Find research datasets worth reusing

restrictedAug 2024View details →

Dataset related to article: SENSE OF OWNERSHIP INFLUENCE ON TACTILE PERCEPTION: IS THE PREDICTIVE CODING ACCOUNT VALID FOR THE SOMATIC RUBBER HAND ILLUSION?

Table including demographic data (sex, age, and education), Laterality Quotient score, Proprioceptive drift data, Body Ownership Questionnaire data, and Two-Point Discrimination score

restrictedNov 2022View details →

Dataset related to article "Nasal Polyposis Quality of Life (NPQ): Development and Validation of the First Specific Quality of Life Questionnaire for Chronic Rhinosinusitis with Nasal Polyps "

This record contains raw data related to article “Nasal Polyposis Quality of Life (NPQ): Development and Validation of the First Specific Quality of Life Questionnaire for Chronic Rhinosinusitis with Nasal Polyps" Abstract To date, no disease-specific tool has been available to assess the impact of chronic rhinosinusitis with nasal polyps (CRSwNP) on health-related quality of life (HRQoL). Therefore, the purpose of this study was to develop and validate a questionnaire specifically designed to this aim: the Nasal Polyposis Quality of Life (NPQ) questionnaire. As indicated in the current guidelines, the development and validation of the NPQ occurred in two separate steps involving different groups of patients. The questionnaire was validated by assessing internal structure, consistency, and validity. Responsiveness and sensitivity to changes were also evaluated. In the development process of NPQ an initial list of 40 items was given to 60 patients with CRSwNP; the 27 most significant items were selected and converted into questions. The validation procedure involved 107 patients (mean age 52.9 ± 12.4). NPQ revealed a five-dimensional structure and high levels of internal consistency (Cronbach's alpha 0.95). Convergent validity (Spearman' coefficient r = 0.75; p < 0.01), discriminant validity (sensitivity to VAS score), and reliability in a sample of patients with a stable health status (Interclass Coefficient 0.882) were satisfactory. Responsiveness to clinical changes was accomplished. The minimal important difference was 7. NPQ is the first questionnaire for the assessment of HRQoL in CRSwNP. Our results demonstrate that the new tool is valid, reliable, and sensitive to individual changes.

restrictedcc-by-4.0Nov 2022View details →

The development and validation of an Inhomogeneous Wind Scheme for Urban Street

This datasets include the following three parts: <ol> <li>the resulting data in paper shown in the Resulting_datasets.zip</li> <li>the source code for IWSUS_v0.1 and the test case are in https://doi.org/10.5281/zenodo.7488104.</li> <li>the CFD simulation for perpendicualr and parallel scenerios at https://doi.org/10.5281/zenodo.7371305 and https://doi.org/10.5281/zenodo.7371804 respectively.</li> <li>The coupling codes for IWSUS-v0.1 with Weather Research and Forecasting model (WRFV3.9.1.1) and Gotherburg cascan be downloaded in https://github.com/krmyArag/IWSUS.</li> </ol> In supplement. docx more CFD simulation results used to develop the IWSUS scheme are included, which cannot be fully presented in the paper due to the limitation of the length of the article.

restrictedJan 2023View details →

Dataset related to article "External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era "

This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era" Abstract Introduction: To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND). Materials and methods: After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis. Results: A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (p = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA. Conclusion: The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.

restrictedMay 2023View details →

Cross-cultural Validation of the Arabic 10-Factors Big Five Scale Using Factor Analysis

In Arab World, the big five aspect scale (BFAS) with 10 factors domain currently lacks validating evidence. The BFAS structure was investigated in Libyan adult sample (N = 1163). The sample completed the BFAS, and the International Personality Item Pool (IPIP) to assess the BFAS validity. Results found that The BFAS Conscientiousness, Extraversion, Introversion Openness/Intellect are correlated positively with IPIP Conscientiousness, Extraversion, Introversion, Intellect, The BFAS Neuroticism was negatively correlated with IPIP emotional stability. Results found that the BFAS scales includes its 10 domains, has good internal consistency, and is strongly related to major personality dimensions assessed by the leading questionnaire. To conclude, the findings indicate that the Arabic BFAS is reliable, valid, and comparable to the original English version. This study provides valuable insights into an individual's personality and behavior, which can have implications for personal, professional, and clinical contexts.

restrictedApr 2023View details →

A Dataset for Evaluating and Validating Blood Perfusion Monitoring During an Occlusion Protocol: Multi-spectral and Plethysmography data

