This dataset contains geo-information about military bases in Belarus and Russia.

It contains five features: - military_base_name: the name of the military base. - coordinate: the coordinate of the military base(format: longitude, latitude). - longitude: the longitude of the military base. - latitude: the latitude of the military base. - description: the description of the military base.

credits: https://twitter.com/Archer83Able/status/1773732406991708277

Why I made this dataset

On the 24th of February Russia has invaded Ukraine.

Despite the fact that there were lots of speculations about the probable invasion in the press, it came as a complete shock for me as for many other Russians. It is so disgusting to start a full-scale war in the 21 century, so it was just unimaginable, no one talked about it seriously at that time. Many of us have friends and family members in Ukraine, others just want to be a part of the civil cosmopolitan world. How could our government so gruesomely attack Ukraine, destroying the lives in both countries?

But maybe it wasn't? I want to see the evolution of the discussion around Ukraine and Russia. So I parsed quite a big number of tweets. Maybe some of you will find it useful as well.

How did I collect the data.

• max 5000 for the day
• I used these words for the search ['ukraine war', 'ukraine troops', 'ukraine border', 'ukraine NATO', 'StandwithUkraine', 'russian troops', 'russian border ukraine', 'russia invade' ]
• for each search, I created the separate CSV
• dates: from 2022 - 01 - 01 to 2022 - 03 -06

Format

The dataset is available as separate CSVs files.

License

As this data was collected from Twitter, its use must abide by the Twitter Developer Agreement. Most notably, the display of individual tweets should satisfy requirements.

Daily losses of the Ukrainian military according to the UALosses (personnel).

The dataset contains information on deceased Ukrainian soldiers between March 2014 and April 2024 for which an online obituary has been found by https://ualosses.org/en/soldiers/. The data include the name of the soldier, the date of birth and date of death whenever available, the location of the soldier (unclear whether it pertains to the origins of the soldier or the location of death), the rank of the soldier, and finally their unit.

Starting from March, 1, UALosses reports the prisoners and missing soldiers, although the date of disappearance is largely missing.

Contents

The Excel file contains 3 sheets: - Database: this is the database as scraped from https://ualosses.org/en/soldiers/. Computed fields are : Male (=1 if male), Rank_L1, Rank_L2, and TypeUnit. - ByDay: the daily total of soldiers killed on that day, the number of which the age is known and the number of which is male and the average age of the soldiers killed that day. - ByDayRank : the daily count of soldiers killed on that day according to their rank (Rank_L2).

The ranks are grouped according to the following rule :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F19808181%2Fe14108b472af93f73b1cfa240c0a4776%2Fsdfqefz.PNG?generation=1718543861089177&alt=media" alt="">

Method of collection

Scraping of the website of https://ualosses.org/en/soldiers/.

Frequency of the dataset

The dataset has a daily frequency, but some holes can exist due to the absence of publication from the source. I plan to update the dataset every two months (on the first of the month).

Companion datasets

All my datasets : - Russian losses (materiel and personnel) according to the Ukrainian Ministry of Defense : https://www.kaggle.com/datasets/ol4ubert/rus-modukr-equipmentpersonnel - Ukrainian losses (materiel and personnel) according to the Russian Ministry of Defense : https://www.kaggle.com/datasets/ol4ubert/ukr-modrus-equipmentpersonnel - Russian losses (materiel) according to ORYX : https://www.kaggle.com/datasets/ol4ubert/rus-oryx-equipment - Ukrainian losses (materiel) according to ORYX : https://www.kaggle.com/datasets/ol4ubert/ukr-oryx-equipment - Russian tank losses according to ORYX : https://www.kaggle.com/datasets/ol4ubert/rus-oryx-tanks - Ukrainian tank losses according to ORYX : https://www.kaggle.com/datasets/ol4ubert/ukr-oryx-tanks - Ukrainian personnel losses (UALosses) : https://www.kaggle.com/datasets/ol4ubert/confirmed-ukrainian-military-personnel-losses - Russian personnel losses (KilledInUkraine) : https://www.kaggle.com/datasets/ol4ubert/confirmed-russian-military-officers-losses - Ukrainian losses in Kursk (materiel and personnel) according to the Russian Ministry of Defense: https://www.kaggle.com/datasets/ol4ubert/ukrainian-military-losses-in-kursk-mod-russia

Any comment is welcome. Please use the Discussion feature or send me an email directly.

