Find research datasets worth reusing

Registration of brands from Germany's most active companies

This bar-plot shows the amount of brand registrations of Germany's most active companies in 2017 and 2019.

Brand registrations from Germany's most active companies

This dataset shows the amount of brand registrations from Germany's most active companies in 2017 and 2019.

CROSSBOW HLU2-UC1-TC1 measurements: Substation measurements gathered during curtailment activation. Second test

. Capture on 21/04/2022 The data comprises the 2 sec measurements from HOPS SCADA system for for the Konjsko substation before, during and after a curtailment in Pometeno Brno plant (21:00 - 22:30 on 21/04/2022). The measurements gathered from the SCADA are: <ul> <li>P, Q and V of the 110, 220 and 400 Kv buses at the substation</li> </ul> P, Q and V of the line connecting the substation with the plant

A comparison of the World Health Organisation's HEAT model results using a non-linear physical activity dose response function with results from the existing tool

Datasets relating to the Wellcome Open Research publication of the same name.

Dataset: Consistent release of volatile organic compounds across an actively degrading permafrost peatland

Here, we conducted in situ measurements of soil and pond VOC emissions across an actively degrading permafrost peatland in subarctic Norway. We used a permafrost thaw gradient that covered bare soil and vegetated palsa plateaus, underlain by intact permafrost, and increasingly degraded permafrost landscapes: thaw slumps, thaw ponds, and vegetated thaw ponds. This dataset includes two excel files: 1) the first one "Finnmark_source_data" is the source data for figures in the publication <a href="https://doi.org/10.1016/j.geoderma.2023.116355">https://doi.org/10.1016/j.geoderma.2023.116355</a>. ii) the second one "Rawdata_of_emission_rate" is the emission rate of the 210 VOC species identified in this study. Results showed that every peatland landscape type was an important and consistent source of atmospheric VOCs, with a large variety species, such as methanol, acetone, monoterpenes, sesquiterpenes, isoprene, hydrocarbons, oxygenated VOCs, etc. VOC composition varied considerably across the measurement period and across the permafrost thaw gradient. We observed enhanced terpenoid emissions following thaw slump degradation, highlighting the potential atmospheric impact of permafrost thaw, due to the high chemical reactivities of terpenoid compounds. Overall, our study demonstrates that VOCs are being emitted in significant quantities and with largely similar composition upon permafrost thawing, inundation, and subsequent vegetation development, despite major differences in microclimate, hydrological regime, vegetation, and permafrost occurrence. Should you have any questions regarding the dataset, please free feel to contact Yi jiao at yi.jiao@bio.ku.dk or the PI of this project Prof. Rinnan at riikkar@bio.ku.dk

Optomotor response (eye movment) and avoidance responses (locomotor activity) of crabs Neohelice granulata for LP-lesioned, control lesioned and control crabs

When an animal rotates (whether it is an arthropod, a fish, a bird, or a human) a drift of the visual panorama occurs over its retina, termed optic flow. The image motion is stabilized by compensatory behaviors (driven by the movement of the eyes, head or the whole body depending on the animal) collectively termed optomotor response (OR). Dipteran lobula plate has been consistently linked with optic flow processing and the control of optomotor responses. Crabs have a neuropil similarly located and interconnected in the optic lobes, therefore referred to as a lobula plate also. Here we show that the crab's lobula plate is required for normal optomotor response since the response was lost or severely impaired in animals whose lobula plate had been lesioned. The effect was behavior-specific, since avoidance responses to approaching visual stimuli were not affected. Crabs require simpler optic flow processing than flies (because they move slower and in 2D instead of 3D), consequently their lobula plates are relatively smaller. Nonetheless, they perform the same essential role in the visual control of behavior. Our findings add a fundamental piece to the current debate on the evolutionary relationship between the lobula plates of insects and crustaceans.

opencc-by-sa-4.0Jan 2022View details →

Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning

This data set and code goes with "Reconstruction of Natural Visual Scenes from Primary Visual Cortical Neural Activity Using Adversarial Learning"

Chemical activities of platinum and gold under a hydrous condition at high pressure with implication for deep volatile storage

This file contains published datasets for "Chemical activities of platinum and gold under a hydrous condition at high pressure with implication for deep volatile storage"

