dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and analyses Our data and analysis scripts are available in the institutional repository of the Max Planck Society at https://dx. doi. org/10. 17617/3. 12. Our analyses were conducted in R v3. 2. 2. For each, we list the approach and specifications in the results below. Generalised and linear mixed models were analysed using the lme4 package  [25] ; because this package does not report p-values for linear mixed models, we considered t-values over 1. 94 as statistically significant and report these below.
The majority of respondents who indicated their field of study (74%; N = 356) were from the same fields as the authors of this study: biology and psychology. Observational data were collected at 247 seminars, from 42 departments of 35 institutions in 10 countries. We retained the pilot data collected at the University of Cambridge and the seminars that were subject to the experimental manipulation, since we found no effect of our manipulation on the time given to questions (see below).
For example, women not feeling clever enough to ask a question was rated as more important by women (M = 3. 21, SD = 1. 13), than by men (M = 2. 57, SD = 1. 17) (Table  1 ). Next, using our observational data, we examined potential predictors of a gender disparity in the questions asked after seminars. We used generalised linear mixed effects models with a binomial response, with questions from female audience members coded as cases, and questions from male audience members coded as noncases. To control for repeated measures, all models included the country, and the department nested within the institution as random effects. We did not include the observer as a random effect because most observers collected data within only one department within an institution. We aimed to test the following fixed effects: the proportion of women in the audience (centred at 0.
Then, each dropped term was added back to the final model, one at a time, to check that it remained a non-significant predictor of the gender disparity. In predicting the proportion of questions asked by women, we could not include the gender of the first person asking a question, since the first person biases the overall gender ratio of questions, in particular when only few questions were asked. We thus ran two sets of analyses using slightly different data and predictors in the starting models to account for this. The first model included the complete dataset and all fixed effects and interactions not including the gender of the first attendee to ask a question. The second model used a reduced dataset, with the first question removed, and included the gender of the first person asking a question as an additional predictor.
Using the complete dataset, we found that the probability that a question was asked by a female audience member was predicted by the proportion of the audience that was female, the proportion of female faculty in the department, the number of questions asked, the gender of the speaker, and whether the speaker was internal or not (Table  2 ; Figure  3 ). Using the baseline values of the minimal model (i. e. with the centred values of the continuous variables and the reference category of the categorical variables) the model predicted that the probability that a question was asked by a woman was 29%. The probability increased by 8% from this baseline (to 37%) when the speaker was male, and by 9% (to 38%) when the speaker was internal. The proportion of female faculty in the department had a positive effect on the proportion of questions asked by women in the audience, however this increase was relatively small-a 5% increase in the proportion of female faculty was associated with a 1.
For example, for a talk that had a male-first question, and totals of 3 questions from women and 5 from males, the new dataset had totals of 3 questions from women and 4 from males, reflecting the numbers of questions asked subsequently to the first one. Additionally, we removed any talks (N = 3) that had only one question-the first one. To control for the factors that affected the proportion of questions asked by women as found in the "complete" model above, our starting model for this second set included the main effects included in the "complete" model of the first set, as well as the main effect of the gender of the first attendee to ask a question and the two interactions involving that effect. Using the reduced dataset with the first question removed, we found an effect of the gender of the first person to ask a question (Table  2 ; Figure  3 ).
Using the baseline values of the model (i. e. with the centred values of the continuous variables and the reference category of the categorical variables) the model predicted that the probability that a question subsequent to the first question was asked by a woman was 33%. When the first question was asked by a male audience member, the proportion of subsequent questions asked by women decreased by 6% (to 27%) compared to when the first question was asked by a woman. All other significant effects detected in the first complete model were retained as significant in the reduced minimal model. As reported earlier, there was significant covariation between the duration of the question time and the number of questions that were asked. When we included in the model the duration of the question time, instead of the total number of questions asked, it was a significant predictor of the proportion of questions asked by women in the reduced dataset (β ± S.
Over the two departments involved in the experiment, we collected data during 30 seminars, including 17 controls and 13 experimental seminars. Our manipulation was not successful: the duration of the time for questions was no longer in our treatment group than in the control group. In fact, there was a significant interaction between the institution and the treatment; on average, the time dedicated to questions increased in one institution from 14 to 16 min, but decreased in the other institution from 10 to 7 min. However, when considering the departments separately, these changes were not significant (p > 0. 05). Thus, unsurprisingly, our manipulation did not have an effect on the proportion questions asked by women (generalised linear model with experimental condition as the only predictor: β ± S.
