Beurteilung der aktuellen Lebenssituation und der politischenund wirtschaftlichen Entwicklung des Landes in Zeiten desökonomischen und gesellschaftlichen Umbruchs. Themen: 1.) Gemeinsamer Befragungsteil für alle teilnehmendenLänder: Wichtigste Probleme des Landes; Auswanderungsabsichtund gewünschtes Zielland; Wunschberuf für das eigene Kind;Beurteilung der Arbeit des Präsidenten des Landes;Befürchtungen und Wünsche für die Zukunft; Einschätzung desaktuellen Lebensstandards und Vergleich mit der Situation vorfünf Jahren und erwartete zukünftige Entwicklung; Beurteilungder Situation des Landes im Zeitvergleich; Präferenz fürFreiheit oder soziale Sicherheit; Einstellung zur Zulassungundemokratischer Parteien; Einstellung zur Pressefreiheit;perzipierte Diskriminierung der Frau; Einstellung zu einerArbeitsteilung bei der Kindererziehung; Beurteilung desVerhaltens der Volksvertreter und der Einstellung zurDemokratie im Lande (Skala); Gefühl politischer Wirksamkeit;interne oder externe Kontrolle; Leistungsorientierung;Einstellung zur Staatswirtschaft; Forderung nach vermehrtemSchutz der Umwelt; Wunsch nach ausländischer Unterstützung fürdas Land; der Staat als Garant für Chancengleichheit undsoziale Sicherheit; Wohlfahrtsstaat; Notwendigkeit derKompromißbereitschaft in der Politik; Politikinteresse aufkommunaler Ebene; abnehmendes Interesse an politischenVorgängen; Selbsteinschätzung als Patriot; Einstellung zurFriedenssicherung durch militärische Stärke; Bereitschaft zurLandesverteidigung; Notwendigkeit der Beteiligung des eigenenLandes an der Weltpolitik; Gebietsansprüche an Nachbarländer;Einstellung zur Kontrolle von Einwanderungen; Einstellung zumEinsatz von Militär zur Wiederherstellung der Weltordnung. Religion und Moral: Wichtigkeit des Gebetes und BedeutungGottes im eigenen Leben; Zweifel an der Existenz Gottes;Einstellung zum Verbot gesellschaftskritischer Bücher und zuSexmagazinen und -filmen; Einstellung zur Meinungsfreiheit auchfür Faschisten; Einschätzung der allgemeinenVertrauenswürdigkeit der Menschen; Aids als Gottesstrafe;Vetreten traditioneller Werte im Bereich der Familie undHeirat; Einstellung zur Abtreibung; klare Vorstellungen von Gutund Böse; perzipierte Verschärfung der Klassenunterschiede;Einschätzung der eigenen Gemeinsamkeiten mit Ungebildeten undmit Personen anderer Rasse oder ethnischer Zugehörigkeit;Zufriedenheit mit der eigenen finanziellen Situation. Demographie: ethnische Zugehörigkeit; zu Hause gesprochene Sprache;Alter bei Schulabschluss; Schulbildung; Geschlecht; Alter (Geburtsjahr);Familienstand; Konfession; Kirchgangshäufigkeit; berufliche Position;Haushaltseinkommen; Haushaltsgröße; Haushaltszusammensetzung;Wohnstatus; Urbanisierungsgrad; Parteimitgliedschaft;Gewerkschaftsmitgliedschaft; Interviedatum; Ortsgröße; Region;Interviewdauer. 2.) Zusätzliche Fragen in dieser Erhebung: Beurteilung derpolitischen wirtschaftlichen Veränderungen der letzten Jahre;besonders positive und negative Veränderungen; Einstellung zurEinführung eines Mehrparteiensystems und zur Einführung derfreien Marktwirtschaft; Beurteilung derÜbergangsgeschwindigkeit zur freien Marktwirtschaft;Einschätzung des Einflusses der Medien und Organisationen sowieInstitutionen auf das Land; vermuteter Einfluß ausländischerStaaten auf das eigene Land; Einstellung zu ausgewähltenethnischen Gruppen (Russen, Georgier, Litauer, Ukrainer, Juden,Armenier, Aserbaijaner und Menschen aus den asiatischenRepubliken; perzipierte Bedrohung des Staates von innen undaußen; Beurteilung der Auswirkungen der gesellschaftlichenVeränderungen auf den Lebensstandard, die Wertvorstellungen,die öffentliche Moral, den Umgang der Menschen miteinander undauf die Beziehung ethnischer Gruppen untereinander; Beurteilungdes Einflusses der Kirche; zuverlässigster Bündnispartner fürdas eigene Land; Länder, von denen die größte Bedrohungausgeht; Einstellung zu Gorbatschow, Bush, den Papst, die UNOund zu ausgewählten Persönlichkeiten des öffentlichen Lebens;Einstellung zu einer Privatisierung ausgewählter Bereiche derWirtschaft und des öffentlichen Dienstes; Einstellung zu einerfreien Preisbildung; Akzeptanz von Arbeitslosigkeit oderPräferenz für staatliche Vollbeschäftigungsgarantie;Einstellung zu einer staatlich festgesetzten Profitbegrenzung;Einstellung zu einer Kreditaufnahme zur Finanzierung vonlanglebigen Wirtschaftsgütern; Beschäftigung innerhalb deseigenen Hauses; Arbeitszufriedenheit; Zufriedenheit mit demArbeitgeber; Einstellung zur kreditfinanziertenUnternehmensgründung; Beurteilung der Geschwindigkeit, mit dersich die Demokratie im Lande entwickelt; Identifikation mit demeigenen Land oder der Sowjetunion; Einschätzung der Kompetenzvon Staat, Armee, Parteien, Kirche und KGB; Einstellung zueiner von der UdSSR unabhängigen Republik; wichtigste Gründefür eine gewünschte Unabhängigkeit; bevorzugte bzw.benachteiligte Republiken innerhalb der UdSSR; präferierteEntwicklung der Gesellschaft in