dataesr/scientific-paragraphs-categorization huggingface dataset:
VIC-SM Large variations in in-situ data and its scarcity have encouraged the use of available global meteorological observations, such as precipitation and temperature, to simulate SM using LSMs such as VIC  (Liang et al. , 1994 (Liang et al. , , 1996)). Sheffield et al. (2004)  used VIC to simulate retrospective SM for the contiguous U. S. using subgrid variability in land cover, elevation, soil water storage capacity, and storm coverage. The VIC model divides the soil column into three layers, with a fixed 10 cm top layer depth and a spatially varying depth for the lower layers  (Sheffield and Wood, 2008a). The downside of using simulated SM data is that such data are prone to large uncertainties since their forcings and land-atmosphere feedback mechanisms are not well understood for many regions of the world  (Sheffield et al.
SPI Since SM responds to precipitation anomalies on a relatively short scale  (Sánchez et al. , 2016) , we compared the SM indices with the SPI at the selected locations to assess whether they provide spatiotemporal consistency. The one-month SPI (SPI-1) is generally related to shortterm SM and crop stress, especially during the growing season and therefore, can approximate conditions represented by the SMOS/SMAP drought index. SPI-1 means that at a given month, we compare the total precipitation for that month in a particular year, with total precipitation of that month in all the years of the time record. In this study, the precipitation data are from the Multi-Source Weighted Ensemble Precipitation (MSWEP) Version 2  (Beck et al. , 2019)  and re-gridded to ~36 km resolution. A drought event occurs any time the SPI-1 is continuously negative, reaches an intensity of -1.
Soil moisture climatology on a global scale Fig. 1  shows the comparison among mean interannual SM from VIC, CCI, SMOS, and SMOS/SMAP. For the CCI SM map, areas of a dense canopy in the mid-latitude have been masked out at the source. The mean SM values of all the groups differ by several volumetric percent. VIC LSM's physical interpretation of the three layers and possession of a smaller dynamic range than remotely sensed data cause a systematic difference against others, showing it to be wetter in general. The spatial patterns of CCI and SMOS maps are highly consistent although SMOS has a higher intensity in dryness. SMOS and SMOS/SMAP agree on dry parts of the globe but disagree on the wetness pattern. The SMOS/SMAP climatology is highly similar to that from SMAP (not shown here). The difference between SMOS/SMAP and SMOS is mostly in the type of configuration algorithms that SMOS and SMAP use for the computation of the Faraday rotation  (Al Bitar et al.
Integrating SMAP and SMOS Satellite remote sensing data are often spatially and temporally discontinuous  (Nijssen et al. , 2001). SMAP provides data in near-real time for operational applications but its 5-year scattered data makes it technically difficult to meaningfully define an index. This raises the challenge of creating a longer-term high temporal resolution global dataset with remote sensing data provided by sensors on different satellites  (Coccia et al. , 2015). We use the global data from SMOS, upscaled from 25 km to 36 km resolution, from the period of 2010/01/15 to 2018/06/29 but since the characteristics of SMAP and SMOS sensors and their viewing geometry used to observe SM are slightly different, their retrievals also generate specific differences. To make a consistent dataset of these two satellites, we use the Bayesian conditional process and CDFM.
In both approaches, we aim to create a continuous SMOS/ SMAP dataset consistent with SMAP, using their 2015-2018 overlapping period. In other words, we treat the SMAP data as the predictand and the SMOS data as the predictor. The Bayesian conditional process approach has been extensively explained by  Coccia et al. (2015)  and  Sadri et al. (2018)  and will result in an extended-dataset consistent with sparse (short record length) remotely sensed data as well as provide in-filling of data due to the orbit gaps. CDFM has been a strong and standard way of rescaling and matching two or more datasets on the basis of their common periods  (Liu et al. , 2012). Both of these approaches are transferable to other areas of hydrology.
Fitting a four-parameter Beta distribution to the integrated datasets Application of the Beta distribution for capturing SM variability was suggested by  (Famiglietti et al. , 1999)  and first tested by  (Ryu and Famiglietti, 2005; Famiglietti et al. , 2008). The Beta distribution is a family of continuous probability distributions, defined on the interval of [0,1], and parameterized by two positive shape parameters p and q. The probability density function (PDF) of the standard Beta distribution, a random variable x between [0,1] with positive shape parameters p and q, is as follows: = > f x p q x x B p q x p q ( ; , ) ( ) (1 ) ( , ) 0 1; , 0 p q ( 1) ( 1) (3) B(p, q) is a normalizing constant known as the Beta function, related to the Gamma function: = B p q u u du ( , ) (1 ) p q 0 1 ( 1) ( 1) (4) Here u is a parameter introduced for the sake of integration.