This dataset contains Russian military equipment losses collected from the open-source WarSpotting API. It is automatically updated multiple times per week using a Python scraper running on GitHub Actions.

The data covers: Full historical scans updated weekly Incremental 30-day scans updated thrice weekly Precise geographic coordinates for equipment loss Equipment type and category details Dates of loss and related metadata

This dataset is designed for researchers, analysts, and developers interested in: Open-source intelligence (OSINT) Conflict monitoring and analysis Machine learning model training Geospatial visualization of battlefield losses

The scraper and automation tools powering this dataset are fully open-source and available on GitHub: https://github.com/lazar-bit/automated-warspotting-scraper

Daily losses of the Russian military according to the Killed in Ukraine Project (personnel).

The dataset contains information on deceased Russian Officers from February 2024 for which an obituary has been found by the Killed in Ukraine project. The Killed in Ukraine Project scans Russian social media posts and local newspapers reports for names of Russian officers killed during the war in Ukraine. It provides information about the rank and date at which the soldier was killed. The raw data is available here.

Contents

The Excel file contains 3 sheets: - Input: this is the database as downloaded from the source. - ByRank: the daily total of soldiers killed on that day, by rank. - ByRankCategory : the daily count of soldiers killed on that day according to their rank (Rank_L2).

The ranks are grouped according to this page.

Method of collection

Download from https://docs.google.com/spreadsheets/d/1InyFVmu1LoSjqcWTHe4iD9cR8CNiL-5Ke5Jiz_Mlvwc/edit?gid=1208816851#gid=1208816851

Frequency of the dataset

The dataset has a daily frequency, but some holes can exist due to the absence of publication from the source. I plan to update the dataset every two months (on the first of the month).

Companion datasets

All my datasets : - Russian losses (materiel and personnel) according to the Ukrainian Ministry of Defense : https://www.kaggle.com/datasets/ol4ubert/rus-modukr-equipmentpersonnel - Ukrainian losses (materiel and personnel) according to the Russian Ministry of Defense : https://www.kaggle.com/datasets/ol4ubert/ukr-modrus-equipmentpersonnel - Russian losses (materiel) according to ORYX : https://www.kaggle.com/datasets/ol4ubert/rus-oryx-equipment - Ukrainian losses (materiel) according to ORYX : https://www.kaggle.com/datasets/ol4ubert/ukr-oryx-equipment - Russian tank losses according to ORYX : https://www.kaggle.com/datasets/ol4ubert/rus-oryx-tanks - Ukrainian tank losses according to ORYX : https://www.kaggle.com/datasets/ol4ubert/ukr-oryx-tanks - Ukrainian personnel losses (UALosses) : https://www.kaggle.com/datasets/ol4ubert/confirmed-ukrainian-military-personnel-losses - Russian personnel losses (KilledInUkraine) : https://www.kaggle.com/datasets/ol4ubert/confirmed-russian-military-officers-losses - Ukrainian losses in Kursk (materiel and personnel) according to the Russian Ministry of Defense: https://www.kaggle.com/datasets/ol4ubert/ukrainian-military-losses-in-kursk-mod-russia

Any comment is welcome. Please use the Discussion feature or send me an email directly.

Daily losses of the Ukrainian military in Kursk according to the Ministry of Defense of Russia (both equipment and personnel).

Contents

The Excel file contains 4 sheets: - Equipment_Level: the cumulative number of pieces of equipment as reported by the Russian MoD, by type of equipment - Equipment_2wnum: the daily total over the last two weeks by type of equipment (e.g. data on August, 21 is the total between August, 08 and August, 21) - Equipment_2wshare: the daily total over the last two weeks by type of equipment as a share of total equipment lost during the same period - Personnel: the cumulative number of Ukrainian personnel lost up to the date from the daily losses in level and in the last two weeks.

The Equipment sheets contain the date in the first column and the type of equipment in the following columns. Some considerations are worth mentioning: - Helicopters and Planes have been grouped under the name "Aircraft" - Artillery and MLRS have been grouped under the name "Artillery"

The Personnel sheets contain the date in the first column and does not make the distinction between Killed/Injured and Surrendered. The second column reports the number of personnel lost in the last two weeks.

