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.

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.

The dataset describes russian and Ukrainian Equipment Losses During The 2022 Russian Invasion Of Ukraine. The dataset was created based on Oryx by scraping Ukrainian losses and russian losses pages. This list only includes destroyed vehicles and equipment of which photo or videographic evidence is available. Therefore, the amount of equipment destroyed is significantly higher than recorded. You can find numbers here 2022 Ukraine Russia War Dataset.

Images data of Equipment Losses

Images data include pictures of Equipment Losses. More than 30k (10 GB) images of destroyed equipment can be found here. Data has been split into different folders by country and type of equipment. You can find the folder structure and some picture examples in Data Overview Notebok.

Tabular data of Equipment Losses

Tabular data includes Equipment Losses, Equipment Models, Countries that produce Equipment, the Number of Equipment Losses, and types of Losses (abandoned, damaged, destroyed, captured, etc.). You can find a basic overview of data in Data Overview Notebok.

Tabular metadata of Equipment Losses

Tabular metadata includes a list of images available in the dataset.

Main Columns - equipment - model - sub_model - manufacturer - losses_total

Tracking Equipment

• Tanks
• Armoured Fighting Vehicles
• Infantry Fighting Vehicles
• Armoured Personnel Carriers
• Mine-Resistant Ambush Protected
• Infantry Mobility Vehicles
• Command Posts And Communications Stations
• Engineering Vehicles And Equipment
• Heavy Mortars
• Towed Artillery
• Self-Propelled Artillery
• Multiple Rocket Launchers
• Anti-Aircraft Guns
• Self-Propelled Anti-Aircraft Guns
• Surface-To-Air Missile Systems
• Radars
• Jammers And Deception Systems
• Aircraft
• Helicopters
• Unmanned Aerial Vehicles
• Naval Ships
• Logistics Trains
• Trucks
• Vehicles and Jeeps

Related Datasets

• Warships Monitoring - the russian warships' movements on the Black Sea, the Sea of Azov, and the Mediterranean Sea since August 2022.
• Massive Missile Attacks on Ukraine (updated weekly) - information about launched and shot down missiles and drones during russian massive missile and drone (UAV) strikes on infrastructure (since October 2022) as part of its invasion of Ukraine.
• Army of Drones (updated monthly) - rusians losses (Equipment & Death Toll) caused by the project Army of Drones.
• 2022 Russia Ukraine War (updated weekly), JSON format on Githab (updated daily) - the russia Equipment and Personnel losses.
• russian navy (Military Maritime Fleet) - all Surface Combatants, Submarines, Littoral Warfare Ships, Rescue, and Auxiliary Ships. Ship losses During The 2022 Russian Invasion Of Ukraine are included.
• Social Media Athletes from russia & belarus - Social Media of russia and belarus athletes that took part in Beijing 2022 Olympic Winter Games.

Dataset History

CURATING DATASETS IS TIME CONSUMING AND DIFFICULT, SO IF YOU FIND THE CONTENT USEFUL, PLEASE LEAVE AN UPVOTE! THANK YOU! 🙏

This is what real world data looks like! It is often messy, complicated, and leaves you wondering what you can even do with it. That is the fun and difficulty of data science. You have information, but what can you do with it? Should you try to use machine learning? Should you use statistics? That is for you to find out! 😄

About the Dataset

This dataset contains information regarding the ongoing Ukrainian and Russian conflict data dating back to 2014. There are two CSV files in this dataset. One contains data from 2014 to 2021, the other contains data from 2018 to 2023. Use your data science skills to better understand a conflict that is happening in real time! This is an excellent project for those looking to better understand global events or who are looking to work on a dataset with greater implications and a larger impact than a cat vs. dog classifier. 👍

I will be contributing to this dataset as new data becomes available, so stay tuned!

About the Conflict

The Ukraine-Russia conflict began in 2014 when Russia annexed Crimea from Ukraine, but the history of these two nations goes back much further than 2014. Since then, pro-Russian separatists have been fighting Ukrainian government forces in the Donbas region of Eastern Ukraine. The conflict has resulted in thousands of deaths and the displacement of over 1.5 million people.

In 2022, the conflict escalated again, with Russia mobilizing its military near the Ukrainian border and launching a large-scale invasion in February. Ukrainian forces have been engaged in heavy fighting with Russian troops and separatist militias, resulting in a humanitarian crisis and significant civilian casualties.

The international community has condemned Russia's actions and imposed economic sanctions on the country. Diplomatic efforts to resolve the conflict, including negotiations and ceasefires, have not been successful so far. The conflict remains ongoing and the situation is highly volatile.

