This dataset is curated to support research and development in natural language processing (NLP), particularly in the area of question answering systems. Focused on the domain of Data Science and Analytics, it contains a diverse collection of question-answer pairs designed to reflect real-world inquiries about key concepts, tools, techniques, and trends within the field.

Each entry includes:

A natural language question related to data science topics such as machine learning, data wrangling, statistical analysis, data visualization, big data technologies, and analytics methods.

A corresponding answer, verified for accuracy and clarity, suitable for use in both retrieval-based and generative QA models.

Optional metadata such as topic category, difficulty level, and source context, where applicable.

Use Cases:

Training and evaluating QA models and chatbots focused on technical domains.

Developing educational tools and intelligent tutoring systems for data science learners.

Benchmarking NLP systems for domain-specific understanding and reasoning.

Target Audience:

AI/ML researchers

Data science educators and students

NLP developers working on domain-specific applications

This dataset aims to bridge the gap between technical knowledge and natural language understanding by providing high-quality QA pairs tailored to one of today’s most in-demand fields.



Question Answering Dataset

DATA Simple Co

Initially, the format of this dataset was .json, so I converted it to .csv for ease of data processing.


"Online articles from the 25 most popular news sites in Vietnam in July 2022, suitable for practicing Natural Language Processing in Vietnamese.

Online news outlets are an unavoidable part of our society today due to their easy access, mostly free. Their effects on the way communities think and act is becoming a concern for a multitude of groups of people, including legislators, content creators, and marketers, just to name a few. Aside from the effects, what is being written on the news should be a good reflection of people’s will, attention, and even cultural standard.

In Vietnam, even though journalists have received much criticism, especially in recent years, news outlets still receive a lot of traffic (27%) compared to other methods to receive information."

Vietnamese Online News .csv dataset

The Yahoo! Answers topic classification dataset is constructed using 10 largest main categories. Each class contains 140,000 training samples and 6,000 testing samples. Therefore, the total number of training samples is 1,400,000 and testing samples 60,000 in this dataset. From all the answers and other meta-information, we only used the best answer content and the main category information.

The file classes.txt contains a list of classes corresponding to each label.

The files train.csv and test.csv contain all the training samples as comma-sparated values. There are 4 columns in them, corresponding to class index (1 to 10), question title, question content and best answer. The text fields are escaped using double quotes ("), and any internal double quote is escaped by 2 double quotes (""). New lines are escaped by a backslash followed with an "n" character, that is "\n".

Yahoo Answers 10 categories for NLP CSV

This data was reformatted from "Midjourney User Prompts & Generated Images (250k)". Give them a ^ +1 and this as well.

TLDR
250k.csv (248k rows) = Good for text search to show images. Contains re-runs, which could have similar output images.
reduced.csv (130k rows) = No re-runs, but may contain duplicate text with different arguments.
raw.csv (251k rows) = messages containing commands and probably unwanted content. URLs are full length.
img_url is the right end of the image URLs.
text is the text portion of the /imagine command, excluding arguments and input URLs.

wc -l *
   251390 midjourney_2022_250k_raw.csv
   248069 midjourney_2022_250k.csv
   130407 midjourney_2022_reduced.csv
The URLs have this removed to save memory. Re-attach like so…
image: https://cdn.discordapp.com/attachments/ + img_url
image: https://media.discordapp.net/attachments/ + img_url
job page: https://www.midjourney.com/app/jobs/ + job_id

The raw version contains errors, chat, server messages, and other anomalies. It's not recommended unless you want to start over.

Mistake: "thumbnail" is just another URL.
Probably all the code you need.

import os,sys,re,glob,json
import pandas as pd
import numpy as np
import glob
from tqdm.notebook import tqdm

def extract_data(filename, ii=0):
    with open(filename) as fh:
        obj = json.loads(fh.read())
    columns = [
    #     'id', 'type', 
        'content', 'timestamp',
    #     'channel_id', 'author', 
        'attachments', 
    #     'embeds', 'mentions', 'mention_roles', 'pinned', 'mention_everyone', 'tts',  'edited_timestamp', 'flags', 'components', 'message_reference', 'hit'
    ]
    data = []
    for xx_lst in obj['messages']:
        for d_yy in xx_lst[:1]:
            zz_lst = d_yy.get('attachments',[])
            content = d_yy.get('content',None)
            timestamp = d_yy.get('timestamp','')
            for d_attach in zz_lst:
                thumb_url = d_attach.get('url','')
                img_url = d_attach.get('proxy_url','')
                data.append([timestamp,content, thumb_url, img_url])
                ii += 1
    return data,ii

def expand_df(df):
    df.drop_duplicates('img_url', inplace=True)
    df['L'] = df._message.apply(len)
    df = df[df.L>0]

