dataesr/scientific-paragraphs-categorization huggingface dataset:
The training process is conducted over 1000 epochs with a batch size of 256. Considering the train/test splits (50%, 50%) for the neural network strategies, the data was split as 50%, 40% and 10% for training, validation and test set respectively  [36]. The model that achieves the best performances on the validation set will be successively blindly evaluated on the test set. Similar to what was proposed for the RF classifiers, two scenarios were first considered for the deep learning approach one using only the S1 time series data and the other using only the NDVI time series. To do this, one of the two branches of the model is completely dropped and the one corresponding to the specific data source is kept. Then, for the third scenario using SAR and optical data, the full model with both branches was used.
Accuracy Assesment For each tested method, the assessment of the classifier results was expressed as a function of the overall accuracy (OA), Kappa and the F-score by applying the generated model on the validation dataset (50%). The precision, recall, and F-score were also calculated for each class (irrigated/non-irrigated). In fact, several studies report the use of these indices to assess the accuracy of the classification  [20, 31, 37, 61]. While OA can be easily interpreted as the percentage of correctly classified plots to the total number of plots, Kappa and F-score can be used to assess the statistical differences between classifications. The obtained accuracy indices were calculated for the obtained results of each method as proposed by  [62] [63] [64]. Table  2  summarizes the equations used to calculate these accuracy indices.
This also reduced the number of variables from 256 coefficients to 18. Figure  9  similarly shows the scatter plot of 2000 irrigated and 2000 non-irrigated plots (randomly selected) with different combinations of the obtained important WT coefficients. As shown with the PC values, Figure  9  also clearly shows that irrigated and non-irrigated plots can be separated using the WT coefficients as they distinctly appear in two different point clouds. The validation of the RF classifier trained using important data only (18 variables) produces slightly lower accuracy than that using the full dataset (Table  4 ). The overall accuracy decreased by 1. 3% only. The F-score of the irrigated class decreased from 0. 87 to 0. 83 while that of the non-irrigated class decreased from 0. 94 to 0. 92. The kappa value also decreased from 0.
Method Class Precision Recall F-Score The validation of the RF classifier trained using important data only (18 variables) produces slightly lower accuracy than that using the full dataset (Table  4 ). The overall accuracy decreased by 1. 3% only. The F-score of the irrigated class decreased from 0. 87 to 0. 83 while that of the non-irrigated class decreased from 0. 94 to 0. 92. The kappa value also decreased from 0. 81 to 0. 75. Although the accuracies reported using the 18 important WT coefficients show lower values compared to those obtained using the whole dataset, the proposed RF classifier keeps a very good performance.
NDVI-RF The RF using NDVI data was first developed with the 17 PC values obtained from the application of the PCA on the NDVI temporal series. The accuracy obtained when validating the model is reported in Table  5. Then, the analysis of the variables' importance allows us to fix a threshold value of 4% for the increase in mean square error. This threshold reduced the number of variables from 17 PC values to seven. The important PC variables obtained includes the first five, the eighth and 11th PC dimensions. The regeneration of the RF classifier using the seven important PC dimensions of NDVI produced approximately similar accuracy to that using all the PC variables. The overall accuracy decreased by only 1% while the kappa and F-score decreased by 2% and 3% respectively. The RF classifier using the 15 important PC variables shows slightly higher accuracy than that using the WT coefficients.
However, the inter-comparison between the classifications' results using NDVI time series shows that 90. 2% of the plots were commonly correctly classified using both models. Among this percentage, 96. 0% of the non-irrigated plots were correctly classified in both maps while 80. 0% of the irrigated plots were commonly classified as irrigated plots. In terms of p-value obtained from the McNemar test, the results also show that the two approaches have significant statistical difference with a p-value < 0. 05. Finally, it is good to mention that, compared to the use of S1 data, the use of the NDVI data performed well in classifying irrigated areas in both approaches. As irrigation in the region usually takes place between May and September, the probability of a plot being irrigated increases when a growing cycle exists in summer season.