Method of collection

Scraping of the website of the Defense Ministry of the Russian Federation.

Frequency of the dataset

The dataset has a daily frequency, but some holes can existe due to the absence of publication from the source. I plan to update the dataset weekly (new version expected every Tuesday).

Companion dataset for the Russian losses

All my datasets : - Russian losses (materiel and personnel) according to the Ukrainian Ministry of Defense : https://www.kaggle.com/datasets/ol4ubert/rus-modukr-equipmentpersonnel - Ukrainian losses (materiel and personnel) according to the Russian Ministry of Defense : https://www.kaggle.com/datasets/ol4ubert/ukr-modrus-equipmentpersonnel - Russian losses (materiel) according to ORYX : https://www.kaggle.com/datasets/ol4ubert/rus-oryx-equipment - Ukrainian losses (materiel) according to ORYX : https://www.kaggle.com/datasets/ol4ubert/ukr-oryx-equipment - Russian tank losses according to ORYX : https://www.kaggle.com/datasets/ol4ubert/rus-oryx-tanks - Ukrainian tank losses according to ORYX : https://www.kaggle.com/datasets/ol4ubert/ukr-oryx-tanks - Ukrainian personnel losses (UALosses) : https://www.kaggle.com/datasets/ol4ubert/confirmed-ukrainian-military-personnel-losses - Russian personnel losses (KilledInUkraine) : https://www.kaggle.com/datasets/ol4ubert/confirmed-russian-military-officers-losses

Any comment is welcome. Please use the Discussion feature or send me an email directly.

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.

This article examines how external security threats shape the acquisition of military capabilities, focusing on 29 European NATO member states’ responses to Russia’s 2014 invasion of Ukraine. Employing a difference-in-differences research design and leveraging data on NATO military capabilities from 2008–2022, the study evaluates how geographic proximity to Russia influenced post-2014 military capability adjustments among member states. Measuring four capability outcomes—active troops, army troops, armored vehicles, and combat aircraft—the results show that NATO members bordering Russia significantly increased these capabilities relative to more geographically distant allies, consistent with expectations that vulnerability drives internal balancing behavior. The findings reveal that while overall NATO defense spending increased following the 2014 invasion, this did not uniformly translate into greater military capability across the alliance. Instead, the analysis shows a widening divergence in force structures: states closest to Russia expanded capabilities to meet heightened deterrence needs, while more distant members continued to contract their conventional militaries. These uneven adjustments reflect enduring free-riding tendencies and the influence of domestic political economies over strategic imperatives.

A list of Russian soldiers, PMC Wagner members and other criminals.

Einstellungen ostdeutscher Jugendlicher. Themen: Verhältnis zwischen Zivilbevölkerung und Volkspolizei;militärischer Zweck der Volkspolizei; Verhältnis zwischenVolkspolizisten und russischen Truppen; Einstellungen zur Volkspolizei;innere Überzeugung der Mitglieder der Volkspolizei; Freiwilligkeit derMitgliedschaft in der Volkspolizei; Flucht von Volkspolizisten in denWesten; Ansehen russischer Truppen in der Zivilbevölkerung;Kontakthäufigkeit zwischen russischen Truppen und der deutschenZivilbevölkerung; persönliche Bekanntschaft mit russischen Soldaten;Hören westlicher Radiosender durch russische Soldaten; Veränderung derAnsichten russischer Soldaten über den Westen durch ihren Aufenthalt inDeutschland. Demographie: Alter; Beruf; Bildung; Geschlecht; Land; Ort. Attitudes of East German young people.Topics:relation between civilian population and People's Police;military purpose of the People's Police;relation between members of the People's Police and Russian troops;attitudes to the People's Police;internal conviction of members of the People's Police;voluntariness of membership in the People's Police;flight of members of the People's Police into the west;reputation of Russian troops in the civilian population;frequency of contact between Russian troops and the German civilian population;personal acquaintance with Russian soldiers;listening to western radio stations by Russian soldiers;change of views of Russian soldiers about the West from their stay in Germany.Demography:age;occupation;education;sex;state;city.

Source: https://milex.sipri.org/sipri

This Dataset aims to show how much the Government of Russia invests into its military. In order to avoid problems with current/historical change rates, it uses indicators. Namely: - What % of the GDP did Russia invest in Defence? - How much did Russia invest per capita? - What % of all government did Russia invest into its military?

