Opinion data from Hungary, Bulgaria and Latvia (including the Russian-speaking minority).

This survey focuses on relations with and attitudes towards Russia in three East European countries with a record of close ties with Russia – Latvia, Hungary, and Bulgaria. The survey was carried out against the backdrop of Russia´s annexation of Crimea and Eastern Ukraine. It may be the very first survey to tap East European reactions to Russia’s drastic attempt to redraw the map of post-war Eastern Europe. The 2015 Post-Crimea Survey asks many of the key questions in the Baltic Barometer questions about identity, democracy, and the European Union (Baltic Barometer 2014).

1117 Russian cities with city name, region, geographic coordinates and 2020 population estimate. How to use from pathlib import Path import requests import pandas as pd url = ("https://raw.githubusercontent.com/" "epogrebnyak/ru-cities/main/assets/towns.csv") # save file locally p = Path("towns.csv") if not p.exists(): content = requests.get(url).text p.write_text(content, encoding="utf-8") # read as dataframe df = pd.read_csv("towns.csv") print(df.sample(5)) Files: towns.csv - city information regions.csv - list of Russian Federation regions alt_city_names.json - alternative city names Сolumns (towns.csv): Basic info: city - city name (several cities have alternative names marked in alt_city_names.json) population - city population, thousand people, Rosstat estimate as of 1.1.2020 lat,lon - city geographic coordinates Region: region_name - subnational region (oblast, republic, krai or AO) region_iso_code - ISO 3166 code, eg RU-VLD federal_district, eg Центральный City codes: okato oktmo fias_id kladr_id Data sources City list and city population collected from Rosstat publication Регионы России. Основные социально-экономические показатели городов and parsed from publication Microsoft Word files. City list corresponds to this Wikipedia article. Alternative dataset is wiki-based Dadata city dataset (no population data). Comments City groups Ханты-Мансийский and Ямало-Ненецкий autonomous regions excluded to avoid duplication as parts of Тюменская область. Several notable towns are classified as administrative part of larger cities (Сестрорецк is a municpality at Saint-Petersburg, Щербинка part of Moscow). They are not and not reported in this dataset. By individual city Белоозерский not found in Rosstat publication, but should be considered a city as of 1.1.2020 Alternative city names We suppressed letter "ё" city columns in towns.csv - we have Орел, but not Орёл. This affected: Белоозёрский Королёв Ликино-Дулёво Озёры Щёлково Орёл Дмитриев and Дмитриев-Льговский are the same city. assets/alt_city_names.json contains these names. Tests poetry install poetry run python -m pytest How to replicate dataset 1. Base dataset Run: download data stro rar/get.sh convert Саратовская область.doc to docx run make.py Creates: _towns.csv assets/regions.csv 2. API calls Note: do not attempt if you do not have to - this runs a while and loads third-party API access. You have the resulting files in repo, so probably does not need to these scripts. Run: cd geocoding run coord_dadata.py (needs token) run coord_osm.py Creates: coord_dadata.csv coord_osm.csv 3. Merge data Run: run merge.py Creates: assets/towns.csv See code at Github: https://github.com/epogrebnyak/ru-cities

Representative samples of populations in Estonia, Latvia and Lithuania. This is our follow-up survey (from 2014) in the three Baltic countries but without additional sampling of their respective Russian speaking minorities. Special focus is on the handling of the covid pandemic in the Baltic countries, but the survey also covers attitudes towards the EU, migration, democracy, and Russia against the backdrop of its aggression in Ukraine.

The second European Union Minorities and Discrimination Survey (EU-MIDIS II) was conducted in 2016 by the European Union Agency for Fundamental Rights (FRA) as a follow-up to the first survey on minorities´ and immigrants´ experiences of discrimination and criminal victimisation conducted by the Agency in 2008. The EU-MIDIS II survey collected information from 25,515 respondents from different ethnic minority and migrant backgrounds, including Roma, in all EU Member States (2016: EU-28 including the UK). The EU-MIDIS II sample is representative of the selected populations that were interviewed. The sample includes persons belonging to ethnic or national minorities, Roma and Russians, persons born outside the EU (first generation respondents) and persons with at least one parent born outside the EU (second generation respondents). All respondents were 16 years or older and had lived in a private household for at least 12 months before the interview. People living in institutional settings - for example, hospitals or prisons - were not interviewed. The selection of groups to be surveyed in each country was based on several criteria, including the size of the target population, the feasibility of surveying the target population in terms of cost and accessibility, the risk of certain groups experiencing ´racial´, ´ethnic´ or ´religious´ discrimination and victimisation, their vulnerability to the risk of social exclusion and, finally, comparability with previous FRA surveys. The target groups of the EU-MIDIS II survey are immigrants and descendants of immigrants from North Africa; immigrants and descendants of immigrants from Turkey; immigrants and descendants of immigrants from Sub-Saharan Africa; immigrants and descendants of immigrants from Asia and South Asia; new immigrants; Roma; members of the Russian minority. In Slovenia and Poland, people who immigrated to the EU in the last 10 years were included, regardless of country of origin. The fieldwork was conducted between September 2015 and September 2016 by Ipsos MORI under the supervision of FRA staff who monitored compliance with strict quality control procedures. The questionnaire includes questions on perceived discrimination in various areas, such as employment, education, housing, health and in the use of public or private services. It also covers police checks, criminal victimisation (including hate crime), and awareness of rights and of institutions that provide victim support. In addition, respondents were asked about issues of social participation and integration, including trust in public institutions and the degree of attachment to the country in which they live.

This data collection includes 'life story' interviews with Russian-speaking women from Russia, Ukraine, and Belarus who have married Chinese citizens and moved for their married lives to the People's Republic of China. Most of the recorded interviews were transcribed verbatim in Russian. Some of the non-recorded conversations are summarised in English. The topics covered in the interviews include the women's journeys to China, their experiences of family, social, and working lives, the challenges of legal, socio-cultural and emotional adaptation, and the questions of citizenship and immigration status for women and their children.The growth of mega-cities and more generally rapid urbanization in China not only include hundreds of millions internal migrants, but an increasing number of foreign (including Taiwanese and returning ethnic Chinese) migrants as well. At present, foreign migrants fill relatively small and specific skills and knowledge gaps, but also include marriage migrants, traders, investors, retirees and unskilled workers. However as China's population growth levels off, population ageing sets in. China's working age population is set to decline, slowly at first but increasingly rapidly, especially roughly after 2025. Moreover, the population's sex imbalance will become ever more pronounced and China will face an increasing shortage of marriageable and working age people. Although international migration is set to make an important contribution to these increasing demographic and labour market shortages in China, little research has as yet been done. Our project will provide estimates and projections of the role of international and internal migration on population dynamics in China. The central focus of our project is on the impact of the second demographic transition in China, including family changes, ageing, migration and regional population changes. We will collect vital data on the interaction between labour markets and population dynamics, the consequences of migration, integration policies in China, EU-China mobility, and shifting patterns of inequality and the cultural division of labour. The project therefore speaks directly to the issues under the theme Understanding Population Change of the Europe - China call for collaborative research. This research data collection includes the transcripts of life story interviews with Russian-speaking women from the Soviet Union who have married a Chinese national and moved for a family life to the People's Republic of China. The research participants for this project were recruited through a snowballing method. A written call for participation and project information were distributed through established contacts and social media, inviting interested parties to contact the researcher. A consent form with the project information was shared with prospective participants prior to the interview. The interviews took place face-to-face or through a video or audio function in Skype or in Wechat, China's most popular social media platform.