Acknowledgements We have compiled a list of resources about various gender biases in academia (e. g. , career progression, authorship, funding, awards…) on our project website: http://diversityinacademia. strikingly. com/. We welcome your help in building and maintaining this list, to benefit anyone who is interested in learning more about these issues. We thank the following people for generously attending seminars near them, counting people and questions, and staying until the very end, even if it took 60 min (listed alphabetically by first name):  Aaron Weidman ,  Adrian Currie ,  Alan McElligot ,  Alexis May ,  Anne Charmantier ,  Anne Goldizen ,  Anne Pisor ,  Caroline Spence ,  Christine Webb ,  Corina Logan ,  Culum Brown ,  Danny Osborne ,  Dominique Knutsen ,  Elva Robinson ,  Federica Dal Pesco ,  Gabrielle Davidson ,  Geoff While ,  Gisela Kopp ,  Greg Brooke ,  Guy Cowlishaw ,  Harry Marshall ,  Helge Gillmeister ,  James Gilbert ,  Joey Cheng ,  Kate Laskowski ,  Kirsty MacLeod ,  Lara Aknin ,  Lauren Brent ,  Lauren Human ,  Matteo Rizzuto ,  Melanie Dahmhann ,  Naomi Langmore ,  Pierre-Olivier Montiglio ,  Sandra Binning ,  Sandrine Muller ,  Sharon Morein ,  Sonja Koski ,  Stefan Fischer ,  Stuart Semple ,  Teri Kirby ,  Theadora Block , and  Wendy King.
15 2. 82 0. 005 Starting hour 0. 013 ± 0. 035 0. 38 0. 70 a Reference category: female b Reference category: false c Reference category: biology Table 2 caption: Reported are the datasets used (see text for details), the predictor variables, their effect size and the associated standard error (β ± S. E. ), the z-value, and the p-value. The minimal model's predictors are indicated in bold type. The terms added in the reduced model are indicated in italics.
Colocalization analysis of GWAS data and STARNET eQTLs. To identify further candidate genes in tissues affected by atherosclerosis that had strong evidence of sharing the same variant for cIMT and carotid plaque as found in our GWAS, we conducted pairwise colocalization analysis of these genetic variants with cis-eQTLs in the STARNET study  10. The pairwise colocalization analysis is based on coloc, a Bayesian statistical methodology that tests pairwise colocalization of SNPs in GWAS with eQTLs and, in this fashion, generates posterior probabilities for each locus weighting the evidence for competing hypothesis of either no colocalization or sharing of a distinct SNP at each locus  10. We used summary statistics from all SNPs within a 200-kb window around each gene covered by the eQTL datasets (N = 18,705, see Methods), and analyzed each p = p-values of association from linear regression analysis, N = total number in meta-analyses a Published cIMT SNP in LD with our most significant SNP: rs11781551 (r 2 = 0.
95 with rs148147734), rs6601530 (r 2 = 0 with rs200482500), and rs445925 (r 2 = 0. 60 with rs7412) b Published plaque SNP in LD with our most significant SNP: rs1878406 (r 2 = 0. 98 with rs11413744), rs17398575 (r 2 = 0. 8 with rs17477177), rs9644862 (r 2 = 0. 79 with rs9632884), and rs4888378 (r 2 = 0. 94 with rs113309773) eQTL-GWAS dataset pair (Supplementary Table  6 ). A posterior probability of ≥75% was considered strong evidence of the tissuespecific eQTL-GWAS pair influencing both the expression and GWAS trait at a particular region. Results for this analysis are shown in Table  3  and Supplementary Figure  5. The strongest evidence for an effect on gene expression within the regions identified in our standard GWAS meta-analysis was for the CCDC71L and PRKAR2B genes at the previously described chromosome 7 cIMT locus (PIK3CG in Table  2 , Fig.
