This data set includes the *.csv data and the used scripts to reproduce the plots of the three different scenarios presented in S. Kiemle, K. Heck, E. Coltman, R. Helmig (2022) Stable water isotopologue fractionation during soil-water evaporation: Analysis using a coupled soil-atmosphere model. (Under review) Water Resources Research. *.csv files The isotope distribution has been analyzed in the vertical and in horizontal direction of a soil column for all scenarios. Therefore, we provide *.csv files generated using the ParaView Tools "plot over line" or "plot over time". Each *.csv file contains information about the saturation, temperature, and component composition for each phase in mole fraction or in the isotopic-specific delta notation. Additionally, information about the evaporation rate is given in a separate file *.txt file. python scripts For each scenario, we provide scripts to reproduce the presented plots. Scenarios We used different free flow conditions to analyze the fractionation processes inside the porous medium. Scenario 1. laminar flow, Scenario 2. laminar flow, but with isolation of parameter affecting the fractionation process, Scenario 3. turbulent flow. Please find below a detailed description of the data labeling and needed scripts to reproduce a certain plot for each scenario. Scenario: The spatial distribution of stable water isotopologues in horizontal (-0.01 m depth) and vertical (at 0.05 m width) inside a soil column at selected days (DoE (Day of Experiment)): Use the python scripts plot_concentration_horizontal_all.py (horizontal direction) and plot_concentration_spatial_all.py (vertical direction) to create the specific plots. In the folder IsotopeProfile_Horizontal and IsotopeProfile_Vertical the belonging *.csv can be found. The *.csv files are named after the selected day (e.g. DoE_80 refers to day 80 of the virtual experiment). The influence of the evaporation rate on isotopic fractionation processes in various depths (-0.001, -0.005, -0.009, and -0.018 m ) during the whole virtual experiment time: Use the python script plot_evap_isotopes_v2.py to create the plots. The data for the isotopologues distribution and the saturation can be found in the folder PlotOverTime. All data is named as PlotOverTime_xxxxm with xxxx representing the respective depth (e.g. PlotOverTime_0.001m refers to -0.001 m depth). The data for the evaporation rate can be found in the folder EvaporationRate. Note, the evaporation rate data is available as a .txt because we extract the information about the evaporation directly during the simulation and do not derive it through any post-processing. Scenario: Process behavior of isolated parameters that influences the isotopic fractionation: Use plot_concentration.py to reproduce the plots either represented in the isotopic-specific delta notation or in mole fraction. The corresponding data can be found in the folder IsotopeProfile_Vertical. The data labeling refers to the single cases (1- no fractionation; 2 - only equilibrium fractionation; 3 - only kinetic fractionation; 4 - only liquid diffusion; 5 - Reference). Scenario: Evaporation rate during the virtual experiment for different flow cases: With plot_evap.py and the .txt files which can be found in the folder EvaporationRate, the evaporation progression can be plotted. The labeling of the .txt files refers to the different flow cases (1 - 0.1 m/s (laminar); 2 - 0.13 m/s (laminar); 3 - 0.5 m/s (turbulent); 4 - 1 m/s (turbulent); 5 - 3 m/s (turbulent)). The isotope profiles in the vertical and horizontal direction of the soil column (similar to Scenario 1) for selected days: With plot_cocentration_horizontal_all.py and plot_concentration_spatial_all.py the plots for the horizontal and vertical distribution of isotopologues can be generated. The corresponding data can be found in the folders IsotopeProfile_Horizontal and IsotopeProfile_Vertical. These folders are structured with subfolders containing the data of selected days of the virtual experiments (DoE - Day of Experiments), in this case, day 2, 10, and 35. The data labeling remains similar to Scenario 3a).

