2017)  [28] , mirBase (for partial miRNA sequences), and the regulatory RNA database (http://www.
regulatoryrna. org/data base/piRNA/download/archive/v1. 0/fasta/) for piRNA sequences. The Arabidopsis sequences were mapped to databases for various non-coding RNAs;ftp://ftp. ensembl genomes. org/pub/release-24/plants/fasta/arabidopsis_tha liana/ncrna/Arabidopsis_thaliana. TAIR10. 24. ncrna. fa. gz), tRNAs (http://gtrnadb2009. ucsc. edu/Athal/Athal-tRNAs. fa) and miRNAs (miRbase). All of these databases were downloaded on October 6, 2017. To generate the heat map shown in Additional file 11: Figure  S10  we used the heatmap. 2 function from the R package gplots (R version 3. 4. 2).
Acknowledgements All library preparation, Illumina sequencing and bioinformatics analyses for this study were performed at the  I2BC Next-Generation Sequencing (NGS) facility. We thank  Dr. Laurence Amar  for a generous gift of RNA material. Dr. Helene Touzet  is gratefully acknowledged for stimulating discussions. The members of the  I2BC NGS  facility are acknowledged for critical reading of the manuscript and helpful suggestions.
Dataset The sample set was analyzed according to the ASTM  [6]  method. The dry samples contain 2. 2% of organic matter on average, destroyed by exposure in a 500 ° C oven. This analyze was carry out on 35 samples. The samples have variable sand amounts ranging from 11% to 90% of the dry mass (Fig. 2 ). The main interest of this sample set is the broad range of concentration from less than 1g/L to more than 10g/L. In the particular case of this flushing event, we observed a correlation between the suspended sediment concentrations and the percentage of sand in the samples. In high shear stress conditions, the sand is moving in suspension through the flow. And during flushing events there is more erosion in the dam's reservoirs, which results in high fine sediment concentration as well.
Optical observation and in situ determination of the hydrogel landscape The optical observations are performed with various techniques: interferometric microscopy, bright field microscopy or epifluorescence microscopy, using inverted microscopes (usually with 2. 5 × or 5 × objectives) or a 0. 8-9. 2 × binocular microscope. For fluorescence studies, the dyes are 0. 8 mM solution of fluorescein in Milli-Q water and 0. 1 mM solution of rhodamine B and we used two-color microscopy. Images are taken using sensitive cameras (Hamamatsu ImageEM and OrcaER) and processed using Matlab software. To measure the hydrogel topography in situ, we use a fluorescence-based technique that consists of filling the device with a dilute fluorescein solution and measuring the intensity field. Supplementary Information 2 shows that in the range of the concentration used, this intensity is proportional to the difference between the hydrogel and the channel heights.
ACKNOWLEDGEMENTS We gratefully thank the  Institut Pierre-Gilles de Gennes (IPGG) , the  French National Research Agency (ANR) ,  EQUIPEX and LABEX IPGG  (Grant Nos. ANR-10-IDEX-0001-02 ,  ANR-10-LABX-31 , PSL*) , the  China Scholarship Council (CSC) ,  ESPCI ,  CNRS ,  PSL , and the  Ministry of Science and Technology of Thailand (MOST)  for their financial support. We are grateful to  Jacques Prost  who initiated the collaboration. We are grateful for the help and discussions with  Emilie Bayon ,  Ghimire Buwan ,  Luce Dauphinot ,  Olivier Mesdjian ,  Jacques Fattacioli , and  Marie Caroline Jullien.
Per isolate and infection stage, the two most representative samples were selected as biological replicates for transcriptome sequencing (Table  1 ). with TopHat2 v2. 0. 9  (Kim et al. , 2013). Read alignments were stored in SAM format, and indexing, sorting, and conversion to BAM format were performed using SAMtools v0. 1. 19 (Li et al. , 2009). Relative abundance of transcripts for predicted genes was calculated in RPKM by Cuffdiff2 v2. 2. 1  (Trapnell et al. , 2013). Raw read counts per gene were estimated with HTSeq v0. 6. 1p1 (Anders, Pyl, & Huber, 2015). Gene coordinates in the Zt05 (Supporting Information Table  S4 ) and Zt10 (Supporting Information Table  S5 ) genomes were obtained by mapping the predicted genes of IPO323 using nucleotide BLAST alignments (e-value cutoff 1e -3 , identity ≥90%, query coverage 90%-110%).
Differential gene expression analyses between Z. tritici infection stages and isolates were performed in R (R Core Team, 2017) using the Bioconductor package DESeq2 v1. 10. 1  (Love, Huber, & Anders, 2014). Significant signals of differential expression for the most strongly changing genes were determined with p adj ≤0. 01 and |log 2 fold change ≥2| as recommended for RNA-seq experiments with a low number of replicates  (Schurch et al. , 2016). The R package topGO  (Alexa, Rahnenführer, & Lengauer, 2006)  was used to perform Gene Ontology (GO) term enrichment analyses. p Values for each GO term  (Grandaubert, Bhattacharyya, & Stukenbrock, 2015)  were calculated using Fischer's exact test applying the topGO algorithm "weight01" considering GO term hierarchy. We reported categories significant with p ≤ 0.
01 for the ontology "Biological Process. " PFAM domain enrichment analyses were performed using a custom python script, and p values were calculated using chi-square tests. To analyze genomic distances between differentially expressed genes and transposable elements (TEs), we annotated TEs as described in  Grandaubert et al. (2015)  for Zt05 (Supporting Information Table  S6 ) and Zt10 (Supporting Information Table  S7 ) and used the published TE annotation of IPO323 for Zt09  (Grandaubert et al. , 2015). Distances between genes of interest and the closest annotated TEs were calculated with bedtools v2. 26  (Quinlan & Hall, 2010). Likewise, we used ChIP-seq peak data  (Schotanus et al. , 2015)  to calculate distances between genes and the closest H3K9me3 and H3K27me3 peaks. Statistical analyses were performed in R.
| Pulsed-field gel electrophoresis A non-protoplast protocol (Supporting Information Text S1) was used to produce DNA plugs for separation of small chromosomes (~0. 2 to 1. 6 Mb) by pulsed-field gel electrophoresis (PFGE)  (Stukenbrock et al. , 2010). Chromosomal DNA of Saccharomyces cerevisiae (Bio-Rad, Munich, Germany) was used as standard size marker. Gels were stained in 1 µg/ml ethidium bromide solution, and chromosome bands were detected with Thyphoon Trio™ (GE Healthcare, Munich, Germany). Alignments were analyzed using a custom python script to extract unique DNA blocks with a minimum length of 1 bp.