dataesr/scientific-paragraphs-categorization huggingface dataset:
Sample We examined the timing of shelter-in-place orders at the county-level since the onset of the COVID-19 pandemic in the United States on January 21. Event history models estimate how different county and state features affect a county's propensity of adopting a policy on any given day. The hazard set for these models is the entirety of U. S. counties between the first record of a case of COVID-19 in the United States on January 21 and April 5, when no new counties issued new shelter-in-place orders. The first failure (i. e. , adoption of a shelter-in-place order) in our data was March 16, when seven San Francisco Bay Area counties became the first to issue a shelter-in-place order effective March 17. Because we were interested in the issue of orders rather than their effect, we determined a failure based on the date on which an order was issued, and not when it went into effect; typically, orders went into effect on the same or the following day.
Dependent variables The shelter-in-place policies of counties and states, tracked on the county-level, come from Painter and Qiu  [4] , who coded the data from the New York Times  [58]  coverage of subnational shelter-in-place policies. We then added to these datasets through systematic manual searches. We describe our search procedures in the appendix, together with a list of all 163 counties that passed shelter-in-place orders prior to their respective state. The primary outcome reported is whether a county had a shelter in place policy, but no state-level order, at any given day. We also estimated the timing of adoption of any shelter-in-place policy, regardless of whether it was issued by the county or the state, to provide a baseline estimate of what models would look like that disregard local autonomy.
In secondary analyses, we also investigate the association between the issuing of countyand state-level policies and subsequent social distancing and COVID-19 growth rates. For social distancing, we drew on the Unacast Social Distancing Scoreboard  [59]. These data included daily measures of the reduction of: a) average distance traveled and b) visits to non-essential places. To combine these two highly correlated variables (r =. 76), we created a factor variable of visits and travel distance. The Cronbach's alpha for this factor is. 86, the Eigenvalue of the first factor is 1. 76 and Eigenvalue of the second factor is. 23; both distancing measures loaded on the first factor with an equal weighting of. 94. To calculate the COVID-19 growth rates, we created a three-day moving average of daily growth in the number of cumulative cases by county.
Independent variables A large variety of datasets inform our model of the covariates of local shelter-in-place policies: (a) Demographic variables came from the American Community Survey (2018 5-year averages) and the 2010 Census. These measures include population, which we logged because of skewness, and population density based on the population by square mile drawn from the Census Our classification of counties by urbanicity followed the simplified National Center for Health Statistics (NCHS) urban-rural classification scheme  [60]. Large central metros are counties in Metropolitan Statistical Areas (MSA) with 1 million or more population and contain the largest principal city of the MSA, have their entire population contained in the largest principal city of the MSA, or contain at least 250,000 inhabitants of any principal city of the MSA.
These findings hold true despite the inclusion of a large number of controls about county demographics and economics, state policy regimes, and exposure to COVID-19. As with statelevel policies, there are statistically significant associations with the daily change in the number of total cases and the average growth rate indicative of temporal patterns. But most orders were issued when the COVID-19 case prevalence was still small (β = -1. 88, p <. 001). The Counties that passed shelter-in-place policies prior to their states have highlighted borders. The majority of local orders were passed in counties that were more Democratic than their state or spatially proximal to such a county. Map illustrates author's data using base map from Tableau, Mapbox, and OpenStreetMap made available under the Open Database License (https://www.
Acknowledgments We thank  Molly King  and two anonymous reviewers for helpful comments on earlier drafts of this manuscript. We also thank  Patrick Bergemann ,  Elisabeth Clemens ,  John W. Hanson ,  Krystal Laryea ,  Nicole Marwell ,  Scott Ortman ,  Woody Powell ,  Amanda Sharkey ,  Nick Sherefkin ,  Satej Soman , and  Austin Wright  for their advice. We are further indebted to  Olivia Paraschos  and  Ana Gonzalez  for their excellent research assistance and to  Marc Painter  and the  New York Times  for generously sharing data on local shelter-in-place orders. Map data copyrighted by OpenStreetMap contributors is openly available on https://www. openstreetmap. org.
