🟢 Name Of Dataset: 1
🟢 Description Of Dataset:
111
🟢 Official Homepage: Not found
🟢 Number of articles that used this dataset: 28
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 NeMo Inverse Text Normalization: From Development To Production
📝 Open Deep Search: Democratizing Search with Open-source Reasoning Agents
📝 Deep Learning in Single-Cell Analysis
📝 Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
📝 UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
📝 Representation Learning with Large Language Models for Recommendation
📝 Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
📝 K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
📝 Semi-supervised Sequence Modeling for Elastic Impedance Inversion
📝 CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
111
🟢 Official Homepage: Not found
🟢 Number of articles that used this dataset: 28
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 NeMo Inverse Text Normalization: From Development To Production
📝 Open Deep Search: Democratizing Search with Open-source Reasoning Agents
📝 Deep Learning in Single-Cell Analysis
📝 Enhancing Fine-grained Sentiment Classification Exploiting Local Context Embedding
📝 UniAnimate-DiT: Human Image Animation with Large-Scale Video Diffusion Transformer
📝 Representation Learning with Large Language Models for Recommendation
📝 Short-Term Aggregated Residential Load Forecasting using BiLSTM and CNN-BiLSTM
📝 K-PLUG: Knowledge-injected Pre-trained Language Model for Natural Language Understanding and Generation in E-Commerce
📝 Semi-supervised Sequence Modeling for Elastic Impedance Inversion
📝 CholecTrack20: A Dataset for Multi-Class Multiple Tool Tracking in Laparoscopic Surgery
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
Telegram
Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
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🟢 Name Of Dataset: MIT-BIH Arrhythmia Database
🟢 Description Of Dataset:
The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included with the database.This directory contains the entire MIT-BIH Arrhythmia Database. About half (25 of 48 complete records, and reference annotation files for all 48 records) of this database has been freely available here since PhysioNet's inception in September 1999. The 23 remaining signal files, which had been available only on the MIT-BIH Arrhythmia Database CD-ROM, were posted here in February 2005.Much more information about this database may be found in theMIT-BIH Arrhythmia Database Directory.
🟢 Official Homepage: https://physionet.org/content/mitdb/1.0.0/
🟢 Number of articles that used this dataset: 31
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
📝 ECG Heartbeat Classification: A Deep Transferable Representation
📝 Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
📝 Subject-Aware Contrastive Learning for Biosignals
📝 Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
📝 A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
📝 AQuA: A Benchmarking Tool for Label Quality Assessment
📝 Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
📝 Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization
📝 MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
The MIT-BIH Arrhythmia Database contains 48 half-hour excerpts of two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979. Twenty-three recordings were chosen at random from a set of 4000 24-hour ambulatory ECG recordings collected from a mixed population of inpatients (about 60%) and outpatients (about 40%) at Boston's Beth Israel Hospital; the remaining 25 recordings were selected from the same set to include less common but clinically significant arrhythmias that would not be well-represented in a small random sample.The recordings were digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range. Two or more cardiologists independently annotated each record; disagreements were resolved to obtain the computer-readable reference annotations for each beat (approximately 110,000 annotations in all) included with the database.This directory contains the entire MIT-BIH Arrhythmia Database. About half (25 of 48 complete records, and reference annotation files for all 48 records) of this database has been freely available here since PhysioNet's inception in September 1999. The 23 remaining signal files, which had been available only on the MIT-BIH Arrhythmia Database CD-ROM, were posted here in February 2005.Much more information about this database may be found in theMIT-BIH Arrhythmia Database Directory.
🟢 Official Homepage: https://physionet.org/content/mitdb/1.0.0/
🟢 Number of articles that used this dataset: 31
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Inter- and intra- patient ECG heartbeat classification for arrhythmia detection: a sequence to sequence deep learning approach
📝 ECG Heartbeat Classification: A Deep Transferable Representation
📝 Multi-module Recurrent Convolutional Neural Network with Transformer Encoder for ECG Arrhythmia Classification
📝 Subject-Aware Contrastive Learning for Biosignals
📝 Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
📝 A Personalized Zero-Shot ECG Arrhythmia Monitoring System: From Sparse Representation Based Domain Adaption to Energy Efficient Abnormal Beat Detection for Practical ECG Surveillance
📝 AQuA: A Benchmarking Tool for Label Quality Assessment
📝 Spot The Odd One Out: Regularized Complete Cycle Consistent Anomaly Detector GAN
📝 Arrhythmia Classifier Using Convolutional Neural Network with Adaptive Loss-aware Multi-bit Networks Quantization
📝 MedFuncta: Modality-Agnostic Representations Based on Efficient Neural Fields
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
www.physionet.org
MIT-BIH Arrhythmia Database v1.0.0
Two-channel ambulatory ECG recordings, obtained from 47 subjects studied by the BIH Arrhythmia Laboratory between 1975 and 1979.
