ABSTRACT:

The World Soil Information Service (WoSIS) provides quality-assessed and standardized soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the ‘WoSIS snapshot 2019’ many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardized in accordance with the licenses specified by the data providers. The source data were contributed by a wide range of data providers, therefore special attention was paid to the standardization of soil property definitions, soil analytical procedures and soil property values (and units of measurement).

We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable.

For each profile we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations as far as these have been specified in the source databases.

Three measures for 'fitness-for-intended-use' are provided: positional uncertainty (for site locations), time of sampling/description, and a first approximation for the uncertainty associated with the operationally defined analytical methods. These measures should be considered during digital soil mapping and subsequent earth system modelling that use the present set of soil data.

DATA SET DESCRIPTION:

The 'WoSIS 2023 snapshot' comprises data for 228k profiles from 217k geo-referenced sites that originate from 174 countries. The profiles represent over 900k soil layers (or horizons) and over 6 million records. The actual number of measurements for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes.

The data are provided in TSV (tab separated values) format and as GeoPackage. The zip-file (446 Mb) contains the following files:

• Readme_WoSIS_202312_v2.pdf: Provides a short description of the dataset, file structure, column names, units and category values (this file is also available directly under 'online resources'). The pdf includes links to tutorials for downloading the TSV files into R respectively Excel. See also 'HOW TO READ TSV FILES INTO R AND PYTHON' in the next section.

• wosis_202312_observations.tsv: This file lists the four to six letter codes for each observation, whether the observation is for a site/profile or layer (horizon), the unit of measurement and the number of profiles respectively layers represented in the snapshot. It also provides an estimate for the inferred accuracy for the laboratory measurements.

• wosis_202312_sites.tsv: This file characterizes the site location where profiles were sampled.

• wosis_2023112_profiles: Presents the unique profile ID (i.e. primary key), site_id, source of the data, country ISO code and name, positional uncertainty, latitude and longitude (WGS 1984), maximum depth of soil described and sampled, as well as information on the soil classification system and edition. Depending on the soil classification system used, the number of fields will vary .

• wosis_202312_layers: This file characterises the layers (or horizons) per profile, and lists their upper and lower depths (cm).

• wosis_202312_xxxx.tsv : This type of file presents results for each observation (e.g. “xxxx” = “BDFIOD” ), as defined under “code” in file wosis_202312_observation.tsv. (e.g. wosis_202311_bdfiod.tsv).

• wosis_202312.gpkg: Contains the above datafiles in GeoPackage format (which stores the files within an SQLite database).

HOW TO READ TSV FILES INTO R AND PYTHON:

A) To read the data in R, please uncompress the ZIP file and specify the uncompressed folder.

setwd("/YourFolder/WoSIS_2023_December/") ## For example: setwd('D:/WoSIS_2023_December/')

Then use read_tsv to read the TSV files, specifying the data types for each column (c = character, i = integer, n = number, d = double, l = logical, f = factor, D = date, T = date time, t = time).

observations = readr::read_tsv('wosis_202312_observations.tsv', col_types='cccciid')
observations ## show columns and first 10 rows

sites = readr::read_tsv('wosis_202312_sites.tsv', col_types='iddcccc') sites

profiles = readr::read_tsv('wosis_202312_profiles.tsv', col_types='icciccddcccccciccccicccci') profiles

layers = readr::read_tsv('wosis_202312_layers.tsv', col_types='iiciciiilcc') layers

Do this for each observation 'XXXX', e.g. file 'Wosis_202312_orgc.tsv':

orgc = readr::read_tsv('wosis_202312_orgc.tsv', col_types='iicciilccdccddccccc')
orgc

Note: One may also use the following R code (example is for file 'observations.tsv'): observations <- read.table("wosis_202312_observations.tsv", sep = "\t", header = TRUE, quote = "", comment.char = "", stringsAsFactors = FALSE )

B) To read the files into python first decompress the files to your selected folder. Then in python:

import the required library

import pandas as pd

Read the observations data

observations = pd.read_csv("wosis_202312_observations.tsv", sep="\t") # print the data frame header and some rows observations.head()

Read the sites data

sites = pd.read_csv("wosis_202312_sites.tsv", sep="\t")

Read the profiles data

profiles = pd.read_csv("wosis_202312_profiles.tsv", sep="\t")

Read the layers data

layers = pd.read_csv("wosis_202312_layers.tsv", sep="\t")

Read the soil property data, e.g. 'cfvo' (do this for each observation)

cfvo = pd.read_csv("wosis_202312_cfvo.tsv", sep="\t")

CITATION: Calisto, L., de Sousa, L.M., Batjes, N.H., 2023. Standardised soil profile data for the world (WoSIS snapshot – December 2023), https://doi.org/10.17027/isric-wdcsoils-20231130

Supplement to: Batjes N.H., Calisto, L. and de Sousa L.M., 2023. Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data, https://doi.org/10.5194/essd-16-4735-2024.

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”.