dataesr/scientific-paragraphs-categorization huggingface dataset:
Cross-identifications When multiple designations exist for the same object, the different names are cross-identified by authors or databases. The best practices with these crossidentifications are: • Confirm the names and positions. Many examples exist in the literature where multiple columns of cross-identifications disagree with each other or with the listed coordinates. We suggest to always verify with established databases that all of the names given to an object are valid crossidentifications for the object and that the listed positions are for the same object. • Cross-match the same objects within the same article. If the same object appears in multiple tables of the same article, but with different designations (e. g. , duplicated detections at the borders between two tiles of a mosaicked image), a cross-matching of the tables should be done by the authors to enable the readers to quickly access related data about the objects.
Photometry The flux or intensity of light radiated by astronomical objects is another key observable in astronomy. To properly represent the photometry data and to enable easy comparisons of the results, we strongly recommend: • State the facility, telescope and instrument used. Specify whether the facility is groundbased or space-based. Authors also need to provide any other relevant instrument configuration information, the specific camera on the instrument, and/or the specific CCD chips of the camera that were used in the analysis. These are crucial to the proper interpretation of the data. Good documentation of the metadata will also facilitate the correct and fast ingestion of photometry data in services such as the NED Spectral Energy Distribution (SED) plots and the VizieR Photome-try viewer 17 , which in return will better serve the community.
Classifications There are many spectral and morphological/phenomenological classifications for astronomical objects, and these classifications are constantly evolving. We suggest: • Utilize established classifications as available. For basic morphological types, use the wellestablished schemes 20. Authors are encouraged to refer to NED's extensive suite of searchable galaxy classifications and attributes 21 or SIMBAD's Object Classification 22 which have been standardized to enable unified queries across journal articles and catalogs. • Define new classifications clearly. If new classifications must be created, define them clearly so they can be easily adopted by other researchers, and quickly integrated into databases.
Figures Figures are commonly used for data visualization in an astronomical article, especially for images, spectra, SED plots, etc. For figures in a publication, we recommend: • Create the graphics with accessibility in mind. Accessibility should be taken into consideration when designing the graphics as it will help a broader audience to properly understand the information conveyed in these plots. For example, color-blind users would benefit from symbols that vary in shape in addition to colors. See the AAS 25  journals' graphics guide 26  for more advice on this. • Include information for "data behind the plots". Make public the original data files used to generate the figures, as this will greatly enhance the ability to reproduce/validate or build upon published results.
Facility credits Proper credit needs to be given to the facility or service used to obtain the data in an article. This may be hardware (telescopes and instruments) or on-line services (databases). Not only are the specifics essential to properly understand the data, the acknowledgement metric is also used as a basis for productivity by organizations maintaining telescopes, archives serving the data, and funding agencies for those facilities. We therefore recommend: • Explicitly indicate the facility involved. Always describe the facilities or services used, and make sure the name is unique. Examples of possible confusion are given in Table  2. • Use standardized keywords when possible. The AAS has created keyword tags 36  to be used with AASTeX \facility and \facilities. Many major astronomical archives, databases, and computational centers have been recently added to this list (e.
Software credits The increasing volume and complexity of the astronomical data require a lot of software processing in obtaining, calibrating, and analyzing the data and building the final product. Similar to acknowledging the facilities and services, we recommend: • List the software and version used in the production of the article. Use the preferred citation if available. If not, include the name of the author(s), title of the program/source code, the code version and a URL link to the code publisher. This will ensure proper credit is given, and at the same time help with reproducibility. The Astrophysics Source Code Library (ASCL 37 ) provides a list of common codes and software packages that are of interest to astronomers and astrophysicists, and their preferred citation methods.
DATA CONTENT KEYWORDS Traditional keywords used in journal articles do not capture information about the types of data presented in the article, for example, whether the article presents new position measurements (astrometry), photometric data, spectroscopic data, etc. This greatly limits the possibility of filtering literature searches based on the availability of these specific types of data. We suggest that authors: • Tag articles with relevant data content keywords from the Unified Astronomy The-saurus (UAT 39 ). The UAT is adopted as a standard by the AAS journals 40 and the broader astronomical community including ADS. Hence we recommend that authors tag their articles with UAT keywords that best describe the types of data contained in the article.
