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dataset for helping and job performance dilemma study
Data Types:
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In the current data article, we present detailed characteristics of voids in carbon/epoxy composite laminates as well as the original image stacks, obtained via X-ray micro-Computed Tomography (micro-CT) . Five different lay-ups are produced with altering the recommended cure cycle in order to intentionally induce voids in the material. For each lay-up, an image stack (consisting of tomographic slices) and a dataset are provided. The image slices are in 8-bit TIF format. The datasets (spreadsheets) include the volume, size parameters, shape parameters, orientation, and location of all the detected voids in the specimen. The segmentation of the images and quantification of voids are performed in VoxTex, an in-house software for processing of micro-CT results. The data is linked to a Data in Brief article "A dataset of voids’ characteristics in multidirectional carbon fiber/epoxy composite laminates, obtained using X-ray micro-computed tomography" and linked to the article "Mehdikhani et al. Detailed characterization of voids in multidirectional carbon fiber/epoxy composite laminates using X-ray micro-computed tomography. Comp Part A. in press.".
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Raw data for experimentation of photosensitivity of fungal substrates
Data Types:
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Smooth muscle cell (SMC) TGFb signaling and expression of relevant genes in the media of normal human aortas and non-Marfan atherosclerotic ascending aorta aneurysms by bulk RNAseq and immunocytochemistry. Related To Fig 1. SMC aneurysms samples show a reduction in TGFb signaling and increased expression of inflammation-related genes compared to normal aortas SMCs.
Data Types:
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We showed that S1P and its receptor S1PR2 are involved in maintaining the epidermal barrier homeostasis by controlling tight junction related proteins, corneodesmosin, and filaggrin2 expression.
Data Types:
  • Software/Code
  • Tabular Data
  • Dataset
Clinical data of patients with arthroscopically confirmed TFCC lesion including preoperative weight bearing capacity tests. We tested the difference between weight bearing capacity of the injured hand compared to the healthy hand and to the injured hand with WristWidget. Further analysis compared the groups: traumatic vs degenerative lesion; stable vs unstable DRUJ determined by the need for a stabilising operation. Data includes Patient ID, age at time of injury/symptom onset (A_Verletzung), handedness (R_L, right=1), injured side (Verl_R_L, right=1, left=2), supposed aetiology (Trauma: 1=traumatic, 2= degenerative), DASH-score (DASH_T0, points), time until examination (Verl_bis_T0, days), pain on forced supination/pronation (fPronation_T0 and fSupination_T0, yes=1, no=2), clinical stability of the DRUJ of the affected hand (DRUG_Stab_T0, 1=unstable, 2=stable), clinical stability of the DRUJ of the other hand (DRUG_Stab_K_T0, 1=unstable, 2=stable), pain on pressure on the ulnar fovea (TFCC_Druck_T0, yes=1, no=2), pain on forced ulnarduction (Abkant_T0, yes=1, no=2), handgrip of both hands (Jamar_R_T0 for right and Jamar_L_T0 for left, in kg), weight bearing capacity in kg of both hands (WB for weight bearing, li for left, re for right, krank for affected hand, gesund for other hand, in kg), weight bearing capacity of both hands with wristwidget (WB for weight bearing, WW for wristwidget), extension of the wrist during weight bearing test (Ext_max_re/li_T0 in degrees), derived variables concerning the weight bearing test (WB_Proz_T0 = WB_krank_T0/WB_gesund_T0; Diff_WW... = WB_WW- WB in kg; Proz_WB_WW... =WB_WW/WB*100), range of motion in degrees for dorsal/palmarflexion (D_re/li_T0 and P_re/li_T0, re for right, li for left), ulnar/radialduction (U_re/li_T0 and R_re/li_T0, re for right, li for left), pro/supination (Sup_re/li_T0 and Pro_re/li_T0, re for right, li for left), range of motion (Summe_ROM_re/li_T0 = D+P+U+R), sum of pronation and supination (Summe_SP_re/li_T0), range of motion of affected hand relative to other hand (ROM_Proz_T0= Summe_ROM_aa_T0 / Summe_ROM_bb_T0*100 with aa=Verl_R_L and re for right and li for left), sum of pronation and supination compared to the other hand (ROM_SP_Proz_T0=Summe_SP_aa_T0/Summe_SP_bb_T0), the same with differentiation between affected hand (krank) and other hand (gesund) differentiation between traumatic and degenerative lesions in the MRI report (MRT_ukb_traumatischdegenerativ, 0=no injury seen, 1=traumatic, 2=degenerative, MRI field strength (Tesla, value in Tesla, some missing values), static ulnar variance (Röntgenbefund_Ulna, , dynamic ulnar variance in mm, weight bearing test capacity during x-ray, derived variables regarding the weight bearing test, information about stabilising operation, information about intraoperative assessment on type of lesion (traumatic/degenerative). Further information on request as description field is limited.
Data Types:
  • Software/Code
  • Dataset
This is a dataset on Ghanaian patients’ perception of how nurses, midwives, and doctors communicate with patients, using Four Habits Patients’ Questionnaire
Data Types:
  • Software/Code
  • Dataset
This data is associated with a submitted manuscript, where processing parameters for Ti6Al4V with and without copper addition is investigated. The two samples are both with 3% copper with different process parameters. The data format is described below and will be of interest to readers of the paper (in Additive Manufacturing journal) for visualization of pores inside the metal samples. Data is in processed format and can be opened using the free software myVGL obtainable from Volume Graphics GmbH, or the compressed image stacks can be opened, which then contains no analysis and only raw data. Voxel size is 0.015 mm isotropic. Cuboids are 10x10x4 mm and analysis region of interest is the central 6x6x2 mm of each sample. The two samples are two different process parameters with power 170 W and scan speed 0.9 m/s, and 340 W with 1.1 m/s
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Study aim: To study Gamma-enhancing neurofeedback learning process and evaluate its efficacy on visual feature binding and fluid intelligence Sample size: 18 healthy female students (mean age: 24.24 ± 1.94 years) Dataset: ----------- 1- Demographics: 18 subjects, Age, BMI, Weight, Height, Handedness, GPA 2- IQ measure: 18 subjects, Pretest and posttest sessions 3- Visual feature binding measure: 18 subjects, Pretest and posttest sessions, Response time and Error rate 4- 4 activity baseline EEG: 18 subjects, Pretest and posttest sessions, Tasks: Eyes open, Eyes closed, Auditory sensory attentiveness, Cognitive effort 5- Neurofeedback training EEG: 8 subjects, 8 training sessions, Eyes closed baseline EEG recorded before and after training in each session, EEG recorded during training in each session
Data Types:
  • Software/Code
  • Image
  • Tabular Data
  • Dataset
  • File Set
This data is used to analyze the structural equation of the article
Data Types:
  • Software/Code
  • Dataset
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