The **Synthetic Oral Cancer Prediction Dataset** is designed for educational and research purposes to analyse factors associated with oral cancer risk, progression, and treatment outcomes. The dataset includes anonymised, synthetic data on various clinical, lifestyle, and demographic factors for individuals diagnosed with oral cancer.

## Dataset Features
- **ID:** Unique identifier for each participant.
- **Country:** Country of residence of the participant.
- **Age:** Age of the participant (in years).
- **Gender:** Gender of the participant (Male/Female).
- **Tobacco Use:** History of tobacco use (Yes/No).
- **Alcohol Consumption:** History of alcohol consumption (Yes/No).
- **HPV Infection:** Presence of human papillomavirus infection (Yes/No).
- **Betel Quid Use:** History of Betel quid use (Yes/No).
- **Chronic Sun Exposure:** History of chronic sun exposure (Yes/No).
- **Poor Oral Hygiene:** Poor oral hygiene habits (Yes/No).
- **Diet (Fruits & Vegetables Intake):** Frequency of consuming fruits and vegetables (Yes/No).
- **Family History of Cancer:** Family history of cancer (Yes/No).
- **Compromised Immune System:** Whether the participant has a compromised immune system (Yes/No).
- **Oral Lesions:** Presence of oral lesions (Yes/No).
- **Unexplained Bleeding:** Presence of unexplained bleeding (Yes/No).
- **Difficulty Swallowing:** Difficulty in swallowing (Yes/No).
- **White or Red Patches in Mouth:** Presence of white or red patches in the mouth (Yes/No).
- **Tumor Size (cm):** Size of the tumor in centimeters.
- **Cancer Stage:** Stage of the oral cancer (1-4).
- **Treatment Type:** Type of treatment received (e.g., Surgery, Radiation, Chemotherapy).
- **Survival Rate (5-Year, %):** 5-year survival rate in percentage.
- **Cost of Treatment (USD):** Total cost of treatment in USD.
- **Economic Burden (Lost Workdays per Year):** Economic burden due to lost workdays each year.
- **Early Diagnosis:** Whether early diagnosis was made (Yes/No).
- **Oral Cancer (Diagnosis):** Diagnosis of oral cancer (Yes/No).

## Distribution
![Synthetic oral cancer dataset plot_output.png](https://storage.googleapis.com/opendatabay_public/09f348fc-a2e8-4132-9f1b-195765d80afc/622bf59174d1_plot_output.png)


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

- **Cancer Research:** Investigate the relationship between various lifestyle, clinical, and demographic factors with oral cancer risk and progression.
- **Predictive Modeling:** Build machine learning models to predict cancer diagnosis, survival rate, or treatment outcomes based on participant data.
- **Healthcare and Public Health:** Study the impact of lifestyle factors (e.g., tobacco, alcohol, diet) on the development and progression of oral cancer.
- **Educational Purposes:** Provide a dataset for students and researchers in oncology, medical data science, and public health fields to analyze cancer risk factors and treatment outcomes.

## Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a wide array of factors that support diverse research and analysis in the oncology and public health domains.

## License
CC0 (Public Domain)

## Who Can Use It
- **Cancer Researchers:** To explore correlations between lifestyle factors, clinical features, and treatment outcomes in oral cancer.
- **Oncologists and Healthcare Providers:** To analyze the effectiveness of different treatments and factors that affect prognosis and survival.
- **Public Health Professionals:** To study the broader societal and economic impacts of oral cancer and develop preventive measures.
- **Data Scientists and Machine Learning Practitioners:** To develop predictive models for diagnosing oral cancer and improving treatment planning.
- **Educators and Students:** As a resource for studying cancer risk analysis, healthcare data science, and public health analytics.

Synthetic Oral Cancer Prediction Dataset

Opendatabay Labs 

The **Synthetic Gastric Cancer Prediction Dataset** has been generated for educational and research purposes to support the analysis of clinical, pathological, and demographic factors associated with gastric cancer. This synthetic, anonymized dataset provides valuable insights into staging, histological characteristics, and invasion patterns that influence disease progression and prognosis.

## Dataset Features
- **Patient:** Unique identifier for each individual.
- **Sex:** Biological sex of the patient (Male/Female).
- **Age:** Age of the patient (in years).
- **T staging:** Tumor size and extent of invasion into nearby tissues (e.g., T1, T2, T3).
- **N staging:** Lymph node involvement status (e.g., N0, N1, N2).
- **M staging:** Metastasis presence (M0: No metastasis, M1: Distant metastasis).
- **Comprehensive Staging:** Combined TNM stage classification.
- **Histological Type:** Cellular classification of the tumor (e.g., adenocarcinoma).
- **Lauren Classification:** Intestinal (0), Mixed (1), or Diffuse (2) type.
- **Lymphovascular Invasion:** Presence of cancer cells in lymphatic or blood vessels (0 = Negative, 1 = Positive).
- **Venous Invasion:** Tumor cells detected in veins (0 = Negative, 1 = Positive).
- **Perineural Invasion:** Cancer spread along or around nerves (0 = Negative, 1 = Positive).
- **Stroma Quantity:** Tumor stroma characteristics (Medullary = 0, Intermediate = 1, Scirrhous = 2).
- **Tumor Infiltration Pattern:** Qualitative assessment of the tumor's infiltration behavior.
- **HER-2:** Human Epidermal Growth Factor Receptor 2 status (0 = Negative, 1 = 1+, 2 = 2+, 3 = 3+).

## Distribution
![Synthetic dataset gastric_cancer_visuals.png](https://storage.googleapis.com/opendatabay_public/00da1b54-b118-40d4-b429-c3e12dbb6fc3/4d500421b95e_gastric_cancer_visuals.png)


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

- **Cancer Research:** Study the relationship between staging, histological subtypes, and tumor invasiveness in gastric cancer.
- **Predictive Modeling:** Train machine learning models to predict outcomes such as HER-2 status, metastatic potential, or overall stage.
- **Clinical Insight:** Explore how combinations of clinical markers relate to patient prognosis and treatment planning.
- **Educational Purposes:** Provide students and researchers with hands-on experience working with real-world-like oncology data.

## Coverage
The dataset contains 100,000 synthetic entries with realistic variation in clinical and pathological features. It is fully anonymized and adheres to data privacy standards, supporting exploratory and predictive analysis across oncology and medical informatics.

## License
CC0 (Public Domain)

## Who Can Use It
- **Medical Researchers and Oncologists:** To evaluate gastric cancer risk factors and invasion markers.
- **Data Scientists:** To develop and benchmark predictive algorithms for cancer staging and HER-2 expression.
- **Healthcare Educators and Students:** As a comprehensive tool for teaching cancer data analysis and medical modeling.

Synthetic Gastric Cancer Prediction Dataset

The **Synthetic Skin Cancer Detection Dataset** is designed for educational and research purposes to analyze factors associated with skin cancer types, their diagnosis, and treatment options. The dataset includes anonymized, synthetic data on various clinical and demographic factors for individuals diagnosed with different types of skin cancer.

## Dataset Features
- **Participant ID:** Unique identifier for each participant.
- **MEL:** Melanoma (Yes/No).
- **NV:** Melanocytic Nevus (Yes/No).
- **BCC:** Basal Cell Carcinoma (Yes/No).
- **AKIEC:** Actinic Keratoses (Yes/No).
- **BKL:** Benign Keratosis (Yes/No).
- **DF:** Dermatofibroma (Yes/No).
- **VASC:** Vascular Lesions (Yes/No).

## Distribution

![Distribution of Synthetic skin cancer dataset yes_diagnosis_counts.png](https://storage.googleapis.com/opendatabay_public/0536d52f-a9dd-4e31-9caf-e1a47fd836d9/8511f90adc6c_yes_diagnosis_counts.png)


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

- **Cancer Research:** Investigate the relationship between various demographic, clinical, and tumour-type factors with the presence of skin cancer.
- **Predictive Modeling:** Build machine learning models to predict the presence or progression of different skin cancer types based on participant data.
- **Clinical Research:** Study the impact of various factors like lesion type, treatment options, and demographic data on skin cancer prognosis.
- **Educational Purposes:** Provide a dataset for students and researchers in oncology, dermatology, and medical data science to analyze skin cancer detection and treatment patterns.

## Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a broad set of factors to support diverse research and analysis in the oncology and medical domains, particularly in dermatology.

## License
CC0 (Public Domain)

## Who Can Use It
- **Cancer Researchers:** To explore correlations between clinical factors, lesion types, and treatment outcomes in skin cancer.
- **Dermatologists and Healthcare Providers:** To analyze how skin lesions and treatments affect cancer progression and recovery.
- **Data Scientists and Machine Learning Practitioners:** To develop predictive models for skin cancer diagnosis, classification, and prognosis.
- **Educators and Students:** As a resource for studying skin cancer analytics and medical data science.

Synthetic Skin Cancer Detection Dataset

The **Synthetic Ovarian Cancer Prediction Dataset** is created for educational and research purposes, particularly for the exploration and development of machine learning models to predict ovarian cancer presence and progression. The dataset includes anonymized, synthetic clinical and laboratory data for 100,000 subjects, simulating real-world patterns of ovarian cancer indicators.