❤5
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This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
5️⃣ Data Analysis
6️⃣ Statistics
7️⃣ Deep Learning
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🟢 Name Of Dataset: RVL-CDIP
🟢 Description Of Dataset:
TheRVL-CDIPdataset consists of scanned document images belonging to 16 classes such as letter, form, email, resume, memo, etc. The dataset has 320,000 training, 40,000 validation and 40,000 test images. The images are characterized by low quality, noise, and low resolution, typically 100 dpi.Source:Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning
🟢 Official Homepage: https://www.cs.cmu.edu/~aharley/rvl-cdip/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/rvl_cdip
huggingface/datasets (rvl-cdip_easyOCR):
https://huggingface.co/datasets/jordyvl/rvl-cdip_easyOCR
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/aharley/rvl_cdip
huggingface/datasets (rvl_cdip_easyocr):
https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr
huggingface/datasets (rvl_cdip_mini):
https://huggingface.co/datasets/dvgodoy/rvl_cdip_mini
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheRVL-CDIPdataset consists of scanned document images belonging to 16 classes such as letter, form, email, resume, memo, etc. The dataset has 320,000 training, 40,000 validation and 40,000 test images. The images are characterized by low quality, noise, and low resolution, typically 100 dpi.Source:Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning
🟢 Official Homepage: https://www.cs.cmu.edu/~aharley/rvl-cdip/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/rvl_cdip
huggingface/datasets (rvl-cdip_easyOCR):
https://huggingface.co/datasets/jordyvl/rvl-cdip_easyOCR
huggingface/datasets (rvl_cdip):
https://huggingface.co/datasets/aharley/rvl_cdip
huggingface/datasets (rvl_cdip_easyocr):
https://huggingface.co/datasets/jordyvl/rvl_cdip_easyocr
huggingface/datasets (rvl_cdip_mini):
https://huggingface.co/datasets/dvgodoy/rvl_cdip_mini
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤5
🟢 Name Of Dataset: FUNSD (Form Understanding in Noisy Scanned Documents)
🟢 Description Of Dataset:
Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.Source:FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
🟢 Official Homepage: https://guillaumejaume.github.io/FUNSD/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets:
https://huggingface.co/datasets/nielsr/FUNSD_layoutlmv2
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.FUNSD
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking.Source:FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents
🟢 Official Homepage: https://guillaumejaume.github.io/FUNSD/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
huggingface/datasets:
https://huggingface.co/datasets/nielsr/FUNSD_layoutlmv2
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.FUNSD
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤4
🟢 Name Of Dataset: IIIT-AR-13K
🟢 Description Of Dataset:
IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection.Source:IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
🟢 Official Homepage: http://cvit.iiit.ac.in/usodi/iiitar13k.php
🟢 Number of articles that used this dataset: 6
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Deep learning for table detection and structure recognition: A survey
📝 RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
📝 The YOLO model that still excels in document layout analysis
📝 IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
📝 Document AI: Benchmarks, Models and Applications
📝 Robust Table Detection and Structure Recognition from Heterogeneous Document Images
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection.Source:IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
🟢 Official Homepage: http://cvit.iiit.ac.in/usodi/iiitar13k.php
🟢 Number of articles that used this dataset: 6
🟢 Dataset Loaders:
Not found
🟢 Articles related to the dataset:
📝 Deep learning for table detection and structure recognition: A survey
📝 RanLayNet: A Dataset for Document Layout Detection used for Domain Adaptation and Generalization
📝 The YOLO model that still excels in document layout analysis
📝 IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents
📝 Document AI: Benchmarks, Models and Applications
📝 Robust Table Detection and Structure Recognition from Heterogeneous Document Images
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
Telegram
Kaggle Data Hub
Your go-to hub for Kaggle datasets – explore, analyze, and leverage data for Machine Learning and Data Science projects.
