dataesr/scientific-paragraphs-categorization huggingface dataset:
The classes are listed in Table  1. The three quantities analysed in the paper and made available in the corresponding database are the rain rate R in millimetres per hour, the drop size distribution N(D) per cubic metre per millimetre (N(D)dD is the number of drops per unit volume in cubic metres with an equivolumic diameter between D and D + dD in mm), and the kinetic energy density flux KE in joules per square metre per hour. Equivalently, we could have worked on a "kinetic energy" expressed per millimetre of rain (i. e. in KE in J m -2 mm -1 ), which is sim- R = π 6 t i,j n i,j D 3 i S eff (D i ) , (1) N (D i ) = 1 S eff (D i ) D i t j n i,j v j , (2) KE = ρ wat π 6 t i,j n i,j D 3 i v 2 j S eff (D i ) , (3) where S eff (D i ) is the sampling area of the device. In the data presented in the paper for the Parsivel 2 , we used S eff (D i ) = The rainfall simulator, with its two separate networks for light and heavy rainfall, is indicated in yellow.
Database This section is actually quite similar to the corresponding one of  Gires et al. (2018a). It contains a description of the database content along with some available scripts. The dataset for this campaign can be found at https://doi. org/10. 5281/zenodo. 3347051 (Gires et al. , 2019). The main differences are the addition of the computation of KE and changes in the calendar, which does not provide links to daily data but to individual tests. The database is organized as follows: -disdrometers_data_base/ -Raw_data_zip/ • Pars1/ • Pasr2/ • Each folder contains the files for its disdrometers. • The name is Raw_DisdroName_YYYYMMDD. zip (e. g. Raw_pars1_20170926. zip). -Daily_data_python/ • Pars1/ • Pasr2/ • Each folder contains the files for its disdrometers. • The name is DisdroName_raw_data_YYYYMMDD.
csv (e. g. Pars1_raw_data_20170926. npy). -Exports/ • Full_matrix/ • KE/ • R/ • Each folder contains the files for all the disdrometers. • The name is DisdroName_DataType_date. csv (e. g. Pars1_KE_30_sec_2017_09_26_00_00_ 00__2017_09_26_23_59_30). -Calendars/ • Data_5_min/ (one file per day; e. g. R_5_min_Sense_ City_2017_09_26_00_ 00_00__2017_09_26_23_59_30. csv • Data_30_sec/ (one file per day; e. g. R_30_sec_Sense_City_2017_09_26_00_ 00_00__2017_09_26_23_59_30. csv) • Calendar_Sense_City. html -Python_scripts/ • It contains the Python scripts (and associated files) to generate and use this database. -Read_me. txt • It contains a short description of the Taranis Sense-City database. It also enables access to the daily quicklooks for each day (there are obviously numerous missing data, since disdrometers were only turned on during the actual tests), as well as of the corresponding rainfall times series with 30 s and 5 min time steps.
"/Raw_data_zip" This folder is actually very similar to the corresponding one of  Gires et al. (2018a) , so the description will simply be repeated: "It contains a folder for each of the three disdrometers. Each of these folders contains a zip file for each day. This. zip file contains the data directly collected from the disdrometer. There is one text file for each 30 s time step. The precise format of these fields can be found in the Python scripts in the heading of the corresponding functions. This corresponds to the raw data. These data have been made available for expert users, but in practice it is believed that the. csv file or the Python scripts should be sufficient for most users. ".
Python scripts/ Again this section is very similar to the corresponding one of  Gires et al. (2018a) , with the addition of the function to export KE. This folder contains some Python scripts that can be used to carry out some initial analysis and data treatment with the database. The tools are located in the script "Tools_data_base_use_Sense_City_v4. py". The main functions are (only a short description is given here -more details, including precise description of the inputs and outputs of the functions, are provided as comments in each script): -Quicklook_and_R_series_generation_Sense_City_ without_PWS generates a quicklook image and the corresponding 30 s and 5 min rain rate time series for a given rainfall event for the Sense_City campaign. -extracting_one_event_Sense_City reads daily.
npy files and generates three lists (one for each disdrometer) containing all the data that can be analysed for the Sense_City campaign. -exporting_full_matrix reads daily NPY files and exports the full matrix in CSV files for a given disdrometer and event. -exporting_R readings daily NPY files and exports R in CSV files for a given disdrometer and event. -exporting_T reads daily NPY files and exports T in CSV files for a given disdrometer and event. -exporting_KE reads daily NPY files and exports KE in CSV files for a given disdrometer and event. Commented examples of use of the functions can be found in the scripts: "Example_of_use_data_base_sense_city. py". Note that Python 3 (https://www. python. org/, last access: 24 March) is required because the NPY files containing the data were saved using Python 3.
