Russia is the largest country in the world by far, with a total area of just over 17 million square kilometers. After Antarctica, the next three countries are Canada, the U.S., and China; all between 9.5 and 10 million square kilometers. The figures given include internal water surface area (such as lakes or rivers) - if the figures were for land surface only then China would be the second largest country in the world, the U.S. third, and Canada (the country with more lakes than the rest of the world combined) fourth. Russia Russia has a population of around 145 million people, putting it in the top ten most populous countries in the world, and making it the most populous in Europe. However, it's vast size gives it a very low population density, ranked among the bottom 20 countries. Most of Russia's population is concentrated in the west, with around 75 percent of the population living in the European part, while around 75 percent of Russia's territory is in Asia; the Ural Mountains are considered the continental border. Elsewhere in the world Beyond Russia, the world's largest countries all have distinctive topographies and climates setting them apart. The United States, for example, has climates ranging from tundra in Alaska to tropical forests in Florida, with various mountain ranges, deserts, plains, and forests in between. Populations in these countries are often concentrated in urban areas, and are not evenly distributed across the country. For example, around 85 percent of Canada's population lives within 100 miles of the U.S. border; around 95 percent of China lives east of the Heihe–Tengchong Line that splits the country; and the majority of populations in large countries such as Australia or Brazil live near the coast.

This graph shows the growth in the U.S. export volume of trade goods to Russia from 1992 to 2023. In 2023, U.S. exports to Russia amounted to about 600 million U.S. dollars. RussiaRussia is the largest country in the world, with a combined land and water area of about 17 million square kilometers between their borders. It covers more than one-eighth of the Earth's inhabited land area. It is larger than Antarctica (14 million square kilometers) and larger than all of Europe (10.5 million square kilometers). In comparison to the country’s huge land mass, the population of Russia is rather minute; with a population of only about 144 million, which comes down to a population density of 8.4 inhabitants per square kilometer. Population density in the United States, which is the fourth largest country in the world, has about 33 inhabitants per sqkm. The Russian population is relatively concentrated around the big metropolitan areas of the country. About 75 percent of Russians live in an urban area. Moscow, the capital of Russia, has about 12 million inhabitants. The greater Moscow metropolitan area has about 16 million inhabitants and is the fifteenth largest metro area in the world. Most of the Russian population lives in the part of the country that is part of the European continent. About 74 percent of all Russians live west of the Ural on an area that encompasses only 23 percent of Russia's total land area.

As of 2025,the combined forces of NATO had approximately 3.44 million active military personnel, compared with 1.32 million active military personnel in the Russian military. The collective military capabilities of the 32 countries that make up NATO outnumber Russia in terms of aircraft, at 22,377 to 4,957, and in naval power, with 1,143 military ships, to 419. In terms of ground combat vehicles, NATO had an estimated 11,495 main battle tanks, to Russia's 5,750. The combined nuclear arsenal of the United States, United Kingdom, and France amounted to 5,559 nuclear warheads, compared with Russia's 5,580. NATO military spending In 2024, the combined military expenditure of NATO states amounted to approximately 1.47 trillion U.S. dollars, with the United States responsible for the majority of this spending, as the U.S. military budget amounted to 967.7 billion dollars that year. The current U.S. President, Donald Trump has frequently taken aim at other NATO allies for not spending as much on defense as America. NATO member states are expected to spend at least two percent of their GDP on defense, although the U.S. has recently pushed for an even higher target. As of 2024, the U.S. spent around 3.38 percent of its GDP on defense, the third-highest in the alliance, with Estonia just ahead on 3.43 percent, and Poland spending the highest share at 4.12 percent. US aid to Ukraine The pause in aid to Ukraine from the United States at the start of March 2025 marks a significant policy change from Ukraine's most powerful ally. Throughout the War in Ukraine, military aid from America has been crucial to the Ukrainian cause. In Trump's first term in office, America sent a high number of anti-tank Javelins, with this aid scaling up to more advanced equipment after Russia's full-scale invasion in 2022. The donation of around 40 HIMARs rocket-artillery system, for example, has proven to be one of Ukraine's most effective offensive weapons against Russia. Defensive systems such as advanced Patriot air defense units have also helped protect Ukraine from aerial assaults. Although European countries have also provided significant aid, it is unclear if they will be able to fill the hole left by America should the pause in aid goes on indefinitely.

Russia was the largest country in the Commonwealth of Independent States (CIS) region, with a total area of over 17 million square kilometers in 2025. Furthermore, Russia was the largest country in the world, followed by Canada, the United States, and China. Ranking second among the CIS countries was Kazakhstan, whose land area comprised about 2.7 million square kilometers.

