Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
1,733
datasets available to search
ShareScore release 0.9.0
Dataset results
1,733 results for “Fatigue”
The Exercise Response to Pharmacologic Cholinergic Stimulation in Myalgic Encephalomyelitis / Chronic Fatigue Syndrome
ClinicalTrials.gov study NCT03674541. IPD Sharing: YES. Countries: 1. Publications: 1.
Omega-3 Fatty Acid in Reducing Cancer-Related Fatigue in Breast Cancer Survivors
ClinicalTrials.gov study NCT02352779. IPD Sharing: NO. Countries: 1. Publications: 2.
Feasibility of Bright Light Therapy on Fatigue, Sleep and Circadian Activity Rhythms in Lung Cancer Survivors
ClinicalTrials.gov study NCT02954809. IPD Sharing: NO. Countries: 1. Publications: 5.
Muscadine Grape Extract to Improve Fatigue
ClinicalTrials.gov study NCT04495751. IPD Sharing: NO. Countries: 1. Publications: 1.
The Effects of Ginseng on Cancer-Related Fatigue
ClinicalTrials.gov study NCT01375114. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data for: Deformable hard tissue with high fatigue resistance in the hinge of bivalve Cristaria plicata
Open the record for dataset details and reuse information.
Data from: Mechanical fatigue in microtubules
Open the record for dataset details and reuse information.
Data from: Subchondral bone fatigue injury in the parasagittal condylar grooves of the third metacarpal bone in Thoroughbred racehorses elevates site-specific strain concentration
Open the record for dataset details and reuse information.
The role of low-grade inflammation in ME/CFS (Myalgic Encephalomyelitis/Chronic Fatigue Syndrome) - associations with symptoms
Open the record for dataset details and reuse information.
A versatile knee exoskeleton mitigates quadriceps fatigue in lifting, lowering, and carrying tasks
Open the record for dataset details and reuse information.
Probabilistic Approach for the Fatigue Strength Prediction of Polymers
<p>The dominating factor in the fatigue of structures made from fiber reinforced polymers (FRP), for example wind turbine blades, is the polymer matrix. Traditionally, experimental stress-life data of polymers is approximated via a linear double-log Basquin model. Recently, the non-linear stress-life formulation by Stüssi was found to provide a better fit of the experimental data with a substantially reduced standard deviation. Moreover, a non-linear constant-life formulation, as proposed by Boerstra, for example, can enhance the representation of the mean stress effect compared to state-of-the-art linear models, i.e., the modified Goodman relation. To this end, we incorporated Stüssi’s model into the Boerstra relation to take account of the mean stress effects of an epoxy. This stress-life formulation was then enhanced with the Weibull probability function. The probabilistic-stress-life model provided a good approximation of the fatigue performance as a function of the stress ratio on the basis of an experimental data set. Finally, we suggested a step-wise engineering approach to derive the permissible stress-life with a view to practical design purposes. The procedure increased the reliability of the fatigue design evaluation compared to the state-of-the-art methodologies.</p> <p>Slides: <a href="https://doi.org/10.5281/zenodo.4443149">https://doi.org/10.5281/zenodo.4443149</a><br> Paper: <a href="https://doi.org/10.2514/6.2021-1289">https://doi.org/10.2514/6.2021-1289</a></p>
Data from: Is the association between health-related quality of life and fatigue mediated by depression in patients with multiple sclerosis? A Spanish cross sectional study
Objectives: To determine the mediating effects of depression on health-related quality of life and fatigue in individuals with multiple sclerosis (MS). Design: A cross-sectional study. Setting: Tertiary urban hospital. Participants: One hundred and eight patients (54% women) with MS participated in this study. Outcome measures: Demographic and clinical data (weight, height, medication, and neurological impairment), fatigue (Fatigue Impact Scale-FIS), depression (Beck Depression Inventory-BDI/II) and health-related quality of life (Short-Form Health Survey 36 - SF36) were collected. Results: Fatigue was significantly associated with bodily pain, physical function, mental health and depression. Depression was associated with bodily pain and mental health. The path analysis found direct effect from physical function, bodily pain and depression to fatigue (all, P<0.01). The path model analysis revealed that depression exerted a mediator effect from bodily pain to fatigue (B=-0.04, P<0.01) and from mental health to fatigue (B=-0.16 P<0.01). The amount of fatigue explained by all predictors in the path model was 37%. Conclusions: This study found that depression mediates the relationship between some health-related quality of life domains and fatigue in people with MS. Future longitudinal studies focusing on proper management of depressive symptoms in individuals with MS will help to determine the clinical implications of these findings.
