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1. Replication Data for: "Promoting Sustainable Travel Modes Through Health and Active Lifestyle Messaging"

   https://doi.org/10.7910/dvn/y8hfxw  

   Anne, Viswa_Sri Rupa; Liu, Yifan; Kibria, Md Gulam; Peeta, Srinivas; Asensio, Omar Isaac; Asensio, Omar Isaac (January 2025, Harvard Dataverse)

   {"Abstract":["<b>Data and Code Repository</b> <br>\nThis repository contains the anonymized dataset and analysis code associated with the paper titled "Promoting Sustainable Travel Modes Through Health and Active Lifestyle Messaging." <br>\n<b>Contents</b>\n<ul>\n <li>anonymized_data.csv: Contains the raw anonymized dataset (1 MB) used in the analysis.</li>\n <li>analysis_v1.ipynb: Python scripts for analysis, and visualization. (Last Modified July 7, 2025, 5:00PM)</li>\n <li>README.md: Description of the repository contents and usage.</li>\n <li>datadictionary.md: A detailed explanation of each variable in the final dataset. 68 unique variables, 4,840 observations.</li>\n</ul>\n<b>Requirements</b> <br>\nPackages required to run this analysis are pandas==2.0.3, numpy==1.24.1, statsmodel.api==0.14.1. This code was tested on Python 3.8.13 and 3.9.2 and on macOS Sequoia 15.5 and Google Colab CPUs. <br>\n<b>Structure of the code</b> <br>\nThe first code block loads the dataset and required packages. The second code block has helper function that generates dataframe for statistical analysis in the later blocks. The third code block has helper variables and functions to load model specifications and formatting model coefficients for analysis in the later blocks Code blocks four and above generate statistical results used in the paper. <br>\n<b>Output</b> <br>\nThis code package generates the necessary derived data consisting of odds ratios and uncertainty for Figure 1 and Figure 2 in the main document:\n<ul>\n <li>Main Document Fig. 1 Treatment effects of air quality impacts targeting bus transit and active lifestyle messaging targeting walking or biking.</li>\n <li>Main Document Fig. 2 Comparative treatment effects of health and active lifestyle messaging for all respondents and those with underlying health conditions.</li>\n</ul>\nThis code package generates the following tables:\n<ul>\n <li>Main Document Table 1 Heterogeneous treatment effects across key subgroups.</li>\n <li>Table S4. Treatment effects of air pollution exposure messaging in Experiment 1 (personal and community benefits targeting bus transit)</li>\n <li>Table S5. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting bus transit)</li>\n <li>Table S6. Treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting walking)</li>\n <li>Table S7. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting walking)</li>\n <li>Table S8. Treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting biking)</li>\n <li>Table S9. Treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting biking)</li>\n <li>Table S10. Treatment effects of all active lifestyle messaging in Experiment 3 (personal and community benefits targeting walking and biking)</li>\n <li>Table S11. Treatment effects of all active lifestyle messaging in Experiment 3 (personal gain targeting biking)</li>\n <li>Table S12. Treatment effects of active lifestyle messaging in Experiment 3 (personal and community benefits targeting biking)</li>\n <li>Table S13. Treatment effects of step count messaging in Experiment 3 (personal and community benefits targeting walking)</li>\n <li>Table S14. Treatment effects of calories burned messaging in Experiment 3 (personal and community benefits targeting walking)</li>\n <li>Table S15. Treatment effects of heart health messaging in Experiment 3 (personal and community benefits targeting walking and biking)</li>\n <li>Table S16. Heterogeneous treatment effects of air pollution exposure messaging in Experiment 1 (personal gain targeting bus transit)</li>\n <li>Table S17. Heterogeneous treatment effects of air pollution exposure messaging in Experiment 1 (personal and community benefits targeting bus transit)</li>\n <li>Table S18. Heterogeneous treatment effects of air quality improvement messaging in Experiment 2 (personal gain targeting bus transit)</li>\n <li>Table S19. Heterogeneous treatment effects of air quality improvement messaging in Experiment 2 (personal and community benefits targeting bus transit)</li>\n <li>Table S20. Heterogeneous treatment effects of active lifestyle messaging in Experiment 3 (personal gain targeting walking)</li>\n <li>Table S21. Heterogeneous treatment effects of active lifestyle messaging in Experiment 3 (personal and community benefits targeting walking)</li>\n <li>Table S22. Heterogeneous treatment effects of different active lifestyle messaging in Experiment 3 targeting walking</li>\n <li>Table S23. Heterogeneous treatment effects of calories burned messaging for commuters with varying daily travel times in Experiment 3 (targeting walking)</li>\n</ul>\n<b>Replication</b> <br>\nSupporting replication code is also available here: https://github.com/asensio-lab/health-active-lifestyle. This code package was last replicated on July 7, 2025 by @YifanLiu0304 <br>\n<b>Declaration of generative AI and AI-assisted technologies in the coding process</b> <br>\nDuring the preparation of this work the authors used ChatGPT in order to debug code errors such as KeyError, syntax errors in python scripts that were used to generate statistical tables. After using this tool/service, the researchers reviewed, edited, and replicated all code."],"TechnicalInfo":["Python, 3.9.2"],"Other":["Supporting replication code is also available here: https://github.com/asensio-lab/health-active-lifestyle"]} 