In 2025, India overtook China as the world's most populous country and now has almost 1.46 billion people. China now has the second-largest population in the world, still with just over 1.4 billion inhabitants, however, its population went into decline in 2023. Global population As of 2025, the world's population stands at almost 8.2 billion people and is expected to reach around 10.3 billion people in the 2080s, when it will then go into decline. Due to improved healthcare, sanitation, and general living conditions, the global population continues to increase; mortality rates (particularly among infants and children) are decreasing and the median age of the world population has steadily increased for decades. As for the average life expectancy in industrial and developing countries, the gap has narrowed significantly since the mid-20th century. Asia is the most populous continent on Earth; 11 of the 20 largest countries are located there. It leads the ranking of the global population by continent by far, reporting four times as many inhabitants as Africa. The Demographic Transition The population explosion over the past two centuries is part of a phenomenon known as the demographic transition. Simply put, this transition results from a drastic reduction in mortality, which then leads to a reduction in fertility, and increase in life expectancy; this interim period where death rates are low and birth rates are high is where this population explosion occurs, and population growth can remain high as the population ages. In today's most-developed countries, the transition generally began with industrialization in the 1800s, and growth has now stabilized as birth and mortality rates have re-balanced. Across less-developed countries, the stage of this transition varies; for example, China is at a later stage than India, which accounts for the change in which country is more populous - understanding the demographic transition can help understand the reason why China's population is now going into decline. The least-developed region is Sub-Saharan Africa, where fertility rates remain close to pre-industrial levels in some countries. As these countries transition, they will undergo significant rates of population growth.

As of May 2025, China had the largest armed forces in the world by active duty military personnel, with about *********** active soldiers. India, the United States, North Korea, and Russia rounded out the top five largest armies. Difference between active and reserve personnel Active personnel, also known as active duty in the United States and active service in the United Kingdom, are those individuals whose full-time occupation is being part of a military force. Active duty contrasts with a military’s reserve force, which are individuals who have both a military role and a civilian career. The number of active duty forces in the U.S. is much larger than its reserve membership. What is the strongest army? The strength of a country’s armed forces is not only determined by how many personnel they maintain, but also the number and quality of their military equipment. For example, looking only at personnel does not factor in the overwhelmingly higher number of nuclear warheads owned by Russia and the United States compared to other countries. One way to answer this question is to look at the total amount of money each country spends on their military, as spending includes both personnel and technology. In terms of countries with the highest military spending, the United States leads the world with an annual budget almost ***** times larger than second-placed China.

About the Project KAPSARC is analyzing the shifting dynamics of the global gas markets, which have turned upside down during the past five years. North America has emerged as a large potential future LNG exporter while gas demand growth has been slowing down as natural gas gets squeezed between coal and renewables. While the coming years will witness the fastest LNG export capacity expansion ever seen, many questions are raised on the next generation of LNG supply, the impact of low oil and gas prices on supply and demand patterns and how pricing and contractual structure may be affected by both the arrival of U.S. LNG on global gas markets and the desire of Asian buyers for cheaper gasKey PointsAround 150 mtpa of LNG export capacity will come to global gas markets over 2015-20. While Asia seems unlikely now to be able to absorb it all, Europe emerges as a residual market for flexible volumes. The question is, therefore, which outcome(s) in the global LNG market could set the stage for a battle for market share in the European gas market between LNG suppliers and the incumbent pipeline suppliers, most importantly Russia, and how that country could respond to the potential challenge of large quantities of LNG supplies flooding European gas markets? Russia’s gas export strategy in Europe so far has been based on value maximization rather than on protecting its market share. But if increasing LNG supply to Europe becomes an extended threat to Russia’s market share, it may change its position from reactive to proactive and attempt to defend it. Whether a confrontation between Russian gas and LNG takes place and how Russia could respond depends crucially on the build-up of total LNG trade and the appetite of China for LNG. Russia has the advantage of being a low cost producer with ample spare productive capacity and underutilized pipeline capacity to Europe. A low price environment (up to $40/bbl) would actually benefit Russia more than a higher price environment, from a market share perspective, as it can reduce its prices below the variable costs of U.S. LNG and can push U.S. volumes out of the European market. In a higher price environment, U.S. LNG would continue to flow. The competition between Russian gas and U.S. LNG in Europe is also about pricing models, driven on one hand by oil market fundamentals, with some influence from Europe spot markets, and on the other hand driven by the fundamentals of the U.S. gas market and the LNG trade. The geopolitical aspect is also important. While relations between Russia and Europe have become frosty, cheap and abundant Russian gas could potentially help mend commercial ties. However, the tensions between the U.S. and Russia have been increased by the Ukraine situation, the war in Syria and sanctions. The competition between U.S. LNG and Russian pipeline gas in Europe is about more than the pure commercial aspects and will be influenced by the geopolitical standoff of the two powers.

