Find research datasets worth reusing

opencc-by-4.0Dec 2019View details →

Continuous multi-sensor wearable data and daily subject-reported fatigue of heathy adults

Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are limited. We collected a total of 28 healthy adult subjects and 973 recording days. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters at 1-minute temporal resolution, and daily questionnaires (patient-reported outcome scores, PROs) on fatigue. When matching both sensor data and PROs, the datasets contains data from 27 subjects and 405 recording days. Analysis of these multimodal digital data to inform, quantify, and augment subjectively captured non-pathological fatigue measures were published at Luo H., et. al. (2020), Assessment of Fatigue Using Wearable Sensors: A Pilot Study. Digit Biomark. Demographics, sensor parameters and other information on this dataset can be found in the aforementioned manuscript and related supplementary material. Files included are <ul> <li>fatiguePROs.csv: daily PROs for all subjects</li> <li>subjectID_*.csv: sensor time series for each subject</li> </ul>

opencc-by-4.0Oct 2024View details →

Short Fatigue Crack Behavior under various Level of Mixed-Mode

This is dataset to paper: Short Fatigue Crack Behavior under various Level of Mixed-Mode

opencc-by-4.0Apr 2018View details →

Dataset Effect of hypnotic suggestion on knee extensor neuromuscular properties in resting and fatigued states

The .xlsx file contains individual data from all figures / tables of the associated manuscript and each .csv file contains information from one figure / table.   Dataset Fig 2 Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre) and after (post) control / hypnosis suggestion Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre) and after (post) control / hypnosis suggestion   Dataset Fig 4 Table 1. Time to task failure (s) of a submaximal isometric contraction performed at 20% maximal voluntary contraction force with the knee extensors for the control session and the hypnosis session   Dataset Fig 5 Table 1. Maximal voluntary contraction force (Newton) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session Table 2. Maximal voluntary activation level (%) from the knee extensor muscles measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session Table 3. Peak doublet force (Newton) evoked from 100 Hz paired stimuli at the knee extensor level measured before (pre exercise) and after (post exercise) exercise during the control session and the hypnosis session   Dataset Fig 6 Table 1. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus lateralis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session Table 2. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the vastus medialis muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session Table 3. Electromyographic activity (in %, expressed as root mean square values normalized to maximal electromyographic activity measured during the maximal voluntary contraction performed before exercise) of the rectus femoris muscle measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session   Dataset Fig 7 Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3). Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6). Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9). Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12). Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15). Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured during the sustained isometric contraction at every 50% of time to task failure for the control session and the hypnosis session. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18). Dataset Fig 8 Table 1. Rate of perceived exertion (6-20 Borg scale) measured during the sustained isometric contraction at every 25% of time to task failure for the control session and the hypnosis session   Dataset Table 1 Table 1. Motor evoked potential peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 2) and the peak-to-peak M-wave amplitude expressed in mV (Table 3). Table 4. Motor evoked potential peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 5) and the peak-to-peak M-wave amplitude expressed in mV (Table 6). Table 7. Motor evoked potential peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the motor evoked potential peak-to-peak amplitude expressed in mV (Table 8) and the peak-to-peak M-wave amplitude expressed in mV (Table 9). Table 10. Short intracortical inhibition peak to peak amplitude from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 11) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 12). Table 13. Short intracortical inhibition peak to peak amplitude from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 14) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 15). Table 16. Short intracortical inhibition peak to peak amplitude from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion. Values are expressed in %, i.e. expressed as a ratio between the short intracortical inhibition peak-to-peak amplitude expressed in mV (Table 17) and the motor evoked potential peak-to-peak amplitude expressed in mV (Table 18).   Dataset table 2 Table 1. M-wave peak to peak amplitude (mV) from the vastus lateralis muscle measured before (pre) and after (post) control / hypnosis suggestion Table 2. M-wave peak to peak amplitude (mV) from the vastus medialis muscle measured before (pre) and after (post) control / hypnosis suggestion Table 3. M-wave peak to peak amplitude (mV) from the rectus femoris muscle measured before (pre) and after (post) control / hypnosis suggestion

opencc-by-4.0May 2023View details →

Fatigue properties of wind turbine rotor blade hybrid epoxy adhesives

This dataset includes the tensile data at two different strain rates and tensile-tensile fatigue data of epoxy adhesives used in wind turbine rotor blades. SPABOND™ 820HTA (non-toughened) and SPABOND™ 840HTA (toughened) epoxy adhesives are combined at different weight proportions to develop the hybrid adhesives. The hybrid and  ASTM D638-22 tensile specimen geometry (Type I and Type II) effects on fatigue performance are determined through instrumented experiments.