The generalized form of Beta distribution is transformed linearly to have lower and upper bounds a, b instead of 0 and 1, with probability density function: = = > + f x a b p q f x a b a p q b a x a b x B p q b a a x b p q ( ; , , , ) ; , /( ) ( ) ( ) ( , )( ) ; , 0 p q p q ( 1) ( 1) ( 1) (5) The Beta distribution has the capability of mapping the behavior of SM as a random variable with limited variability range  (Sheffield et al. , 2014; Sadri et al. , 2018) , however, the fact that the SMOS/SMAP calibration data spans only 8 years can be still a statistical challenge for meaningful assessment of its indices. Even with long enough datasets, we still want to extend the tails of the distribution beyond the observed values for those extreme drought and flood events. To the authors' knowledge, no existing methodology works for this case, although there have been efforts to establish such procedures in the past.
Therefore, the in-situ data from these two grids are eliminated from the study. Our study period for point validation spans from January 2012 to December 2017. According to the Canadian Drought Monitor (CDM), there were at least two periods of drought in the prairies during this period. One severe drought occurred in 2012 and the other occurred in 2017. In contrast, the year 2016 was a very wet year with above-normal precipitation during summer and fall. In the next section, we look for these events in our results. from CDFM in column 2 revealed to have lower RMSE (and bias) and higher correlation with SMAP over the globe compared with the dataset obtained from the Bayesian process in column 1. It can be, therefore, concluded that there is less uncertainty associated globally with the CDFM dataset.
Global and regional goodness-of-fit of the Beta distribution For the SMOS/SMAP datasets, we performed the KS test on both CDFM and the Bayesian conditional process integrated datasets at a 95% confidence margin to filter the grids whose time series for each calendar month fit a Beta distribution. The SMOS/SMAP SM dataset from CDFM showed less flexibility to fit the Beta distribution regionally and globally. The number of grids that passed the KS test varied in each month. We summed the total number of passed grids in every month for 12 months and divided them by the total number of grids that were overpassed for 12 months to get the percentage of grids that fit the Beta distribution both globally and for the CPE region. On the global scale, we eliminated 18,872 grids (from the total of 100,500 grids) that were permanent bare soil/deserts from the percentages of KS passing calculation, whereas for the CPE, only 4 grids were identified as bare soil and so we did not eliminate them.
The summary of results is presented in Table  2. For comparison, we did a similar analysis of VIC and CCI datasets. Although there is a chance that the results in Table  2  are affected by spatial autocorrelation which may affect the total passing percentages, considerably more grids passed the KS test using the Bayesian conditional process than the CDFM approach both globally and in the CPE. While the CDFM process is widely practiced for integrating data, the assumption of perfect correlation between SMOS and SMAP, the process might not lead to better parametric fit. Since we used monthly batches to extract indices, we performed a "breakpoint" analysis to check for temporal biases in month-to-month changes. For every grid, we made two frequency distributions: one by calculating the differences between the indices of every two consecutive dates that fall inside of the same calendar month; the second by calculating the difference between the indices of every two consecutive dates that fall on the last day of the old and first day of the new month.
In-situ comparison For the 6 validation grids in the CPE, we calculated BIAS, RMSE, correlation coefficient, and the Nash-Sutcliffe Efficiency (NSE) coefficient between the average in-situ SM data and the SM from SMOS, SMAP, SMOS/SMAP (from both CDFM and the Bayesian process). We tried two combinations of SMOS/SMAP SM time series. One approach was to fully simulate the SMOS/SMAP series even for the days that SMAP satellite had a retrieval. The other approach was to partially simulate the SMOS/SMAP series meaning that we replace the SMAP retrievals back in the SMOS/SMAP datasets for the days that they existed. Fig. 10  shows the boxplots of summary statistics between the average in-situ SM values and SMOS, SMAP, fully simulated SMOS/ SMAP using CDFM, partially simulated SMOS/SMAP using CDFM, and partially simulated SMOS/SMAP using Bayesian process.