    def func(x):
        if '*' not in x: return None
        x = x.replace('\n',' ')
        arr = re.findall(r'[*]{2,20}(.+)[*]{2,20}', x)
        cmd = None
        if len(arr)>0:
            cmd = arr[0].replace('—','--')
        return cmd    
    df['cmd'] = df._message.apply(func)
    df = df[~df['cmd'].isnull()]

    def func(x):
        return x.replace('https://media.discordapp.net/attachments/','')
    df['img_url'] = df.img_url.apply(func)
    df = df[df.img_url.str.contains('png')]

    def func(x):
        job_ids = re.findall('[a-f0-9-]{36,36}',x)
        return job_ids[0] if len(job_ids)>0 else None
    df['job_id'] = df._thumb_url.apply(func)
    df.drop_duplicates('job_id', inplace=True)

    def func(x):
        x = re.sub(r'[<](.+)[>]', '', x)
        return x.split('--')[0].strip()
    df['text'] = df['cmd'].apply(func)
    return df.sample(frac=1.0)

files = glob.glob(f'{DATA_DIR}*.json')
ii = 0
data = []
for filename in tqdm(files):
    dd,ii = extract_data(filename, ii)
    data += dd

columns = 'timestamp,_message,_thumb_url,img_url'.split(',')
df = pd.DataFrame(data, columns=columns)
df.to_csv(BASE_DIR+'midjourney_2022_250k_raw.csv')
df = expand_df(df)
df = df[[c for c in df.columns if len(c)>0 and str(c)[0]!='_']]
df.to_csv(BASE_DIR+'midjourney_2022_250k.csv')
df = df.sample(frac=1.0).drop_duplicates(['cmd'])
df.sort_values('L', ascending=True, inplace=True)
df.to_csv(BASE_DIR+'midjourney_2022_reduced.csv')

Midjourney 2022 - 250k [CSV]

Context
This is a dataset consisting of Drake lyrics and other information gathered from Genius.com. I'm currently working on a side project while enrolled in the Data Science program at Flatiron. I've lovingly entitled this project "Ye-Spirations" which will essentially be a motivational poster generator that renders high-quality images with random lines of text generated from hip hop lyrics. I have a simple prototype built out with Kanye lyrics but I would like to continue working on it and add additional features before sharing a final version. I intend on expanding the artist options for lyrics which brings me here. This dataset is a byproduct of the expansion (which I am excited to call "Inspo-Papi") and I wanted to publish this for others to use for their own projects or even my future self.

Content
This dataset contains 3 files.
.txt - lyrics only
.json - lyrics, song title, album title, url, view count (at this time)
.csv - lyrics, song title, album title, url, view count (at this time)

Acknowledgements
Data gathered from the GOAT of lyrics and annotations - Genius.com

Requests
I intend on expanding to multiple artists so if y'all have any requests feel free to shout em out!

Drake Lyrics

Overview

Dive into the Extrovert vs. Introvert Personality Traits Dataset, a rich collection of behavioral and social data designed to explore the spectrum of human personality. This dataset captures key indicators of extroversion and introversion, making it a valuable resource for psychologists, data scientists, and researchers studying social behavior, personality prediction, or data preprocessing techniques.

Context

Personality traits like extroversion and introversion shape how individuals interact with their social environments. This dataset provides insights into behaviors such as time spent alone, social event attendance, and social media engagement, enabling applications in psychology, sociology, marketing, and machine learning. Whether you're predicting personality types or analyzing social patterns, this dataset is your gateway to uncovering fascinating insights.

Dataset Details

Size: The dataset contains 2,900 rows and 8 columns.

Features:

    - Time_spent_Alone: Hours spent alone daily (0–11).
    - Stage_fear: Presence of stage fright (Yes/No).
    - Social_event_attendance: Frequency of social events (0–10).
    - Going_outside: Frequency of going outside (0–7).
    - Drained_after_socializing: Feeling drained after socializing (Yes/No).
    - Friends_circle_size: Number of close friends (0–15).
    - Post_frequency: Social media post frequency (0–10).
    - Personality: Target variable (Extrovert/Introvert).*
Data Quality: Includes some missing values, ideal for practicing imputation and preprocessing.
Format: Single CSV file, compatible with Python, R, and other tools.*

Data Quality Notes

Contains missing values in columns like Time_spent_Alone and Going_outside, offering opportunities for data cleaning practice.
Balanced classes ensure robust model training.
Binary categorical variables simplify encoding tasks.
Potential Use Cases

Build machine learning models to predict personality types.
Analyze correlations between social behaviors and personality traits.
Explore social media engagement patterns.
Practice data preprocessing techniques like imputation and encoding.
Create visualizations to uncover behavioral trends.
Please Upvote if you like the dataset ☺️

Extrovert vs. Introvert Behavior Data

This dataset contains detailed demographic and health-related information for individuals alongside their corresponding medical insurance charges. It includes features such as age, sex, BMI, number of children, smoking status, region, and total insurance cost. This dataset is covered from the USA.

