The total population in Russia was estimated at 146.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - Russia Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Russia by race. It includes the distribution of the Non-Hispanic population of Russia across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Russia across relevant racial categories.

Key observations

Of the Non-Hispanic population in Russia, the largest racial group is White alone with a population of 82 (97.85% of the total Non-Hispanic population).

https://i.neilsberg.com/ch/russia-oh-population-by-race-and-ethnicity.jpeg" alt="Russia Non-Hispanic population by race">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Russia
• Population: The population of the racial category (for Non-Hispanic) in the Russia is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Russia total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Russia Population by Race & Ethnicity. You can refer the same here

Context

You found Russian Demography (1990-2017) Dataset. It contains demographic features like natural population growth, birth rate, urbanization, etc. Data was collected from various Internet resources.

Content

Dataset has 2380 rows and 7 columns. Keys for columns:

• "year" - year (1990-2017)
• "region" - name of a federal subject of Russia. It could be oblast, republic, krai, autonomous okrug, federal city and a single autonomous oblast
• "npg" - natural population growth by 1000 people. Calculating as the difference between birth rate and death rate
• "birth_rate" - number of births by 1000 people
• "death_rate" - number of deaths by 1000 people
• "gdw" - general demographic weight (how many people of non-woking age account for 100 people of working age). Working age for men 16-60 years, for women 16-55 years
• "urbanization" - % of urban population

Acknowledgements

ЕМИСС (UIISS) - Unified interdepartmental information and statistical system

Inspiration

You can analyze the relationships between various years, find best regions by each feature and compare them.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Russia township by race. It includes the distribution of the Non-Hispanic population of Russia township across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Russia township across relevant racial categories.

Key observations

With a zero Hispanic population, Russia township is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is White alone with a population of 47 (100% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Russia township
• Population: The population of the racial category (for Non-Hispanic) in the Russia township is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Russia township total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Russia township Population by Race & Ethnicity. You can refer the same here

Welcome to the Russian Chain of Thought prompt-response dataset, a meticulously curated collection containing 3000 comprehensive prompt and response pairs. This dataset is an invaluable resource for training Language Models (LMs) to generate well-reasoned answers and minimize inaccuracies. Its primary utility lies in enhancing LLMs' reasoning skills for solving arithmetic, common sense, symbolic reasoning, and complex problems.

Dataset Content

This COT dataset comprises a diverse set of instructions and questions paired with corresponding answers and rationales in the Russian language. These prompts and completions cover a broad range of topics and questions, including mathematical concepts, common sense reasoning, complex problem-solving, scientific inquiries, puzzles, and more.

Each prompt is meticulously accompanied by a response and rationale, providing essential information and insights to enhance the language model training process. These prompts, completions, and rationales were manually curated by native Russian people, drawing references from various sources, including open-source datasets, news articles, websites, and other reliable references.

Our chain-of-thought prompt-completion dataset includes various prompt types, such as instructional prompts, continuations, and in-context learning (zero-shot, few-shot) prompts. Additionally, the dataset contains prompts and completions enriched with various forms of rich text, such as lists, tables, code snippets, JSON, and more, with proper markdown format.

Prompt Diversity

To ensure a wide-ranging dataset, we have included prompts from a plethora of topics related to mathematics, common sense reasoning, and symbolic reasoning. These topics encompass arithmetic, percentages, ratios, geometry, analogies, spatial reasoning, temporal reasoning, logic puzzles, patterns, and sequences, among others.

These prompts vary in complexity, spanning easy, medium, and hard levels. Various question types are included, such as multiple-choice, direct queries, and true/false assessments.

Response Formats

To accommodate diverse learning experiences, our dataset incorporates different types of answers depending on the prompt and provides step-by-step rationales. The detailed rationale aids the language model in building reasoning process for complex questions.

These responses encompass text strings, numerical values, and date and time formats, enhancing the language model's ability to generate reliable, coherent, and contextually appropriate answers.

Data Format and Annotation Details

This fully labeled Russian Chain of Thought Prompt Completion Dataset is available in JSON and CSV formats. It includes annotation details such as a unique ID, prompt, prompt type, prompt complexity, prompt category, domain, response, rationale, response type, and rich text presence.