Developmental temperature alters the thermal sensitivity of courtship activity and signal-preference relationships, but not mating rates

Mating behaviours are susceptible to novel or stressful thermal conditions, particularly for ectothermic organisms. One way to deal with changes in thermal conditions is to exhibit developmental plasticity, whereby the thermal sensitivity of mating behaviours depend on developmental conditions. We test how developmental temperature affects the thermal sensitivity of courtship behaviour and mating rates, as well as mating signal and preference coupling. We rear treehoppers under two temperature regimes and then test how a range of ambient temperature affects behaviours involved in the coordination of mating. We also test for sex-specific thermal sensitivity and developmental plasticity. We find developmental plasticity in the thermal sensitivity of courtship behaviour and mating signals for males. However, we found no developmental plasticity in females, and no change in the thermal sensitivity of mating rates. We discuss the implications of signal-preference decoupling for sexual selection, how reversible acclimation may drive sex-specific results, and the potential for mismatches between developmental and mating thermal environments under future climate change predictions.

Reef effect of offshore structures on the occurrence and foraging activity of harbour porpoises

With increasing numbers of offshore structures to be decommissioned, a better understanding of their effect on marine predators is timely. There is some evidence that oil and gas platforms may attract marine mammals acting as artificial reefs. However, it is unclear whether different man-made structure designs have similar effects. Further, due to the lack of baseline data prior to installation, it is unknown whether artificial structures modify the diel patterns of occurrence and foraging behaviour of marine mammals. Here, we used passive acoustics to investigate the occurrence and foraging activity of harbour porpoises (Phocoena phocoena) around three artificial structures of different age and complexity. We deployed an array of echolocation click detectors (CPODs) in 2021, along a gradient of distances to these structures and assessed the extent to which porpoises were attracted to them. We also investigated the effect of these structures on the diel patterns of occurrence and foraging activity of porpoises. The probability of porpoise occurrence and foraging activity decreased with distance from offshore structures. A significant increase in porpoise occurrence and foraging was detected during night-time compared to daytime around all three offshore structures (< 200 m). Comparing pre- and post-installation porpoise detections, the daily patterns of occurrence and foraging activity shifted from a weak <a>diel</a> pattern before the structure was installed, to a strong nocturnal pattern when the structure was present. These findings provide evidence that marine mammals are attracted to man-made structures and that porpoises modify their diel patterns of occurrence and foraging activity around them. This research suggests that offshore structures play an important role as foraging areas for marine mammals and provides key information for the decommissioning process.

Data and code for: No evidence of metabolic costs following adaptive immune activation or reactivation in house sparrows

We examined the energetic costs of immune activation by measuring both basal (BMR) and exercise-induced maximal metabolic rates (MMR) in house sparrows before and after being injected with either saline (sham) or two novel antigens (keyhole limpet haemocyanin and sheep red blood cells; KLH and SRBC, respectively) after the primary and two subsequent vaccinations. We also examined the effect of experimentally-induced breeding levels of testosterone (T) on immune responses and their metabolic costs in both males and females.

Mechanical behavior of textile reinforced alkali-activated mortar based on fly ash, metakaolin and ladle furnace slag

Data corresponding to all figures and tables presented in the publication

Additional data; Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities

This dataset contains additional data for the research article Biotechnologically produced chitosans with nonrandom acetylation patterns differ from conventional chitosans in properties and activities by Sruthi Sreekumar, Jasper Wattjes, Anna Niehues, Tamara Mengoni, Ana C. Mendes, Edwin R. Morris, Francisco M. Goycoolea, and Bruno M. Moerschbacher. The directory Fig1ab_SuppFig1ab contains data, code, and results of enzymatic mass-spectrometric fingerprinting experiments. The directory cosms contains source code and instructions to create a conda environment in which the code to create Figures 1 A+B and Supplementary Figures 1 A+B can be executed. The directory Fig23456_SuppFig_2567 contains Excel Worksheets (.xlsx files) with data underlying Figures 2, 3, 4, 5 and 6, and Supplementary Figures 2, 5, 6 and 7. The directories Fig5b and SuppFig7d contain raw MS data (Bruker .d files) of oligomeric hydrolysis products produced by incubating different chitosans with different chitinolytic enzymes.