05. The use of the NDVI temporal series only shows approximately similar performance between the RF classifier and the CNN in terms of overall accuracy (89. 5% and 91. 6% for RF and CNN respectively). However, the inter-comparison between the classifications' results using NDVI time series shows that 90. 2% of the plots were commonly correctly classified using both models. Among this percentage, 96. 0% of the non-irrigated plots were correctly classified in both maps while 80. 0% of the irrigated plots were commonly classified as irrigated plots. In terms of p-value obtained from the McNemar test, the results also show that the two approaches have significant statistical difference with a p-value < 0. 05. Finally, it is good to mention that, compared to the use of S1 data, the use of the NDVI data performed well in classifying irrigated areas in both approaches.
Strength, Limitations and Future Directions The S1 satellite with the 6 days revisit time allows more precise temporal follow-up of agricultural crops. S1 is now the only operational satellite system providing dense time series in free and open access mode with global coverage. Thus, any method based on the use of S1 time series could interest a wide range of end users. Moreover, the advantage of using S1 temporal series rather than the optical time series is that radar sensors are not limited to good weather conditions. The cloud cover, that may be present in the optical images of certain geographical regions, could be a drawback when working with optical satellites. Even though the combined use of optical and radar data did not highly enhance the classification results, the synergic use of microwave and optical data could increase the possibility of transferring the model across different regions.
Conclusions In this paper, a new methodology for mapping irrigated areas using Sentinel-1 time series was introduced. First, a temporal series of SAR backscattering coefficients from the S1 data was obtained at plot scale in VV and VH polarization. To remove the ambiguity between rainfall and irrigation events, the S1 signal at plot scale was compared to that obtained at grid scale (10 km × 10 km). Then, two different transformations including the PCA and the WT were applied to both the S1 temporal series at plot scale σ 0 P and the temporal series of the difference between plot and grid ∆σ 0 PG. Using the PC dimensions and the WT coefficients, irrigated areas were classified in two different random forest classifiers. The results show a good overall accuracy (OA) for the RF classifier using the PC values (90.
7%) and the WT coefficients (89. 1%). Moreover, the PC transformation was applied on the NDVI time series obtained from Sentinel-2 optical images. Results showed that the RF classifier using optical data (NDVI) performs well with OA = 89. 5%. The combined use of optical and SAR data (in PC values) slightly improved the classification accuracy (OA = 92. 3%). Another approach tested in this study was the one-dimensional convolutional neural network (CNN). The convolutional neural network was applied to the S1 time series obtained at plot scale σ 0 P and difference between plot and grid scales ∆σ 0 PG. The results of the validation of CNN approach showed very high accuracy (OAm = 94. 1%) compared to that obtained using the RF classifiers. The accuracy of the irrigated class increased when using the CNN approach thus allowing better detection of irrigated plots.
The use of NDVI data only in a CNN classifier produced lower overall accuracy (91. 5%) than that obtained using S1 data only. However, the combined use of both data types in the CNN did not significantly improve the accuracy. The proposed approach should be applied to other agricultural areas to better assess its relevance and possible usage in operational mode. Given the very good accuracy obtained and the fact that S1 data is free and open access, the use of Sentinel-1 data is relevant. Even though the optical data presents good results, its use could be problematic in certain geographical areas due to the presence of cloud cover. However, machine learning algorithms including classical or advanced approaches still require training data to obtain good classification results. Thus, future work should also focus on building effective approaches to detect irrigation using less training data.
Acknowledgments: The authors wish to thank the  French Space Study Center  ( TOSCA 2019 ) and the  National Research Institute of Science and Technology for Environment and Agriculture (IRSTEA). The authors wish also to thank the  European Space Agency (ESA)  for the Sentinel-1 and Sentinel-2 data and Theia pole for the calibration of the Sentinel-2 images. Finally, authors would like to thank the  General Direction of Rural Development of the Department of Agriculture, Livestock, Fishing and Food of the Generalitat of Catalunya  for providing the SIGPAC data.
Background: Reads assignment to taxonomic units is a key step in microbiome analysis pipelines. To date, accurate taxonomy annotation of 16S reads, particularly at species rank, is still challenging due to the short size of read sequences and differently curated classification databases. The close phylogenetic relationship between species encountered in dairy products, however, makes it crucial to annotate species accurately to achieve sufficient phylogenetic resolution for further downstream ecological studies or for food diagnostics. Curated databases dedicated to the environment of interest are expected to improve the accuracy and resolution of taxonomy annotation.