Quality and Accuracy

Our dataset upholds the highest standards of quality and accuracy. Each prompt undergoes meticulous validation, and the corresponding responses and rationales are thoroughly verified. We prioritize inclusivity, ensuring that the dataset incorporates prompts and completions representing diverse perspectives and writing styles, maintaining an unbiased and discrimination-free stance.

The Russian version is grammatically accurate without any spelling or grammatical errors. No copyrighted, toxic, or harmful content is used during the construction of this dataset.

Continuous Updates and Customization

The entire dataset was prepared with the assistance of human curators from the FutureBeeAI crowd community. Ongoing efforts are made to add more assets to this dataset, ensuring its growth and relevance. Additionally, FutureBeeAI offers the ability to gather custom chain of thought prompt completion data tailored to specific needs, providing flexibility and customization options.

License

The dataset, created by FutureBeeAI, is now available for commercial use. Researchers, data scientists, and developers can leverage this fully labeled and ready-to-deploy Russian Chain of Thought Prompt Completion Dataset to enhance the rationale and accurate response generation capabilities of their generative AI models and explore new approaches to NLP tasks.

The number of employed persons in Russia remained unchanged at 75 Million in August of 2025 from 75 Million in July of 2025. This dataset provides - Russia Employed Persons - actual values, historical data, forecast, chart, statistics, economic calendar and news.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Russian Mission by race. It includes the distribution of the Non-Hispanic population of Russian Mission across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Russian Mission across relevant racial categories.

Key observations

With a zero Hispanic population, Russian Mission is 100% Non-Hispanic. Among the Non-Hispanic population, the largest racial group is Two or more races (multiracial) with a population of 154 (76.24% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Russian Mission
• Population: The population of the racial category (for Non-Hispanic) in the Russian Mission is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Russian Mission total Non-Hispanic population. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Russian Mission Population by Race & Ethnicity. You can refer the same here

RCS Data Russia is an authentic dataset that you can get now. The database also comes with a replacement guarantee, meaning if any numbers are incorrect, they will be replaced. This makes sure you only get valid numbers. You won’t have to worry about old numbers, as the system automatically removes invalid data. This helps you connect with real people interested in your offers. Therefore, it makes your outreach more effective and reliable. It saves you from wasting time on dead numbers or inactive users. In addition, RCS Data Russia is the key to staying ahead in marketing. With updated information, you can always rely on accurate contacts. This helps build trust with potential customers. Invalid data can harm your campaign, but this database removes all outdated or incorrect information. The focus stays on real, active contacts. As a result, your marketing efforts will be more successful. Russia RCS data is reliable and you can easily filter by gender, age, relationship status, and location. This makes finding the right audience super simple. The data is always valid, which means you won’t waste time on incorrect numbers. You can trust the accuracy of this database. Also, 24/7 support is always available. If you have questions, there is someone ready to help anytime. With valid data, reaching out to people who match your needs becomes easy and quick. You will save time and money while getting the best results for your business. Moreover, Russia RCS data is perfect for marketers and businesses. You can create targeted campaigns with the help of this database. This ensures you reach the right people who might have an interest in your product or service. Filtering by various details like age and location helps make your campaign specific and effective.

Introduction

Welcome to the Russian Language In-car Speech Dataset, a comprehensive collection of audio recordings designed to facilitate the development of speech recognition models specifically tailored for in-car environments. This dataset aims to support research and innovation in automotive speech technology, enabling seamless and robust voice interactions within vehicles for drivers and co-passengers.

Speech Data

This dataset comprises over 5,000 high-quality audio recordings collected from various in-car environments. These recordings include scripted wake words and command-type prompts.

Participant Diversity:

- Speakers: 50+ native Russian speakers from the FutureBeeAI Community.

- Regions: Ensures a balanced representation of Russia1 accents, dialects, and demographics.

- Participant Profile: Participants range from 18 to 70 years old, representing both males and females in a 60:40 ratio, respectively.

Recording Nature: Scripted wake word and command type of audio recordings.

- Duration: Average duration of 5 to 20 seconds per audio recording.

- Formats: WAV format with mono channels, a bit depth of 16 bits. The dataset contains different data at 16kHz and 48kHz.