The dataset is ideal for building and evaluating machine learning models that predict healthcare costs based on personal and lifestyle factors.

#### Dataset Features
**1. age:**
Age of the individual in years.

**2. sex:**
Biological sex of the individual (male or female).

**3. BMI:**
Body Mass Index — the numeric measure of body fat based on height and weight.

**4. children:**
Number of dependent children covered by the insurance plan.

**5. smoker:**
Smoking status of the individual (yes or no).

**6. region:**
Geographic region of the individual within the United States (northeast, northwest, southeast, or southwest).

**7. charges:**
Individual medical insurance cost billed by the insurer.



#### Distribution
- **Format:** 
CSV (Comma-Separated Values)

- **Data Volume:**
Rows: 1,338 records 

- **7 Columns:**
age, sex, BMI, children, smoker, region, charges

- **File Size:** Approximately 56 KB


#### Usage
This dataset is ideal for a variety of applications:

Medical Cost Prediction: Train regression models to estimate insurance charges based on demographic and lifestyle factors

Health Economics Research: Analyze how factors like smoking, BMI, and age impact healthcare costs.
  

#### Geographic Coverage:
- **United States:** the dataset includes individuals from four regions: northeast, northwest, southeast, and southwest.

- **Time Range:** The exact dates of data collection are not specified, but the data reflects typical insurance and demographic patterns observed in recent years.

- **Demographics:** Includes a diverse range of individuals:
Age Range: From 18 to 64 years old
Gender: Male and female
Lifestyle Factors: Smoking status and BMI
Dependents: Number of children covered by the insurance  


#### License
CC0

#### Who Can Use It

- **Data Scientists:** For training machine learning models.  
- **Researchers:** For academic or scientific studies.  
- **Businesses:** For analysis, insights, or AI development.  

    

Medical Insurance Dataset 

Quantrix Intelligence 

A curated dataset of 241,000+ English-language comments labeled for sentiment (negative, neutral, positive). Ideal for training and evaluating NLP models in sentiment analysis.


#### Dataset Features
 **1. text:**
Contains individual English-language comments or posts sourced from various online platforms.

**2. label:**
Represents the sentiment classification assigned to each comment. It uses the following encoding:

0 — Negative sentiment
1 — Neutral sentiment
2 — Positive sentiment

#### Distribution
- **Format:**
  CSV (Comma-Separated Values)
- **2 Columns:**
text: The comment content
label: Sentiment classification (0 = Negative, 1 = Neutral, 2 = Positive)
- **File Size:**
Approximately 23.9 MB
- **Structure:**
Each row contains a single comment and its corresponding sentiment label.

#### Usage
This dataset is ideal for a variety of applications:

- **1. Sentiment Analysis Model Training:**
Train machine learning or deep learning models to classify text as positive, negative, or neutral.

- **2. Text Classification Projects:**
Use as a labeled dataset for supervised learning in text classification tasks.

- **3. Customer Feedback Analysis:**
Train models to automatically interpret user reviews, support tickets, or survey responses.

#### Coverage
- **Geographic Coverage:**
Primarily English-language content from global online platforms

- **Time Range:**
The exact time range of data collection is unspecified; however, the dataset reflects contemporary online language patterns and sentiment trends typically observed in the 2010s to early 2020s.

- **Demographics:**
Specific demographic information (e.g., age, gender, location, industry) is not included in the dataset, as the focus is purely on textual sentiment rather than user profiling.


#### License
CC0

#### Who Can Use It

- **Data Scientists:** For training machine learning models.  
- **Researchers:** For academic or scientific studies.  
- **Businesses:** For analysis, insights, or AI development.  


    

Text Classification Dataset 

The **Synthetic Eye Cancer Patient Records Dataset** is a comprehensive, anonymised synthetic dataset created for research and educational purposes. It captures demographic, clinical, genetic, and treatment-related variables to support the study of eye cancer types, progression, treatment efficacy, and survival outcomes.

## Dataset Features
- **Age:** Age of the patient at diagnosis (in years).
- **Gender:** Biological sex of the patient (Male/Female/Other).
- **Cancer_Type:** Type of eye cancer diagnosed (e.g., Lymphoma, Melanoma, Other).
- **Laterality:** Location of the cancer in the eye (Left, Right, Bilateral).
- **Date_of_Diagnosis:** Date when cancer was diagnosed.
- **Stage_at_Diagnosis:** Clinical stage at diagnosis (e.g., Stage I, Stage II, Stage III, Stage IV).
- **Treatment_Type:** Primary treatment modality (e.g., Surgery, Radiation, Chemotherapy).
- **Surgery_Status:** Whether surgery was performed (True/False).
- **Radiation_Therapy:** Duration or number of radiation therapy sessions (integer).
- **Chemotherapy:** Number of chemotherapy cycles administered (integer).
- **Outcome_Status:** Patient outcome (e.g., In Remission, Active, Deceased).
- **Survival_Time_Months:** Survival time after diagnosis (months).
- **Genetic_Markers:** Presence of genetic mutations or markers (e.g., BRAF Mutation), with some missing values.
- **Family_History:** Family history of cancer (True/False).
- **Country:** Patient's country of residence.