opencc-by-4.0Jun 2023View details →

Multivariate Time Series data of Fatigued and Non-Fatigued Running from Inertial Measurement Units

The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin. The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here are segmented strides from the two 400m runs of each of the 19 participants. The labels on the data represent the participant number and whether it was a fatigued stride ('F') or a not fatigued stride ('NF'). The data used from the sensors includes data from the accelerometer in three directions (X, Y, Z) and the gyroscope in three directions (X, Y, Z). The direction of each of the axis is relative to the sensor. Two extra signals, magnitude acceleration and magnitude gyroscope were derived from the component signals and included in the analysis.Kindly cite one of the following papers when using this data:B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a>

opencc-by-4.0May 2024View details →

SWIFTIES (Subject and Wearables data for Investigation of Fatiguing Tasks In Extension/flexion of Shoulder/elbow)

32 subjects (17 males, 15 females) performed 8 shoulder and elbow flexion-extension tasks with the dominant arm until fatigue. Tasks were performed at 25% and 45% of the maximum voluntary contraction (MVC) force in a static and dynamic manner. Every combination of movement (static or dynamic) x joint (shoulder or elbow) x load (25% or 45% of MVC Force) was performed by every subject. Subject data includes information on demographic and anthropometrics, rate of perceived exertion (RPE), endurance time of tasks, MVC forces, and loads lifted. Sensor data includes inertial measurement unit (IMU), electromyography (EMG), motion capture (MC) and pressure insole (PI) data.

opencc-by-4.0Jun 2022View details →

316L L-PBF fatigue dataset

This file contains 316L Laser Powder Bed Fusion fatigue tests dataset. Experiments were carried on a MTS Landmark 100 kN servohydraulic fatigue test machine. This experimental campaign took place in the context of a PhD grant from the French region Pays de la Loire (see https://pastel.archives-ouvertes.fr/tel-03688021 for the thesis manuscript). Fatigue tests were carried : - in air or in salt-spray - on different batches (polished, pre-corroded, with artificial defects,...) - at R=-1 and R=0.1

opencc-by-4.0Jul 2022View details →

Supplementary data on microcantilever fatigue tests

This data publication contains the results of microcantilever fatigue tests on a metallic glass and tungsten. It supplements the publication “A new method for microscale cyclic crack growth characterization from notched microcantilevers and application to single crystalline tungsten and a metallic glass” DOI: <a href="http://dx.doi.org/10.1557/s43578-022-00618-x">10.1557/s43578-022-00618-x</a>. During the experiments four Zr48Cu36Ag8Al8 metallic glass microcantilevers and five tungsten single crystal microcantilevers were tested. The compositions, orientations and the production of the microcantilevers are described in the publication. The fatigue tests were performed by S. Gabel under the supervision of B. Merle at the Institute I - General Materials Properties of the Friedrich-Alexander Universität Erlangen-Nürnberg <a href="http://www.ww1.tf.fau.de/">(Martensstr.5, 91058 Erlangen, Germany)</a> using a NMT 03 in-situ nanoindenter <a href="https://www.femtotools.com/">(FemtoTools, Switzerland)</a> inside a 1540EsB Secondary Electron Microscope <a href="https://www.zeiss.com/corporate/int/home.html">(Carl Zeiss, Germany)</a>. The indenter was equipped with a wedge diamond tip, which had a length of ~10 µm. The experiments were performed, until the tip lost contact with the microcantilever due to bending. The data for the fatigue tests are provided in nine .csv files, each corresponding to a single test and being divided into two folders, sorted by the material. The numbers in the files names describe the initial cyclic stress intensity factor ΔKI. This  is also used in the publication to describe the single tests. The data in each .csv file are organized as follows: Each row contains thirteen semicolon separated characters, forming thirteen columns. The data description are given in row 1, e.g., "Stiffness". The physical unit is given in row 2, e.g., "N/m". From row 3 on the data for the individual experiment are given. The following information can be found in the respective columns: 1: Cycle number. 2: Mean force Pm in µN. 3: Force amplitude ΔP/2 in µN. 4: Displacement amplitude Δh/2 in µm. 5: Stiffness SCSM in N/m. 6: Crack growth length Δa in µm. 7: Stress intensity factor range ΔKI in MPa√m. 8: Initial crack length a in µm. 9: Cantilever width W in µm. 10: Cantilever thickness T in µm. 11: Span length L in µm. 12: Phase shift in °. 13: Temperature in °C.   One test with metallic glass was performed with a higher recording rate of 400 Hz, which can be found in the folder: "Metallic_Glass_High_Recording_Rate". The data in the .csv file are sorted differently to the other tests: Each row contains fifteen semicolon separated characters, forming fifteen columns The data description is given in row 1, e.g., "Stiffness". The physical unit is given in row 2, e.g., "N/m". From row 3 on the data for the individual experiment are given. The following information can be found in the respective columns: 1: Time in s. 2: Cycle number. 3: Mean displacement hm in µm. 4: Mean force Pm in µN. 5: Force amplitude ΔP/2 in µN. 6: Displacement amplitude Δh/2 in µm. 7: The force P in µN. 8: Displacement h in µm. 9: Stiffness SCSM in N/m. 10: Initial crack length a in µm. 11: Cantilever width W in µm. 12: Cantilever thickness T in µm. 13: Span length L in µm. 14: Phase shift in °. 15: Temperature in °C.