SUMMARY We hope the guidelines provided here will assist authors in preparing and publishing their data in a way that allows science claims to be clearly communicated, and readily understood and validated by readers. These best practices are intended to be used not only by authors during the preparation and submission stages of a publication, but also by referees, editors and publishers during the refereeing and editing stages before final publication. This will improve the quality of the published research record, expedite the integration of data into the databases with more efficiency and accuracy, and result in long-term preservation and reuse of valuable data. This will in turn enable more scientific discoveries that would otherwise not be possible or practical, and increase citations for authors.
(d) Include all references in the bibliography section. (e) Distinguish original data in your article and data taken from other work. 13. Facility credits ( §5. 2) (a) Indicate the facilities involved, such as telescopes, instruments, and databases. (b) Use standard keywords when possible. (c) Include facility's own statement if available. 14. Software credits ( §5. 3) (a) List the software and version used in the production of the article. 15. Digital object identifiers ( §5. 4) (a) Use DOIs to cite data sets, software and services if available. 16. Data content keywords ( §6) (a) Tag articles with relevant data content keywords from the UAT.
Computational details All DFT and TDDFT calculations were carried out by using the Gaussian 16 package  68. The ground state and excited state geometries for the molecule of compound 1 were optimized at the DFT/B3LYP/6-31G(d,p) and TDDFT/M062X/TZVP levels, respectively. Based on the optimized geometries, the excited-state properties were calculated at the TDDFT/ B3LYP/6-31G(d,p) level. In all calculations, the environment was modeled by using the polarizable continuum model (PCM) with dichloromethane (ε = 8. 93) as solvent.
Single crystal X-ray diffraction The single-crystal X-ray diffraction data were collected on a Rigaku Synergy-DS Diffractometer. The data collection, integration, scaling, and absorption corrections were performed by using the Bruker Apex 3 software  69. The structures were solved with direct methods using Olex2  70  and refined by using the full-matrix least-squares method on F 2. The non-hydrogen atoms were refined anisotropically. The positions of the hydrogen atoms were calculated and refined isotropically. The program PLATON was used for the geometrical calculations  71. The graphics related to the structures were generated by using Mercury 4. 2. 0  72. The crystallographic details are available in Supplementary Data  1  and 2.
Existing literature contains many more studies characterizing airborne dispersal of plant pathogens compared with splash dispersal  (McCartney et al. 2006 ):  Fitt et al. (1987)  found 305 datasets on airborne dispersal in the field, only 10 datasets on splash dispersal in controlled conditions, and no studies on splash dispersal in the field. A possible explanation for this disparity is that commercially available spore traps or disease assessments readily capture spatial scales of meters or longer, characteristic of airborne dispersal, but those are more challenging to apply over submeter scales typical for splash dispersal. Zymoseptoria tritici (formerly Mycosphaerella graminicola) is a major fungal pathogen of wheat  (Dean et al. 2012; Jørgensen et al. 2014 ) that causes Septoria tritici blotch (STB) disease.
Thus, we measured the conditional disease intensity using only the numbers of pycnidia per leaf. We estimated the number of asexual reproduction and dispersal events between t 0 and t 1 using the following arguments. First, based on the data from  Shaw (1990;  as revisited in Figure  A1  of  Karisto et al. 2018) , the latent period after inoculation was estimated to be longer than 20 days (average daily temperature during first 19 days was 19°C). Thus, there was likely no spread from the inoculation areas during the rainy period 13 to 17 DAI (Fig. 1F ). This was further confirmed by a visual assessment on 8 June (22 DAI), when we observed only a few tiny lesions and mostly no pycnidia on plants inside the inoculation areas, concluding that substantial spread had not yet occurred. Second, at t 0 (14 June, 28 DAI) we observed substantial disease (Fig.
Estimation of dispersal and transmission parameters The data used in the analysis were obtained in the following way. First, we conducted STB incidence measurements and acquired conditional STB intensity measurements from the digital image analysis. Second, the STB intensity values in each measurement line were multiplied by the corresponding STB incidence to obtain the full or "unconditional" intensity values, hereafter called "intensity" for the sake of simplicity. Third, we calculated the average full intensity in each measurement line to obtain five data points (from five replicates) for each treatment (at distances x 0 , x ±1 , x ±2 , x ±3, and x ±4 ). These average values over each measurement line were used for fitting the model functions. The dispersal gradient functions (equations 2 and 3) were fitted to the data to estimate α and I 0 β.
