This dataset is about countries per year in Russia. It has 64 rows. It features 4 columns: country, continent, and urban population living in areas where elevation is below 5 meters .

About this dataset

Context

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

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 14(4.68%) households where the householder is under 25 years old, 109(36.45%) households with a householder aged between 25 and 44 years, 97(32.44%) households with a householder aged between 45 and 64 years, and 79(26.42%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the village of Russia, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

About this dataset

Context

The dataset presents the median household incomes over the past decade across various racial categories identified by the U.S. Census Bureau in Russia town. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. It also showcases the annual income trends, between 2013 and 2023, providing insights into the economic shifts within diverse racial communities.The dataset can be utilized to gain insights into income disparities and variations across racial categories, aiding in data analysis and decision-making..

Key observations

• White: In Russia town, the median household income for the households where the householder is White increased by $32,051(56.20%), between 2013 and 2023. The median household income, in 2023 inflation-adjusted dollars, was $57,028 in 2013 and $89,079 in 2023.
• Black or African American: Even though there is a population where the householder is Black or African American, there was no median household income reported by the U.S. Census Bureau for both 2013 and 2023.
• Refer to the research insights for more key observations on American Indian and Alaska Native, Asian, Native Hawaiian and Other Pacific Islander, Some other race and Two or more races (multiracial) households

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 town.
• 2010: 2010 median household income
• 2011: 2011 median household income
• 2012: 2012 median household income
• 2013: 2013 median household income
• 2014: 2014 median household income
• 2015: 2015 median household income
• 2016: 2016 median household income
• 2017: 2017 median household income
• 2018: 2018 median household income
• 2019: 2019 median household income
• 2020: 2020 median household income
• 2021: 2021 median household income
• 2022: 2022 median household income
• 2023: 2023 median household income
• Please note: All incomes have been adjusted for inflation and are 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 town median household income by race. You can refer the same here

This dataset is about countries per year in Russia. It has 1 row and is filtered where the date is 2021. It features 3 columns: country, and population.

RuBQ 1.0: A Russian Knowledge Base Question Answering Data Set

Introduction

We present RuBQ (pronounced [`rubik]) -- Russian Knowledge Base Questions, a KBQA dataset that consists of 1,500 Russian questions of varying complexity along with their English machine translations, corresponding SPARQL queries, answers, as well as a subset of Wikidata covering entities with Russian labels. 300 RuBQ questions are unanswerable, which poses a new challenge for KBQA systems and makes the task more realistic. The dataset is based on a collection of quiz questions. The data generation pipeline combines automatic processing, crowdsourced and in-house verification, see details in the paper. To the best of our knowledge, this is the first Russian KBQA and semantic parsing dataset.

Links

ISWC 2020 paper (newest) :page_facing_up:

arXiv paper :page_facing_up:

Test and Dev subsets

RuWikidata sample

Dataset is also published on Zenodo

Usage

The dataset is thought to be used as a development and test sets in cross-lingual transfer, few-shot learning, or learning with synthetic data scenarios.

Format

Data set files are presented in JSON format as an array of dictionary entries. See full specifications here.

Examples

SELECT ?answer 
WHERE {
 wd:Q2222 wdt:P50 ?answer .
}

SELECT ?answer
WHERE {
 wd:Q845176 p:P161 [
  ps:P161 ?answer; 
  pq:P453 wd:Q2737140
 ] .
}

SELECT ?answer 
WHERE {
 wd:Q181517 wdt:P366 [
  wdt:P3095 ?answer
 ] .
}

SELECT ?answer 
WHERE {
 ?answer p:P2046/
   psn:P2046/
   wikibase:quantityAmount ?sq .
 ?answer wdt:P31 wd:Q9430 .
}
ORDER BY ASC(?sq)
LIMIT 1

RuWikidata8M Sample

We provide a Wikidata sample containing all the entities with Russian labels. It consists of about 212M triples with 8.1M unique entities. This snapshot mitigates the problem of Wikidata’s dynamics – a reference answer may change with time as the knowledge base evolves. The sample guarantees the correctness of the queries and answers. In addition, the smaller dump makes it easier to conduct experiments with our dataset.

We strongly recommend using this sample for evaluation.