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2. Data from: Evolutionary responses to historic drought across the range of scarlet monkeyflower

   https://doi.org/10.5061/dryad.18931zd7g  

   Sheth, Seema; Albano, Lucas; Blanchard, Charles; Cook, Emily; Diaz, Rosalinda; Gomez-Vega, Xitlaly; Kutella, Katelin; Moazed, Mariam; Patel, Macy; Prange, Julia; et al (January 2025, Dryad)

   {"Abstract":["Adaptive evolution is a key means for populations to persist under\n environmental change, yet whether populations across a species’ range can\n adapt quickly enough to keep pace with climate change remains unknown. The\n breeder’s equation predicts the evolutionary change in a trait from one\n generation to the next as the product of the selection differential and\n the narrow-sense heritability in that trait. Incorporating these aspects\n of the breeder’s equation, we performed a resurrection study with the\n scarlet monkeyflower (Mimulus cardinalis) to evaluate whether traits\n associated with drought adaptation have evolved in populations across a\n species’ range in response to extreme drought. We compared trait and\n fitness differences of pre-drought ancestors and post-drought descendants\n from six populations transplanted into three latitudinally-arrayed common\n gardens and quantified phenotypic selection and trait heritabilities. The\n strength, direction, and mode of selection varied among traits and\n gardens. Trait heritabilities were relatively low and did not differ\n dramatically among populations or gardens. Overall, instances of\n evolutionary responses between ancestors and descendants were few and\n small in magnitude, but the magnitude of these evolutionary differences\n varied among gardens. Together, these results suggest that the expression\n of genetic variation, and thus traits, depends on the environment, and\n that environmental variability in field settings may mask the genetic\n variation that is often detected in greenhouse environments. "],"TechnicalInfo":["# Data from: Evolutionary responses to historic drought across the range\n of scarlet monkeyflower\n [https://doi.org/10.5061/dryad.18931zd7g](https://doi.org/10.5061/dryad.18931zd7g) ## Description of the data and file structure These data are associated with a common garden study of scarlet monkeyflower (*Mimulus cardinalis*). In 2023, we transplanted pre-drought 2010 ancestors alongside post-drought 2017 descendants from two northern-edge, two central, and two southern-edge populations into three experimental gardens near the northern range edge, latitudinal range center, and southern range edge in the western United States. We collected data on several physiological and leaf traits associated with adaptation to drought, along with proxies for fitness, including survival and reproductive output.   ### Files and variables 1\\. **PERSIST_2023_data.csv**: 2023 trait data for all populations and cohorts in all gardens * garden: experimental garden (north, center, south) - block: experimental randomized block in garden (1 - 10 in north, 1 - 11 in center, 1 - 10 in south) * garden_block: variable that combines garden and block - row: row (y-coordinate) of experimental garden; with 4 rows per block; rows 101 - 104 are in block 1; rows 1101 - 1104 are in block 11, etc. * position: position (x-coordinate) of experimental garden; corresponds to a unique plant ID (Cross_ID_Rep), or has no plant (NA) - rowPosition: variable that combines row and position * Cross_ID_Rep: variable that combines unique ID for each full-sibling family and replicate of that family within a particular garden - Cross_ID: unique ID for each full-sibling family; each Cross_ID has a unique mom and dad * Sire_ID: unique ID for sire (father); plants with the same Sire_ID and different Dam_IDs are half-sibs - Dam_ID: unique ID for sire (mother); dams are nested within sires to yield a nested half-sib/full-sib design * Population: Population of scarlet monkeyflower (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) - Year: Year that seeds were collected in the field (2010 ancestors and 2017 descendants) * Year1: Alternate coding for year corresponding to "ancestor" and "descendant" - Date_early: Date of early-season li-600 data collection * Time_early: Time of early-season li-600 data collection - VPDleaf_early: Leaf vapor pressure deficit at the time of early-season li-600 data collection * gsw_early: Early-season stomatal conductance to water vapor, measured at the leaf level with a li-600 porometer in units of mmol/m²/s - freshMass_g: Fresh leaf mass in grams (CDM please add something here about leaf selection) * dryMass_g: Oven-dried leaf mass in grams (CDM please add something here about leaf selection) - leafArea_cm2: Leaf area in square centimeters, derived from leaf scans (CDM please clarify) * lma_g_per_m2: Dry leaf mass in grams per area in meters squared (CDM please clarify) - ldmc: Leaf dry matter content, measured as dry leaf mass in grams divided by fresh leaf mass in grams * sla_cm2_per_g: specific leaf area, measured as leaf area in squared centimeters divided by dry leaf mass in grams - L1: Length of the primary or longest stem at first flower in centimeters * L2: Length of the second longest stem at first flower in centimeters - L3: Length of the third longest stem at first flower in centimeters * totalStemLen: Sum of the lengths of the three longest stems at first flower in centimeters - first_flower_date: Date of first flower * first_flower_doy: Day of year of first flower - last_flower_date: Date of last flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season * last_flower_doy: Day of year of last flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season - flowering_duration: Duration of flowering expressed as the difference between the date of last flower and the date of first flower; note this is not reliable because we did not continue collecting data after a certain point in the growing season * Date_late: Date of late-season li-600 data collection - Time_late: Time of late-season li-600 data collection * VPDleaf_late: Leaf vapor pressure deficit at the time of late-season li-600 data collection - gsw_late: Late-season stomatal conductance to water vapor, measured at the leaf level with a li-600 porometer in units of mmol/m²/s * maxHeight: Maximum stem height in centimeters at the end of the growing season - repBranchN: Number of major reproductive branches at the end of the growing season * RScount1: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on the stem with the most reproductive structures at the end of the growing season - RScount2: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on a representative major reproductive branch at the end of the growing season * RScount3: Number of reproductive structures (flowers, fruits, buds, and pedicels) counted on a representative major reproductive branch at the end of the growing season - totalRS: An estimate of the total number of reproductive structures (flowers, fruits, buds, and pedicels) on a plant, calculated as described in Supplementary Methods and Results 2\\. **PERSIST_populations_gardens_1901-2021SY.csv**: annual climate data (1951-2021) for focal populations and experimental gardens. Downloaded from climateNA v. 7.30 on 2022-09-14 (Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. [https://doi.org/10.1371/journal.pone.0156720](https://doi.org/10.1371/journal.pone.0156720)) * Year: year to which climate data corresponds - ID1: identifier corresponding to population (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) or experimental garden (N_garden: northern garden; C_garden: central garden; S_garden: southern garden) * ID2: identifier that ranks population from northernmost (1) to southernmost (6) - Latitude: y-position of each population or garden in decimal degrees * Longitude: x-position of each population or garden in decimal degrees - Elevation: meters above sea level of each population or garden All other columns are climate variables with units and definitions defined here: [https://climatena.ca/Help2](https://climatena.ca/Help2) 3\\. **subset_correlations.csv**: 2023 fitness data collected on a subset of individuals from each garden * rowPos: Variable that combines row and position (unique plant ID within each garden) - garden: Experimental garden (north, central, south) * population: Population of scarlet monkeyflower (N1 and N2: northern populations; C1 and C2: central populations; S1 and S2: southern