Finally, we grouped these unconditional means over measurement lines into sets representing the five replicates. As a result, we obtained 100,000 bootstrap replicates of the entire experiment. The one-dimensional disease gradient in equation 5 was fitted to each of the 100,000 bootstrap replicates. The disease gradient function in equation 3 was fitted to a subset of 10,000 bootstrap replicate datasets for computational reasons. As a result, we obtained a large number of bootstrap estimates of parameters α and βI 0 for each treatment and direction. These estimates were used to conduct statistical tests.
Statistics implementation All data analysis was implemented in Python (versions 3. 5. 2 and 3. 6. 0), and the code is provided together with the data. Fitting was performed using the 'Model. fit' function of the lmfit package (v. 0. 9. 10; Newville et al. 2014) , with the methods 'brute' for initial guess, 'leastsq' for actual fitting, and occasionally 'tnc' (truncated Newton) when 'leastsq' failed. Numerical integrations were implemented with 'quad' and 'dblquad' functions in the SciPy package (v. 1. 0. 1; Virtanen et al. 2020). Fitting of the two-dimensional functions (equations 2 and 3) was performed using the high-performance computing cluster Euler of ETH Zurich. The Kruskal-Wallis test was conducted with the 'Kruskal' function in the SciPy package and Dunn's test with the 'posthoc_dunn' function in the scikit-posthocs package  (v.
RESULTS The second inoculations with strains 1A5 and 3D7, and their mixture, were successful: At t 0 , we observed increased disease levels in the inoculation areas of all three treatments compared with controls (Fig. 2A ). At time point t 1 , there was a gradient of disease intensity from higher levels at x ±1 to lower levels at x ±4 (Fig. 2B ). Genotyping the reisolated strains confirmed the successful spread (Supplementary File S1, Appendix A). In to-tal, 4,190 plants were inspected in the course of incidence measurements, 2,527 leaves were collected and analyzed using the digital image analysis, and 153 isolates were genotyped. The entire dataset including raw data, bootstrap replicates, best fitting parameter estimates, and weather data is available in Dryad data repository (https://doi.
17, parameter βI 0 : P βI0 = 0. 13, Fig. 3A ), nor in the mixed inoculation treatment (P 2D = 0. 74, P α = 0. 60, P βI0 = 0. 95). This similarity between directions suggests isotropic dispersal during the experiment. Considering this isotropy, we estimated the parameters using the dataset that combined the two directions. We established a significant difference between 3D7 and mixed inoculation treatments (P 2D = 0. 014, P α = 0. 020, P βI0 = 0. 018, Fig. 3B ). The dispersal parameter α was higher in mixed inoculation, whereas βI 0 was higher in the 3D7 treatment (Table  1 ). We thus highlight that following the mixed inoculation, the pathogen had dispersed further but caused lower disease transmission compared with 3D7 treatment.
For the strain 3D7, the mean dispersal distances in one-and two-dimensional models would be 15 cm and 27 cm, respectively. Clearly, a one-dimensional model of dispersal should not be used for deriving dispersal distances. Second, regarding the computational time, the onedimensional model (equation 4) was fitted to 100,000 bootstrap datasets on a standard PC in a few hours, whereas fitting the two-dimensional model (equation 3) required a few days of computational time for only 10,000 replicates. When using the most complete model (equation 2), it took more than 12 h on a PC to obtain the estimates for only one replicate (the observed data). We conclude that considering the spatial extent of the source by means of two-dimensional modeling pays off in the increased accuracy of the parameter estimates, even though the computational demand increases.
Sampling distances The measurement lines were at closest 20 ± 5 cm from the edge of the inoculated area. Measuring the disease intensity closer to the source and even inside the source might have improved the estimates, as the differences between the gradients are more pronounced close to the source. However, closer to source, the reliability of measurements may suffer from saturation and from dispersal via direct contact  (Fitt et al. 1989). We also measured the disease inside the inoculation area, but those data points were excluded from fitting because our aim was to analyze only the newly spread infection and capture the primary disease gradients. The increase of disease intensity in the source area from t 0 to t 1 was not only due to secondary infections but likely also from latent infections that can become symptomatic after an extended period  (Karisto et al.
