Since 1800, more than 37 million people worldwide have died while actively fighting in wars.

The number would be much higher still if it also considered the civilians who died due to the fighting, the increased number of deaths from hunger and disease resulting from these conflicts, and the deaths in smaller conflicts that are not considered wars.1

Wars are also terrible in many other ways: they make people’s lives insecure, lower their living standards, destroy the environment, and, if fought between countries armed with nuclear weapons, can be an existential threat to humanity.

Looking at the news alone, it can be difficult to understand whether more or less people are dying as a result of war than in the past. One has to rely on statistics that are carefully collected so that they can be compared over time.

While every war is a tragedy, the data suggests that fewer people died in conflicts in recent decades than in most of the 20th century. Countries have also built more peaceful relations between and within them.

How many wars are avoided, and whether the trend of fewer deaths in them continues, is up to our own actions. Conflict deaths recently increased in the Middle East, Africa, and Europe, stressing that the future of these trends is uncertain.

Dataset Description:

This dataset offers insights into countries experiencing ongoing conflicts, providing estimates of fatalities resulting from these conflicts across various years. It serves as a valuable resource for understanding the global landscape of conflict and its human toll.

Measuring armed conflicts and conflict deaths across the world helps us understand how people’s lives and livelihoods are affected by large-scale violence.

But this comes with many challenges. People do not always agree on what characteristics define an armed conflict. Even once defined, these characteristics — especially how many people died in them — are difficult to assess.

The people affected are not always asked who has died around them due to the conflict.

The conflict parties may underreport deaths to claim success, or overreport them to encourage intervention by third parties.

Independent observers may also struggle to be present in all places and document a conflict’s death toll.

So how do researchers address these challenges?

In our work on War and Peace, we provide data from six sources that identify armed conflicts and count their deaths:

Uppsala Conflict Data Program (UCDP)1 Project Mars by Jason Lyall (2022)2 Militarized Interstate Events by Douglas Gibler and Steven Miller3 Correlates of War (CoW)4 Peace Research Institute Oslo (PRIO)5 Conflict Catalog by Peter Brecke (1999)6 These sources all measure armed conflicts and their deaths; they cover many countries and years, and researchers and policymakers frequently use them.

Syria SY: Battle-Related Deaths: Number of People data was reported at 24,950.000 Person in 2017. This records a decrease from the previous number of 43,936.000 Person for 2016. Syria SY: Battle-Related Deaths: Number of People data is updated yearly, averaging 41,218.000 Person from Dec 2004 (Median) to 2017, with 8 observations. The data reached an all-time high of 69,086.000 Person in 2013 and a record low of 1.000 Person in 2004. Syria SY: Battle-Related Deaths: Number of People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Syrian Arab Republic – Table SY.World Bank.WDI: Population and Urbanization Statistics. Battle-related deaths are deaths in battle-related conflicts between warring parties in the conflict dyad (two conflict units that are parties to a conflict). Typically, battle-related deaths occur in warfare involving the armed forces of the warring parties. This includes traditional battlefield fighting, guerrilla activities, and all kinds of bombardments of military units, cities, and villages, etc. The targets are usually the military itself and its installations or state institutions and state representatives, but there is often substantial collateral damage in the form of civilians being killed in crossfire, in indiscriminate bombings, etc. All deaths--military as well as civilian--incurred in such situations, are counted as battle-related deaths.; ; Uppsala Conflict Data Program, http://www.pcr.uu.se/research/ucdp/.; Sum;

Data Description: Since 1800, more than 37 million people worldwide have died while actively fighting in wars.

The number would be much higher still if it also considered the civilians who died due to the fighting, the increased number of deaths from hunger and disease resulting from these conflicts, and the deaths in smaller conflicts that are not considered wars.

Wars are also terrible in many other ways: they make people’s lives insecure, lower their living standards, destroy the environment, and, if fought between countries armed with nuclear weapons, can be an existential threat to humanity.

Looking at the news alone, it can be difficult to understand whether more or less people are dying as a result of war than in the past. One has to rely on statistics that are carefully collected so that they can be compared over time.

How many wars are avoided, and whether the trend of fewer deaths in them continues, is up to our own actions. Conflict deaths recently increased in the Middle East, Africa, and Europe, stressing that the future of these trends is uncertain.

In this dataset, there are 6 csv files in one zip one. Everything is clear but if you have any question, feel free to ask. Good luck.