The dataset contains multispectral images and PPG data from 45 subjects who underwent an occlusion protocol  to induce changes in blood perfusion in the dominant hand. All the partipiants signed an informed consent. Blood perfusion Blood perfusion refers to the passage of oxygen and other nutrients through the circulatory system. We look to provide data for the evaluation of non-invasive approaches based on multi-spectral images. PPG reference data from the thumb is provided for validation purposes. Occlusion Protocol The occlusion protocol for each participant lasts around 10 minutes. The participants were seated, and their superior limbs were extended on a table.  An automatic blood pressure monitor measured their systolic and diastolic pressures. A blood cuff was placed in their dominant arm, and a PPG sensor MAX30102 (Maxim Integrated, CA, United States) was placed in their thumb finger.  A multispectral camera was positioned above to record the hand palm throughout the protocol. The camera employed is a CMS-V1-C-EVR1M-USB3 (SILIOS Technologies SA., France), which records 9 multispectral channels in the VNIR region of the spectrum. . The protocol is divided into 5 stages, each one lasts 2 minutes. <ol> <li>Start: No pressure is applied</li> <li>Vascular Occlusion: a fixed pressure of 60 mmHg is applied through the blood cuff</li> <li>Rest: The pressure is liberated</li> <li>Total Occlusion:  A pressure equal to the initial systolic pressure plus 20 mmHg is constantly applied.</li> <li>Hyperemia: The pressure is released.</li> </ol> Data available: The multispectral datasets for each subject are contained in a P#.7z file. Single-channel images are included. The name of each file specifies the participant ID, the time (minutes_seconds_milliseconds) of the acquisition with respect to the start of the occlusion protocol, the multispectral dataset, and the corresponding multispectral channel. Example: the following file name specifies a file for participant number 21. The data was captured at 9 minutes and 59.5290 seconds after the start of the occlusion protocol. The image corresponds to the first channel (the count starts at 0). P21_09_59_5290_d02459_channel0.png Files containing masks for each dataset are available for the whole hand region (hand_masks.7z) and the middle finger (finger_masks.7z). Finger masks for participants with a lot of movement are not available. The PPG data is stored in a simple csv file with txt extension for each participant. It contains the Red and Infrared channel data as well as the acquisition time of each sample. The PPG data for all the participants are contained in the compressed file PPG.7z. Reference multispectral images for calibration are also provided in the file Calibration.7z. This file contains multispectral images with the cap on (Dark_Reference) plus three folders with reference images taken during the same session as the participants.  reference_set_A:  = P1 to P10 reference_set_B:  = P11 to P30 reference_set_C:  = P31 to P45 each folder contains multispectral images of different white reference materials which can be employed for different calibration methods:  WhiteA = white tile  WhiteB = white reference bar  WhiteP = sheet of white paper WhiteT = Teflon (PTFE) sheets The participant’s data is summarized in the next table. <table> <tbody> <tr> <td> ID </td> <td> Age </td> <td> Gender </td> <td> Skin type </td> <td> Systolic Pressure </td> <td> # Multi-spectral Datasets </td> </tr> <tr> <td> P1 </td> <td> 20 </td> <td> M </td> <td> 4 </td> <td> 109 </td> <td> 2,456 </td> </tr> <tr> <td> P2 </td> <td> 20 </td> <td> M </td> <td> 3 </td> <td> 129 </td> <td> 2,458 </td> </tr> <tr> <td> P3 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 117 </td> <td> 2,460 </td> </tr> <tr> <td> P4 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 110 </td> <td> 2,454 </td> </tr> <tr> <td> P5 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,458 </td> </tr> <tr> <td> P6 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 111 </td> <td> 2,471 </td> </tr> <tr> <td> P7 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 91 </td> <td> 2,462 </td> </tr> <tr> <td> P8 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 102 </td> <td> 2,474 </td> </tr> <tr> <td> P9 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 113 </td> <td> 2,468 </td> </tr> <tr> <td> P10 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 108 </td> <td> 2,461 </td> </tr> <tr> <td> P11 </td> <td> 18 </td> <td> M </td> <td> 4 </td> <td> 137 </td> <td> 2,461 </td> </tr> <tr> <td> P12 </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 132 </td> <td> 2,457 </td> </tr> <tr> <td> P13 </td> <td> 18 </td> <td> M </td> <td> 4 </td> <td> 119 </td> <td> 2,461 </td> </tr> <tr> <td> P14 </td> <td> 18 </td> <td> F </td> <td> 4 </td> <td> 100 </td> <td> 2,460 </td> </tr> <tr> <td> P15 </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 104 </td> <td> 2,460 </td> </tr> <tr> <td> P16 </td> <td> 18 </td> <td> M </td> <td> 3 </td> <td> 126 </td> <td> 2,462 </td> </tr> <tr> <td> P17* </td> <td> 18 </td> <td> F </td> <td> 3 </td> <td> 105 </td> <td> 2,469 </td> </tr> <tr> <td> P18 </td> <td> 21 </td> <td> F </td> <td> 4 </td> <td> 122 </td> <td> 2,461 </td> </tr> <tr> <td> P19 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 130 </td> <td> 2,462 </td> </tr> <tr> <td> P20 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 106 </td> <td> 2,457 </td> </tr> <tr> <td> P21 </td> <td> 23 </td> <td> M </td> <td> 3 </td> <td> 133 </td> <td> 2,459 </td> </tr> <tr> <td> P22 </td> <td> 23 </td> <td> M </td> <td> 3 </td> <td> 134 </td> <td> 2,471 </td> </tr> <tr> <td> P23 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 115 </td> <td> 2,472 </td> </tr> <tr> <td> P24 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 111 </td> <td> 2,445 </td> </tr> <tr> <td> P25 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 116 </td> <td> 2,460 </td> </tr> <tr> <td> P26 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 107 </td> <td> 2,462 </td> </tr> <tr> <td> P27 </td> <td> 21 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,460 </td> </tr> <tr> <td> P28 </td> <td> 24 </td> <td> F </td> <td> 4 </td> <td> 106 </td> <td> 2,460 </td> </tr> <tr> <td> P29 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 125 </td> <td> 2,460 </td> </tr> <tr> <td> P30 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 125 </td> <td> 2,461 </td> </tr> <tr> <td> P31 </td> <td> 21 </td> <td> F </td> <td> 3 </td> <td> 100 </td> <td> 2,472 </td> </tr> <tr> <td> P32 </td> <td> 22 </td> <td> M </td> <td> 3 </td> <td> 120 </td> <td> 2,460 </td> </tr> <tr> <td> P33 </td> <td> 21 </td> <td> M </td> <td> 4 </td> <td> 127 </td> <td> 2,461 </td> </tr> <tr> <td> P34 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 100 </td> <td> 2,457 </td> </tr> <tr> <td> P35 </td> <td> 19 </td> <td> F </td> <td> 3 </td> <td> 108 </td> <td> 2,460 </td> </tr> <tr> <td> P36 </td> <td> 20 </td> <td> F </td> <td> 3 </td> <td> 136 </td> <td> 2,471 </td> </tr> <tr> <td> P37 </td> <td> 19 </td> <td> M </td> <td> 4 </td> <td> 129 </td> <td> 2,457 </td> </tr> <tr> <td> P38* </td> <td> 19 </td> <td> M </td> <td> 3 </td> <td> 128 </td> <td> 2,463 </td> </tr> <tr> <td> P39* </td> <td> 18 </td> <td> M </td> <td> 3 </td> <td> 111 </td> <td> 2,479 </td> </tr> <tr> <td> P40 </td> <td> 23 </td> <td> M </td> <td> 4 </td> <td> 110 </td> <td> 2,462 </td> </tr> <tr> <td> P41 </td> <td> 21 </td> <td> M </td> <td> 3 </td> <td> 130 </td> <td> 2,465 </td> </tr> <tr> <td> P42 </td> <td> 20 </td> <td> F </td> <td> 3 </td> <td> 126 </td> <td> 2,461 </td> </tr> <tr> <td> P43 </td> <td> 22 </td> <td> F </td> <td> 3 </td> <td> 125 </td> <td> 2,459 </td> </tr> <tr> <td> P44 </td> <td> 22 </td> <td> M </td> <td> 3 </td> <td> 138 </td> <td> 2,467 </td> </tr> <tr> <td> P45 </td> <td> 22 </td> <td> M </td> <td> 4 </td> <td> 104 </td> <td> 2,461 </td> </tr> </tbody> </table> Table 1.- Participant's ID and relevant information.Participants with an * presented a lot of movement during the recording.

