Social media platforms have become integral tools in the conduct of foreign policy for many nations, including India. This dataset serves as a resource for analyzing ‘Social Media and India’s Foreign Policy: The Case Study of ‘X’ Diplomacy during the Covid-19 Pandemic.’ The data were collected through a web-based questionnaire distributed primarily to people aged 18 – 61 and above in India. A total of 171 valid data were collected from 17 states offering extensive geographic coverage and stored in Mendeley. The 15 contributor states are Goa, Maharashtra, Tamil Nadu, Gujarat, Delhi, Assam, Haryana, Jammu and Kashmir, Karnataka, Kerala, Punjab, Rajasthan, Tripura, Uttar Pradesh and West Bengal. It encompasses diverse question formats, including single-choice, multiple-choice, quizzes, and open-ended. The study underscores the opportunities and challenges of employing 'X' diplomacy in India's foreign policy. Thus, there were two hypotheses. First, India's effective use of 'X' diplomacy positively impacts public perception of India's foreign policy effectiveness. Second, India's adept use of 'X' diplomacy during the COVID-19 pandemic enhances its ability to manage and respond to the crisis effectively. This data shows public perception of the effective use of social media by the Government of India, particularly in the crisis situation. Data also highlight the significant change in India’s narrative through its ‘X’ diplomacy, effectively setting the narratives, public perceptions, and diplomatic strategies. This data can be fully utilized in the study of the significance of social media in India’s foreign policy, the role of social media like ‘X’ in the making of India’s foreign policy, how effective social media like ‘X’ was during the Covid-19 pandemic and how Indian government utilized social media like ‘X’ to delivered messages and to set the narrative in the international politics.

Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey dataThe National Family Health Survey (NFHS), India data is publicly available data set and can be accessed on request. It can be downloaded upon registration from the Demographic and Health Survey (DHS) website upon registration at The DHS Program - Request Access To Datasets. We have used data from the fourth and fifth round of NFHS, which can be accessed after registration from the link given here for NFHS 4 and NFHS 5 https://dhsprogram.com/data/dataset/India_Standard-DHS_2015.cfm?flag=0 and here https://dhsprogram.com/data/dataset/India_Standard-DHS_2020.cfm?flag=0 respectively. These datasets (HR file) have been used to obtain this combined dataset of a paper entitled "Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data" submitted to BMJ Global Health August 2023.

About this dataset

Context

The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Indian Trail: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 184(1.41%) households where the householder is under 25 years old, 4,951(37.86%) households with a householder aged between 25 and 44 years, 5,924(45.30%) households with a householder aged between 45 and 64 years, and 2,019(15.44%) households where the householder is over 65 years old.
• The age group of 25 to 44 years exhibits the highest median household income, while the largest number of households falls within the 45 to 64 years bracket. This distribution hints at economic disparities within the town of Indian Trail, showcasing varying income levels among different age demographics.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Income brackets:

• Less than $10,000
• $10,000 to $14,999
• $15,000 to $19,999
• $20,000 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $59,999
• $60,000 to $74,999
• $75,000 to $99,999
• $100,000 to $124,999
• $125,000 to $149,999
• $150,000 to $199,999
• $200,000 or more

Variables / Data Columns

• Household Income: This column showcases 16 income brackets ranging from Under $10,000 to $200,000+ ( As mentioned above).
• Under 25 years: The count of households led by a head of household under 25 years old with income within a specified income bracket.
• 25 to 44 years: The count of households led by a head of household 25 to 44 years old with income within a specified income bracket.
• 45 to 64 years: The count of households led by a head of household 45 to 64 years old with income within a specified income bracket.
• 65 years and over: The count of households led by a head of household 65 years and over old with income within a specified income bracket.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Indian Trail median household income by age. You can refer the same here

This dataset contains 10,000 simulated sales transaction records, each represented in natural language with diverse sentence structures. It is designed to mimic how different users might describe the same type of transaction in varying ways, making it ideal for Natural Language Processing (NLP) tasks, text-based data extraction, and accounting automation projects.

Each record in the dataset includes the following fields:

Sale Date: The date on which the transaction took place. Customer Name: A randomly generated customer name. Product: The type of product purchased. Quantity: The quantity of the product purchased. Unit Price: The price per unit of the product. Total Amount: The total price for the purchased products. Tax Rate: The percentage of tax applied to the transaction. Payment Method: The method by which the payment was made (e.g., Credit Card, Debit Card, UPI, etc.). Sentence: A natural language description of the sales transaction. The sentence structure is varied to simulate different ways people describe the same type of sales event.