## Dataset Features
The dataset includes 51 features representing a wide range of blood biomarkers, demographics, and diagnostic markers commonly associated with ovarian cancer. These include:

- **SUBJECT_ID:** Unique identifier for each patient.
- **Age:** Age of the patient (in years).
- **Menopause:** Menopausal status (encoded as integers).
- **AFP, CA125, CA19-9, CA72-4, HE4, CEA:** Tumor markers relevant to cancer diagnostics.
- **Various Blood Markers:** Including ALB, AST, ALT, ALP, BUN, CREA, TP, TBIL, DBIL, IBIL, GGT, GLU, and others.
- **Electrolytes and Ions:** Such as Na, K, Ca, Mg, CL, PHOS, CO2CP.
- **Hematological Measures:** Including HGB, HCT, RBC, WBC subtypes (NEU, LYM, MONO, EO, BASO), PLT, RDW, MCH, MCV, MPV, PDW, and PCT.
- **TYPE:** Target variable indicating cancer classification (encoded as integers).

## Distribution
![Synthetic dataset ovarian_cancer_visuals.png](https://storage.googleapis.com/opendatabay_public/27816028-a67a-429c-84f6-ce88d59ae85f/65e968756c92_ovarian_cancer_visuals.png)


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

- **Cancer Research:** Explore associations between biomarkers and ovarian cancer diagnosis or prognosis.
- **Predictive Modeling:** Develop machine learning or statistical models to classify or predict ovarian cancer types based on lab results and demographic data.
- **Clinical Research:** Study the diagnostic value of various tumor markers in ovarian cancer.
- **Educational Purposes:** Serve as a resource for training students and professionals in data science, bioinformatics, and healthcare analytics.

## Coverage
The dataset is fully synthetic and anonymized, adhering to privacy standards and suitable for open-access educational and research purposes. It captures realistic data distributions for key clinical metrics involved in ovarian cancer detection and monitoring.

## License
CC0 (Public Domain)

## Who Can Use It
- **Medical Researchers and Oncologists:** To identify biomarker trends and diagnostic criteria for ovarian cancer.
- **Data Scientists and Machine Learning Engineers:** To build and evaluate predictive models on medical data.
- **Healthcare Educators and Students:** For academic instruction, project work, and data analysis practice in clinical research settings.

Synthetic Ovarian Cancer Prediction Dataset

The **Synthetic Diarrhea Etiology Dataset** is a comprehensive, anonymized dataset created for educational and research purposes. It simulates pediatric clinical records related to diarrhea episodes, collected across different locations and times. The dataset supports analysis of symptom progression, clinical signs, treatment practices, and socio-environmental conditions related to diarrhea etiology and outcomes.

## Dataset Features
- **Study ID:** Unique identifier for each patient.
- **Admit Date & Time:** Timestamp of hospital admission.
- **Gender:** Biological sex of the patient (Male/Female).
- **Age (Months):** Patient age in months.
- **Diarrhea Hours/Days/Episodes:** Duration and frequency of diarrhea symptoms.
- **Nurse Name & Exam Time:** Attending nurse and examination timestamp.
- **Vital Signs:** Temperature, respiration rate.
- **Stool & Symptom Reporting:** Observations and reports of blood in stool, fever, vomiting, and breastfeeding status.
- **Anthropometrics:** Height and mid-upper arm circumference (MUAC).
- **Seasonality:** Time of year when case occurred.
- **Patient Status:** Classification codes for patient condition.
- **% Viral:** Estimated viral etiology percentage.
- **Socioeconomic Indicators:** Parent education levels, household income, number of people at home, shared pot use.
- **Medications:** Usage and type of medication administered.
- **Stool Sample Collection:** Time, date, and location of sample collection.

## Distribution
![Synthetic Diarrhea Etiology Dataset Distribution image on Opendatabay.png](https://storage.googleapis.com/opendatabay_public/965cfd39-e906-425d-b73b-c18b8978998f/6f7573e2ea48_side_by_side_summary_plots.png)



## Usage
This dataset can be used for:

- **Epidemiological Research:** Explore the relationships between demographic, environmental, and clinical factors affecting diarrhea in children.
- **Predictive Modelling:** Build models to predict symptom severity, etiology (viral vs. bacterial), or recovery time.
- **Clinical Insights:** Analyse symptom presentation, vital signs, and treatment effectiveness.
- **Educational Purposes:** Serve as a realistic dataset for students in global health, pediatric epidemiology, and data science.

## Coverage
This synthetic dataset is fully anonymized and adheres to privacy standards. It is designed to support robust statistical and machine learning analysis without compromising personal data.

## License
CC0 (Public Domain)

## Who Can Use It
- **Public Health Researchers:** To study pediatric diarrhea patterns and intervention impacts.
- **Medical and Clinical Practitioners:** To understand how vital signs and symptoms relate to diagnosis and treatment.
- **Data Scientists and Machine Learning Experts:** To experiment with clinical classification and prediction tasks.
- **Students and Educators:** As a case study in health informatics, biostatistics, and data-driven public health strategies.

Synthetic Diarrhea Etiology Dataset

The **Synthetic Respiratory Symptoms Dataset** is created for educational and research use to analyze associations between respiratory symptoms, underlying diseases, treatment types, and the severity of the condition. This synthetic dataset helps simulate real-world scenarios involving respiratory illnesses while ensuring privacy compliance through full anonymization.

## Dataset Features
- **Symptoms:** Descriptions of reported respiratory symptoms (e.g., yellow cough, chest congestion).
- **Age:** Age of the individual (in years).
- **Sex:** Biological sex of the individual (Male/Female).
- **Disease:** Diagnosed respiratory condition (e.g., Mesothelioma, bronchitis).
- **Treatment:** Medical intervention applied (e.g., Chemotherapy, Antibiotic, or none).
- **Nature:** Severity of the case (e.g., low, medium, high).

## Distribution
![Synthetic Respiratory Symptoms Dataset Distribution by Disease Count](https://storage.googleapis.com/opendatabay_public/38f90264-2312-43c5-9666-e1b18e7ce7e2/4fb39ac546e8_disease_count.png)

![Synthetic Respiratory Symptoms Dataset Distribution by Nature](https://storage.googleapis.com/opendatabay_public/38f90264-2312-43c5-9666-e1b18e7ce7e2/9b0990ccf2df_age_by_nature.png)


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

- **Respiratory Disease Research:** Study the correlation between symptoms, severity, and diagnosed diseases in synthetic patients.
- **Predictive Modelling:** Train machine learning models to predict disease types or severity based on symptoms and demographic data.
- **Clinical Insights:** Analyse how different symptoms align with specific respiratory conditions and the common treatments used.
- **Educational Purposes:** Serve as a teaching tool for learners and educators in public health, respiratory medicine, and medical data science.

## Coverage
This synthetic dataset is fully anonymised and complies with modern data privacy standards. It incorporates a diverse array of symptom profiles, diseases, and treatments relevant to respiratory illnesses, enabling a broad range of analytical and educational applications.

## License
CC0 (Public Domain)

## Who Can Use It
- **Medical Researchers:** To investigate symptom-disease-treatment relationships in respiratory medicine.
- **Pulmonologists and Healthcare Professionals:** To explore treatment trends and symptom patterns in respiratory conditions.
- **Data Scientists and Machine Learning Engineers:** To build and evaluate predictive models for diagnosis or severity classification.
- **Educators and Students:** As a practice dataset for courses in health data analytics and medical informatics.

Synthetic Respiratory Symptoms Dataset


Creative Commons 3D Printing Models dataset to access a diverse collection of legally shareable and modifiable 3D models designed for 3D printing. Popular use cases include prototyping, educational projects, product design, custom 3D printing applications, and training AI models for 3D object recognition and generation.

The Creative Commons 3D Models dataset offers a diverse collection of legally shareable and modifiable 3D models, ideal for a wide range of applications. This dataset is perfect for prototyping, educational projects, product design, custom 3D printing applications, and training AI models for 3D object recognition and generation.

With models available under Creative Commons licenses, users can freely use, adapt, and distribute them while respecting creator rights. This dataset is a valuable resource for designers, educators, researchers, and developers seeking high-quality, customizable 3D assets for their projects.

#### Dataset Features

- **url:** Direct link to the 3D model or its hosting page.  
- **id:** Unique identifier for each 3D model.  
- **domain:** Source platform or website hosting the model.  
- **title:** Name of the 3D model.  
- **description:** Summary of what the model is and its intended use.  
- **license:** The Creative Commons license under which the model is shared.  
- **images:** URLs or thumbnails of the model in visual form.  
- **creator_details:** Information about the model's creator (username, profile link, etc.).  
- **tags:** Keywords or categories related to the model (e.g., robot, toy, mechanical).  
- **date_posted:** Date when the model was uploaded or published.  
- **download_url:** Direct link to download the 3D model files.  
- **num_comments:** Number of user comments or feedback on the model.  
- **num_likes:** Number of likes or favourites from users.  
- **num_files:** Total number of downloadable files associated with the model.  
- **print_settings:** Optional details about how to 3D print the model (e.g., layer height, supports, infill).  