Details

Sample is a collection of several RDF files in Turtle.

• wdt_all.ttl contains all the truthy statements.
• names.ttl contains Russian and English labels and aliases for all entities. Names in other language also provided when needed.
• onto.ttl contains all Wikidata triples with relation wdt:P279 - subclass of. It represents some class hierarchy, but remember that there is no class or instance concepts in Wikidata.
• pch_{0,6}.ttl contain all statetment nodes and their data for all entities.

Evaluation

rdfs:label and skos:altLabel predicates convention

Some question in our dataset require using rdfs:label or skos:altLabel for retrieving answer which is a literal. In cases where answer language doesn't have to be inferred from question, our evaluation script takes into account Russian literals only.

Reference

If you use RuBQ dataset in your work, please cite:

@inproceedings{RuBQ2020,
 title={{RuBQ}: A {Russian} Dataset for Question Answering over {Wikidata}},
 author={Vladislav Korablinov and Pavel Braslavski},
 booktitle={ISWC},
 year={2020},
 pages={97--110}
}

This work is licensed under a "http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License.

Dataset description: This dataset contains corpus data used in the paper described below. The dataset set consists of html-pages that contain the results for corpus searches in the Russian National Corpus (RNC) as described in the methodology of the corresponding paper and in the methodological information of this README file. Furthermore, it contains the scripts that were used to save these html-pages and to extract the relevant information from them. The scripts created csv files which were then imported into a LibreOffice Calc document with the ".ods" extension. Article description: The present small-scale study compares the usage of the verbal prefix do- in contemporary Russian and Ukrainian using the Ukrainian parallel corpus of the Russian National Corpus. Two datasets were analyzed: In the first one, translations of Russian do- verbs into Ukrainian were analyzed, whereas the second dataset dealt with translations of Ukrainian do- verbs into Russian. The focus of the discussion was on cognate translations with different prefixes. While the amount of data does not allow any strong conclusions, it is shown that in both languages do- prefixes can express the same meanings, namely REACH, REACH (ABSTRACT), ADD, CONVEY, and, when used together with postfix -sja, EXCESS. As the discussion shows, there is reason to believe that the CONVEY meaning is less productive in Russian where it is used in words restricted to official contexts and in fixed expressions. A quantitative analysis showed that among cognate translations from Ukrainian into Russian, the prefix was more often different than in translations from Russian into Ukrainian. This can be seen as a further clue for a wider application of Ukrainian do- compared to its Russian counterpart.

Abstract

The fourth edition of the Global Findex offers a lens into how people accessed and used financial services during the COVID-19 pandemic, when mobility restrictions and health policies drove increased demand for digital services of all kinds.

The Global Findex is the world's most comprehensive database on financial inclusion. It is also the only global demand-side data source allowing for global and regional cross-country analysis to provide a rigorous and multidimensional picture of how adults save, borrow, make payments, and manage financial risks. Global Findex 2021 data were collected from national representative surveys of about 128,000 adults in more than 120 economies. The latest edition follows the 2011, 2014, and 2017 editions, and it includes a number of new series measuring financial health and resilience and contains more granular data on digital payment adoption, including merchant and government payments.

The Global Findex is an indispensable resource for financial service practitioners, policy makers, researchers, and development professionals.

Geographic coverage

National coverage

Analysis unit

Individual

Kind of data

Observation data/ratings [obs]

Sampling procedure

In most developing economies, Global Findex data have traditionally been collected through face-to-face interviews. Surveys are conducted face-to-face in economies where telephone coverage represents less than 80 percent of the population or where in-person surveying is the customary methodology. However, because of ongoing COVID-19 related mobility restrictions, face-to-face interviewing was not possible in some of these economies in 2021. Phone-based surveys were therefore conducted in 67 economies that had been surveyed face-to-face in 2017. These 67 economies were selected for inclusion based on population size, phone penetration rate, COVID-19 infection rates, and the feasibility of executing phone-based methods where Gallup would otherwise conduct face-to-face data collection, while complying with all government-issued guidance throughout the interviewing process. Gallup takes both mobile phone and landline ownership into consideration. According to Gallup World Poll 2019 data, when face-to-face surveys were last carried out in these economies, at least 80 percent of adults in almost all of them reported mobile phone ownership. All samples are probability-based and nationally representative of the resident adult population. Phone surveys were not a viable option in 17 economies that had been part of previous Global Findex surveys, however, because of low mobile phone ownership and surveying restrictions. Data for these economies will be collected in 2022 and released in 2023.