Use Cases: NLP Training: This dataset is suitable for training models to extract structured information (e.g., date, customer, amount) from natural language descriptions of sales transactions. Accounting Automation: The dataset can be used to build or test systems that automate posting of sales transactions based on unstructured text input. Text Data Preprocessing: It provides a good resource for developing methods to preprocess and standardize varying formats of text descriptions. Chatbot Training: This dataset can help train chatbots or virtual assistants that handle accounting or customer inquiries by understanding different ways of expressing the same transaction details.

Key Features: High Variability: Sentences are structured in numerous ways to simulate natural human language variations. Randomized Data: Names, dates, products, quantities, prices, and payment methods are randomized, ensuring no duplication. Multi-Field Information: Each record contains key sales information essential for accounting and business use cases.

Potential Applications: Use for Named Entity Recognition (NER) tasks. Apply for information extraction challenges. Create pattern recognition models to understand different sentence structures. Test rule-based systems or machine learning models for sales data entry and accounting automation.

License: Ensure that the dataset is appropriately licensed according to your intended use. For general public and research purposes, choose a CC0: Public Domain license, unless specific restrictions apply.

About this dataset

Context

The dataset tabulates the median household income in Indian Village. It can be utilized to understand the trend in median household income and to analyze the income distribution in Indian Village by household type, size, and across various income brackets.

Content

The dataset will have the following datasets when applicable

Please note: The 2020 1-Year ACS estimates data was not reported by the Census Bureau due to the impact on survey collection and analysis caused by COVID-19. Consequently, median household income data for 2020 is unavailable for large cities (population 65,000 and above).

• Indian Village, IN Median Household Income Trends (2010-2021, in 2022 inflation-adjusted dollars)
• Median Household Income Variation by Family Size in Indian Village, IN: Comparative analysis across 7 household sizes
• Income Distribution by Quintile: Mean Household Income in Indian Village, IN
• Indian Village, IN households by income brackets: family, non-family, and total, in 2022 inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Interested in deeper insights and visual analysis?

Explore our comprehensive data analysis and visual representations for a deeper understanding of Indian Village median household income. You can refer the same here

Life expectancy is an estimate of how long a person would live, on average.

Life expectancy is affected by many factors such as: • Socioeconomic status, including employment, income, education and economic wellbeing. • The quality of the health system and the ability of people to access it; health behaviors such as tobacco and excessive alcohol consumption, poor nutrition and lack of exercise. • Social factors; genetic factors; and environmental factors including overcrowded housing, lack of clean drinking water and adequate sanitation, etc.

With the help of the above-mentioned factors, I tried to analyse t the data and come up with measurable solutions to improve the Life Expectancy.

SUMMARY:

Vumonic provides its clients email receipt datasets on weekly, monthly, or quarterly subscriptions, for any online consumer vertical. We gain consent-based access to our users' email inboxes through our own proprietary apps, from which we gather and extract all the email receipts and put them into a structured format for consumption of our clients. We currently have over 1M users in our India panel.

If you are not familiar with email receipt data, it provides item and user-level transaction information (all PII-wiped), which allows for deep granular analysis of things like marketshare, growth, competitive intelligence, and more.

VERTICALS:

• Ecommerce (Amazon, Flipkart, Myntra, Nykaa)
• Taxi (Uber, Ola)
• Food Delivery (Swiggy, Zomato)
• OTT (Netflix, Amazon Prime Video, Disney+)
• Appstore (Apple App Store and Google Playstore)
• OTA (Expedia, Booking.com, GoIbibo)
• E-wallets (PhonePe, PayTM)
• Education (Byju's, Unacademy)

PRICING/QUOTE:

Our email receipt data is priced market-rate based on the requirement. To give a quote, all we need to know is:

• what vertical you are interested in
• how often do you wish to receive the data, and
• do you want any backdata (e.g. from 2019 onwards)

Send us over this info and we can answer any questions you have, provide sample, and more.

https://store.poidata.xyz/in Point-of-interest (POI) is defined as a physical entity (such as a business) in a geo location (point) which may be (of interest).