Richtung Sozialismus oderKapitalismus; größte Zukunftsängste; Einstellung zu privatemEigentum und zum Landverkauf durch die Bauern; Präferenz füreine demokratische Regierung oder eine starke Führung;Einstellung zu einer sorgfältigen Diskussion von politischenStreitfragen; Karriere der Emporkömmlinge aufgrund von Leistungoder durch Beziehungen; Attribution von Mißerfolgen alsgesellschaftlich oder persönlich bedingt; Egoismus odergesellschaftliche Bedingungen als Gründe für die materielleUnterversorgung; Charakterisierung des eigenen Volkes anhandeiner Eigenschaftsliste; Nationalität. Judgement on current situation in life and political and economicdevelopment of the country in times of economic and social upheaval. Topics: 1. Common part of the survey for all participating countries:most important problems of the country; intent to emigrate and countryof choice; desired occupation for one´s own child; judgement on thework of the president of the country; fears and desires for the future;assessment of current standard of living and comparison with thesituation five years ago and expected future development; judgement onthe situation of the country in comparison over time; preference forfreedom or social security; attitude to admission of undemocraticparties; attitude to freedom of the press; perceived discriminationagainst women; attitude to division of labor in raising children;judgement on conduct of parliamentary representatives and attitude todemocracy in the country (scale); feeling of political effectiveness;internal or external control; achievement orientation; attitude to thenational economy; demand for increased environmental protection; desirefor foreign support for one´s country; the government as guarantor ofequal opportunities and social security; welfare state; necessity ofthe willingness to compromise in politics; interest in politics atmunicipal level; diminishing interest in political events;self-assessment as patriot; attitude to securing of peace throughmilitary strength; readiness for national defense; necessity ofparticipation of one´s country in world politics; claims to territoryin neighboring countries; attitude to restriction on immigration;attitude to use of military for restoration of world order. Religion and morals: importance of prayer and significance of God inone´s own life; doubt in the existence of God; attitude to prohibitionof books critical of society and to sex magazines and films; attitudeto freedom of speech even for fascists; assessment of the generaltrustworthiness of people; AIDS as punishment by God; representation oftraditional values in the area of family and marriage; attitude toabortion; clear concepts of Good and Evil; perceived intensification ofclass differences; assessment of personal things in common withuneducated and with persons of another race or ethnic affiliation;satisfaction with one´s own financial situation. Demography: party membership; union membership; residential status;city size; religiousness. 2. Additional questions in this survey: judgement on political andeconomic changes of the last few years; particularly positive andnegative changes; attitude to introduction of a multi-party system andintroduction of the free market economy; judgement on the speed oftransition to the free market economy; assessment of the influence ofthe media and organizations as well as institutions on the country;assumed influence of foreign nations on one´s own country; attitude toselected ethnic groups (Russians, Georgians, Lithuanians, Ukrainians,Jews, Armenians, Azerbaijanis) and people from the asiatic republics;perceived threat to the nation from within and without; judgement onthe effects of the social changes on the standard of living, moralconcepts, public morals, dealing of the people with one another and onthe relation of ethnic groups with each other; judgement on theinfluence of the church; most reliable alliance partner for one´scountry; countries from which the greatest threat originates; attitudeto Gorbachev, Bush, the Pope, the UN and selected personalities ofpublic life; attitude to privatization of selected areas of the economyand civil service; attitude to free establishing of prices; acceptanceof unemployment or preference for government guarantee offull-employment; attitude to a government-specified profit limitation;attitude to borrowing to finance durable economic goods; occupationwithin one´s own home; work satisfaction; satisfaction with employer;attitude to founding business with credit financing; judgement on thespeed with which democracy is developing in the country; identificationwith one´s country or the Soviet Union; assessment of the ability ofgovernment, army, parties, church and KGB; attitude to a Republicindependent of the USSR; most important reasons for desiredindependence; privileged or disadvantaged republics within the USSR;preferred development of society in the direction of