Dataset Diversity

Apart from participant diversity, the dataset is diverse in terms of different wake words, voice commands, and recording environments.

Different Automobile Related Wake Words: Hey Mercedes, Hey BMW, Hey Porsche, Hey Volvo, Hey Audi, Hi Genesis, Hey Mini, Hey Toyota, Ok Ford, Hey Hyundai, Ok Honda, Hello Kia, Hey Dodge.

Different Cars: Data collection was carried out in different types and models of cars.

Different Types of Voice Commands:

- Navigational Voice Commands

- Mobile Control Voice Commands

- Car Control Voice Commands

- Multimedia & Entertainment Commands

- General, Question Answer, Search Commands

Recording Time: Participants recorded the given prompts at various times to make the dataset more diverse.

- Morning

- Afternoon

- Evening

Recording Environment: Various recording environments were captured to acquire more realistic data and to make the dataset inclusive of various types of noises. Some of the environment variables are as follows:

- Noise Level: Silent, Low Noise, Moderate Noise, High Noise

- Parking Location: Indoor, Outdoor

- Car Windows: Open, Closed

- Car AC: On, Off

- Car Engine: On, Off

- Car Movement: Stationary, Moving

Metadata

The dataset provides comprehensive metadata for each audio recording and participant:

Participant Metadata: Unique identifier, age, gender, country, state, district, accent, and dialect.

Other Metadata: Recording transcript, recording environment, device details, sample rate, bit depth, file format, recording time.

This metadata is a powerful tool for understanding and characterizing the data, enabling informed decision-making in the development of Russian voice assistant speech recognition models.

License

This Russian In-car audio dataset is created by FutureBeeAI and is available for commercial use.

Why I made this dataset

On the 24th of February Russia has invaded Ukraine.

Despite the fact that there were lots of speculations about the probable invasion in the press, it came as a complete shock for me as for many other Russians. It is so disgusting to start a full-scale war in the 21 century, so it was just unimaginable, no one talked about it seriously at that time. Many of us have friends and family members in Ukraine, others just want to be a part of the civil cosmopolitan world. How could our government so gruesomely attack Ukraine, destroying the lives in both countries?

But maybe it wasn't? I want to see the evolution of the discussion around Ukraine and Russia. So I parsed quite a big number of tweets. Maybe some of you will find it useful as well.

How did I collect the data.

• max 5000 for the day
• I used these words for the search ['ukraine war', 'ukraine troops', 'ukraine border', 'ukraine NATO', 'StandwithUkraine', 'russian troops', 'russian border ukraine', 'russia invade' ]
• for each search, I created the separate CSV
• dates: from 2022 - 01 - 01 to 2022 - 03 -06

Format

The dataset is available as separate CSVs files.

License

As this data was collected from Twitter, its use must abide by the Twitter Developer Agreement. Most notably, the display of individual tweets should satisfy requirements.

Introduction

Welcome to the Russian General Conversation Speech Dataset — a rich, linguistically diverse corpus purpose-built to accelerate the development of Russian speech technologies. This dataset is designed to train and fine-tune ASR systems, spoken language understanding models, and generative voice AI tailored to real-world Russian communication.

Curated by FutureBeeAI, this 30 hours dataset offers unscripted, spontaneous two-speaker conversations across a wide array of real-life topics. It enables researchers, AI developers, and voice-first product teams to build robust, production-grade Russian speech models that understand and respond to authentic Russian accents and dialects.

Speech Data

The dataset comprises 30 hours of high-quality audio, featuring natural, free-flowing dialogue between native speakers of Russian. These sessions range from informal daily talks to deeper, topic-specific discussions, ensuring variability and context richness for diverse use cases.

Topic Diversity

The dataset spans a wide variety of everyday and domain-relevant themes. This topic diversity ensures the resulting models are adaptable to broad speech contexts.

Transcription

Each audio file is paired with a human-verified, verbatim transcription available in JSON format.

These transcriptions are production-ready, enabling seamless integration into ASR model pipelines or conversational AI workflows.

Metadata

The dataset comes with granular metadata for both speakers and recordings:

Such metadata helps developers fine-tune model training and supports use-case-specific filtering or demographic analysis.