## Distribution
![Synthetic eye cancer data visualization.png](https://storage.googleapis.com/opendatabay_public/87c2f659-2bc7-4eb3-a35b-d7c09dae080b/48b9294cd355_cancer_dataset_visualization.png)



## Usage
This dataset can be used for:

- **Cancer Research:** Investigate relationships among demographics, clinical features, genetics, and treatment outcomes.
- **Predictive Modeling:** Build models to predict survival time, treatment response, or disease progression.
- **Healthcare Analytics:** Explore epidemiological trends by cancer type, stage, and geography.
- **Educational Use:** Provide realistic data for training in oncology data analysis and bioinformatics.

## Coverage
The data is fully anonymized and synthetically generated to ensure privacy while preserving clinically relevant variability for academic and data science applications.

## License
CC0 (Public Domain)

## Who Can Use It
- **Oncologists and Medical Researchers:** To study eye cancer patterns and outcomes.
- **Data Scientists and Bioinformaticians:** For developing predictive and survival models.
- **Healthcare Educators and Students:** As a detailed dataset for hands-on learning in oncology and medical data analysis.

Synthetic Eye Cancer Patient Records Dataset

Opendatabay Labs 

The **Synthetic Prostate Cancer Dataset** has been generated for educational and research purposes to support the analysis of morphological and textural attributes associated with prostate cancer diagnosis. This synthetic, anonymised dataset enables exploration of clinical and imaging-like features often used in medical decision-making and cancer detection.

## Dataset Features
- **ID:** Unique identifier for each patient record.
- **Diagnosis Result:** Indicates whether the tumor is malignant (M) or benign (B).
- **Radius:** Measure of the radius of the cell nuclei.
- **Texture:** Standard deviation of grayscale values, representing texture variation.
- **Perimeter:** The perimeter of the tumor region.
- **Area:** The area enclosed by the tumor perimeter.
- **Smoothness:** Local variation in radius lengths.
- **Compactness:** Perimeter² / Area - 1.0, describing shape compactness.
- **Symmetry:** Symmetry of the tumor shape.
- **Fractal Dimension:** Measure of tumor boundary complexity.

## Distribution
![Synthetic Prostate Cancer Dataset Visuals.png](https://storage.googleapis.com/opendatabay_public/9ec115f8-9091-4cdf-ac5b-c10330a114b9/c32bd07f611d_prostate_cancer_visuals.png)


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

- **Cancer Research:** Explore patterns and differences in tumour morphology related to benign and malignant cases.
- **Predictive Modelling:** Train and test machine learning algorithms for binary classification of cancer status.
- **Clinical Insight:** Analyse how combinations of geometrical and textural features correlate with diagnosis outcomes.
- **Educational Purposes:** Provide students and researchers with practical experience in medical data analysis and model development.

## Coverage
The dataset is synthetically generated and fully anonymised. It includes both numeric and categorical data types, supporting a wide range of supervised learning and exploratory analysis techniques in medical informatics and cancer detection research.

## License
CC0 (Public Domain)

## Who Can Use It
- **Medical Researchers and Oncologists:** To examine tumor characteristics and evaluate diagnostic indicators.
- **Data Scientists:** To develop and benchmark machine learning models for cancer detection.
- **Healthcare Educators and Students:** As a robust resource for learning about biomedical data processing and analysis.


Synthetic Prostate Cancer Detection Dataset for AI Training

The **Synthetic Colon Cancer Treatment Dataset** has been created for educational and research purposes to support the analysis of treatment responses, patient characteristics, and survival outcomes in colon cancer. This synthetic, anonymized dataset provides rich details on clinical interventions, pathological findings, and patient survival statuses.

## Dataset Features
- **ID:** Unique identifier for each patient.
- **Study:** Study or trial group identifier.
- **RX (Treatment):** Type of treatment received (e.g., Lev+5FU, Obs).
- **Sex:** Biological sex of the patient (M/F).
- **Age:** Age of the patient at diagnosis (in years).
- **Obstruct:** Indicates if the tumor caused bowel obstruction (Yes/No).
- **Perfor:** Indicates if the tumor caused bowel perforation (Yes/No).
- **Adhere:** Indicates tumor adhesion to surrounding structures (Yes/No).
- **Nodes:** Number of positive lymph nodes.
- **Status:** Vital status of the patient (Alive/Dead).
- **Differ (Differentiation):** Histological differentiation of the tumor (e.g., Moderate, Poor).
- **Extent:** Extent of disease spread (e.g., Distant, Regional).
- **Surg:** Surgical intervention status (e.g., Surgery, No Surgery).
- **Node4:** Binary flag indicating whether 4 or more nodes are involved (Yes/No).
- **Time:** Survival time in days.
- **Etype (Event Type):** Classification of death or survival outcome (e.g., Cancer Death, Other Death).