We strive to provide the most accurate, complete and up to date point of interest datasets for all countries of the world. The India POI Dataset is one of our worldwide POI datasets.

This is our process flow:

Our machine learning systems continuously crawl for new POI data
Our geoparsing and geocoding calculates their geo locations
Our categorization systems cleanup and standardize the datasets
Our data pipeline API publishes the datasets on our data store

POI Data is in a constant flux - especially so during times of drastic change such as the Covid-19 pandemic.

Every minute worldwide on an average day over 200 businesses will move, over 600 new businesses will open their doors and over 400 businesses will cease to exist.

In today's interconnected world, of the approximately 200 million POIs worldwide, over 94% have a public online presence. As a new POI comes into existence its information will appear very quickly in location based social networks (LBSNs), other social media, pictures, websites, blogs, press releases. Soon after that, our state-of-the-art POI Information retrieval system will pick it up.

We offer our customers perpetual data licenses for any dataset representing this ever changing information, downloaded at any given point in time. This makes our company's licensing model unique in the current Data as a Service - DaaS Industry. Our customers don't have to delete our data after the expiration of a certain "Term", regardless of whether the data was purchased as a one time snapshot, or via a recurring payment plan on our data update pipeline.

The main differentiators between us vs the competition are our flexible licensing terms and our data freshness.

The core attribute coverage for India is as follows: Poi Field Data Coverage (%) poi_name 100 brand 3 poi_tel 17 formatted_address 100 main_category 100 latitude 100 longitude 100 neighborhood 7 source_url 24 email 2 opening_hours 26

The dataset may be viewed online at https://store.poidata.xyz/in and a data sample may be downloaded at https://store.poidata.xyz/datafiles/in_sample.csv

Abstract

Well-functioning financial systems serve a vital purpose, offering savings, credit, payment, and risk management products to people with a wide range of needs. Yet until now little had been known about the global reach of the financial sector - the extent of financial inclusion and the degree to which such groups as the poor, women, and youth are excluded from formal financial systems. Systematic indicators of the use of different financial services had been lacking for most economies.

The Global Financial Inclusion (Global Findex) database provides such indicators. This database contains the first round of Global Findex indicators, measuring how adults in more than 140 economies save, borrow, make payments, and manage risk. The data set can be used to track the effects of financial inclusion policies globally and develop a deeper and more nuanced understanding of how people around the world manage their day-to-day finances. By making it possible to identify segments of the population excluded from the formal financial sector, the data can help policy makers prioritize reforms and design new policies.

Geographic coverage

The sample excludes the Northeast states and remote islands. The excluded area represents approximately 10% of the total adult population.

Analysis unit

Individual

Universe

The target population is the civilian, non-institutionalized population 15 years and above.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Global Findex indicators are drawn from survey data collected by Gallup, Inc. over the 2011 calendar year, covering more than 150,000 adults in 148 economies and representing about 97 percent of the world's population. Since 2005, Gallup has surveyed adults annually around the world, using a uniform methodology and randomly selected, nationally representative samples. The second round of Global Findex indicators was collected in 2014 and is forthcoming in 2015. The set of indicators will be collected again in 2017.

Surveys were conducted face-to-face in economies where landline telephone penetration is less than 80 percent, or where face-to-face interviewing is customary. The first stage of sampling is the identification of primary sampling units, consisting of clusters of households. The primary sampling units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households by means of the Kish grid.

Surveys were conducted by telephone in economies where landline telephone penetration is over 80 percent. The telephone surveys were conducted using random digit dialing or a nationally representative list of phone numbers. In selected countries where cell phone penetration is high, a dual sampling frame is used. Random respondent selection is achieved by using either the latest birthday or Kish grid method. At least three attempts are made to teach a person in each household, spread over different days and times of year.

The sample size in India was 3,518 individuals.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was designed by the World Bank, in conjunction with a Technical Advisory Board composed of leading academics, practitioners, and policy makers in the field of financial inclusion. The Bill and Melinda Gates Foundation and Gallup, Inc. also provided valuable input. The questionnaire was piloted in over 20 countries using focus groups, cognitive interviews, and field testing. The questionnaire is available in 142 languages upon request.

