United States Imports from Russia was US$3.27 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Russia - data, historical chart and statistics - was last updated on July of 2025.

United States Imports from Russia of Fertilizers was US$1.3 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports from Russia of Fertilizers - data, historical chart and statistics - was last updated on July of 2025.

Exports to Russia in the United States increased to 35.95 USD Million in February from 29.98 USD Million in January of 2024. This dataset includes a chart with historical data for the United States Exports to Russia.

This dataset is about books. It has 5 rows and is filtered where the book subjects is United States-Relations-Russia. It features 9 columns including author, publication date, language, and book publisher.

United States Import: Customs: Russia: Crude Materials, Inedible, ex Fuels data was reported at 26.065 USD mn in May 2018. This records an increase from the previous number of 22.391 USD mn for Apr 2018. United States Import: Customs: Russia: Crude Materials, Inedible, ex Fuels data is updated monthly, averaging 8.115 USD mn from Jan 1996 (Median) to May 2018, with 269 observations. The data reached an all-time high of 35.996 USD mn in Aug 2008 and a record low of 1.970 USD mn in Jul 2009. United States Import: Customs: Russia: Crude Materials, Inedible, ex Fuels data remains active status in CEIC and is reported by US Census Bureau. The data is categorized under Global Database’s USA – Table US.JA063: Trade Statistics: Portugal and Russia: SITC.

This data release is a subset of the National Hydrography Dataset (NHD) water bodies, specifically lakes Mendocino and Sonoma in the Russian River watershed. The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic ...

About this dataset

Context

The dataset tabulates the Russia town population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Russia town across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Russia town was 2,268, a 0.40% increase year-by-year from 2022. Previously, in 2022, Russia town population was 2,259, a decline of 0.40% compared to a population of 2,268 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Russia town decreased by 219. In this period, the peak population was 2,603 in the year 2011. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Russia town is shown in this column.
• Year on Year Change: This column displays the change in Russia town population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Russia town Population by Year. You can refer the same here

Activities done by an astronaut or cosmonaut outside a spacecraft beyond the Earth's appreciable atmosphere.

About this dataset

Context

The dataset tabulates the Russia township population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Russia township across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Russia township was 26, a 0% decrease year-by-year from 2022. Previously, in 2022, Russia township population was 26, a decline of 3.70% compared to a population of 27 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Russia township decreased by 7. In this period, the peak population was 38 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Russia township is shown in this column.
• Year on Year Change: This column displays the change in Russia township population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Russia township Population by Year. You can refer the same here

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

Monthly river flow rates and maximum yearly flow rates for selected rivers in Russia were obtained from Esther Munoz and Seelye Martin at University of Washington, School of Oceanography. The data were originally compiled by United Nations Educational, Scientific and Cultural Organization (UNESCO) and the State Hydrological Institute in St. Petersburg, Russia, which is part of the Federal Service for Hydrometeorology and Environmental Monitoring, Moscow, Russia.

This occurrence dataset provides primary data on repeated tree measurement of two inventories on the permanent sampling plot (8.8 ha) established in the old-growth polydominant broadleaved forest stand in the “Kaluzhskie Zaseki” State Nature Reserve (center of the European part of Russian Federation). The time span between the inventories was 30 years, and a total of more than 11 000 stems were included in the study (11 tree species and 3 genera). During the measurements, the tree species (for some trees only genus was determined), stem diameter at breast height of 1.3 m (DBH), and life status were recorded for every individual stem, and some additional attributes were determined for some trees. Field data were digitized and compiled into the PostgreSQL database. Deep data cleaning and validation (with documentation of changes) has been performed before data standardization according to the Darwin Core standard.

Представлены первичные данные двух перечетов деревьев, выполненных на постоянной пробной площади (8.8 га), заложенной в старовозрастном полидоминантном широколиственном лесу в заповеднике “Калужские засеки”. Перечеты выполнены с разницей в 30 лет, всего исследовано более 11 000 учетных единиц (деревья 11-ти видов и 3-х родов). Для каждой учетной единицы определяли вид, диаметр на высоте 1.3 м и статус, для части деревьев также измеряли дополнительные характеристики. Все полевые данные были оцифрованы и организованы в базу данных в среде PostgreSQL. Перед стандартизацией данных в соответствии с Darwin Core выполнена их тщательная проверка, все внесенные изменения документированы.