Data availability The database presented in this paper has been made available by the Hydrology, Meteorology and Complexity laboratory of École des Ponts ParisTech (HM&Co-ENPC) and the Sense-City consortium at https://doi. org/10. 5281/zenodo. 3686594 (Gires et al. , 2019). The following citation should be used for every use of the data: -The following citation should be used for this paper:  Gires et al. (2020) , https://doi. org/10. 5194/essd-12-835-2020. -The following citation should be used for the database:  Gires et al. (2019) , https://doi. org/10. 5281/zenodo. 3686594. This dataset is available for download free of charge. Licence terms apply. Finally it should mentioned that these disdrometers and others have been and are used in other measurement campaigns by HM&Co. Regular updates of their status along with updates of the database are to be provided through the lab's website (https://hmco.
Conclusions The 30 s disdrometer data from a measurement campaign beneath a rainfall simulator are presented in this paper. Raw data as well as Python-formatted data with the associated scripts for basic manipulation are described and made available to the community for further use. In order to discuss the features the rainfall generated by the simulator, an illustrative comparison is made with actual rainfall. It appears the properties of the rainfall generator remain steady over time, which is the desired quality. In terms of more refined properties, the drop size distribution generated is thinner than actual rainfall and centred on smaller drops. In addition the height of the simulator is not sufficient for larger drops (> 1 mm) to reach their terminal fall velocity. As a consequence the kinetic energy of the simulated rainfall is smaller than in actual rainfall for a similar rain rate.
Figure3displays a snapshot of the page "Calen-dar_Sense_City. html" of the database. It summarizes the campaign and gives access to quicklooks of the measurements for each location and rainfall intensity. These quicklooks can also be directly accessed in the folder Quicklooks/. It also enables access to the daily quicklooks for each day (there are obviously numerous missing data, since disdrometers were only turned on during the actual tests), as well as.
Acknowledgements. The authors greatly acknowledge partial financial support from the  Chair of Hydrology for Resilient Cities  (endowed by  Veolia) of the École des Ponts ParisTech , and the  Îlede-France region RadX@IdF Project. We also want to thank  ANR , the  French Funding Agency, for supporting Sense City Equipex. Financial support. This study received financial support from the  Chair of Hydrology for Resilient Cities  (endowed by  Veolia) of the École des Ponts ParisTech , and the  Île-de-France region RadX@IdF Project. Review statement. This paper was edited by  Scott Stevens  and reviewed by two anonymous referees.
, 2017). The Deblur workflow is a novel method for obtaining sequences that describe community composition at the sub-operational taxonomic unit (sOTU) level using Illumina error profile  (Amir et al. , 2017). A total of 1,110,070 reads remained across the 280-sample dataset with 2,016 unique sequences from the Deblur workflow. The loss of ∼80% of reads in the workflow likely reflects the large proportion of host coral mitochondrial sequences (<250 base pairs) amplified by the primers, which is a known issue in using the 515F-806R primers on coral tissues. The resulting sOTU table from the Deblur workflow was processed in QIIME 2 2019. 7  (Bolyen et al. , 2019). Taxonomy was assigned with the q2-feature-classifier plugin  (Bokulich et al. , 2018)  which employs the classify-sklearn naïve Bayes taxonomy classifier against the Silva 132 99% OTUs reference sequences from the 515F/806R region  (Quast et al.
, 2012). Next, sOTUs were removed from the dataset if they annotated as mitochondrial or chloroplast sequences or were only present in a single sample further reducing the number of reads per sample to a median value of 1,210 with a variance of 1. 2. The remaining sOTUs were aligned with mafft  (Katoh et al. , 2002)  (via q2-alignment) and used to construct a phylogeny with fasttree2  (Price et al. , 2010)  (via q2-phylogeny). Alpha rarefaction curves were visualized using the q2-diversity plugin to pick a minimum frequency of 881 reads per sample as a sufficient rarefying depth (Supplementary Table  1  and Supplementary Figure  1 ). The sOTU table was rarefied resulting in 159 remaining samples with unbalanced replication across treatments and coral hosts (Table  1 ) using the package phyloseq (v1.