0003 and rs55917128, chr10:100023359, LOXL4, p = 0. 0005). ADAMTS9 LOXL4 PRKAR2B CCDC71L C I M T -A O R C I M T -M A M C I M T -S F C I M T -S K L M C I M T -V A F C I M T -L I V C I M T -B l o o d P L A Q U E -A O R P L A Q U E -M A M P L A Q U E -S F P L A Q U E -S K L M P L A Q U E -V A F P L A Q U E -L I V P L A Q U E -B Colocalization of CHD and stroke GWAS and STARNET eQTLs. We next assessed if the four genes (CCDC71L, PRKAR2B, ADAMTS9, LOXL4) identified through colocalization of cIMT/ carotid plaque with tissue-specific eQTLs also showed evidence for colocalization with CHD and stroke traits (Supplementary Data 1 and Supplementary Figure  6 ). We used GWAS summary data for CHD (CARDIoGRAMPlusC4D), and stroke subtypes (MEGASTROKE) and AOR and MAM STARNET tissue eQTLs for these analyses.
This gene has suggestive evidence of pairwise colocalization with carotid plaque (67% of probability of shared variant between carotid plaque and eQTL in MAM), as well as a high probability of shared variant between MAM eQTL expression of this gene, GWAS carotid plaque or cIMT, and CHD traits (Supplementary Table  7 ). We note, however, that the GWAS signal for outcomes across the datasets did not reach genome-wide significance and larger sample sizes may be needed to strengthen the evidence for involvement in disease outcomes. Genetic correlations of cIMT/carotid plaque and clinical outcomes. To provide etiological insights into the role of measures of subclinical atherosclerosis and major atherosclerotic disease outcomes such as CHD and ischemic stroke, we quantified the genetic correlation using cross-trait LD score regression, a method that estimates genetic correlation across different traits using summary level data  15.
These findings suggest that there may be important differences in vascular bed regulation at distinctive regions for atherosclerotic cardiovascular and stroke outcomes that may help to identify genes and specific targets for CHD or stroke prevention and treatment. Additional studies in diverse and large samples across the multiple datasets are needed to explore these results further. As more summary statistics become available for other clinical endpoints beyond stroke and CHD (both in terms of larger sample size and richer genome coverage), and as further refinements in clinical phenotypes emerge (e. g. from CHD to acute coronary syndrome sub-components), strategies to integrate this knowledge using methods such as moloc 10 and eCAVIAR 24 will continue to be essential for harnessing genome-wide findings in the drug-discovery process.
We used data from the baseline examination or the first examination in which carotid ultrasonography was obtained. cIMT was defined by the mean of the maximum of several common carotid artery measurements, measured at the far wall or the near wall. For most studies, this was an average of multiple measurements from both the left and right arteries. We also examined a carotid plaque phenotype, defined by atherosclerotic thickening of the carotid artery wall or the proxy measure of luminal stenosis greater than 25% (Supplementary Table  2 ). Genotyping, imputation, and study-level quality control. Genotyping arrays and QC pre-imputation are shown in Supplementary Table  3. Each GWAS study Table 4 Genetic correlation between CHD and stroke traits with cIMT and plaque, and cIMT with plaque using LD score and meta-GWAS conducted genome-wide imputation using a Phase 1 integrated (March 2012 release) reference panel from the 1000G Consortium using IMPUTE2  25  or MaCH/ minimac  26  , and used Human Reference Genome Build 37.
Sample QC was performed with exclusions based on call rates, extreme heterozygosity, sex discordance, cryptic relatedness, and outlying ethnicity. SNP QC excluded variants based on call rates across samples and extreme deviation from Hardy-Weinberg equilibrium (Supplementary Table  3 ). Non-autosomal SNPs were excluded from imputation and association analysis. Pre-meta-analysis GWAS study-level QC was performed using EasyQC software  27. This QC excluded markers absent in the 1000G reference panel; non A/ C/G/T/D/I markers; duplicate markers with low call rate; monomorphic SNPs and those with missing values in alleles, allele frequency, and beta estimates; SNPs with large effect estimates or standard error (SE) ≥10; and SNPs with allele frequency difference >0. 3 compared to 1000G reference panel.