Astrocytic Gi-GPCR activation enhances stimulus-evoked extracellular glutamate

Astrocytes perform critical functions in the nervous system, many of which are dependent on neurotransmitter-sensing through G protein-coupled receptors (GPCRs). However, whether specific astrocytic outputs follow specific GPCR activity remains unclear, and exploring this question is critical for understanding how astrocytes ultimately influence brain function and behavior. Here, we investigate the outputs of astrocytic Gi-GPCRs, a family of GPCRs which we previously showed is sufficient to increase slow-wave neural activity (SWA) during sleep when activated in cortical astrocytes1. We focus on two putative outputs by astrocytes in vivo, the regulation of extracellular glutamate and GABA, by combining fiber photometry recordings of the extracellular indicators iGluSnFR and iGABASnFR with astrocyte-specific chemogenetic Gi-GPCR activation. We find that Gi-GPCR activation does not change spontaneous dynamics of extracellular glutamate or GABA. However, Gi-GPCR activation does specifically increase visual stimulus-evoked extracellular glutamate. Together, these data point towards a complex relationship between astrocytic inputs and outputs in vivo that may depend on behavioral context. Further, they suggest an extracellular glutamate-specific mechanism underlying some astrocytic Gi-GPCR-dependent behaviors, including the regulation of sleep SWA.

Epilelptiform coritcal activity - EEG simulations

Source-space brain activity was generated using a physiologically-grounded computational model, named COALIA. It generates brain-scale electrophysiology activity while accounting for the macro- (between regions) as well as the micro-circuitry (within a single region) of the brain (for details, see [1]). We considered a scenario inspired from a general scheme of the organization of human partial seizures presented in [2], and proposing the existence of an epileptogenic subnetwork as well as a propagation subnetwork. In the present study, the two subnetworks were located in the left hemisphere. The epileptogenic subnetwork included four cortical regions: the rostral middle frontal gyrus, pars opercularis, pars triangularis, and pars orbitalis; the propagation subnetwork included the supramarginal, banks superior temporal sulcus, and transverse temporal cortex. Regions affiliations were based on the Desikan-Killiany atlas [3]. Epileptiform activity was generated in the epileptogenic and propagation subnetworks, while background activity was assigned to the remaining cortical regions. All sources belonging to a single patch were synchronized at a zero lag, while a delay of 30 ms was introduced between the two subnetworks to reflect the propagation of spikes between relatively distant regions in the brain. A timeseries of ~6 min at 2048 Hz was simulated. Scalp EEG can be estimated by solving the forward problem Citation: Allouch, S., M. Yochum, A. Kabbara, J. Duprez, M. Khalil, F. Wendling, M. Hassan, and J. Modolo. 2022. “Mean-Field Modeling of Brain-Scale Dynamics for the Evaluation of EEG Source-Space Networks.” Brain Topography 35 (1): 54–65. References: [1] Bensaid, S., Modolo, J., Merlet, I., Wendling, F., & Benquet, P. (2019). COALIA: A Computational Model of Human EEG for Consciousness Research. Frontiers in Systems Neuroscience, 13, 1–18. https://doi.org/10.3389/fnsys.2019.00059 [2] Bartolomei, F., Guye, M., & Wendling, F. (2013). Abnormal binding and disruption in large scale networks involved in human partial seizures. EPJ Nonlinear Biomedical Physics, 1(4), 1–16. https://doi.org/10.1140/epjnbp11  [3] Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., … Killiany, R. J. (2006). An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage, 31, 968–980. https://doi.org/10.1016/j.neuroimage.2006.01.021

opencc-by-4.0Jun 2022View details →

New observations of recently active wrinkle ridges in the lunar mare: Implications for the timing and origin of lunar tectonics

Enclosed are the for the tectonically deformed lunar impact crater and recently active wrinkle ridge datasets produced in Nypaver and Thomson, 2022 (GRL). These data are presented in Figure 2 of that Manuscript and are readable in a GIS-based software (shapefile) and the LROC Quickmap interface (json). Lat/Long coordinates for all tectonically deformed craters are also presented in CSV format.

opencc-zeroJun 2022View details →

Data from: Active regulation of ultraviolet light exposure overrides thermal preference behaviour in eastern fence lizards