The United States' total Imports in 2024 were valued at US$3.36 Trillion, according to the United Nations COMTRADE database on international trade. The United States' main import partners were: Mexico, China and Canada. The top three import commodities were: Machinery, nuclear reactors, boilers; Electrical, electronic equipment and Vehicles other than railway, tramway. Total Exports were valued at US$2.06 Trillion. In 2024, The United States had a trade deficit of US$1.29 Trillion.

Bathymetric contours were generated from soundings collected during surveys and cruises by the Hydrographic Office, National Ocean Survey, and Coast and Geodetic Survey. The region covered by the map is the Bering Sea Shelf from Bristol Bay, Alaska to the Gulf of Anadyr, Russia. Bathymetry is in meters at 10 m intervals, with 5 m supplemental contours. The digitized portion includes the Anadyr Gulf and Bering Strait in Russian waters (west of the Exclusive Economic Zone), to supplement digitized National Ocean Service maps of U.S. waters (Coastal Shelf Bathymetry of the Bering, Chukchi, and Beaufort Seas). The original paper map was produced by the Geological Society of America and published in 1974. The map is no longer in print from the Geological Society of America (3300 Penrose Place, Boulder, CO 80301) but may be available at natural resource agency libraries that include literature on Alaska and/or Russia. In 1997, the USGS digitized the bathymetric contours for research pur ...

