dataesr/scientific-paragraphs-categorization huggingface dataset:
B. Cloud detection by disparity phase analysis for pushbroom satellite imagers The article  [19]  and its demo  9  address the problem of detecting clouds in a single satellite image. It focuses on satellites equipped with pushbroom sensors. The method exploits the parallax effect caused by pushbroom sensors, which induces an observable shift between color bands. The magnitude of the shift is different between cloudy and non-cloudy areas. The algorithm has several steps such as rank transform of the input image, inter-bands optical flow estimation and feature-based discrimination of cloudy and cloud free areas. Experiments have been carried out on 9854 patches of size 366×366 pixels extracted from the Sentinel-2 dataset of Hollstein et al. [10]. The online demo allows to evaluate the algorithm on a subset of these patches, and to vary the parameters of the method.
C. Workshop on Relative Radiometric Normalization of multisatellite image time series This workshop 10 demonstrates the relative radiometric normalization of long image time series, described in  [20]. This algorithm allows to merge time series from different satellites, thus allowing to increase the revisit time under comparable conditions. This method is illustrated by the fusion of time series of Landsat-8 and Sentinel-2 images. Hence, the workshop includes several time series made of Sentinel-2 images at processing levels L1C and L2A, as well as Landsat-8 images. The nature of this problem does not allow to have a ground truth. Hence the comparison with other methods is only possible through the use of a fixed dataset. We provide in the workshop three satellite image time series. 10 https://ipolcore.
ipol. im/demo/clientApp/demo. html?id=77777000114 Sentinel-2 blue, green and red bands have a pixel size of 10m, while Landsat-8 panchromatic band is sampled at 15m and its blue, green and red bands are sampled at 30m. To create a time series with images from both satellites, the pixel sizes must be made uniform. We thus use the panchromatic band to pansharpen Landsat-8 color bands to 15m/pixel and then upsample them to 10m/pixel by interpolation. GDAL 11 pansharpening tool is used for the first step. The time series is then registered with sub-pixel accuracy using the phase correlation method  [21] , as described in the following section IV-D. The resampling of the images for both the change of pixel size and registration are performed by spline interpolation  [22]. The dataset name in IPOL is RelativeRadiometricNormalization.
The misaligned images are removed from the time series. The verification is made using an alignment graph; nodes that are not connected are discarded. The third and final step is interpolation, performed using b-splines  [22]. The dataset in this workshop (ImageTimeSeriesRegistration) thus contains several misaligned time series from Sentinel-2 and Landsat-8. Their misalignment is due to native inaccuracies in the satellite's position and attitude used for geo-referencing the images.
We provide images from several satellites: Planet, Sentinel-2 (correction level L1C and L2A), and Landsat-8. These datasets in IPOL are CloudDetection_Sentinel2_L1C_RGB, CloudDetection_TimeSeries_Sentinel2_L2A, CloudDetection_Registered_RGB_TS_Landsat8, and CloudDetection_Registered_RGB_TS_Planet. We created one dataset by satellite and correction level. All provided image time series are registered by phase correlation as described in Sec. IV-D. The demos presented here use common datasets with images or groups of images whose size allows to execute the algorithms in a reasonable time (typically, 30 seconds). The IPOL demo system allows demos to use interactive controls over the images 14 , thus allowing to choose particular points or regions over a reduced view of a large satellite image, if needed.
V. GOOD PRACTICES FOR REPRODUCIBLE RESEARCH IN REMOTE SENSING The experience of IPOL publishing computational reproducible research (first focused on classic image processing, and then expanded to other fields and data types) enabled our editors to identify good practices and make recommendations. We shall list the most important here. • Reusable and publicly available datasets: as discussed in Secs. III, the use of datasets that can be re-used by different demos allows to compare methods and helps establishing the true state of the art. • Reliable datasets: making available the datasets is not enough. A detailed description of how a dataset was built is needed. In case this is unavoidable (for example, the program runs GPU code with specific libraries), this needs to be clearly explained in the README.