8°C•days for the 32 batches; Figure  S5 ), as the percentage of the inoculated leaf surface covered by pycnidia (sporulating area assessed, from 0 to 100%, in increments of 5%). The complete data set is presented in supplementary file F5. A strain was considered virulent ('vir') against Stb16q when pycnidia were observed on at least one of the six leaves of Cellule (at least 5% of sporulating area on the leaf, the minimum for visual quantification in our experimental conditions). The strains were otherwise considered avirulent ('avr'). The frequency of virulent strains ݂ t,v,vir (t ∈ [t1; t2], v ∈ [A; C]) was calculated for each cultivar in each treatment (Figure  1b-d ). The effect of the treatment on the number of virulent individuals was assessed with a ߯ ଶ test, or with Fisher's exact test when the expected numbers were small, with Bonferroni correction for pairwise comparison.
, 2018; Crété et al. , 2020). Our experimental findings confirm that cultivar mixtures may also be used to extend the lifetime of resistance sources, this time at within-field scale, through the introduction of heterogeneity. File F1 (F1_Field_disease_severity_rawdata. csv). Field disease severity raw data. File F2 (F2_t2_2018_field-severity_means. txt). File F16 (F16_Severity_analysis. R). Main script for field data analysis. File F17 (F17_Severity_function. R). Function used in main script for field data analysis. File F18 (F18_Virulence_analysis. R). Main script for population-phenotyping data analysis. File F19 (F19_Virulence_function. R). Function used in main script for populationphenotyping data analysis. File F20 (F20_Realpop_analysis. R). Main script to calculate the proxy for the size of the virulent subpopulations.
Acknowledgements We thank  Christophe Montagnier  ( INRAE Experimental Unit, Thiverval-Grignon, FR ),  Nathalie Retout ,  Laurent Gerard ,  Nicolas Lecutier ,  Auriane Pinton ,  Miléna Bonnard ,  Jérôme Lageyre ,  Martin Willigsecker  and  Benjamin Boudier  ( INRAE BIOGER, Thiverval-Grignon, FR ) for technical assistance. We thank  Dr. Cyrille Saintenac  ( INRAE GDEC, Clermont-Ferrand, FR ) for providing us with the Chinese Spring isogenic lines of wheat. We thank  Dr. Romain Valade  ( ARVALIS-Institut du Végétal, Thiverval-Grignon, FR ) and  Dr. Thierry Marcel  ( INRAE BIOGER, Thiverval-Grignon, FR ) for sharing information and providing relevant advices throughout this project. We thank  Dr. Julie Sappa  for her help correcting our English.
Funding This research was supported by a grant from the  'Fond de Soutien à l'Obtention Végétale' (FSOV PERSIST  project; 2019-2022 ) and by a  PhD fellowship  from the  French Ministry of Education and Research (MESR)  awarded to  Carolina Orellana-Torrejon  for the 2018-2022 period. The BIOGER laboratory also receives support from  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
, 2016). It is typically measured as the average of the oxygen uptake rate measurements that are collected to estimate SMR, although some portions of the dataset may not be considered; for example, high rates of oxygen uptake when the animal is stressed by handling for placement in the respirometer  (Chabot et al. , 2016; Steffensen, 1989). The RMR can, in theory, lie anywhere between SMR and MMR, but it is expected to be closer to SMR if animals are undisturbed  (Chabot et al. , 2016). RMR is often used to infer a metabolic response to a stimulus or stressor (e. g. perceived predator threat)  (Hall and Clark, 2016; Palacios et al. , 2016). Also note that the abbreviation 'RMR' is sometimes used to refer to 'resting metabolic rate', a term often used as a less strict equivalent of SMR. Maximum metabolic rate (MMR) This is the maximum rate of oxygen uptake that an animal can achieve to create ATP aerobically  (Norin and Clark, 2016).
, 2016). Note that there are excellent R packages available e. g. respR (https://github. com/ januarharianto/respR;  Harianto et al. , 2019)  and FishResp (https:// CRAN. R-project. org/package=FishResp; Morozov et al. , 2020)  that are specifically designed for calculation of oxygen uptake rates using data obtained through intermittent-flow respirometry. These packages include various options for estimation of SMR, RMR and MMR, as well as corrections for background respiration. Wider use of these packages would help facilitate replication and reproducibility. Although reporting for MMR was relatively good when compared with the other criteria categories (appearing in a mean of 61% of papers across criteria), there were still important details   1. Each point represents one criterion; numbered labels correspond to criteria numbering in Table  1.