Admin: @HusseinSheikho || @Hussein_Sheikho
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🟢 Name Of Dataset: ICDAR 2013
🟢 Description Of Dataset:
TheICDAR 2013dataset consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark dataset for evaluating near-horizontal text detection.Source:Single Shot Text Detector with Regional Attention
🟢 Official Homepage: https://rrc.cvc.uab.es/?ch=2
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
activeloopai/Hub:
https://docs.activeloop.ai/datasets/icdar-2013-dataset
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.IC13
tanglang96/DataLoaders_DALI:
https://github.com/tanglang96/DataLoaders_DALI
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
TheICDAR 2013dataset consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark dataset for evaluating near-horizontal text detection.Source:Single Shot Text Detector with Regional Attention
🟢 Official Homepage: https://rrc.cvc.uab.es/?ch=2
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
activeloopai/Hub:
https://docs.activeloop.ai/datasets/icdar-2013-dataset
mindee/doctr:
https://mindee.github.io/doctr/latest/datasets.html#doctr.datasets.IC13
tanglang96/DataLoaders_DALI:
https://github.com/tanglang96/DataLoaders_DALI
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤3
🟢 Name Of Dataset: UFPR-ALPR
🟢 Description Of Dataset:
This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus.We collected 1,500 images with each camera, divided as follows:- 900 of cars with gray license plates;- 300 of cars with red license plates;- 300 of motorcycles with gray license plates.The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters.Source:A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
🟢 Official Homepage: https://web.inf.ufpr.br/vri/databases/ufpr-alpr/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
ultralytics/yolov5:
https://github.com/ultralytics/yolov5
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
This dataset includes 4,500 fully annotated images (over 30,000 license plate characters) from 150 vehicles in real-world scenarios where both the vehicle and the camera (inside another vehicle) are moving.The images were acquired with three different cameras and are available in the Portable Network Graphics (PNG) format with a size of 1,920 × 1,080 pixels. The cameras used were: GoPro Hero4 Silver, Huawei P9 Lite, and iPhone 7 Plus.We collected 1,500 images with each camera, divided as follows:- 900 of cars with gray license plates;- 300 of cars with red license plates;- 300 of motorcycles with gray license plates.The dataset is split as follows: 40% for training, 40% for testing and 20% for validation. Every image has the following annotations available in a text file: the camera in which the image was taken, the vehicle’s position and information such as type (car or motorcycle), manufacturer, model and year; the identification and position of the license plate, as well as the position of its characters.Source:A Robust Real-Time Automatic License Plate Recognition Based on the YOLO Detector
🟢 Official Homepage: https://web.inf.ufpr.br/vri/databases/ufpr-alpr/
🟢 Number of articles that used this dataset: Unknown
🟢 Dataset Loaders:
ultralytics/yolov5:
https://github.com/ultralytics/yolov5
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤2
🟢 Name Of Dataset: PHM2017
🟢 Description Of Dataset:
PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:self-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., "However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer."other-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., "Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world"awareness. The tweet contains the disease name, but does not mention a specific person, e.g., "A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals"non-health. The tweet contains the disease name, but the tweet topic is not about health. "Now I can have cancer on my wall for all to see <3"Source:Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
🟢 Official Homepage: https://github.com/emory-irlab/PHM2017
🟢 Number of articles that used this dataset: 7
🟢 Dataset Loaders:
emory-irlab/PHM2017:
https://github.com/emory-irlab/PHM2017
🟢 Articles related to the dataset:
📝 PHMD: An easy data access tool for prognosis and health management datasets
📝 Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
📝 Incorporating Emotions into Health Mention Classification Task on Social Media
📝 A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
📝 Neural Architecture Search For Fault Diagnosis
📝 Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
📝 Multi-task Learning for Personal Health Mention Detection on Social Media
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
🟢 Description Of Dataset:
PHM2017 is a new dataset consisting of 7,192 English tweets across six diseases and conditions: Alzheimer’s Disease, heart attack (any severity), Parkinson’s disease, cancer (any type), Depression (any severity), and Stroke. The Twitter search API was used to retrieve the data using the colloquial disease names as search keywords, with the expectation of retrieving a high-recall, low precision dataset. After removing the re-tweets and replies, the tweets were manually annotated. The labels are:self-mention. The tweet contains a health mention with a health self-report of the Twitter account owner, e.g., "However, I worked hard and ran for Tokyo Mayer Election Campaign in January through February, 2014, without publicizing the cancer."other-mention. The tweet contains a health mention of a health report about someone other than the account owner, e.g., "Designer with Parkinson’s couldn’t work then engineer invents bracelet + changes her world"awareness. The tweet contains the disease name, but does not mention a specific person, e.g., "A Month Before a Heart Attack, Your Body Will Warn You With These 8 Signals"non-health. The tweet contains the disease name, but the tweet topic is not about health. "Now I can have cancer on my wall for all to see <3"Source:Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
🟢 Official Homepage: https://github.com/emory-irlab/PHM2017
🟢 Number of articles that used this dataset: 7
🟢 Dataset Loaders:
emory-irlab/PHM2017:
https://github.com/emory-irlab/PHM2017
🟢 Articles related to the dataset:
📝 PHMD: An easy data access tool for prognosis and health management datasets
📝 Did You Really Just Have a Heart Attack? Towards Robust Detection of Personal Health Mentions in Social Media
📝 Incorporating Emotions into Health Mention Classification Task on Social Media
📝 A Novel Approach to Train Diverse Types of Language Models for Health Mention Classification of Tweets
📝 Neural Architecture Search For Fault Diagnosis
📝 Improving Health Mentioning Classification of Tweets using Contrastive Adversarial Training
📝 Multi-task Learning for Personal Health Mention Detection on Social Media
==================================
🔴 For more datasets resources:
✓ https://t.me/Datasets1
❤1
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Forwarded from Machine Learning with Python
This channels is for Programmers, Coders, Software Engineers.
0️⃣ Python
1️⃣ Data Science
2️⃣ Machine Learning
3️⃣ Data Visualization
4️⃣ Artificial Intelligence
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📝 1. BASIC DATASET INFO
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Title: E. coli Resistance Dataset
Basic Description: Antibiotic resistance profiles in E. coli clinical isolates
📖 2. FULL DATASET DESCRIPTION
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Full Description:
This dataset contains 195,000+ raw records of Escherichia coli clinical isolates and their antimicrobial susceptibility test results. The data was extracted from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), a public repository funded by NIAID.
Each entry captures how a specific E. coli genome responds to a given antibiotic, along with phenotypic interpretation, lab methods, measurement values (e.g., MIC), and supporting publication links.
📥 3. API DOWNLOAD INFORMATION
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API Download Link: https://www.kaggle.com/api/v1/datasets/download/valeriamaciel/e-coli-resistance-dataset
Dataset Size: Download dataset as zip (3 MB)
📊 4. FILE COUNT
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File count not found
📈 5. VIEWS & DOWNLOADS
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Views: 418
Downloads: 72
📚 6. RELATED NOTEBOOKS
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1. Antibiotic Dataset
Upvotes: 38
URL: https://www.kaggle.com/datasets/kanchana1990/antibiotic-dataset
2. Malaria Dataset
Upvotes: 35
URL: https://www.kaggle.com/datasets/miracle9to9/files1
3. SARS-CoV-2 Genetics
Upvotes: 13
URL: https://www.kaggle.com/datasets/rtwillett/sarscov2-genetics
4. E.coli_Data_cleaning
Upvotes: 7
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-cleaning
5. E.coli_data_analysis
Upvotes: 4
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-analysis
📝 1. BASIC DATASET INFO
----------------------------------------
Title: E. coli Resistance Dataset
Basic Description: Antibiotic resistance profiles in E. coli clinical isolates
📖 2. FULL DATASET DESCRIPTION
----------------------------------------
Full Description:
This dataset contains 195,000+ raw records of Escherichia coli clinical isolates and their antimicrobial susceptibility test results. The data was extracted from the Bacterial and Viral Bioinformatics Resource Center (BV-BRC), a public repository funded by NIAID.