ACKNOWLEDGMENTS The Genetic Diversity Centre at ETH Zurich helped with the genetic analysis. PK and AM thank  Andreas Hund  and  Hansueli Zellweger  for maintenance of the field experiment,  Bruce McDonald  for helpful discussions, and  Marcello Zala  for support in preparing inoculations and other laboratory work. AM is grateful to  Jonathan Levine  for his advice on designing the experiment. PK is grateful to  Fanny Hartmann  for help in designing PCR primers. FS thanks  Christophe Montagnier ,  Sandrine Gélisse , and  Nicolas Lecutier  for maintenance of the similar field experiment prepared at the facilities of  INRAE Bioger in Thiverval-Grignon, France.
Data Processing Raw data quality was checked using FastQC (0. 11. 7). Illumina TruSeq adapters were trimmed and short reads (<15bp) removed using fastp (version 0. 21. 0) (Chen et al. , 2018). The fastq files were pooled according to the phenotypic sex of the samples, resulting in a pool of females reads (with 10 samples) and a pool of males reads (with 10 samples). The two pools were mapped to the latest O. niloticus reference genome available on NCBI (O_niloticus_UMD_NMBU, Genbank ID: GCA_001858045. 3) using BWA mem mapper (version 0. 7. 15)  (Li and Durbin, 2009). Sam files were then converted to Bam files and sorted using SAMtools (version 1. 9)  (Li et al. , 2009)  and read groups were added to the two pools using Picard AddOrReplaceReadGroups (2. 7. 0)  (Broad Institute, 2019). The output files were then combined with SAMtools mpileup (version 1.
9). Using Popoolation2 mpileup2sync. pl (version 1. 2. 2)  (Kofler et al. , 2011) , the mpileup file was transformed to a synchronized file (sync). Finally, the sync file was processed with Sex_SNP_finder_GA. pl  (Gammerdinger et al. , 2014)  to calculate the Fst between males and females. This pipeline allowed us to identify sites that are fixed (or nearly fixed) in one sex and at intermediate frequencies in the other sex. The following parameters were used in Sex_SNP_finder: fixed_threshold = 0. 9; minimum_polymorphic_frequency = 0. 3; maximum_polymorphic_frequency = 0. 7; minimum_read_depth = 10; maximum_read_depth = 100; minimum_read_count = 2; sex_SNP_finder_window_size = 10000. Finally, the Fst were plotted on a manhattan plot using R and the qqman package (version 0. 1. 4) (Turner, 2018).
Subsetting of Read Datasets, Identification of Y Specific Reads and Assembly Using the results of the Illumina short reads (2 × 150bp) whole genome sequencing published in  Triay et al. (2020) , we built an expected Y haplotype for the LG23 sex region (Figure  1 ). This expected Y haplotype is based on the sequence of the X haplotype of the reference genome (an XX female), to which the region corresponding to the duplication of the oaz1 to dot1l genes was inserted according to the breakpoints described in  Triay et al. (2020). We kept the nucleotides matching the small deletions (<500bp) found using the short read data in this expected haplotype, as we assumed the Nanopore reads to be long enough to cross short deletions. However, we deleted the sequences corresponding to the two large (>5 kb) deletions to obtain the final sequence of the theoretical Y.
Oxford Nanopore MinION raw reads were used to build a BLAST database  (Camacho et al. , 2009)  against which were blasted the sequences of the oaz1, amh and dot1l genes extracted from the latest available O. niloticus XX female reference genome (O_niloticus_UMD_NMBU, Genbank ID: GCA_001858045. 3). Since the first intron of the dot1l gene is very similar to the sequence of the unrelated vasa gene, we split the sequence of dot1l in order to remove this intron and limit its influences in the blast results. All raw reads for which at least one of the sequences presented blast hits were kept. This subset of reads was then trimmed for 23bp at the 3′ and 5′ ends to remove regions with low quality bases. This set of trimmed reads was then mapped onto the theoretical Y haplotype in order to identify Y specific reads according to the presence of Y specific indels described in  Triay et al.
