dataesr/scientific-paragraphs-categorization huggingface dataset:
Funding information R. B. is supported by the  International Leibniz Research School for Microbial and Biomolecular Interactions (ILRS)  and  Jena School for Microbial Communication (JSMC, DFG). The financial support of the  Boehringer Ingelheim Foundation , the  Daimler Benz foundation  and the  German research Foundation  ( CRC 1127 ,  ChemBioSys  and  BE-4799/3-1 ) to C. B. is greatly acknowledged.
5. 1) via Mascot Daemon. MS/MS data acquired on yeast histones were matched to a database made up of yeast proteins from Swiss-Prot and their decoy version (13,458 protein sequences), plus a list of about 500 contaminants commonly observed in MS analyses (keratins, trypsin, etc. ). The following modifications were considered as variable ones: N-terminal protein acetylation; Lys acetylation; Lys and Arg mono-and di-methylation, Lys trimethylation and Ser and Thr phosphorylation. For all Mascot searches, the tolerance on mass measurement was set to 5 ppm for peptides and to 0. 6 Da for fragment ions. Up to five tryptic missed cleavages were allowed for histones H2A, H2B and H4, whereas allowing up to 3 missed cleavages was sufficient for H3. It is uncommon to consider so many variable modifications and possible tryptic missed cleavages; however, this does not impact the Mascot score indicative of the unlikelihood that the suggested sequence/spectrum match is a random event.
Paired identifications of sequences HLQLAIR and HLQLAIRme2 of which we provide the MS/MS spectra annotated by Mascot are such examples (Additional file 3). MS/MS data quantification Quantification of the abundance of modified peptides was performed using a label free method consisting of measuring the MS intensity of the corresponding chromatographic peak at the top of the curve. Normalization was done by dividing the raw MS signals of modified peptides by the signals of reference non-modified peptides for each histone, namely HLQLAIR and AGLTFPVGR for H2A, KETYSSYIYK and ETYSSYIYK for H2B, STELLIR for H3 and ISGLI-YEETR and DNIQGITKPAIR for H4. Raw and normalized data are presented in Additional file 5.
Funding This work has been funded by the  National Institute of Health  ( R01 GM055360  and  HD06817  to SLB,  F32HD086939  to LJL), the  Fond d'Intervention of the University Grenoble Alpes  (to JG), the  Agence Nationale de la Recherche  ( ANR-11-PDOC-0011  to J. G. ,  ANR-14-CE19-0014-02  to D. P. ), and the  European Union   FP7 Marie Curie Action "Career Integration Grant " (304003 to J. G. ),  Fondation pour la Recherche Médicale  ( SPF20140129159  to E. G. O. ). This study also received financial support from the  French National Research Agency   ANR-10-INBS-08   ProFI (Proteomics French Infrastructure ) and  GRAL  project ( ANR-10-LABX-49-01 ).
86 metric tonnes per day (or 313 metric tonnes per year), equivalent to 42,840 cubic feet per day of natural gas, and a daily gas consumption of approximately 214 U. S. households. Moreover, the surveys and analyses done for Danbury and New London also reveal problematic leaks, particularly for Danbury with an estimated 3. 6 leaks per mile. Although road miles covered in New London were more limited, the survey revealed leak-prone areas, albeit with a range of methane readings lower than those in Hartford and Danbury. Data collection methods for all studies is first reported here and are readily transferable to similar urban settings. This work demonstrates the actionable value that can be gained from data-driven evaluations of urban pipeline performance, and if supplemented with a map of leak-prone pipe geo-location, and information on pipeline operating pressures, will provide a spatial database facilitating proactive repair and replacement of leak-prone urban pipes, a considerable improvement compared to reactive mitigation of humanreported leaks.
While confirming that each leak is from a methane pipe is beyond the scope of this study, the leak indications found were too numerous to have significant representation from sewer pipelines, wetlands or landfills. Surveys were conducted mid-February through late March 2016 in Hartford, and again in mid-late March in 2019 in Hartford, Danbury, and New London, CT, a timing that suppressed alternative methane emission signals from possible wetland, landfill or other subsurface sources, because of cold temperatures. Danbury and New London were selected for surveys owing to a similar aging infrastructure, and in order to test the hypothesis that similar leak-prone pipeline data would be observed. The 2016 survey in Hartford was nearly a complete census of all 225 road miles in the town (Connecticut Department of Transportation, 2014), whereas the 2019 surveys done in Danbury and New London were samples of road miles in each town, targeting areas in which pipelines are most likely to be present, based on the judgment of the field team.
