The **Synthetic Smoking Effect Dataset** is designed for educational and research purposes to analyze the impact of smoking on various health indicators. It provides anonymized, synthetic data on individuals' demographics, physiological measurements, and medical history.

## Dataset Features
- **ID:** Unique identifier for each individual.
- **Gender:** Gender of the individual (Male/Female).
- **Age:** Age of the individual.
- **Height (cm):** Height of the individual in centimeters.
- **Weight (kg):** Weight of the individual in kilograms.
- **Waist (cm):** Waist circumference in centimeters.
- **Eyesight (Left/Right):** Vision acuity for left and right eyes.
- **Hearing (Left/Right):** Hearing ability for left and right ears.
- **Systolic:** Systolic blood pressure measurement.
- **Relaxation:** Diastolic blood pressure measurement.
- **Fasting Blood Sugar:** Blood sugar levels after fasting.
- **Cholesterol:** Total cholesterol level.
- **Triglyceride:** Triglyceride level in the blood.
- **HDL:** High-density lipoprotein (good cholesterol) level.
- **LDL:** Low-density lipoprotein (bad cholesterol) level.
- **Hemoglobin:** Hemoglobin concentration in the blood.
- **Urine Protein:** Presence of protein in urine.
- **Serum Creatinine:** Level of creatinine in the blood.
- **AST:** Aspartate aminotransferase enzyme level.
- **ALT:** Alanine aminotransferase enzyme level.
- **GTP:** Gamma-glutamyl transferase enzyme level.
- **Oral Health:** Presence of oral health issues (Yes/No).
- **Dental Caries:** Presence of cavities (Yes/No).
- **Tartar:** Presence of tartar build-up (Yes/No).
- **Smoking:** Smoking status (Yes/No).

## Distribution
![smoking .png](https://storage.googleapis.com/opendatabay_public/b0cc9638-615a-4646-8099-6f851b74fb0c/8ce999766ca6_smoking_.png)


## Usage
This dataset can be used for the following applications:

- **Smoking Impact Analysis:** Assess how smoking affects health parameters such as blood pressure, cholesterol, and lung function.
- **Medical Research:** Explore correlations between smoking and chronic diseases.
- **Preventative Healthcare:** Identify risk factors associated with smoking-related conditions.
- **Machine Learning Models:** Develop predictive models for health risk assessment.
- **Public Health Insights:** Guide policies and initiatives to reduce smoking-related health risks.

## Coverage
This synthetic dataset is fully anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles to facilitate comprehensive analysis.

## License
CC0 (Public Domain)

## Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For predictive modeling of smoking-related health effects.
- **Healthcare Researchers:** To study smoking's impact on various health indicators.
- **Public Health Professionals:** For insights into smoking-related population health trends.
- **Educators and Students:** As a teaching resource for health analytics and data science projects.



Smoking Effect Dataset (Synthetic)

Opendatabay Labs 

The **Synthetic Leukemia Risk Dataset** is designed for educational and research purposes to analyze health-related factors contributing to leukemia risk. It provides anonymized, synthetic data on individuals’ medical history, lifestyle, and blood test results.

### Dataset Features
- **Age:** Age of the individual.
- **Gender:** Gender of the individual (Male/Female).
- **Country:** Country of residence.
- **WBC_Count:** White Blood Cell count.
- **RBC_Count:** Red Blood Cell count.
- **Platelet_Count:** Platelet count per unit volume.
- **Hemoglobin_Level:** Level of hemoglobin in the blood.
- **Bone_Marrow_Blasts:** Percentage of blasts in the bone marrow.
- **Genetic_Mutation:** Presence of genetic mutations linked to leukemia (Yes/No).
- **Family_History:** Family history of leukemia (Yes/No).
- **Smoking_Status:** History of smoking (Yes/No).
- **Alcohol_Consumption:** History of alcohol consumption (Yes/No).
- **Radiation_Exposure:** Prior exposure to radiation (Yes/No).
- **Infection_History:** History of infections (Yes/No).
- **BMI:** Body Mass Index.
- **Chronic_Illness:** Presence of chronic diseases (Yes/No).
- **Immune_Disorders:** Presence of immune system disorders (Yes/No).
- **Ethnicity:** Ethnic group of the individual.
- **Socioeconomic_Status:** Economic status of the individual (Low/Medium/High).
- **Urban_Rural:** Whether the individual resides in an urban or rural area.
- **Leukemia_Status:** Leukemia diagnosis prediction (Positive/Negative).

### Distribution
![download.png](https://storage.googleapis.com/opendatabay_public/fe4b6677-dba3-4a9e-b104-a79243448324/dd2a4494bcf5_download.png)


### Usage
This dataset can be used for the following applications:

- **Leukemia Risk Prediction:** Build models to predict leukemia status based on medical and lifestyle factors.
- **Health Behavior Analysis:** Study correlations between lifestyle factors and leukemia risk.
- **Medical Research:** Explore associations between genetic, environmental, and clinical factors with leukemia.
- **Preventative Healthcare:** Identify key predictors for early detection and risk assessment.
- **Policy and Decision Making:** Provide insights into population health trends and guide public health initiatives.

### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to leukemia risk prediction.
- **Healthcare Researchers:** To study risk factors and the prevalence of leukemia.
- **Public Health Professionals:** For insights into population health trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Leukemia Risk Dataset (Synthetic)

The Synthetic Cardiovascular Disease Dataset is designed for educational and research purposes to analyze health-related factors contributing to cardiovascular disease risk. It provides anonymized, synthetic data on individuals’ demographic, medical, and lifestyle factors.

### Dataset Features
- **age:** Age of the individual (in years).
- **anaemia:** Presence of anaemia (Yes/No).
- **creatinine_phosphokinase:** Level of creatine phosphokinase enzyme (U/L).
- **diabetes:** Presence of diabetes (Yes/No).
- **ejection_fraction:** Percentage of blood leaving the heart per contraction.
- **high_blood_pressure:** Presence of high blood pressure (Yes/No).
- **platelets:** Platelet count (per mL).
- **serum_creatinine:** Level of creatinine in the blood (mg/dL).
- **serum_sodium:** Level of sodium in the blood (mEq/L).
- **sex:** Gender of the individual (M/F).
- **smoking:** Smoking status (Yes/No).
- **time:** Follow-up period (days).
- **death_event:** Cardiovascular-related death occurrence (Yes/No).

### Distribution
![cardio1.png](https://storage.googleapis.com/opendatabay_public/b88efcc6-65e3-400b-b63c-e06cb0eda35c/a7f9da477c9f_cardio1.png)

![Cardio2.png](https://storage.googleapis.com/opendatabay_public/b88efcc6-65e3-400b-b63c-e06cb0eda35c/cd0a893357ee_Cardio2.png)

### Usage
This dataset can be used for the following applications:

- **Cardiovascular Disease Risk Prediction:** Build models to predict cardiovascular disease and mortality risk based on health indicators.
- **Health Behavior Analysis:** Study the impact of anaemia, diabetes, and smoking on cardiovascular health.
- **Medical Research:** Explore correlations between blood markers and cardiovascular outcomes.
- **Preventative Healthcare:** Identify key predictors to inform early detection and prevention strategies.
- **Policy and Decision Making:** Provide insights into cardiovascular health trends to guide public health initiatives.
### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to cardiovascular disease prediction and risk analysis.
- **Healthcare Researchers:** To study risk factors and prevalence of cardiovascular disease in a synthetic population.
- **Public Health Professionals:** For insights into cardiovascular health trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Cardiovascular Disease Patient Records (Synthetic)

The Synthetic Lower Back Pain Symptoms Dataset is designed for educational and research purposes, focusing on analyzing various anatomical and biomechanical factors contributing to lower back pain. It includes anonymized, synthetic data on spinal angles, pelvic alignment, and other related features to assess the condition's presence.

### Dataset Features
- **pelvic_incidence:** Angle between the line from the center of the femoral head to the sacral endplate and the line from the center of the femoral head to the center of the sacral promontory.
- **pelvic_tilt:** Angle between the vertical line and the line from the center of the femoral head to the center of the sacral promontory.
- **lumbar_lordosis_angle:** Angle of lumbar curvature, a key factor in assessing spinal posture.
- **sacral_slope:** Angle between the horizontal line and the sacral endplate.
- **pelvic_radius:** The distance between the center of the sacral promontory and the center of the femoral head.
- **degree_spondylolisthesis:** Degree of forward displacement of a vertebra.
- **pelvic_slope:** Angle formed between the line from the center of the sacral endplate and the center of the femoral head.
- **Direct_tilt:** Tilt of the sacrum relative to the vertical axis.
- **thoracic_slope:** Angle of the thoracic spine relative to the horizontal.
- **cervical_tilt:** Tilt angle of the cervical spine.
- **sacrum_angle:** The angle between the horizontal plane and the sacral endplate.
- **scoliosis_slope:** Degree of curvature in the spine indicative of scoliosis.
- **Attribute class:** Classification of the lower back condition as either "Normal" or "Abnormal."


### Distribution

### Usage
This dataset can be used for the following applications:

- **Lower Back Pain Prediction:** Build models to classify individuals based on anatomical and biomechanical factors influencing lower back pain.
- **Biomechanical Research:** Explore the relationships between pelvic alignment, spinal angles, and the presence of back pain.
- **Clinical Studies:** Study the impact of pelvic tilt, lumbar curvature, and other spinal factors on the likelihood of lower back pain.
- **Preventative Healthcare:** Identify key anatomical factors that predict lower back pain for early intervention and preventative strategies.
- **Orthopedic Decision-Making:** Aid clinicians in understanding how variations in spinal alignment correlate with pain symptoms.
### Coverage
This synthetic dataset adheres to data privacy standards and is anonymized for research and educational purposes. It represents diverse individuals with different spinal and pelvic postures, providing valuable insights into back pain analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to back pain prediction and analysis of spinal and pelvic factors.
- **Biomechanics and Orthopedic Researchers:** To study the correlation between spinal alignment and back pain.
- **Healthcare Professionals:** For clinical decision-making and understanding the role of spinal health in lower back pain.
- **Educators and Students:** As a teaching resource for analyzing musculoskeletal data and building predictive models.