## Distribution
![colon cancer dataset data distribution visuals.png](https://storage.googleapis.com/opendatabay_public/2fbb27fd-5e09-4a7b-88db-f8523ea6a394/a50f9246c0ce_colon_cancer_visuals.png)



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

- **Treatment Outcome Research:** Analyze the relationship between treatment types and patient survival.
- **Predictive Modeling:** Develop survival prediction models based on clinical and pathological features.
- **Clinical Decision Support:** Evaluate which combinations of factors influence prognosis, aiding evidence-based treatment planning.
- **Educational Purposes:** Enable students and researchers to explore real-world-like oncology datasets with survival outcomes.

## Coverage
The dataset is fully anonymized and designed to replicate the complexity of real-world colon cancer cases. It is suitable for both statistical modeling and machine learning applications, particularly in survival analysis, treatment efficacy, and decision-making support tools.

## License
CC0 (Public Domain)

## Who Can Use It
- **Medical Researchers and Oncologists:** To evaluate treatment effects and prognostic indicators.
- **Data Scientists:** To build and test survival analysis models and classification algorithms.
- **Healthcare Educators and Students:** As a comprehensive dataset for studying outcomes-based oncology data.

Synthetic Colon Cancer Patient Treatment Dataset

The **Synthetic Gastrointestinal Disease Dataset** has been generated to support research, model development, and education related to gastrointestinal (GI) health. This comprehensive dataset captures a wide range of patient features, lifestyle factors, test results, symptoms, and clinical diagnoses to simulate real-world diagnostic complexity.

## Dataset Features
- **Age:** Age of the patient in years.
- **Gender:** Biological sex of the patient (M/F).
- **BMI:** Body Mass Index.
- **Body_Weight:** Patient's weight in kilograms.
- **Obesity_Status:** Categorized as Normal, Overweight, or Obese based on BMI.
- **Ethnicity:** Ethnic background (e.g., White, Hispanic, Asian, etc.).
- **Family_History:** Indicates presence of family history of GI conditions (Yes/No).
- **Genetic_Markers:** Count of relevant genetic risk markers detected.
- **Microbiome_Index:** Numerical score representing gut microbiota diversity or imbalance.
- **Autoimmune_Disorders:** Presence of autoimmune conditions (Yes/No).
- **H_Pylori_Status:** Helicobacter pylori infection status (Yes/No).
- **Fecal_Calprotectin:** Inflammatory marker measured in stool (numeric count).
- **Occult_Blood_Test:** Result of hidden blood detection in stool (Positive/Negative).
- **CRP_ESR:** Combined C-Reactive Protein / Erythrocyte Sedimentation Rate value, an inflammation marker.
- **Endoscopy_Result / Colonoscopy_Result / Stool_Culture:** Clinical test results (e.g., Normal, Abnormal).
- **Diet_Type:** Type of diet followed (e.g., Vegetarian, Western, etc.).
- **Food_Intolerance:** Reported intolerances (Yes/No).
- **Smoking_Status / Alcohol_Use / Physical_Activity:** Lifestyle habits.
- **Stress_Level:** Reported level of psychological stress (Low/Moderate/High). *Note: Some entries missing.*
- **GI Symptoms:** Includes:
  - **Abdominal_Pain, Bloating, Diarrhea, Constipation**
  - **Rectal_Bleeding, Appetite_Loss, Weight_Loss**
- **Bowel_Habits:** Overall pattern (e.g., Normal, Frequent, Irregular).
- **Bowel_Movement_Frequency:** Number of bowel movements per week.
- **Medication Use:** Includes:
  - **NSAID_Use (e.g., ibuprofen), Antibiotic_Use, PPI_Use (proton-pump inhibitors), Medications (Yes/No)**
- **Disease_Class:** Primary GI-related condition diagnosed (e.g., Blood in stool, Nausea or vomiting, Abdominal cramps or pain, Unexplained weight loss).

## Distribution
![Synthetic Gastrointestional Disease Patient Records Data Distribution.png](https://storage.googleapis.com/opendatabay_public/02185296-ec00-4159-ba19-2df70ea680f6/e72683f668b8_eda_summary_plots.png)


## Usage
This dataset is ideal for:

- **Disease Classification:** Predict GI disease categories using symptoms and clinical test results.
- **Feature Importance Analysis:** Understand contributing factors in diagnosis.
- **Pattern Mining:** Detect associations among lifestyle, symptoms, and microbiome/genetic indicators.
- **Model Training:** Useful for supervised learning (e.g., random forest, XGBoost) or unsupervised clustering.