CompanyData.com powered by BoldData provides verified, high-quality company information sourced directly from official trade registers around the world. We help businesses unlock insights, drive compliance and scale with confidence using reliable data.

Our Russia company database contains over 3,178,171 verified business records, covering companies across all regions of the Russian Federation. Each record includes in-depth firmographic data such as industry codes, company size and turnover, as well as corporate hierarchies, and verified contact information including emails, phone numbers and decision-makers when available.

This comprehensive dataset supports a wide range of business applications - Compliance and KYC verification - AML checks and regulatory reporting - Sales and business development - B2B marketing and targeting - CRM enrichment and database cleansing - AI training and market research

We offer flexible delivery options to suit your business requirements - Custom company lists filtered by location, sector or company size - Full Russian company database delivered in Excel or CSV - Real-time API access for seamless integration - Data enrichment services for updating and validating your internal records

Russia is part of our global coverage of more than 3,178,171 verified companies across 200+ countries, making CompanyData.com a trusted partner for international data-driven growth. Whether you are navigating compliance in Eastern Europe or expanding your sales footprint, our verified data ensures you move forward with clarity and confidence.

Partner with CompanyData.com to gain access to the most reliable company data in Russia—delivered how and when you need it.

The World Bank Group is interested in gauging the views of clients and partners who are either involved in development in Russia or who observe activities related to social and economic development. The World Bank Group Country Opinion Survey will give the World Bank Group's team that works in Russia, greater insight into how the Bank's work is perceived. This is one tool the World Bank Group uses to assess the views of its stakeholders, and to develop more effective strategies that support development in Russia. The survey was designed to achieve the following objectives: Assist the World Bank Group in gaining a better understanding of how stakeholders in Russia perceive the World Bank Group; Obtain systematic feedback from stakeholders in Russia regarding: · Their views regarding the general environment in Russia; · Their overall attitudes toward the World Bank Group in Russia; · Overall impressions of the World Bank Group’s effectiveness and results, knowledge work and activities, and communication and information sharing in Russia; · Perceptions of the World Bank Group’s future role in Russia. Use data to help inform Russia country team’s strategy.

20,000 geo-referenced distribution data of freshwater fishes of Russia and adjacent countries More information on this dataset can be found in the Freshwater Metadatabase - BFE_106 (http://www.freshwatermetadata.eu/metadb/bf_mdb_view.php?entryID=BFE_106).