populations) - cohort: Year that seeds were collected in the field (2010 ancestors and 2017 descendants) * repBranchN: The number of reproductive branches on an individual (used to calculate total number of reproductive structures/successful fruits) - biomass: mass of the whole plant in grams * L1: Length of the primary (usually longest) stem at first flower in centimeters - L2: Length of the second longest stem at first flower in centimeters * L3: Length of the third longest stem at first flower in centimeters - SC1: Successful fruit count for stem 1 * SC2: Successful fruit count for stem 2 - SC3: Successful fruit count for stem 3 * TC1: Total reproductive structure count for stem 1 - TC2: Total reproductive structure count for stem 2 * TC3: Total reproductive structure count for stem 3 Missing data code: NA ## Code/software #### Code and objects associated with "Evolutionary responses to historic drought across the range of scarlet monkeyflower" Manuscript is in review at The American Naturalist #### STEPS #### A. Download entire repository to desired location B. Open PERSIST-general.Rproj file in R Studio C. Install associated R packages listed at the beginning of each script. D. Create a new subdirectory with the structure "figures/2024_AmNat/manuscript" #### DIRECTORY DESCRIPTIONS data/2024_AmNat: raw data files used in analyses and figures r/2024_AmNat: script files to reproduce analyses in manuscript, numbered sequentially objects/2024_AmNat: output files created by R scripts PERSIST.Rproj: R Studio project file README.txt: text file that contains descriptions of each data file and R script #### SCRIPTS 01a_anomalies_climateNA.R: Calculate winter precipitation anomalies, make Fig. 2b and c 01b_Cardinalis_map.R: Make Fig. 2 (map of Mimulus cardinals populations and experimental gardens combined with panels from script 01a) 02_R_analyses.R: Run models with each trait as response variable to estimate trait medians, evolutionary change between ancestors and descendants and quantitative genetic parameters for each population and cohort in each garden 03_selection_analyses.R: Run models with fitness as response variable and each trait as a predictor to estimate phenotypic selection in each garden 04a_summary_R_h2_NCS.R: Summarize trait models from script 02 for traits measured in all gardens 04b_summary_R_h2_NS.R: Summarize trait models from script 02 for traits only measured in northern and southern gardens 05_model_selection_Va.R: Compare different models of additive genetic variance and make Table S9 06_plot_R_h2.R: Make figures and tables of trait medians (Fig. 3, Table S6), evolutionary change between ancestors and descendants (Fig. 6, Table S10) and quantitative genetic parameters for each population and cohort in each garden (Fig. 5, Table S8) 07a_summary_S_NCS.R: Summarize selection models from script 03 for traits measured in all gardens 07b_summary_S_NS.R: Summarize selection models from script 03 for traits only measured in northern and southern gardens 08_plot_S.R: Make figures and tables of phenotypic selection (Fig. 5, Table S7) 09_fitness-subset-correlations.R: Perform simple correlation tests among various fitness proxies measured on a subset of plants in each garden and make Fig. S1 and S2 and Table S3. 10_sample_sizes_sires_dams.R: Extract sample sizes reported in Tables S2 and S4. 11_brms_vs_mcmcglmm.R: Compare global brms model including data from all populations, cohorts, and gardens, to sub-models in brms and MCMCglmm built from each ancestral cohort of each population x garden combination (Table S5, Figure S3). 12_gxe_plot.R: Visualize genotype-by-environment interactions by plotting breeding values of each population across each pair of gardens (Figure S4). #### OBJECTS The scripts produce several intermediate objects. These are included in the repository but are not individually listed and described here. ## Access information Climate data were downloaded from climateNA v. 7.30 on 2022-09-14 (Wang T, Hamann A, Spittlehouse D, Carroll C (2016) Locally Downscaled and Spatially Customizable Climate Data for Historical and Future Periods for North America. PLoS ONE 11(6): e0156720. [https://doi.org/10.1371/journal.pone.0156720](https://doi.org/10.1371/journal.pone.0156720))"]} 