In economies where face-to-face surveys are conducted, the first stage of sampling is the identification of primary sampling units. These units are stratified by population size, geography, or both, and clustering is achieved through one or more stages of sampling. Where population information is available, sample selection is based on probabilities proportional to population size; otherwise, simple random sampling is used. Random route procedures are used to select sampled households. Unless an outright refusal occurs, interviewers make up to three attempts to survey the sampled household. To increase the probability of contact and completion, attempts are made at different times of the day and, where possible, on different days. If an interview cannot be obtained at the initial sampled household, a simple substitution method is used. Respondents are randomly selected within the selected households. Each eligible household member is listed, and the hand-held survey device randomly selects the household member to be interviewed. For paper surveys, the Kish grid method is used to select the respondent. In economies where cultural restrictions dictate gender matching, respondents are randomly selected from among all eligible adults of the interviewer's gender.

In traditionally phone-based economies, respondent selection follows the same procedure as in previous years, using random digit dialing or a nationally representative list of phone numbers. In most economies where mobile phone and landline penetration is high, a dual sampling frame is used.

The same respondent selection procedure is applied to the new phone-based economies. Dual frame (landline and mobile phone) random digital dialing is used where landline presence and use are 20 percent or higher based on historical Gallup estimates. Mobile phone random digital dialing is used in economies with limited to no landline presence (less than 20 percent).

For landline respondents in economies where mobile phone or landline penetration is 80 percent or higher, random selection of respondents is achieved by using either the latest birthday or household enumeration method. For mobile phone respondents in these economies or in economies where mobile phone or landline penetration is less than 80 percent, no further selection is performed. At least three attempts are made to reach a person in each household, spread over different days and times of day.

Sample size for Russian Federation is 2011.

Mode of data collection

Landline and mobile telephone

Research instrument

Questionnaires are available on the website.

Sampling error estimates

Estimates of standard errors (which account for sampling error) vary by country and indicator. For country-specific margins of error, please refer to the Methodology section and corresponding table in Demirgüç-Kunt, Asli, Leora Klapper, Dorothe Singer, Saniya Ansar. 2022. The Global Findex Database 2021: Financial Inclusion, Digital Payments, and Resilience in the Age of COVID-19. Washington, DC: World Bank.

This data collection contains various data about current-economic and social practices in the border region shared between Russia, China and Mongolia, combining historical and anthropological methods of research. It contains informal interviews and pictures of representatives (informants) of the main focus groups, such as ethnic communities who straddle the border, such as the Nanai, Russians, and Mongols, border traders, and cultural activists among them. Interviews reflect their cross-border connections, including re-establishing of kinship ties, religious practices and social memory of separation and political upheavals between China and Russia, which greatly affected their everyday life at the border. Data reflects research findings to answer the question, how border society operates and how both countries manage their border economies, trade and migration. Along with detailed genealogies of some Buryat lineages, collection contains GIS maps of the Russian border region with China in Transbaikal region and fieldwork reports from various locations. Collection also includes data on research structure, workshops, publications, lectures and public talks of the Project members to share Project findings with a wider audience. The ‘Where Rising Powers Meet’ project aims to investigate what the Russian-Chinese border can reveal about the differing political economies of the two countries and their trajectories in the post-1991 era. Since each state exercises full sovereignty right up to their mutual border, there is no better place to compare the two remarkably dissimilar ways that economic development, the rule of law, citizen rights, migration, and inequality are managed. Yet state policies encounter volatile, more or less independent activities across this border. An important question the project will address is: how stable is this situation and what do the trends visible today indicate about the future of the two ‘rising powers’? This project, based at Cambridge but working in collaboration with colleagues in China, Russia, Mongolia, France and Denmark, is both multidisciplinary and multi-sited. The research team, composed of anthropologists, sociologists and economists, will be carrying out research at various sites along the border, from Mongolia in the west to Vladivostok in the east. The project has obtained the ethical approval of the University of Cambridge. Formal and informal interviews, photographs, digital audio recordings, surveys, GIS mapping, archival research. Data was collected during fieldwork in the border region, namely in border cities, such as Manzhouli, Blagoveshchensk, Vladivostok, Zabaikal'sk, Kyakhta and Suifenghe, including some archival research on history of the Sino-Russian trade relations (caravan trade) and cross-border migration. Focus groups include: - cross-border and transborder ethnic groups living in border area shared by China, Russia and Mongolia; Russian and Chinese border traders; Chinese seasonal labour migrants to Russia; Russian female border traders to China; border guards; mixed marriage couples. Interviews and surveys among Chinese and Russian border traders aimed to find new social stratification of the Sino-Russia border society in post-Socialist period.