In addition, Permutational Analyses of Multivariate Dispersions (PERMDISP;  Anderson, 2006)  were used to test for homogeneity of multivariate dispersions between groups. PERMANOVA and PERMDISP were performed using the functions adonis and betadisper in the package vegan (v2. 5. 5) followed by a pairwise analysis of variance with pairwiseAdonis (v0. 01) and permutest in vegan, respectively, with FDR adjusted p-values. The betadisper command also was used to calculate the distance to centroid for each sampling group. All analyses were initially conducted on all microbiome data controlling for host taxa so that patterns of change driven by time and treatment were assessed across all samples with coral genus (Porites, Acropora, or Pocillopora) as an independent variable. When there was a significant interaction between treatment and coral genus, analyses were repeated for each individual host genus to discern differences in main effects between coral genera that may have been masked when all genera were combined.
Sea Surface Temperatures, Thermal Stress, and Nitrogen Exposure The 2015/2016 El Niño event increased the probability that corals would experience thermal stress and bleaching, providing us with   (Pratchett et al. , 2013). Thus, for a total of 45 days, including 37 consecutive days from mid-March to mid-April, corals at our site experienced thermal stress sufficient to cause bleaching. Average monthly SST is reported in Table  1  and a graph of average daily temperatures during the experiment can be found in Supplementary Figure  2. Of the corals analyzed here, only seven colonies bleached (Acropora: n = 6, Porites: n = 1) in May 2016 all of which had no signs of bleaching in July 2016 (Supplementary Table  1 ). Due to this low sample size of bleached corals and the absence of bleaching-induced mortality in the dataset, bleaching was not included in statistical analyses.
Bacterial Community Composition Varied Over Time The dominant bacterial taxon in the dataset (n = 159) belonged to the genus Endozoicomonas (mean relative abundance 0. 448 ± 0. 033 SEM); this genus was present in all but 21 samples (Figure  1 ). The next most abundant taxa across the dataset belonged to the genera Vibrio (0. 060 ± 0. 011), Acinetobacter (0. 059 ± 0. 008), Pseudomonas (0. 049 ± 0. 006), and Candidatus Amoebophilus (0. 038 ± 0. 011). Generally, Endozoicomonas relative abundance was lowest in March (0. 277 ± 0. 050) and July 2016 (0. 186 ± 0. 047) and highest in January 2017 (0. 817 ± 0. 058) for all corals combined. Despite low replication for Pocillopora samples in May of 2016 (Table  1 ), coral samples from all three genera had high relative abundance of Endozoicomonas. The decrease in relative abundance of Endozoicomonas in March, May, and July coincided with an increase in the relative abundance of minor taxa including Vibrio, Pseudomonas, Staphylococcus, and Halobacteriovorax, all of which decreased or disappeared in January 2017 (Figure  1 ).
Previous work supports the hypothesis that the coral host and microbiome have parallel responses under stress  (Ziegler et al. , 2017). Our selection of samples that survived the 2016 bleaching event may have inadvertently biased our dataset to corals that did not bleach (bleached n = 7). This may have prevented us from detecting any effects by nitrogen on the microbiome that parallel the significant interaction between temperature and nitrate and the significant differences between nitrate and urea observed in the coral host response  (Burkepile et al. , 2019). Our results suggest that thermal stress likely overwhelmed the coral microbiome such that additional nutrient stress had no measurable effect. We found no significant interactions on microbiome diversity between nitrogen enrichment and increased seawater temperatures.
ACKNOWLEDGMENTS We thank  M. Anskog ,  A. Duran ,  C. Fuchs ,  K. Landfield ,  S. Leung ,  K. Neumann ,  K. Seydel ,  A. M. S. Correa ,  A. T. S. Tang ,  A. Thurber ,  R. Welsh , and  S. Wise  for field and laboratory assistance. We are grateful to  T. Lamy  for his assistance in preliminary data analysis. Research was completed under permits issued by the  Territorial Government of French Polynesia  ( Délégation à la Recherche ) and the  Haut-commissariat de la République en Polyneésie Francaise (DTRT)  (Protocole d'Accueil 2015-2017).
FUNDING This work was funded by the  National Science Foundation  Grants # 1442306  to RV, # OCE-1635913  to RV, # 1314109-DGE  to RLM, # OCE-1619697  to SH, DB, and  RS ,  OCE-1547952  to DB, and  OCE-1236905  and  OCE-1637396  for the Mo'orea Coral Reef LTER to RS and SH. This work was also funded by the  Swiss National Science Foundation  Fellowship # P400PB_ 183867  to LE.