Lower Back Pain Symptoms Dataset (Synthetic)

The Synthetic Gestational Diabetes Dataset is designed for educational and research purposes to analyze health-related factors contributing to gestational diabetes risk. It provides anonymized, synthetic data on individuals’ demographic, medical, and hereditary information.

### Dataset Features
- **age:** Age of the individual (in years).
- **pregnancy_no:** Number of pregnancies.
- **weight:** Weight of the individual (in kg).
- **height:** Height of the individual (in cm).
- **bmi:** Body Mass Index (BMI) of the individual.
- **heredity:** Family history of diabetes (Yes/No).
- **prediction:** Gestational diabetes diagnosis (Yes/No).

### Distribution

![diabetes x 1.png](https://storage.googleapis.com/opendatabay_public/b133ee2a-5e6c-4d88-9b75-ff32f37f835c/3f59788358b7_diabetes_x_1.png)

![diabetes x 2.png](https://storage.googleapis.com/opendatabay_public/b133ee2a-5e6c-4d88-9b75-ff32f37f835c/ff1e65bb0374_diabetes_x_2.png)


### Usage
This dataset can be used for the following applications:

- **Gestational Diabetes Risk Prediction:** Build models to predict gestational diabetes status based on demographic and medical factors.
- **Health Behavior Analysis:** Study the impact of BMI, heredity, and other health indicators on gestational diabetes prevalence.
- **Medical Research:** Explore correlations between pregnancy count, weight, and diabetes risk.
- **Preventative Healthcare:** Identify key predictors for early detection and prevention strategies.
- **Policy and Decision Making:** Provide insights into maternal health trends to guide public health initiatives.
### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to gestational diabetes prediction and risk analysis.
- **Healthcare Researchers:** To study risk factors and prevalence of gestational diabetes in a synthetic population.
- **Public Health Professionals:** For insights into maternal health trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Gestational Diabetes Dataset (Synthetic)

The Synthetic Diabetes Prediction Dataset is designed for educational and research purposes to analyze health-related factors contributing to diabetes risk. It provides anonymized, synthetic data on individuals’ demographic information, medical history, and lifestyle choices.

### Dataset Features
- **gender:** Gender of the individual (Male/Female).
- **age:** Age of the individual (in years).
- **hypertension:** Presence of hypertension (Yes/No).
- **heart_disease:** Presence of heart disease (Yes/No).
- **smoking_history:** Smoking history (e.g., never, formerly, current, No Info).
- **bmi:** Body Mass Index (BMI) of the individual.
- **HbA1c_level:** Hemoglobin A1c level (a measure of blood sugar over time).
- **blood_glucose_level:** Random blood glucose level (mg/dL).
- **diabetes:** Diabetes diagnosis (Yes/No).

### Distribution
![diabetes1.png](https://storage.googleapis.com/opendatabay_public/37949600-753b-48f7-b9fa-dc13e940abd5/b26216038976_diabetes1.png)

![diabetes2.png](https://storage.googleapis.com/opendatabay_public/37949600-753b-48f7-b9fa-dc13e940abd5/3b67bd20bb14_diabetes2.png)


### Usage
This dataset can be used for the following applications:

- **Diabetes Risk Prediction:** Build models to predict diabetes status based on demographic, medical, and lifestyle factors.
- **Health Behavior Analysis:** Study the impact of BMI, smoking history, and other health indicators on diabetes prevalence.
- **Medical Research:** Explore correlations between hypertension, heart disease, and diabetes.
- **Preventative Healthcare:** Identify key predictors to inform early detection and prevention strategies for diabetes.
- **Policy and Decision Making:** Provide insights into population health to guide public health initiatives.
### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to diabetes prediction and risk analysis.
- **Healthcare Researchers:** To study risk factors and prevalence of diabetes in a synthetic population.
- **Public Health Professionals:** For insights into population health trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Diabetes Prediction Dataset (Synthetic) 

The Synthetic Osteoporosis Prediction Dataset is designed for educational and research purposes to analyze health-related factors contributing to osteoporosis risk. It provides anonymized, synthetic data on individuals’ demographic, medical, and lifestyle factors.

### Dataset Features
- **age:** Age of the individual (in years).
- **gender:** Gender of the individual (Male/Female).
- **hormonal_changes:** Presence of hormonal changes (Normal/Postmenopausal).
- **family_history:** Family history of osteoporosis (Yes/No).
- **race_ethnicity:** Race or ethnicity of the individual.
- **body_weight:** Weight classification (Underweight/Normal/Overweight).
- **calcium_intake:** Level of calcium intake (Adequate/Insufficient).
- **vitamin_d_intake:** Level of vitamin D intake (Sufficient/Insufficient).
- **physical_activity:** Physical activity level (Sedentary/Active).
- **smoking:** Smoking status (Yes/No).
- **alcohol_consumption:** Alcohol consumption level (None/Moderate/High).
- **medical_conditions:** Presence of medical conditions such as hyperthyroidism or rheumatoid arthritis.
- **medications:** Medications taken (e.g., corticosteroids, none).
- **prior_fractures:** History of prior fractures (Yes/No).
- **osteoporosis:** Osteoporosis diagnosis (Yes/No).
### Distribution
### Usage
This dataset can be used for the following applications:

- **Osteoporosis Risk Prediction:** Build models to predict osteoporosis based on demographic, medical, and lifestyle factors.
- **Health Behavior Analysis:** Study the impact of physical activity, calcium intake, and other factors on osteoporosis prevalence.
- **Medical Research:** Explore correlations between hormonal changes, body weight, and osteoporosis.
- **Preventative Healthcare:** Identify key predictors to inform early detection and prevention strategies.
- **Policy and Decision Making:** Provide insights into osteoporosis risk factors to guide public health initiatives.
### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to osteoporosis prediction and risk analysis.
- **Healthcare Researchers:** To study risk factors and prevalence of osteoporosis in a synthetic population.
- **Public Health Professionals:** For insights into osteoporosis risk trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Osteoporosis Patient Records (Synthetic)

The Synthetic Tuberculosis Symptom Dataset is designed for educational and research purposes to analyze health-related factors contributing to tuberculosis risk. It provides anonymized, synthetic data on individuals’ symptoms and medical history.

### Dataset Features
- **fever_for_two_weeks:** Presence of fever lasting two weeks (Yes/No).
- **coughing_blood:** Occurrence of coughing up blood (Yes/No).
- **sputum_mixed_with_blood:** Presence of blood in sputum (Yes/No).
- **night_sweats:** Experiencing excessive sweating at night (Yes/No).
- **chest_pain:** Presence of chest pain (Yes/No).
- **back_pain_in_certain_parts:** Experiencing localized back pain (Yes/No).
- **shortness_of_breath:** Difficulty in breathing (Yes/No).
- **weight_loss:** Significant unexplained weight loss (Yes/No).
- **body_feels_tired:** Experiencing fatigue and tiredness (Yes/No).
- **lumps_armpits_neck:** Appearance of lumps around the armpits and neck (Yes/No).
- **cough_and_phlegm_two_to_four_weeks:** Persistent cough and phlegm for two to four weeks (Yes/No).
- **swollen_lymph_nodes:** Presence of swollen lymph nodes (Yes/No).
- **loss_of_appetite:** Decreased appetite (Yes/No).
- **Prediction:** Tuberculosis diagnosis prediction (Yes/No).
  
### Distribution


### Usage
This dataset can be used for the following applications:

- **Tuberculosis Risk Prediction:** Build models to predict tuberculosis status based on symptom patterns.
- **Health Behavior Analysis:** Study the impact of symptoms and their correlation with tuberculosis.
- **Medical Research:** Explore associations between common symptoms and tuberculosis diagnosis.
- **Preventative Healthcare:** Identify key predictors to inform early detection and prevention strategies for tuberculosis.
- **Policy and Decision Making:** Provide insights into population health trends and guide public health initiatives.
### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and health profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to tuberculosis prediction and risk analysis.
- **Healthcare Researchers:** To study risk factors and prevalence of tuberculosis in a synthetic population.
- **Public Health Professionals:** For insights into population health trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Tuberculosis Symptom Dataset (Synthetic) 

The Synthetic Brain Tumor Dataset is designed for educational and research purposes to analyze health-related factors contributing to brain tumor classification. It provides anonymized, synthetic data on individuals’ tumor characteristics and demographic information.

### Dataset Features
- **tumor_type:** Type of brain tumor (e.g., Glioblastoma, Oligodendroglioma, Astrocytoma, Meningioma).
- **location:** Location of the tumor in the brain (e.g., Frontal Lobe, Parietal Lobe, Cerebellum).
- **size_cm:** Size of the tumor in centimeters.
- **grade:** Tumor grade based on severity (I-IV).
- **patient_age:** Age of the patient (in years).
- **gender:** Gender of the patient (Male/Female).
### Distribution
### Usage
This dataset can be used for the following applications:

- **Brain Tumor Classification:** Build models to predict tumor type based on its characteristics.
- **Medical Imaging Analysis:** Study tumor size, location, and grade distributions.
- **Health Risk Assessment:** Explore correlations between age, gender, and tumor characteristics.
- **Preventative Healthcare:** Identify key predictors for early detection and treatment strategies.
- **Policy and Decision Making:** Provide insights into population health trends and guide public health initiatives.
### Coverage
This synthetic dataset is anonymized and adheres to data privacy standards. It represents diverse demographics and tumor profiles, enabling broad applications in healthcare research and analysis.

### License
CC0 (Public Domain)

### Who Can Use It
- **Data Scientists and Machine Learning Practitioners:** For classification tasks related to brain tumor prediction and risk analysis.
- **Healthcare Researchers:** To study risk factors and prevalence of brain tumors in a synthetic population.
- **Public Health Professionals:** For insights into population health trends and preventative care.
- **Educators and Students:** As a teaching resource for analyzing health-related datasets and building predictive models.

Brain Tumor Dataset (Synthetic) 

This dataset contains details of real estate transactions, including property sales, mortgages, and registrations in Dubai. It provides information about property types, locations, prices, and nearby landmarks. The dataset is valuable for real estate market analysis, price trend forecasting, and investment decision-making.