To avoid double-counting leaks that were driven past multiple times, a procedure was used to eliminate multiple outliers within a spatial window of 30 m radius from the highest peak methane concentration in the vicinity. A spatial window was used from as small as 5 m up to 30 m. It was found that there was relative insensitivity of the total leak count in this range, while apparent leak count decreased substantially in window sizes above 30 m. Since vehicle lane widths are generally approximately 10 m or less, the 30-meter window is large enough to prevent doublecounting but small enough to avoid incorrectly combining separate observed leaks into one. Once each town's survey was complete, the corresponding data files described above were created (. DAT and. KML) for use in subsequent analysis, the essential steps of which are outlined in the Appendix, which also contains the file control lists, software (R code) for processing the data files, and the resulting outlier files (predicted leaks).
Results from 2016 study In the 2016 study, a total of 716 distinct methane leaks over 225. 45 road miles in Hartford were detected, resulting in a leak frequency of 3. 2 leaks per road mile. This leak frequency compares to 4. 3 leaks per mile previously discovered in Boston, MA  (Phillips et al. , 2013). Over a one-year period covering the same area (15 October, 2015 to 15 October, 2016), PURA recorded138 leaks (data provided by PURA). When the researchers who performed the 2016 study compared the number of leaks found in a period of weeks to the number reported by gas companies that are recorded and monitored by PURA over an entire year, they objectively measured approximately five times more leaks than were recorded by PURA. The difference between the number of leaks identified by pro-active, objective measurement is significantly larger than those found by incidental finding or report.
LDCs are required by PURA to record any leak reported by customers or employees into their data. Any hazardous leaks (e. g. , Grade 1) are repaired immediately. Leaks that represent a potential future hazard (e. g. , Grade 2) are periodically monitored and scheduled for repair. Grade 3 (the lowest risk category) leaks are monitored annually, but data are not available in detail. A preliminary estimate of the leakage rate from the leaks found during the survey was made using the leak size distribution data from a prior Boston study  (Hendrick et al. , 2016). Assuming leaked natural gas volume from the Hartford pipes had the same distribution found in the Boston study, with a log-normal average of 1. 2 kg CH 4 per day, a preliminary estimate would find 0. 86 metric tonnes/42,840 cubic feet of gas was lost to the atmosphere each day, resulting in 313 metric tonnes per year.
Recently we have developed a universal running model which builds on concepts in exercise physiology, depends only a minimal set of key performance indices that are required to predict race performance, contains no additional individual-dependent quantities and has been validated with running world-records  14. Here, we show that it is also possible to obtain novel insights into individual's running performance by applying this model to big exercise datasets. Exercise data are a valuable source of information about individual long-term training protocols. Endurance training leads to a wide spectrum of physiological responses. However, in practice, training is prescribed often only by anecdotal evidence and personal experience. This might be due to a lack of knowledge of statistically significant correlations between the relevant physiological parameters and training characteristics for large groups of individuals with different fitness status.
Unfortunately, we cannot determine from the available data set if performance was achieved during an actual racing event. For our approach however, it is only required that the recorded performance corresponds to the maximal effort over a given running distance achieved during the racing season. Exercise data. An overview of the data analysis design is provided in Fig. 1. All available subjects and activities in the data set of the exercise tracking platform were grouped by SID and marathon date, combining all individual running activities during the 180 days before the marathon, defining a season. For each season, activities with the fastest time for the four fixed race distances defined a racing season. We imposed the condition that each racing season contains at least two races. If a season contained 30 or more total running activities they were defined as training season.
| Data collection and processing Wandering albatrosses on Possession Island, Crozet Islands (46°24ʹS; 51°46ʹE) and Bird Island, South Georgia (54°00ʹS, 38°03ʹW) were individually marked and sexed from field observations (size and plumage, copulatory position) or from genetic analyses. GPS loggers (IgotU 120/600 Mobile Action Technology © ) were deployed on incubating albatrosses from Crozet in 2010-2016 (n = 276) and from South Georgia in 2012 (n = 42), and in 2016, X-GPS radar loggers were also deployed on birds at Crozet (n = 47; see  Weimerskirch, Filippi, Collet, Waugh, & Patrick, 2018  for details). IgotU devices were programmed to record a location (longitude and latitude) every 15 and 25 min, at Crozet and South Georgia, respectively, and X-GPS devices recorded locations at a much finer sampling rate (1 Hz).