Each entry captures how a specific E. coli genome responds to a given antibiotic, along with phenotypic interpretation, lab methods, measurement values (e.g., MIC), and supporting publication links.
📥 3. API DOWNLOAD INFORMATION
----------------------------------------
API Download Link: https://www.kaggle.com/api/v1/datasets/download/valeriamaciel/e-coli-resistance-dataset
Dataset Size: Download dataset as zip (3 MB)
📊 4. FILE COUNT
----------------------------------------
File count not found
📈 5. VIEWS & DOWNLOADS
----------------------------------------
Views: 418
Downloads: 72
📚 6. RELATED NOTEBOOKS
----------------------------------------
1. Antibiotic Dataset
Upvotes: 38
URL: https://www.kaggle.com/datasets/kanchana1990/antibiotic-dataset
2. Malaria Dataset
Upvotes: 35
URL: https://www.kaggle.com/datasets/miracle9to9/files1
3. SARS-CoV-2 Genetics
Upvotes: 13
URL: https://www.kaggle.com/datasets/rtwillett/sarscov2-genetics
4. E.coli_Data_cleaning
Upvotes: 7
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-cleaning
5. E.coli_data_analysis
Upvotes: 4
URL: https://www.kaggle.com/code/valeriamaciel/e-coli-data-analysis
❤2
Dataset Name: 1.88 Million US Wildfires
Basic Description: 24 years of geo-referenced wildfire records
📖 FULL DATASET DESCRIPTION:
==================================
This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2015. It is the third update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 1.88 million geo-referenced wildfire records, representing a total of 140 million acres burned during the 24-year period.
This dataset is an SQLite database that contains the following information:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rtatman/188-million-us-wildfires
🔴 Dataset Size: Download dataset as zip (176 MB)
📊 Additional information:
==================================
File count not found
Views: 411,000
Downloads: 38,600
📚 RELATED NOTEBOOKS:
==================================
1. Exercise: Creating, Reading and Writing | Upvotes: 453,001
URL: https://www.kaggle.com/code/residentmario/exercise-creating-reading-and-writing
2. Exercise: Indexing, Selecting & Assigning | Upvotes: 319,639
URL: https://www.kaggle.com/code/residentmario/exercise-indexing-selecting-assigning
3. Exercise: Summary Functions and Maps | Upvotes: 269,410
URL: https://www.kaggle.com/code/residentmario/exercise-summary-functions-and-maps
4. Next Day Wildfire Spread | Upvotes: 40
URL: https://www.kaggle.com/datasets/fantineh/next-day-wildfire-spread
5. Fire statistics dataset | Upvotes: 8
URL: https://www.kaggle.com/datasets/sujaykapadnis/fire-statistics-dataset
Basic Description: 24 years of geo-referenced wildfire records
📖 FULL DATASET DESCRIPTION:
==================================
This data publication contains a spatial database of wildfires that occurred in the United States from 1992 to 2015. It is the third update of a publication originally generated to support the national Fire Program Analysis (FPA) system. The wildfire records were acquired from the reporting systems of federal, state, and local fire organizations. The following core data elements were required for records to be included in this data publication: discovery date, final fire size, and a point location at least as precise as Public Land Survey System (PLSS) section (1-square mile grid). The data were transformed to conform, when possible, to the data standards of the National Wildfire Coordinating Group (NWCG). Basic error-checking was performed and redundant records were identified and removed, to the degree possible. The resulting product, referred to as the Fire Program Analysis fire-occurrence database (FPA FOD), includes 1.88 million geo-referenced wildfire records, representing a total of 140 million acres burned during the 24-year period.