The Survey of Russian Elites consists of one dataset that covers the years 1993, 1995, 1999, 2004, 2008, 2012, 2016, and 2020. The data were collected as part of a larger study of mass-elite interactions in post-Soviet Russia, with particular emphasis on the links between the domestic political economy and foreign policy perspectives. The dataset includes questions on international relations, threat perceptions, foreign policy decision-making, domestic politics, and economic issues. In particular, respondents were asked their opinions about the national interests of Russia, the role of military force in international relations, the greatest threats to stability and security, the enlargement of the European Union, NATO expansion, the need for order in Russia, and how closely Russia should follow the path of developed countries. Additional questions asked respondents about their level of influence on foreign policy decision-making, how they find out about world and domestic events, how often they discuss such events with friends or family members, whether the United States represents a threat to Russia, whether they favor giving military aid to other countries, the importance of various foreign policy goals to Russia, the influence of various governmental institutions on foreign policy, and the permissibility of using military force. Questions focusing on domestic issues asked about their attitudes regarding government spending, the rights of individuals versus those of society, the role of political competition, the responsibility of the government to its citizens, the importance and meaning of democracy, and the type of political system that is most appropriate for Russia. Respondents were also asked about Russia's relations with other countries (e.g., Ukraine, Belarus, Japan, and China), various forms of property ownership, their willingness to open a new business, national pride, political protests, the environment, their views toward immigrants and other societal groups, and Russia's political and economic future. Demographic questions include nationality, education, occupation of the respondent and other family members, sex, age, country of birth, marital status, language spoken in the home, income level, household composition, political party affiliation, religion, military service, and foreign travel.

This proposal relates to the call of the PSA PSA COVID-19 Rapid Project 003 data collection effort in Russia. To slow the transmission of COVID-19, governments around the world are asking their citizens to participate in social distancing, that is, to stay at home as much as possible. In most countries, individuals have some choice over whether or not they follow recommendations for social distancing. Thus, understanding how to best motivate social distancing has become a public health priority. This study tests, in a confirmatory manner, whether self-determination theory-guided message framing impacts people’s motivation to participate in social distancing. Specifically, we expect autonomy-supportive messages that help people understand the value of behavior change to a) increase ‘buy in’, or autonomous motivation, for social distancing, b) reduce feelings of defiance in response to those messages, and c) increase behavioral intentions to socially distance, relative to neutral and controlling messages. Further, we expect controlling messages that pressure people to change using shame, guilt, and demands, may backfire and a) decrease ‘buy in’ for social distancing, b) increase defiance, relative to the control condition, and c) reduce behavioral intentions to socially distance. We also expect ‘buy in’, or autonomous motivation, to explain why messages impact defiance and behavioral intentions. Exploratory tests will examine whether the effects of message framing on motivation, defiance, and behavioral intentions are moderated by culture, providing sufficient variability on this measure is obtained. This work has direct relevance for how public officials, health professionals, journalists, and others can communicate about solving this and future public health crises in ways that motivate people more effectively.

https://imgur.com/h4ObE3v.jpg" alt="World Trade">

TL;DR

• I'd recommend going through the Content to understand the Data.
• This data is Clean
• The data contains Exports made by Russia to the world i. 225 Partnering countries ii. ~**20** Years iii. ~**3000** unique commodities SITC Code description
• Starter Code to optimize performance

The world trade is going through a massive change after the COVID-19 Situation fueled by the recent Russia-Ukraine conflict, This is Russia's part in the World Trade for around 15years with 225 countries.
Reference to sanctions might help drawing conclusions

Context & Inspiration

The COVID-19 pandemic is likely to be known as that inflection point in the history which changed the nature of the post-World Trade Organization (WTO) global trade policy environment. The last time the world witnessed a similar situation was in 1995 when WTO was established, creating a rule-based global trading system.

The war in Ukraine is causing worldwide disruptions to trade and investment, affecting auto makers in Europe, hoteliers in Georgia and the Maldives, as well as impacting consumers of food and fuel globally . Although the world’s poor—who spend a large part of their incomes on life’s necessities—are the most vulnerable, no country, region, or industry is left untouched by these disruptions.

Content

Classification - SITC Version 4( Latest ) Code description | more
Year - Year when the trade was made 📆
Commodity Code - Code of the Commodity
Commodity - Name of the Commodity more
Qty Unit - Unit of the Item / Quantity
Qty - Quantity of Item Netweight (kg) - Item weight in kilograms ⚓️
Trade Value (US$) - Trade value in US Dollars 💵
Aggregate Level - 5 Levels ( 1 to 5 ) You can choose one / All ( Group is the sweet spot ) more | Aggregate Level | Level Name | Code Format | Number of Items | | --- | --- | --- | --- | | 1 | Section | 0 | 10 Items | | 2 | Division | 01 | 67 Items | | 3 | Group | 012 | 261 Items | | 4 | Subgroup | 012.1 | 1033 Items | | 5 | Item | 012.13 | 3121 Items |

