Historical dataset showing Russia literacy rate by year from 1989 to 2021.

Comprehensive dataset containing 1 verified Literacy program businesses in Mordovia Republic, Russia with complete contact information, ratings, reviews, and location data.

Techsalerator’s Business Funding Data for Russia

Techsalerator’s Business Funding Data for Russia offers a comprehensive and insightful collection of information crucial for businesses, investors, and financial analysts. This dataset provides a detailed examination of funding activities across various sectors in Russia, capturing and categorizing data related to funding rounds, investment sources, and financial milestones.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact.

Techsalerator’s Business Funding Data for Russia

Techsalerator’s Business Funding Data for Russia delivers an in-depth and insightful overview of essential information for businesses, investors, and financial analysts. This dataset provides a thorough examination of funding activities across diverse sectors in Russia, detailing data related to funding rounds, investment sources, and key financial milestones.

Top 5 Key Data Fields

1. Company Name: Identifies the company receiving funding. This information helps investors spot potential opportunities and enables analysts to track funding trends within specific industries.

2. Funding Amount: Shows the total amount of funding a company has received. Understanding these amounts offers insights into the financial health and growth potential of businesses and the scale of investment activities.

3. Funding Round: Indicates the stage of funding, such as seed, Series A, Series B, or later stages. This helps investors assess a business’s maturity and growth trajectory.

4. Investor Name: Provides details about the investors or investment firms involved. Knowing the investors helps gauge the credibility of the funding source and their strategic interests.

5. Investment Date: Records when the funding was completed. The timing of investments can reflect market trends, investor confidence, and potential impacts on a company’s future.

Top 5 Funding Trends in Russia

1. Technology and Innovation: Significant investments are being made in technology startups, including IT, fintech, and AI. These investments are essential for fostering innovation and driving digital transformation in Russia.

2. Energy and Resources: With Russia's rich natural resources, funding is directed towards energy projects, including oil, gas, and renewable energy sources, aiming to enhance energy efficiency and sustainability.

3. Healthcare and Biotechnology: Increased funding is flowing into healthcare infrastructure, biotechnology, and health tech to address the healthcare needs of the population and support medical research and innovation.

4. Agriculture and Food Technology: Funding is being allocated to modernize agricultural practices, enhance food security, and support agritech solutions that improve productivity and sustainability in the sector.

5. Education and Research: Investments are directed towards educational initiatives and research programs aimed at improving literacy rates, advancing scientific research, and fostering technological development.

Top 5 Companies with Notable Funding Data in Russia

1. Yandex: A major tech company providing a range of services including search engines, e-commerce, and digital advertising, Yandex has received substantial funding to enhance its technology and expand its offerings.

2. Sberbank: Russia’s largest bank, Sberbank, has secured significant investment to support its digital transformation, expand its financial services, and enhance its technological capabilities.

3. Mail.ru Group: A prominent internet company in Russia, Mail.ru Group has garnered notable funding to expand its digital services, including social networking and online gaming.

4. Tinkoff Bank: An innovative online bank, Tinkoff Bank has attracted substantial investment to support its growth, enhance its digital services, and strengthen its position in the Russian financial market.

5. Kaspersky Lab: A global cybersecurity company based in Russia, Kaspersky Lab has received significant funding to advance its cybersecurity solutions and support research and development in the field.

Accessing Techsalerator’s Business Funding Data

To obtain Techsalerator’s Business Funding Data for Russia, contact info@techsalerator.com with your specific needs. Techsalerator will provide a customized quote based on the required data fields and records, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Company Name
• Funding Amount
• Funding Round
• Investor Name
• Investment Date
• Funding Type (Equity, Debt, Grants, etc.)
• Sector Focus
• Deal Structure
• Investment Stage
• Contact Information

For detailed insights into funding activities and fi...

This dataset tracks annual reading and language arts proficiency from 2011 to 2022 for Russia Elementary School vs. Ohio and Russia Local School District

The dataset contains the results of developing alternative text for images using chatbots based on large language models. The study was carried out in April-June 2024. Microsoft Copilot, Google Gemini, and YandexGPT chatbots were used to generate 108 text descriptions for 12 images. Descriptions were generated by chatbots using keywords specified by a person. The experts then rated the resulting descriptions on a Likert scale (from 1 to 5). The data set is presented in a Microsoft Excel table on the “Data” sheet with the following fields: record number; image number; chatbot; image type (photo, logo); request date; list of keywords; number of keywords; length of keywords; time of compilation of keywords; generated descriptions; required length of descriptions; actual length of descriptions; description generation time; usefulness; reliability; completeness; accuracy; literacy. The “Images” sheet contains links to the original images. Data set is presented in Russian.