Institutional settings play a key role in shaping land cover and land use. Our goal was to understand the effects of institutional changes on agricultural land abandonment in different countries of Eastern Europe and the former Soviet Union after the collapse of socialism. We studied 273 800 km**2 (eight Landsat footprints) within one agro-ecological zone stretching across Poland, Belarus, Latvia, Lithuania and European Russia. Multi-seasonal Landsat TM/ETM+ satellite images centered on 1990 (the end of socialism) and 2000 (one decade after the end of socialism) were used to classify agricultural land abandonment using support vector machines. The results revealed marked differences in the abandonment rates betweencountries. The highest rates of land abandonment were observed in Latvia (42% of all agricultural land in 1990 was abandoned by 2000), followed by Russia (31%), Lithuania (28%), Poland (14%) and Belarus (13%). Cross-border comparisons revealed striking differences; for example, in the Belarus-Russia cross-border area there was a great difference between the rates of abandonment of the two countries (10% versus 47% of abandonment). Our results highlight the importance of institutions and policies for land-use trajectories and demonstrate that radically different combinations of institutional change of strong institutions during the transition can reduce the rate of agricultural land abandonment (e.g., in Belarus and in Poland). Inversely, our results demonstrate higher abandonment rates for countries where the institutions that regulate land use changed and where the institutions took more time to establish (e.g., Latvia, Lithuania and Russia). Better knowledge regarding the effects of such broad-scale change is essential for understanding land-use change and for designing effective land-use policies. This information is particularly relevant for Northern Eurasia, where rapid land-use change offers vast opportunities for carbon balance and biodiversity, and for increasing agricultural production on previously cultivated lands.

The Russian River Watershed (RRW) covers about 1,300 square miles (without Santa Rosa Plain) of urban, agricultural, and forested lands in northern Sonoma County and southern Mendocino County, California. Communities in the RRW depend on a combination of Russian River water and groundwater to meet their water-supply demands. Water is used primarily for agricultural irrigation, municipal and private wells supply, and commercial uses - such as for wineries and recreation. Annual rainfall in the RRW is highly variable, making it prone to droughts and flooding from atmospheric river events. In order to better understand surface-water and groundwater issues, the USGS is creating a Coupled Ground-Water and Surface-Water Flow Model (GSFLOW; Markstrom and others, 2008) of the RRW. This model will include climate, geology, surface-water, groundwater, and land-use data. These climate data are temperature, precipitation, solar radiation, and reference evapotranspiration observations from stations in the Russian River watershed. These data were used for the Russian River Integrated Hydrologic Model (RRIHM).

The data set presents about 40,000 trees which where surveyed during several Russian-German expeditions by North-Eastern Federal University Yakutsk and Alfred-Wegener-Institute Potsdam to the North-East of the Russian Federation between the years 2011 and 2021. The purpose was to gather information on trees and forests in this region, which was then used to understand tree line migration, stand infilling and natural disturbance and succession processes and to initialize and validate a forest model. Trees are located on more than 160 vegetation plots, each of which has a size of several hundred square meters. For every tree, height was estimated, and the species recorded. Some individuals were subject to more detailed inventory, including diameters at base and at breast height, crown diameters, and other information.