Russia - Ukraine War Tweets

Tweets about ongoing Russia - Ukraine war

By [source]

About this dataset

This dataset consists of tweets relating to the Russian invasion of Ukraine that were scraped for this study. Only tweets of which user features were available are included in the dataset. The tweets and corresponding user features can be rehydrated using the Twitter API. However, it could be that some tweets or users might be deleted or put on private and are therefore no longer available. Moreover, user and tweet features might change over time

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

The dataset consists of tweets relating to the Russian invasion of Ukraine that were scraped for this study. Only tweets of which user features were available are included in the dataset. The tweets and corresponding user features can be rehydrated using the Twitter API. However, it could be that some tweets or users might be deleted or put on private and are therefore no longer available. Moreover, user and tweet features might change over time This dataset can be used to study the change in sentiment, and topics over time as the war continues

Research Ideas

• Find out which tweets are most popular among people interested in the Russian invasion of Ukraine
• Identify which user attributes are associated with tweets about the Russian invasion of Ukraine
• Study the change in sentiment and public opinion on the war as events unfold.

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: after_invasion_tweetids.csv | Column name | Description | |:--------------|:-----------------------| | id | The tweet id. (String) |

File: before_invasion_tweetids.csv | Column name | Description | |:--------------|:-----------------------| | id | The tweet id. (String) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit .

Between 2022 and 2024, over **** million articles published on the internet in ** languages contained the word "Ukraine," and around **** million mentioned "Russia." Online media coverage of the two countries increased in 2022 due to the Russian invasion of Ukraine that began in February of that year. Prior to the war, the number of online articles mentioning Russia was higher than those mentioning Ukraine. The country with the most articles on Ukraine published on the internet in 2022 was the United States, and in 2023 and 2024 it was Ukraine.

This dataset is a subset of the larger (🌇Sunset) 🇺🇦 Ukraine Conflict Twitter Dataset, available on Kaggle, focusing specifically on tweets related to the ongoing conflict between Ukraine and Russia.

Data Source: The data was originally collected from the Twitter API by Creator. The dataset contains tweets spanning a significant timeframe, capturing public sentiment, news updates, and discussions related to the conflict between Ukraine and Russia.

File Size and Format: Given the extensive size of the original dataset (approximately 48GB), we've extracted and curated a smaller subset of approximately 4GB, focusing specifically on tweets relevant to the Ukraine conflict. The files have been renamed for ease of access and loading, making them more manageable for analysis and exploration.

Usage: Researchers, data scientists, and analysts interested in studying the discourse surrounding the Ukraine-Russia conflict, social media sentiment analysis, or geopolitical dynamics may find this dataset particularly valuable. It can be used for tasks such as sentiment analysis, topic modeling, trend analysis, and understanding public perceptions and reactions to unfolding events.

Disclaimer: While efforts have been made to ensure the accuracy and relevance of the data, users are encouraged to exercise caution and verify the information as Twitter data can be subject to biases, noise, and misinformation. Additionally, please adhere to Twitter's terms of service and guidelines when using this dataset for research or analysis purposes.

The economic effects of wars and recessions can have significant impacts on consumer attitudes and behaviors. Understanding how these attitudes and behaviors change impacts during challenging economic times is crucial for financial education and management. A survey was conducted to investigate the financial attitudes and behaviors of individuals during a recessionary period. The survey included questions about age, financial education level, savings behavior, attitudes towards debt, gender and financial management behavior, age and financial education, and income and savings behavior. Data were analyzed using t-tests and ANOVA. Participants with higher income level had higher levels of savings and investing behaviors than those with lower income levels. Participant with a higher level of formal education in finance had higher levels of budgeting and investing behaviors than those with a lower level of formal education in finance. Additionally, participants who reported higher levels of self-rated financial knowledge had higher levels of all financial management behaviors (budgeting, saving, investing, and debt management) compared to those with lower self-rated financial knowledge. The findings suggest that financial education and management programs should target individuals with lower income levels and less formal education in finance. Additionallt, promoting self-rated financial knowledge may be a useful strategy for improving financial management behaviors. Future research could explore the effectiveness of different financial education and management programs on improving financial attitudes and bevaiors during recessionary periods.

WAR, day 1369

Data will be updated weekly
Each new record accumulates data from previous days

This dataset describes Equipment Losses & Death Toll & Military Wounded & Prisoner of War of russians in 2022 Ukraine russia War. All data are official and additionally structured by myself. A lot of civilians and children have already been killed by russia troops. Ukraine is in war flame and under missile attack now. We are strong. Stand with Ukraine.