This dataset is an SQLite database that contains the following information:
📥 DATASET DOWNLOAD INFORMATION
==================================
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/rtatman/188-million-us-wildfires
🔴 Dataset Size: Download dataset as zip (176 MB)
📊 Additional information:
==================================
File count not found
Views: 411,000
Downloads: 38,600
📚 RELATED NOTEBOOKS:
==================================
1. Exercise: Creating, Reading and Writing | Upvotes: 453,001
URL: https://www.kaggle.com/code/residentmario/exercise-creating-reading-and-writing
2. Exercise: Indexing, Selecting & Assigning | Upvotes: 319,639
URL: https://www.kaggle.com/code/residentmario/exercise-indexing-selecting-assigning
3. Exercise: Summary Functions and Maps | Upvotes: 269,410
URL: https://www.kaggle.com/code/residentmario/exercise-summary-functions-and-maps
4. Next Day Wildfire Spread | Upvotes: 40
URL: https://www.kaggle.com/datasets/fantineh/next-day-wildfire-spread
5. Fire statistics dataset | Upvotes: 8
URL: https://www.kaggle.com/datasets/sujaykapadnis/fire-statistics-dataset
❤1
Dataset Name: Yahoo NSFW as Mobile Net V2 Bottlenecks
Basic Description: Yahoo NSFW as MobileNetV2 Bottlenecks
📖 FULL DATASET DESCRIPTION:
==================================
This dataset is meant to aid development of effective and computationally light NSFW filtering that can be run on low powered devices. To understand why I'm posting this dataset, see this article.
NSFW machine learning requires NSFW images, which are best not distributed on public sites (and usually against Terms of Service). Instead, this dataset contains the model outputs of 200K mostly pornographic images having been sent through the first layers of MobileNetV2. Additionally, the output of the Yahoo NSFW model are included.
Transfer learning principles can then be applied to this dataset. Using the MobileNetV2 outputs as bottlenecks, and the Yahoo NSFW outputs as target values, one can build a model which tries to mimic the Yahoo NSFW model.
The files are in several archives (it was the only way to upload this much data with a 2GB limit per file). Inside the archives are npz files (compressed numpy arrays), containing 2000 input and target tensors.
Keras was used to create the MobileNetV2 output, and you can see in the tutorial kernel how it can be utilized.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (6 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/nmurray1234/yahoo-nsfw-as-mobilenetv2-bottlenecks
📊 Additional information:
==================================
File count not found
Views: 13,600
Downloads: 416
📚 RELATED NOTEBOOKS:
==================================
1. Tutorial: Yahoo NSFW as MobileNetV2 | Upvotes: 16
URL: https://www.kaggle.com/code/nmurray1234/tutorial-yahoo-nsfw-as-mobilenetv2
2. EfficientNet Keras Full Weights | Upvotes: 11
URL: https://www.kaggle.com/datasets/xhlulu/efficientnet-keras-weights
3. Starter: Yahoo NSFW as MobileNetV2 bbf75635-2 | Upvotes: 6
URL: https://www.kaggle.com/code/kerneler/starter-yahoo-nsfw-as-mobilenetv2-bbf75635-2
4. CNN Models from Yahoo NSFW | Upvotes: 5
URL: https://www.kaggle.com/code/jimojung/cnn-models-from-yahoo-nsfw
5. EfficientnetV1-V2 no-top Keras models | Upvotes: 2
URL: https://www.kaggle.com/datasets/hamzaboulahia/efficientnetsv2-keras-notop-models
Basic Description: Yahoo NSFW as MobileNetV2 Bottlenecks
📖 FULL DATASET DESCRIPTION:
==================================
This dataset is meant to aid development of effective and computationally light NSFW filtering that can be run on low powered devices. To understand why I'm posting this dataset, see this article.
NSFW machine learning requires NSFW images, which are best not distributed on public sites (and usually against Terms of Service). Instead, this dataset contains the model outputs of 200K mostly pornographic images having been sent through the first layers of MobileNetV2. Additionally, the output of the Yahoo NSFW model are included.
Transfer learning principles can then be applied to this dataset. Using the MobileNetV2 outputs as bottlenecks, and the Yahoo NSFW outputs as target values, one can build a model which tries to mimic the Yahoo NSFW model.
The files are in several archives (it was the only way to upload this much data with a 2GB limit per file). Inside the archives are npz files (compressed numpy arrays), containing 2000 input and target tensors.