### Dataset Features  

- **DP_ID:** Unique identifier for each transaction.
- **procedure_name:** The type of transaction (e.g., Sell, Mortgage Registration).
- **date:** The date of the transaction.  
- **property_type:** The general classification of the property (e.g., Unit, Villa).  
- **property_sub_type:** The specific sub-category (e.g., Flat, Hotel Apartment).  
- **property_usage:** The intended use of the property (e.g., residential, hospitality).
- **area_name:** The name of the locality where the property is situated.
- **building_name:** The specific building where the property is located.
- **master_project:** The larger development under which the property falls.  
- **nearest_landmark:** A nearby significant location for reference.
- **nearest_metro:** The closest metro station to the property.
- **nearest_mall:** The nearest shopping mall.
- **bedrooms:** Number of bedrooms in the property.
- **Parking:** Number of parking spaces available. 
- **built_up_area_(sqft):** Total built-up area in square feet. 
- **selling_price:** The final sale price of the property. 
- **sale_price_per_foot:** Price per square foot of the property. 

### Distribution  

- **Data Volume:** 513581 rows and 17 columns.
- **Format:** Tabular dataset suitable for analysis in CSV, Excel, or database formats.

### Usage  

This dataset is ideal for a variety of applications: 

- **Real Estate Market Analysis:** Studying property trends and price fluctuations.
- **Investment Decision-Making:** Identifying high-value properties and potential growth areas.
- **Price Forecasting:** Developing models to predict property prices based on historical data.
- **Urban Planning:** Understanding property distribution and infrastructure development.

### Coverage  

- **Geographic Coverage:** Dubai, UAE. 
- **Time Range:** Includes real estate transactions from 2011 to 2024.
- **Demographics:** Covers different property types, including residential.

### License  
CC0  

### Who Can Use It  

- **Data Scientists:** For predictive modeling of real estate prices.
- **Researchers:** To analyze urban development and housing market trends.
- **Businesses:** For property valuation, investment analysis, and market forecasting.
- **Government & Planners:** To assess real estate growth and infrastructure needs.

Dubai Housing Market Insights

Vdt. Data


This dataset contains information about airports and heliports worldwide, including their locations, identifiers, elevation, and classification. It is valuable for aviation research, geographic analysis, and air transportation studies, providing insights into airport distribution, accessibility, and infrastructure.

### Dataset Features  

- **WA_ID:** Unique identifier for each airport or heliport.
- **Ident:** The official airport identifier.
- **Type:** Classification of the facility (e.g., heliport, small airport).
- **Name:** The official name of the airport or heliport.
- **elevation_ft:** The elevation of the airport above sea level (in feet).
- **iso_country:** The country where the airport is located (ISO code).
- **iso_region:** The region within the country where the airport is located (ISO code).
- **Municipality:** The city or town where the airport is situated.
- **gps_code:** The GPS code assigned to the airport.
- **local_code:** The local code assigned to the airport by regional authorities.
- **Coordinates:** The latitude and longitude of the airport location.

### Distribution  

- **Data Volume:** 28285 rows and 11 columns.
- **Format:** Tabular dataset suitable for analysis in CSV, Excel, or database formats.

### Usage  

This dataset is ideal for a variety of applications: 

- **Aviation Analysis:** Studying airport distribution, infrastructure, and accessibility.
- **Geospatial Research:** Mapping airport locations and analyzing their geographic significance.
- **Air Traffic Management:** Supporting air transportation planning and logistics.
- **Emergency Planning:** Identifying nearby airports for disaster response and relief efforts.

### Coverage  

- **Geographic Coverage:** Global (includes airports from different countries and regions).
- **Demographics:** Covers aviation infrastructure rather than population demographics.

### License  

CC0  

### Who Can Use It  

- **Data Scientists:** To develop models for airport accessibility and aviation insights.
- **Researchers:** To study aviation networks, transportation logistics, and infrastructure planning.
- **Businesses:** To enhance travel-related applications, logistics, and navigation systems.
- **Government & Planners:** For transportation planning and emergency response coordination.


Aviation Infrastructure Database

This dataset contains customer reviews of clothing items, including product ratings, recommendations, and detailed feedback. It provides insights into customer preferences, satisfaction levels, and purchasing behaviors, making it useful for fashion retailers, e-commerce platforms, and sentiment analysis.

### Dataset Features  

- **WC_ID:** Unique identifier for each review.
- **Clothing ID:** Identifier for the specific clothing item being reviewed.
- **Age:** Age of the reviewer.
- **Title:** The title or summary of the review.
- **Review Text:** The full text of the review written by the customer.
- **Rating:** Numerical rating of the product (1-5).
- **Recommended IND:** Boolean indicating whether the reviewer recommends the product (1 = Yes, 0 = No).
- **Positive Feedback Count:** Number of upvotes received for the review.
- **Division Name:** The major category of the clothing item (e.g., General, General Petite).
- **Department Name:** The specific department of the item (e.g., Dresses, Tops).
- **Class Name:** The subcategory of the item (e.g., Blouses, Knits).

### Distribution  

- **Data Volume:** 19662 rows and 11 columns.
- **Format:** Tabular dataset, suitable for analysis in CSV.