Analytical Dashboards

• russian losses application - is a monitoring dashboard that describes russian losses during the 2022 russia invasion of Ukraine.
• Cargo200rus - is a telegram bot representing the official losses of the russian armed forces in Ukraine. GitHub.

Related Datasets

• Warships Monitoring - the russian warships' movements on the Black Sea, the Sea of Azov, and the Mediterranean Sea since August 2022.
• Army of Drones - rusians losses (Equipment & Death Toll) caused by the project Army of Drones.
• Massive Missile Attacks on Ukraine - detailed information about launched and shot down missiles and drones during Massive Missile Attacks.
• 2022 Ukraine Russia War, Losses, Oryx + Images - Ukraine and russia Equipment Losses based on Oryx Investigation.
• russian navy (Military Maritime Fleet) - all Surface Combatants, Submarines, Littoral Warfare Ships, Rescue, and Auxiliary Ships. Ship losses During The 2022 Russian Invasion Of Ukraine are included.
• Social Media Athletes from russia & belarus - Social Media of russia and belarus athletes that took part in Beijing 2022 Olympic Winter Games.
• Dataset in JSON format - The same data on Github (Updated daily).

Code Starter

• Python - 2022 Ukraine Russia War: visualization
• R - Losses of Russia (Visualization)
• Data Daily + Plot Example

Data

• russia_losses_personnel.csv- contains Personnel Losses during the war
• russia_losses_equipment.csv - contains Equipment Losses during the war
• russia_losses_equipment_correction.csv - contains some data correction in russia_losses_equipment.csv (date: 2022-10-13, date: 2023-05-27)

Data Preprocessing

I recommend the Daily Data Notebook if you need daily bases data. It contains a full code that shows how to convert the data or you can adapt the below code snippet. df = df.diff().fillna(df).fillna(0).astype(int)

Data Sources

1. Main data sources are General Staff of the Armed Forces of Ukraine and Ministry of Defence of Ukraine. Data from different battlefields are gathered. The calculation is complicated by the high intensity of hostilities. Data are being updated.
2. Invaders - Russians Prisoner of War (POW). The project is not active since May of 2022.
3. Oryxspioenkop - Ukraine and russia Equipment Losses. This list only includes destroyed vehicles and equipment of which photo or videographic evidence is available. Therefore, the amount of equipment destroyed is significantly higher than recorded here.
4. Interactive Maps:
   • Liveuamap - Live Interactive Map with events that happened.
   • DeepStateMap, Wiki page - an interactive open-source intelligence online map run by the non-governmental and volunteer-led Deep State UA, which focuses primarily on the military operations of the Russian and Ukrainian armies in the Russian invasion of Ukraine.
   • Alerts Map - Air Raid Alert Map of Ukraine
   • eMap - Air Raid Alert Map of Ukraine
5. Sanctions Databases:
   • [Ukraine Russia related s...

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.

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.

ObjectivesTo examine the association between food insecurity (FI) and loneliness in countries of the former Soviet Union (FSU).MethodsData were analysed from 15,568 adults in Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, and Ukraine, collected in the Health in Times of Transition (HITT) survey in 2010–2011. Information was obtained on both FI and loneliness with single-item measures. Logistic regression was used to examine associations.ResultsIn a fully adjusted analysis, moderate (OR: 1.35, 95% CI: 1.16–1.57) and severe FI (OR: 1.94, 95% CI: 1.58–2.38) were associated with significantly higher odds of loneliness in the pooled sample. In sex- and age-stratified analyses, severe FI was associated with loneliness in all population subgroups, with odds ratios ranging from 1.69 to 1.99. However, moderate FI was linked to loneliness in only three of the five subgroups. In further analyses, FI was associated with loneliness in six of the nine countries.ConclusionFI is associated with loneliness in FSU countries. Efforts to increase food security in these countries may be important for improving physical and psychological well-being among adults in the general population.

Inflation Rate in Russia decreased to 7.70 percent in October from 8 percent in September of 2025. This dataset provides - Russia Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

IntroductionRomania shares the longest UE border with Ukraine, and since the Russian invasion of Ukraine began, many have been involved in helping the refugees. Consequently, children and adolescents might be directly and indirectly exposed to war-related trauma. In the present exploratory research, we investigated Romanian adolescents’ potential risk and protective factors related to the psychological outcomes of war exposure. Our cross-sectional study was conducted shortly after February 24th (i.e., the first invasion day).MethodsThe sample included 90 Romanian adolescents aged 11 to 15 (M = 12.90, SD = 1.17), residents in Iași, Romania (i.e., 205,7 km from the Ukrainian border). Participants completed self-reported measures of peritraumatic dissociative experiences, knowledge about the conflict in Ukraine, personal, school, and family implications in volunteering/helping behavior, discussions about the conflict, threat perception (self and perceived parental threat), anxiety, social media engagement, resilience, and moral elevation.ResultsThe main findings suggested that participants involved in helping behaviors toward Ukrainian refugees present higher peritraumatic dissociative experiences, anxiety symptoms, and higher moral elevation than boys and participants not involved in these behaviors. Moreover, anxiety symptoms were positively associated with threat perception, peritraumatic dissociation, and social media engagement and negatively related to resilience.DiscussionsFinally, we discuss the implications of our findings concerning their practical utility in managing peritraumatic exposure to war by using interventions designed to increase adolescents’ resilience during difficult times.