Comparing fully simulated SMOS/SMAP using CDFM (orange bars) and partially simulated SMOS/SMAP using CDFM (dark brown bars), we see that they have similar biases but in the partially simulated dataset, we get a better correlation and NSE with the in-situ data. Therefore, we decided to proceed with the partially simulated integrated time series in general. We then compare the partially simulated SMOS/SMAP using CDFM (dark brown) and the partially simulated SMOS/SMAP using the Bayesian conditional (light brown) series. In this latter case, the two, again, have very similar bias and RMSE statistics. Some of the bias values from both the CDFM and the Bayesian process are slightly in the negative range for both datasets. But the average bias values are less than 0. 04 m 3 /m 3 and close to zero.
10 ). This means SMOS/SMAP on average has less sum of differences with the in-situ data while it can capture the in-situ seasonality reasonably well. The SMOS data also capture seasonality but show a bigger bias. Fig. 11  (as well the P-P plots of the SMAP vs. the SMOS/SMAP) confirm that the SMOS/SMAP dataset has captured the SMAP pattern while using the information that SMOS provided (having more revisit frequency and a longer period of record).
Joint distribution heat maps Although studying the monthly pattern of indices show the overall agreement and disagreement of the indices, to understand the similarity of the daily index maps, we generated two-by-two comparison heat maps from the actual daily indices of SMOP/SMAP, VIC, and CCI. Fig. 14  shows heat maps from such comparisons for selected grids (2,3, and 6) for the entire period of 2010 to 2018. Percentiles from 1 to 100 are partitioned into 5-percentile increments for each of the indices and the number of times that two indices at the time fall within the same bin is counted. For each grid, a heat map denser diagonally would be more ideal because it shows that the two indices identify similar severity of drought or pluvial conditions numerically. There are warmer colors in higher percentiles between VIC and SMOS/SMAP and between VIC and CCI percentiles meaning that, in the CPE region, the daily maps of indices from VIC datasets most likely agree more with CCI or SMOS/SMAP in terms of severity of the pluvial conditions but agrees less in terms of drought conditions.
All indices comparison In comparing all indices, we want to answer two questions. Firstly, which of these indices are more reliable, and secondly, which of these indices are more suitable and has the potential of assessing agricultural drought impacts. Reliability is a matter of matching ground truth; as far as the CPE region is concerned, SMAP datasets showed to be the most reliable, with the partially simulated SMOS/SMAP next in reliability. Suitability of use can be assessed by more statistical tests to find which index is more harmonized with major drought and pluvial conditions and can be used as a monitoring and prediction tool. We compared all 382 grids in the CPE in terms of their statistics and plotted the CDFs of CCI, SMOS/SMAP, and VIC indices in Fig. 15  knowing that, in estimating the indices, every grid on any given day should have an equal chance of having all the percentiles.
Data and maps availability For accessibility and as an outreach tool, we have developed an online platform that overlays these maps over Google Maps and stores the archive for download. The website can be reached at http:// hydrology. princeton. edu/ sadri/nasasmap_globe/index. html. The CDM monthly maps are available at https://open. canada. ca/data/en/dataset/292646cd-619f-4200-afb1-8b2c52f984a2. USDM weekly maps of the U. S. are available at https://droughtmonitor. unl. edu/Maps/MapArchive. aspx. GRACE-based assimilated weekly maps are available at https:// nasagrace. unl. edu/Archive. aspx. The 10-day European Drought Monitor maps are from https://edo. jrc. ec. europa. eu/edov2/php/index. php?id=1111. AFDM is generated by Princeton Climate Analytics (PCA) at https://platform. princetonclimate.
Conlusions In this study, we developed GSMIM, the first operational global SMbased index product using merged near-real-time remote sensing data. The constructed dataset used SMAP observations as predictand while taking advantage of the spatial information and temporal coverage that SMOS provides since 2010. We make the index maps available online every 24 h. In its current form, the system only monitors the SM index state of the globe for studying drought and flood evolutions and does not address impacts. The GSMIM is distinguished from other operating systems in that it uses 4-parameter Beta distributions with asymptotics to describe its limiting behavior and extend its endpoints beyond the observed data. Furthermore, the value of this study is enhanced by the use of remotely sensed data which characterizes the drought condition spatially and temporally in a near-real-time capacity.
The algorithm developed in this study is implemented operationally for the use of SMAP/SMOS dataset as the basis for GSMIM, however, the methodology for integrating two or more datasets, fitting a non-standard Beta distribution whose extreme tails go beyond the observed data, and translating the incoming integrated soil moisture into index percentile is essentially applicable to any datasets in any regional or temporal scales. Since GSMIM uses a parametric distribution, it also has great potential for being developed further into a prediction tool. In this current stage, the 7-8 day temporal latency of GSMIM comes from the latency of the SMOS SM and if that is addressed, the GSMIM can easily be synced with SMAP's latency (3-day). Compared with the C3S SM, GSMIM's 7-8 day lag is lower which may be critical for some applications.