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3. "Cohorts of immature Pteropus bats show interannual variation in Hendra virus serology

   https://doi.org/10.5281/zenodo.15982444  

   ymous, anon (January 2025, Zenodo)

   {"Abstract":[" LUMINEX Wildlife Disease Analysis Pipeline\n\n  Overview\n\n  Bayesian analysis pipeline for "Cohorts of immature Pteropus bats show interannual variation in Hendra virus serology"\n\nSummary \n\n  Prerequisites\n\n  Software Requirements\n\n  - R (≥ 4.0.0)  - Stan (≥ 2.21)\n\n  Required R Packages\n\n  # Core packages  install.packages(c("rstan", "tidyverse", "here", "loo", "bayesplot"))  # Additional packages    install.packages(c("RColorBrewer", "cowplot", "boot", "see", "factoextra", "bestNormalize", "LaplacesDemon", "ggpubr", "plyr", "see", "pscl"))    # Core utility functions (CRITICAL DEPENDENCY)  source(here("R", "useful_functions.R"))  # Bayesian analysis functions  source(here("R", "paper_theme.R"))      # Plotting themes    Hardware Requirements\n\n  - Storage: ≥5GB free space  - CPU: Multi-core processor recommended\n\n  Data Requirements\n\n  /raw_sharable folder\n\n  Execution Workflow\n\n  ⚠️ CRITICAL: Execute in This Order\n\n  Phase 1: Core Data Processing\n\n  1. Initial Data Cleaning  source("R/create_datasets_for_stan_part_1.R")    - Runtime: ~5-10 minutes    - Outputs: data_for_cohort_model.csv, luminex_igg_igm.csv  2. Serology Classification  rmarkdown::render("R/mixture_model_final.R")    - Runtime: ~30-60 minutes (Stan model fitting)    - Outputs: serology_prob.csv  3. Age/Cohort Assignment  rmarkdown::render("R/cohort_model_2025_05_12.Rmd")    - Runtime: ~1-3 hours (complex Stan models)    - Outputs: age_predictions.csv & a species comparison analysis   4. Dataset Integration  source("R/create_datasets_for_stan_part_2_06_30_24.R")    - Runtime: ~10-15 minutes    - Outputs: All analysis-ready datasets with time-alive variables\n\n  Phase 2: Primary Analyses\n\n  5. Prevalence Smoothing Analysis  rmarkdown::render("R/gaussian_smooth_prevalence_06_30_24.Rmd")    - Runtime: ~10+ hours (multiple Stan models)    - Key outputs: Prevalence curves, model comparisons    - Additional outputs:       -Basic prevalence smoothing (4 pathogens × 4 cohorts)      -Site-specific analysis (8-cohort models)      -Sex-stratified analysis      -Stringent cohort cutoff analysis      -Multiple cutoff threshold testing (5 different cutoffs)      -Date-based modeling      -Batch effect testing      -Adult vs juvenile comparisons  6. Logistic Regression Analysis  source("R/logistic_models_fig_3.R")  source("R/logistic_models_fig_2.R")    - Runtime: ~30-60 minutes    - Outputs: Figure 2 & 3 plots\n\n  Phase 3: Supporting Analyses (Optional)\n\n  7. Additional Analyses (run as needed):    - adult_prevalence_curves.Rmd - Adult dynamics    - PCA_new_analysis.R - Multivariate analysis    - additional_figures.R - Supplementary figures\n\n  Key Functions (useful_functions.R)\n\n  - fit_4cohort_model() - Bayesian 4-cohort prevalence model  - compile_stan_results() - Extract and format Stan results  - create_time_sequence() - Generate prediction timepoints  - compute_loo_cv() - Leave-one-out cross-validation  - plot_parameter_diagnostics() - Model diagnostic plots\n\n  Troubleshooting\n\n  Common Issues\n\n  Stan compilation errors:  # Recompile Stan models  rstan_options(auto_write = TRUE)  options(mc.cores = parallel::detectCores())\n\n  Memory issues:  - Reduce Stan iterations: ITER = 1000 instead of ITER = 2000  - Run analyses sequentially, not in parallel\n\n  Missing dependencies:  # Load all utility functions  source(here("R", "useful_functions.R"))  source(here("R", "paper_theme.R"))\n\n  Resume from Saved Results\n\n  Many scripts save intermediate results:  # Check for existing model fits  if(file.exists("model_results.RData")) {    load("model_results.RData")  } else {    # Run full analysis  }\n\n  Output Structure\n\n  Luminex_figs/r_figs/     # All generated figures  Data_for_publication/    # Final analysis datasets  stan/                    # Stan model files  R/                       # Analysis scripts\n\n  Expected Runtime\n\n  Full pipeline: 10-20 hours on modern hardware  \n\n \n\n "]} 