Results: We provide a manually curated database composed of 10'290 full-length 16S rRNA gene sequences from prokaryotes tailored for dairy products analysis (https://github. com/marcomeola/DAIRYdb). The performance of the DAIRYdb was compared with the universal databases Silva, LTP, RDP and Greengenes. The DAIRYdb significantly outperformed all other databases independently of the classification algorithm by enabling higher accurate taxonomy annotation down to the species rank. The DAIRYdb accurately annotates over 90% of the sequences of either single or paired hypervariable regions automatically. The manually curated DAIRYdb strongly improves taxonomic annotation accuracy for microbiome studies in dairy environments. The DAIRYdb is a practical solution that enables automatization of this key step, thus facilitating the routine application of NGS microbiome analyses for microbial ecology studies and diagnostics in dairy products.
More 16S specific taxonomy predictors have been developed, including RDP Naive Bayesian Classifier (NBC)  [19] , a naive Bayesian Classifier based on k-mers, GAST  [20] , MEGAN  [21] , Metaxa2  [22] , riboFrama  [23] , SPINGO  [24] , PROTAX  [25] , SINTAX  [26] , DynamiC  [27] , Humidor  [28] , MAPseq  [29] , microclass  [30] , q2feature-classifier  [31] , IDTAXA  [32]  and other tools implemented in the most current 16S pipelines like mothur  [33] , Qiime v1  [34] , Qiime v2  [35]  and FROGS  [36]. Although classification prediction algorithms have strongly improved, manually curated databases containing only authoritative full-length 16S sequences from type strains and cultivated reference strains can potentially compensate the limitations of short read sequences annotations by means of sophisticated algorithms.
To date, three main independent universal repositories dedicated to universal 16S sequences from prokaryotes are widely used: Silva, The Ribosomal Database Project (RDP), and Greengenes (GG)  [37]. Silva is the universal 16S repository with the highest number of sequences. The latest release of Silva SSU/LSU 132 (www. arb-silva. de) contained 6'073'181 16S sequences of at least 300 bp, with 2'090'668 good quality sequences with at least 900 bp length  [38] [39] [40]. Taxonomic rank information of Silva and Living Tree Project (LTP) are based on the Bergey's Taxonomic Outlines and the List of Prokaryotic Names with Standing Nomenclature (LPSN)  [41]. Minimal training sets, such as the SSU Ref NR 99 or the LTP  [42] , offer a reduced number of sequences for faster classification but still covering the broadest currently known biodiversity.
The second biggest repository, the Ribosomal Database Project (RDP Release 11, Update 5; http://rdp. cme. msu. edu)  [43] , contained at the time of writing 3'356'809 16S sequences from the International Nucleotide Sequence Database Collaboration (INSDC)  [44]. The nomenclature is based on the Bacterial Nomenclature Up-to-Date and the taxonomic rank information on the Bergey's Manual. Greengenes v13_5  [45]  contains 1'800'000 quality filtered 16S sequences. Classification nomenclature is based on automatic de novo tree construction and rank mapping with the NCBI Taxonomy database  [46]. Although frequently used in community studies together with Qiime  [34] , the last update dates back to 2013 with no indication for an imminent update. Taxonomic classification of the 16S is not trivial and requires both familiarity with prokaryotic phylogeny and often manual intervention due to poor annotation of the OTUs or by the available 16S databases  [47].
Fast and accurate, thus automatized classification of the OTUs is not yet possible at the biologically most significant species rank due to the short sequence fragments and the absence of food-dedicated, thoroughly curated 16S databases. Manually curated databases are of paramount importance to improve reproducibility, speed during the bioinformatics process of microbiome studies, and communication between researchers  [48]. Previous studies have highlighted the importance of high-quality data for improving the classification of the obtained OTUs  [17, 49, 50]. Although universal 16S databases cover vast prokaryotic biodiversity, they often fail to guarantee accurate classification to the species rank for sequences obtained from a highly studied environment, such as dairy products. In fact, annotation accuracy at lower taxonomic ranks increases with a standard training set encompassing only full-length and good quality representative sequences innate to the investigated environment  [17, 48, [50] [51] [52].