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

The dataset contains data on fish species in recreational fishing catches, obtained during ichthyological studies in the upper and middle parts of the Kakhovka reservoir (within the Zaporizhia region). The dataset contains data on the records of 26 fish species registered during the study of catches in the specified territory, as well as information on the number of individuals, their biomass and the fishing effort that was spent on their catch (category and number of fishing gear, duration of fishing at the time of the study). The dataset provides important reptrospective data on distribution of fish species in past years which is preserved and will be useful for studying of consequences of Kakhovka dam destruction by russian military forces in 2023 for nature.

This dataset captures detailed geolocated conflict events inside Ukraine, from the beginning of the 2022 Russia–Ukraine war to early 2025. It includes battles, airstrikes, protests, shelling, and more — sourced from the respected ACLED project.

📌 What’s Inside? Event Date — Exact date of each incident.

Event Type — Battles, Explosions, Violence Against Civilians, etc.

Involved Parties — Ukrainian forces, Russian forces, and others.

Location Data — Province, District, Town, Latitude, Longitude.

Fatalities — Estimated deaths for each event.

Detailed Notes — Short summaries of every event.

🌍 Source Curated from ACLED (Armed Conflict Location & Event Data Project).

Data complies with public research usage under ACLED’s policies.

🔥 Potential Uses Conflict Timeline 📅

Predictive Modeling of War Escalation/De-escalation 🔮

Geospatial Analysis & Mapping 🗺️

Death Toll and Intensity Trend Studies 📊

Machine Learning: Severity and Impact Prediction 🧠

⚡ Latest Update: April 2025 Covers only events happening within Ukraine.

Updated for 2025 ongoing conflict events.

Fork it, analyze it, visualize it, and help the world understand this important situation!

If you like this project, please give an upvote Data will be updated monthly

The Army of Drones is a joint project of the General Staff of the Armed Forces, the State Special Communications Service, and the Ministry of Digital Transformation. You can read about the project on United24 and Army of Drones.

This dataset documents Russian losses (equipment and personnel) attributed specifically to:

I. Army of Drones.
- Timeframe: Summer 2023 (project start) → Mid-summer 2024
- Scope: Only losses caused by project drones are included.
- Exclusions: Losses caused by other drones are not part of this dataset.

II. Unmanned Systems Forces - Timeframe: Starting 2025-06-24

Data

images_Army-Drones/ - contains images of weekly reports covering the period 2023-06-25 → 2024-07-08.

images_Unmanned-Systems-Forces/- contains images of daily reports from Unmanned Systems Forces, starting 2025-06-24.

russia_losses.csv - structured dataset automatically extracted from images in the images_Army-Drones/ folder. - APV - Ammunition/fuel depot - Anti-aircraft warfare systems - Cannons - MRL - Mortar/ATGM/MG - Radio equipment - Self-propelled artillery, - Strongpoint - Tanks - Vehicles - Personnel

Related Datasets

• Warships Monitoring - the russian warships' movements on the Black Sea, the Sea of Azov, and the Mediterranean Sea since August 2022.
• 2022 Ukraine Russia War, Losses, Oryx + Images(updated quarterly) - Ukraine and russia Equipment Losses based on Oryx Investigation.
• 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.
• 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.

This HTML file is an interactive map, showing areas from which we know Russia launched missiles and drones at least ten times between 09/28/22 and 11/24/24, based on data posted to Kaggle by Piter FM, here:

https://www.kaggle.com/datasets/piterfm/massive-missile-attacks-on-ukraine/data.

How to Use This File Download the HTML File:

Click the "Download" button on this dataset page to save the file to your computer. Open the File:

Locate the downloaded .html file on your computer.

Double-click it to open it in your default web browser.

This dataset describes the russian warships' movements on the Black Sea, the Sea of Azov, and the Mediterranean Sea since August 2022.

The dataset was created based on the images published on official social media such as Facebook or Telegram of the Ukrainian Navy.

The paper Створення набору даних «Моніторинг військових кораблів» за допомогою GenAI-підходу(ukrainian) discusses the creation of the "Warships Monitoring" dataset using a Generative AI approach with Gemini-2.0-Flash-Experimental.