Questions on insurance, mobile payments, and loan purposes were asked only in developing economies. The indicators on awareness and use of microfinance insitutions (MFIs) are not included in the public dataset. However, adults who report saving at an MFI are considered to have an account; this is reflected in the composite account indicator.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country- and indicator-specific standard errors, refer to the Annex and Country Table in Demirguc-Kunt, Asli and L. Klapper. 2012. "Measuring Financial Inclusion: The Global Findex." Policy Research Working Paper 6025, World Bank, Washington, D.C.

Context

Government of India(GoI) does Census of entire country every ten years, last census was done in 2011 and next will be done in 2021. Purpose of census is to get good understanding of the country population and other associated things, these data helps GoI to create and enhance the the policy and new reforms.

Content

The attached CSV file has data related to Literacy in India according to India Census 2011. - First Column has simple serial number - Second column has the District name - Third column has State name corresponding to the district from second column. - Last column has the Literacy data corresponding to the district from second column.

Acknowledgements

All thanks to GoI and volunteers who help in collecting dataset.

Inspiration

This can be used to get insight about the education, as well as it can used along with other datasets as per need.

The dataset was created as part of an ESRC-sponsored study, ‘British economic, social, and cultural interactions with Asia, 1760-1833’. It contains statistics relating to the trade and domestic finances of the monopolistic English East India Company primarily between 1755 and 1834, the year in which the Company ceased to function as a commercial organization. Until now quantitative data derived from original sources has only been available in time series for the Company’s trade and some aspects of its domestic finances for the years before 1760. But many of the details, patterns, and trends of trade and finance in the decades after 1760, a most important period when the Company fully embarked on the interlinked processes of military, political, and commercial expansion in Asia, have remained unclear. In creating this dataset, the aim was thus two-fold: i) to establish for the first time a set of statistics detailing the changing value, volume, and geographical structure of the East India Company’s overseas trade for the period when the Company began to exert imperial control over large parts of the Indian subcontinent; and ii) to generate select statistics relating to the Company’s domestic finances, thereby enabling analysis to be undertaken of a range of Company interactions with Britain’s economy and society.

Vehicle Information Vehicle Type: This column represents the type of vehicle. Possible values include:

Car: A standard passenger vehicle. Truck: A larger vehicle used for transporting goods. Bus: A vehicle designed to carry multiple passengers. Motorcycle: A two-wheeled motor vehicle. Fuel Type: This column indicates the type of fuel the vehicle uses. Possible values are:

Petrol: Also known as gasoline, a common fuel for internal combustion engines. Diesel: A type of fuel used in diesel engines, often found in larger vehicles like trucks and buses. Electric: Vehicles powered by electric batteries. Hybrid: Vehicles that use a combination of an internal combustion engine and electric propulsion. Engine Size: The size of the vehicle's engine, measured in liters. Larger engines typically produce more power and can lead to higher emissions.

Age of Vehicle: The age of the vehicle in years. Older vehicles may have higher emissions due to wear and tear or outdated technology.

Mileage: The total distance the vehicle has traveled, measured in kilometers or miles. Higher mileage can indicate more wear and potentially higher emissions.

Driving Conditions Speed: The average speed of the vehicle during the measurement period, measured in kilometers per hour (km/h) or miles per hour (mph). Vehicle emissions can vary with speed.

Acceleration: The rate at which the vehicle's speed increases, measured in meters per second squared (m/s²). Rapid acceleration can lead to higher emissions.

Road Type: The type of road the vehicle is driving on. Possible values include:

Highway: Major roads designed for fast travel. City: Urban roads with frequent stops and lower speeds. Rural: Country roads that may have varying conditions. Traffic Conditions: The level of traffic during the measurement period. Possible values include:

Free flow: Minimal traffic, allowing for smooth travel. Moderate: Some traffic, but generally steady movement. Heavy: High traffic, often leading to stop-and-go conditions. Environmental Conditions Temperature: The ambient temperature during the measurement period, measured in degrees Celsius (°C) or Fahrenheit (°F). Temperature can affect engine performance and emissions.

Humidity: The relative humidity of the air during the measurement period, measured as a percentage. Humidity can affect the combustion process and emissions.

Wind Speed: The speed of the wind during the measurement period, measured in meters per second (m/s) or kilometers per hour (km/h). Wind can influence the dispersion of emissions.

Air Pressure: The atmospheric pressure during the measurement period, measured in hectopascals (hPa). Air pressure can affect engine efficiency and emissions.

Emission Data CO2 Emissions: The amount of carbon dioxide emitted by the vehicle, measured in grams per kilometer (g/km). CO2 is a major greenhouse gas contributing to climate change.

