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Kaggle Inc data sources a open access dataset
Data Sources A Open Access Dataset, supplied by Kaggle Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/open+source+datasets/access+dataset+open/pm41598615-76-0-5
Average 86 stars, based on 1 article reviews

data sources a open access dataset - by Bioz Stars, 2026-09

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Article Title: X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer’s Disease Prediction Using MRI Scans
Article Snippet: The author utilized an open-source dataset taken from Kaggle, including MR brain images, with an accuracy of 94%.

Article Title: Generative adversarial networks and hyperparameter-optimized XGBoost for enhanced heart disease prediction
Article Snippet: In this research study, an open-source dataset has been used, which is freely available on Kaggle at the following link: https://www.kaggle.com/code/alawdisoft/personal-key-indicators-of-heart-diseases/notebook? select=heart\_2020\_cleaned.csvKaggle Notebook: Personal Key Indicators of Heart Diseases.

Article Title: Developing Synthetic Orthopantomogram Datasets Through Generative Models
Article Snippet: Additionally, OPGs from the TUFTS Dental Dataset (TDD) and an open-source dataset from Kaggle were used to increase the diversity of the images., , A total of 5,383 OPGs of permanent dentition were included in the model training.

Article Title: Improving predictive reliability and automation of smart grids using the StarNet ensemble model
Article Snippet: The bagging classifier technique obtained the highest accuracy of 97.9% compared to other classifiers in load prediction using an open-access dataset from Kaggle and running trials in a Python simulation environment.

Article Title: Generative adversarial networks and hyperparameter-optimized XGBoost for enhanced heart disease prediction.
Article Snippet: In this research study, an open-source dataset has been used, which is freely available on Kaggle at the following link: https://www.kaggle.com/code/alawdisoft/personal-key-indicators-of-heartdiseases/notebook?select=heart_2020_cleaned.csvKaggle Notebook: Personal Key Indicators of Heart Diseases

Article Title: Large language model assisted hyper-heuristic evolutionary algorithm for groundwater level prediction.
Article Snippet: Dataset I was sourced from open-source data on the Kaggle Data Mining and Forecasting Competition platform.

Magnetic Resonance Imaging:

Article Title: X-ViTCNN: A Novel Network-Level Fusion of Transfer Learning and Customized Vision Transformer for Multi-Stage Alzheimer's Disease Prediction Using MRI Scans.
Article Snippet: .. The author utilized an open-source dataset taken from Kaggle, including MR brain images, with an accuracy of 94%. https://doi.org/10.3390/diagnostics16060835 In addition, Rallabandi et al. [26] developed an Inception-ResNet wrapper model to classify AD using MRI and PET scans. ..

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$a ) and b ) Gene expression changes within infarcted left ventricle (‘infarct’) are marked at 24- and 48-hours post-MI, as assessed by hierarchical clustering and principal component analyses of the open-source GEO dataset GSE4648 . Analyses carried out using Geo2R and the open-source BART bioinformatics tool. c ) Differential gene expression patterns identified in 24-hour post-MI ventricle compared with sham control. cIAP2 (Birc3) is significantly upregulated. d ) LV infarct region gene expression timecourse of cIAP2 (maroon) and related transcripts cIAP1 (blue) and Xiap (green), contrasted with measured cardiac marker tnnt2 (Cardiac Troponin T) and inflammatory marker cxcl1. e ) Compiled expression timecourse of representative myeloid-related transcripts (maroon) and lymphoid transcripts (blue) from LV infarct region post-MI. ptprc (black) = hematopoietic marker gene transcript. f ) Gene Ontogeny (GO) pathway enrichment following MI identifies multiple networks requiring cIAP2. g ) NOD1 −/− mice have reduced systolic dysfunction and cardiac inflammation following MI relative to WT control mice. Control-operated mice: N = 3; MI-operated mice: N = 5. Ejection fraction: N = 4 MI-operated per group. h ) cIAP2 is expressed at lower levels, along with inflammatory genes RIPK1, IRAK4 and TRAF6, in multiple danger signaling receptor-deficient mouse models following MI. Image is representative of two similar experiments. Error bars denote Mean +/- SD; p values were calculated using one-way ANOVA with Bonferroni’s correction ( g , upper panel) or two-sided t-test ( g , lower panel).$

Journal: Nature Cardiovascular Research

Article Title: Hematopoietic expression of cIAP2 drives inflammation and heart failure after myocardial infarction

doi: 10.1038/s44161-026-00782-x

Figure Lengend Snippet: a ) and b ) Gene expression changes within infarcted left ventricle (‘infarct’) are marked at 24- and 48-hours post-MI, as assessed by hierarchical clustering and principal component analyses of the open-source GEO dataset GSE4648 . Analyses carried out using Geo2R and the open-source BART bioinformatics tool. c ) Differential gene expression patterns identified in 24-hour post-MI ventricle compared with sham control. cIAP2 (Birc3) is significantly upregulated. d ) LV infarct region gene expression timecourse of cIAP2 (maroon) and related transcripts cIAP1 (blue) and Xiap (green), contrasted with measured cardiac marker tnnt2 (Cardiac Troponin T) and inflammatory marker cxcl1. e ) Compiled expression timecourse of representative myeloid-related transcripts (maroon) and lymphoid transcripts (blue) from LV infarct region post-MI. ptprc (black) = hematopoietic marker gene transcript. f ) Gene Ontogeny (GO) pathway enrichment following MI identifies multiple networks requiring cIAP2. g ) NOD1 −/− mice have reduced systolic dysfunction and cardiac inflammation following MI relative to WT control mice. Control-operated mice: N = 3; MI-operated mice: N = 5. Ejection fraction: N = 4 MI-operated per group. h ) cIAP2 is expressed at lower levels, along with inflammatory genes RIPK1, IRAK4 and TRAF6, in multiple danger signaling receptor-deficient mouse models following MI. Image is representative of two similar experiments. Error bars denote Mean +/- SD; p values were calculated using one-way ANOVA with Bonferroni’s correction ( g , upper panel) or two-sided t-test ( g , lower panel).

Article Snippet: Left ventricular expression of gene products over a 48-hour timecourse was catalogued in the National Center for Biotechnology Information (NCBI)-curated, open-source dataset GSE4648 ( GDS2329 ) (for reference, see https://www.ncbi.nlm.nih.gov/sites/GDSbrowser?acc=GDS2329 ).

Techniques: Gene Expression, Control, Marker, Expressing