Reporter Code	Reporter	Reporter ISO
Reporting Countries Code	Name of country reporting	Reporting countries ISO Code
644 (Constant)	Russia (Constant)	RUS (Constant)

Partner Code	Partner	Partner ISO
Partner ( Receiver ) Countries Code	Partner countries name	Partner ISO Code
3 Digit code	225 Unique countries	3 Digit code)

• Starter Code to optimize performance

Please leave a upvote if you found this helpful ☮️

Hello 👋, If you are enjoying so far, Please checkout my other Datasets, I would love to hear your support & feedback on it, Thank you !

<---	(❁´◡`❁)	--->

Checkout my other Datasets & Notebooks

The number of LinkedIn users in Russia was forecast to continuously increase between 2022 and 2028 by in total 0.4 million users (+5.26 percent). According to this forecast in 2028, the LinkedIn user base will have increased for the sixth consecutive year to 8.04 million users. Notably, the number of LinkedIn users was continuously increasing over the past years.User figures, shown here with regards to the platform LinkedIn, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of LinkedIn users in countries like Northern Europe and Central & Western Europe.

https://store.poidata.xyz/ru

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 Russia POI Dataset is one of our worldwide POI datasets with over 58% coverage.

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 is as follows:

Poi Field Data Coverage (%) poi_name 100 brand 10 poi_tel 56 formatted_address 100 main_category 98 latitude 100 longitude 100 neighborhood 10 source_url 47 email 9 opening_hours 47

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

Russia RU: Total Population data was reported at 145,845.591 Person th in 2021. This records a decrease from the previous number of 146,459.802 Person th for 2020. Russia RU: Total Population data is updated yearly, averaging 145,976.470 Person th from Dec 1981 (Median) to 2021, with 41 observations. The data reached an all-time high of 148,538.190 Person th in 1992 and a record low of 139,221.500 Person th in 1981. Russia RU: Total Population data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Russian Federation – Table RU.OECD.MSTI: Population, Labour Force and Employment: Non OECD Member: Annual.

In response to Russia's large-scale aggression against Ukraine, the OECD Council decided on 8 March 2022 to immediately suspend the participation of Russia and Belarus in OECD bodies. In view of this decision, the OECD suspended its solicitation of official statistics on R&D from Russian authorities, leading to the absence of more recent R&D statistics for this country in the OECD database. Previously collected and compiled indicators are still available.

The business enterprise sector includes all organisations and enterprises whose main activity is connected with the production of goods and services for sale, including those owned by the state, and private non-profit institutions serving the above-mentioned organisations. In practice however, R&D performed in this sector is carried out mostly by industrial research institutes other than enterprises. This particularity reflects the traditional organisation of Russian R&D.

Headcount data include full-time personnel only, and hence are underestimated, while data in full-time equivalents (FTE) are calculated on the basis of both full-time and part-time personnel. This explains why the FTE data are greater than the headcount data.

New budgetary procedures introduced in 2005 have resulted in items previously classified as GBARD being attributed to other headings and have affected the coverage and breakdown by socio-economic objective.

Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team, except for aggregation of individual case count data into daily counts when that was the best data available for a disease and location. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretability. We also formatted the data into a standard data format. All geographic locations at the country and admin1 level have been represented at the same geographic level as in the data source, provided an ISO code or codes could be identified, unless the data source specifies that the location is listed at an inaccurate geographical level. For more information about decisions made by the curation team, recommended data processing steps, and the data sources used, please see the README that is included in the dataset download ZIP file.