NOx Emissions: The amount of nitrogen oxides emitted by the vehicle, measured in grams per kilometer (g/km). NOx contributes to air pollution and can cause respiratory problems.

PM2.5 Emissions: The amount of particulate matter with a diameter of 2.5 micrometers or smaller emitted by the vehicle, measured in grams per kilometer (g/km). PM2.5 can penetrate deep into the lungs and cause health issues.

VOC Emissions: The amount of volatile organic compounds emitted by the vehicle, measured in grams per kilometer (g/km). VOCs contribute to the formation of ground-level ozone and smog.

SO2 Emissions: The amount of sulfur dioxide emitted by the vehicle, measured in grams per kilometer (g/km). SO2 can contribute to acid rain and respiratory problems.

Target Variable Emission Level: This column categorizes the overall emission level of the vehicle into three classes: Low: Vehicles with low emissions. Medium: Vehicles with moderate emissions. High: Vehicles with high emissions. Summary Categorical Features: Vehicle Type, Fuel Type, Road Type, Traffic Conditions, Emission Level. Continuous Numerical Features: Engine Size, Age of Vehicle, Mileage, Speed, Acceleration, Temperature, Humidity, Wind Speed, Air Pressure, CO2 Emissions, NOx Emissions, PM2.5 Emissions, VOC Emissions, SO2 Emissions.

India Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 9.800 % in 2021. This records a decrease from the previous number of 10.000 % for 2020. India Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 6.200 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 10.300 % in 2019 and a record low of 5.100 % in 2004. India Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Labor Force Participation Rate in India remained unchanged at 50.40 percent in the fourth quarter of 2024 from 50.40 percent in the third quarter of 2024. This dataset provides - India Labor Force Participation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Description:

👉 Download the dataset here

We are pleased to present a unique and valuable dataset collect from ancient stone inscriptions locate at the historic Venkatesa Perumal Temple in Kanchipuram, Tamil Nadu, India. The temple, which dates back over a millennium, is adorned with stone inscriptions that are equally ancient, offering insight into the language, culture, and history of the Tamil people. This dataset is a critical resource for those interest in studying Tamil epigraphy, paleography, and the preservation of heritage through 3D reconstruction techniques.

For each inscription, we have meticulously capture 15 to 25 digital images from multiple angles using high-resolution cameras. These images are utilize to reconstruct 3D models of the inscriptions, enabling a detail and comprehensive analysis. Additionally, we employed LiDAR technology to create high-accuracy 3D models, preserving these invaluable inscriptions in digital form for future generations.

Download Dataset

Inscribed Inscriptions: These are inscriptions that remain intact and are carved deeply into the stone. They are relatively well-preserve and legible, providing clear information.

Eroded Inscriptions: Over time, weathering has eroded parts of these inscriptions. While some characters may still be discernible, much of the original text has fade.

Projected Inscriptions: These inscriptions are carved in such a way that the text projects outward from the stone surface. The unique style often makes them easier to read but also more susceptible to damage.

Rural Paleographic Inscriptions: These inscriptions represent a specific style of writing used in rural areas. They often reflect the simpler, everyday language and script of the common people of the time.

Urban Paleographic Inscriptions: In contrast, urban paleographic inscriptions were typically create in more sophisticate scripts. These were often commissioned by rulers or temple authorities and represent higher levels of literacy and formal language use.

3D Model Construction

The images capture for each inscription are use to construct 3D models of the stones, providing a digital preservation of these historical records. The number of input images plays a significant role in the clarity and detail of the final model. Our experiments show that with a minimum of 10 input images, a clear 3D model can be constructed. However, when 15-25 images are use, the resulting models are much sharper, offering greater detail and accuracy for further study and analysis.

The inclusion of LiDAR technology further enhances the accuracy of the 3D models by capturing intricate details in the inscriptions’ surfaces. This technology is especially useful for eroded or projected inscriptions, where traditional photography may struggle to capture the full depth and nuance of the text.

List of Inscriptions

Inscription 1 – Eroded Inscriptions: Despite the wear and tear, some segments of the text are still visible, offering fragments of the original narrative.

Inscription 2 – Urban Paleographic Inscriptions: Displaying the formal urban writing style, these inscriptions are a testament to the sophistication of the era’s linguistic and cultural practices.