The invasive shrub, Russian olive (Elaeagnus augustifolia), is widely established within riparian areas across the western United States (U.S.). Limited information on its distribution and invasion dynamics in northern regions has hampered understanding and management efforts. Given this lack of spatial and ecological information we worked with local stakeholders and developed two main objectives: 1) map the distribution of Russian olive along the Powder River (Montana and Wyoming, U.S.) with field data and remote sensing; and 2) relate that distribution to environmental variables to understand its habitat suitability and community/invasion dynamics. In the study watershed, field data showed Russian olive has reached near equal canopy cover (18.3%) to native plains cottonwood (Populus deltoides; 19.1%), with higher cover closer to the channel and over a broader range of elevations. At the basin scale, we modeled Russian olive distribution using field surveys, ocular sampling of aerial imagery, and spectral variables from Sentinel-2 MultiSpectral Instrument using a random forest model. A statistical model linking the resulting Russian olive percent cover detection map (RMSE = 15.42, R2 = 0.64) to environmental variables for the entire watershed indicated Russian olive cover increased with flow accumulation and groundwater depth, decreased with elevation, and was associated with poorer soil types. We attribute the success of Russian olive to its broad habitat suitability combined with changing hydrologic conditions favoring it over natives. This study provides a repeatable Russian olive detection methodology due to the use of Sentinel-2 imagery that is available worldwide, and provides insight into its ecological relationships and success with relevance for management across areas with similar environmental conditions. Methods Model Training Data To predict Russian olive percent cover across the Powder River Basin, we created a spectral detection model for the year 2020. The model was trained using two different data collection methods: (1) field data and (2) ocular samples from NAIP 2019 aerial imagery. Field data were collected in June 2021 (Figure 1A). Ten meter radius plots were placed on transects (25 on the east bank and 17 on the west bank) perpendicular to the river and about 50 m apart, for a total of 276 plots (Figure 1A). Within each plot, vegetation cover was estimated for each woody species, including Russian olive, plains cottonwood (Populus deltoides), and tamarisk (Tamarix ramosissima), and height of the tallest woody plant was measured using a survey rod or clinometer. Of the 276 field data plots, 185 contained Russian olive. To increase the dataset size and spatial representation, we conducted randomized ocular image sampling using NAIP 2019 true and false color imagery following a similar sampling procedure as described in (Woodward et al. 2018ab). NAIP 2019 false color imagery was referenced to help with species classification. We used Google Earth Engine (GEE) to collect 10-meter radial plots, matching the size of the field plots. We visually determined the percentage of Russian olive coverage present on a scale of 0-100 %, with 0 % being no Russian olive present and 100 % being full Russian olive cover, within each 10-meter radial plot (Figure 2). Prior to making formal observations, all five observers went through a calibration process to train and reduce bias. Due to the rarity of Russian olive in our random sample, we also opportunistically collected 478 additional plots with Russian olive. Most opportunistic aerial imagery ocular sampling points fell along the Powder River between Clear Creek and Crazy Woman Creek in Wyoming. In preliminary model runs, low to moderate Russian olive cover was unrealistically predicted in cropland areas, such as areas of Barley (Categorization Code “21”), Winter Wheat (“24”), Alfalfa (“36”), and Other Hay (“37”), so we created a simple mask to remove most crops from the final analysis. The mask was created using land cover classifications from the 2020 USDA National Agricultural Statistics Service Cropland Data Layer (NASSCD; 2021). Land cover types where Russian olive is known to occur such as Shrubland (NASSCD attribute code “152”), Grassland/Pasture (“176”), Woody Wetlands (“190”), and Herbaceous Woodlands (“195”) were retained. Table S1.2 contains a detailed list of land cover types that were not masked from the final model. All NASSCD agricultural land cover types from 0-61, 66-77, and 204-254 were excluded from the Russian olive model. The mask was also used to remove the ocular samples to build the model on sampled points that did not fall within agricultural areas. We built our model on 2,160 points (1,407 random ocular samples, 477 opportunistic ocular samples, 276 field samples), 595 of which had Russian olive present (419 ocular samples). Random Forest Model We created a mosaic of 2020 imagery from Copernicus Sentinel-2 MultiSpectral Instrument Level-1C data to cover the Powder River Basin study area, obtained in GEE. We filtered images for those with low cloud cover (<20-30 %), then created a median composite image for each relevant season – spring (2020-04-01 to 2020-05-15), summer (2020-05-16 to 2020-07-31), and fall (2020-08-01 to 2020-09-30) – to account for seasonal phenological variation (Gorelick et al. 2017). Spectral bands and vegetation indices were derived from the images, which included a Normalized Difference Vegetation Index (NDVI), Normalized Difference Moisture Index (NDMI), Normalized Burn Ratio (NBR), Simple Ratio (SR), Tasseled Cap transformation, and others (Table S1.1). The resulting Tasseled Cap brightness, greenness, and wetness (BGW) indices, named for the features they emphasize, improve vegetation classifications because they are sensitive to phenological changes (Crist and Cicone 1984). We also differenced indices between summer and spring and summer and fall to capture seasonal variation of different species to aid Russian olive detection (Evangelista et al. 2009). We modeled Russian olive percent cover and evaluated predictor variable performance using the ‘randomForest’ package in RStudio (Liaw et al. 2002) using spectral bands and vegetation indices from GEE. Our independent variable was Russian olive percent cover and all 61 predictor variables are identified in Table S1.1. The number of trees (ntree) and number of variables randomly sampled at each split (mtry) were set to 1,000 and 3, respectively (Liaw et al. 2002). We valued a model with fewer predictor variables and removed predictors that did not improve the model to achieve better model performance and greater interpretability (Evans et al. 2010). We first ran a model using all predictor variables to evaluate initial out-of-bag model performance using the R2 value and root mean squared error (RMSE), a standard measure of the magnitude of model error. We then evaluated correlations between variables, removing one variable from pairs correlated by greater than 0.7 (Dormann et al. 2013), leaving us with 18 variables after the initial run. With the remaining 18 uncorrelated variables, Wwe ran 1276 additional models using backwards selection to remove the one or two variables with the lowest variable importance as measured by the increase in mean squared error. Variables with partial dependence plots that suggested the variable contributed to over-fitting or had a weak relationship were removed (Friedman 2001). The greater the R2 value and the smaller the RMSE, the better the model performed. The final model had six variables. Finally, we summarized the random forest model results by 5 km hexagon to show trends of Russian olive cover across the study area. Species Composition and Russian Olive Habitat Suitability at the Watershed Scale Plot data collected along transects perpendicular to the Powder River channel allowed for additional insights when paired with lidar and topographic data, particularly because a robust suite of woody species was recorded in addition to Russian olive. Topographic position index (TPI) was derived from 2016 lidar data (Ackerman 2016). TPI is a measure of position by comparing elevation at a given point to the mean elevation in a surrounding window (Weiss 2001). In this case, a 100 cell (100 m) radius was used and can be interpreted as position relative to the detrended channel. We also derived a Canopy Height Model (CHM), calculated as the digital terrain model minus the digital surface model, calculated in OpenTopography.org. We extracted mean TPI, CHM, and distance from channel centerline to field plots to investigate how these varied by species by considering variable distributions (i.e., boxpolots and basic statistical moments and distributions) by dominant plot species. Additionally, we used the complete suite of species and cover data in a k-means grouping analysis that included distance from the channel in ArcGIS Map. The k-means grouping method is an unsupervised classification method where every point is assigned a group based on their similarity (Davies and Bouldin, 1979). The Pseudo F-Statistic was used to determine how many groups to include in the final analysis. This allowed inference regarding the spatial relationships among Russian olive, cottonwood, and tamarisk, which was not possible in the watershed-scale modeling. Previous work (Nagler et al. 2011) describes factors at the continental to reach scale known to influence Russian olive distribution. Robust species cover data for an entire watershed is rare. As such, here we have a unique opportunity to bridge the reach and continental scales (Nagler et al. 2011). At watershed scales, surface and groundwater flow conditions, and their regulation, are known to influence native and invasive riparian species distributions across North America (McShane et al. 2015). Surface flows have declined through time in the Powder

The USD/RUB exchange rate fell to 77.1688 on December 2, 2025, down 0.72% from the previous session. Over the past month, the Russian Ruble has strengthened 4.39%, and is up by 26.50% over the last 12 months. Russian Ruble - values, historical data, forecasts and news - updated on December of 2025.