Crack nucleation using combined crystal plasticity modelling, HR-DIC and HR-EBSD in a superalloy containing non-metallic inclusions under fatigue
<p>The uploaded data are required to reproduce the experimental results in the paper. </p> <p>To replicate figure 4, both GID.mat and thermal_E11.mat should be loaded into matlab. </p> <p>To replicate figure 6, strain_11.mat should be uploaded. Then fDIC_GB.m should be executed. </p> <p>If the reader has further questions, please contact Tiantian Zhang at tiantian.zhang08@imperial.ac.uk or tzhang6@wpi.edu</p>
Investigation of slip transfer across HCP grain boundaries with application to cold dwell facet fatigue
<p>Data for "Investigation of slip transfer across HCP grain boundaries with application to cold dwell facet fatigue"<br> http://dx.doi.org/10.1016/j.actamat.2017.01.021</p> <p>This Data folder contains 4 data files:<br> (1) Data_for_Figure_3.xlsx<br> (2) Data_for_Figure_4.xlsx<br> (3) Data_for_Figure_9.xlsx<br> (4) Data_for_Figure_13.xlsx</p> <p>-<br> If readers need further information, please feel free to contact:<br> zebang.zheng12@imperial.ac.u</p>
Dataset for article "Fatigue crack initiation and propagation relation at notched specimens with welded joint characteristics"
<p>The dataset presents is a collection of fatigue test data obtained from artificially notched specimens with weld characteristics. The data was used to investigate the relation between crack initiation and propagation in welded joints of different notch acuity (different radii and opening angle) by excluding the effect of geometrical variation along weld seams. The experiments show that the investigated relationship basically depends on the notch acuity, the load level and the stress ratio.</p> <p> </p> <p>For detailed information about the tests and the assessment please refer to the article:</p> <p>Braun M, Fischer C, Baumgartner J, Hecht M, Varfolomeev I. Fatigue Crack Initiation and Propagation Relation of Notched Specimens with Welded Joint Characteristics. <em>Metals</em>. 2022; 12(4):615. https://doi.org/10.3390/met12040615 </p>
A tactical video-based task does not elicit mental fatigue and does not impair soccer performance in a subsequent small-sided game
<p>Dataset of the publication "A tactical video-based task does not elicit mental fatigue and does not impair soccer performance in a subsequent small-sided game."</p>
Plots for the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history"
<p>These are the raw plot files from the publication "Lidar-assisted model predictive control of wind turbine fatigue via online rainflow-counting considering stress history".</p> <p>The files have been created with MATLAB 2019, and labeled according to their corresponding figure number(s) in the publication.</p>
Humans trade-off energetic cost with fatigue avoidance while walking
<p>See ReadMe.txt</p>
FATIGUE MODEL DATASET
<p>The fatigue model dataset contains the power and fatigue damage data of four case studies presented in <em>D6.4 Fatigue assessment</em> of the LiftWEC project. The deliverable can be downloaded <a href="https://liftwec.com/d6-4-fatigue-assessment/?utm_source=rss&utm_medium=rss&utm_campaign=d6-4-fatigue-assessment">here</a> and the four cases consist of: 1) Fixed v-frame structure, 2) Compliant v-frame structure, 3) Passive radial motion of foil and 4) Passive pitching of foil. The theoretical considerations for each case are presented in <em>D6.4 Fatigue assessment. </em>The fatigue damage is computed in one of the fixed ends of the foil. This is because this is a stress hot spot as identified previously in <em>D6.1 Extreme Event LiftWEC ULS Assessment</em>. The fatigue damage is computed assuming a curve D of offshore steel with cathodic protection. A summary of the data is included in the file LW-WP01-AAG-T01-1x0 LiftWEC DATASET FATIGUE ASSESSMENT.</p>
Digital image correlation displacements and strains around a growing fatigue crack in an AA2024-T3 aluminium alloy
<p>This repository contains the data used in the research article:</p> <p>Strohmann, Melching, Paysan, Dietrich, Requena, Breitbarth. Next generation fatigue crack growth experiments of aerospace materials. <em>Scientific Reports</em>, 2024, <a href="https://doi.org/10.1038/s41598-024-63915-x" target="_blank" rel="noopener">https://doi.org/10.1038/s41598-024-63915-x</a>.</p> <p> </p> <p><strong>General Description</strong><br>The dataset contains digital image correlation (DIC) data of a growing fatigue crack in an AA2024-T3 alloy. For the experiment, two independent DIC measurement devices were used – a global full-field DIC and a local microscopic DIC. Thus, the dataset consists of two main directories. One for the 3D DIC data ("global_3d_dic") and a second one for the 2D microscopic DIC data ("local_2d_dic"). Both of these are described and connected by rich metadata.