We used gene expression datasets from multiple tissues from patients with CHD of the STAR-NET study, including blood, MAM, AOR, subcutaneous fat (SF), visceral fat (VAF), skeletal muscle (SKLM), and liver (LIV) obtained from 600 patients during open heart surgery  11. Pairwise colocalization was tested between these expression disease tissue datasets and GWAS results from our cIMT/carotid plaque GWAS meta-analysis. We used GWAS and eQTL summary statistics of SNPs within a 200-kb window around each gene covered by the eQTL datasets. A posterior probability of colocalization ≥0. 75 was considered a strong evidence for a causal gene. Next, we reported the gene(s) in the STARNET datasets that had the strongest evidence of sharing the same variant with cIMT or carotid plaque genome-wide. In an alternative analysis, we also tested loci with an SNP that reached a threshold of significant or suggestive genome-wide significance for cIMT or carotid plaque (reported in Table  1 , Supplementary Table  5 ).
For each region 200kb around the SNP with the lowest association p-value, we report the gene with the highest probability of being responsible for the GWAS signal (Supplementary Table  6 ). Pairwise colocalization for these genes was also tested for publicly available GWAS for CHD case-controls (CARDIoGRAMPlusC4D) and stroke case-controls (MEGASTROKE consortium). The MEGASTROKE dataset uses genotypes imputed to the 1000G phase I haplotype panel. The European ancestry sample used to generate these results consisted of 40,585 stroke cases and 406,111 controls from 15 cohorts and two consortia: the METASTROKE and CHARGE consortia  29. The phenotypes used in this analysis were any stroke (n = 39,067 cases, total n = 442,142), ischemic stroke (IS, n = 32,686 cases, total n = 423,266), and etiologic stroke subtypes:cardioembolic stroke (CE, n = 6,820 cases, total n = 314,368), large vessel disease (n = 4,113, total n = 202,263), and small vessel disease (SVD, n = 4,975, total n = 242,250).
9 and directly overlap the genomic annotations considered. Overall genetic correlation analysis. Genetic correlation between cIMT/carotid plaque, CHD, and stroke traits were calculated using LD score regression approach LD-score, which uses GWAS summary statistics and is not affected by sample overlap. This method relies on the fact that the χ 2 association statistic for a given SNP includes the effects of all SNPs that are in LD with it and it calculates genetic correlation by partitioning the SNP heritabilities  15. Genetic correlations between stroke traits (IS, CE, large vessel disease, and SVD) and cIMT and carotid plaque were calculated using software available at http://github. com/bulik/ldsc with GWAS summary statistics for our cIMT/carotid plaque GWAS, CARDIO-GRAMPlusC4D data, and stroke GWAS.
We used the LD-scores  15  , which are based on the 1000 Genomes European population and estimated within 1-cM windows. Based on ten tests performed (two subclinical traits and five outcomes), we set the significance threshold to p = 0. 005. PATHWAY ANALYSES. Methods for GO Slim: The Ensembl identifiers of all protein-coding genes identified as in LD with the 12 variants for cIMT and 15 variants for carotid plaque (including variants from main and suggestive signals, Table  1  and Supplementary Table  5 ), and five genes for which there is strong evidence of colocalization (Table  3 ), were mapped to UniProt accession numbers, using the UniProt ID mapping service (http://www. uniprot. org/uploadlists/). A GO Slim analysis was performed on this list using QuickGO (www. ebi. ac. uk/QuickGO) and the Generic GO Slim.
The GO terms used in the final slim analysis were further refined by adding/removing GO terms to provide more detailed information about the processes covered. Methods for GO term enrichment analysis: The VLAD gene list analysis and visualization tool (http://proto. informatics. jax. org/prototypes/vlad/) was used to perform a GO term enrichment analysis on the same UniProt accessions as listed for the GO Slim. The background annotation set was obtained from the goa_human. gaf file (dated 21 November 2017, downloaded from ftp://ftp. ebi. ac. uk/ pub/databases/GO/goa/HUMAN/) and the ontology data was obtained from the go-basic. obo file provided in the VLAD tool (analysis run 28 November 2017). The LD block around top SNPs associated with cIMT and carotid plaque was constructed using LD information from the 1000 Genomes panel, as previously outlined in Finan et al.