Categorical scatterplots with R for biologists: a step-by-step guide

Benjamin Petre1, Aurore Coince2, Sophien Kamoun1

1 The Sainsbury Laboratory, Norwich, UK; 2 Earlham Institute, Norwich, UK

Weissgerber and colleagues (2015) recently stated that ‘as scientists, we urgently need to change our practices for presenting continuous data in small sample size studies’. They called for more scatterplot and boxplot representations in scientific papers, which ‘allow readers to critically evaluate continuous data’ (Weissgerber et al., 2015). In the Kamoun Lab at The Sainsbury Laboratory, we recently implemented a protocol to generate categorical scatterplots (Petre et al., 2016; Dagdas et al., 2016). Here we describe the three steps of this protocol: 1) formatting of the data set in a .csv file, 2) execution of the R script to generate the graph, and 3) export of the graph as a .pdf file.

Protocol

• Step 1: format the data set as a .csv file. Store the data in a three-column excel file as shown in Powerpoint slide. The first column ‘Replicate’ indicates the biological replicates. In the example, the month and year during which the replicate was performed is indicated. The second column ‘Condition’ indicates the conditions of the experiment (in the example, a wild type and two mutants called A and B). The third column ‘Value’ contains continuous values. Save the Excel file as a .csv file (File -> Save as -> in ‘File Format’, select .csv). This .csv file is the input file to import in R.

• Step 2: execute the R script (see Notes 1 and 2). Copy the script shown in Powerpoint slide and paste it in the R console. Execute the script. In the dialog box, select the input .csv file from step 1. The categorical scatterplot will appear in a separate window. Dots represent the values for each sample; colors indicate replicates. Boxplots are superimposed; black dots indicate outliers.

• Step 3: save the graph as a .pdf file. Shape the window at your convenience and save the graph as a .pdf file (File -> Save as). See Powerpoint slide for an example.

Notes

• Note 1: install the ggplot2 package. The R script requires the package ‘ggplot2’ to be installed. To install it, Packages & Data -> Package Installer -> enter ‘ggplot2’ in the Package Search space and click on ‘Get List’. Select ‘ggplot2’ in the Package column and click on ‘Install Selected’. Install all dependencies as well.

• Note 2: use a log scale for the y-axis. To use a log scale for the y-axis of the graph, use the command line below in place of command line #7 in the script.

7 Display the graph in a separate window. Dot colors indicate

replicates

graph + geom_boxplot(outlier.colour='black', colour='black') + geom_jitter(aes(col=Replicate)) + scale_y_log10() + theme_bw()

References

Dagdas YF, Belhaj K, Maqbool A, Chaparro-Garcia A, Pandey P, Petre B, et al. (2016) An effector of the Irish potato famine pathogen antagonizes a host autophagy cargo receptor. eLife 5:e10856.

Petre B, Saunders DGO, Sklenar J, Lorrain C, Krasileva KV, Win J, et al. (2016) Heterologous Expression Screens in Nicotiana benthamiana Identify a Candidate Effector of the Wheat Yellow Rust Pathogen that Associates with Processing Bodies. PLoS ONE 11(2):e0149035

Weissgerber TL, Milic NM, Winham SJ, Garovic VD (2015) Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm. PLoS Biol 13(4):e1002128