About this dataset

Context

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

Key observations

• Upon closer examination of the distribution of households among age brackets, it reveals that there are 33(10.93%) households where the householder is under 25 years old, 117(38.74%) households with a householder aged between 25 and 44 years, 47(15.56%) households with a householder aged between 45 and 64 years, and 105(34.77%) households where the householder is over 65 years old.
• The age group of 45 to 64 years exhibits the highest median household income, while the largest number of households falls within the 25 to 44 years bracket. This distribution hints at economic disparities within the village of Russia, showcasing varying income levels among different age demographics.

Content

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

Income brackets:

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

Variables / Data Columns

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

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

The database on Desalination plants in the EU was created in 2021 by Cogea srl for the European Marine Observation and Data Network (EMODnet). It is the result of the harmonization of a dataset provided by GWI DesalData. The dataset provides spatial information (point) on the centroid of the municipality where a given plant is located. It is available for viewing and download on EMODnet - Human Activities web portal (https://emodnet.ec.europa.eu/en/human-activities) and will be updated every year. The dataset covers the following countries: Albania, Austria, Belgium, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, Denmark, Finland, France, Georgia, Germany, Gibraltar, Greece, Guernsey, Hungary, Ireland, Italy, Jersey, Latvia, Liechtenstein, Lithuania, Luxembourg, Malta, Moldova, Netherlands, Norway, Poland, Portugal, Romania, Russia, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Turkey, UK, Ukraine. Where available, each point has the following attributes: site code (ID), Location Name, Country, Position Source, Commune Name, NUTS code (NUTS 3 ID), Latitude, Longitude. A relational table provides further information for each desalination plant where data are available: Location Type, Project, Capacity (m3/d), Size, Units, Unit Size (m3/d), Technology, Feedwater, Plant Type, Award Date, Online Date, Plant Status, Customer Type, Industry Type, Customer, Holding Company, Plant Owner. More plant-specific information is available in the original dataset.

This dataset is about countries per year in Russia. It has 1 row and is filtered where the date is 2021. It features 4 columns: country, birth rate, and male population.

This dataset is about book subjects. It has 5 rows and is filtered where the books is Russia 1905-1941. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

The dataset on ocean energy in the European seas was created in 2014 by AZTI for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources from all across the European countries. It is updated every year, and is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points representing Ocean Energy project sites in the following countries: Belgium, Denmark, Finland, France, Ireland, Italy, Norway, Portugal, Russia, Spain, Sweden, The Netherlands and United Kingdom. Where available, each point has the following attributes: site code (ID_1), project code (ID), name, location, country, sea basin, sea, distance to coast (metres), resource type (wave, tidal, salinity gradient, wave/wind), starting year, ending year, lease status, technology (Based on www.aquaret.com/), device, device scale (Full scale, prototype, etc.), project scale (Commercial, Demonstrator Array, etc.), project status (operational, completed, etc.), project capacity (KW), promoter, position info (it indicates if the attribute value is original from the source or has been estimated or calcultated the polygon centroid) and the studies conducted for the environmental assessment (EIA). In 2016, a feature on areas for ocean energy test sites was included. It contains polygons representing Ocean Energy test sites in the following countries: Denmark, France, Ireland, Norway, Portugal, Spain, Sweden, The Netherlands and United Kingdom. Where available, each polygon has the following attributes: test site code, name, location, country, sea basin, sea, distance to coast (metres), resource type (wave, tidal), starting year, ending year, lease status, site status, capacity (kW), depth (metres), area (square km), grid connection, number of berths, developer, position info (it indicates if the attribute value is original from the source or has been estimated) and the studies conducted for the Environmental Assessment (EIA). In 2023, new data has been included and existing data has been updated.

This dataset is about books. It has 11 rows and is filtered where the book subjects is Russia-History-1613-1689. It features 9 columns including author, publication date, language, and book publisher.

This dataset is about politicians. It has 68 rows and is filtered where the political party is LDPR (Russia). It features 10 columns including birth date, death date, country, and gender.

This dataset is about politicians. It has 104 rows and is filtered where the political party is KPRF (Russia). It features 5 columns: birth date, death date, country, and political party.

This dataset is about books. It has 9 rows and is filtered where the book subjects is Ukraine-Relations-Russia. It features 9 columns including author, publication date, language, and book publisher.