1. Over a century of ecophysiological studies on lizards have perpetuated the assumption that basking and shuttling movements between sun and shade function solely for temperature regulation. However, these behaviors also modulate exposure to ultraviolet (UV) wavelengths that are essential for maintaining physiological homeostasis as well as ensuring proper growth and development and enhancing long-term fitness. 2. An alternative hypothesis is that lizards also actively regulate their UV exposure. In this scenario, UV needs may even override temperature needs (or vice versa), generating asymmetries in the ability of a lizard to regulate both conditions equally. We test this hypothesis using field and laboratory data collected on adult Sceloporus undulatus. 3. We found that S. undulatus actively regulate UV exposure and prioritize UV over temperature, favoring body temperatures much higher than preferred values to sustain preferred UV exposure. In stark contrast, temperature had no reciprocal impact on UV regulation behavior. Our field data support these patterns, suggesting that lizards may even seek out hotter environments despite thermal costs to enhance UV exposure. 4. We conclude that S. undulatus actively regulate for UV as well as temperature. Unfortunately, outside of zoos and private hobbyists, appreciation of the importance of UV for ectotherm survival and reproductive success has been minimal. Addressing this deficit will therefore be vital to improve our understanding of the factors shaping the evolution of ectotherm photoregulation behavior in nature.

opencc-by-4.0Aug 2021View details →

Sex-specific tuning of modular muscle activation patterns for locomotion in young and older adults

There is increasing evidence that including sex as a biological variable is of crucial importance to promote rigorous, repeatable and reproducible science. In spite of this, the body of literature that accounts for the sex of participants in human locomotion studies is small and often produces controversial results. Here, we investigated the modular organization of muscle activation patterns for human locomotion using the concept of muscle synergies with a double purpose: i) uncover possible sex-specific characteristics of motor control and ii) assess whether these are maintained in older age. We recorded electromyographic activities from 13 ipsilateral muscles of the lower limb in young and older adults of both sexes walking (young and old) and running (young) on a treadmill. The data set obtained from the 215 participants was elaborated through non-negative matrix factorization to extract the time-independent (i.e., motor modules) and time-dependent (i.e., motor primitives) coefficients of muscle synergies. We found sparse sex-specific modulations of motor control. Motor modules showed a different contribution of hip extensors, knee extensors and foot dorsiflexors in various synergies. Motor primitives were wider (i.e., lasted longer) in males in the propulsion synergy for walking (but only in young and not in older adults) and in the weight acceptance synergy for running. Moreover, the complexity of motor primitives was similar in younger adults of both sexes, but lower in older females as compared to older males. In essence, our results revealed the existence of small but defined sex-specific differences in the way humans control locomotion and that these strategies are not entirely maintained in older age. In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; c) the touchdown and lift-off timings of the recorded limb, d) the code to process the data. In total, 520 trials from 215 participants are included in the supplementary data set. The file “metadata.dat” is available in ASCII format and contains: <ul> <li>Code: the participant’s code</li> <li>Group: the participant's group (G1=young adults, walking; G2=old adults, walking; G3=young adults, running)</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (walking or running)</li> <li>Speed: the speed at which the recordings were conducted in [m/s]</li> <li>Speed_type: the distinction between fixed (decided by the researchers) or preferred (selected by the participant) speed</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> 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. Trials are named like “ID0020_M_YOUNG_TW_01,” where the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> 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 one row for each gait cycle and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_ID0020_M_YOUNG_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “ID0020” indicate the participant number (in this example the 20th), the character “M” indicates the sex, the characters “YOUNG” indicate the age group, the characters “TW” indicate the locomotion type and environment (T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. 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 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. Each trial is saved as an element of a single R list. Trials are named like “RAW_EMG_ID0003_F_OLD_TW_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “ID0003” indicate the participant number (in this example the 3rd), the character “F” indicates the sex, the characters “OLD” indicate the age group, the characters “TW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”. The latest version of this code can be found at https://github.com/alesantuz/musclesyneRgies.

opencc-by-4.0Jul 2020View details →

Muscle activation patterns are more constrained and regular in treadmill than in overground human locomotion