https://cran.r-project.org/

http://ggplot2.org/

This dataset contains all the data and code needed to reproduce the analyses in the manuscript: Penn, H. J., & Read, Q. D. (2023). Stem borer herbivory dependent on interactions of sugarcane variety, associated traits, and presence of prior borer damage. Pest Management Science. https://doi.org/10.1002/ps.7843 Included are two .Rmd notebooks containing all code required to reproduce the analyses in the manuscript, two .html file of rendered notebook output, three .csv data files that are loaded and analyzed, and a .zip file of intermediate R objects that are generated during the model fitting and variable selection process. Notebook files 01_boring_analysis.Rmd: This RMarkdown notebook contains R code to read and process the raw data, create exploratory data visualizations and tables, fit a Bayesian generalized linear mixed model, extract output from the statistical model, and create graphs and tables summarizing the model output including marginal means for different varieties and contrasts between crop years. 02_trait_covariate_analysis.Rmd: This RMarkdown notebook contains R code to read raw variety-level trait data, perform feature selection based on correlations between traits, fit another generalized linear mixed model using traits as predictors, and create graphs and tables from that model output including marginal means by categorical trait and marginal trends by continuous trait. HTML files These HTML files contain the rendered output of the two RMarkdown notebooks. They were generated by Quentin Read on 2023-08-30 and 2023-08-15. 01_boring_analysis.html 02_trait_covariate_analysis.html CSV data files These files contain the raw data. To recreate the notebook output the CSV files should be at the file path project/data/ relative to where the notebook is run. Columns are described below. BoredInternodes_26April2022_no format.csv: primary data file with sugarcane borer (SCB) damage Columns A-C are the year, date, and location. All location values are the same. Column D identifies which experiment the data point was collected from. Column E, Stubble, indicates the crop year (plant cane or first stubble) Column F indicates the variety Column G indicates the plot (integer ID) Column H indicates the stalk within each plot (integer ID) Column I, # Internodes, indicates how many internodes were on the stalk Columns J-AM are numbered 1-30 and indicate whether SCB damage was observed on that internode (0 if no, 1 if yes, blank cell if that internode was not present on the stalk) Column AN indicates the experimental treatment for those rows that are part of a manipulative experiment Column AO contains notes variety_lookup.csv: summary information for the 16 varieties analyzed in this study Column A is the variety name Column B is the total number of stalks assessed for SCB damage for that variety across all years Column C is the number of years that variety is present in the data Column D, Stubble, indicates which crop years were sampled for that variety ("PC" if only plant cane, "PC, 1S" if there are data for both plant cane and first stubble crop years) Column E, SCB resistance, is a categorical designation with four values: susceptible, moderately susceptible, moderately resistant, resistant Column F is the literature reference for the SCB resistance value Select_variety_traits_12Dec2022.csv: variety-level traits for the 16 varieties analyzed in this study Column A is the variety name Column B is the SCB resistance designation as an integer Column C is the categorical SCB resistance designation (see above) Columns D-I are continuous traits from year 1 (plant cane), including sugar (Mg/ha), biomass or aboveground cane production (Mg/ha), TRS or theoretically recoverable sugar (g/kg), stalk weight of individual stalks (kg), stalk population density (stalks/ha), and fiber content of stalk (percent). Columns J-O are the same continuous traits from year 2 (first stubble) Columns P-V are categorical traits (in some cases continuous traits binned into categories): maturity timing, amount of stalk wax, amount of leaf sheath wax, amount of leaf sheath hair, tightness of leaf sheath, whether leaf sheath becomes necrotic with age, and amount of collar hair. ZIP file of intermediate R objects To recreate the notebook output without having to run computationally intensive steps, unzip the archive. The fitted model objects should be at the file path project/ relative to where the notebook is run. intermediate_R_objects.zip: This file contains intermediate R objects that are generated during the model fitting and variable selection process. You may use the R objects in the .zip file if you would like to reproduce final output including figures and tables without having to refit the computationally intensive statistical models. binom_fit_intxns_updated_only5yrs.rds: fitted brms model object for the main statistical model binom_fit_reduced.rds: fitted brms model object for the trait covariate analysis marginal_trends.RData: calculated values of the estimated marginal trends with respect to year and previous damage marginal_trend_trs.rds: calculated values of the estimated marginal trend with respect to TRS marginal_trend_fib.rds: calculated values of the estimated marginal trend with respect to fiber content Resources in this dataset:Resource Title: Sugarcane borer damage data by internode, 1993-2021. File Name: BoredInternodes_26April2022_no format.csvResource Title: Summary information for the 16 sugarcane varieties analyzed. File Name: variety_lookup.csvResource Title: Variety-level traits for the 16 sugarcane varieties analyzed. File Name: Select_variety_traits_12Dec2022.csvResource Title: RMarkdown notebook 2: trait covariate analysis. File Name: 02_trait_covariate_analysis.RmdResource Title: Rendered HTML output of notebook 2. File Name: 02_trait_covariate_analysis.htmlResource Title: RMarkdown notebook 1: main analysis. File Name: 01_boring_analysis.RmdResource Title: Rendered HTML output of notebook 1. File Name: 01_boring_analysis.htmlResource Title: Intermediate R objects. File Name: intermediate_R_objects.zip

This dataset is about books. It has 1 row and is filtered where the book is Select sermons : with an account of his life by J.C.Ryle and a summary of his doctrine by R.Elliot. It features 7 columns including author, publication date, language, and book publisher.