#### Distribution

- **Data Volume:** 15 Columns and 24.58K Rows  
- **Format:** CSV 

#### Usage
Ideal for use cases including:

- **3D Printing:** Directly use models for physical prototyping, hobby printing, or functional parts.  
- **AI Training:** Train models on 3D object recognition, generation, or segmentation tasks.  
- **Educational Projects:** Incorporate into STEM learning, CAD training, or maker programs.  
- **Design Inspiration:** Source ideas for product development, mechanical designs, and artistic modeling.  
- **Simulation and VR:** Use models for populating scenes in VR/AR environments or physical simulations.  

#### Coverage

- **Geographic Coverage:** Global user-generated content 

#### License

CUSTOM  

*Please review the respective licenses below:* 

1.	Data Provider's License  
    - [Bright Data Master Service Agreement](https://brightdata.com/license)  

#### Who Can Use It

- **Data Scientists:** To develop or evaluate 3D object recognition and generation models.  
- **Researchers:** For computer vision, human-computer interaction, or additive manufacturing studies.  
- **Businesses:** To jumpstart rapid prototyping or integrate printable assets into their workflows.  
- **Educators & Students:** For hands-on STEM education, 3D modeling lessons, and practical fabrication tasks.  
- **Designers:** To remix or build upon existing models for creative or commercial projects.  

----

#### Suggested Dataset Name

1.	Open 3D Print Hub  
2.	Commons3D Models Dataset  
3.	CreativeFab Collection  
4.	PrintForge Open Dataset  
5.	3D Commons Library  

----
#### Pricing 
##### Based on Delivery frequency


~ Min order **$250**

Get the complete dataset each delivery, including all records.
Retrieve only the data you need with the flexibility to set Smart Updates.


- Monthly

New snapshot each month, 12 snapshots/year
Paid monthly

- Quarterly

New snapshot each quarter, 4 snapshots/year
Paid quarterly

- Bi-annual

New snapshot every 6 months, 2 snapshots/year
Paid twice-a-year

- One-time purchase

New snapshot one-time delivery
Paid once
    

Creative Commons 3D Models

Bright Data


Zoopla Properties Listing dataset to explore detailed property information, including pricing, location, and features. Popular use cases include real estate market analysis, property valuation, and investment research.

Use our Zoopla Properties Listing Information dataset to explore detailed property listings, including property details, pricing, location, and market trends across various regions. This dataset provides valuable insights into property valuations, consumer preferences, and real estate dynamics, enabling businesses and researchers to make data-driven decisions.

Tailored for real estate professionals, investors, and market analysts, this dataset supports market trend analysis, property valuation assessments, and investment strategy development. Whether you're evaluating property investments, tracking market conditions, or conducting competitive analysis, the Zoopla Properties Listing Information dataset is a key resource for navigating the real estate landscape.

**Dataset Features**

- **url:** The original listing URL on Zoopla.  
- **property_type:** Type of property (e.g., Flat, Detached, Terraced).  
- **property_title:** Title or headline of the listing.  
- **address:** Full postal address of the property.  
- **google_map_location:** Geographical coordinates (latitude, longitude).  
- **virtual_tour:** Link to a virtual walkthrough or 360° tour.  
- **street_view:** Link to the Google Street View of the property.  
- **url_property:** Zoopla-specific property page URL.  
- **currency:** Currency in which the property is priced.  
- **deposit:** Security deposit required (typically for rentals).  
- **letting_arrangements:** Letting details (e.g., short-term, long-term).  
- **breadcrumbs:** Category breadcrumbs for location and type navigation.  
- **availability:** Availability status (e.g., Available now, Under offer).  
- **commonhold_details:** Information about commonhold ownership.  
- **service_charge:** Annual service charge (for leasehold properties).  
- **ground_rent:** Annual ground rent cost.  
- **time_remaining_on_lease:** Lease duration remaining in years.  
- **ecp_rating:** Energy Performance Certificate rating.  
- **council_tax_band:** Council tax band.  
- **price_per_size:** Price per square meter or foot.  
- **tenure:** Tenure type (Freehold, Leasehold, etc.).  
- **tags:** Descriptive tags (e.g., New build, Chain-free).  
- **features:** List of property features (e.g., garden, garage, en-suite).  
- **property_images:** URLs to property photos.  
- **additional_links:** Other related links (e.g., brochures, agents).  
- **listing_history:** Changes in price, listing dates, and status over time.  
- **agent_details:** Information about the listing agent or agency.  
- **points_ofInterest:** Nearby landmarks or facilities (schools, transport).  
- **bedrooms** Number of bedrooms.  
- **price:** Listed price of the property.  
- **bathrooms:** Number of bathrooms.  
- **receptions:** Number of reception rooms (living, dining, etc.).  
- **country_code:** Country code of the listing (e.g., GB for UK).  
- **energy_performance_certificate:** Detailed EPC documentation or summary.  
- **floor_plans:** URL or data related to property floor plans.  
- **description:** Detailed property description from the listing.  
- **price_per_time:** Price frequency for rentals (e.g., per week, per month).  
- **property_size:** Area of the property (in sq ft or sq m).  
- **market_stats_last_12_months:** Market stats for the area over the past year.  
- **market_stats_renta_opportunities:** Data on rental yields and opportunities.  
- **market_stats_recent_sales_nearby:** Sales history for nearby properties.  
- **market_stats_rental_activity:** Local rental activity trends.  
- **uprn:** Unique Property Reference Number for UK properties.  
- **listing_label:** Label/category of the listing.  

**Distribution**

- **Data Volume:** 44 Columns and 95.92K Rows  
- **Format:** CSV  

**Usage**

This dataset is ideal for a variety of high-impact applications:

- **Property Valuation Models:** Train ML models to estimate market value using features like size, location, and amenities.  
- **Real Estate Market Analysis:** Identify pricing trends, demand patterns, and neighbourhood growth over time.  
- **Investment Research:** Analyse rental yields, price per square foot, and historical price changes for investment opportunities.  
- **Recommendation Systems:** Develop intelligent recommendation engines for property buyers and renters.  
- **Urban Planning & Policy Making:** Use location and infrastructure data to guide city development.  
- **Sentiment & Description Analysis:** NLP-driven insights from listing descriptions and agent narratives.  

**Coverage**

- **Geographic Coverage:** Global  
- **Time Range:** Ongoing collection; historical data may span multiple years  

**License**

CUSTOM  

*Please review the respective licenses below:* 

1.	Data Provider's License  
    - [Bright Data Master Service Agreement](https://brightdata.com/license)  

**Who Can Use It**

- **Data Scientists:** Build predictive models, conduct time-series analysis, etc.  
- **Researchers:** Study urban migration, housing affordability, and socio-economic factors.  
- **Real Estate Professionals:** Competitive benchmarking and portfolio evaluation.  
- **Government & Policy Analysts:** Understand housing supply/demand at the local level.  
- **Startups & PropTech Platforms:** Develop tools for smart search, price alerts, and rental management.

---

**Dataset Name Suggestions**

1.	Zoopla Real Estate Listings Dataset  
2.	UK Property Market Insights (Zoopla)  
3.	Zoopla Property Data for Real Estate Analysis  
4.	Zoopla Property Intelligence & Pricing Trends  
5.	Zoopla Residential Property Dataset  

----
#### Pricing 
##### Based on Delivery frequency


~Up to **$0.0025** per record. Min order **$250**

Approximately 95.3K records are updated each month.
Get the complete dataset each delivery, including all records.
Retrieve only the data you need with the flexibility to set Smart Updates.


- Monthly

New snapshot each month, 12 snapshots/year
Paid monthly

- Quarterly

New snapshot each quarter, 4 snapshots/year
Paid quarterly

- Bi-annual

New snapshot every 6 months, 2 snapshots/year
Paid twice-a-year

- One-time purchase

New snapshot one-time delivery
Paid once



Zoopla properties listing information


Explore a diverse range of fashion items, home goods, and more, with insights into pricing, availability, ratings, and reviews. Popular use cases include trend forecasting, pricing optimization, and inventory management in the fast-fashion market.

The Shein.com Products dataset provides a detailed overview of the extensive product range available on Shein, offering key insights into the fast-fashion market. This dataset includes essential details such as product names, prices, discounts, descriptions, materials, product images, SKUs (Stock Keeping Units), low-stock indicators, and more.

Ideal for eCommerce professionals, fashion analysts, and market strategists, this dataset supports trend analysis, pricing strategies, and inventory management. Whether you're benchmarking competitors, identifying emerging trends, or optimizing your product offerings, the Shein.com Products dataset delivers valuable insights to stay ahead in the dynamic fashion industry.