Data We obtained migraine data from the International Migraine Headache Genetics Consortium. For migraineall, this GWAS is a meta-analysis of 22 different cohorts. 5 or migraine with aura and migraine without aura, the GWAS excluded the 23andMe cohort, leading to a smaller sample size for the migraine subtypes. We obtained the UK Biobank GWAS from [https://data. broadinstitute. org/alkesgroup/UKBB/]. We used the effective sample sizes (N eff ) provided with the data for these traits. For our calcium analysis, we could only obtain genomewide calcium summary statistics for the discovery analysis from O'Seaghdha et al. 26  However, summary statistics for the meta-analysis from O'Seaghdha are available for their lead single nucleotide polymorphisms (SNPs). Therefore, we used the statistics from the meta-analysis for our calcium MR, and used the discovery analysis statistics for calcium at each of the diastolic blood pressure instrumental variables in the multivariable MR analysis.
Mendelian randomization We performed our Mendelian randomization analyses using the MR-base R package. 48 We generated the instrumental variables for each BOLT-LMM GWAS trait using the clump_data feature with default parameters, and filtered out SNPs with INFO scores below 0. 9. For the remaining GWAS, we obtained instruments using plink's clump_data function with a R 2 < 0. 001. 51 For the MR-PRESSO analysis, we used the same instruments as in our standard MR analysis. We used default parameters, except we increased the NbDistribution parameter until MR-PRESSO could compute empirical P-values. 14.
Genetic correlation with stroke resulted in a P-value of 0. 25 (Supplementary Table  6 , available as Supplementary data at IJE online). These analyses suggest that cardiovascular-related traits, like blood pressure or lipids levels, rather than the specific disease endpoints, may be driving the genetic correlation observed in the UKB analysis. We then turned to evaluate genetic correlation between measurements of blood pressure and migraine. We found a compelling genetic correlation between diastolic blood pressure and migraine (r g ¼0. 1, P ¼ 5. 4 Â 10 À5 , Figure  1 ; Supplementary Table  1 , available as Supplementary data at IJE online) with nominal significance in migraine with and without aura. We attempted replication of the genetic correlation finding in a meta-analysis of GWAS of blood pressure, which included individuals from the Million Veterans Project and the International Consortium of Blood Pressure (ICBP).
Genetic correlation between migraine and psychiatric and educational attainment traits Next, we measured the genetic correlation between education level and migraine. We observed an inverse genetic correlation between migraine and both years of education (r g ¼ À0. 09, P ¼ 2. 0 Â 10 À5 ) and having a college or university degree (r g ¼ À0. 12, P ¼ 1. 1 Â 10 À9 ) (Figure  1 ; Supplementary Table  1 , available as Supplementary data at IJE online). Replication of the genetic correlation in the largest GWAS for educational attainment to date was strong (r g ¼ À0. 11, P ¼ 1. 9 Â 10 À8 ) (Supplementary Table  10 , available as Supplementary data at IJE online). 20 We next tested for a genetic association between cognitive performance and migraine using LD score regression but did not observe association (Supplementary Table  10 , available as Supplementary data at IJE online).
8 Observational studies have also found a correlation between the occurrence of these diseases. 41 However, consistent with what we report here, Pickrell et al. found no genetic correlation using cross-trait LD score regression on their migraine dataset. One possible explanation for the discordance between these crosstrait LD score results and other types of evidence could be that heart disease is not pleiotropic enough for a highpowered genetic correlation analysis. An additional explanation is that coronary artery disease and migraine share only a subset of causal single nucleotide polymorphisms (SNPs), diluting the genetic correlation signal. Consistent with our results, lipid levels have been previously associated with migraine severity. 42, 43 We also observed a novel positive genetic correlation with type 2 diabetes.
9 Fe 0. 1 O 2. A detailed comparison of these two samples is provided in Figure  1d -f, revealing that Ce is purely tetravalent  [20, 21]  in both cases, while Fe is trivalent  [21, 22]  in Ce 0. 9 Fe 0. 1 O 2 (see Supporting Information for a detailed discussion of the spectra). For a further analysis of the oxidation states and local structure, we performed X-ray absorption spectroscopy (XAS) measurements, collecting data at both the Ce L III -edge and Fe K-edge. The XANES spectra at the Ce L III -edge of CeO 2 and Ce 0. 9 Fe 0. 1 O 2 (Figure  1g ) confirm that Ce has the same local environment in both phases. [23] The position and the shape of the main absorption edge indicate that Ce is in its tetravalent state (Figure  S2a , Supporting Information). Furthermore, results from the EXAFS region, which can be fitted according to the model obtained from the XRD data, and the unit cell parameters as estimated on the basis of the Ce-O distance, are in good agreement with the results obtained by XRD (Figure  S2b ,c, Table  S2 , and Table  S3 , Supporting Information).