• Archival and easy-referencing: authors are encouraged to submit their source code to an archival platform. In the case of IPOL, the code is transparently submitted to Software Heritage 16. • Online demo: this helps readers to get a quick idea of the method and use it without technical knowledge. The main benefit for the authors is receiving citations of their work given that the experimentation is easier and the method convincing. • Show failures: the article should not only show successful experiments, but also failure cases and bad results. This helps to understand the limitations of the proposed method and to establish to which particular application it is better suited.
VI. CONCLUSION IPOL started in 2009 as a fully reproducible research journal, with a special emphasis on detailed mathematical descriptions of the published methods. Each publication comes with the text of the article (as in a classic journal), but including as well under the same DOI the source code and the data of the methods, as a whole. Endowed with on online demos and growing large experimental archives, it has helped establishing the true state of the art in image processing on several basic problems such as image comparison or edge detection. IPOL has expanded to remote sensing. In Sec. IV we gave four emblematic examples of articles or workshops on this topic: they address the first basic problem faced by any researcher in the analysis of time series of satellite images: registration, cloud detection and radiometry equalization.
A README file included in the published code should specify which libraries and versions are required. • Use virtual environments in Python: in the case of Python, virtual environments are a solution to the versioning problem mentioned in the previous point. They allow to declare which libraries are needed to run the program and set their exact versions. In IPOL this is a mandatory requirement. • Rich datasets: remote sensing image datasets should contain images acquired on a variety of locations, at different dates spanning all seasons and atmospheric con- ditions. Datasets built for machine learning applications are usually made of small image crops extracted from a few satellite acquisitions. Particular attention should be made when preparing such datasets to ensure that they contain image crops coming from different satellite acquisitions.
• Machine-independent sources: authors should try their best not to write code that only runs in a particular architecture, or hardcoding capabilities instead of letting the compiler determine if they are present in the under- lying architecture. Typical pitfalls: assuming that all ar- chitectures have AVX instructions or any vendor-specific capabilities • Accurate metadata: every image provided in a dataset should include all the metadata needed to retrieve the original files (distributed by a satellite operator) from which the image comes from. For example, every Sentinel-2 image should be provided with the identifier of the corresponding product distributed in SAFE format by Copernicus Open Access Hub and band number. Similarly, PlanetScope images should be provided with their item_id (product identifier) and asset_type (processing level) as listed in Planet Explorer and Planet API catalogs.
This however requires the ability of making predictions for large classes of models, both at colliders and for what concerns cosmology. The FeynRules package  [4]  offers such a possibility, as from a unique FeynRules implementation of any given dark matter model, it is subsequently possible to generate model files suitable for various high-energy physics tools such as MG5 aMC  [5] , MadDM  [6]  or MicrOMEGAs  [7]. Following the general strategy for new physics computations outlined in ref. [8] , such a joint usage of various packages has two major advantages in the dark matter context. First, it allows for the straightforward and automatic calculation of the dark matter relic density, as well as of the direct and indirect detection cross sections to verify the cosmological viability of any model.
The resulting partonic events are matched with parton showers as described by the Pythia 8 package  [45] , following the MC@NLO prescription  [46]. We also use Pythia 8 to handle hadronisation. We then reconstruct the hadronlevel events by clustering hadrons according to the antik T algorithm with a separation parameter set to ∆R = 0. 4  [47] , as implemented in the FastJet software  [48]  that we drive from MadAnalysis 5  [9, 49]. The latter programme is also used for the generation of the differential distributions studied in section 3. 3, and the reinterpretation analysis of the LHC results in section 3. 4.
Collider constraints on S3D uR dark matter As visible from the results of section 3. 3, most of the monojet signal arises, in the considered S1 and S2 benchmark scenarios, from the production of heavy mediators (by pairs or in association with dark matter) that then decay into dark matter and jets. In the following, we reinterpret the results of typical LHC dark matter searches probing final states featuring a large amount of missing transverse energy (carried away by the dark matter particles) and an important hadronic activity. We recast two of such ATLAS analyses for which reimplementations within the MadAnalysis 5 Public Analysis Database (PAD) of recasted LHC analyses  [58]  2 exist. Starting from Monte Carlo simulations of the XX + XY + Y Y dark matter signal as described in section 3.