Various water column variables, including salinity, dissolved inorganic nutrients, pH, total alkalinity, dissolved inorganic carbon, radio-carbon isotopes were measured in samples collected using a Niskin-bottle rosette at selected depths from sites offshore of California and Oregon from October to November 2019 during NOAA Ship Lasker R-19-05 (USGS field activity 2019-672-FA). CTD (Conductivity Temperature Depth) data were also collected at each depth that a Niskin-bottle sample was collected and are presented along with the water sample data. This data release supersedes version 1.0, published in August 2020 at https://doi.org/10.5066/P9ZS1JX8. Versioning details are documented in the accompanying VersionHistory_P9JKYWQU.txt file.

Petition subject: Against railroad discrimination with focus on white passengers Original: http://nrs.harvard.edu/urn-3:FHCL:10956457 Date of creation: (unknown) Petition location: Sudbury Legislator, committee, or address that the petition was sent to: Francis R. Gourgas, Concord Selected signatures:J.H. BrownSally BrownLoring Eaton Total signatures: 76 Legal voter signatures (males not identified as non-legal): 31 Female signatures: 37 Unidentified signatures: 8 Female only signatures: No Identifications of signatories: inhabitants, [females] Prayer format was printed vs. manuscript: Printed Signatory column format: not column separated Additional non-petition or unrelated documents available at archive: no additional documents Additional archivist notes: 11057/4 written on back Location of the petition at the Massachusetts Archives of the Commonwealth: House Unpassed 1842, Docket 1153 Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.

One of the methods for the separation of stable isotopes is the thermal diffusion column. The advantages of this method include small-scale operations because of apparatus simplicity and a small inventory, especially in gas phase operations. These features attract attention to the tritium and noble gas separation system. In this research, the R cascade was used for designing and determining the number of columns. Moreover, the square cascade was adopted for the final design because of its flexibility. Calculations were performed as an example for the separation of 20Ne and 22Ne isotopes. Accordingly, all R cascades that have enriched Ne isotopes to more than 99% were investigated, and the number of columns was determined. Also, using the specified columns, the square cascade parameters were optimized. A calculation code entitled ''RSQ_CASCADE'' was developed in this regard. The unit separation factor of 3 was considered, and the number of stages was studied in the range of 10 to 20. The results showed that the column separation power, the relative total flow rate, and the required columns were linearly related to the number of stages. The separation power and relative total flow decreased with the increase of stage number while the number of columns increased. Therefore, the cascade of 85 columns was recommended to separate the Ne stable isotopes. These calculations resulted in the 17-stage square cascade, with five columns in each stage. By changing the stages cut, feed point, and cascade feed flow rate, the best parameters of square cascade were determined according to the cascade separation power and column separation power. As the column separation power had the maximum value in the cascade feed 50, it was selected for separating Ne isotopes.

Petition subject: Execution case Original: http://nrs.harvard.edu/urn-3:FHCL:12233039 Date of creation: (unknown) Petition location: Uxbridge Selected signatures:Luther RistSusan R. UsherHarriett N. Moury Total signatures: 175 Legal voter signatures (males not identified as non-legal): 64 Female signatures: 84 Unidentified signatures: 27 Female only signatures: No Identifications of signatories: inhabitants, [females] Prayer format was printed vs. manuscript: Manuscript Signatory column format: not column separated Additional non-petition or unrelated documents available at archive: additional documents available Additional archivist notes: Leander Thompson Location of the petition at the Massachusetts Archives of the Commonwealth: Governor Council Files, April 17, 1847, Case of Leander Thompson Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.