Criteria for each category were scored across n=202 studies, except for the MMR category, where n=123 (several papers did not include data for MMR, see Appendix 1). The grey line is the overall average across all criteria. Boxplot lower and upper hinges represent the 25th and 75th percentiles, respectively; the horizontal line within the box represents the median; the length of whiskers represents the range of data points between either the upper or lower hinge and 1. 5× the difference between the 25th and 75th percentiles. that were often neglected. For example, when measuring oxygen uptake immediately after exhaustion, many studies did not report whether the animal was exposed to air before placement in the chamber (criterion 45). The time taken to initiate measurements of oxygen uptake (e.
It is more important than ever to ensure the collection and reporting of reliable and replicable data, as metabolic rates are increasingly becoming a focus for understanding the ability of animals to cope with environmental and climate change. Accurate reporting of methodologies is particularly important in cases where data may be used to inform conservation efforts. The availability of a checklist of important methodological details should also be useful to new researchers entering this rapidly developing field, and we hope that our checklist will be a valuable resource to both new and experienced researchers in this area. Appendix 1 Methods for the literature survey and scoring of criteria Literature survey and criteria scoring Focussing on studies with fish, we performed a survey of the literature to determine variation in the reporting of methods and the extent to which various criteria are (or are not) reported.
In total, 202 studies from 71 journals were assessed from between the years 1993 and 2021 (data are available at Mendeley Data: https://doi. org/10. 17632/fky5n2nt9x. 2; and are also included with this submission). This consisted of 123 studies that measured both SMR (or RMR) and MMR, and 79 studies that measured or SMR (or RMR) only, without MMR. Each study was scored for whether they satisfied each criterion in the checklist. Studies were awarded a point for a given criterion if they gave a clear, unambiguous description of that methodological detail, without the need for reader assumptions or calculations. Importantly, scores were not based on the quality of a methodology itselfthey were simply based on whether a given detail was provided. For example, if a paper had stated that the respirometer was made of Swiss cheese, the criterion 'provide material of respirometer' (criterion 8; Table  1 ) would be considered satisfied and a point would be awarded, without judgement of whether Swiss cheese is an appropriate material for respirometer construction.
Statistical analysis A generalised linear mixed model (GLMM) with a binomial distribution (logit link) was constructed to examine factors affecting methods reporting across published papers. The score for each criterion per paper (0 or 1) was used as the response variable, and criteria category, scaled year, scaled journal impact factor and all interactions among these variables were initially included as explanatory variables. Paper ID (coded anonymously by title) and scorer were included as random effects. Non-significant interactions were dropped sequentially and the model re-run. All analyses were conducted using R v. 4. 0. 3 (https://www. r-project. org/) using the function glmm in package lme4 (https://CRAN. R-project. org/ package=lme4;  Bates et al. , 2015). All R scripts are available at Mendeley Data: https://doi.
Funding S. S. K. was supported by a  Natural Environment Research Council   Standard Grant   NE/T008334/1. E. A. F. C. was supported by the  Carlsberg Foundation  (Carlsbergfondet  CF19-0400 ). T. N. was supported by funding from the  European Union 's  Horizon 2020 Framework Programme  under the  Marie Skłodowska-Curie  grant agreement no. 713683. L. Z. was supported by the  Austrian Science Fund, FWF ,  Lise Meitner Program  (Project  M2742   BBL ). J. J. H. N. was supported by funding from the  European Union 's  Horizon 2020 Framework Programme  under the  Marie Skłodowska-Curie  grant agreement no. 839039. Open access funding provided by  University of Glasgow. Deposited in PMC for immediate release.
RESULTS Psychrobacter forms a clade whose basal members are of the Moraxella genus. To explore the evolutionary history of Psychrobacter, we built a phylogeny based on 400 conserved marker genes using publicly available whole genomes derived from cultured isolates of 51 species from the Moraxellaceae family. We included 18 species each of Acinetobacter and Moraxella obtained from NCBI and 15 Psychrobacter genomes that we generated in this study (see Table  S1  in the supplemental material). For all genomes, we incorporated phenotypic data collected from previously published type-strain research. The 51 species formed 3 distinct clades, each with robust bootstrap support. The Acinetobacter clade consists uniquely of Acinetobacter species (labeled A in Fig. 1A ). The Acinetobacter clade is a sister taxon to the Moraxella (M) clade, consisting entirely of Moraxella species, and to the Psychrobacter (P) clade, which contains all of the Psychrobacter species, as well as 4 Moraxella species-M.