| Wind data We obtained hourly zonal (i. e. latitudinal, V u ) and meridional (i. e. longitudinal, V v ) wind speed components from the European Centre for Medium Range Weather Forecasts (ECMWF) ERA5 reanalysis dataset (Copernicus Climate Change Service (C3S), 2017). Data were available at a spatial resolution of 0. 28°, corresponding to around a 15-30 km resolution given the latitudes used by the tracked birds. We selected winds at 10 m a. s. l. as this is similar to the mean observed flight height (8 m;  Pennycuick, 1982). Wind data nearest in time to each tracking location were extracted using the raster package  (Hijmans et al. , 2019). We computed wind speed and direction at each location from zonal and meridional components and calculated flight direction relative to wind direction (hereafter relative wind direction), which was the absolute difference between the bearing of the bird and wind direction, scaled to between 0° (tailwind) and 180° (headwind) to remove directionality.
| Comparing wind conditions We first assessed whether wind speeds experienced by birds varied by population and sex using linear mixed effects models (LMMs) in the Lme4 package  (Bates et al. 2015). Wind speed was modelled as the response variable with a Gaussian error distribution, and the factors population, sex, their two-way interaction and year included as covariates. A random effect of trip identity nested within individual identity was included to account for variation in the number and duration of trips per individual respectively. We performed multi-model inference on the full set of predictor combinations and assessed the best-supported model as that with the lowest Akaike Information Criterion (AIC). As small differences in AIC are not considered to be meaningful  (Burnham & Anderson, 2002) , if multiple models were within two AIC units, the best model was deemed to be that which had the fewest number of parameters (i.
| Behavioural classification We fitted three-state HMMs to the interpolated tracks within the momentuhmm package  (McClintock & Michelot, 2018)  in order to: (a) identify states as proxies of discrete behaviours and (b) model the effect of covariates on the probability of transitioning between states  (Grecian, Lane, Michelot, Wade, & Hamer, 2018; Patterson et al. , 2009). We considered the three states using two input variables, step lengths and turning angles; directed flight (high speeds, shallow turning angles), ARS (moderate speeds, moderate to wide turning angles) and rest (low speeds, shallow to moderate turning angles). A gamma distribution was chosen for step lengths and a von Mises distribution for turning angles. We used the Viterbi algorithm to estimate the most likely sequence of behavioural states from the fitted model  (Rabiner, 1989).
2019) , the likelihood of encounter is substantially lower and so is less likely to influence our results  (Corbeau et al. , 2019; Weimerskirch et al. , 2018). As foraging and flight behaviour of albatrosses varies by day and night  (Phalan et al. , 2007; Weimerskirch et al. , 1997) , for each GPS location we also assigned daylight (including civil twilight, when the sun is 6° below the horizon) or darkness using the r package maptooLs  (Bivand et al. , 2020)  and included this variable (hereafter LoD) in models to test for different responses to wind by day and night. We ran a series of HMMs including all combinations of the covariates sex, wind speed, LoD, as well as the two-way interactions between wind speed and LoD, and wind speed and sex. For models which included wind speed, three further model combinations were run, including (a) the quadratic effect of wind speed to capture non-linear responses, (b) relative wind direction and (c) relative wind direction and its interaction with wind speed.
| Model limitations State classifications were remarkably similar between the two populations suggesting that the models were biologically meaningful, however, we acknowledge that due to the GPS sampling resolution, we could not resolve behaviour at finer scales. Moreover, global circulation models are designed to represent an averaged state of the atmosphere and currently do not resolve wind patterns on micro-and mesoscale levels, for example, from turbulence and gusts (Shamoun-  Baranes et al. , 2017). As biases were similar across groups (sex and population) and as major transitions between resting and flight (either directed flight or ARS) were still detected, these factors should not have material effects on our results. When we compared HMM-assigned states to finer scale immersion activity for South Georgia birds, the two datasets accorded well for directed flight and rest, but during ARS flight we were not able to capture sequences of take-offs and landings, likely associated with prey capture  (Weimerskirch et al.