As a matter of fact, the crystal structure is very well preserved after a complete dis-/charge cycle as confirmed by the ex situ HRTEM micrographs depicted in Figure  4c, d. Cycled Ce 0. 9 Fe 0. 1 O 2 reveals the same lattice fringes as the pristine sample, i. e. , 0. 31, 0. 27, and 0. 19 for the (111), (200), and (220) planes, respectively (Figure  4c ), as well as an essentially perfect atomic ordering even at the very edge of the particle (Figure  4d ). To complement this study, ex situ XAS analysis was performed. XANES spectra were recorded at the Ce L III -edge of CeO 2 and Ce 0. 9 Fe 0. 1 O 2 before and after lithiation and are presented in Figure  4e ,f and Figure  S2a  (Supporting Information). For both cases, the XANES data clearly show a complete reduction of Ce 4+ to Ce 3+ (Figure  S2a , Supporting Information).
9 Fe 0. 1 O 2 -C/LNMO lithium-ion full-cell presented herein (based on the mass of the anode and cathode active material) and earlier reported lithium-ion batteries (LIBs; marked with colored triangles) as well as lithium-ion capacitors (LIC; marked with colored asterisks). The corresponding data are also listed in Table  S6 , Supporting Information. this result by itself is already remarkable, we may anticipate that particularly the highly reversible, selective reduction of the dopant on the atomic scale within the maintained crystalline CeO 2 lattice will allow for reconsidering the introduction of (transition) metal dopants in insertion-type battery materials and, ideally, also for the investigation of metallic atoms and their general properties as such when being confined in physically stable host structures.
Ex Situ XANES and EXAFS Analysis: For the ex situ XANES and EXAFS analysis of Ce 0. 9 Fe 0. 1 O 2 and CeO 2 , half-cells were discharged at 20 mA g -1 to 0. 01 V and then opened in an argon-filled glove box. The electrodes were rinsed with DMC to remove any residual electrolyte, then dried and sealed within polyethylene (PE) foil to avoid any potential air contamination during the transport to the synchrotron and during the measurement. DFT Calculations: DFT calculations have been conducted with the Vienna ab initio simulation package (VASP). [57] VASP is a periodic plane wave code, in which the electron-ion interaction is described by pseudopotentials for effective computation. Spin-polarized calculations were conducted using the projector augmented wave method,  [58]  while the exchange and correlation were accounted for by the generalized gradient approximation in the formulation of Perdew, Burke, and Ernzerhof.
Acknowledgements Financial support from the  Chinese Scholarship Council  (CSC; Y. -J. M. and Y. M. ), the  Vector Foundation  within the framework of the  NEW E 2 project , and the  Helmholtz Association  is kindly acknowledged. Part of this work was performed on the supercomputer ForHLR, funded by the  Ministry of Science, Research and the Arts Baden-Württemberg  and by the  Federal Ministry of Education and Research. Furthermore , the authors would like to thank  Oliver Mendoza Reyes  and  Ruihao Gong  for conducting the UV-vis experiments including the very valuable discussion. LISA  CRG  staff is kindly acknowledged for the provision of in-house beamtime on BM08. The authors also acknowledge  DESY (Hamburg, Germany) , a member of the  Helmholtz Association HGF , for the provision of experimental facilities.
HEPB-adapted derivatives were confirmed as B. vietnamiensis strain G4 by random amplified polymorphic DNA (RAPD) analysis  (50). Analysis of growth dynamics. The growth dynamics of the wild-type B. vietnamiensis strain G4, HEPB-adapted derivatives of strain G4, and P. aeruginosa (ATCC 19429) were evaluated using a broth dilution assay in a Bioscreen C microbiological growth analyzer (Labsystems, Finland). Starting cultures were standardized by OD as described by Rushton et al. (2) , and quadruplicate 200-l cultures in the multiwell plate were inoculated with 1 ϫ 10 6 CFU. Turbidity readings were taken using a wide-band filter (450 to 580 nm) every 15 min after shaking of the microplates for 10 s at medium amplitude. Experiments were repeated to obtain three biological replicates. The mean ODs of the uninoculated media were subtracted from those of the test wells, and the data were transformed by log 10 ϩ1 to obtain a log 10 -adjusted OD for growth curve analysis.