#### Dataset Features
- **product_name**: The name/title of the product listed.  
- **description**: A brief description of the product, including features or materials.  
- **initial_price**: The original price of the product before any discounts.  
- **final_price**: The actual selling price after applying discounts.  
- **currency**: The currency in which the price is listed (e.g., USD).  
- **in_stock**: Availability status of the product (True if in stock, otherwise False).  
- **color**: Available color(s) for the product.  
- **size**: Size(s) available (e.g., S, M, L, or custom sizes).  
- **reviews_count**: Number of user reviews the product has received.  
- **main_image**: URL to the primary product image.  
- **category_url**: Link to the category page the product belongs to.  
- **url**: Direct link to the product page.  
- **category_tree**: Hierarchical path of the product category.  
- **country_code**: Country code indicating where the product is available.  
- **domain**: The Shein domain where the product was found (e.g., shein.com, shein.uk).  
- **image_count**: Total number of product images.  
- **image_urls**: List/array of URLs for all images related to the product.  
- **model_number**: The product’s model or SKU number.  
- **offers**: Details of promotions or discounts available.  
- **other_attributes**: Miscellaneous product features or labels (e.g., eco-friendly, plus-size).  
- **product_id**: Unique identifier for the product.  
- **rating**: Average user rating (typically on a 5-star scale).  
- **related_products**: List of similar or related products.  
- **root_category**: The broadest category classification (e.g., "Women", "Home").  
- **top_reviews**: Highlighted customer reviews.  
- **category**: Specific product category (e.g., "Bikinis", "T-Shirts").  
- **brand**: Brand name (often "Shein" or sub-brands).  
- **all_available_sizes**: List of all size options for the product.  
- **category_details**: Additional metadata about the product category.  
- **initial_price_usd**: Original price converted to USD.  
- **final_price_usd**: Final price converted to USD.  
- **discount_price**: Price discount amount (initial - final).  
- **discount_price_usd**: Discount amount in USD.  
- **colors**: All color variants of the product.  
- **store_details**: Information about the store or seller.  
- **shipping_details**: Information about shipping costs and delivery time.  
- **shipping_type**: Type of shipping offered (e.g., standard, express).  
- **product_parent_id**: ID representing a grouped product variant.  
- **tags**: Keywords or tags associated with the product.  
- **model_data**: Additional attributes from the product model (could include fit, cut, etc.).  

#### Distribution
- **Data Volume**: 40 Columns and 42.35 M Rows  
- **Format**: CSV  

#### Usage
This dataset is ideal for a wide range of practical and analytical applications:
- **Trend Forecasting**: Identify emerging fashion trends based on product popularity and review sentiment.  
- **Pricing Optimization**: Analyze discount strategies and dynamic pricing patterns.  
- **Inventory Management**: Monitor stock availability and detect low-stock patterns.  
- **Recommendation Systems**: Build personalized fashion recommendations using product attributes and user ratings.  
- **Market Benchmarking**: Compare Shein's offerings with competitors or across regions.  
- **Computer Vision**: Use product images for training models in visual fashion recognition.  

#### Coverage
- **Geographic Coverage**: Global  
- **Time Range**: Varies by data collection; generally recent and can be updated periodically.  

#### License

CUSTOM  

*Please review the respective licenses below:* 

1.	Data Provider's License  
    - [Bright Data Master Service Agreement](https://brightdata.com/license)  

#### Who Can Use It
- **Data Scientists**: For training ML models like price predictors, review sentiment classifiers, or image-based search engines.  
- **Researchers**: To study fast fashion, consumer behavior, or global eCommerce trends.  
- **Businesses**: For competitive analysis, dynamic pricing, or trend-based product design.  
- **Retail Analysts**: To monitor category performance and stock insights.  

---

#### Dataset Name Suggestions
1. Shein Fashion & Lifestyle Product Dataset  
2. Shein.com Global eCommerce Dataset  
3. Fast-Fashion Insights from Shein  
4. Shein Product Listings with Pricing & Reviews  
5. Shein eCommerce Intelligence Dataset

----
#### Pricing 
##### Based on Delivery frequency


~Up to **$0.0025** per record. Min order **$250**
Get the complete dataset each delivery, including all records.
Retrieve only the data you need with the flexibility to set Smart Updates.


- Monthly

New snapshot each month, 12 snapshots/year
Paid monthly

- Quarterly

New snapshot each quarter, 4 snapshots/year
Paid quarterly

- Bi-annual

New snapshot every 6 months, 2 snapshots/year
Paid twice-a-year

- One-time purchase

New snapshot one-time delivery
Paid once


Shein Products

Access a wealth of information, including article titles, raw text, images, and structured references. Popular use cases include knowledge extraction, trend analysis, and content development.

Use our Wikipedia Articles dataset to access a vast collection of articles across a wide range of topics, from history and science to culture and current events. This dataset offers structured data on articles, categories, and revision histories, enabling deep analysis into trends, knowledge gaps, and content development.

Tailored for researchers, data scientists, and content strategists, this dataset allows for in-depth exploration of article evolution, topic popularity, and interlinking patterns. Whether you are studying public knowledge trends, performing sentiment analysis, or developing content strategies, the Wikipedia Articles dataset provides a rich resource to understand how information is shared and consumed globally.

**Dataset Features**
- **url**: Direct URL to the original Wikipedia article.  
- **title**: The title or name of the Wikipedia article.  
- **table_of_contents**: A list or structure outlining the article's sections and hierarchy.  
- **raw_text**: Unprocessed full text content of the article.  
- **cataloged_text**: Cleaned and structured version of the article’s content, optimized for analysis.  
- **images**: Links or data on images embedded in the article.  
- **see_also**: Related articles linked under the “See Also” section.  
- **references**: Sources cited in the article for credibility.  
- **external_links**: Links to external websites or resources mentioned in the article.  
- **categories**: Tags or groupings classifying the article by topic or domain.  
- **timestamp**: Last edit date or revision time of the article snapshot.  

**Distribution**
- **Data Volume**: 11 Columns and 2.19 M Rows  
- **Format**: CSV  

**Usage**
This dataset supports a wide range of applications:
- **Knowledge Extraction**: Identify key entities, relationships, or events from Wikipedia content.  
- **Content Strategy & SEO**: Discover trending topics and content gaps.  
- **Machine Learning**: Train NLP models (e.g., summarisation, classification, QA systems).  
- **Historical Trend Analysis**: Study how public interest in topics changes over time.  
- **Link Graph Modeling**: Understand how information is interconnected.  

**Coverage**
- **Geographic Coverage**: Global (multi-language Wikipedia versions also available)  
- **Time Range**: Continuous updates; snapshots available from early 2000s to present.  

**License**

CUSTOM  

*Please review the respective licenses below:* 

1.	Data Provider's License  
    - [Bright Data Master Service Agreement](https://brightdata.com/license)  

**Who Can Use It**
- **Data Scientists**: For training or testing NLP and information retrieval systems.  
- **Researchers**: For computational linguistics, social science, or digital humanities.  
- **Businesses**: To enhance AI-powered content tools or customer insight platforms.  
- **Educators/Students**: For building projects, conducting research, or studying knowledge systems.  

---

**Suggested Dataset Names**
1. Wikipedia Corpus+  
2. Wikipedia Stream Dataset  
3. Wikipedia Knowledge Bank  
4. Open Wikipedia Dataset  

----
#### Pricing 
##### Based on Delivery frequency


~Up to **$0.0025** per record. Min order **$250**

Approximately 283 new records are added each month.
Approximately 1.12M records are updated each month.
Get the complete dataset each delivery, including all records.
Retrieve only the data you need with the flexibility to set Smart Updates.


- Monthly

New snapshot each month, 12 snapshots/year
Paid monthly

- Quarterly

New snapshot each quarter, 4 snapshots/year
Paid quarterly

- Bi-annual

New snapshot every 6 months, 2 snapshots/year
Paid twice-a-year

- One-time purchase

New snapshot one-time delivery
Paid once

Wikipedia articles

The **Laryngeal Voice Disorder Dataset (Synthetic)** is designed for educational and research purposes to analyze the acoustic features of vocal disorders, such as Paresis, Dysphonia, and healthy voices. The dataset includes anonymized, synthetic data on various speech features for individuals with different voice conditions, facilitating research in voice disorder analysis and classification.

## Dataset Features
- **Jitter:** The variation in pitch over time.
- **Shimmer:** The variation in amplitude over time.
- **GNE (Glottal Noise Excursion):** The extent of noise in the glottal signal.
- **Irregularity:** A measure of irregularity in voice.
- **Noise:** The amount of noise present in the signal.
- **OverallSeverity:** A severity score representing the overall condition of the voice.
- **mean_F0:** The average fundamental frequency of the voice.
- **SD_F0:** The standard deviation of the fundamental frequency.
- **Max_F0:** The maximum fundamental frequency.
- **Min_F0:** The minimum fundamental frequency.
- **Diagnosis:** The diagnosed voice condition (Paresis, Dysphonia, or healthy).

## Distribution
![Synthetic Laryngeal Voice Disorder Dataset Distribution](https://storage.googleapis.com/opendatabay_public/633d7964-9714-45e3-bb93-faf7331c7575/84d2ca5d3ca3_larynx2.png)


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

- **Voice Disorder Diagnosis:** Train machine learning models to classify and predict voice disorders based on acoustic features.
- **Speech Therapy Research:** Analyze the effects of various voice conditions on speech production.
- **Healthcare Analytics:** Investigate the relationship between voice disorders and other medical conditions.
- **Educational Purposes:** Provide a dataset for students in speech pathology, data science, and healthcare analytics to explore real-world issues in voice disorder diagnosis.

## Coverage
This synthetic dataset is fully anonymized and adheres to privacy standards. It includes a variety of acoustic features to support research in voice disorder detection, severity analysis, and classification.