2, we make use of MadAnalysis 5 to automatically simulate the response of the ATLAS detector by means of appropriate tunes of the Delphes 3 programme  [59]. We then assess the sensitivity of the considered analyses to the S1 and S2 signals by using the CL s method  [60]. We consider two ATLAS analyses of 36. 2 fb -1 of LHC data targeting the production of missing energy recoiling against at least one hard jet and a subleading hadronic activity. We recast the ATLAS-EXOT-2016-27 analysis  [53] [54] [55]  in which the selection imposes that the dark matter system is produced together with 2 to 4 extra hard jets with quite stringent kinematic requirements. The analysis includes an ensemble of signal regions that are distinguished by different inclusive and exclusive constraints on the missing transverse energy.
Acknowledgements The authors are grateful to  L. Lopez Honorez ,  K. Mohan ,  A. Pukhov  and  T. Tait  for their help and useful comments during the model validation steps. CA is supported by the  Innoviris   ATTRACT 2018 104 BECAP 2  agreement. This work has received funding from the  European Union 's  Horizon 2020 research and innovation programme  as part of the  Marie Sk lodowska-Curie Innovative Training Network MCnetITN3  (grant agreement no. 722104 ).
Traditional breeding may not be sufficient to address those challenges so that it is critical to develop genomic tools helping to improve breeding programs efficiency. To date, few molecular resources have been developed on Lavandula angustifolia and no SNP (Single Nucleotide Polymorphism) markers have been reported. As of June 2019, 340 nucleotide sequences had been deposited in the NCBI's GenBank database for Lavandula species with more than 50% being sequences of Lavandula angustifolia. Most of them (~25%) are related to synthesis or regulation of essential oils  [14] [15] [16] [17] [18] [19] [20] [21] ; or are isolated from the chloroplast genome (~30%). Moreover, EST-derived SSR (Simple Sequence Repeat) markers have been recently developed and successfully tested for transferability between Lavandula species  [22].
The quality of the raw assemblies was assessed with TRANSRATE v1. 0. 2  [27]. TRANS-RATE maps paired-end reads back to the raw contigs and calculate metrics (percentage of reads mapping back to contigs in proper pairs, number of contigs with a predicted open reading frame, estimation of fragmented transcripts. ), in order to assess how well the assembled contigs are supported by the sequencing data. On this basis, a score is determined for each contig and the set of contigs with best scores are gathered into an "optimized assembly". TRANSRATE also computes a global score that allows comparing two or more assemblies performed with the same initial dataset but with different tools or settings. To evaluate the completeness of the assemblies, we performed a BLASTx+ (e-value 1e-20) alignment of the optimized assemblies against SwissProt database (http://www.
uniprot. org/) using a script developed by the TRINITY developer's team (https://github. com/trinityrnaseq/ trinityrnaseq/wiki/Counting-Full-Length-Trinity-Transcripts). This analysis allowed to determine (i) the number of unique best blast hit (BBH) that align against contigs and (ii) the percentage of coverage of the hit sequence by the contig. We finally took advantage of both assemblies to build the reference Unigene. This Unigene is compound of the sequences assembled with TRINITY or CLC that covered more than 70% of their BBH in SwissProt database with more than 30% identity. In case of redundancy within an assembly (i. e, several contigs matched the same hit in the database), we only kept the longest contig in the Unigene. Likewise, when two sequences built by CLC and TRINITY had the same BBH, we only kept the longest form in the Unigene or, in cases of equality, the one with the highest alignment similarity percentage.
In silico functional annotation of the reference Unigene. The completeness of the reference Unigene was evaluated with regard to the 1440 plant-specific genes in the Benchmarking Universal Single-Copy Orthologs (BUSCO, Embryophyta data set)  [28]. To further describe biological functions related to the Unigene sequences, we ran the Trinotate pipeline (https:// trinotate. github. io/) which is a comprehensive annotation suite adapted to in silico annotation of de novo assembled transcriptome. The analysis includes homology searches (evalue 1e-5) to SwissProt database, protein domain identification using HMMER and Pfam databases, protein signal peptide and transmembrane domain prediction with Signalp and tmHMM servers, and search for Gene Ontology (GO) terms and KEGG pathways  [29, 30]. Insofar as the ability to annotate sequences based on similarity search against databases depends on the completeness of these databases, we completed the annotations using supplementary databases and tools.