Island and Bird Island, in particular Hannah Froy for considerable assistance with GPS tracking. We thank Dominique Fillipi for developing the X-GPS loggers, Jeff Zeyl and Emiel van Loon for useful discussions, Andy Wood, Karine Delord and Dominique Besson for database support and the European Centre for Medium Range Weather Forecasts (ECMWF) for making modelled wind data available. We would also like to thank the two anonymous reviewers for their constructive comments, which helped improve the manuscript. The Institut Polaire Français and the Terres Australes Antarctique Françaises (TAAF) provided logistical and financial support at the Crozet Islands. T. A. C. , S. C. P. , R. J. , S. C. T. , M. B. , O. d. O. and J. A. were funded by a Human Frontier Science Program Young Investigator Grant (SeabirdSound; RGY0072/2017).
GPS data can be downloaded from the BirdLife International Seabird Tracking Database (http://seabi rdtra cking. org/mappe r/; dataset ids: 1133, 1134, 1135, 1136, 1137, 1138, 1503). The datasets and R codes to run analyses and reproduce Figures  2, 4  and 5 , are freely available on Zenodo https://doi. org/10. 5281/zenodo. 3824064 (Clay et al. , 2020). The ECMWF ERA5 data are available through the.
By iteratively building genome trees for each clade, we significantly improved the initial automated assignment, frequently up to the genus level. We recovered MAGs from most of the termite-specific clusters in the radiation of, for example, Planctomycetes, Fibrobacteres, Bacteroidetes, Euryarchaeota, Bathyarchaeota, Spirochaetes, Saccharibacteria, and Firmicutes, which to date contained only few or no representative genomes. Moreover, the MAGs included abundant members of the termite gut microbiota. This dataset represents the largest genomic resource for arthropod-associated microorganisms available to date and contributes substantially to populating the tree of life. More importantly, it provides a backbone for studying the metabolic potential of the termite gut microbiota, including the key members involved in carbon and nitrogen biogeochemical cycles, and important clues that may help cultivating representatives of these understudied clades.
Metagenomic datasets To cover a wide range of microbial diversity, we used 30 metagenomic datasets representing the main gut compartments (crop, midgut, P1-P5 proctodeal compartments of the hindgut) and main feeding groups present in higher-termites (see Table  1 ). Eight species of higher termites, identified by both morphological criteria and analysis of the mitogenome, were considered: Cornitermes sp. , Cubitermes ugandensis, Microcerotermes parvus, Nasutitermes corniger, Neocapritermes taracua, Termes hospes  (Dietrich & Brune, 2016) , Labiotermes labralis, and Embiratermes neotenicus  (Hervé & Brune, 2017). Field experiments were approved by the French Ministry for the Ecological and Solidarity Transition (UID: ABSCH-CNA-FR-240495-2; permit TREL1902817S/118). Processing of the termite samples and DNA extraction and purification were described previously  (Rossmassler et al.
, 2015). Metagenomic libraries were prepared, sequenced, quality controlled, and assembled at the Joint Genome Institute (Walnut Creek, CA, USA). DNA was sequenced using Illumina HiSeq 2000 or Illumina HiSeq 2500 (Illumina Inc. , San Diego, CA, USA). Quality-controlled reads were assembled and uploaded to the Integrated Microbial Genomes (IMG/M ER) database  (Markowitz et al. , 2014). Accession numbers and information about these 30 metagenomes can be found in Table  S1.
Genome reconstruction For each metagenomic dataset, both quality-controlled (QC) and assembled (contigs) reads were downloaded from IMG/M ER in August 2017. To obtain coverage profile of contigs from each metagenomic assembly, the QC reads were mapped to contigs using BWA v0. 7. 15 with the bwa-mem algorithm  (Li & Durbin, 2009). This generated SAM files that were subsequently converted into BAM files using SAMtools v1. 3  (Li et al. , 2009). Combining coverage profile and tetranucleotide frequency information, genomes were reconstructed from each metagenome with MetaBAT version 2. 10. 2 with default parameters  (Kang et al. , 2019). Quality of the reconstructed genomes was estimated with CheckM v1. 0. 8  (Parks et al. , 2015). Only MAGs that were at least 50% complete and with less than 10% contamination, were retained for subsequent analyses.