Petition subject: Execution case Original: http://nrs.harvard.edu/urn-3:FHCL:12232985 Date of creation: 1843-09-11 Petition location: Roxbury Selected signatures:Charles W. LillieStephen R. DoggettCaroline Williams Total signatures: 13 Legal voter signatures (males not identified as non-legal): 9 Female signatures: 4 Female only signatures: No Identifications of signatories: inhabitants, [females] Prayer format was printed vs. manuscript: Manuscript Signatory column format: not column separated Additional non-petition or unrelated documents available at archive: additional documents available Additional archivist notes: Isaac Leavitt Location of the petition at the Massachusetts Archives of the Commonwealth: Governor Council Files, September 22, 1843, Case of Isaac Leavitt Acknowledgements: Supported by the National Endowment for the Humanities (PW-5105612), Massachusetts Archives of the Commonwealth, Radcliffe Institute for Advanced Study at Harvard University, Center for American Political Studies at Harvard University, Institutional Development Initiative at Harvard University, and Harvard University Library.

LLM selection results by FS procedure in terms of R-values, L-values, and ω-values (Numeric values in the 2nd column represent rule IDs).

Walking and running are mechanically and energetically different locomotion modes. For selecting one or another, speed is a parameter of paramount importance. Yet, both are likely controlled by similar low-dimensional neuronal networks that reflect in patterned muscle activations called muscle synergies. Here, we investigated how humans synergistically activate muscles during locomotion at different submaximal and maximal speeds. We analysed the duration and complexity (or irregularity) over time of motor primitives, the temporal components of muscle synergies. We found that the challenge imposed by controlling high-speed locomotion forces the central nervous system to produce muscle activation patterns that are wider and less complex relative to the duration of the gait cycle. The motor modules, or time-independent coefficients, were redistributed as locomotion speed changed. These outcomes show that robust locomotion control at challenging speeds is achieved by modulating the relative contribution of muscle activations and producing less complex and wider control signals, whereas slow speeds allow for more irregular control.

In this supplementary data set we made available: a) the metadata with anonymized participant information, b) the raw EMG, c) the touchdown and lift-off timings of the recorded limb, d) the filtered and time-normalized EMG, e) the muscle synergies extracted via NMF and f) the code to process the data, including the scripts to calculate the Higuchi's fractal dimension (HFD) of motor primitives. In total, 180 trials from 30 participants are included in the supplementary data set.

The file “metadata.dat” is available in ASCII and RData format and contains:

Code: the participant’s code

Group: the experimental group in which the participant was involved (G1 = walking and submaximal running; G2 = submaximal and maximal running)

Sex: the participant’s sex (M or F)

Speeds: the type of locomotion (W for walking or R for running) and speed at which the recordings were conducted in 10*[m/s]

Age: the participant’s age in years

Height: the participant’s height in [cm]

Mass: the participant’s body mass in [kg]

PB: 100 m-personal best time (for G2).

The "RAW_DATA.RData" R list consists of elements of S3 class "EMG", each of which is a human locomotion trial containing cycle segmentation timings and raw electromyographic (EMG) data from 13 muscles of the right-side leg. Cycle times are structured as data frames containing two columns that correspond to touchdown (first column) and lift-off (second column). Raw EMG data sets are also structured as data frames with one row for each recorded data point and 14 columns. The first column contains the incremental time in seconds. The remaining 13 columns contain the raw EMG data, named with the following muscle abbreviations: ME = gluteus medius, MA = gluteus maximus, FL = tensor fasciæ latæ, RF = rectus femoris, VM = vastus medialis, VL = vastus lateralis, ST = semitendinosus, BF = biceps femoris, TA = tibialis anterior, PL = peroneus longus, GM = gastrocnemius medialis, GL = gastrocnemius lateralis, SO = soleus. Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17). All the other trials consist of 30 gait cycles. Trials are named like “P20_R_20,” where the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”.