With the launch of Landsat 7, data are no longer copyright protected and these data may be freely distributed. EOS-WEBSTER, in an effort to provide access to earth science data, has designed an interim system to make Landsat data that we have in our database available to other users. In many cases, in-house researchers have acquired these data directly from the USGS EROS Data Center (EDC) for their research projects. They have provided copies of their data to EOS-WEBSTER for distribution to a wide audience. Boreal Russian Landsat data are also being housed.

Therefore, our data holdings come from several different sources and can have a variety of different processing levels associated with them. We have attempted to document, to the best of our ability, the processing steps each Landsat scene has been through. Our data are currently served in two output formats: BSQ and ERDAS Imagine, and three different spectral types (when available): multispectral, panchromatic, and thermal. A header file is provided with each ordered image giving the specifics of the image.

Please refer to the references to learn more about Landsat and the data this satellite acquires. We hope to add more data as it becomes available to EOS-WEBSTER. If you have any Landsat data, which you are willing to share, EOS-WEBSTER would like to provide access to it to a broad audience by adding it to our database. Landsat 7 data and Landsat 5 data older than 10 years can be distributed without copyright restrictions. Please contact our User Services Personnel if you would like to distribute your Landsat data, or other earth science products, via EOS-WEBSTER's FREE data distribution mechanism.

See more detailed information regarding these data and data access privilages at "http://eos-earthdata.sr.unh.edu/" or contact the Data Center Contact above.

We assessed changes in the population size, density, and diet composition of wolves inhabiting the Romincka Forest (RF), an area of 480 km2 situated along the state border between Poland, Russian Federation (Kaliningrad), and Lithuania. We compared the results of our research in 2020-2021 with data from other projects conducted since 1999. We found that both packs living in RF had transboundary territories. The number of packs was stable over 21 years, the average pack size almost doubled (from 4-4.5 to 7.5-8 wolves per pack), the total wolf number increased 1.8 times, reaching 15-16 wolves, the density increased 1.5 times up to 3.1-3.3 wolves/100 km2 in winter 2020/2021. Our analyses of 165 scats revealed that beavers Castor fiber made up 45.6% of food biomass in the wolf diet in 2020, which was 3.4 times more than in 1999-2004 (n=84 scats, 13.4%). Wild ungulates constituted 44.8% of the wolf food biomass in 2020, 1.6 times less than before (71.1%). In our study, among wild ungulates, wolves primarily consumed roe deer Capreolus capreolus (22.6% of food biomass), then wild boars Sus scrofa (13.7%) and red deer Cervus elaphus (5.0%), while moose Alces alces was eaten rarely (0.4%). We also recorded domestic dogs (4.9% of food biomass) and cattle (3.1%). The food niche breadth was wider (B=2.31) than in the earlier period (B=1.84), and the Pianka index showed moderate similarity in food composition between both periods (α=0.816). In November 2022, due to the migration crisis, a 199 km impermeable fence along the state border with Kaliningrad was erected, which blocked access to 48% of the RF area that was regularly used by the resident wolf packs. This may cause wolf numbers to decrease and isolation from the central part of the Baltic wolf population to which they belong, according to our DNA analyses. Methods Tracking. We tracked wolves by foot or by car, using the regular and dense network of dirt roads, routes, and other linear structures, and the plowed strip of soil along the borderline, across the whole Polish portion of RF, that wolves used for traveling and scent-marking. In snow-free seasons, we found tracks on mud or sand and followed them as far as were visible, usually at distances of 100-300 m, while in winter, snow cover allowed us to follow wolf tracks up to 10 km. Species identification was based on the shape and size of tracks and evidence of animal behavior during scent-marking. Additionally, track identification was verified with genetic analysis of scat and urine samples collected during tracking. In winter, we estimated the number of wolves in the tracked group on snow by counting the number of individual trails when wolves split, which usually happened on road junctions and was associated with intense scent-marking. We measured the length of the footprint of the front paw with claws and the distance between the heels of subsequent footprints in the track to distinguish between adults and pups. To record adult wolves and pups, we used camera traps Browning Spec Ops Advantage and Browning Spec Ops Edge, USA, which were set up to register 30s-long videos with a 1s delay between recordings. Altogether, we set up camera traps in 28 locations, mostly on roads and road junctions, which were defined based on evidence of wolf presence (tracks and scent marks) found during tracking and wolf response to our howl stimulation. Cameras worked for 112 camera days. Genotyping. To distinguish between wolf family groups in RF, we applied genetic fingerprinting. Because our main goal was to genotype and distinguish between parental pairs and their relatives, which scent-mark the pack territory mostly on roads and junctions, we were looking for non-invasive samples (fresh scats and urine from snow) during wolf tracking on forest roads and trails within the whole forest tract. We put 3-4 cm long fragments of fresh wolf scats into plastic tubes (30 ml) fixed with 96% ethanol and stored at +4°C, while urine samples collected from snow, we mixed with two volumes of 96% ethanol and sodium acetate (100 mM final concentration) and kept at −20°C. All tubes with samples were described with date, GPS coordinates, and, whenever possible, the presumed status (breeder, subadult, male, or female) based on the behaviour of the tracked wolf. DNA isolation was performed in a separate cleanroom to avoid contamination. DNA from scats was isolated either with QIAamp DNA Stool Mini Kit (Qiagen) or Exgene™ Stool DNA Mini kit (GeneAll Biotechnology), while precipitated urine samples were isolated with Exgene™ Tissue SV kit (GeneAll Biotechnology). Genetic fingerprinting was based on 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054, FH2087L, FH2088, FH2096, FH2137, FH2140, FH2161, vWF, PEZ17 and CPH5. Additionally, DBX intron 6 and DBY intron 7 were used as sex markers. Details of laboratory analyses are described in the paper of Szewczyk et al. (2019. Sci. Rep.). Furthermore, we assigned the local wolves to the source population, comparing microsatellite genotypes from the study area with reference genotypes from the Central and Eastern European wolf populations (Baltic, Carpathian, and Central European Lowland). From each population, we selected 30 genotypes (from the dataset from Szewczyk et al. 2019, Sci. Rep.) and processed them together with genotypes from RF using STRUCTURE 2.3.4 software. Analyses were performed for a number of clusters (K) ranging from 1 to 10, with 10 iterations per K. The Evanno method applied in STRUCTURE HARVESTER was used to infer the most likely number of clusters. Wolf diet. The diet composition was assessed by analyzing the content of wolf scats. Scats were collected opportunistically year-round on the dense grid of forest roads and other linear structures spanning the whole Polish RF and during long-distance tracking on snow. Each scat was placed in a paper envelope and described with date and GPS coordinates. Then scats were dried for a minimum of five days at 70°C in a laboratory drier to kill parasites. Socked scats were washed through a 0.5 mm-mesh sieve and then dried. The species eaten by wolves were identified based on hair, bones, teeth, and hooves found in scats using hair or osteological keys and reference material collected during earlier studies on the wolf foraging ecology. In cases where the identification of food items in the scat based on the hair and bones was inconclusive (n=19 samples), we determined the species based on the mtDNA isolated from the hair from each sample. We extracted DNA using Exgene™ Genomic DNA micro kit (GeneAll Biotechnology) with modification of manufacturer protocol with the elongated time of first incubation (until complete dissolution, from 2.5 hours to overnight incubation), with the result of 50 ul of DNA extract. The PCR solution, reaction conditions, and further analyses were conducted as described in Szewczyk et al. (2019, Sci. Rep.) with primers L15995 (Taberlet and Bouvet 1994) and H16498 (Fumagalli et al. 1996) proposed by Pun et al. (2009) for species identification from mixed samples.