### Usage  

This dataset is ideal for a variety of applications: 

- **Sentiment Analysis:** Extracting customer sentiment from review texts.
- **Product Improvement:** Identifying design flaws and areas for enhancement.
- **Recommendation Systems:** Training AI models to suggest relevant clothing items.
- **Marketing & Consumer Insights:** Understanding customer preferences and purchase behavior. 

### Coverage  

- **Geographic Coverage:** Global.
- **Demographics:** Includes a wide age range of reviewers with diverse fashion preferences.

### License  

CC0  

### Who Can Use It  

- **Data Scientists:** For training machine learning models on sentiment analysis.
- **Researchers:** To study consumer behavior and preferences in the fashion industry.
- **Businesses:** To improve product designs, marketing strategies, and customer satisfaction.
- **E-commerce Platforms:** For enhancing recommendation systems and customer engagement.


Customer Clothing Reviews & Ratings

Forager

Datrix AI

Quanzine LLMs

Appnalysis

Review Nexus

Data Bank

TV Audience Tracker

Synthetic IDD

This synthetic healthcare dataset has been generated to serve as an educational resource for data science, machine learning, and data analysis applications in the healthcare industry. It mirrors real-world patient records, allowing users to practice data manipulation and develop analytical skills in a healthcare context.

#### Dataset Features:
- Age: Age of the patient at admission (in years).
- Gender: Gender of the patient.
- Blood Type: Patient's blood type (e.g., "A+", "O-").
- Medical Condition: Primary diagnosis or medical condition, such as "Diabetes," "Hypertension," or "Asthma."
- Date of Admission: The date when the patient was admitted.
- Insurance Provider: Name of the patient’s insurance provider (e.g., "Aetna," "Cigna").
- Billing Amount: Total charges for healthcare services provided (in monetary units).
- Room Number: Room number assigned to the patient during their stay.
- Admission Type: Type of admission, categorized as "Emergency," "Elective," or "Urgent."
- Discharge Date: Date when the patient was discharged.
- Test Results: Outcome of a medical test, categorized as "Normal," "Abnormal," or "Inconclusive."

#### Visualisation:
![Distribution](https://storage.googleapis.com/opendatabay_public/images/synthetic-healthcare.jpg "Distribution")


#### Usage:

This dataset can be used for:
- Healthcare research: To explore trends and patterns in patient admissions, conditions, and outcomes.
- Educational training: To teach data cleaning, transformation, and visualization techniques specific to healthcare data.
- Predictive modelling: To develop models that predict healthcare outcomes based on various patient and admission factors.

#### Coverage:
This dataset is synthetic and anonymized, making it a safe tool for experimentation and learning without compromising real patient privacy.

#### License:
CCO (Public Domain)

#### Who can use it:
- Researchers and educators: For studies or teaching purposes in healthcare analytics and data science.
- Data science enthusiasts: For learning, practising, and applying healthcare data manipulation and analysis techniques.


Synthetic Healthcare Dataset

A comprehensive set of data that includes the usage percentage of iOS Operating Systems between 2018 and 2023.

### Features:
- Average Marketshare percentage for the month
- Operating System Marketshare key
- Platform
- Region
- Version
- Year

### Usage:
Market analysis, business strategy development.

### Coverage:
This will cover the Marketshare data from the last 6 years, showing the OS usage on each platform across a variety of regions around the world.

### License:
CC0

### Who can use it:
Product marketing teams or product owners looking at potential territories to target their future mobile app towards. Depending on the adoption of a certain iOS OS could dictate what the minimum version of Android the app should support.

Market Share Data iOS

Synthetic Retina Images for AI Training" dataset by Synthetic IDD offers a comprehensive collection of high-resolution synthetic retina images designed to train AI and machine learning models for medical applications. With 1 million images, this dataset provides an extensive resource for researchers, medical professionals, and developers interested in enhancing diagnostic accuracy and creating robust AI models for retinal disease detection.
![Sample](https://storage.googleapis.com/opendatabay_public/images/retina_example.jpg)

### Features:
- **Global Coverage:** Includes synthetic retina images representing a diverse range of conditions and demographics from around the world.
- **Extensive Volume:** Contains 1 million high-resolution images, providing a robust dataset for in-depth analysis and model training.
- **Format:** Available in high-resolution JPEG format, making it easy to integrate with various image analysis tools and AI platforms.
- **Detailed Annotations:** Each image is accompanied by detailed annotations, including synthetic diagnoses such as diabetic retinopathy, glaucoma, and macular degeneration.

### Usage:
This dataset can be used for a variety of purposes, including but not limited to:
- Training AI models for the detection and classification of retinal diseases.
- Developing diagnostic tools and applications for ophthalmology.
- Researching retinal disease patterns and characteristics.
- Enhancing the accuracy and robustness of existing medical imaging AI systems.

### License:
The dataset is provided under the CC0 (Public Domain) license, allowing users to freely use, modify, and distribute the data without any restrictions.

Synthetic Retina Images

The British Supermarket Product Nutrient Database (BSPND) is a comprehensive collection of nutritional information for a wide array of products available in British supermarkets. Covering diverse categories from dairy to snacks, sauces to grains, the dataset provides detailed insights into the energy content and nutritional composition of each item.