Here we present a comprehensive reference database, DAIRYdb (Database, Agroscope, Inra, Ribosomal, accu-racY), for 16S OTUs classification of next generation sequencing (NGS) reads from dairy products. The main goal was to develop a dedicated database that allows researchers to accurately and automatically annotate short reads of 16S down to the species level. The performance of the DAIRYdb was compared with the universal databases Silva, RDP, Greengenes and LTP using three predictors based on different algorithms and programming languages  [53] , such as Blast+  [54] , Metaxa2,  [22, 55] , and SINTAX  [26]. Manual curation of the database and its restriction to the biodiversity expected in dairy products strongly improves accuracy and reproducibility of phylogenetic classification at all taxonomic ranks.
Construction The 16S sequence database of dairy products DAIRYdb was constructed using a set of over 390'000 sequences associated to the selected keywords (cheese, milk, teat, dairy, starter, whey) deposited in NCBI GenBank and ENA/EMBL, as well as sequences with 97% average nucleotide identity (ANI) from Silva, RDP and Greengenes (Fig. 1 ). About 10'000 best quality reference sequences were retained after filtering based on sequence length (>1300 bp), quality (pintail >75) and potential chimeras. Finally, 16S sequences of important species from cheese and dairy environments  [56, 57]  lost during the clustering were added subsequently. The final number of 16S sequences consequently reached 10'290. The observed distribution among the different key words might reflect the unequal distribution of microbiome studies predominantly performed on cheese, dairy and milk samples, as compared to teats and whey.
About 1933 sequences of the DAIRYdb were shared among all Fig. 1  Development of the DAIRYdb consisted in three main steps: construction, curation and validation. For construction, dairy products specific 16S sequences were retrieved from Silva, RDP and Greengenes using Genbank NCBI, EMBL, Agroscope and INRA sequences. Curation was performed based on the cross-validation results from the leave-one-out test of SATIVA and highly iterated RAxML tree, followed by manual curation of taxonomic assignment and consistency throughout all taxonomic ranks, with a particular focus on singleton taxons with no reference sequence. Validation was performed comparing taxonomy annotation accuracy of single and HVR pairs by the five databases (Greengenes 13. 8, LTP version, Silva 128 NR99, RDP version and DAIRYdb) Fig.
2  Origin of sequences in the DAIRYdb. a Five-factors Venn diagram comparing the origins of the sequences (9'948) retrieved from the public repositories Genbank NCBI and EMBL associated to the keywords "cheese", "dairy", "milk", "teat" and "whey/starter". About 12. 7% (1'263) sequences were only detected in cheese and 15. 1%  (1'507)  were detected in all three cheese, milk and dairy environments. b Total number of sequences associated to a particular keyword. c Number of sequences shared by 1 to 5 keywords. About 19. 4% (1'933) sequences were detected in all 5 keywords, while 17. 1% (1'700) sequences were unique to one keyword keywords (Fig. 2a ) and 1778 were shared among the keywords dairy, cheese and milk. In fact, the majority of the sequences composing the DAIRYdb were linked to those three keywords (Fig.
Curation During the first step of data curation, the sequences were taxonomically annotated with Silva by means of SINA  [58]. The resulted annotation at all taxonomic ranks underwent a first manual check and cleaning for taxonomic inconsistencies through cross-comparison with the other members of the same taxonomic rank in a phylogenetic tree. No taxonomic overlaps comparable to other databases are present in the DAIRYdb, where different species of the same genus fall under different taxonomic lineages  [48]. A maximum of three closest neighbour type strains (CN) with authoritative taxonomy from Silva sharing 99% global sequence similarity to each sequence in the DAIRYdb were added to the 10'290 sequences in the DAIRYdb as reference during the curation process and removed at the end of the curation process.