SMOS/SMAP indices might not show the extreme drought condition as severe as they are on the ground albeit capturing their presence and spatial extent. This can be due to the fact that both CCI and VIC SM datasets have longer record periods than SMOS/SMAP SM datasets. The CCI still has biases to be recognized and removed, but it performed closest to an index. The CCI, however, is not currently available in nearreal-time. SMOS/SMAP's indices performed better than VIC indices. Because SMOS and SMAP monitor soil moisture directly and provide critical information for drought early warning, GSMIM can be a reasonable global index with operational capacity and competitive quality. It is important, however, to acknowledge that such an operational tool relies on continuous efforts to improve the system, e.
Annotations of Transposable Elements The latest genome assemblies available for B. rapa 'Z1'  (Belser et al. , 2018) and 'Chiifu' v. 3. 0 (Zhang et al. , 2018)  were retrieved from the Genoscope website 1  and the Brassica database 2  , respectively. These genomes were subjected to the same TE annotation package REPET V2. 5  (Quesneville et al. , 2005; Flutre et al. , 2011; Hoede et al. , 2014). Briefly, the REPET pipeline was used for the detection, classification (TEdenovo) and annotation of TEs (TEannot). The TEdenovo pipeline identifies TE copies present on a reference genome using a curated database of TEs: Repbase (V 20. 05 for both nucleotides and amino acid sequences). Then, TEdenovo creates de novo consensus sequences when at least five copies of the same TE family are retrieved within the genome.
Next, TEannot pipeline uses these consensus sequences as well as Repbase to annotate the provided reference genomes. Default parameters were set for this last step of annotation. Finally, two files containing the structural annotation of 'Z1' and 'Chiifu' were output in GFF3 format (Generic Feature Format version 3). The resulting GFF3 files were hand curated using custom python scripts (Python version 2. 7. 12).
Comparative Structural Analyses To evaluate large-scale genome structural rearrangements, the assembled genomes of B. rapa 'Z1' and 'Chiifu, ' as well as the A subgenome of B. napus 'Darmor Bzh' V4. 1, were retrieved from the Brassica database 2 and compared. The package MUMmer V3. 23  (Kurtz et al. , 2004)  was used on each of the 10 chromosomes using the following settings:id 95, -l 4,000. Inversions or deletions observed in 'Z1' when compared to 'Chiifu' were further verified using BioNano raw data from  Belser et al. (2018). To better characterize these genomic regions, gene annotations were retrieved from GFF files. Gene Ontology (GO) enrichment was performed on GO terms of the identified genes present in specifically inserted regions of 'Z1' using Agrigo V2  (Tian et al. , 2017). Finally, we assessed the duplication status of these genes using proto-Calepineae karyotype (PCK;  Murat et al.
The chromosomes were mounted and counterstained in Vectashield (Vector Laboratories) containing 2. 5 µg/mL 4 ,6-diamidino-2-phenylindole (DAPI). Fluorescence images were captured using an ORCA-Flash4 (Hamamatsu, Japan) on an Axioplan 2 microscope (Zeiss, Oberkochen, Germany) and analyzed using Zen 2 PRO software (Carl Zeiss, Germany). Finally, the lengths of both A06 chromosomes in the hybrid 'Z1' × 'Chiifu' were estimated on 14 cells at the mitotic metaphase stage and measured using the visualization software Zen 2 PRO (Carl Zeiss, Germany).
Copy Number Estimation of Highly Repeated Sequences: Ribosomal DNA and Gypsy LTR Retrotransposon CNVs of 45S rDNA (otherwise called 25S or 35S rDNA) and gypsy LTR Retrotransposons were obtained using Shotgun data for B. rapa var. pekinensis 'Chiifu' (from 2 ) and B. rapa var. trilocularis 'Z1' (from 3  ), applying a method based on a calibration using the CNV (based on read depth approach) of single copy genes (scripts available at github/juboutte). First, 17,954 putative single copy genes in 'Chiifu' and 'Z1' were identified using custom-made python scripts based on PCK genes from B. rapa  (Murat et al. , 2015). Second, 38 rDNA sequences, corresponding to Brassica rDNA full-length sequences downloaded from the NCBI (last accessed August 5, 2019) and 96 gypsy sequences downloaded from the gypsy database (last accessed August 5, 2019) 4  , were retrieved as reference sequences.