Inscription 3 – Projected Inscriptions: These inscriptions protrude from the surface of the stone, creating a unique visual experience and allowing easier identification of certain characters.

Inscription 4 – Inscribed Inscriptions: These deeply carved inscriptions remain remarkably preserved, providing a clearer representation of the historical texts.

Inscription 5 – Rural Paleographic Inscriptions: These inscriptions offer valuable insight into the language and script of rural Tamil Nadu, reflecting a simpler style.

Inscription 6 – Eroded Inscriptions: As with Inscription 1, this inscription has suffered from the ravages of time, but still offers partial data for epigraphic studies.

This dataset is sourced from Kaggle.

Covid 19 emerged as a threat to India. But Endurance to fight against it helped.

Indians suffering and overcoming it . Inspired most to me to make this dataset.

How India overcome this Pandemic. This dataset will show.

the dataset has 44 columns and approx. 2 years of data. till Dec 15, 2021

This heart disease dataset is acquired from one o f the multispecialty hospitals in India. Over 14 common features which makes it one of the heart disease dataset available so far for research purposes. This dataset consists of 1000 subjects with 12 features. This dataset will be useful for building a early-stage heart disease detection as well as to generate predictive machine learning models.

Content:

This dataset contains information about total number of human trafficking cases reported per State/Union Territories in India, number of victims trafficked/rescued, nationality of the victims, age-group, purpose of trafficking, police and court disposal of cases, and number of culprits arrested/acquitted.

To know more about the Indian states and Union Territories, you may refer Know India

Till 2019, India had 29 states and 7 Union Territories. But in 2020, there were changes in the demographics and now, there are 28 states and 8 union territories.

2018 - (29 states and 7 Union Territories)

• Rate of Cognizable Crimes- this column refers to cases reported per 1 lakh population.
• Due to non-receipt of data from Assam & Jharkhand for 2018, data furnished for 2017 has been used.
  ###### 2019 - (29 states and 7 Union Territories)
• Due to non-receipt of data from West Bengal in time for 2019, data furnished for 2018 has been used.
  ###### 2020 - (28 states and 8 Union territories).
• The earlier two UTs of D & N Haveli and Daman & Diu were combined into 1.
• State of Jammu and Kashmir was changed to Union Territories of:
  1. Jammu & Kashmir
  2. Ladhak
• The number of cases reported in 2018, 2019 for Jammu & Kashmir includes data for Ladhak too.

Here is a short description about few terms present in the dataset. For further reading, you may refer this site.

1. Charge Sheet- is the complaint of a private individual on which criminal proceedings are initiated. When the charge sheet is sent by police to Magistrate, the preliminary stage of investigation and preparation is over.
2. Final report- The charge sheet is followed by the Final Report. It records the conclusion arrived at by the Police after the investigation process.

So, if Final Report column contains 0, it implies that the investigation is not yet complete.

Acknowledgement

The data has been taken from the National Crime Records Bureau portal of India.

Inspiration

I recently watched some movies/documentaries on Human Trafficking which prompted me to compile this dataset.

Abstract

Vadu Rural Health Program, KEM Hospital Research Centre Pune has a rich tradition in health care and development being in the forefront of needs-based, issue-driven research over almost 35 years. During the decades of 1980 and 1990 the research at Vadu focused on mother and child with epidemiological and social science research exploring low birth weight, child survival, maternal mortality, safe abortion and domestic violence. The research portfolio has ever since expanded to include adult health and aging, non-communicable and communicable diseases and to clinical trials in recent years. It started with establishment of Health and Demographic Surveillance System at Vadu (HDSS Vadu) in August, 2002 that seeks to establish a quasi-experimental design setting to allow evaluation of impact of health interventions as well as monitor secular trends in diseases, risk factors and health behavior of humans.

The term "demographic surveillance" means to keep close track of the population dynamics. Vadu HDSS deals with keeping track of health issues and demographic changes in Vadu rural health program (VRHP) area. It is one of the most promising projects of national relevance that aims at establishing a quasi-experimental intervention research setting with the following objectives: 1) To create a longitudinal data base for efficient service delivery, future research, and linking all past micro-studies in Vadu area 2) Monitoring trends in public health problems 3) Keeping track of population dynamics 4) Evaluating intervention services

This dataset contains the events of all individuals ever resident during the study period (1 Jan. 2009 to 31 Dec. 2015).