## Coverage
The data integrates symptoms, lifestyle, inflammation markers, test outcomes, and genetics—making it valuable for both biological and behavioral models of disease. It reflects realistic distributions of obesity, diet, and ethnicity found in contemporary populations.

## License
CC0 (Public Domain)

## Who Can Use It
- **Medical Researchers and GI Specialists:** For testing diagnostic hypotheses and exploring symptom clusters.
- **Data Scientists and ML Engineers:** For building diagnostic classifiers or recommender systems.
- **Educators and Students:** For practical exercises in predictive modeling and health analytics.

Synthetic Gastrointestinal Disease Patient Records Dataset

Bright Data

FACTS AND DIMENSIONS

Forager

Datrix AI

Appnalysis

Data Bank

TV Audience Tracker

Synthetic IDD

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


Zillow Properties Listing dataset to access detailed real estate listings, including property prices, locations, and features. Popular use cases include market analysis, property valuation, and investment decision-making in the real estate sector.

Use our Zillow Properties Listing Information dataset to access detailed real estate listings, including property features, pricing trends, and location insights. This dataset is perfect for real estate agents, investors, market analysts, and property developers looking to analyze housing markets, identify investment opportunities, and assess property values.

Leverage this dataset to track pricing patterns, compare property features, and forecast market trends across different regions. Whether you're evaluating investment prospects or optimizing property listings, the Zillow Properties dataset offers essential information for making data-driven real estate decisions.