To characterize Psychrobacter phenotypes under a number of different conditions, we assessed the strain collection for resistance to bile salts as well as ability to grow under 24 different conditions: two different media combined with four different salt concentrations and with three different temperatures (Materials and Methods). We calculated growth probabilities, or the fraction of growth-positive conditions out of total conditions tested, for every strain and given variable of the growth curve The Moraxellaceae family phylogeny was constructed with 51 diverse Moraxellaceae genomes using the software PhyloPhlAn, which constructs a phylogeny using FastTree with 1,000 bootstraps, refined by RAxML under the PROTCATG model. Amino acid sequences from 400 marker genes were used in the alignment.
Branches with bootstrap support greater than 70% are represented by filled circles. The scale bar represents the average amino acid substitutions per site. Pseudomonas aeruginosa was used as an outgroup. Clades are highlighted in colored blocks, and branches are colored by genus. Isolation source is depicted in a color strip, along with a heatmap of scaled notable genome characteristics that differ between the genera, with 0 representing the smallest value present and 1 the largest value (P. aeruginosa not included). GS, genome size, ranging between 1. 8 Mb and 4. 5 Mb. PCoA, PC1 values from a PCoA based on gene presence/absence data. CD, genome coding density, ranging from 80% to 89%. Growth temperature range data were collected from type strain publications. (B) PC1 and PC2 of a PCoA of a binary matrix of gene presence/absence for 51 species of Moraxellaceae, explaining 32% and 14% of the variation, respectively.
have functional and structural genomic differences. When exploring gene presence/absence via PCoA, accessions from the basal FE-only subclade cluster closely together, indicating similar gene content. In agreement with the phylogeny, these basal FE accessions are separated from the other accessions on PC1, while the derived FE and RE accessions are more scattered, indicating more diverse gene content (Fig. 4A ). According to an envfit analysis, the separation is most strongly driven by genes from the COG categories T, signal transduction (r 2 = 0. 65, P value = 0. 001); U, trafficking and secretion (r 2 = 0. 63, P value = 0. 001); P, inorganic ion transport and metabolism (r 2 = 0. 56, P value = 0. 001), and X, unassigned or no homologs in the COG database (r 2 = 0. 51, P value = 0. 001) (Table  S2 ).
Given the higher number of transposons in FE strains, we next examined the number of predicted pseudogenes between the ecotypes. FE genomes are predicted to have a higher proportion of pseudogenes than RE strains (WRS, W = 997, P value = 0. 03, phylogenetic P value = 0. 6) (Fig. 4C ). Finally, we compared the average numbers of genes per genome between the ecotypes, as bacterial genomes are known to strongly select against the accumulation of pseudogenes resulting in gene deletions  (71) , and found that FE strains have significantly fewer genes than RE strains (WRS, W = 547, P value = 0. 05, phylogenetic P value = 0. 6). We looked for specific gene clusters differentiating the FE and RE ecotypes while controlling for population structure by performing a microbial pangenome-wide association analysis with the R package treeWAS  (72).
This returned four gene clusters that significantly correlated with ecotype, each of which is enriched in RE strains compared to FE strains (Table  1 ). The gene with the most significant enrichment is annotated as a transcriptional regulator in the TetR/AcrR family with the KEGG KOs of K16137 and K19335, which is predicted to be either nemR or bdcR. Other significant results include lpdA, dihydrolipoamine dehydrogenase related to amino acid metabolism (KEGG KO K00382/383), a hypothetical protein with the IG domain, and a hypothetical protein with no homologs in eggNOG database and no predicted annotation. Each of these genes has paralogs that are not significantly enriched in one ecotype over the other. Polar bear feces collected from the Arctic ice have a high abundance of Psychrobacter.
b ***, the paralogs identified as significantly associated with ecotype by the pangenome-wide association software treeWAS. pacificensis was able to persist, while closely related RE strains P. namhaensis and P. okhotskensis were not. Phylogenetic placement does correlate with colonization density, however, as the two most basal strains tested, P. lutiphocae and P. ciconiae, colonize at significantly higher densities than the most derived strain that was successful, P. pacificensis (pairwise WRS, both adjusted P values = 0. 0007).
