dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We thank all participants for their participation and valuable contributions. This research has been conducted using the  UK Biobank Resource  under application number  12010. The work of N. V. was supported by  NWO VENI  grant  016. 186. 125. We thank 23andMe and the  23andMe Research Team  for their contribution sharing their data and performing the GWAS analysis in the 23andMe cohort. P. V. received an unrestricted grant from  GlaxoSmithkline  to build the CoLaus study. N. J. T. is a  Wellcome Trust Investigator  ( 202802/Z/16/Z ), is the PI of the  Avon Longitudinal Study of Parents and Children  ( MRC  &  WT 217065/Z/19/Z ), is supported by the  University of Bristol NIHR Biomedical Research Centre  ( BRC-1215-2001 ), the  MRC Integrative Epidemiology Unit  ( MC_UU_00011/ 1 ) and works within the  CRUK   Integrative Cancer Epidemiology Program  ( C18281/A29019 ).
Data availability All data supporting the findings described in this manuscript are available in the article and its Supplementary Information files. The genome-wide summary statistics, excluding the 23andMe data, generated in this study have been deposited in a Mendeley database available through https://data. mendeley. com/datasets/9b725x7mvb/ draft?a=f8619d91-5c4d-4e4f-8f44-a73692a332a5. The top 10,000 genetic variants, including the 23 and Me data, can be downloaded from the same repository. The full GWAS summary statistics, including 23andMe data, will be made available through 23andMe to qualified researchers under an agreement with 23andMe that protects the privacy of the 23andMe participants. Datasets will be made available at no cost for academic use. Please visit https://research.
SOURCES OF FUNDING This work was supported by:  IHU OPeRa  ( ANR-10-IBHU-004 ) program " Investissements d'Avenir " operated by the  French National Research Agency (ANR) ,  RHU MARVELOUS  ( ANR-16-RHUS-0009 ) of  Université de Lyon , program " Investissements d'Avenir " operated by the  French National Research Agency (ANR) ,  INSERM  and  UCBL  annual funding of the  CarMeN Laboratory. DISCLOSURES None.
Electrophysiology After a delay of at least 3 weeks to allow the 6-OHDA-induced lesion to stabilize, rats (N = 70; Sham = 25; Partial DA lesion = 22; Total DA lesion = 23) were anaesthetized with an intra-peritoneal injection of ethyl carbamate (urethane, 1. 25 g/kg, Sigma-Aldrich) and placed in a stereotaxic frame. Body temperature was maintained at 37 °C with a thermostatic heating blanket throughout the recording sessions. Two stainless steel electrodes (E363-1, Plastics One, Roanoke, VA) were inserted into the left hindpaw for noxious stimulation. One was placed under the skin of the plantar surface of the foot, the other was placed under the skin of the medial aspect of the lower leg. Three craniotomies were then performed to allow simultaneous access to the STN, SNr and PBN. Extracellular voltage excursions were amplified, band-pass filtered (300 Hz-10 kHz), digitized at 10 kHz and recorded directly onto computer disc using a Micro 1401 data acquisition system (Cambridge Electronic Design [CED] Systems, Cambridge, UK) running CED data capture software (Spike 2).
Noxious electrical footshocks were applied throughout this period, until either the effects of the drug wore off, or the PBN cell was lost. After a complete trial, further PBN neurons were tested in the same way. Electrophysiological analysis. Following the recording sessions, the artefact of the stimulation was removed from our recordings using a script developed by Cambridge electronic design limited (CED, script ArtRem, https://ced. co. uk/fr/downloads/scriptsigedit), peri-stimulus time interval histograms (PSTHs) were constructed based on SNr multi-unit (bin width 1 ms) and STN/PBN single-unit data (bin width 10 ms). PSTHs were imported into an Excel program (Peter Furness, University of Sheffield  16, 18, 24, 28, 29  ) which allowed us to analyze the following response characteristics: (i) firing rate: the mean number of single/multi-unit events during the 500 ms prior to the footshock; (ii) Response latency: the latency of a nociceptive evoked response marked as the point when the value of post-stimulation events exceeded 1.