#### **Dataset Features**

- **zpid:** Unique property identifier assigned by Zillow.  
- **city:** The name of the city where the property is located.  
- **state:** The state in which the property is located.  
- **homeStatus:** Indicates the current status of the property  
- **address:** The full address of the property, including street, city, and state.  
- **isListingClaimedByCurrentSignedInUser:** This field shows if the current Zillow user has claimed ownership of the listing.  
- **isCurrentSignedInAgentResponsible:** This field indicates whether the currently signed-in real estate agent is responsible for the listing.  
- **bedrooms:** Number of bedrooms in the property.  
- **bathrooms:** Number of bathrooms in the property.  
- **price:** Current asking price of the property.  
- **yearBuilt:** The year the home was originally constructed.  
- **streetAddress:** Specific street address (usually excludes city/state/zip).  
- **zipcode:** The postal ZIP code of the property.  
- **isCurrentSignedInUserVerifiedOwner:** This field indicates if the signed-in user has verified ownership of the property on Zillow.  
- **isVerifiedClaimedByCurrentSignedInUser:** Indicates whether the user has claimed and verified the listing as the current owner.  
- **listingDataSource:** The original source of the listing. Important for data lineage and trustworthiness.  
- **longitude:** The longitudinal geographic coordinate of the property.  
- **latitude:** The latitudinal geographic coordinate of the property.  
- **hasBadGeocode:** This indicates whether the geolocation data is incorrect or problematic.  
- **streetViewMetadataUrlMediaWallLatLong:** A URL or reference to the Street View media wall based on latitude and longitude.  
- **streetViewMetadataUrlMediaWallAddress:** A similar URL reference to the Street View, but based on the property’s address.  
- **streetViewServiceUrl:** The base URL to Google Street View or similar services. Enables interactive visuals of the property’s surroundings.  
- **livingArea:** Total internal living area of the home, typically in square feet.  
- **homeType:** The category/type of the home.  
- **lotSize:** The size of the entire lot or land the home is situated on.  
- **lotAreaValue:** The numerical value representing the lot area is usually tied to a measurement unit.  
- **lotAreaUnits:** Units in which the lot area is measured (e.g., sqft, acres).  
- **livingAreaValue:** The numeric value of the property's interior living space.  
- **livingAreaUnitsShort:** Abbreviated unit for living area (e.g., sqft), useful for compact displays.  
- **isUndisclosedAddress:** Boolean indicating if the full property address is hidden, typically used for privacy reasons.  
- **zestimate:** Zillow’s estimated market value of the home, generated via its proprietary model.  
- **rentZestimate:** Zillow’s estimated rental price per month, is helpful for rental market analysis.  
- **currency:** Currency used for price, Zestimate, and rent estimate (e.g., USD).  
- **hideZestimate:** Indicates whether the Zestimate is hidden from public view.  
- **dateSoldString:** The date when the property was last sold, in string format (e.g., 2022-06-15).  
- **taxAssessedValue:** The most recent assessed value of the property for tax purposes.  
- **taxAssessedYear:** The year in which the property was last assessed.  
- **country:** The country where the property is located.  
- **propertyTaxRate:** The most recent tax rate.  
- **photocount:** This column provides a photo count of the property.  
- **isPremierBuilder:** Boolean indicating whether the builder is listed as a premier (trusted) builder on Zillow.  
- **isZillowOwned:** Indicates whether the property is owned or managed directly by Zillow.  
- **ssid:** A unique internal Zillow identifier for the listing (not to be confused with network SSID).  
- **hdpUrl:** URL to the home’s detail page on Zillow (Home Details Page).  
- **tourViewCount:** Number of times users have viewed the property tour.  
- **hasPublicVideo:** This column indicates if there’s a public-facing video available for the listing.  
- **lastSoldPrice:** The price at which the home was most recently sold.  
- **livingAreaUnits:** Full-text description of living area units (e.g., square feet).  
- **hasApprovedThirdPartyVirtualTourUrl:** Boolean indicating if there’s a verified virtual tour from a third-party source.  
- **streetViewTileImageUrlMediumLatLong:** A URL to the Street View tile image of the location, based on latitude and longitude.  
- **isNonOwnerOccupied:** This indicates whether the property is not owner-occupied (e.g., rented out).  
- **zestimateMinus30:** Zestimate minus 30%, used to show a lower-bound pricing estimate for analysis or negotiations.  
- **restimateMinus30:** Rent Zestimate minus 30%, used for conservative rent estimates.  
- **zestimateLowPercent:** The lower bound percentage of the Zestimate confidence interval. Useful for understanding pricing uncertainty.  
- **zestimateHighPercent:** The upper bound percentage of the Zestimate confidence interval.  
- **restimateLowPercent:** The lower bound percentage estimate of the Rent Zestimate.  
- **restimateHighPercent:** The upper bound percentage estimate of the Rent Zestimate.  
- **description:** Text description of the property, typically written by the listing agent or owner. Includes features, amenities, and upgrades.  
- **parcelId:** Unique identifier for the property parcel, usually linked to public records or tax data.  
- **taxHistory:** A record of previous property taxes paid over different years.  
- **priceHistory:** A record of previous listing prices or sale prices, along with corresponding dates.  
- **nearbyHomes:** Data or links related to nearby homes, useful for comparisons or neighborhood exploration.  
- **schools:** List of nearby schools along with ratings, distances, and types (elementary, middle, high school).  
- **totalCount:** Total number of records retrieved in the current dataset response—often used for pagination.  
- **mortgageRates:** Interest rates applicable at the time, possibly including fixed and variable mortgage options.  
- **isInstantOfferEnabled:** Boolean flag showing whether the home qualifies for Zillow’s Instant Offer program.  
- **zillowOfferMarket:** Indicates whether Zillow is actively buying/selling in the market area for the property.  
- **isRentalListingOffMarket:** This indicates whether a rental listing has been taken off the market.  
- **nearbyCities:** List of neighboring cities or towns, useful for geographical or real estate market context.  
- **nearbyNeighborhoods:** Neighborhoods close to the listed property, useful for comparative market analysis.  
- **nearbyZipcodes:** Other zip codes geographically close to the current property’s location.  
- **abbreviatedAddress:** A shortened version of the address, typically for display in map cards or search results.  
- **daysOnZillow:** Total number of days the property listing has been active on Zillow.  
- **rentalApplicationsAcceptedType:** Indicates the type of rental applications accepted (e.g., online, in-person, third-party platforms).  
- **brokerageName:** Name of the real estate brokerage representing the property.  
- **propertyTypeDimension:** Describes the type of property, such as Single Family, Condo, Apartment, etc.  
- **hdpTypeDimension:** Home Detail Page property classification used by Zillow for frontend display logic.  
- **timeZone:** Time zone of the property's location, which can affect scheduling tours or viewing times.  
- **tourEligibility:** Flags that determine if and how a user can tour the property.  
- **virtualTourUrl:** Link to a virtual tour of the property, usually in video or 360° walkthrough format.  
- **selfTour:** Indicates if the property is eligible for a self-guided tour without an agent.  
- **photos:** Collection of URLs or metadata for images associated with the listing.  
- **resoFacts:sewer:** Type of sewer system servicing the property (e.g., Public Sewer, Septic Tank).  
- **resoFacts:waterSource:** Type of water supply source (e.g., Municipal, Well).  
- **utilities:** Details on utilities available or connected to the property (electricity, gas, water, etc.).  
- **url:** Direct Zillow URL for the listing detail page.  
- **countyFIPS:** Federal Information Processing Standard (FIPS) code identifying the county.  
- **countyID:** Zillow’s internal identifier for the county.  
- **dateSold:** The most recent date the property was sold.  
- **isFeatured:** This indicates if the listing is currently featured on Zillow.  
- **isHousingConnector:** Indicates whether the property is listed through the Housing Connector program (for affordable housing).  
- **isRentalsLeadCapMet:** Shows whether the rental lead cap has been met for the listing, limiting further inquiries.  
- **listingTypeDimension:** Classification of the listing type.  
- **postingContact:** Contact details for the listing agent or landlord.  
- **homeValuation:** Estimated market value of the home, may include confidence ranges or trends.  
- **isOffMarket:** Boolean flag indicating whether the home is no longer available on the market.  
- **resofacts_sewer:** Duplicate or alternative reference to the sewer data in RESO (Real Estate Standards Organization) format.  
- **resofacts_water_source:** Duplicate or alternative reference to the water source data in RESO format.  
- **citySearchUrl:** Zillow search URL prefiltered by the city of the listing.  
- **county:** Name of the county in which the property is located.  
- **is_showcased:** Indicates if the property is being specially highlighted or advertised.  
- **interior:** General description of interior features such as flooring, lighting, appliances, etc.  
- **overview:** Short summary of the property’s major highlights and selling points.  
- **is_listed_by_management_company:** This indicates if the rental listing is managed by a third-party company.  
- **management_company_phone_number:** Contact the phone number for the management company, if applicable.  
- **listing_provided_by:** Information on who submitted the listing (agent, owner, builder, etc.).  
- **hoa_details:** Information about any homeowner’s association (fees, policies, amenities).  
- **financial:** Financial information about the property, such as price breakdowns, taxes, and estimated mortgage.  
- **interior_full:** Detailed list of interior features (e.g., number of fireplaces, flooring types, appliance details, etc.).  
- **property:** General property details like zoning, lot characteristics, and architectural style.  
- **construction:** Information on construction materials, year built, and building techniques (e.g., wood frame, brick). 
- **tag:** Descriptive tags Zillow uses for filtering or highlighting listings (e.g., “New Listing”, “Price Reduced”).  
- **contingent_listing_type:** Indicates if the listing is under a contingent status (e.g., contingent on inspection, loan approval).  
- **unit_number:** Apartment or unit number if the property is part of a multi-unit building.  
- **unit_amenities:** Features within the unit itself (e.g., in-unit laundry, balcony, air conditioning).  
- **open_house_details:** Scheduled dates and times for open house events.  
- **community_details:** Information about the neighbourhood or development community (e.g., amenities, HOA features, age restrictions).  
- **mls_id:** Unique identifier assigned by the MLS (Multiple Listing Service).  
- **base_rent:** The base rental price, excluding utilities, fees, or additional charges.  
- **sqft:** Total square footage of the property.  
- **availability_date:** Date from which the property will be available for move-in or purchase.  
- **special_offer:** Promotional offers tied to the listing (e.g., “First Month Free”, “Reduced Deposit”).  
- **originating_mls:** The MLS platform from which the listing originated.  
- **tags:** Keywords or phrases used for categorization or search optimization.  
- **getting_around_scores:** Accessibility scores like Walk Score, Transit Score, and Bike Score.  
- **days_on_zillow:** Number of days the listing has been on Zillow.  
- **getting_around:** Narrative description or metrics about walkability, public transport, commute options, etc.  
- **other:** Miscellaneous or unstructured information not categorized in other fields.  