Geographic coverage

Vadu HDSS falls in two administrative blocks: (1) Shirur and (2) Haweli of Pune district in Maharashtra in western India. It covers an area of approximately 232 square kilometers.

Analysis unit

Individual

Universe

Vadu HDSS covers as many as 50,000 households having 140,000 population spread across 22 villages.

Kind of data

Event history data

Frequency of data collection

Two rounds per year

Sampling procedure

Vadu area including 22 villages in two administrative blocks is the study area. This area was selected as this is primarily coverage area of Vadu Rural Health Program which is in function since more than four decade. Every individual household is included in HDSS. There is no sampling strategy employed as 100% population coverage in the area is expected.

Mode of data collection

Proxy Respondent [proxy]

Research instrument

Language of communication is in Marath or Hindi. The form labels are multilingual - in English and Marathi, but the data entered through the forms are in English only.

The following forms were used: - Field Worker Checklist Form - The checklist provides a guideline to ensure that all the households are covered during the round and the events occurred in each household are captured. - Enumeration Form: To capture the population details at the start of the HDSS or any addition of villages afterwards. - Pregnancy Form: To capture pregnancy details of women in the age group 15 to 49. - Birth Form: To capture the details of the birth events.
- Inmigration Form: To capture inward population movement from outside the HDSS area and also for movement within the HDSS area. - Outmigration Form: To capture outward population movement from inside the HDSS area and also for movement within the HDSS area. - Death Form: To capture death events.

Cleaning operations

Entered data undergo a data cleaning process. During the cleaning process all error data are either corrected in consultaiton with the data QC team or the respective forms are sent back to the field for re collection of correct data. Data editors have the access to the raw dataset for making necessary editing after corrected data are bought from the field.

For all individuals whose enumeration (ENU), Inmigration (IMG) or Birth (BTH) have occurred before the left censoring date (2009-01-01) and have not outmigrated (OMG) or not died (DTH) before the left censoring date (2009-01-01) are included in the dataset as Enumeration (ENU) with EventDate as the left censored date (2009-01-01). But the actual date of observation of the event (ENU, BTH, IMG) is retained in the dataset as observation date for these left censored ENU events. The individual is dropped from the dataset if their end event (OMG or DTH) is prior to the left censoring date (2009-01-01)

Response rate

On an average the response rate is 99.99% in all rounds over the years.

Sampling error estimates

Not Applicable

Data appraisal

Data is cleaned to an acceptable level against the standard data rules using Pentaho Data Integration Comminity Edition (PDI CE) tool. After the cleaning process, quality metrics were as follows:

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate IN021 MicroDataCleaned Starts 1 301112 301113 0. 2017-05-31 20:06
IN021 MicroDataCleaned Transitions 0 667010 667010 0. 2017-05-31 20:07
IN021 MicroDataCleaned Ends 301113 2017-05-31 20:07
IN021 MicroDataCleaned SexValues 29 666981 667010 0. 2017-05-31 20:07
IN021 MicroDataCleaned DoBValues 575 666435 667010 0. 2017-05-31 20:07

Note: Except lower under five mortality in 2012 and lower adult mortality among females in 2013, all other estimates are fairly within expected range. Data underwent additional review in terms of electronic data capture, data cleaning and management to look for reasons for lower under five mortality rates in 2013 and lower female adult mortality in 2013. The additional review returned marginally higher rates and this supplements the validity of collected data. Further field related review of 2012 and 2013 data are underway and any revisions to published data/figures will be shared at a later stage.

Conflict and disaster population movement (flows) data for India. The data is the most recent available and covers a 180 day time period.

Internally displaced persons are defined according to the 1998 Guiding Principles as people or groups of people who have been forced or obliged to flee or to leave their homes or places of habitual residence, in particular as a result of armed conflict, or to avoid the effects of armed conflict, situations of generalized violence, violations of human rights, or natural or human-made disasters and who have not crossed an international border.

The IDMC's Event data, sourced from the Internal Displacement Updates (IDU), offers initial assessments of internal displacements reported within the last 180 days. This dataset provides provisional information that is continually updated on a daily basis, reflecting the availability of data on new displacements arising from conflicts and disasters. The finalized, carefully curated, and validated estimates are then made accessible through the Global Internal Displacement Database (GIDD). The IDU dataset comprises preliminary estimates aggregated from various publishers or sources.