BackgroundHealth literacy (HL) is considered to be an important precondition for health. HL research often identifies migrants as vulnerable for low HL. However, in-depth data on HL among migrants especially in its domains of health care, disease prevention and health promotion and its determinants are still scarce.ObjectiveThe aim of this study was therefore to analyse the current status of HL among migrants and their descendants from Turkey and from the former Soviet Union (FSU) in Germany and factors associated with it. This has not been studied using large-scale data and bilingual interviews. We differentiate between dimensions of HL, namely the domains of health care, disease prevention and health promotion which goes beyond many previous studies. In addition, we explore new mechanisms by testing the explanatory power of self-efficacy and interethnic contacts for migrants' HL.MethodsThe study includes 825 first- and second-generation adult migrants from two of the largest immigration groups in Germany, from Turkey and FSU, who were interviewed face-to-face in German, Turkish or Russian in late summer 2020. HL was measured using the HLS19-Q47 instrument. Age, gender, educational level, social status and financial deprivation, chronic illness, health-related literacy skills, self-efficacy, interethnic contacts, migration generation, duration of stay and region of origin were considered as possible determinants. Ordinary least square regressions were estimated.ResultsThe average general HL score was 65.5. HL in health promotion and disease prevention was lower than in health care. Low financial deprivation, health-related literacy skills, and self-efficacy were positively correlated with each HL domain. Educational level, social status, age, gender, duration of stay and interethnic contacts were positively correlated with HL in some domains. Region of origin was only correlated with the domain of disease prevention until interethnic contact was accounted for.ConclusionOur study contributes to the existing knowledge by analyzing different domains of HL and testing its correlations with self-efficacy and interethnic contact among migrants. We reveal that migrants cannot generally be considered as vulnerable for low HL, as oftentimes outlined. There is a need for interventions e.g. to enhance the understanding of health information among subgroups with lower HL.

Abstract

Financial services sector, like other economic sectors of Azerbaijan, has been characterized with fast development rate. Banking, insurance and post services hold leading positions among those services. Individuals are one of the major consumers of those services. Thus, more than 3.6 million people already use payment cards and about 500,000 people take consumer credits. Increase of financial literacy and better protection of consumer rights contribute to more efficient access of population to financial services. First of all, current status of financial literacy of population should be studied and problems revealed, to this end.

Increase of financial literacy and better protection of consumer rights became more urgent issues over the last decade. Fast integration of Azerbaijan into the world economy made it necessary to study those issues and implement appropriate measures in the country.

In view of the above mentioned facts, the Central Bank of the Republic of Azerbaijan, World Bank and SECO decided to carry out a financial literacy research of the population. The main objective of that project was to conduct a "Financial Literacy Survey", create a Single Database and prepare a Report reflecting outcomes of the survey.

Geographic coverage

The survey covered Baku (including 11 administrative districts), Ganja, Sumgait, Shirvan, Khirdalan, Sheki, Lankaran, Yevlakh, Nakhchivan, Guba, Gusar, Aghsu, Bilesuvar, Berde, Tovuz, Masalli cities, 2 settlements and 37 villages (see: table 1.1 of the survey report). 54% of survey participants live in urban (Baku- 23%) and 46% in rural areas. This is a similar pattern to the national demographic status.

Analysis unit

Household, individual

Universe

The survey was carried out among people above 18 years old (18 also included) (except for those not capable of being interviewed) with the latest birthday date within a year.

Kind of data

Sample survey data [ssd]

Sampling procedure

Definition of sampling frame and scale

1200 respondents were defined as a sample frame in 8 economic regions (2 economic regions of the country are under occupation) and Baku city. The main reason for conducting the survey among 1200 respondents is to ensure representativeness and financial feasibility of the project. Urban and rural ratio was set at 54% and 46% in line with statistic indicators. For detailed information see Table 1.1 of the survey report.

Preparation of the survey plan and implementation of survey sampling

Sampling was carried out at 2 stages: i) at the first stage, it was conducted while taking into account distribution of population by capital city, other urban and rural areas and economic regions with preliminary sampling units being street and villages (each preliminary sampling unit includes 15 respondents); ii) At the second stage, streets within the sampled cities and villages within economic regions were randomly selected. For example, according to results of the first stage of the sampling, a survey should be carried out among 45 respondents in Guba region and 15 respondents should be selected in urban areas and 30 respondents in rural areas. In view of the fact that primary sampling unit consists 15 respondents, 1 street within Guba town or its settlements and 2 villages among rural areas should be randomly selected.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was prepared based on the analogical questionnaire used in Russia and submitted by the Central Bank. The questionnaire was translated into Azerbaijani language, questions were adjusted to the country context, irrelevant questions were removed and new ones introduced. Meetings were arranged with representatives of the Central Bank and other relevant organizations, as well as their comments were discussed through e-mail during the preparation period of the questionnaire. The final version of the questionnaire was consisted of 65 questions and mainly covered such issues as registration of household's income and expenditures, financial awareness, financial literacy on basic calculations, violation of consumer rights during the use of financial services, access to financials services, payments cards and socio-demographic status of respondents. The questionnaire was prepared in Azerbaijani language and then, translated into English.

Cleaning operations

Entering and cleaning data, and creation of a Single Database

An operator entered and analyzed data through relevant software (SPSS). All questionnaires were coded during the entering process of data. An database specialist undertook additional control and regulation works to clean data. A Single Database was checked through preliminary analysis after major logic examination.

A Single Database was created at SPSS software based on questions of the questionnaire. Answers given by 1207 respondents were entered into the Single Database.