## License
CC0 (Public Domain)

## Who Can Use It
- **Voice Disorder Researchers:** To explore the relationship between voice features and specific voice disorders.
- **Speech Therapists and Clinicians:** To study how different voice disorders manifest in speech and to develop treatment plans.
- **Data Scientists and Machine Learning Practitioners:** To build predictive models for diagnosing voice disorders based on acoustic data.
- **Educators and Students:** As a resource for studying voice analysis, speech pathology, and healthcare analytics.

Synthetic Laryngeal Voice Disorder Dataset

The **EMI - Bone Tumor, Tumour Detection Dataset, Patient Records (Synthetic)** is designed for educational and research purposes to analyze factors associated with bone tumors, their progression, and treatment options. The dataset includes anonymized, synthetic data on various clinical and demographic factors for individuals diagnosed with different types of bone tumors.

## Dataset Features
- **Participant ID:** Unique identifier for each participant.
- **Sex:** Sex of the participant (Male/Female).
- **Age:** Age of the participant (in years).
- **Grade:** Grade of the tumor (Low/Intermediate/High).
- **Histological Type:** Type of tumor (e.g., pleomorphic sarcoma, synovial sarcoma).
- **MSKCC Type:** Type of malignancy based on the Memorial Sloan Kettering Cancer Center classification (e.g., Leiomyosarcoma).
- **Site of Primary STS:** Location of the primary soft tissue sarcoma (e.g., right thigh, left thigh).
- **Status:** Current status of the participant (NED: No Evidence of Disease, AWD: Alive with Disease, D: Deceased).
- **Treatment:** Type of treatment received (e.g., Radiotherapy, Surgery, Chemotherapy, or a combination).

## Distribution
![EMI - Bone Tumor, Tumour Detection Dataset, Patient Records Distribution](https://storage.googleapis.com/opendatabay_public/860f7112-989d-4f87-a3b8-d833290cae6c/7ddc3b9fc35c_bone_tumor.png)


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

- **Cancer Research:** Investigate the relationship between various demographic, clinical, and treatment factors with the presence and progression of bone tumors.
- **Predictive Modeling:** Build machine learning models to predict tumor status or prognosis based on participant data.
- **Clinical Research:** Study the impact of tumor grade, histological type, and treatment options on survival and recovery.
- **Educational Purposes:** Provide a dataset for students and researchers in oncology, medical data science, and healthcare fields to analyze cancer progression and treatment patterns.

## Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a broad set of factors to support diverse research and analysis in the oncology and medical domains.

## License
CC0 (Public Domain)

## Who Can Use It
- **Cancer Researchers:** To explore correlations between clinical factors, tumor types, and treatment outcomes.
- **Oncologists and Healthcare Providers:** To analyze how tumor characteristics and treatments affect prognosis and survival rates.
- **Data Scientists and Machine Learning Practitioners:** To develop predictive models for bone tumor diagnosis, progression, and survival.
- **Educators and Students:** As a resource for studying cancer research analytics and medical data science.

Synthetic EMI Bone Tumor Detection Dataset

FACTS AND DIMENSIONS

Forager

Datrix AI

Synthetic IDD

Data Bank

TV Audience Tracker

Appnalysis


Consumer price inflation time series. Comprehensive database of time series covering measures of inflation data for the UK including CPIH, CPI and RPI.
Includes Consumer price inflation time series. CDID to Title Lookup.

This dataset provides historical data on consumer price inflation in the United Kingdom, covering a period from 1800 to the present. It includes key inflation indicators such as Retail Price Index (RPI), Consumer Price Index (CPI), and other inflation-related indices. With over 4,000 entries and detailed time series data, this dataset offers valuable insights into long-term inflation trends, weights, and indexes crucial for economic research, financial analysis, and policy-making.

Original Data Source: [Office,For National Statistics, Consumer price inflation time series](https://www.ons.gov.uk/economy/inflationandpriceindices/datasets/consumerpriceindices)


#### Dataset Features
For Inflation_Data_Time_Series.csv:
- **CDID:** Unique identifier matching the consumer data series.
- **CDID_Title:** Descriptive title for each index or series.
- **PreUnit:** The unit of measurement before adjustment.
- **Unit:** The unit of measurement (e.g., percentage, index points).
- **Release_Date:** Date the data was released.
- **Next_Release:** Date for the next scheduled data release.
- **Important_Notes:** Additional context or notes about the data.
- **CPI_Period:** Time period for which CPI data is available.
- **CPI_Period_End_Date:** End date of the CPI period.
- **CPI_Period_Type:** Type of the CPI period (e.g., monthly, yearly).
- **Value:** The inflation value or index for the given period.
- **Effective_Snapshot_Date:** Date when the snapshot of data was taken.
- **Report_Period_Length:** Length of the report period (e.g., month, year).

For CDID_Titles_Lookup_SCD.csv:
- **CDID (Consumer Data Identifier):** Unique identifier for each inflation index or dataset entry.
- **Title:** Descriptive title of the inflation index or dataset.
- **In_Source_Data:** Indicator of whether the data is sourced from internal data (1 = Yes, 0 = No).
- **Effective_Snapshot_Date:** Date when the data snapshot was taken.
- **Is_Latest:** Indicates whether the data is the most recent (1 = Yes, 0 = No).
- **Effective_From:** The starting date when the data is applicable.
- **Effective_To:** The end date when the data is applicable, if available.

#### Distribution
- **rpt_Inflation_Data_Time_Series.csv:** 5.9 million rows, 12 columns
- **dim_CDID_Titles_Lookup_SCD.csv:** 4056 rows, 7 columns
- **File Format:** CSV
- **Data Structure:** Tabular format with columns representing inflation data attributes.

![plot3 (1).png](https://storage.googleapis.com/opendatabay_public/28107fd7-d2af-40f6-8b3d-f6c393830660/1c19c927dd14_plot3__1_.png)

![plot2 (2).png](https://storage.googleapis.com/opendatabay_public/28107fd7-d2af-40f6-8b3d-f6c393830660/ed8c7c6bd9af_plot2__2_.png)

![plot1 (3).png](https://storage.googleapis.com/opendatabay_public/28107fd7-d2af-40f6-8b3d-f6c393830660/66f28d862475_plot1__3_.png)


#### Usage

This dataset is ideal for a variety of applications:
- **Economic Research:** Analyze long-term inflation trends, historical consumer price indexes, and inflation weights.
- **Financial Analysis:** Model inflation impacts on markets, investments, and economic forecasting.
- **Policy-Making:** Assist government agencies and policymakers in understanding inflation over long periods and making informed decisions.

#### Coverage
- **Geographic Coverage:** United Kingdom
- **Time Range:** 1800 - Present
- **Demographics:** General population; focused on consumer price indices and inflation metrics, relevant for economic research and policy.

#### License
CUSTOM

*Please review the respective licenses below:*

1. Data Source License
   - [ONS OGL Public Licence](https://www.ons.gov.uk/help/termsandconditions)


2. Data Provider's License
   - [FACTS AND DIMENSION - Custom License](https://factsanddimensions.co.uk/Facts%20And%20Dimensions%20Licence%20Agreement%202018_05_18.pdf)


*You must comply with the licensing terms set by both the source and the data provider.*

#### Who Can Use It
- **Data Scientists:** For time series forecasting, trend analysis, and model training.
- **Researchers:** For academic research, publications, or economic analysis related to inflation.
- **Economists and Policy Makers:** For policy decision-making and long-term economic planning.
- **Businesses:** For financial modeling, inflation prediction, and economic planning.

This dataset provides critical insights for understanding long-term inflation trends in the UK, offering an invaluable resource for various economic analyses.

#### Dataset Name Suggestions
- UK Consumer Inflation 1800 Plus
- UK Inflation 4000 Indexes Since 1800
- Historical Inflation And Indexes UK 1800
- UK Inflation Index Archive 1800 CPI CPIH RPI
- Consumer_Price_Inflation

#### Video
[Video](https://www.youtube.com/watch?v=ZmxNGev4dXo)



This video provides an overview of the complete data collection within this topic.
Interested in accessing additional datasets from this collection? Contact us for more information!

----
##### Need Help with Open Data?

Struggling to access, curate, or organize open data? Let us know the source, and we’ll add it for you! If we already have the data you need, we can list it on Opendatabay for your convenience.

[Request To Add Open Data](https://www.opendatabay.com/request-data)

----
##### Our Open Data Offering

We currently host 7,217 open data tables across 381 schemas, all accessible via leading cloud platforms like Azure and Snowflake.

[Full Data Product Catalog](https://factsanddimensions.co.uk/product)




---


UK Consumer Inflation 4000+ Indexes Since Year 1800 - OFNS

This publication supersedes 'Mental health:** physical health checks for people with severe mental illness', which was based on the Strategic Data Collection Service (SDCS) collection and published on the NHS England website.

This dataset provides information regarding the number of individuals registered with severe mental illness (SMI) within the National Health Service (NHS). It includes detailed records of crucial health metrics assessed during routine checks conducted by general practitioner (GP) practices. These metrics encompass various health indicators such as blood pressure, alcohol consumption, smoking habits, body mass index (BMI), and blood glucose levels.