The online tools KAAS (http://www. genome. jp/kegg/kaas/) [31]  was used with default parameters to decipher associated KEGG pathways by aligning Unigene sequences against the KEGG GENES database. The online tool TRAPID (http://bioinformatics. psb. ugent. be/webtools/ trapid/)  [32]  was also used with default parameters. TRAPID performs open-reading frames detection and similarity searches against implemented databases (OrthoMCL-DB version 5 and PLAZA 2. 5). Results were then combined in order to identify coding sequences, assign transcripts to gene families, and generate GO annotation. Iterative targeted assembly of lavender genes. The Unigene sequences and the reads from the DNA sequencing of the clone 'Maillette' were used to perform the iterative mapping and assembly steps to build a set of reference gene sequences (with exons and introns) as described in Aluome et al  [33] (Fig 1, S1 File), hereafter called Genespace.
Mapping was less stringent at the first iteration (step 1, Fig 1 : 100% of the read length with a similarity percent of 95%) than for other iterations (step 4, Fig 1 : 100% of the read length with a similarity percent of 98%) in order to take into account exons/introns junctions at the first step of mapping. For assembly steps, we used idba_ud v. 1. 0. 9 software  [34]. This tool offers the advantage to use the new sequences assembled at the iteration i-1 to 'guide' the assembly at the iteration i (-input-long-read option) and thus allowing to keep the information build at the iteration i-1 while assembling new sequences at iteration i. Parameters used for idba_ud for all iterations were a minimum k-mer size of 25 nucleotides and a maximum k-mer size of 100 nt (-mink 25-maxk 100), with an increment of 5 nucleotides (-step 5) and a similarity value of 1 nucleotides (-similar 1).
Finally, we checked the collinearity and identity (for exons) of the GeneSpace sequences compared to their homolog sequences in the Unigene with a BLASTN alignment (e-value 1e-6). SNP discovery. Filtered DNA-seq paired-end reads of the 15 lavender clones and of the lavandin 'Grosso' were mapped against the Genespace sequences with CLC software. To be included in the mapping, at least 95% (90% for 'Grosso') of the read (length fraction = 0. 95 or 0. 90) must be aligned to the reference sequence with at least 90% identity (80% for 'Grosso') (similarity fraction = 0. 90 or 0. 80). Moreover, reads with non-specific matches (i. e. reads mapping equally well at several alignment positions) were excluded from the mapping. SNP detection was performed with CLC and GATK v 3. 6  [35] [36] [37]  tools.
Finally, in order to prevent from spurious SNP calling, we applied SelectVariant tool to extract SNP that were concordant (position and genotype information) between CLC and GATK vcf output files to get a cleaned set of putative SNPs. Genetic distances analysis. From the filtered putative SNPs set, we selected SNPs that generated no missing genotype data. The analyses were based on the 'Population Structure' workflow from the Grundwald laboratory (https://grunwaldlab. github. io/Population_Genetics_in_ R/Pop_Structure. html). The R packages 'poppr' version 2. 7. 1  [39, 40] , 'adegenet'  [41, 42]  and 'ade4'  [43]  were used to calculate absolute genetic distances with 'Prevosti's method'  [44]  (a method suited to SNP data), to perform a principal component analysis (PCA) and built an UPGMA phylogenetic tree with 1000 bootstraps.
De novo RNA-seq assembly Results of de novo assemblies are presented on Table  1. Overall, TRINITY generated more contigs (280,062 sequences versus187,584 sequences for CLC assembly) of a longer size than (N50 of 1250bp versus 739 bp for CLC assembly). Likewise, there were almost twice more contigs with a predicted ORF within the TRINITY assembly. The TRANSRATE tool used reads data in pairs to assess the quality of assemblies (Table  1 ). A total of 66,107,704 paired-end reads (92% of cleaned reads) remained in pairs after independent trimming of the reads 1 and the reads 2. More than 62 million of pairs mapped back to the contigs assembled with TRINITY and CLC. Of these mapped paired-end reads, almost 60% were assessed to be mapped in "good" pairs according to the TRANSRATE standards that are: both members of the pair are aligned on the same contig, in the correct orientation and the total length of the reads align against the contig.