Keras was used to create the MobileNetV2 output, and you can see in the tutorial kernel how it can be utilized.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (6 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/nmurray1234/yahoo-nsfw-as-mobilenetv2-bottlenecks
📊 Additional information:
==================================
File count not found
Views: 13,600
Downloads: 416
📚 RELATED NOTEBOOKS:
==================================
1. Tutorial: Yahoo NSFW as MobileNetV2 | Upvotes: 16
URL: https://www.kaggle.com/code/nmurray1234/tutorial-yahoo-nsfw-as-mobilenetv2
2. EfficientNet Keras Full Weights | Upvotes: 11
URL: https://www.kaggle.com/datasets/xhlulu/efficientnet-keras-weights
3. Starter: Yahoo NSFW as MobileNetV2 bbf75635-2 | Upvotes: 6
URL: https://www.kaggle.com/code/kerneler/starter-yahoo-nsfw-as-mobilenetv2-bbf75635-2
4. CNN Models from Yahoo NSFW | Upvotes: 5
URL: https://www.kaggle.com/code/jimojung/cnn-models-from-yahoo-nsfw
5. EfficientnetV1-V2 no-top Keras models | Upvotes: 2
URL: https://www.kaggle.com/datasets/hamzaboulahia/efficientnetsv2-keras-notop-models
Dataset Name: AI vs. Human-Generated Images
Basic Description: A Curated Dataset of AI-Generated and Authentic Images
📖 FULL DATASET DESCRIPTION:
==================================
Official dataset for the 2025 Women in AI Kaggle Competition: https://www.kaggle.com/competitions/detect-ai-vs-human-generated-images
The dataset consists of authentic images sampled from the Shutterstock platform across various categories, including a balanced selection where one-third of the images feature humans. These authentic images are paired with their equivalents generated using state-of-the-art generative models. This structured pairing enables a direct comparison between real and AI-generated content, providing a robust foundation for developing and evaluating image authenticity detection systems.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (10 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/alessandrasala79/ai-vs-human-generated-dataset
📊 Additional information:
==================================
Total files: 85,500
Views: 29,800
Downloads: 9,793
📚 RELATED NOTEBOOKS:
==================================
1. [99.97% LB ] Baseline with timm | Upvotes: 229
URL: https://www.kaggle.com/code/vyacheslavshen/99-97-lb-baseline-with-timm
2. [0.73298] ConvNeXT Classifier | Upvotes: 116
URL: https://www.kaggle.com/code/madhavdhage201/0-73298-convnext-classifier
3. 2025WAI ViT Image Classification | Upvotes: 64
URL: https://www.kaggle.com/code/ucas0v0zhuoqunli/2025wai-vit-image-classification
4. ShutterStock Dataset for AI vs Human-Gen. Image | Upvotes: 16
URL: https://www.kaggle.com/datasets/shreyasraghav/shutterstock-dataset-for-ai-vs-human-gen-image
5. Flickr-Face-HQ and GenAI Dataset (FF-GenAI) | Upvotes: 1
URL: https://www.kaggle.com/datasets/argonautex/flickr-face-hq-and-genai-dataset-ff-genai
==================================
⭐️ By: https://t.me/datasets1
Basic Description: A Curated Dataset of AI-Generated and Authentic Images
📖 FULL DATASET DESCRIPTION:
==================================
Official dataset for the 2025 Women in AI Kaggle Competition: https://www.kaggle.com/competitions/detect-ai-vs-human-generated-images
The dataset consists of authentic images sampled from the Shutterstock platform across various categories, including a balanced selection where one-third of the images feature humans. These authentic images are paired with their equivalents generated using state-of-the-art generative models. This structured pairing enables a direct comparison between real and AI-generated content, providing a robust foundation for developing and evaluating image authenticity detection systems.
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (10 GB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/alessandrasala79/ai-vs-human-generated-dataset
📊 Additional information:
==================================
Total files: 85,500
Views: 29,800
Downloads: 9,793
📚 RELATED NOTEBOOKS:
==================================
1. [99.97% LB ] Baseline with timm | Upvotes: 229
URL: https://www.kaggle.com/code/vyacheslavshen/99-97-lb-baseline-with-timm
2. [0.73298] ConvNeXT Classifier | Upvotes: 116
URL: https://www.kaggle.com/code/madhavdhage201/0-73298-convnext-classifier
3. 2025WAI ViT Image Classification | Upvotes: 64
URL: https://www.kaggle.com/code/ucas0v0zhuoqunli/2025wai-vit-image-classification
4. ShutterStock Dataset for AI vs Human-Gen. Image | Upvotes: 16
URL: https://www.kaggle.com/datasets/shreyasraghav/shutterstock-dataset-for-ai-vs-human-gen-image
5. Flickr-Face-HQ and GenAI Dataset (FF-GenAI) | Upvotes: 1
URL: https://www.kaggle.com/datasets/argonautex/flickr-face-hq-and-genai-dataset-ff-genai
==================================
⭐️ By: https://t.me/datasets1
❤3
Dataset Name: Arabic Handwritten Characters Dataset
Basic Description: Arabic Handwritten Characters Data-set
📖 FULL DATASET DESCRIPTION:
==================================
• A. El-Sawy, M. Loey, and H. EL-Bakry, “Arabic handwritten characters recognition using convolutional neural network,” WSEAS Transactions on Computer Research, vol. 5, pp. 11–19, 2017.