This database contains tobacco consumption data from 1970-2015 collected through a systematic search coupled with consultation with country and subject-matter experts. Data quality appraisal was conducted by at least two research team members in duplicate, with greater weight given to official government sources. All data was standardized into units of cigarettes consumed and a detailed accounting of data quality and sourcing was prepared. Data was found for 82 of 214 countries for which searches for national cigarette consumption data were conducted, representing over 95% of global cigarette consumption and 85% of the world’s population. Cigarette consumption fell in most countries over the past three decades but trends in country specific consumption were highly variable. For example, China consumed 2.5 million metric tonnes (MMT) of cigarettes in 2013, more than Russia (0.36 MMT), the United States (0.28 MMT), Indonesia (0.28 MMT), Japan (0.20 MMT), and the next 35 highest consuming countries combined. The US and Japan achieved reductions of more than 0.1 MMT from a decade earlier, whereas Russian consumption plateaued, and Chinese and Indonesian consumption increased by 0.75 MMT and 0.1 MMT, respectively. These data generally concord with modelled country level data from the Institute for Health Metrics and Evaluation and have the additional advantage of not smoothing year-over-year discontinuities that are necessary for robust quasi-experimental impact evaluations. Before this study, publicly available data on cigarette consumption have been limited—either inappropriate for quasi-experimental impact evaluations (modelled data), held privately by companies (proprietary data), or widely dispersed across many national statistical agencies and research organisations (disaggregated data). This new dataset confirms that cigarette consumption has decreased in most countries over the past three decades, but that secular country specific consumption trends are highly variable. The findings underscore the need for more robust processes in data reporting, ideally built into international legal instruments or other mandated processes. To monitor the impact of the WHO Framework Convention on Tobacco Control and other tobacco control interventions, data on national tobacco production, trade, and sales should be routinely collected and openly reported. The first use of this database for a quasi-experimental impact evaluation of the WHO Framework Convention on Tobacco Control is: Hoffman SJ, Poirier MJP, Katwyk SRV, Baral P, Sritharan L. Impact of the WHO Framework Convention on Tobacco Control on global cigarette consumption: quasi-experimental evaluations using interrupted time series analysis and in-sample forecast event modelling. BMJ. 2019 Jun 19;365:l2287. doi: https://doi.org/10.1136/bmj.l2287 Another use of this database was to systematically code and classify longitudinal cigarette consumption trajectories in European countries since 1970 in: Poirier MJ, Lin G, Watson LK, Hoffman SJ. Classifying European cigarette consumption trajectories from 1970 to 2015. Tobacco Control. 2022 Jan. DOI: 10.1136/tobaccocontrol-2021-056627. Statement of Contributions: Conceived the study: GEG, SJH Identified multi-country datasets: GEG, MP Extracted data from multi-country datasets: MP Quality assessment of data: MP, GEG Selection of data for final analysis: MP, GEG Data cleaning and management: MP, GL Internet searches: MP (English, French, Spanish, Portuguese), GEG (English, French), MYS (Chinese), SKA (Persian), SFK (Arabic); AG, EG, BL, MM, YM, NN, EN, HR, KV, CW, and JW (English), GL (English) Identification of key informants: GEG, GP Project Management: LS, JM, MP, SJH, GEG Contacts with Statistical Agencies: MP, GEG, MYS, SKA, SFK, GP, BL, MM, YM, NN, HR, KV, JW, GL Contacts with key informants: GEG, MP, GP, MYS, GP Funding: GEG, SJH SJH: Hoffman, SJ; JM: Mammone J; SRVK: Rogers Van Katwyk, S; LS: Sritharan, L; MT: Tran, M; SAK: Al-Khateeb, S; AG: Grjibovski, A.; EG: Gunn, E; SKA: Kamali-Anaraki, S; BL: Li, B; MM: Mahendren, M; YM: Mansoor, Y; NN: Natt, N; EN: Nwokoro, E; HR: Randhawa, H; MYS: Yunju Song, M; KV: Vercammen, K; CW: Wang, C; JW: Woo, J; MJPP: Poirier, MJP; GEG: Guindon, EG; GP: Paraje, G; GL Gigi Lin Key informants who provided data: Corne van Walbeek (South Africa, Jamaica) Frank Chaloupka (US) Ayda Yurekli (Turkey) Dardo Curti (Uruguay) Bungon Ritthiphakdee (Thailand) Jakub Lobaszewski (Poland) Guillermo Paraje (Chile, Argentina) Key informants who provided useful insights: Carlos Manuel Guerrero López (Mexico) Muhammad Jami Husain (Bangladesh) Nigar Nargis (Bangladesh) Rijo M John (India) Evan Blecher (Nigeria, Indonesia, Philippines, South Africa) Yagya Karki (Nepal) Anne CK Quah (Malaysia) Nery Suarez Lugo (Cuba) Agencies providing assistance: Iranian Tobacco Co. Institut National de la Statistique (Tunisia) HM Revenue & Customs (UK) Eidgenössisches Finanzdepartement EFD/Département...