If the TRANSRATE score obtained for CLC raw assembly (0. 154) was better than for TRINITY raw assembly (0. 108), the scores were similar for optimized assemblies with a score of 0. 184 and 0. 198 for CLC and TRINITY assemblies, respectively. Finally, a pool of 147,672 contigs from CLC raw assembly and 194,706 contigs from TRINITY raw assembly were selected as two optimized assemblies for downstream analyses. To assess the completeness of the optimized assemblies, we used a TRINITY utility (see Methods) to perform a BLASTX alignment against SwissProt database (e-value 1e-20) (Table  1 ). A total of 51,821 (35%) contigs built with CLC and 78,929 (40. 5%) contigs built with TRINITY had a hit in the database. There was redundancy in both optimized assemblies since the 51,821 sequences from CLC optimized assembly matched 16,270 unique proteins and the 78,929 sequences from TRINITY optimized assembly matched 18,024 unique proteins of SwissProt database.
In silico annotation of the reference Unigene Of the 1440 BUSCO genes, 791 genes (55%) were recovered in the reference Unigene. Among these 791 genes, 729 (92%) were complete and single-copy, 62 genes (7. 8%) were complete and  annotation. Search of conserved domain against Pfam database allowed to increase the rate of sequence annotated with a GO term or a KEGG ontology (S3 Table ). The identification of various GO terms and KEGG pathways indicated that we assembled sequences related to a relatively diversified panel of protein functions.
Primary SNP discovery was performed independently with CLC and GATK. The selection of the subset of concordant SNP between the two tools (position + genotype calling) led to the construction of a genotyping matrix of 16 individuals � 359,323 sites (S4 Table ). These SNP spread on 7,332 Genespace sequences with a mean value of 1 SNP per 90 bp. The average  ). Moreover, 30,144 putative private SNP including 991 SNP at the homozygous state were identified between the lavandin 'Grosso' and all the lavender clones. For downstream analyses, we used a subset of 9, 505 SNP generating no missing data across the 16 individuals (see vcf file in S5 Table ). Of these 9505 SNP, 66% had a minor allele frequency (MAF) ranging from 0. 4 to 0. 5, 13. 5% had a MAF value in the range 0. 05-0. 4 and 20. 5% had a MAF value inferior or equal to 0.
05. Rare alleles were deliberately preserved in the dataset to maximize our power to discriminate among the individuals. The majority (98. 7%) of the conserved SNP were biallelic (S6 Table ). The genotype of the 16 clones at these 9,505 SNP was mainly heterozygous with one reference and one alternate allele (66. 7 to 80. 9% of the genotype calls, S6 Table ).
Genetic distances analysis Pairwise genetic distances calculated from the 9,505 SNP ranged from 0. 023 (Ruffinato/5. 90 pair) to 0. 167 (77. 13/Grosso pair) (S7 Table ). Most of the clones are genetically close: 75% of the calculated distance pairs were between 0. 05 and 0. 07. However, all the clones were successfully discriminated with the set of SNPs. As expected, the clone 'Grosso' appeared to be divergent from the lavender clones with a mean genetic distance to other lavenders of 0. 16. The distance matrix was used to perform a UPGMA analysis with 1000 bootstraps (Fig  4 ). Three main cluster seems to be highlighted: C15. 50/B7/Barthee, Gabelle/Frisee (the two "blue lavender" of the dataset), and Maillette/Diva/77. 13/Ruffinato/5. 90. To better assess the intra-species diversity, a PCA was performed between the lavenders (Fig  5 ).
Construction of a good-quality gene sequences using RNA and DNA sequencing data We built gene sequences from 84. 5 million of RNA paired-end reads and 145 million of DNA paired-end reads sequenced from leaves, roots, and flower buds of the lavender clone 'Maillette'. We used different licensed and free softwares for our analyses, however this can be done with any of one's preferred tool. The bioinformatic platforms provide users with suite of tools and pipelines to make and validate a de novo assembly of RNA-seq such as DRAP  [45]  or the Trinity protocol  [26]. Likewise, the transcriptome-guided DNA-seq assembly protocol can be performed with any tool allowing to map reads to a reference genome and then extract reads that mapped in proper and broken pairs. De novo transcriptome assembly. The quality of an assembly is primary based on the quality and the quantity of sequencing reads.