These MAGs have been deposited at the Sequence Read Archive (SRA) under the BioProject accession number PRJNA560329; genomes are available with accession numbers SRR9983610-SRR9984198 (Table  S2 ). Additionally, the MAGs have been deposited at the NCBI's Assembly Database under the accessions WQRH00000000-WRNX00000000 (Table  S2 ). For each MAG, CheckM was also used to extract 16S rRNA gene sequences as well as a set of 43 phylogenetically informative marker genes consisting primarily of 29 ribosomal proteins (PF00466, PF03946, PF00298, PF00572, PF00238, PF00252, PF00861, PF00687, PF00237, PF00276, PF00831, PF00297, PF00573, PF00281, PF00673, PF00411, PF00164, PF00312, PF00366, PF00203, PF00318, PF00189, PF03719, PF00333, PF00177, PF00410, PF00380, PF03947, PF00181), nine RNA polymerase domains (PF04563, PF04997, PF00623, PF05000, PF04561, PF04565, PF00562, PF04560, PF01192), two tRNA ligases (TIGR00344 and TIGR00422), a signal peptide binding domain (PF02978), a translation-initiation factor 2 (PF11987) and a TruB family pseudouridylate synthase (PF01509).
Placement of MAGs in a 16S rRNA-based phylogenetic framework All 16S rRNA gene sequences recovered from the respective bins were classified using the phylogenetic framework of the current SILVA reference database (SSURef NR 99 release 132)  (Quast et al. , 2013). The database was manually curated to extend the taxonomic outline of all relevant lineages to genus level by linking the taxonomy to the termite-specific groups to that of the DictDb v3 database  (Mikaelyan et al. , 2015b). 16S rRNA gene sequences contained in the MAGs were aligned with SINA version 1. 2. 11 (Pruesse, Peplies & Glöckner, 2012)  and imported into the reference database. Sequences longer than >100 bp were added to the reference trees using the parsimony tool of ARB version 6. 0. 6  (Ludwig et al. , 2004). If none of the MAGs in a cluster contained a 16S rRNA gene longer than 100 bp, or if the placement of the 16S rRNA genes in the bin conflicted with the results of the phylogenomic analysis (indicating a contamination), the phylogenomic classification was used.
Estimation of the relative abundance of the MAGs in each metagenome For each metagenome, raw reads were mapped against MAGs using BWA  (Li & Durbin, 2009)  with default parameters. Unmapped reads and reads mapped to more than one location were removed by using SAMtools  (Li et al. , 2009)  with parameters: F 0x904. Reads mapped to each MAGs were summarized using the "pileup. sh" script (BBmap 38. 26)  (Bushnell, 2014). The relative abundance of each MAG was calculated as the total number of reads mapped to a MAG divided by the total number of reads in the corresponding metagenome sample, as described in  Hua et al. (2019). Similarly, the MAG coverage was estimated by multiplying the mapped reads by the read length and dividing it by the MAG length.
The present study focused on these 589 MAGs, which showed on average a 38. 6-fold coverage (Table  S2 ). The number of MAGs recovered from the different metagenomes did not show a Gaussian distribution. Instead, we found a significant and positive relationship between the number of metagenome-assembled reads and the number of MAGs recovered (r = 0. 85, p < 0. 0001), indicating that assembly success and sequencing depth were important predictors of genome reconstruction success (Fig. 1 ). This is in agreement with benchmarking reports on metagenomic datasets  (Sczyrba et al. , 2017)  and underscore that a good quality assembly is a prerequisite for high binning recovery, which is important to consider when designing a metagenomic project for the purpose of binning. A significantly higher number of assembled reads and of MAGs recovered was observed in the current dataset compared to the Rossmassler et al.
MAGs taxonomy and abundance We investigated the phylogenomic context of the 589 MAGs. An initial automated classification of the MAGs using CheckM and when available, the taxonomic assignment of the 16S rRNA gene, identified representatives of 15 prokaryotic phyla (Table  S3 ). Initially, 142 MAGs (24% of the dataset) remained unclassified at the phylum level, and key taxa of the termite microbiota, such as Fibrobacteres and Treponema, were absent or only poorly represented. This is partly explained by the lack of representative genomes for certain taxa in the reference genome tree provided in the current version of CheckM (e. g. , only one Fibrobacteres genome and one Elusimicrobia genome, and an absence of Bathyarchaeota and Kiritimatiellaeota genomes). New tools incorporating larger databases, such as GTDB-Tk  (Parks et al.