TBD

Data

Table1. data_monitoring.csv

• date - observation time;
• img_name - image name;
• black_sea.enemy_ships - number warships in the Black Sea;
• black_sea.kalibr_carriers - number warships ('Kalibr' carriers) in the Black Sea;
• black_sea.total_salvo - total salvo in the Black Sea;
• azov_sea.enemy_ships - number warships in the Sea of Azov;
• azov_sea.kalibr_carriers - number warships ('Kalibr' carriers) in the Sea of Azov;
• azov_sea.total_salvo - total salvo in the Sea of Azov;
• mediterranean_sea.enemy_ships - number warships in the Mediterranean Sea;
• mediterranean_sea.kalibr_carriers - number warships ('Kalibr' carriers) in the Mediterranean Sea;
• mediterranean_sea.total_salvo - total salvo in the Mediterranean Sea;
• kerch_strait_passage.black_sea.total - number of ships passage the Kerch Strait from the Sea of Azov to the Black Sea;
• kerch_strait_passage.black_sea.moved_towards_bosporus - number of ships passage the Kerch Strait from the Sea of Azov to the Black Sea - Bosporus direction;
• kerch_strait_passage.azov_sea.total - number of ships passage the Kerch Strait from the Black Sea to the Sea of Azov;
• kerch_strait_passage.azov_sea.moved_from_strait_bosporus - number of ships passage the Kerch Strait from the Black Sea to the Sea of Azov - Bosporus direction. #### Table2. data_posts.csv
• id - post id;
• text - text;
• date - post time;
• views - number of views on the day the data was received
• img_path - image name;
• reactions - number of reactions on the day the data was received;
• date_create - download date. #### Table3. metadata_images.csv #### Table4. metadata_images_test.csv #### Folder1. images Input images for creating data_monitoring.csv. #### Folder2.images_test Images subset for testing

Related Datasets

• Massive Missile Attacks on Ukraine - detailed information about launched and shot down missiles and drones during Massive Missile Attacks.
• 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.
• 2022 Ukraine Russia War, Losses, Oryx + Images(updated quarterly) - 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.

NATO Eastern Flank Expansion (Airforces)

is a dataset that highlights NATO's Eastern Flank's military might and cooperation (русский: Расширение восточного фланга НАТО (ВВС)).

NATO Eastern Flank Expansion (Airforces) dataset contains the military capability and alliance information, including:

• Aircraft models in deployment
• Contributor countries
• Military base or group type
• If any country has contributed multiple aircraft model types

Source:

The information has been taken from NATO's official website.

License:

NATO allows to use their data only if the source credit is given to NATO.

File type: .csv

Note:

Although NATO has declared to expand and continue its military presence in the Eastern Europe for stronger vigilance and defence of the NATO members, Russia sees the east-ward expansion of NATO as a threat. The Russian government often claims to see NATO's eastern expansion as a problem and threat to Russia's security (Россия заявляет, что расширение НАТО на восток представляет угрозу безопасности России). The data has been sourced from NATO's official website as mentioned clearly here and converted from PDF (infographic) into .csv by the owner of this dataset Farial Mahmod Tishan.

Context

I've created this dataset as I want to compare the Russia and the NATO's armies.

Content

You will find a ranking with a Power Index calculated for every country by the "Global Fire Power" organization : this is not simple to make a fair comparison between Russia and the 27 countries of the NATO because we can't really summarize what "would be" the Power Index of an alliance.

Acknowledgements

Thanks for your support, if you use this dataset consider giving me credits or share with me your works !

This dataset contains daily geospatial data of Russian-occupied areas in Ukraine, automatically extracted from the open-source DeepStateMap project. The dataset is generated using a Python-based pipeline and updated daily via GitHub Actions.

The data includes:

Daily GeoJSON-based snapshots of territorial control Centroid coordinates and area for each polygonal zone Polygon geometries in WKT (Well-Known Text) format Cleaned, structured, and aggregated into a single CSV for ease of use

This dataset is intended for:

Open-source intelligence (OSINT) workflows Conflict zone mapping and territorial change analysis Machine learning model training on spatial or temporal patterns Geospatial dashboards and time series visualizations

The full data processing pipeline and automation scripts are openly available on GitHub: https://github.com/lazar-bit/deepstate-map-data-analytics