This study conducts a comparative analysis of how geopolitical risk (GPR) and innovation impact stock returns in the defense industry based on data from 75 defense companies across 17 countries and 4 continents. With daily datasets spanning from January 1, 2014 to March 29, 2024, wavelet coherence and wavelet phase differences were used to conduct the analysis. The results revealed that innovation had a greater and more pronounced impact during the entire analysis period compared with the influence of GPR events. GPRs exerted an uneven and heterogeneous impact on global defense stocks and had a concentrated impact during events that generated uncertainty. Overall, we found significant time-varying dependence across a large number of companies at different time frequencies. The COVID-19 pandemic did not have a major impact on companies in the defense industry. Further, GPR events led to increased volatility during the Russia–Ukraine war, leading to increased uncertainty. In addition to the dominant role they play in the world defense market, US companies served as a robust hedge, especially from 2021 to 2022. Defense companies in the UK are more sensitive to both GPR events and innovation, followed by companies in Germany and France. Comparative analysis of the scalograms of China reveals a greater influence of innovation compared with GPR events. Thus, diversification opportunities have been extended from the defense industry in China, offering investors a promising way to capitalize on refuge opportunities during periods of disruption. To mitigate the global rearmament trend, we suggest alternative investment opportunities for different time horizons.

The World Values Survey (WVS) is an international research program devoted to the scientific and academic study of social, political, economic, religious and cultural values of people in the world. The project’s goal is to assess which impact values stability or change over time has on the social, political and economic development of countries and societies. The project grew out of the European Values Study and was started in 1981 by its Founder and first President (1981-2013) Professor Ronald Inglehart from the University of Michigan (USA) and his team, and since then has been operating in more than 120 world societies. The main research instrument of the project is a representative comparative social survey which is conducted globally every 5 years. Extensive geographical and thematic scope, free availability of survey data and project findings for broad public turned the WVS into one of the most authoritative and widely-used cross-national surveys in the social sciences. At the moment, WVS is the largest non-commercial cross-national empirical time-series investigation of human beliefs and values ever executed. World Values Survey Interview Mode of collection: mixed mode. Face-to-face interview: CAPI (Computer Assisted Personal Interview). Face-to-face interview: PAPI (Paper and Pencil Interview). Telephone interview: CATI (Computer Assisted Telephone Interview). Self-administered questionnaire: CAWI (Computer-Assisted Web Interview). Self-administered questionnaire: Paper. In all countries, fieldwork was conducted on the basis of detailed and uniform instructions prepared by the WVS scientific advisory committee and WVSA secretariat. The main data collection mode in WVS 2017-2022 is face to face (interviewer-administered). Several countries employed self-administered interview or mixed-mode approach to data collection: Australia (CAWI & postal survey); Canada (CAWI); Hong Kong SAR (PAPI & CAWI); Malaysia (CAWI & PAPI); Netherlands (CAWI); USA (CAWI & CATI). The WVS Master Questionnaire was provided in English, Arabic, Russian and Spanish. Each national survey team had to ensure that the questionnaire was translated into all the languages spoken by 15% or more of the population in the country. WVSA Secretariat and Data archive monitored the translation process; every translation is subject to multi-stage validation procedure before the fieldwork can be started. The target population is defined as: individuals aged 18 (16/17 is acceptable in the countries with such voting age) or older (with no upper age limit), regardless of their nationality, citizenship or language, that have been residing in the [country/ territory] within private households for the past 6 months prior to the date of beginning of fieldwork (or in the date of the first visit to the household, in case of random-route selection). Research area: Andorra (AD); Argentina (AR); Armenia (AM); Australia (AU); Bangladesh (BD); Bolivia (BO); Brazil (BR); Canada (CA); Colombia (CO); Chile (CL); China (CN); Cyprus (CY); Ecuador (EC); Egypt (EG); Ethiopia (ET); Germany (DE); Greece (GR); Guatemala (GT); Hong Kong SAR PRC (HK); Indonesia (ID); Iran (IR); Iraq (IQ); Japan (JP); Jordan (JO); Kazakhstan (KZ); Kenya (KE); Kyrgyzstan (KG); Lebanon (LB); Libya (LY); Macao SAR PRC (MO); Malaysia (MY); Maldives (MV); Mexico (MX); Mongolia (MN); Morocco (MA); Myanmar (MM); Netherlands (NL); New Zealand (NZ); Nicaragua (NI); Nigeria (NG); Pakistan (PK); Peru (PE); Philippines (PH); Puerto Rico (PR); Romania (RO); Russia (RU); Serbia (RS); Singapore (SG); South Korea (KR); Taiwan ROC (TW); Tajikistan (TJ); Thailand (TH); Tunisia (TN); Turkey (TR); Ukraine (UA); United States (US); Venezuela (VE); Vietnam (VN); Zimbabwe (ZW). The sampling procedures differ from country to country; probability sample: Multistage Sample, Probability Sample, Simple Random Sample Representative single stage or multi-stage sampling of the adult population of the country 18 (16) years old and older was used for the WVS 2017-2021. Sample size was set as effective sample size: 1200 for countries with population over 2 million, 1000 for countries with population less than 2 million. Countries with great population size and diversity (e.g. India, China, USA, Russia, Brazil etc.) are requirred to reach an effective sample of N=1500 or larger. Only 2 countries (Argentina, Chile) deviated from the guidelines with an effective sample size below the set threshold. Sample design and other relevant information about sampling were reviewed by the WVS Scientific Advisory Committee and approved prior to contracting of fieldwork agency or starting of data collection. The sampling was documented using the Survey Design Form delivered by the national teams which included the description of the sampling frame and each sampling stage as well as the calculation of the planned gross and net sample size to achieve the required effective sample. Additionally, it included the analytical description of the inclusion probabilities of the sampling design that are used to calculate design weights.