opencc-by-4.0Oct 2022View details →

Experimental fatigue test on I-CAR structure

In order to validate the welding procedures developed during AVANGARD project, a fatigue test of the vehicle structure was performed. As can be seen in the video, the vehicle underwent the entire test without visible damage. On the other hand, once the test was completed, non-destructive inspection with penetrate liquids verified that there were no cracks. The quality of the joints is therefore corroborated.

opencc-by-4.0May 2024View details →

Time Series data from wearable sensors to capture the onset of Fatigue in Runners

The data captured came from mounting a single Shimmer3 IMU on the lumbar of 19 recreational runners. The participants were all regular runners and injury free. The study protocol was reviewed and approved by the human research ethics committee at University College Dublin. The data was collected in three segments; in the first, the participant completed a 400m run at a comfortable pace; the second segment consisted of a beep test which acted as the fatiguing protocol for this study; and the last segment where the runner was required to complete the 400m run at their comfortable pace, this time in their fatigued state. The beep test requires the runner to continuously run between two points 20m apart following an audio which produces `beeps' indicating when the person should begin running from one end to the other. The test eventually requires the runner to increase their pace as the interval between the `beeps' reduces as the test progresses. The fatiguing protocol ends when the runner is unable to keep up the increase in pace. The runs were all done on an outdoor running track. The sensor captured acceleration, angular velocity and magnetometer data throughout the three stages of the trials at a sampling rate of 256Hz. The data included here consists of the raw readings from the sensors across the three phases of the run. The data is saved seperately as 'F' for Fatigued, 'NF' for Not Fatigued, and 'BeepTest' for the data collected during the fatiguing process. For the processed and labelled fatigue and non fatigue data, see: https://zenodo.org/records/7997851 Kindly cite one of the following papers when using this data: B. Kathirgamanathan, B. Caulfield and P. Cunningham, "Towards Globalised Models for Exercise Classification using Inertial Measurement Units," 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1–4, doi: 10.1109/BSN58485.2023.10331612 B. Kathirgamanathan, T. Nguyen, G. Ifrim, B. Caulfield, P. Cunningham. Explaining Fatigue in Runners using Time Series Analysis on Wearable Sensor Data, XKDD 2023: 5th International Workshop on eXplainable Knowledge Discovery in Data Mining, ECML PKDD, 2023, <a href="http://xkdd2023.isti.cnr.it/papers/223.pdf">http://xkdd2023.isti.cnr.it/papers/223.pdf</a>

opencc-by-4.0Sep 2024View details →

Data on the material characterization of cast and additively manufactured IN939 subjected to room-temperature low-cycle fatigue load

The original data to the research paper termed "Room-temperature low-cycle fatigue behaviour of cast and additively manufactured IN939 superalloy" are enclosed. Two specimen orientations of L-PBF IN939 - horizontal and vertical, and two thermodynamical states - without subsequent heat treatment (non-treated) and standard aged according to Delargy et al., 1986, were investigated. The paper concerns the low-cycle fatigue performance of cast and additively manufactured IN939 superalloy. It brings a comprehensive account on the damage and deformation behaviour of the tested alloy, combining the test analyses with high-resolution SEM and TEM observations.