9)  (Narasimhan et al. , 2016). For the two outgroups Etia nguti and Hemichromis elongatus, and for the O. niloticus reference genome, we used the fasta files available on NCBI (Genbank IDs: GCA_015106755. 1, GCA_015108515. 1 and GCA_001858045. 3 respectively). When needed, we produced a reverse complement in order to obtain sequences in identical sense to the sequences of  Meyer et al. (2015). The 42 sequences of all samples were then combined into a unique fasta and we added some of the species of  Meyer et al. (2015). All sequences were then aligned using MAFFT (v7. 475)  (Katoh and Standley, 2013)  and sites presenting more than 50% missing data among all species/ samples, usually corresponding to species-specific insertions, were removed using trimAl (version 1. 4. rev22)  (Capella-Gutiérrez et al.
Mitochondrial Phylogeny We assembled the mitochondrial genomes from Koka, Hora and Kpandu O. niloticus wild populations obtained from the short reads of our Whole Genome Sequencing trimmed data using MitoFinder (version 1. 4)  (Allio et al. , 2020). We used the reference mitochondrial genome as the basis for annotation (Genbank ID: GU238433. 1). In addition, we used the closely related species Haplochromis burtoni as outgroup (NCBI ID: NC_027289. 1). In order to use the same basis as the one used for gene annotation, we also ran the reference mitochondrial genome and Haplochromis burtoni through Mitofinder to extract individual genes. The sequences of the 13 mitochondrial protein- coding genes and the two ribosomal subunits (Supplementary Table  S2 ) were then aligned using MAFFT (v7. 475)  (Katoh and Standley, 2013).
2. 5. 3. Phylogeny of Sex Haplotypes Because of the low coverage of long reads data and due to the high error rate of Oxford Nanopore MinION data, we could not use the Y haplotypes built from the de novo analyses. Thus, we focused on the amh gene region and more precisely on the LG23 region from 34,489,620 to 34,532,693 bp according to the XX reference genome and analyzed only female individuals. This corresponds to the region defined as carrying the major sex-determinant in previous studies  (Li et al. , 2015; Cáceres et al. , 2019; Triay et al. , 2020; Taslima et al. , 2021). This region was extracted from Kpandu, Koka and Hora female mapping files using SAMtools (version 1. 9)  (Li et al. , 2009). These regions were then converted to consensus sequences using Geneious (version 9. 0. 5) (Kearse et al.
No Y Specific Reads in Hora Males in the Expected LG23 Sex-Linked Region We next performed a long-reads nanopore sequencing to study in Hora males in more depth the region around the amh gene that is duplicated and male-specific in the Koka and Kpandu populations. We sequenced on a MinION a male from each population. We obtained for the Hora male a total of 777,765 long nanopore reads corresponding to 4,034,783,321 bp, 4,474,365 reads corresponding to 22,830,560,109 bp for the Kpandu male and for the Koka male a total of 5,266,388 reads corresponding to 14,776,650,374 bp. The average error rate was 6. 44%. This resulted in 228 reads of Kpandu, 287 of Koka and 236 reads of Hora that passed the oaz1, amh and dot1l BLAST filter. The expected Y haplotype structure we constructed is correct since Kpandu and Koka reads mapped to the Y haplotype with high quality, confirming the structure we previously described using WGS short reads data  (Triay et al.
aureus was also observed when using mtDNA but they were differentiated in phylogenetic trees derived from nuclear DNA markers (SNPs)  (Syaifudin et al. , 2019). Discordance between mtDNA and nuclear DNA has been shown in several studies within the Oreochromine cichlids  (Ford et al. , 2019). Mitochondria DNA reflects only maternal inheritance, whereas species topology is probably more reliable when using multi-nuclear markers  (Meyer et al. , 2015; Ford et al. , 2019). Ancient hybridization was suggested as being the most likely explanation for the discordance between mtDNA and nuDNA  (Dunz and Schliewen, 2013). If this is the case, our mitochondrial analysis is not suitable to infer the relative position of Kpandu to our other O. niloticus samples. By contrast, our nuclear DNA datasets should not be affected by mitochondrial introgressions.