restrictedJul 2023View details →

Validation of a clinicopathological classification for predicting outcomes of pituitary tumours: retrospective cohort study in a pituitary tumour centre of excellence, 2013–2023

Simple Summary: This retrospective study aims to classify a series of pituitary neuroendocrine tumours (PitNETs), typified according to the WHO 2017 recommendations, using Trouillas et al.’s clinicopathological classification from 2013. We analysed 166 patients who underwent PitNET surgery from 2013 to 2023. The tumours were identified according to the gene and immunohistochemistry expression of pituitary transcription factors and adenohypophyseal hormones. The PitNETs were graded based on the invasion observed in MRI and the Ki-67 index. The study found that grade 2a and 2b tumours, T2 signal intensity ratio (SIR), and silent corticotroph tumours were associated with lower progression-free survival rates. Tumour volume and T2 SIR were independent predictors of recurrence/progression, with a T2 SIR of 2 or more showing a significantly higher risk. These findings emphasise the prognostic value of the five-grade classification and underscore the importance of radiological evaluation for managing PitNETs.   Abstract: Immunostaining of transcription factors allows a more exact classification of pituitary neuroendocrine tumours (PitNETs), but not a better prediction of their clinical behaviour. This retrospective, single-centre study aims to classify a series of PitNETs using Trouillas et al.’s clinicopathological classification from 2013. We analysed 166 patients undergoing PitNET surgery in 2013–2023. Tumours were identified according to the gene and immunohistochemical expression of PitNET transcription factors plus adenohypophyseal hormones. Tumours were classified according to a grading system based on MRI invasion and Ki-67 index. Eighty-one (48.8%) patients had grade 2a tumours; 71 (42.8%), grade 1a; 8 (4.8%), 2b; and 6 (3.6%), 1b. At a mean follow-up of 57.8 (standard deviation 30) months, 13.9% (n=23) showed recurrence/progression; independent predictors of recurrence were tumour volume (p=0.031) and T2 signal intensity ratio (SIR) (p<0.001). This risk was 18.6-fold higher for a T2 SIR of 2 or more. Grade 2a and 2b tumours, T2 SIR, and silent corticotroph adenomas (SCAs) were associated with lower progression-free survival. Our results add more evidence to the prognostic value of the five-grade PitNET classification and suggest higher clinical surveillance of patients with SCAs is warranted. The MRI findings highlight the increasing value of radiological evaluation for managing PitNETs.

A Study to Validate a New Oral Fluid/Saliva Antibody Test for Novel H1N1v Influenza

ClinicalTrials.gov study NCT01192958. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Validity of Different Prognostic Models Assessing 1 Year Survival After Liver Transplantation

ClinicalTrials.gov study NCT02412280. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Validation of Sleepware G3 Autoscoring Algorithm

ClinicalTrials.gov study NCT01564472. IPD Sharing: Not stated. Countries: 0. Publications: 0.

To Validate the Specific Patterns of Magnetocardiography, a Non-invasive Non-contact Examination for Patients With Ischemic Heart Disease or Cardiac Arrhythmia

ClinicalTrials.gov study NCT00965614. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Electrocardiogram (ECG) Validation Study

ClinicalTrials.gov study NCT06414447. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Validation of a Historical Physical Activity Survey

ClinicalTrials.gov study NCT00005414. IPD Sharing: Not stated. Countries: 0. Publications: 0.

Larsen & Toubro (L&T) Noninvasive Blood Pressure Module Performance Validation

ClinicalTrials.gov study NCT01097603. IPD Sharing: Not stated. Countries: 0. Publications: 0.

closedIPD-NOFeb 2026View details →

Development and Validation of a Serum Biomarker for the Diagnostic Work-up of Asthma

ClinicalTrials.gov study NCT04377958. IPD Sharing: NO. Countries: 0. Publications: 0.

closedIPD-NOFeb 2026View details →

In Search of the Correct Diagnosis of Beta-lactam Allergy: From the Validation of a Risk Stratification Tool to Accurate Endotyping

ClinicalTrials.gov study NCT07011212. IPD Sharing: NO. Countries: 0. Publications: 0.

openGEO-OpenDec 2016View details →

caArray_willm-00090: Gene Expression Profiling Reveals Novel Genes for Improved Risk Classification and Outcome Prediction in Pediatric Acute Lymphoblastic Leukemia: Identification, Cloning, and Valid

GEO Series GSE92708. Homo sapiens. 254 samples. Type: Expression profiling by array.

openGEO-OpenJul 2025View details →

Developing a Prognostic Stratification Model Based on Glutathione Metabolism in Thyroid Cancer and Validating RRM2’s Tumor‑Promoting Role

GEO Series GSE302548. Homo sapiens. 6 samples. Type: Expression profiling by high throughput sequencing.

openGEO-OpenJan 2026View details →

CBTi-seq validates specialized alternative splicing patterns in gene-edited cells

GEO Series GSE304004. Mus musculus. 3 samples. Type: Expression profiling by high throughput sequencing.