opencc-by-4.0Oct 2023View details →

WSD4FEDSRM (Wearable sensor data for fatigue estimation during shoulder rotation movements)

The dataset comprises a collection of many data types during shoulder internal rotation, and external rotation exercises from 34 participants, including demographic information, anthropometric measurements, maximum voluntary isometric contraction force measurements, inertial measuring unit data, surface electromyography recordings, photoplethysmogram data from wearable sensors, as well as measurements from the Borg rating of perceived exertion scale and the Karolinska sleepiness scale.

opencc-by-4.0Sep 2016View details →

X-ray CT data: fatigue damage in glass fibre/polyester composite used for wind turbine blades

These data are obtained using a Zeiss Xradia Versa 520 scanner to scan a uni-directional glass fibre reinforced polyester composite made from a non-crimp fabric used for wind turbine blades. The scans were performed to study the fatigue damage progression in this material. The data is published together with the below journal paper, in which more information can be found. The present videos of the data relate directly to the figures in this paper. Jespersen, K. M., Zangenberg Hansen, J., Lowe, T., Withers, P. J., & Mikkelsen, L. P. (2016). Fatigue damage assessment of uni-directional non-crimp fabric reinforced polyester composite using X-ray computed tomography. Composites Science and Technology, 136, 94–103. DOI:10.1016/j.compscitech.2016.10.006 For use of these data, please remember to cite the above mentioned paper. Corresponding author, K. M. Jespersen, e-mail kmun@dtu.dk

opencc-by-4.0Aug 2017View details →

Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading

Ex-situ X-ray CT fatigue testing data sets published as a data in brief: "Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading", Data in brief, 2017, doi.org/10.1016/j.dib.2017.10.074. Together with the following article: K. M. Jespersen and L. P. Mikkelsen, “Three dimensional fatigue damage evolution in non-crimp glass fibre fabric based composites used for wind turbine blades,” Compos. Sci. Technol. (In press), 2017, 10.1016/j.compscitech.2017.10.004.

dryad40/100

Cerebral microstructural alterations in Post-COVID-condition are related to cognitive impairment, olfactory dysfunction, and fatigue

After contracting COVID-19, a substantial number of individuals develop a Post-COVID-Condition (PCC), marked by neurologic symptoms such as cognitive deficits, olfactory dysfunction, and fatigue, which can have detrimental socioeconomic consequences. Despite this, biomarkers and pathophysiological understandings of this condition remain limited. Employing magnetic resonance imaging, we conduct a comparative analysis of cerebral microstructure among patients with post-COVID condition, healthy controls, and individuals who contracted COVID-19 without long-term symptoms. This reveals widespread alterations in cerebral microstructure, attributed to a shift in volume from neuronal compartments to free fluid, associated with the severity of the initial infection. Correlating these alterations with cognition, olfaction, and fatigue unveils distinct affected networks, which are in a close anatomical-functional relationship with the respective symptoms. This plausibility of symptom-specific networks not only provides insights into the disease's pathophysiological foundations, which align well with an accelerated aging process but also underscores the significance of microstructure as an imaging biomarker.

opencc-zeroMar 2024View details →

opencc-by-4.0Nov 2024View details →

Surface EMG recordings of the biceps brachii muscle during voluntary isometric contractions - pilot study on fatigue

This publication contains the data recorded during a study on muscle fatigue and its evaluation using surface electromyography (EMG). There were a total of 16 participants. Each directory, with an alphanumeric value contains data from a single participant (.mat and .json files). The README.pdf file  in the root directory provides details on the experimental set-up and protocol, and data in each file.  The EMG data are provided in matlab files and in addition to those, there are recordings of the <a href="https://doi.org/10.1016/j.jelekin.2019.05.012">perceived fatigue of the participants using a modified Borg CR-10 scale</a> in the json file, in which further participant and experimental information are provided. For each participant information on gender, age (in aggregate form) and height (in aggregate form) are provided.

opencc-by-4.0Dec 2024View details →

Dataset for publication:Dataset for publication: "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys"

The dataset contains a set of experimental data used in preparation for the manuscript "Application of shot peening to improve fatigue properties via enhancement of precipitation response in high-strength Al-Cu-Li alloys." The dataset contains results of fatigue tests, residual stress measurements, nanoindentation measurements, and surface roughness data.