Usage and Applications

This dataset is a versatile resource for multiple Russian speech and language AI applications:

Introduction

This Russian Call Center Speech Dataset for the Travel industry is purpose-built to power the next generation of voice AI applications for travel booking, customer support, and itinerary assistance. With over 30 hours of unscripted, real-world conversations, the dataset enables the development of highly accurate speech recognition and natural language understanding models tailored for Russian -speaking travelers.

Created by FutureBeeAI, this dataset supports researchers, data scientists, and conversational AI teams in building voice technologies for airlines, travel portals, and hospitality platforms.

Speech Data

The dataset includes 30 hours of dual-channel audio recordings between native Russian speakers engaged in real travel-related customer service conversations. These audio files reflect a wide variety of topics, accents, and scenarios found across the travel and tourism industry.

Topic Diversity

Inbound and outbound conversations span a wide range of real-world travel support situations with varied outcomes (positive, neutral, negative).

These scenarios help models understand and respond to diverse traveler needs in real-time.

Transcription

Each call is accompanied by manually curated, high-accuracy transcriptions in JSON format.

Metadata

Extensive metadata enriches each call and speaker for better filtering and AI training:

Usage and Applications

This dataset is ideal for a variety of AI use cases in the travel and tourism space:

An increase in morbidity and mortality due to COVID-19 in 2020-2022 has forced various countries to introduce lockdowns. Due to unfavorable economic consequences, this measure often caused a negative attitude toward the population, leading to sabotage and even protests. In this study, we question whether it is possible to change the population's attitude towards lockdown by emphasizing economic loss prevention. Based on the results of an online survey of 23,064 residents of Russia, we show that mentioning the negative economic consequences of a lockdown reduces the level of support for it. In contrast, mentioning the possibility of avoiding long-term negative consequences for the economy reinforces this support. The influence of economic loss prevention treatment holds for the poor and people with full-time employment, although these are groups that the lockdown can affect in the first place. Moreover, we show that economic loss prevention treatment can even influence people's opinions who were initially firmly against the lockdown. However, loss prevention treatment is not significant for people who have already experienced the pandemic's direct negative economic consequences.

This dataset is about book series. It has 1 row and is filtered where the books is A prodigal saint : Father John of Kronstadt and the Russian people. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Introduction

This Russian Call Center Speech Dataset for the Healthcare industry is purpose-built to accelerate the development of Russian speech recognition, spoken language understanding, and conversational AI systems. With 30 Hours of unscripted, real-world conversations, it delivers the linguistic and contextual depth needed to build high-performance ASR models for medical and wellness-related customer service.

Created by FutureBeeAI, this dataset empowers voice AI teams, NLP researchers, and data scientists to develop domain-specific models for hospitals, clinics, insurance providers, and telemedicine platforms.

Speech Data

The dataset features 30 Hours of dual-channel call center conversations between native Russian speakers. These recordings cover a variety of healthcare support topics, enabling the development of speech technologies that are contextually aware and linguistically rich.

Topic Diversity

The dataset spans inbound and outbound calls, capturing a broad range of healthcare-specific interactions and sentiment types (positive, neutral, negative).

These real-world interactions help build speech models that understand healthcare domain nuances and user intent.

Transcription

Every audio file is accompanied by high-quality, manually created transcriptions in JSON format.

Metadata

Each conversation and speaker includes detailed metadata to support fine-tuned training and analysis.

Usage and Applications

This dataset can be used across a range of healthcare and voice AI use cases:

About this dataset

Context

This list ranks the 365 cities in the Florida by Russian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Russian Population: This column displays the rank of city in the Florida by their Russian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Russian Population: The Russian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Russian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Florida Russian Population: This tells us how much of the entire Florida Russian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

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/.