https://doi.org/10.1007/978-3-319-48308-5_54
https://link.springer.com/chapter/10.1007/978-3-319-48308-5_54
• A. El-Sawy, H. EL-Bakry, and M. Loey, “CNN for handwritten arabic digits recognition based on lenet-5,” in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, vol. 533, pp. 566–575, Springer International Publishing, 2016.
https://www.wseas.org/multimedia/journals/computerresearch/2017/a045818-075.php
https://arxiv.org/abs/1706.06720
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (25 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mloey1/ahcd1
📊 Additional information:
==================================
Total files: 16,800
Views: 128,000
Downloads: 17,100
📚 RELATED NOTEBOOKS:
==================================
1. Automated feature selection with sklearn | Upvotes: 198
URL: https://www.kaggle.com/code/residentmario/automated-feature-selection-with-sklearn
2. Arabic MNIST with detection | Upvotes: 152
URL: https://www.kaggle.com/code/yehyachali/arabic-mnist-with-detection
3. Understanding Convolutional Neural Network | Upvotes: 101
URL: https://www.kaggle.com/code/bbloggsbott/understanding-convolutional-neural-network
4. Arabic-Handwritten-Chars | Upvotes: 12
URL: https://www.kaggle.com/datasets/rashwan/arabic-chars-mnist
5. Handwritten Isolated English Character Dataset | Upvotes: 3
URL: https://www.kaggle.com/datasets/fayed02/handwritten-isolated-english-character-dataset
==================================
⭐️ By: https://t.me/datasets1
Basic Description: Arabic Handwritten Characters Data-set
📖 FULL DATASET DESCRIPTION:
==================================
• A. El-Sawy, M. Loey, and H. EL-Bakry, “Arabic handwritten characters recognition using convolutional neural network,” WSEAS Transactions on Computer Research, vol. 5, pp. 11–19, 2017.
https://doi.org/10.1007/978-3-319-48308-5_54
https://link.springer.com/chapter/10.1007/978-3-319-48308-5_54
• A. El-Sawy, H. EL-Bakry, and M. Loey, “CNN for handwritten arabic digits recognition based on lenet-5,” in Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016, vol. 533, pp. 566–575, Springer International Publishing, 2016.
https://www.wseas.org/multimedia/journals/computerresearch/2017/a045818-075.php
https://arxiv.org/abs/1706.06720
📥 DATASET DOWNLOAD INFORMATION
==================================
🔴 Dataset Size: Download dataset as zip (25 MB)
🔰 Direct dataset download link:
https://www.kaggle.com/api/v1/datasets/download/mloey1/ahcd1
📊 Additional information:
==================================
Total files: 16,800
Views: 128,000
Downloads: 17,100
📚 RELATED NOTEBOOKS:
==================================
1. Automated feature selection with sklearn | Upvotes: 198
URL: https://www.kaggle.com/code/residentmario/automated-feature-selection-with-sklearn
2. Arabic MNIST with detection | Upvotes: 152
URL: https://www.kaggle.com/code/yehyachali/arabic-mnist-with-detection
3. Understanding Convolutional Neural Network | Upvotes: 101
URL: https://www.kaggle.com/code/bbloggsbott/understanding-convolutional-neural-network
4. Arabic-Handwritten-Chars | Upvotes: 12
URL: https://www.kaggle.com/datasets/rashwan/arabic-chars-mnist
5. Handwritten Isolated English Character Dataset | Upvotes: 3
URL: https://www.kaggle.com/datasets/fayed02/handwritten-isolated-english-character-dataset
==================================
⭐️ By: https://t.me/datasets1
❤5