This is a collection of full-text datasets based on contents extracted from the websites of Russian state institutions.

All datasets do not include items published after 31 December 2023.

These datasets have been introduced in the following book chapter, which offers additional context:

Comai, Giorgio (2025, forthcoming), "Text-mining on-line sources from Russia openly", in Autocracy, Influence, War: Russian Propaganda Today, edited by Paul Goode

The name of each corpus is composed of the bare domain name, a two letter code of the main language of the contents, and the year of release of the dataset, separated by an underscore, e.g. kremlin.ru_ru_2024 for the Russian-language version of Kremlin.ru.

This release includes the following websites: - Russia's president, kremlin.ru, in English, filename: kremlin.ru_en_2024, from 1999-12-31 to 2023-12-31. Items included: 33 165 - Russia's president, kremlin.ru, in Russian, filename: kremlin.ru_ru_2024, from 1999-12-31 to 2023-12-31. Items included: 45 538 - Russia's MFA, mid.ru, in English, filename: mid.ru_en_2024, from 2003-01-04 to 2023-12-31. Items included: 25 943 - Russia's MFA, mid.ru, in Russian, filename: mid.ru_ru_2024, from 2003-01-02 to 2023-12-31. Items included: 56 203 - Russia's government, government.ru, in Russian, filename: government.ru_ru_2024, from 2006-06-22 to 2023-12-30. Items included: 17 135 - Russia's government (archived version), archive.government.ru, in Russian, filename: archive.government.ru_ru_2024, from 2008-05-07 to 2013-05-21. Items included: 7 103 - Russia's prime minister (archived version), archive.premier.gov.ru, in Russian, filename: archive.premier.gov.ru_ru_2024, from 2008-05-07 to 2012-05-07. Items included: 3 323 - Russia's Duma, duma.gov.ru, in Russian, filename: duma.gov.ru_ru_2024, from 2006-04-05 to 2023-12-30. Items included: 29 094 - Russia's Duma (transcripts), transcript.duma.gov.ru, in Russian, filename: transcript.duma.gov.ru_ru_2024, from 1994-01-11 to 2023-12-15. Items included: 6 032

File formats: compressed csv files (.csv.gz); Open Document Spreadsheets (.ods)

A web version of the documentation accompanying this release is available online: https://tadadit.xyz/datasets/2024/russian_institutions_2024/

Explore through a basic web interface: https://explore.tadadit.xyz/2024/ru_institutions_2024/

This dataset is about book subjects. It has 6 rows and is filtered where the books is Bering : the Russian discovery of America. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Over the past several decades, many climate datasets have been exchanged directly between the principal climate data centers of the United States (NOAA's National Climatic Data Center (NCDC)) and the former-USSR/Russia (All-Russian Research Institute for Hydrometeorological Information-World Data Center (RIHMI-WDC)). This data exchange has its roots in a bilateral initiative known as the Agreement on Protection of the Environment (Tatusko 1990). CDIAC has partnered with NCDC and RIHMI-WDC since the early 1990s to help make former-USSR climate datasets available to the public. The first former-USSR daily temperature and precipitation dataset released by CDIAC was initially created within the framework of the international cooperation between RIHMI-WDC and CDIAC and was published by CDIAC as NDP-040, consisting of data from 223 stations over the former USSR whose data were published in USSR Meteorological Monthly (Part 1: Daily Data). The database presented here consists of records from 518 Russian stations (excluding the former-USSR stations outside the Russian territory contained in NDP-040), for the most part extending through 2010. Records not extending through 2010 result from stations having closed or else their data were not published in Meteorological Monthly of CIS Stations (Part 1: Daily Data). The database was created from the digital media of the State Data Holding. The station inventory was arrived at using (a) the list of Roshydromet stations that are included in the Global Climate Observation Network (this list was approved by the Head of Roshydromet on 25 March 2004) and (b) the list of Roshydromet benchmark meteorological stations prepared by V.I. Kodratyuk, Head of the Department at Voeikov Main Geophysical Observatory. For access to the data files, click this link to the CDIAC data transition website: http://cdiac.ess-dive.lbl.gov/ndps/russia_daily518.html