The data were imported into MATLAB, binned and compared using a Wilcoxon signed-rank test. STN/PBN firing patterns were also assessed using MATLAB according to the methodology developed by Piallat et al. 83  Neurons were classified as irregular, regular or bursting according to the interspike distributions and autocorrelograms. Burst activity showed a wide or bimodal interspike interval distribution and a significant single peak in the autocorrelation function. Irregular activity was characterized by a wide interspike interval distribution and a flat autocorrelogram. Regular activity was characterized by a narrow interval interspike distribution and an autocorrelogram with multiple regular peaks.
The slides were then developed with Kodak rapid-fix diluted at 1:6 in distilled water, dehydrated in alcohols and coverslipped in DPX. Sections were allowed to dry at room temperature for 72 h and were then cleaned and scanned. Slides stained with the Golgi-Cox technique were scanned using Axio Scan Z at 400x magnification. 10 Z-stacks with a 2. 0 μm step were taken, centered on the PBN region. Images were imported into Reconstruct software that utilizes the distinct geometric characteristics of spines as the basis for their categorization (see Risher et al. for details)  32. Dendritic sections of neurons clearly located in the PBN were analyzed. Parameters such as spine length, width and number were measured. The software then classified the spine morphology automatically based on these measurements, displaying information such as dendrite length/ width, total spine count, percentage of mushroom/ stubby/ filopodia/ branched spines, and spine density.
Electron microscopy Three rats per group (total N = 9) were examined using electron microscopy. Sections centered on the PBN were first washed with PB, post-fixed with 1% osmium tetroxide in PB, 0. 1 M pH 7. 2, for 25 min, stained in 1% uranyl acetate at pH 4 for 40 min in the dark and dehydrated through a graded ethanol series before being embedded in epoxy resin (Flukka) between two sheets of Aclar film. Samples were left to polymerize for 3 days at 60 °C. Ultrathin sections of the sample (50 nm) were cut with an ultra-microtome (Leica Ultracut). Sections were then stained in uranyl acetate and lead citrate before being viewed under a transmission electron microscope at 80 kV (JEOL 1200EX). Images of synapses were taken at 12, 000 x and 50, 000 x magnification with a digital camera (Veleta, Olympus) (n = 50 / animal) and morphometric parameters were measured with iTEM software (Olympus, Soft Imaging System).
ACKNOWLEDGEMENTS We thank  Anne Bertrand  and the personal from the animal facility for their technical contributions. This work was supported by the  Photonic Imaging Center of Grenoble Institute Neuroscience (Univ Grenoble Alpes-Inserm   U1216 ) which is part of the  ISdV core facility  and certified by the  IBiSA label. The work was funded by  Inserm , the  "Communauté de Recherche Académique", Qualité de Vie et Vieillissement» (PhD funding) , the association « France Parkinson» , the  University Grenoble Alpes  (PhD funding) and the  "Network of Centers of Excellence in Neurodegeneration (CoEN) ".
Sequencing and Bioinformatic Analysis Genomic DNA libraries and whole genome sequencing (WGS) were performed by Macrogen (Seoul, Republic of Korea) using the Illumina MiSeq platform. Raw data were provided as fastq files, using an Illumina bcl2fastq package. Data were analyzed using an inhouse pipeline. Quality analysis and trimming of reads were performed by FastQC (https: //www. bioinformatics. babraham. ac. uk/projects/fastqc/ (accessed on 31 March 2022)) and TrimGalore (https://www. bioinformatics. babraham. ac. uk/projects/trim_galore/ (accessed on 31 March 2022)). Genome assembly was performed using UniCycler (https: //journals. plos. org/ploscompbiol/article?id=10. 1371/journal. pcbi. 1005595 (accessed on 31 March 2022)) and annotated by Galaxy (https://usegalaxy. eu/ (accessed on 30 April 2022)).
Molecular Characterization of mgrB To characterize genetic modifications in the mgrB genes, gDNAs were obtained from the isolates of interest and were subjected to a polymerase chain reaction (PCR) using specific primers spanning the promoter and open reading frame (ORF) of the gene (mgrB_U111_F: 5 CGTTTTGAAACAAGTCGATGA 3 and mgrB_D248_R: 5 ATTCTGC-CGCTTTTGCTG 3 )  [67]. The obtained PCR product was purified and Sanger sequenced to identify mutations. The IS finder database (https://www-is. biotoul. fr (accessed on 31 May 2022)) was used to identify insertion sequence elements.