### Key Insights

Containing data fields such as energy (in kilocalories), fat, saturated fat, carbohydrates, sugars, fiber, protein, and salt (measured in grams), BSPND facilitates detailed analysis and comparison of nutritional profiles across various products. This dataset is indispensable for consumers, health professionals, and researchers seeking to make informed dietary choices or conduct nutritional studies.

With entries ranging from popular yogurt drinks like Actimel to staple pantry items like Allinson's bread flour, BSPND offers a comprehensive snapshot of the nutritional landscape of British supermarket offerings. Whether one is managing dietary requirements, crafting meal plans, or conducting nutritional research, this dataset serves as a valuable resource for understanding the nutritional content of everyday grocery items available in the UK.

### Who can use this data

Researchers and policymakers can utilise BSPND to analyse trends in nutritional composition across product categories, monitor changes in dietary habits over time, or inform public health initiatives aimed at promoting healthier eating habits. By providing transparency and accessibility to nutritional data, BSPND contributes to promoting consumer awareness and facilitating evidence-based decision-making regarding dietary choices in the British context.

British Supermarket Products

Synthetic Traffic Flow and Incident Data dataset by Synthetic IDD offers a comprehensive collection of synthetic traffic metrics and incident reports from around the globe. With 100 million lines of data, this dataset provides an extensive resource for researchers, urban planners, and developers interested in understanding traffic patterns, congestion points, and incident occurrences.

### Usage:
This dataset can be used for a variety of purposes, including but not limited to:
- Analyzing traffic patterns and congestion hotspots globally
- Building predictive models for traffic management and incident prediction
- Researching the impact of road conditions and incidents on traffic flow
- Developing applications for real-time traffic monitoring and navigation

### License:
The dataset is provided under the CC0 (Public Domain) license, allowing users to freely use, modify, and distribute the data without any restrictions.

Traffic Flow and Incident Data

Viewership trends across 5000 TV shows on Netflix, including last 5 years of ratings and audience numbers.

A vast collection of data including the 100 New Free Applications in the App Store for each day since February 2024.

### Features:
- Date of chart
- Rank
- App name
- App identifier
- Chart collection

### Usage:
Market trend analysis, business strategy development.

### Coverage:
This will cover the new free app chart data from the UK iOS App store.

### License:
CCO

### Who can use it:
Product Owners or Project Managers can use this data set.

### How to use it:
The data set could be used to track specific applications and their position within the App store chart over time.

App Store Charts - New Free Apps

This collection of data will report for the Top Play Store chart and include information for whether the applications have in-app purchases.

### Features:
- App Identifier
- App Name
- App Version
- Chart Collection
- Chart Created
- Has IAP

### Usage:
Market trend analysis, business strategy development.

### Coverage:
This will cover the top free app chart in the UK Android Play store.

### License:
CCO

### Who can use it:
Product Owners or Project Managers can use this data set.

### How to use it:
The data set could be used to track applications and whether they have in-app purchases within the app.

Play Store Chart - App Monetisation

Sea Mapping Data by British Marine Tech

Description:
The "Synthetic Sea Mapping Data" dataset by British Marine Tech offers a comprehensive collection of high-resolution synthetic sea maps and bathymetric data designed to train AI and machine learning models for marine applications. With 2 million data points, this dataset provides an extensive resource for researchers, marine engineers, and developers interested in enhancing marine navigation, safety, and environmental monitoring systems.

### Usage:
This dataset can be used for a variety of purposes, including but not limited to:
- Training AI models for marine navigation and mapping systems
- Developing advanced maritime safety and collision avoidance tools
- Researching underwater topography and marine environmental conditions
- Enhancing the accuracy and functionality of existing marine mapping technologies

### Features:
- **Global Coverage:** Includes synthetic sea maps and bathymetric data representing diverse marine regions worldwide.
- **Extensive Volume:** Contains 2 million data points, providing a robust dataset for in-depth research and model training.
- **Format:** Available in high-resolution TIFF and CSV formats, making it easy to integrate with various mapping and analysis tools.
- **Detailed Annotations:** Each map includes detailed annotations, such as depth contours, underwater features, and synthetic marine traffic patterns.

### License:
The dataset is provided under the CC0 (Public Domain) license, allowing users to freely use, modify, and distribute the data without any restrictions.

Sea Mapping Data

British Marine Tech

Synthetically Generated X-ray Images of Lung Cancer Patients" dataset offers an extensive collection of high-quality synthetic X-ray images designed for the training and development of AI and machine learning models in medical imaging. With over 1 million images, this dataset serves as a valuable resource for researchers, medical professionals, and developers focused on improving diagnostic accuracy and creating advanced AI systems for lung cancer detection.

#### Features:
- **Global Coverage:** Includes synthetic X-ray images representing a wide variety of lung cancer conditions and patient demographics from across the globe.
- **Extensive Volume:** Contains over 1 million high-resolution images, providing a substantial dataset for comprehensive analysis and model training.
- **Format:** Available in high-resolution JPEG format, facilitating easy integration with various image analysis tools and AI platforms.
- **Detailed Annotations:** Each image is accompanied by precise annotations, including synthetic diagnoses such as different stages of lung cancer, tumor locations, and other relevant clinical information.