This data release contains the boundaries of assessment units, assessment input data tables and resulting fact sheet data tables for the assessment of undiscovered oil and gas resources in the West Siberian Province in Russia. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. Machine-readable tables are provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

Time series data for the data Current Account and Its Components - Current USD, TTM for the country Russian Federation. The Current Account and Its Components The current account is a component of a country's balance of payments that records the transactions of goods, services, income, and current transfers between residents of the country and the rest of the world. It consists of four main components:

a. Trade in Goods Balance

b. Trade in Services Balance

c. Primary Income Balance

d. Secondary Income Balance

1. Trade in Goods Balance Definition: This includes the export and import of physical items such as machinery, food, clothing, etc.

Credit Example: A German car manufacturer exports cars to the United States (value of exported cars).

Debit Example: A German electronics retailer imports smartphones from South Korea (value of imported smartphones).

1. Trade in Services Balance Definition: This includes the export and import of services such as tourism, financial services, consulting, transportation, etc.

Credit Example: A German IT company provides software development services to a client in Japan (value of exported services).

Debit Example: A German tourist books a hotel room in France (value of imported tourism services).

1. Primary Income Balance Definition: This includes earnings from the provision of factors of production such as labor, financial assets, land, and natural resources. It covers income from interest, profits, and dividends.

Credit Example: A German investor receives dividends from shares held in a U.S. company (value of received dividends).

Debit Example: Foreign investors receive interest payments on bonds issued by a German company (value of interest payments).

1. Secondary Income Balance Definition: This includes current transfers such as foreign aid, remittances, and other one-way payments that do not involve an exchange of goods or services.

Credit Example: Remittances sent by German residents working abroad to their families in Germany (value of received remittances).