The use of motorized treadmills as convenient tools for the study of locomotion has been in vogue for many decades. However, despite the widespread presence of these devices in many scientific and clinical environments, a full consensus on their validity to faithfully substitute free overground locomotion is still missing. Specifically, little information is available on whether and how the neural control of movement is affected when humans walk and run on a treadmill as compared to overground. Here, we made use of linear and nonlinear analysis tools to extract information from electromyographic recordings during walking and running overground and on an instrumented treadmill. We extracted synergistic activation patterns from the muscles of the lower limb via non-negative matrix factorization. We then investigated how the motor modules (or time-invariant muscle weightings) were used in the two locomotion environments. Subsequently, we examined the timing of motor primitives (or time-dependent coefficients of muscle synergies) by calculating their duration, the time of main activation, and their Hurst exponent, a nonlinear metric derived from fractal analysis. We found that motor modules were not influenced by the locomotion environment, while motor primitives resulted overall more regular in treadmill than in overground locomotion, with the main activity of the primitive for propulsion shifted earlier in time. Our results suggest that the spatial and sensory constraints imposed by the treadmill environment forced the central nervous system to adopt a different neural control strategy than that used for free overground locomotion. A data-driven indication that treadmills induce perturbations to the neural control of locomotion.   In this supplementary data set we made available: a) the metadata with anonymized participant information; b) the raw EMG, already concatenated for the overground trials; 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; f) the code to process the data. In total, 120 trials from 30 participants are included in the supplementary data set. The file “metadata.dat” is available in ASCII and RData format and contains: <ul> <li>Code: the participant’s code</li> <li>Sex: the participant’s sex (M or F)</li> <li>Locomotion: the type of locomotion (W=walking, R=running)</li> <li>Environment: to distinguish between overground (O) and treadmill (T)</li> <li>Speed: the speed at which the recordings were conducted in [m/s] (1.4 m/s for walking, 2.8 m/s for running)</li> <li>Age: the participant’s age in years</li> <li>Height: the participant’s height in [cm]</li> <li>Mass: the participant’s body mass in [kg].</li> </ul> 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 running overground trials of participants P0001, P0007, P0008 and P0009 consist of 21, 29, 29 and 26 cycles, respectively. All the other trials consist of 30 gait cycles. Trials are named like “P0003_OR_01”, where the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data are named, following the same rules, like “FILT_EMG_P0003_OR_01”. Old versions not compatible with the R package <a href="https://CRAN.R-project.org/package=musclesyneRgies">musclesyneRgies</a> 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 30 rows (one for each gait cycle) and two columns. The first column contains the touchdown incremental times in seconds. The second column contains the duration of each stance phase in seconds. Each trial is saved as an element of a single R list. Trials are named like “CYCLE_TIMES_P0020_TW_01,” where the characters “CYCLE_TIMES” indicate that the trial contains the gait cycle breakdown times, the characters “P0020” indicate the participant number (in this example the 20th), the characters “TW” indicate the locomotion type and environment (O=overground, T=treadmill, W=walking, R=running), and the numbers “01” indicate the trial number. Please note that the running overground trials of participants P0001, P0007, P0008 and P0009 only contain 21, 29, 29 and 26 cycles, respectively. 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 30000 rows (one 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 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_P0003_OR_01”, where the characters “RAW_EMG” indicate that the trial contains raw emg data, the characters “P0003” indicate the participant number (in this example the 3rd), the characters “OR” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. The filtered and time-normalized emg data is named, following the same rules, like “FILT_EMG_P0003_OR_01”. The files containing the muscle synergies extracted from the filtered and normalized EMG data are available in RData format, in the file named “SYNS.RData”. Each element of this R list represents one trial and contains the factorization rank (list element named “synsR2”), the motor modules (list element named “M”), the motor primitives (list element named “P”), the reconstructed EMG (list element named “Vr”), the number of iterations needed by the NMF algorithm to converge (list element named “iterations”), and the reconstruction quality measured as the coefficient of determination (list element named “R2”). The motor modules and motor primitives are presented as direct output of the factorization and not in any functional order. Motor modules are data frames with 13 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 M), one for each synergy and for each muscle. 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 P), 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. Trials are named like “SYNS_ P0012_OW_01”, where the characters “SYNS” indicate that the trial contains muscle synergy data, the characters “P0012” indicate the participant number (in this example the 12th), the characters “OW” indicate the locomotion type and environment (see above), and the numbers “01” indicate the trial number. 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. All the code used for the pre-processing of EMG data and the extraction of muscle synergies is available in R format. Explanatory comments are profusely present throughout the script “muscle_synergies.R”.