#### Preview:
![xray_628fce2b-16d1-4eb2-a543-2ab92e6bc1f8.jpg](https://storage.googleapis.com/opendatabay_public/images/xray_628fce2b-16d1-4eb2-a543-2ab92e6bc1f8.jpg)

#### Usage:
This dataset can be utilized for various applications, including but not limited to:
- Training AI models for the detection and classification of lung cancer.
- Developing diagnostic tools and applications for pulmonology and oncology.
- Researching lung cancer patterns, stages, and characteristics.
- Enhancing the accuracy and robustness of existing medical imaging AI systems.

#### License:
The dataset is provided under the CC0 (Public Domain) license, allowing users to freely use, modify, and distribute the data without any restrictions.

Synthetic X-ray Images of Lung Cancer Patients

A diverse range of dietary patterns among individuals across different age groups and genders. Spanning various meal habits, ingredient preferences, and dining behaviors, it offers invaluable insights into contemporary dietary trends and their implications for human health. The dataset is meticulously curated, capturing nuanced details such as meal frequency, cooking preferences, food allergies, and weekly intake frequencies of distinct food categories and beverages.

### Usage:
Researchers and nutritionists can leverage data to discern prevalent dietary patterns and tailor dietary interventions accordingly. With its granular information on meal compositions, consumption frequencies, and lifestyle choices, the dataset serves as a valuable resource for understanding the complex interplay between diet, health, and lifestyle factors. Moreover, policymakers and public health advocates can utilise these insights to formulate targeted interventions aimed at promoting healthier dietary practices and combating nutritional deficiencies.

### Key Insights:
Dataset unravels intriguing patterns in dietary preferences, revealing a spectrum of dietary inclinations ranging from traditional omnivorous diets to more specialised regimes like vegetarianism and pescatarianism. Moreover, it sheds light on emerging trends such as meal skipping, home-cooked meal preferences, and the increasing prevalence of plant-based diets among younger demographics. By elucidating the factors driving dietary choices and consumption behaviours, Dataset equips stakeholders with the knowledge needed to address evolving nutritional needs and promote holistic well-being in modern society.

### Future Directions:
As dietary preferences continue to evolve in response to cultural, economic, and environmental factors, Dataset remains a dynamic repository of dietary data poised to inform future research endeavours and policy initiatives. By incorporating longitudinal studies and expanding the demographic scope, future iterations of the dataset can provide deeper insights into evolving dietary landscapes and facilitate evidence-based decision-making in the realms of nutrition, public health, and food policy.

Dietary patterns

This dataset offers insights into fetal health by analyzing features extracted from Cardiotocogram (CTG) exams. CTG exams are widely used in prenatal care to monitor fetal heart rate, movements, and uterine contractions, enabling early detection of potential health risks. The dataset has been classified by expert obstetricians into three categories: Normal, Suspect, and Pathological, providing valuable data for the development of predictive models aimed at reducing child and maternal mortality.

Fields

- Baseline Fetal Heart Rate (FHR): Baseline value of the fetal heart rate, measured in beats per minute.
- Accelerations: Frequency of heart rate accelerations per second.
- Fetal Movement: Number of fetal movements per second.
- Uterine Contractions: Number of uterine contractions per second.
- Light Decelerations: Frequency of mild decelerations in heart rate per second.
- Severe Decelerations: Frequency of severe decelerations in heart rate per second.
- Prolonged Decelerations: Number of prolonged heart rate decelerations per second.
- Abnormal Short-Term Variability: Percentage of time with abnormal short-term variability in heart rate.
- Mean Value of Short-Term Variability: Average value of short-term variability in heart rate.
- Percentage of Time with Abnormal Long-Term Variability: Proportion of time showing abnormal long-term variability.
- Fetal Health Classification: Labels indicating fetal health status:
  1. 	Normal: No evident risks to fetal health.
	2. Suspect: Potential risks, requiring further monitoring or intervention.
	3. Pathological: Critical condition necessitating immediate medical attention.

Usage

This dataset is well-suited for developing machine learning models for multiclass classification, particularly in the field of healthcare and prenatal diagnostics. Potential applications include:

- Predictive Modeling: Build machine learning models to classify fetal health into Normal, Suspect, or Pathological states.
- Risk Assessment: Identify key indicators of fetal health to inform early intervention strategies.
- Feature Analysis: Examine the relationship between CTG features and fetal health outcomes.
- Healthcare Optimization: Improve decision-making tools for healthcare professionals by integrating automated fetal health assessment.

Coverage

The dataset contains 2,126 records of CTG exams, reflecting a variety of fetal health states. The data is comprehensive and has been labeled with high reliability by obstetricians, making it a valuable resource for research into fetal health and maternal care.

License

The dataset is available under the CC0 (Public Domain) license, enabling unrestricted use for research, educational, and commercial purposes.

Who Can Use It

This dataset is ideal for:

- Data scientists and machine learning engineers focusing on healthcare.
- Researchers and medical professionals studying prenatal diagnostics.
- Students and educators exploring classification techniques in healthcare data.

How to Use

- Train machine learning models for fetal health classification to support early diagnosis.
- Analyze correlations between CTG features and health outcomes to identify key indicators.
- Create healthcare tools that integrate CTG data for real-time risk assessment.
- Design educational tools for obstetrics training using labeled CTG datasets.

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