Acknowledgments: We are grateful to the  University Hospital Paz (Madrid, Spain)  and the  University Hospital Marques of Valdecilla (Santander, Spain)  for allowing us to use the K. pneumoniae samples. We also thank  Juan Antonio Marín-Sanz  and  Fernando Abollo-Jiménez  of the  Maimonides Biomedical Research Institute of Cordoba/University of Cordoba  for performing the assembly of the fastq sequences. The authors would like to give a special mention to  Juan J. Román-Camacho  for his critical review of the manuscript. The authors are grateful to  Martha E. Gaustad  for revising the English.
Phylogenetic models further reveal that the strength of sexual selection is explained by temperature seasonality coupled with a suite of climate-associated factors, including migration, diet, and territoriality. Overall, these analyses suggest that climatic conditions leading to short, intense breeding seasons, or highly abundant and patchy food resources, increase the potential for polygamy in birds, driving latitudinal gradients in sexual selection. Our findings help to resolve longstanding debates about spatial variation in evolutionary mechanisms linked to reproductive biology, and provide a comprehensive species-level dataset for further studies of selection and phenotypic evolution in the context of global climatic change.
2021). Furthermore, information on breeding can be coupled with uniquely comprehensive datasets on avian phylogenetic relationships, geographical distribution, life history and ecology  (Jetz et al. 2012; Tobias et al. 2020 Tobias et al. , 2022)). By combining these resources for all birds, along with GIS-based mapping and Bayesian models, we test key hypotheses about fundamental drivers of macroevolutionary patterns in sexual selection  (Cornwallis & Uller 2010; Macías-Ordóñez et al. 2013). We begin by assessing whether sexual selection scores are correlated with the latitude at which species occur, and then -because latitudinal gradients are best viewed as emergent properties shaped by multiple underlying mechanisms  (Machado et al. 2016 ) -we evaluate the role of climate and ecology as fundamental drivers of variation in sexual selection across species.
Results We scored sexual selection by assigning 10671 bird species  (Clements et al. 2021) , including all 9988 valid bird species included in the global BirdTree phylogeny (Extended Data Fig. 1a )  (Jetz et al. 2012) , to one of five categories ranging from 0 (strict monogamy) to 4 (extreme polygamy). These scores were assigned according to thresholds based on the degree of polygamy, extra-pair paternity (EPP) or operational sex ratio (OSR); see Methods. Most BirdTree species (n = 8271; 83%) were scored as strictly monogamous, with higher levels of sexual selection (scores 1-4) assigned to the remainder (n = 1721, 17%; see Extended Data Fig. 1 ). The predominance of strict monogamy may partly reflect a tendency to underestimate sexual selection in the literature. For example, species observed in socially monogamous pairs are generally assumed to be genetically monogamous, whereas further study often reveals them to have higher rates of polygamy and EPP  (Brouwer & Griffith 2019).
Accordingly, when we focused exclusively on the best-known BirdTree species with highest certainty data (n = 2883), the proportion of strictly monogamous species was lower (n = 1937, 67%), while a third of species (n = 946, 33%) were classified as either polygamous or subject to sexual selection via EPP or OSR (scores 1-4). Sexual selection and data-certainty scores for all bird species, aligned with both Clements and BirdTree taxonomy, are presented along with sources in Supplementary Dataset 1. To assess the validity of our sexual selection scores, we compared them against three alternative metrics of sexual selection: residual testes mass as an index of postcopulatory sperm competition  (Dunn et al. 2001) , Bateman gradients reflecting the increase in reproductive success obtained from additional matings  (Bateman 1948) , and opportunity for sexual selection (IS) reflecting variance in mating success  (Wade 1979;  for a full explanation, sources and derivation of these metrics see Supplementary Information).
Sexual selection scores for all birds: validity, robustness and research priorities Our analyses offer the most comprehensive synthesis of geographical variation in sexual selection, estimated for all 9988 valid bird species aligned with a global phylogeny  (Jetz et al. 2012 ). We provide this information on sexual selection, data sources and data certainty for all species in Supplementary Dataset 1, representing a major step-change in data availability from the most recent compilations of sexual selection scores, covering 3250 passerine species  (Dale et al. 2015; Gonzalez-Voyer et al. 2022 ). We extended these earlier datasets, not only in terms of species coverage, but by scoring sexual selection based on a wider accumulation of evidence, including molecular evidence of polygamy and extra-pair paternity.