Since 2008, KIIS has been tracking public opinion in Ukraine regarding Russia by asking the question 'What is your general attitude towards Russia now?' with a 4-point scale from 'very good' to 'very bad.' To gain a deeper understanding of the situation, every few years the surveys also included additional questions about attitudes towards Russians (residents of Russia) and the Russian leadership. Each survey wave in Ukraine was carried out on a sample representative of Ukraine's adult population (aged 18 and older), with an average sample size of about 2,000 respondents. The merged dataset contains data from 49 waves of the survey conducted in Ukraine from 2008 to 2022 with a total of 98,575 respondents. The background information includes respondents' socio-demographic profiles (gender, age, education, nationality, occupation, self-assessment of financial situation) and place of residence (oblast, type of settlement). These data enable tracking Ukrainian public opinion regarding Russia for the period of 14 years, from 2008 to 2022, both among the population as a whole and among its different subpopulations. This monitoring of public opinion in Ukraine on Russia is a part of a joint project with the Levada Center, which simultaneously tracked public opinion in Russia on Ukraine, using the same question wording. However, only the data from the polls conducted in Ukraine are presented in this data collection.

Different countries have different health outcomes that are in part due to the way respective health systems perform. Regardless of the type of health system, individuals will have health and non-health expectations in terms of how the institution responds to their needs. In many countries, however, health systems do not perform effectively and this is in part due to lack of information on health system performance, and on the different service providers. The aim of the WHO World Health Survey is to provide empirical data to the national health information systems so that there is a better monitoring of health of the people, responsiveness of health systems and measurement of health-related parameters. The overall aims of the survey is to examine the way populations report their health, understand how people value health states, measure the performance of health systems in relation to responsiveness and gather information on modes and extents of payment for health encounters through a nationally representative population based community survey. In addition, it addresses various areas such as health care expenditures, adult mortality, birth history, various risk factors, assessment of main chronic health conditions and the coverage of health interventions, in specific additional modules. The objectives of the survey programme are to: 1. develop a means of providing valid, reliable and comparable information, at low cost, to supplement the information provided by routine health information systems. 2. build the evidence base necessary for policy-makers to monitor if health systems are achieving the desired goals, and to assess if additional investment in health is achieving the desired outcomes. 3. provide policy-makers with the evidence they need to adjust their policies, strategies and programmes as necessary.

Real estate ads in Russia are published on the websites avito.ru, realty.yandex.ru, cian.ru, sob.ru, youla.ru, n1.ru, moyareklama.ru. The ads-api.ru service allows you to upload real estate ads for a fee. The parser of the service works strangely and duplicates real estate ads in the database if the authors extended them after some time. Also in the Russian market there are a lot of outbids (bad realtors) who steal ads and publish them on their own behalf. Before publishing this dataset, my task was to select the original ad from a bunch of ads. Russian real estate services allow ad authors to manually write data about an apartment or house. Therefore, it often happens that a user can publish an ad with errors or typos. Also, the user may not know, for example, the type of walls near his house. The user also specifies the address of the object being sold. He may make a mistake and simply indicate the address, for example, "Moscow". Which street? Which house? We will never know.

Dataset

The real estate market in Russia is of two types, in the dataset it is used as object type 0 - Secondary real estate market; 2 - New building. I found it necessary to determine the geolocation for each ad address and add the coordinates to this dataset. Also there is a number of the region of Russia. For example, the number of the Chuvash region is 21. Additionally, there is a house number that is synchronized through the federal public database of the Federal Tax Service "FIAS". Since the data is obtained through a paid third party service, I cannot publish the results, however, I can anonymize them and publish parameters such as Street ID and House ID. Basically, all houses are built from blocks such as brick, wood, panel and others. I marked them with numbers: building type - 0 - Don't know. 1 - Other. 2 - panel. 3 - Monolithic. 4 - Brick. 5 - blocky. 6- Wooden

The number of rooms can also be as 1, 2 or more. However, there is a type of apartment that is called a studio apartment. I've labeled them "-1".

Ideas

I hope that the publication of this dataset will improve developments in the field of global real estate. You can create apartment price forecasts. You can analyze real estate markets. You can understand that there is a need to publish free real estate datasets. And much more

Others

The license for this dataset is public, you can use it in your scientific research, design work and other works. The only condition is the publication of a link to this dataset. You can send suggestions (or complaints) on the dataset by mail daniilakk@gmail.com

About this dataset

Context

The dataset presents the median household income across different racial categories in Russia. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Russia population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 99.05% of the total residents in Russia. Notably, the median household income for White households is $76,458. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $76,458.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Russia.
• Median household income: Median household income, adjusting for inflation, presented in 2023-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/.

Recommended for further research

This dataset is a part of the main dataset for Russia median household income by race. You can refer the same here