#### **Distribution**

- **Data Volume:** 128 Columns and 134.86M Rows  
- **Format:** CSV  

#### **Usage**
This dataset is ideal for:
 - **Real Estate Market Analysis:** Analyze property trends, pricing patterns, or hot markets.
 - **Machine Learning Projects:** Build regression models to predict house prices or classify home types.
 - **Business Intelligence Dashboards:** Create interactive dashboards in Power BI or Tableau.
 - **Geospatial Visualization:** Plot homes on maps to explore spatial pricing patterns.
 - **Property Investment Research:** Identify undervalued areas or rental investment opportunities.

#### **Coverage**

- **Geographic Coverage:** Primarily U.S.-based  
- **Time Range:** Includes historical sales data, though specific start/end date not defined  

#### **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 modeling, feature engineering, and price prediction tasks.  
- **Researchers:** For academic studies on urbanization, housing economics, or real estate investment.  
- **Businesses & Startups:** For developing real estate tools, valuation models, or customer-targeting platforms.  
- **Students:** For hands-on practice with a real-world, high-dimensional dataset.

---

**Dataset Name Ideas**

1. **Zillow Real Estate Listings Dataset**  
2. **US Property Insights – Zillow Data**  
3. **Zillow Housing Market Dataset**  
4. **Zillow Property & Listing Metadata**  
5. **Zillow Home Intelligence Dataset**  
6. **Zillow Real Estate & Rental Dataset**  

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

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

Approximately 15.1M 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


Zillow Properties Listing Information Dataset


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!

----
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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 20,841 unique identities from around the world.


**Distribution**

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

-Total Size: 2.7TB

-Total Videos: 47,547

-Identities Covered: 20,841

-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

Romanian: 1144 videos

Arabic: 1691 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

**Updates**
Added Arabic and Romanian languages 28.04.2025

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 dataset

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:
![DSynthetic Healthcare Patient Records Data 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 Patient Records 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.

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