The dataset serves as a valuable resource for understanding the range and effectiveness of healthcare services delivered to people suffering from severe mental illnesses. It enables the tracking of the completion rates for essential health checks and procedures over a specified quarterly time frame, allowing for a clear evaluation of healthcare engagement and accessibility.
The data is essential for monitoring healthcare outcomes for individuals on the SMI register. It supports timely care, improving health trajectories, quality of life, and overall wellbeing for those with severe mental illnesses. Additionally, insights from this data inform health policy, optimize resource allocation, and enhance the quality of care within the NHS.

Original Data Source: [NHS Digital, Physical Health Checks for People with Severe Mental Illness](https://digital.nhs.uk/data-and-information/publications/statistical/physical-health-checks-for-people-with-severe-mental-illness)

#### Dataset Features
- **Reporting_Period_Start:** The start date of the reporting period for the data.
- **Reporting_Period_End:** The end date of the reporting period for the data.
- **National:** The country where the data is collected (e.g., England).
- **Region:** The geographic region within the country (e.g., North West).
- **ICB_Code:** The code identifying the Integrated Care Board (ICB), which is a regional healthcare management entity (e.g., QE1, QF7).
- **ICB_Code_Name:** The name of the Integrated Care Board (e.g., "NHS LANCASHIRE AND SOUTH CUMBRIA INTEGRATED CARE BOARD").
- **Sub_ICB_Code:** The code identifying a sub-region within the ICB (e.g., 01A).
- **Sub_ICB_Code_Name:** The name of the sub-region within the ICB (e.g., "NHS LANCASHIRE AND SOUTH CUMBRIA ICB - 01A").
- **PCN_Code:** The code identifying the Primary Care Network (PCN) (e.g., U11269).
- **PCN_Code_Name:** The name of the Primary Care Network (e.g., "RIBBLESDALE PCN").
- **Practice_Code:** The unique code of the GP practice (e.g., P81017).
- **Practice_Code_Name:** The name of the GP practice (e.g., "WHALLEY MEDICAL CENTRE").
- **Measure_ID:** The identifier for the specific measure or metric being reported (e.g., PHS008a).
- **Measure_Name:** The name of the measure being reported (e.g., "Number_on_SMI_Register_Blood_Pressure_Check").
- **Measure_Value:** The numerical value of the measure (e.g., 95).
- **Measure_Value_Str:** The string representation of the measure value (e.g., "95").
- **Effective_Snapshot_Date:** The date the data was recorded or reported.
- **Report_Period_Length:** The frequency of the report (e.g., Quarterly).
- **Demographic_Field:** The demographic field associated with the demographic data.
- **Demographic_Value:** The demographic value corresponding to the demographic field.

#### Distribution
- **Data Volume:** The dataset consists of 264104 rows and 20 columns.
- **Format:** Tabular structure with structured data fields.

![plot1 (1).png](https://storage.googleapis.com/opendatabay_public/9d4fc16b-dee4-4da7-890d-72307f2682d5/db1eb360e1da_plot1__1_.png)

![plot2 (1).png](https://storage.googleapis.com/opendatabay_public/9d4fc16b-dee4-4da7-890d-72307f2682d5/b524d3499027_plot2__1_.png)


#### Usage
This dataset is ideal for the following applications:
- **Healthcare Monitoring:** It can be used to track the completion of health checks for individuals on the SMI register, ensuring that all necessary checks (e.g., blood pressure, BMI, blood glucose) are carried out.
- **Performance Evaluation:** Helps evaluate how well healthcare providers, including GP practices and primary care networks, are meeting health check requirements for patients on the SMI register.
- **Health Research:** Useful for researchers studying the effectiveness of healthcare interventions for individuals with severe mental illness and for analyzing health disparities.

#### Coverage
- **Geographic Coverage:** The dataset covers healthcare data within UK.
- **Time Range:** The data includes quarterly reports spanning June 2024, September 2024, and December 2024.

#### License
CUSTOM

*Please review the respective licenses below:*

1. Data Source License
   - [NHS England OGL Public Licence](https://www.england.nhs.uk/terms-and-conditions-2/)


2. Data Provider's License
   - [FACTS AND DIMENSION - Custom License](https://factsanddimensions.co.uk/Facts%20And%20Dimensions%20Licence%20Agreement%202018_05_18.pdf)


*You must comply with the licensing terms set by both the source and the data provider.*



#### Who Can Use It
- **Data Scientists:** For creating models to predict health check completion rates and analyzing healthcare services.
- **Researchers:** For academic research related to mental health, healthcare provision for SMI patients, or public health studies.
- **Healthcare Providers:** For monitoring performance and ensuring that the necessary health checks are carried out for individuals on the SMI register.
- **Policymakers:** For assessing the effectiveness of healthcare policies and making data-driven decisions regarding mental health services.

#### Dataset Name Suggestions
- SMI Health Checks
- NHS SMI Register Health Check Report 
- Quarterly SMI Health Metrics for GP Practices
- Mental Health and Physical Health Monitoring Data
- Health Checks for Severe Mental Illness Patients - NHS Report
- NHS Physical Health Check Data for People with SMI
- Integrated Mental and Physical Health Monitoring Dataset
- Severe Mental Illness Health Check Performance Report
- Physical_Health_Checks_Severe_Mental_Illness.rpt_Data_V2


#### Video
[Video](https://www.youtube.com/watch?v=XBCUU_t6-jE)

This video provides an overview of the complete data collection within this topic.
Interested in accessing additional datasets from this collection? Contact us for more information!

----
##### Need Help with Open Data?

Struggling to access, curate, or organize open data? Let us know the source, and we’ll add it for you! If we already have the data you need, we can list it on Opendatabay for your convenience.

[Request To Add Open Data](https://www.opendatabay.com/request-data)

----
##### Our Open Data Offering

We currently host 7,217 open data tables across 381 schemas, all accessible via leading cloud platforms like Azure and Snowflake.

[Full Data Product Catalog](https://factsanddimensions.co.uk/product)

Physical Health Checks for Severe Mental Illness (SMI) - NHS Dataset


Index of cancer survival: 1-year, 5-year and 10-year survival (%) for all cancer combined, breast, colorectal, lung , by calendar year of diagnosis, by sex and age group: all adults (aged 15 to 99 years), Clinical Commissioning Groups, Sustainability and Transformation Partnerships, Cancer.

This dataset offers comprehensive information on cancer survival rates throughout various regions in the United Kingdom, organized systematically by Cancer Alliances, Clinical Commissioning Groups (CCGs), and Sustainability and Transformation Partnerships (STPs). It presents detailed survival percentages for multiple types of cancers, including colorectal, breast, and lung cancers, among others. The data is thoughtfully stratified by demographic factors such as age and sex, as well as by specific cancer sites, enabling a nuanced analysis of survival rates.

The dataset incorporates important statistical metrics, including confidence intervals and estimates of precision, which provide deeper insights into the reliability of the data. This combination of detailed demographic stratification and robust statistical measures makes the dataset an invaluable resource for researchers and healthcare professionals aiming to assess cancer survival trends and outcomes across different healthcare regions in the UK. Such analysis can further inform public health policies and strategies aimed at improving cancer care and survival rates.

Original Data Source: [NHS England, Cancer Survival in England](https://digital.nhs.uk/data-and-information/publications/statistical/cancer-survival-in-england)


#### Dataset Features

- **Geography_Type:** Specifies the type of geographical region (e.g., CCG, Cancer Alliance, STP).
- **Geography_Code:** A unique identifier for the geographical region.
- **Geography_Code_Geography_Name:** The name of the geographical region associated with the Geography_Code.
- **Parent_Cancer_Alliance:** The overarching Cancer Alliance that the region falls under.
- **Parent_STP:** The Sustainability and Transformation Partnership that the region is part of.
- **Cancer_Site:** The type of cancer being tracked (e.g., lung, colorectal, breast).
- **Sex:** Specifies the gender (e.g., Persons, Male, Female) for which survival data is reported.
- **Age_At_Diagnosis:** The age category of individuals at the time of cancer diagnosis.
- **Standardisation_Type:** Describes how the data is standardized (e.g., Age; Sex, Cancer site; sex).
- **Diagnosis_Year:** The year in which the cancer diagnosis occurred.
- **Years_Since_Diagnosis:** The number of years since the diagnosis was made.
- **Patient_Number:** The number of patients included in the dataset for that specific region and cancer site.
- **Survival_Pct:** The percentage of patients surviving cancer for the specified duration.
- **Lower_CI:** The lower bound of the confidence interval for the survival percentage.
- **Upper_CI:** The upper bound of the confidence interval for the survival percentage.
- **Precision:** The precision of the survival percentage estimate.
- **Standard_Error:** The standard error for the survival percentage estimate.
- **Substituted_By_Parent_Geography:** Indicates whether the data has been substituted by data from a parent geography.
- **Effective_Snapshot_Date:** The date on which the data was collected or reported.
- **Report_Period_Length:** The frequency of the data report (e.g., Annual).
- **Precision_Str:** A string representation of the precision for easier interpretation.

#### Distribution

- **Data Volume:** The dataset contains 224892 records and 21 columns
- **Format:** Tabular structure with structured data fields.