MATERIALS AND METHODS Moraxellaceae family genomics and phenotypic data. The Moraxellaceae family includes 3 wellcharacterized genera: Moraxella, Acinetobacter, and Psychrobacter. To build a family-level phylogeny, we downloaded genomes of 18 species of Acinetobacter and 18 species of Moraxella from the National Center for Biotechnology Information (NCBI) genome database (86) (June 2020) (see Table  S2  in the supplemental material). We also included 15 Psychrobacter genomes generated in this study (described below). We determined the phylogenetic relationship between genomes using the whole-genome marker gene analysis software PhyloPhlAn (87) (v2. 0, diversity = "medium. " set to "accurate") and determined genome quality and summary characteristics using CheckM (88) (v1. 0. 18) and Prokka (89) (v1.
14. 6, kingdom = "bacteria"). The Moraxellaceae phylogenetic tree was visualized and annotated using the interactive Tree of Life (iTOL) web interface (90) (v5. 6. 1). We analyzed the Moraxellaceae pangenome using the PanX pipeline (91) (v1. 6. 0, core gene cutoff = 0. 9). The input genomes used by PanX were initially annotated using Prokka. After their assignment into orthologous clusters using MCL (v14. 137), we reannotated gene clusters using eggNOG-Mapper (92) (v1. 0. 3). We explored genome content by calculating a distance matrix using the Jaccard metric through the R package ecodist (93) (v0. 3. 0) from a binary gene presence-absence table, followed by dimensional reduction of that distance matrix through principal-coordinate decomposition (PCoA) with the cmdscale function from the R package stats.
We investigated variables contributing to the separation of the PCoA using the envfit function from the R package vegan (94) (v2. 5-6); we tested whether genes significantly contributing to separation were "core" (genes present in 90% to 100% of strains), "shell" (genes present in greater than two strains, but in fewer than 90%), or "cloud" (genes present in only one strain) and if general gene function-summarized by Cluster of Orthologous Groups (COG) category (95)-contributed to the separation. We collected growth temperature range data from type strain publications  (16, 17, 20, 21, 26,. We used the R package phytools (96) (v0. 7-47) to map the temperature ranges onto the phylogeny. Psychrobacter strains. We obtained 92 isolates of Psychrobacter from strain catalogues for phenotypic and genotypic characterization.
12. 8, cov_cutoff = off, set to "careful. " minimum scaffold length = 500 bp), followed by refinement with Pilon (v1. 22, chunksize = 10,000,000). Finally, we assessed the assemblies for quality using CheckM. We assigned taxonomy using Sourmash (v2. 0. 0a4, scaled = 10,000, k = 31) and GTDB-Tk (v1. 0. 2, min_perc_aa = 10) and annotated assemblies using Prokka. For genomes that were particularly fragmented (having greater than 250 contigs), we performed additional long-read sequencing (Table  S4 ). We constructed Oxford Nanopore libraries using the ligation sequencing and native barcode ligation kits (Oxford Nanopore, Oxford, United Kingdom). We sequenced the libraries using the MinION system run with software MinKNOW (Oxford Nanopore). We basecalled and demultiplexed reads using ont-Guppy (v3.
We followed the quality control cutoffs suggested by CheckM and removed two genomes for having contamination higher than 5% and one for having completion less than 90%. We removed two additional genomes as the taxonomic classification was outside the Psychrobacter genus. These accessions were removed from genomic and phenotypic analysis. All further analyses use the 85 accessions which passed all quality control (QC) measures. For a full description of sequencing for each accession, see Table  S4. We annotated genomes with Prokka and eggNOG-Mapper. A phylogeny of the accessions was generated using PhyloPhlAn, with Moraxella lincolnii as an outgroup. Again, the phylogeny was visualized and annotated using iTOL. We used PanX to analyze the Psychrobacter pangenome and R to explore gene presence-absence data as described above with the Moraxellaceae family.