For ranks at which no taxonomic annotation was possible with certainty due to the lack of authoritative type strains within the same clade (i. e. , commonly labelled "unknown", "uncultured" etc. in universal databases), the lowest common rank (LCR)  [52]  was used down to the species rank with the addition of the unclassified rank. As an example, a sequence assigned to the LCR, the genus Sporichthya, was named at species rank Sporichthya_Species. This approach avoids the merging of abundance values from different unknown species to biologically uninformative groups, thus improving communication among scientists  [60]. DAIRYdb version 1. 1 contains 2 kingdoms (Bacteria and Archaea), 47 phyla, 136 classes, 249 orders, 463 families, 1'757 genera and 4'030 unique species-like groups/species complexes (Fig.
lactis and Lactococcus lactis subsp. cremoris  [61]. It still is unresolved whether Streptococcus thermophilus is a species on its own or a subspecies of Streptococcus salivarius  [62] [63] [64]. The DAIRYdb is composed of sequences retrieved from the Silva database along with their respective taxonomy, which was manually inspected for nomenclature hierarchy conflicts based on the phylogenetic position within the tree. However, some conflicting annotations of the same sequence were detected between the Silva taxonomy and the Bacterial Diversity Metadatabase, such as the species assignment of the type strain sequence Accession AB008205, which is labelled as L. casei in Silva and L. paracasei in Bacterial Diversity Metadatabase (BacDive)  [65]. For the reference sequences of the most crucial species, bacterial names listed in the actual "List of prokaryotic names" according to BacDive were used.
However, further disagreements between Silva and BacDive cannot be completely excluded. Moreover, some crucial genera in dairy products may undergo a radical genome-based relabelling in the future to create more homogeneous clusters  [64]. Different approaches were applied on impure taxa, i. e. taxa that overlap in the tree despite being assigned to different nomenclature  [48]  in the universal databases. For instance, for the genera Escherichia and Shigella, Silva, LTP and RDP use the combined genus name Escherichia-Shigella but retain well-established species names, such as Escherichia coli. Differently, Greengenes leaves their sequences unclassified at ranks below the family Enterobacteriaceae  [48]. The different taxonomic nomenclature references used by the three databases have an impact on revisions to resolve conflicts with sequence-based phylogenies and the labelling of new candidate groups identified in environmental sequences.
However, discussion on the taxonomic inconsistencies and limitations of the universal databases (Silva, LTP, RDP and Greengenes), which the DAIRYdb was compared with, goes beyond the scope of this study and was extensively discussed elsewhere  [48, 52]. The DAIRYdb will undergo regular updates in accordance to update on bacterial nomenclature  [64] , integrating the novelties or correcting the changes. Finally, the inclusion of full-length and high-quality 16S sequences from reference type strains leads to a more robust and confident taxonomic classification  [49].
after  [68]  355R_v2r S-D-Bact-0355-a-A-18 v2R 338-355 18 GCWGCCTCCCGTAGGAGT 66 mod. after  [69]  338F_v3f S-D-Bact-0338-a-S-20 v3F 337-354 20 ACWCCTACGGGWGGCAGCAG 65 mod. after  [70]  534R_v3r consideration of their coverage in diversity with respect to the studied environment  [82]. Although researchers tend to use primers as universal as possible to catch the entire diversity present in the samples, it might be a pragmatic approach to lose some universality while increasing specificity for the studied environment. For dairy products, the DAIRYdb achieves both, covering all the biodiversity expected in these environments, while achieving specificity in taxonomic annotation. Annotation accuracy of the DAIRYdb was compared with other universal databases, such as Silva128, RDP trainset v16, LTP and Greengenes analysing fragments of single HVRs or HVR pairs extracted from the sequences in the DAIRYdb with V-Xtractor and in silico PCR.
About 1000 subsamples, each composed of synthetic HVRs extracted from 100 randomly selected sequences from the DAIRYdb, by either V-Xtractor or in silico PCR, were assigned to all taxonomic ranks by the means of three different classification predictors (Blast+, Metaxa2 and SINTAX) and the aforementioned databases. Single HVRs extracted with V-Xtractor and annotated with the DAIRYdb using SINTAX achieved annotation accuracy above 75% at all taxonomic ranks (Fig. 5a ). Accuracy was highest for the even HVRs (V2, V4, V6 and V8) as compared to the odd HVRs (V1, V3, V5, V7 and V9). The region V2 presented the highest taxonomy annotation accuracy, which is in line with other findings showing that the regions V1 and V2 resulted in a more accurate OTU clustering at 97%, 98% and 99%  [27]. Overall, the universal databases were less accurate with decreasing taxonomic rank (Fig.