Then, rDNA and gypsy sequences of 'Chiifu' and 'Z1' were identified by performing BLASTn and tBLASTn algorithms (evalue threshold of 10 -5 ;  Altschul et al. , 1997)  between the rDNA and gypsy reference sequences and the two assembled genomes 'Chiifu' v. 3. 0  (Zhang et al. , 2018)  and 'Z1'  (Belser et al. , 2018). Finally, consensus sequences (two rDNA and 211 gypsy) were obtained overall for 'Z1' and 'Chiifu' using python custom script (sequences presenting more than 90% identity were combined together) and aligned with Mafft v. 7. 245; option -auto  (Katoh and Standley, 2013). Visual checking of the alignments was done using Geneious v. 10. 0. 9  (Kearse et al. , 2012). 'Chiifu' and 'Z1' genomic reads were then mapped on the two 45S rDNA consensus, the 211 gypsy RT consensus and the 17,954 putative single copy genes using Bowtie 2, v.
Phylogenetic Analyses To study the evolutionary relationships of 45S rDNA and gypsy sequences of B. rapa 'Chiifu' and 'Z1, ' phylogenetic analyses were performed. First, 45S rDNA and gypsy sequences (first translated to protein sequences and merged to the 96 gypsy database sequences) of 'Chiifu' and 'Z1' were aligned separately using Mafft alignment v. 7. 245; option -auto  (Katoh and Standley, 2013). Second, beginning and end of the matrices with less than 25% of the sequences were deleted, as well as indels present in more than 25% of the sequences. Phylogenetic analyses were performed using Geneious tree builder with the Jukes-Cantor model and the neighbor-joining method (45S rDNA clade support was determined by 1,000 bootstrap replicates). To avoid Long Branch Attraction error, sequences presenting long branches were deleted, and new phylogenetic analyses were performed using the parameters described above.
Moreover, in B. rapa, the individual mitotic metaphase chromosome A06 ranged from 2. 1 to 5. 6 µm in length (observed from 14 metaphase sets). The chromosome A06 from 'Chiifu' was on average 17. 6% ( ±6. 96%) smaller compared to the chromosome A06 of 'Z1' (Supplementary Table  4 ). Genome Size Variation and Presence of Inversions Among B. rapa Accessions DNA content was obtained for 'Z1, ' 'Chiifu, ' and 10 accessions representing B. rapa core collection. After checking normality of data (Shapiro test, P = 0. 487), the significative impact of accessions was tested (ANOVA; df = 11, P = 4. 19e-13), followed by Tukey pairwise comparisons and group assignment based on P < 0. 05. Of particular interest, the two focal varieties 'Chiifu' and 'Z1' have significantly different 1C DNA content with 0.
ACKNOWLEDGMENTS We acknowledge the  BrACySol BRC (INRA Ploudaniel, France)  that provided us with the seeds of B. rapa accessions and  Marie-Madeleine Gilet  for growing and taking care of the accessions. We would also like to thank all the technical staff of the greenhouse for management of the plant material (especially  L. Charlon ,  P. Rolland ,  J. -P. Constantin ,  J. -M. Lucas , and  F. Letertre ).
FUNDING This work was made possible by financial support of the  European Union  and a  Marie Sklodowska-Curie grant  (No. 791908 ) awarded to JF as well as a BOOST'EUROPE financed by the  Conseil régional de Bretagne. FB was supported by a fellowship from  BAP INRAE  and  Conseil régional de Bretagne. This work was additionally supported by the  France Génomique National  infrastructure, funded as part of the  « Investissements d'Avenir » program  managed by the  Agence Nationale pour la Recherche  (contract  ANR-10-INBS-09 ).
Data analysis To evaluate the effect of location and entry in Zn, Fe and PA concentration, a 2-factor analysis of variance model was used for each parboiling and milling type (NPBDOM0, NPBDOM7. 5, NPBDOM10, PB13DOM0, PB13DOM7. 5, PB13DOM10, PB16DOM0 and PB16DOM7. 5). The effect processing step (parboiling and non-parboiling) and grain type (biofortified and non-biofortified) on Zn, Fe and PA TR was evaluated using a 2-factor analysis of variance model. The effect of processing on Zn, Fe and PA TR for each entry was evaluated using a one-way analysis of variance. Analysis of variance was done using the GLM procedure in SAS 9. 4 (SAS Institute, Cary, NC). Means separation was calculated using PLM procedure and Tukey-Kramer multiple comparison method. Differences between means were considered significant at p < 0.