Our results also reveal a strong latitudinal gradient in data certainty (Fig. 2c, d ), reflecting a long-standing bias towards ornithological research in the temperate zone  (Stutchbury & Morton 2000)  as well as the difficulty of tracking down relevant literature published in languages other than English. However, we note that most highcertainty species in our dataset are found in the tropics (Fig. S2 ), suggesting that biases in knowledge to some extent reflect the sheer number of tropical species compared with higher latitudes. The availability of high-certainty scores across all latitudes and ecological categories is crucial, allowing us to re-run our analyses with a more conservative dataset, confirming that all our main results are robust to biases in knowledge. We hope that the open release of comprehensive estimates of sexual selection for over 10,000 bird species provides a template for further macro-scale research.
Hypotheses linking sexual selection to evolutionary mechanisms and ecological contexts  (Tobias & Seddon 2009; Biagolini-Jr. et al. 2017 ) can now be examined at unprecedented scale. We also hope our global dataset can re-energize efforts to improve the quality of evidence. In particular, the distribution of species with low-certainty data (Fig. 2c  & S1) offers a detailed map of research priorities, highlighting the urgent need for observational and molecular studies, especially in the tropics, to quantify polygamy, EPP and OSR in species with low to moderate levels of sexual selection.
Finally, species with highly elaborate mating display behaviours, including social lekking (e. g. Birds-of-Paradise, Paradisaeidae) or permanent display locations (e. g. bellbirds, Procnias), were given the highest score (4) because variance in the reproductive success of males is likely to reach its peak in these systems. Unlike previous bi-directional scoring systems  (Dale et al. 2015) , we treated sexual selection as a unidirectional variable. That is, we make no distinction between male or female-biased mating systems, instead grouping polyandry and polygyny under the general definition of polygamy. To facilitate any future research that relies on discriminating between female-biased and male-biased sexual selection, we provide an updated list of sex-role-reversed bird species (Supplementary Dataset 1) so that a bidirectional score can be generated by reversing the sign on scores for sex-rolereversed species.
To enable phylogenetic analyses, we aligned sexual selection scores and associated geographical range data to the most comprehensive phylogeny available  (Jetz et al. 2012). The taxonomy used in this phylogeny requires updating, so we also provide an additional dataset of sexual selection scores aligned with 10671 species included for a more recent taxonomy  (Clements et al. 2021 ). Although we do not use this dataset in our own analyses, we hope that it will enable future studies using forthcoming phylogenetic datasets, as well as several rich sources of information, including AVONET  (Tobias et al. 2022) , Birds of the World  (Billerman et al. 2022 ) and eBird citizen-science data  (Sullivan et al. 2009 ), all of which use the same updated taxonomic format  (Clements et al. 2021). Sexual selection scores under both taxonomic treatments, along with the sources of information supporting these assignments, are presented in Supplementary Dataset 1.
Testing sexual selection metrics To validate the accuracy of our proposed scoring system in estimating sexual selection, we conducted a comparative analysis against three widely used measures of sexual selection from existing published datasets: residual testes mass  (Pitcher et al. 2005; Baker et al. 2020 ), Bateman gradients (βSS)  (Fromonteil et al. 2023) , and the opportunity for sexual selection (IS)  (Janicke & Morrow 2018). Given the scarcity of studies estimating sexual selection, we also derived additional estimates of zero IS from studies that recorded 0% extrapair paternity (EPP) rates, as summarized by  Brouwer & Griffith (2019). We assessed the strength of association between our scores and each metric using Bayesian phylogenetic models (Supplementary Information), with each metric as a single predictor.