<br>The 3D DIC data directory contains two subdirectories (a "Nodemaps" directory and a "Connections" directory). Both of them contain 797 '.txt'-files. The "Nodemaps" give the DIC results (i.e. coordinates, displacement and strains) for every timestep throughout the experiment. These are usually maximum, minimum, and mean load of a certain load cycle. However, for few crack lengths, we obtained DIC data for a higher number (ca. 100) of images within one load cycle. The nodemaps' format and structure is optimized for data processing in the open-source Python package <a title="CrackPy" href="https://doi.org/10.5281/zenodo.10990494" target="_blank" rel="noopener">CrackPy</a>. The last integer number of each filename can be interpreted as a 'timestep' throughout the experiment. The "Connection" files represent the connections of the DIC facet center coordinates. These are necessary to export the "Nodemap" data to any mesh like dataset, e.g. for VTK. <br>The 2D microscopic DIC directory contains 3 subdirectories ("80", "90", "95") for predefined positions with respect to the specimen coordinate system. For each location, a number of DIC data are stored, again within two subdirectories "Nodemaps" and "Connections" as '.txt'-files. For the 2D DIC data, the last integer of each name cannot be correlated to a timestep. Instead, we provide a descriptive file "local_2d_microscopic_coordinates_by_nodemaps.csv" linking each and every "Nodemap"-file to its respective coordinates and timestep (i.e. the load cycles).</p> <p>To describe the data, we distinguish between<br>1. Higher-level metadata - these data contain information about the experiment and material. The data do not change between timesteps and are given within this description.<br>2. Timestep metadata - these data contain information about one timestep of the experiment and are stored in the header of each "Nodemap"-file.</p> <p> </p> <p><strong>Higher-level Metadata</strong><br>The experiment is described in detail in the reference publication by <a title="Strohmann et al. (2024)" href="https://www.researchsquare.com/article/rs-3128435/v1" target="_blank" rel="noopener">Strohmann et al. (2024)</a> and a summary is given below. Moreover, we provide a dictionary in javascript object notation explaining terms which are used in the higher-level metadata. We use such a dictionary since no standardized ontology is currently available. This dictionary is stored in the main directory as "higher_level_metadata_dictionary.json".</p> <p><em>Material </em><br>A commercially available AA2024-T3 aluminum alloy was tested in L-T orientation, i.e. rolling direction, L, parallel to the load axis. The specimen had a width W = 160 mm cut from a rolled sheet of 2 mm.</p> <p><em>Digital image correlation</em><br>For 3D DIC, we used a GOM Aramis 12M system with a facet size of 20 x 20 pixels and a 16 pixels facet distance. One facet, therefore, covers ~0.614 x 0.614 mm². For the 2D microscopic DIC we captured images using a Zeiss STEMI 206C light optical microscope (LOM), equipped with a Basler a2A5320-23µmPro global shutter CMOS camera. One image has a size of 10.2 x 5.7 mm², 5328 x 3040 Pixels and a facet size of 40x40 pixels (distance of facet center points 30 pixels). The LOM was mounted to a robotic arm, a KUKA lbr Iiwa Cobot.</p> <p><em>Fatigue crack growth</em><br>We used a standard uniaxial servo-hydraulic testing rig. We applied a cyclic load ranging from Fmin = 4.5 kN to Fmax = 15 kN, i.e. R=Fmin/Fmax = 0.3. Throughout the experiment, we measured the crack length using direct current potential drop (DCPD).</p> <p><em>Image acquisition during fatigue crack growth</em><br>We acquired reference images for the DIC calculations before the experiment. For the global DIC, this is simply an image of the unloaded specimen. For the local microscopic DIC, the reference images are acquired in a checker board pattern with an overlap of 70 %. The depth of focus was calibrated for each image individually following (see <a title="Paysan et al. (2023)" href="https://doi.org/10.1007/s11340-023-00964-9" target="_blank" rel="noopener">Paysan et al. (2023)</a>). Images were acquired every 0.5 mm of crack extension at minimum, maximum and 0.5(Fmax- Fmin).</p> <p> </p> <p><strong>Timestep Metadata</strong><br>The timestep-wise metadata is stored in the individual DIC output files, "Nodemaps". We explain the terms used in a second dictionary, "timestep_level_metadata_dictionary.json". For all DIC data, we stored all data coming from the machine controller, i.e. number of cycles, force, displacement of the cylinder and also potential and crack length calculated from the potential as well as current values for back face strain gauges at both back faces of the MT specimen. In addition, for the local microscopic DIC data, we also store the current location of the center point of the image with respect to the global coordinate system provided by the current position of the robot carrying the LOM.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.