Debit Example: Germany sends humanitarian aid to a developing country (value of sent aid). Trade in Goods Balance (USD)The indicator "Trade in Goods Balance (USD)" stands at 125.52 Billion United States Dollars as of 3/31/2025, the lowest value since 3/31/2024. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -5.57 Billion United States Dollars compared to the value the year prior.The 1 year change is -5.57 Billion United States Dollars.The 3 year change is -119.74 Billion United States Dollars.The 5 year change is -25.87 Billion United States Dollars.The 10 year change is -62.30 Billion United States Dollars.The Serie's long term average value is 139.34 Billion United States Dollars. It's latest available value, on 3/31/2025, is -13.82 Billion United States Dollars lower, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 6/30/2002, to it's latest available value, on 3/31/2025, is +85.62 Billion.The Serie's change in United States Dollars from it's maximum value, on 9/30/2022, to it's latest available value, on 3/31/2025, is -197.02 Billion.Secondary Income Balance (USD)The indicator "Secondary Income Balance (USD)" stands at -3.24 Billion United States Dollars as of 3/31/2025. Regarding the One-Year-Change of the series, the current value constitutes an increase of 4.61 Billion United States Dollars compared to the value the year prior.The 1 year change is 4.61 Billion United States Dollars.The 3 year change is 1.19 Billion United States Dollars.The 5 year change is 5.37 Billion United States Dollars.The 10 year change is 4.04 Billion United States Dollars.The Serie's long term average value is -5.45 Billion United States Dollars. It's latest available value, on 3/31/2025, is 2.21 Billion United States Dollars higher, compared to it's long term average value.The Serie's change in United States Dollars from it's minimum value, on 12/31/2019, to it's latest available value, on 3/31/2025, is +6.98 Billion.The Serie's change in United States Dollars from it's maximum value, on 3/31/2001, to it's latest available value, on 3/31/2025, is -3.07 Billion.Primary Income Balance (USD)The indicator "Primary Income Balance (USD)" stands at -29.02 Billion United States Dollars as of 3/31/2025, the lowest value since 12/31/2023. Regarding the One-Year-Change of the series, the current value constitutes an decrease of -5.37 Billion United States Dollars compared to the value the year prior.The 1 year change is -5.37 Billion United States Dollars.The 3 year change is 19.99 Billion United States Dollars.The 5 year change is 19.20 Billion United States Dollars.The 10 year change is 31.14 Billion United States Dollars.The Serie's long term average value is -37.25 Billion United States Dollars. It's latest available value, on 3/31/2025, is 8.23 ...

Two dominant explanations for ethnic bias in distributional outcomes are electoral incentives and outgroup prejudice. The paper proposes a novel, complementary explanation for this phenomenon: variation in legibility across ethnic groups. I argue that states will allocate fewer resources to groups from which they cannot gather accurate information and collect taxes. I support this argument using original data on state aid during the 1891-92 famine in the Russian Empire. Qualitative and quantitative analyses show that districts with a larger Muslim population experienced higher famine mortality, but received less generous public assistance. Historically ruled via religious intermediaries, the Muslims were less legible and generated lower fiscal revenues. State officials could not guarantee the repayment of food loans or collect tax arrears from Muslim communes, so they were more likely to withhold aid. State relief did not vary with the presence of other minorities, which were more legible and generated more revenue.

This digital database is the product of collaboration between the U.S. Geological Survey, the Alfred Wegener Institute for Polar and Marine Research Potsdam, Foothill College GeoSpatial Technology Certificate Program, and the Geophysical Institute at the University of Alaska. The primary goal for creating this digital database is to enhance current estimates of organic carbon stored in deep permafrost, in particular Late Pleistocene syngenetic ice-rich loess permafrost deposits, called Yedoma. This deposit is vulnerable to thermokarst and erosion due to natural and anthropogenic disturbances. The original paper maps were issued by the Department of Natural Resources of the Russian Federation or its predecessor the Department of Geology of the Soviet Union and have their foundation in decades of geological field and remote sensing work and mapping at scales 1:50,000 to 1:500,000 by Russian geologists and cartographers in the respective regions. Eleven paper maps were scanned and digitized to record the geology unit boundaries, genetic type and clast size of each geologic unit, and borehole and outcrop locations. We also calculated area in km2, perimeter in km for each polygon. These attributes were used in support of (Grosse and others, 2013) which focused on extracting geologic units interpreted as Yedoma, based on lithology, ground ice conditions, geochronology, geomorphologic, and spatial association.