Palm Image Biometric Data

FileMarket provides an extensive Biometric Data set that includes 20,000 high-quality images of human palms sourced from diverse geographical locations, such as Bangladesh, Russia, Nigeria, Ukraine, and other countries. Each individual in the dataset is represented by a minimum of 20 images (10 left and 10 right), capturing the palms from slightly different angles. This multi-angle approach is specifically designed to enhance the accuracy and effectiveness of computer vision and AI verification models.

This dataset is meticulously curated to support the development and training of robust AI models, making it an invaluable resource for researchers and developers in biometric verification, gesture recognition, and VR/AR applications. The versatility of this dataset extends across multiple technological advancements, including identity verification, security, and more.

Key Features of the Palm Image Biometric Data:

Geographical Diversity: Sourced from various countries including Bangladesh, Russia, Nigeria, and Ukraine. Multi-Angle Captures: Each individual is represented by 20 images, offering diverse palm angles for enhanced model accuracy. Versatile Applications: Ideal for biometric verification, gesture recognition models, and VR/AR applications. In addition to this palm image dataset, FileMarket offers specialized datasets across Object Detection Data, Machine Learning (ML) Data, Large Language Model (LLM) Data, and Deep Learning (DL) Data. Each category is designed to meet the specific needs of cutting-edge AI and machine learning projects.

Customizable Data Collection: Upon request, we can expand this dataset by collecting additional palm images through our community-driven data collection method, ensuring the dataset meets your specific needs and requirements.

By leveraging this comprehensive dataset, you can significantly improve the performance of your models in tasks such as identity verification, security, and beyond, while also exploring new frontiers in gesture recognition and virtual/augmented reality.

Russia RU: Poverty Headcount Ratio at $6.85 a Day: 2017 PPP: % of Population data was reported at 2.000 % in 2021. This records an increase from the previous number of 1.700 % for 2020. Russia RU: Poverty Headcount Ratio at $6.85 a Day: 2017 PPP: % of Population data is updated yearly, averaging 2.800 % from Dec 2014 (Median) to 2021, with 8 observations. The data reached an all-time high of 3.500 % in 2016 and a record low of 1.700 % in 2020. Russia RU: Poverty Headcount Ratio at $6.85 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Russian Federation – Table RU.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $6.85 a day is the percentage of the population living on less than $6.85 a day at 2017 international prices.;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).

Historical chart and dataset showing Russia death rate by year from 1950 to 2025.

Russia is the 154 least corrupt nation out of 180 countries, according to the 2024 Corruption Perceptions Index reported by Transparency International. This dataset provides the latest reported value for - Russia Corruption Rank - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.