Old versions not compatible with the R package musclesyneRgies

The files containing the gait cycle breakdown are available in RData format, in the file named “CYCLE_TIMES.RData”. The files are structured as data frames with as many rows as the available number of gait cycles and two columns. The first column named “touchdown” contains the touchdown incremental times in seconds. The second column named “stance” contains the duration of each stance phase of the right foot in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P20_R_20,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P20” indicate the participant number (in this example the 20th), the character “R” indicate the locomotion type (W=walking, R=running), and the numbers “20” indicate the locomotion speed in 10*m/s (in this case the speed is 2.0 m/s). Please note that the following trials include less than 30 gait cycles (the actual number shown between parentheses): P16_R_83 (20), P16_R_95 (25), P17_R_28 (28), P17_R_83 (24), P17_R_95 (13), P18_R_95 (23), P19_R_95 (18), P20_R_28 (25), P20_R_42 (27), P20_R_95 (25), P22_R_28 (23), P23_R_28(29), P24_R_28 (28), P24_R_42 (29), P25_R_28 (29), P25_R_95 (28), P26_R_28 (29), P26_R_95 (28), P27_R_28 (28), P27_R_42 (29), P27_R_95 (24), P28_R_28 (29), P29_R_95 (17).

The files containing the raw, filtered and the normalized EMG data are available in RData format, in the files named “RAW_EMG.RData” and “FILT_EMG.RData”. The raw EMG files are structured as data frames with as many rows as the amount of recorded data points and 13 columns. The first column named “time” contains the incremental time in seconds. The remaining 12 columns contain the raw EMG data, named with muscle abbreviations that follow those reported above. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_P03_R_30”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P03” indicate the participant number (in this example the 3rd), the character “R” indicate the locomotion type (see above), and the numbers “30” indicate the locomotion speed (see above). The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P03_R_30”.

The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the files named “SYNS_H.RData” and “SYNS_W.RData”. The muscle synergies files are divided in motor primitives and motor modules and are presented as direct output of the factorisation and not in any functional order. Motor primitives are data frames with 6000 rows and a number of columns equal to the number of synergies (which might differ from trial to trial) plus one. The rows contain the time-dependent coefficients (motor primitives), one column for each synergy plus the time points (columns are named e.g. “time, Syn1, Syn2, Syn3”, where “Syn” is the abbreviation for “synergy”). Each gait cycle contains 200 data points, 100 for the stance and 100 for the swing phase which, multiplied by the 30 recorded cycles, result in 6000 data points distributed in as many rows. This output is transposed as compared to the one discussed in the methods section to improve user readability. Each set of motor primitives is saved as an element of a single R list. Trials are named like “SYNS_H_P12_W_07”, where the characters “SYNS_H” indicate that the trial contains motor primitive data, the characters “P12” indicate the participant number (in this example the 12th), the character “W” indicate the locomotion type (see above), and the numbers “07” indicate the speed (see above). Motor modules are data frames with 12 rows (number of recorded muscles) and a number of columns equal to the number of synergies (which might differ from trial to trial). The rows, named with muscle abbreviations that follow those reported above, contain the time-independent coefficients (motor modules), one for each synergy and for each muscle. Each set of motor modules relative to one synergy is saved as an element of a single R list. Trials are named like “SYNS_W_P22_R_20”, where the characters “SYNS_W” indicate that the trial contains motor module data, the characters “P22” indicate the participant number (in this example the 22nd), the character “W” indicates the locomotion type (see above), and the numbers “20” indicate the speed (see above). Given the nature of the NMF algorithm for the extraction of muscle synergies, the supplementary data set might show non-significant differences as compared to the one used for obtaining the results of this paper.

The files containing the HFD calculated from motor primitives are available in RData format, in the file named “HFD.RData”. HFD results are presented in a list of lists containing, for each trial, 1) the HFD, and 2) the interval time k used for the calculations. HFDs are presented as one number (mean HFD of the primitives for that trial), as are the interval times k. Trials are named like “HFD_P01_R_95”, where the characters “HFD” indicate that the trial contains HFD data, the characters “P01” indicate the participant number (in this example the 1st), the character “R” indicates the locomotion type (see above), and the numbers “95” indicate the speed (see above).

All the code used for the pre-processing of EMG data, the extraction of muscle synergies and the calculation of HFD is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.

The collection of datasets published contain the input parameters and output simulations from a single column 1D radiative transfer simulations using the Rapid Radiative Transfer Model for General Circulation Model (GCM) applications (RRTMG).

The simulations are focused on a selected case study of low-level-stratus clouds during the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing observations, which were synergistically used with the Cloudnet algorithm to derive macro and microphysical properties of clouds. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.