![survival_percentage_distribution.png](https://storage.googleapis.com/opendatabay_public/707c66a1-6fb7-41f9-bb48-060658786091/fa323288439f_survival_percentage_distribution.png)

![avg_survival_by_cancer_site.png](https://storage.googleapis.com/opendatabay_public/707c66a1-6fb7-41f9-bb48-060658786091/01072cd74925_avg_survival_by_cancer_site.png)

![avg_survival_by_geography_type.png](https://storage.googleapis.com/opendatabay_public/707c66a1-6fb7-41f9-bb48-060658786091/48ed29ba7817_avg_survival_by_geography_type.png)

![survival_percentage_over_time.png](https://storage.googleapis.com/opendatabay_public/707c66a1-6fb7-41f9-bb48-060658786091/674ab044448f_survival_percentage_over_time.png)


#### Usage
This dataset is ideal for several applications:
- **Cancer Survival Analysis:** To assess cancer survival rates across different geographical regions and cancer types.
- **Healthcare Planning:** For policymakers and healthcare providers to identify regions with lower survival rates and focus on improving care quality.
- **Public Health Research:** To analyze trends and disparities in cancer outcomes based on geographical region, age, and sex.
- **Machine Learning:** For building predictive models to forecast cancer survival outcomes based on regional and demographic factors.
Coverage
- **Geographic Coverage:** The dataset covers various regions within the United Kingdom, including NHS Clinical Commissioning Groups (CCGs), Cancer Alliances, and Sustainability and Transformation Partnerships (STPs).
- **Time Range:** The data spans from 2002 to 2020 with annual updates.
  
#### License
CUSTOM

*Please review the respective licenses below:*

1. Data Source License
   - [NHS England OGL Public Licence](https://www.england.nhs.uk/terms-and-conditions-2/)


2. Data Provider's License
   - [FACTS AND DIMENSION - Custom License](https://factsanddimensions.co.uk/Facts%20And%20Dimensions%20Licence%20Agreement%202018_05_18.pdf)


*You must comply with the licensing terms set by both the source and the data provider.*




#### Who Can Use It

- **Data Scientists:** For building predictive models and performing statistical analysis on cancer survival.
- **Researchers:** For academic or scientific studies in oncology, public health, and epidemiology.
- **Healthcare Providers:** For assessing cancer care outcomes and improving treatment strategies.
- **Policymakers:** For making informed decisions related to cancer care, regional healthcare initiatives, and resource allocation.

#### Dataset Name Suggestions

- UK Cancer Survival Data by Region and Type
- Cancer Survival Rates in the UK
- Cancer Diagnosis and Survival by Region and Demographics
- UK Cancer Outcome Analysis by CCG, STP, and Cancer Alliance
- Regional Cancer Survival Data for the UK 
- Cancer_Survival.rpt_Data_Complete

#### Video
[Video](https://www.youtube.com/watch?v=i5MiVUHZjzA)


This video provides an overview of the complete data collection within this topic.
Interested in accessing additional datasets from this collection? Contact us for more information!

----
##### Need Help with Open Data?

Struggling to access, curate, or organize open data? Let us know the source, and we’ll add it for you! If we already have the data you need, we can list it on Opendatabay for your convenience.

[Request To Add Open Data](https://www.opendatabay.com/request-data)

----
##### Our Open Data Offering

We currently host 7,217 open data tables across 381 schemas, all accessible via leading cloud platforms like Azure and Snowflake.

[Full Data Product Catalog](https://factsanddimensions.co.uk/product)
    

UK Cancer Survival Data by Region and Type

**About**

We provide a comprehensive talking-head video dataset with over 50,000 videos, totaling more than 500 hours of footage and featuring 23,841 unique identities from around the world.


**Distribution**

Detailing the format, size, and structure of the dataset:

-Total Size: 2.5TB

-Total Videos: 48,200

-Identities Covered: 24,100

-Resolution: 60% 4k(1980), 33% fullHD(1080)

-Formats: MP4

-Full-length videos with visible mouth movements in every frame.

-Minimum face size of 400 pixels.

-Video durations range from 20 seconds to 5 minutes.

-Faces have not been cut out, full screen videos including backgrounds.


**Usage**

**This dataset is ideal for a variety of applications**:

Face Recognition & Verification: Training and benchmarking facial recognition models.

Action Recognition: Identifying human activities and behaviors.

Re-Identification (Re-ID): Tracking identities across different videos and environments.

Deepfake Detection: Developing methods to detect manipulated videos.

Generative AI: Training high-resolution video generation models.

Lip Syncing Applications: Enhancing AI-driven lip-syncing models for dubbing and virtual avatars.

Background AI Applications: Developing AI models for automated background replacement, segmentation, and enhancement.


**Coverage**

Explaining the scope and coverage of the dataset:

Geographic Coverage: Worldwide

Time Range: Time range and size of the videos have been noted in the CSV file.

Demographics: Includes information about age, gender, ethnicity, format, resolution, and file size.


**Languages Covered (Videos):**

English: 23,038 videos

Portuguese: 1,346 videos

Spanish: 677 videos

Norwegian: 1,266 videos

Swedish: 1,056 videos

Korean: 848 videos

Polish: 1,807 videos

Indonesian: 1,163 videos

French: 1,102 videos

German: 1,276 videos

Japanese: 1,433 videos

Dutch: 1,666 videos

Indian: 1,163 videos

Czech: 590 videos

Chinese: 685 videos

Italian: 975 videos

Philipeans: 920 videos

Bulgaria: 340 videos



**Who Can Use It**

List examples of intended users and their use cases:

Data Scientists: Training machine learning models for video-based AI applications.

Researchers: Studying human behavior, facial analysis, or video AI advancements.

Businesses: Developing facial recognition systems, video analytics, or AI-driven media applications.


**Additional Notes**

Ensure ethical usage and compliance with privacy regulations.
The dataset’s quality and scale make it valuable for high-performance AI training.
Potential preprocessing (cropping, down sampling) may be needed for different use cases.
Dataset has not been completed yet and expands daily, please contact for most up to date CSV file.
The dataset has been divided into 100GB zipped files and is hosted on a private server (with the option to upload to the cloud if needed).
To verify the dataset's quality, please contact me for the full CSV file. I’d be happy to provide example videos selected by the potential buyer.

https://lipsynthesis.com/dataset

THVD (Talking Head Full Video Dataset)

LipSynthesis

LinkedIn profiles dataset to make informed business decisions, enhance customer relations, and support strategic hiring. Popular use cases include tracking talent movement, sourcing new talent, lead generation, and alternative investment insights.

Use our LinkedIn People Profiles dataset to access detailed information on professionals across industries, including job titles, skills, experience, and education. This dataset is ideal for recruiters, talent acquisition teams, marketers, and business analysts looking to enhance recruitment efforts, refine marketing strategies, and identify key talent.

With this dataset, you can track career movements, analyze skill trends, and perform targeted talent searches to support hiring, lead generation, and networking strategies. Gain valuable insights into professional profiles, stay ahead of industry trends, and optimize your human resource and business development initiatives.

#### Dataset Features

- **timestamp:** The time when the data was captured.
- **linkedin_num_id:** A unique identifier for the LinkedIn profile.
- **url:** The URL of the LinkedIn profile.
- **name:** Name of the LinkedIn user.
- **country_code:** Country code of the user's location.
- **city:** The city where the user is based.
- **about:** A brief description or summary of the user's professional background.
- **followers:** Number of people following the user's profile.
- **connections:** Number of connections the user has on LinkedIn.
- **position:** The user's current job title or position.
- **experience:** The number of years of professional experience the user has.
- **current_company:** The name of the company where the user currently works.
- **current_company_name:** An additional field possibly representing the company's name for consistency.
- **current_company_company_id:** A unique identifier for the current company.
- **posts:** posts made by the user.
- **activity:** User's recent activity or engagement on LinkedIn.
- **education:** The user's highest level of education.
- **educations_details:** Detailed information about the user's educational background.
- **courses:** Courses the user has completed or is enrolled in.
- **certifications:** Any professional certifications the user holds.
- **honors_and_awards:** Recognitions, honours, or awards the user has received.
- **volunteer_experience:** Any volunteer work the user has participated in.
- **organizations:** Organizations or groups the user is associated with.
- **recommendations_count:** recommendations the user has received.
- **recommendations:** Specific recommendations provided by other LinkedIn users.
- **languages:** Languages spoken by the user.
- **projects:** Projects the user has worked on.
- **patents:** Patents held by the user.
- **publications:** Publications authored by the user.
- **avatar:** URL to the user's profile picture.
- **default_avatar:** Indicates whether the user has a default avatar or a custom one.
- **banner_image:** URL to the user's banner image.
- **similar_profiles:** LinkedIn profiles similar to the given user profile.
- **people_also_viewed:** Profiles that were also viewed by people who viewed the user's profile.
- **memorialized_account:** Indicates whether the user's account has been memorialized.
- **input_url:** The source URL from which the data was scraped.
- **linkedin_id:** A unique identifier for the LinkedIn profile.

#### Distribution
- **Data Volume:** 38 columns and 525.94 M rows
- **Data Format:** CSV

#### Usage
This dataset is ideal for a variety of applications:
- **Social Network Analysis:** Analyzing LinkedIn users' connections, followers, and activities to understand professional networking patterns.
- **Career Development Tools:** Building tools or platforms that provide career advice based on the user's profile and activity.
- **Machine Learning:** Training machine learning models for tasks such as job recommendation systems or professional skill assessments.
- **Business Intelligence:** Analyzing user profiles for market research, hiring trends, or business development.