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4. Replication Data for: Physics potential of the IceCube Upgrade for atmospheric neutrino oscillations

   https://doi.org/10.7910/dvn/ospldg  

   Collaboration, IceCube; Collaboration, IceCube (January 2025, Harvard Dataverse)

   {"Abstract":["Data and plotting scripts for reproducing plots from "Physics potential of the IceCube Upgrade for atmospheric neutrino oscillations". Please refer to the related publication for a detailed explanation of the sample and analysis.\n<br><br>\nContents: \n<ol>\n<li> <code>README.md</code> contains useful information about the contents of this data release.</li>\n<li> <code>example.py</code> Example script demonstrating how to load the csv files, plot the chi squared map, and extract the 90% sensitivity contours shown in Figures 11 and 15.</li>\n<li> <code>modchi2map_nufitwoSK.csv</code> chi2 map used to produce Figure 11 (left), in which the injected truth point is the best fit point from <a href="http://www.nu-fit.org/sites/default/files/v52.tbl-parameters.pdf">NuFit 5.2 w/o SK (upper panel)</a>, sin²(&theta;₂₃)=0.572 and &Delta;m²₃₂=2.43&times;10^-3 eV².</li>\n<li> <code>modchi2map_nufitwSK.csv</code> chi2 map used to produce Figure 11 (right), in which the injected truth point is the best fit point from <a href="http://www.nu-fit.org/sites/default/files/v52.tbl-parameters.pdf">NuFit 5.2 w/ SK (lower panel)</a>, sin²(&theta;₂₃)=0.451 and &Delta;m²₃₂=2.43&times;10^-3 eV².</li>\n<li> <code>modchi2map_icecube.csv</code> chi2 map used to produce Figure 15, in which the injected truth point is the best fit point from the latest published IceCube DeepCore oscillation result <a href="https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.134.091801">Phys. Rev. Lett. 134, 091801 (2025)</a>, sin²(&theta;₂₃)=0.54 and &Delta;m²₃₂=2.40&times;10^-3 eV².</li>\n</ol>\n\nPlease note: The CSV files are available for download through dataverse in either csv or tab format."]} 

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5. NIOZ-DOM-Analysis/ABCDom: Coral_thermal_stress_and_bleaching_enrich_and_restructure_reef_microbial_communities_via_altered_organic_matter_exudation

   https://doi.org/10.5281/zenodo.10214648  

   Arts, Milou; Sparagon, Wesley (January 2023, Zenodo)

   Authors: Wesley J. Sparagon, Milou G.I. Arts, Zachary Quinlan3, Irina Koester, Jacqueline Comstock, Jessica A. Bullington, Craig A. Carlson, Pieter C. Dorrestein, Lihini I. Aluwihare, Linda Wegley Kelly, Andreas F. Haas and Craig E. Nelson Contains relevant raw data, R Code for analysis and figure generation, and output data frames used for figures. 

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