Ecological variables To assess the role of evolutionary drivers, we extracted data on species-specific traits from published datasets  (Tobias et al. 2016 (Tobias et al. , 2022)). We selected three fundamental ecological traits previously proposed to influence levels of sexual selection: trophic level (linked to both spacing and constraints on parental care;  Snow 1971; Beehler 1983 Beehler , 1987)) , migration (linked to breeding synchrony or constraints on the duration of breeding seasons;  García-Peña et al. 2009) , and territoriality (linked to movement and spacing patterns;  Davies & Lundberg 1984). To facilitate the comparison of categorical and continuous data, we converted trophic level, migration, and territoriality to binary variables  (Gelman 2008 ). For trophic level, we group species as either obligate primary consumers, defined as those with more than 60% of their energetic requirements provided by plant material, or secondary consumers, including omnivores, carnivores, and scavengers from higher trophic levels.
Climatic seasonality To account for the effects of climate seasonality in models of sexual selection, we included species-specific data on average temperature seasonality, extracted from the CHELSA bioclim dataset v2. 1  (Karger et al. 2017). We selected temperature seasonality (bio4) as our primary measure of climate variability, based on extensive research connecting temperature, mating systems, and sexual selection  (García-Roa et al. 2020; Leith et al. 2022). The temperature seasonality metric in CHELSA (bio4) provides an index of local intra-annual temperature variation, with grid cells equal to the standard deviation of monthly mean temperatures across each year, using global data from 1981-2010 at 30 arcsecond resolution. To extract an average seasonality estimate for each species, we overlaid climate data with expert-drawn breeding ranges provided by BirdLife International (2021) (Supplementary Information).
Statistical analysis To assess macroevolutionary patterns in sexual selection, we used Bayesian regression models from the R package brms  (Bürkner 2017 ) (Supplementary Information). Given the ordinal nature of our sexual selection scores, we used a cumulative family distribution, which can be interpreted similarly to standard generalized linear models  (McCullagh 1980). Initially, we ran models including all species (n = 9836), with absolute latitude (distance from the equator) as the sole predictor. We also ran similar models with data certainty score as the response variable to identify any latitudinal bias in data certainty. Given a strong relationship between latitude and data certainty, we ran subsequent latitudinal models predicting sexual selection, limiting data certainty to species with moderate to high certainty (scored 3-4; n = 7592 species), and high certainty (scored 4; n = 2851 species).
Extended data Extended Data Table  1. Criteria for scoring the intensity of sexual selection in birds. All species were scored from literature by applying this classification system to textual descriptions of mating systems and breeding behaviour, including territoriality and offspring provisioning roles. By our definition, monogamy involves both sexes maintaining the same breeding partner for the duration of a reproductive event or season, and thus includes both sequential and perennial (lifelong) monogamy. Where available, we used quantitative estimates of polygamy, EPP and OSR to refine our scores. The quantitative thresholds used for scores 0-3 were based on previous studies  (Verner & Willson 1966; Møller 1986; Owens & Hartley 1998). Scores for all species are presented in Supplementary Dataset 1 along with literature sources and estimates of data certainty (see Extended Data Table  2 ).
Taxonomy To allow the use of phylogenetic analyses, we followed the species names used in the global BirdTree phylogeny  (Jetz et al. 2012). Given the rapid state of flux in avian taxonomy, the species limits and family classifications in BirdTree are increasingly obsolete, particularly because the phylogenetic tree itself is likely to be replaced with new phylogenies using updated taxonomic treatments. To maximise future utility of our dataset, we rescored sexual selection and all other variables according to  Clements (2021)  taxonomy. This will allow integration of our dataset with ecological and life history data published in Birds of the World (https://birdsoftheworld. org/bow), as well as spatially explicit citizen-science data from eBird (https://ebird. org), and forthcoming global phylogenetic datasets based on Clements taxonomy.
Approximately 93% of species in the BirdTree dataset were directly matched with Clements species, including many that needed to be matched manually because of changes in spelling or higher taxonomic classification. In all these cases, we transferred the same sexual selection and data uncertainty scores from BirdTree to the Clements dataset. For the remaining 7% of species, 512 were taxonomic splits, where a BirdTree species was separated into multiple Clements species. In these cases, we identified the parent (nominate) species and transferred trait data directly from BirdTree to the parent lineage. For the remaining daughter species, we inferred sexual selection scores from the behaviour of the parent. For example, under BirdTree taxonomy, the House Sparrow Passer domesticus is considered a single species, with the Italian Sparrow considered a subspecies (P.