16. Briefly, the boundaries of the LD region were defined as the positions of the variants furthest upstream and downstream of a GWAS SNP with an r 2 value of ≥0. 5 and within a 1-Mbp flank on either side of the GWAS variant. Associated variants that were not present in the 1000 Genomes panel that were not in LD with any other variants were given a nominal flank of 2. 5 kbp on either side of the association. Gene annotations using Ensembl version 79 were then overlapped to the LD region. Druggable genes. We examined the druggability status for the nearest coding genes identified in our GWAS analysis on cIMT and carotid plaque, including significant (novel and replicated) and suggestive ones, as well as genes identified through colocalization analysis. The druggable gene set was calculated using the previously described criteria: novel targets of first-in-class drugs licensed since 2005; the targets of drugs currently in late phase clinical development; pre-clinical phase small molecules with protein binding measurements reported in the ChEMBL database; and genes encoding secreted or plasma membrane proteins that are targets of monoclonal antibodies and other bio-therapeutics  16.
We defined three tiers of druggable gene sets based on their drug development. In Tier 1, 1427 genes were targets of approved small molecules and biotherapeutic drugs and clinical-phase drug candidates. Tier 2 comprised 682 genes encoding targets with known bioactive drug-like small molecule binding partners and those with significant sequence similarity to approved drug targets. Tier 3 contained 2370 genes encoding secreted or extracellular proteins, proteins with more distant similarity to approved drug targets, and druggable genes not included in Tier 1 or 2 such as GPCRs, nuclear hormone receptors, ion channels, kinases, and phosphodiesterases. URLs. For GTEx, see http://gtexportal. org/. For Coloc, see https://cran. r-project. org/web/packages/coloc/coloc. pdf. For, Moloc, see https://github.
com/clagiamba/ moloc/blob/master/man/moloc-package. Rd. For CARDIoGRAMPlusC4D, see www. cardiogramplusc4d. org/. For LD scores, www. broadinstitute. org/~bulik/ eur_ldscores/. For UniProt ID, www. uniprot. org/uploadlists/. For QuickGO, www. ebi. ac. uk/QuickGO. For VLAD tool, see http://proto. informatics. jax. org/ prototypes/vlad/. for work unrelated to the present project. M. A. N. 's participation in this project was supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH, Bethesda, MD, USA. M. A. N. also consults for Illumina Inc. , the Michael J. Fox Foundation, and University of California Healthcare. B. M. P. serves on the DSMB of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson.
Acknowledgements The work was supported by the following grants:  National Institute of Health  grants:  R21HL123677 ,  R21-HL140385 ,  DK104806-01A1 ,  R01-MD012765-01A1  (NF),  National Institutes of Health  awards  R01HG009120 ,  R01HG006399 ,  U01CA194393 ,  T32NS048004  (CG), the  American Heart Association  Grant # 17POST33350042  (PV), the  British Heart Foundation  ( RG/13/5/30112 ) and the  National Institute for Health Research University College London Hospitals Biomedical Research Centre  (RCL and RPH), the  British Heart Foundation   FS/14/55/30806  (JCH), the  German Federal Ministry of Education and Research (BMBF)  in the context of the e: Med program  (e:Ather-oSysMed), the  DFG  as part of the  CRC 1123  ( B3 ), and the  FP7/2007-2103 European Union project CVgenes@target  (grant agreement number  Health-F2-2013-601456 ).
Data availability All relevant summary statistics data that support the findings of this study have been deposit in the database of Genotypes and Phenotypes (dbGaP) under the CHARGE acquisition number (https://www. ncbi. nlm. nih. gov/projects/gap/cgi-bin/ study. cgi?study_id=phs000930. v6. p1; accession phs000930. v6. p1). GWAS data for most US studies are already available in dbGAP.
That tool is implemented as an Interactive Molecular Dynamic in the popular VMD software  [18]. AutoIMD enables one to manipulate the molecules during MD simulations with real-time force feedback and a graphical display. There is also a wide range of packages containing all-atom force fields which enable one (after manual parametrization) to perform the CNT simulation in the all-atom resolution (e. g. , AMBER  [19] , GROMACS  [20] , and NAMD  [21] ). On the other hand, the systems containing a CNT and a protein can sometimes be too big to simulate at the all-atom resolution in a reasonable timescale. Therefore, coarse-grained models of the CNTs were developed. One such model was created by Chen et al. [22]  using the open source LAMMPS  [23]  platform, where each CNT is simplified as a multi-bead chain.