More than 40 percent of Russians believed that military actions in Ukraine would continue for longer than one year, according to a survey from January 2024. The second most common expectation was that the war would end in six to twelve months.

A detailed list of destroyed and captured vehicles and equipment of both sides can be seen below. This list is constantly updated as additional footage becomes available.

This list only includes destroyed vehicles and equipment of which photo or videographic evidence is available. Therefore, the amount of equipment destroyed is significantly higher than recorded here. Small arms, munitions, civilian vehicles, trailers and derelict equipment (including aircraft) are not included in this list. All possible effort has gone into discerning the status of equipment between captured or abandoned. Many of the entries listed as 'abandoned' will likely end up captured or destroyed. Similarly, some of the captured equipment might be destroyed if it can't be recovered. ATGMs and MANPADS are included in the list but not included in the ultimate count. The Soviet flag is used when the equipment in question was produced prior to 1991.

(Click on the numbers to get a picture of each individual captured or destroyed vehicle)

Pro-Kremlin disinformation campaigns have long targeted Ukraine. We investigate susceptibility to this pro-Kremlin disinformation from a cognitive science perspective. Is greater analytic thinking associated with less belief in disinformation, as per classical theories of reasoning? Or does analytic thinking amplify motivated system 2 reasoning (or “cultural cognition”), such that analytic thinking is associated with more polarized beliefs (and thus more belief in pro-Kremlin disinformation among pro-Russia Ukrainians)? In online (N=1,974) and face-to-face representative (N=9,474) samples of Ukrainians, we find support for the classical reasoning account. Analytic thinking, as measured using the Cognitive Reflection Test, was associated with greater ability to discern truth from disinformation – even for Ukrainians who are strongly oriented towards Russia. We find similar, albeit weaker, results when operationalizing analytic thinking using the self-report Active Open-minded Thinking scale. These results demonstrate a similar pattern to prior work using American participants. Thus, the positive association between analytic thinking and the ability to discern truth versus falsehood generalizes to the qualitatively different information environment of post-communist Ukraine. Despite low trust in government and media, weak journalistic standards, and years of exposure to Russian disinformation, Ukrainians who engage in more analytic thinking are better able to tell truth from falsehood.

The yield on Russia 10Y Bond Yield rose to 14.35% on December 1, 2025, marking a 0.02 percentage points increase from the previous session. Over the past month, the yield has fallen by 0.23 points and is 1.82 points lower than a year ago, according to over-the-counter interbank yield quotes for this government bond maturity. Russia 10-Year Government Bond Yield - values, historical data, forecasts and news - updated on December of 2025.

The collection "Protests Ukraine 2013 – 2014: Video Database" is part of a larger and ongoing collection of videos on protest events in the post-Soviet region. It contains a database describing 763 videos (mp4) of protests in late 2013 and 2014 (mainly Kyiv area) triggered by the governmental denial to sign the association agreement with the EU. We compiled the material in 2016 and 2017. All data is processed in an MS Excel database with metadata. For this kind of collections on protest events we take into account all videos that 1) are event related AND show actions of this event, 2) are fully accessible, 3) we can find with our search words during a particular period. We strictly aim at a systematic and unbiased selection and organized processing of protest-related videos. We identify particular event-related search words or phrases after intense research on the event. According to the snowball principle, we then start the search of videos and try to describe as much relevant content as possible. However, we cannot guarantee the completeness of protest videos on the particular event. We search the videos and include them into the collection until a particular degree of saturation has been reached. The videos have been posted mainly by the participants of the events. Therefore, the material is only an extract and biased by the perspective of the single creator. Due to data privacy regulations and copyright restrictions, we are only allowed to give access to the database of the collected video files including the hyperlinks with its metadata and not to the videos themselves. YouTube's user guidelines do not allow us to store copies of the video collection described above.

As of 2025, the total number of aircraft of Russia outnumbered that of the Air Forces of Ukraine by more than 13 times. The number of Russian fighter aircraft amounted to 833, whereas Ukraine had 70 fighter aircraft.