#### Coverage

- **Geographic Coverage:** Global coverage, with data from users located in various countries and cities.
- **Time Range:** The data collection period varies but may include data from recent years.

#### License

CUSTOM

*Please review the respective licenses below:*

1. Data Provider's License
   - [Bright Data Master Service Agreement](https://brightdata.com/license)


#### Who Can Use It

- **Data Scientists:** For training machine learning models, especially in social network analysis and career recommendation systems.
- **Researchers:** For academic or scientific studies related to professional networks, career trends, or data analysis.
- **Businesses:** For market analysis, talent scouting, recruitment efforts, or customer insights.

#### Potential Names for the Dataset:

- **LinkedIn Profile Analytics Dataset**
- **Professional Network Insights Dataset**
- **LinkedIn User Data Collection**
- **Career Profile Dataset**

----
#### Pricing 
##### Based on Delivery frequency

~Up to **$0.0025** per record. Min order **$500**

Approximately 6.4M new records are added each month.
Approximately 398M records are updated each month.
Get the complete dataset each delivery, including all records.
Retrieve only the data you need with the flexibility to set Smart Updates.


- Monthly

New snapshot each month, 12 snapshots/year
Paid monthly

- Quarterly

New snapshot each quarter, 4 snapshots/year
Paid quarterly

- Bi-annual

New snapshot every 6 months, 2 snapshots/year
Paid twice-a-year


- One-time purchase

New snapshot one-time delivery
Paid once


LinkedIn people profiles

The **Dry Eye Disease Patient Records (Synthetic)** is designed for educational and research purposes to analyze patterns in sleep behavior, stress levels, lifestyle factors, and their potential links to dry eye disease. It provides anonymized, synthetic data on various health conditions and behavioral habits.

## Dataset Features
- **Gender:** Gender of the individual (Male/Female).
- **Age:** Age of the individual.
- **Sleep Duration:** Average sleep duration in hours.
- **Sleep Quality:** Subjective assessment of sleep quality (scale-based).
- **Stress Level:** Measured stress level (scale-based).
- **Heart Rate:** Resting heart rate (bpm).
- **Daily Steps:** Number of steps taken per day.
- **Physical Activity:** Minutes of physical activity per day.
- **Height & Weight:** Individual’s height (cm) and weight (kg).
- **Sleep Disorder:** Presence of a diagnosed sleep disorder (Yes/No).
- **Wake Up During Night:** Frequency of waking up during the night (Yes/No).
- **Feel Sleepy During Day:** Self-reported daytime sleepiness (Yes/No).
- **Caffeine Consumption:** Frequency of caffeine intake (Yes/No).
- **Alcohol Consumption:** Frequency of alcohol intake (Yes/No).
- **Smoking:** Smoking habits (Yes/No).
- **Medical Issue:** Presence of any medical conditions (Yes/No).
- **Ongoing Medication:** Use of any ongoing medication (Yes/No).
- **Smart Device Before Bed:** Usage of smart devices before sleeping (Yes/No).
- **Average Screen Time:** Daily screen time in hours.
- **Blue-Light Filter:** Use of blue-light filters on devices (Yes/No).
- **Eye Discomfort & Strain:** Presence of discomfort and eye strain (Yes/No).
- **Redness in Eye:** Occurrence of eye redness (Yes/No).
- **Itchiness/Irritation in Eye:** Symptoms of eye itchiness or irritation (Yes/No).
- **Dry Eye Disease:** Diagnosis of Dry Eye Disease (Yes/No).

## Distribution
![Dry Eye Disease Patient Records Synthetic Data](https://storage.googleapis.com/opendatabay_public/f4e9ad52-5d13-4d2e-ac19-207a5b71522e/2e2e949519d7_eye.png)


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

- **Healthcare Analytics:** Identify patterns between lifestyle factors and dry eye disease.
- **Predictive Modeling:** Develop machine learning models to predict eye health risks.
- **Clinical Research:** Investigate associations between screen time, sleep habits, and eye conditions.
- **Educational Purposes:** Provide a dataset for students in medical, data science, and public health fields to analyze real-world health trends.

## Coverage
This synthetic dataset is fully anonymized and complies with data privacy standards. It includes a variety of demographic and lifestyle factors to support a broad range of research and analysis.

## License
CC0 (Public Domain)

## Who Can Use It
- **Healthcare Researchers:** To explore correlations between lifestyle habits and dry eye disease.
- **Clinicians and Medical Practitioners:** To analyze factors contributing to eye health issues.
- **Data Scientists and Machine Learning Practitioners:** To develop predictive models for eye-related conditions.
- **Educators and Students:** As a resource for studying health analytics and medical research.

Synthetic Dry Eye Disease Patient Records 

This dataset contains cybersecurity incident records, documenting various attack types, their severity, response actions, and potential data exfiltration. The dataset is useful for analyzing cybersecurity threats, response strategies, and risk assessment in digital environments. It provides insights into how different threats are detected, mitigated, and eradicated.

### Dataset Features  

- **AM_ID:** Unique identifier for each cybersecurity incident.
- **Event ID:** A unique identifier for tracking the event.
- **Timestamp:** Date and time when the security incident occurred.
- **User Agent:** The browser or system details used during the attack.
- **Attack Type:** The classification of the security threat (e.g., Ransomware, DDoS, Insider Threat).
- **Attack Severity:** The level of threat posed by the attack (e.g., critical, high, medium).
- **Data Exfiltrated:** Boolean indicating whether sensitive data was stolen (TRUE/FALSE).
- **Threat Intelligence:** Additional context or information about the attack.
- **Response Action:** The action taken to mitigate the threat (e.g., eradicated, contained, recovered).

### Distribution  

- **Data Volume:** 20000 rows and 9 columns.
- **Format:** Tabular dataset suitable for analysis in CSV, Excel, or database formats.

### Usage  

This dataset is ideal for a variety of applications: 

- **Threat Intelligence Analysis:** Identifying attack trends and assessing security risks.
- **Incident Response Optimization:** Evaluating how organizations respond to cyber threats.
- **Machine Learning for Cybersecurity:** Training models to detect and predict security incidents.
- **Risk Assessment:** Understanding the impact of various cyber threats on IT infrastructure.

### Coverage  

- **Geographic Coverage:** Global (cybersecurity incidents are location-agnostic).
- **Time Range:** Covers events from 2020 to 2024.
- **Demographics:** Focuses on cybersecurity threats across different industries and sectors.

### License  

CC0  

### Who Can Use It  

- **Cybersecurity Analysts:** To assess and improve security response strategies. 
- **Data Scientists:** For building predictive models for threat detection. 
- **Researchers:** To study trends in cyber threats and attack patterns.
- **Businesses:** To analyze and enhance their cybersecurity defenses. 


Threat Intelligence & Response Data

Vdt. Data


This dataset contains information about Airbnb listings in New York City, including features like room type, pricing, neighbourhood, and reviews. It is designed to predict factors like price and availability based on various attributes of the listing. The dataset provides a useful resource for building models focused on rental pricing and demand prediction in the tourism and hospitality industry.

#### Dataset Features:
- **Id:** Unique identifier for the listing.
- **Name:** Name or title of the listing.
- **Host id:** Unique identifier for the host.
- **Hostname:** Name of the host.
- **Neighbourhood group:** The broader geographic area, e.g., Brooklyn, Manhattan.
- **Neighbourhood:** Specific neighbourhood within the group, e.g., Kensington, Midtown.
- **Latitude:** Latitude of the listing's location.
- **Longitude:** Longitude of the listing's location.
- **Room type:** Type of the room offered (e.g., entire home/apartment, private room, and shared room).
- **Price:** Price per night for the listing. (range of 10 to 1000)
- **Minimum nights:** Minimum number of nights required for booking.
- **Number of reviews:** Total number of reviews received by the listing.
- **Last review:** Date of the most recent review.
- **Reviews per month:** Average number of reviews per month.
- **Calculated host listings:** Number of listings a host has on the platform.
- **Availability:** Number of days the listing is available for booking in a year.

#### Usage:
This dataset is ideal for training and testing machine learning models in the following areas:
- Predicting rental prices based on location, room type, and other factors.
- Analysing availability patterns and trends.
- Evaluating the impact of reviews on pricing and demand.
- Comparing the performance of different neighbourhoods in terms of rental prices and reviews.

#### Coverage:
The dataset provides insights into Airbnb listings across various neighbourhoods in New York City, covering diverse room types, pricing strategies, and host characteristics. This makes it suitable for predictive modelling, pricing analysis, and market trend exploration.

#### License:
CC0 (Public Domain)

#### Who can use it:
This dataset is intended for data scientists, machine learning practitioners, researchers, and students interested in exploring predictive analytics in the real estate and hospitality sectors.

#### How to use it:
- Develop models to predict pricing and availability based on listing attributes.
- Analyse the effect of host behaviour and reviews on rental success.
- Explore trends in pricing across different neighbourhoods and room types.


Airbnb data of NYC

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

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

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

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