Uses of this digital geologic map should not violate the spatial resolution of the data. Although the digital form of the data removes the constraint imposed by the scale of a paper map, the detail and accuracy inherent in map scale are also present in the digital data. The data was edited at a scale of 1:1,000,000 and higher resolution information is not present in the dataset. Plotting at scales larger than 1:1,000,000 will not yield greater real detail, although it may reveal fine-scale irregularities below the intended resolution of the database. Similarly, where this database is used in combination with other data of higher resolution, the resolution of the combined output will be limited by the lower resolution of these data. Acknowledgment of the U.S. Geological Survey would be appreciated in products derived from these data.

Citation

In order to use these data, you must cite this data set with the following citation:

Bryant, R.N., Robinson, J.E., Taylor, M.D., Harper, William, DeMasi, Amy, Kyker-Snowman, Emily, Veremeeva, Alexandra, Schirrmeister, Lutz, Harden, Jennifer and Grosse, Guido, 2017, Digital Database and Maps of Quaternary Deposits in East and Central Siberia: U.S. Geological Survey data release, https://doi.org/10.5066/F7VT1Q89.

Russia's military capabilities outnumbered those of Ukraine for most indicators as of 2025. For example, the number of aircraft at the disposal of the Russian Army was close to 4,300, while the Ukrainian Armed Forces possessed 324 aircraft. Russia's naval fleet was 4.7 times larger than Ukraine's. Moreover, Russia was one of the nine countries that possessed nuclear weapons. As of early 2024, Russia held the world's largest inventory of nuclear warheads. How many soldiers does Ukraine have? Ukraine's Army counted approximately 2.2 million military personnel as of 2025. Of them, 900,000 were active military staff. Furthermore, 1.2 million soldiers were part of the country's reserve forces. To compare, Russia had approximately 1.32 million active military personnel and two million of reserve military personnel. Russia's active soldier count was the fourth-largest worldwide, while Ukraine's ranked sixth. Ukraine's tank strength Ukraine's Armed Forces possessed over 1,100 tanks as of 2025, which was more than five times less than Russia's. To support Ukraine during the Russian invasion, several Western countries made commitments to deliver tanks to Ukraine, including Leopard 2, Challenger 2, and M1 Abrams. Furthermore, Ukraine received other types of armored vehicles from Western countries, such as M133 armored personnel carriers from the United States and Mastiff (6x6) protected patrol vehicles from the United Kingdom.

This data release contains the boundaries of assessment units and input data for the assessment of undiscovered gas resources in the Volga-Ural Basin and Timan-Pechora Basin Provinces of Russia. The Assessment Unit is the fundamental unit used in the National Assessment Project for the assessment of undiscovered oil and gas resources. The Assessment Unit is defined within the context of the higher-level Total Petroleum System. The Assessment Unit is shown herein as a geographic boundary interpreted, defined, and mapped by the geologist responsible for the province and incorporates a set of known or postulated oil and (or) gas accumulations sharing similar geologic, geographic, and temporal properties within the Total Petroleum System, such as source rock, timing, migration pathways, trapping mechanism, and hydrocarbon type. The Assessment Unit boundary is defined geologically as the limits of the geologic elements that define the Assessment Unit, such as limits of reservoir rock, geologic structures, source rock, and seal lithologies. Machine-readable tables are provided that contain the input and results for each assessment unit summarized in the USGS Fact Sheet. Methodology of assessments are documented in USGS Data Series 547 for continuous assessments (https://pubs.usgs.gov/ds/547) and USGS DDS69-D, Chapter 21 for conventional assessments (https://pubs.usgs.gov/dds/dds-069/dds-069-d/REPORTS/69_D_CH_21.pdf). See supplemental information for a detailed list of files included this data release.

The globally distributed causal agent of powdery mildew on wheat, Blumeria graminis f. sp. tritici (Bgt), is one of the most rapidly adapting plant pathogens and requires monitoring for shifts in virulence to wheat resistance (Pm) genes. Virulence frequencies were assessed in a total of 346 Bgt isolates from several countries that had either lately recorded increasing powdery mildew epidemics (Brazil, South Africa, and Australia) or not recently been surveyed (Turkey and Russia). The results were compared to previously published surveys of United States and Egyptian Bgt (390 isolates). Many of the Pm genes that have potentially been employed longer (Pm1a–Pm17) were shown to have lost effectiveness, and the complexity of virulence to those genes was higher among Brazilian isolates than those from any other country. Some cases of high virulence frequency could be linked to specific Pm gene deployments, such as the widespread planting of cultivar Wyalkatchem (Pm1a) in Australia. Virulence was also assessed to a set of Pm genes recently introgressed from diploid and tetraploid wheat relatives into a hexaploid winter wheat background and not yet commercially deployed. The isolate collections from Fertile Crescent countries (Egypt and Turkey) stood out for their generally moderate frequencies of virulence to both the older and newer Pm genes, consistent with that region’s status as the center of origin for both host and pathogen. It appeared that the recently introgressed Pm genes could be the useful sources of resistance in wheat breeding for other surveyed regions.