About this dataset

Context

The dataset illustrates the median household income in Russia town, spanning the years from 2010 to 2023, with all figures adjusted to 2023 inflation-adjusted dollars. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2023, the median household income for Russia town increased by $17,656 (25.21%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $5,602 (7.68%) between 2010 and 2023.

Analyzing the trend in median household income between the years 2010 and 2023, spanning 13 annual cycles, we observed that median household income, when adjusted for 2023 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 8 years and declined for 5 years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021, 2022, 0223

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2023
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2023 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

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. You can refer the same here

Our dataset includes 7632 records of trematode occurrences in small mammals (Erinaceomorpha, Soricomorpha, Rodentia and Chiroptera) from the Middle Volga region (European part of Russia) including 4645 digenean records in Samara Region, 2986 records in the Republic of Mordovia and one trematode record in Ulyanovsk Region. The dataset presents data on 46 species of trematodes from 23 genera and 11 families found in the Middle Volga region. Our studies of trematodes in micromammals were conducted using the method of complete helminthological necropsy. The data on trematodes in 29 species of micromammals belonging to 15 genera are presented. In total, the number of collected trematode specimens in our dataset was 155.061. The dataset summarises micromammals’ trematode occurrences obtained by long-term field helminthological studies of erinaceomorphs, soricomorphs, bats and rodents in the territory of the Middle Volga region during more than 20-year period (1999–2024). Each occurrence record contains the trematode species name, basis of record, locality of host species, geographic coordinates, coordinate accuracy, date of the record and authors of the record and species identification. All occurrence records are georeferenced. The dataset is based on research of the staff of the Institute of Ecology of the Volga River basin of the Russian Academy of Sciences and the Joint Directorate of the Mordovia State Nature Reserve and National Park “Smolny”. Our research in the Samara Region was funded by Russian Science Foundation, grant number 23-24-10021.

This data release contains digital data generated by the U.S. Geological Survey under cooperative agreements with Sonoma County Water Agency and the California State Water Resources Control Board to characterize the three-dimensional hydrogeology of the Russian River Watershed, located in the northern part of the California Coast Ranges section of the Pacific Border province. This dataset contains geospatial data of a three-dimensional hydrogeologic framework model (3D HFM). The 3D HFM was constructed using methods from previously published reports. Sources of 3D HFM construction methods can be found in the metadata. The geospatial database contains a polygon feature class that is a 2-dimensional representation of the 3D HFM. The polygon feature class is a cellular array where each model cell has multiple attributes including XY location, and interpolated elevations and thicknesses of hydrogeologic units.

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: Irani... Visit https://dataone.org/datasets/sha256%3Aaa1b4aae69c3399c96bfbf946da54abd8f7642332d12ccd150c42ad400e9699b for complete metadata about this dataset.

Explore real GDP growth projections dataset, including insights into the impact of COVID-19 on economic trends. This dataset covers countries such as Spain, Australia, France, Italy, Brazil, and more.

growth rate, Real, COVID-19, GDP

Spain, Australia, France, Italy, Brazil, Argentina, United Kingdom, United States, Canada, Russia, Turkiye, World, China, Mexico, Korea, India, Saudi Arabia, South Africa, Germany, Indonesia, JapanFollow data.kapsarc.org for timely data to advance energy economics research..Source: OECD Economic Outlook database.- India projections are based on fiscal years, starting in April. The European Union is a full member of the G20, but the G20 aggregate only includes countries that are also members in their own right. Spain is a permanent invitee to the G20. World and G20 aggregates use moving nominal GDP weights at purchasing power parities. Difference in percentage points, based on rounded figures.