This dataset belongs to Ourworldindata By: Bastian Herre, Lucas Rodés-Guirao, Max Roser, Joe Hasell and Bobbie Macdonald

A dataset to advance the study of life-cycle interactions of biomedical and socioeconomic factors in the aging process. The EI project has assembled a variety of large datasets covering the life histories of approximately 39,616 white male volunteers (drawn from a random sample of 331 companies) who served in the Union Army (UA), and of about 6,000 African-American veterans from 51 randomly selected United States Colored Troops companies (USCT). Their military records were linked to pension and medical records that detailed the soldiers������?? health status and socioeconomic and family characteristics. Each soldier was searched for in the US decennial census for the years in which they were most likely to be found alive (1850, 1860, 1880, 1900, 1910). In addition, a sample consisting of 70,000 men examined for service in the Union Army between September 1864 and April 1865 has been assembled and linked only to census records. These records will be useful for life-cycle comparisons of those accepted and rejected for service. Military Data: The military service and wartime medical histories of the UA and USCT men were collected from the Union Army and United States Colored Troops military service records, carded medical records, and other wartime documents. Pension Data: Wherever possible, the UA and USCT samples have been linked to pension records, including surgeon''''s certificates. About 70% of men in the Union Army sample have a pension. These records provide the bulk of the socioeconomic and demographic information on these men from the late 1800s through the early 1900s, including family structure and employment information. In addition, the surgeon''''s certificates provide rich medical histories, with an average of 5 examinations per linked recruit for the UA, and about 2.5 exams per USCT recruit. Census Data: Both early and late-age familial and socioeconomic information is collected from the manuscript schedules of the federal censuses of 1850, 1860, 1870 (incomplete), 1880, 1900, and 1910. Data Availability: All of the datasets (Military Union Army; linked Census; Surgeon''''s Certificates; Examination Records, and supporting ecological and environmental variables) are publicly available from ICPSR. In addition, copies on CD-ROM may be obtained from the CPE, which also maintains an interactive Internet Data Archive and Documentation Library, which can be accessed on the Project Website. * Dates of Study: 1850-1910 * Study Features: Longitudinal, Minority Oversamples * Sample Size: ** Union Army: 35,747 ** Colored Troops: 6,187 ** Examination Sample: 70,800 ICPSR Link: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/06836

This is the first attempt to record the Jewish soldiers who became casualties in the numerous Wars between the Habsburg Monarchy and Revolutionary and Napoleonic France. Jewish military service in the Austrian and Austro-Hungarian army from the mid-19th century onwards, especially during the First World War, is well known and documented. By contrast, nothing comparable has been done for the very first Jewish soldiers in modern history. The time has come to set the record straight!

The current database was compiled from the personal records of the War Archive (Kriegsarchiv) of the Austrian State Archives. At that time, the Habsburg army did not publish casualty lists other than mentioning the names of the most senior officers. To find individual Jewish soldiers who became casualties, one must identify serving Jewish soldiers in the regular musters and revision papers. Those found so far can be seen in the database Jewish Soldiers of the Habsburg Army (1788-1820), which should be used in parallel with this one. The current database offers an outtake with a separate list of Jewish soldiers who were killed, wounded, missing in action, or taken prisoner. The first version has 253 entries. These are arranged chronologically based on the date the soldier first became a casualty. The name of the battle or the action shows at the top of the table. Under each such action, up to four sub-categories are given:

1. K/KIA (Killed in Action) – Soldier killed outright in combat. Readers might be surprised how few such cases appear in the database. There are several possible reasons. Firstly, since 1781 the Habsburg manpower reports began to omit the rubric Vor Feind geblieben (left in front of the enemy) denoting soldiers killed in battle. This was part of a broader rationalisation of military records in the early days of Joseph II’s rule. Whichever was the cause of their death, all fatalities were now perceived as irrecoverable manpower wastage. Soldiers who died in service were now simply marked as gestorben. Identifying combat deaths is only possible by looking at monthly reports called Standes- und Diensttabellen. Even then, the number of combat deaths remains extraordinarily low. It appears that the Habsburg army formally recorded a soldier as ‘killed in action’ only if the body was identified. For this to happen, the army had to remain in control of the battlefield – in other words, the battle had to be won. For much of the Revolutionary and Napoleonic period, this was rarely the case on the Austrian side. It appears that most combat deaths in the period landed in the rubric as ‘missing-in-action’.

2. W/WIA (Wounded in Action) – Muster rolls did not record wounds at all. Monthly tables did so very rarely. The latter were intended primarily as financial documents to record the source of the men’s pay. When a soldier entered hospital, his pay was issued from the hospital fund whose accounts were later reimbursed by the man’s regiment. While dates of hospitalisation were meticulously recorded, the cause of hospitalisation was not mentioned. In most cases, identifying wounded soldiers can only be done indirectly. When dozens or hundreds of men from the same unit were hospitalised on the same day directly after a major battle, it can be reasonably assumed that these were combat casualties. A sure way of identifying a wounded soldier was through the medical evaluation papers (Superarbitrierungs-Liste), which were filed for men no longer fit for wartime service. These papers always mentioned combat wounds, as this was a major argument in favour of making the soldier eligible for admission into the invalids. Unfortunately, the survival rate of these documents is variable and the majority simply do not exist. This database employs two categories for wounded soldiers. When medical papers or hospitalisation date allows clear identification, a soldier is entered into the database as a certain case. When broader context allows (such as wartime service and numerous other hospitalisations from the same company on the same day, suggesting a skirmish), such men are entered as probable cases.

3. P/POW (Prisoner of War) – Unlike the previous two rubrics, the Habsburg military records usually mentioned soldiers taken prisoner (Kriegsgefangen/ In Kriegsgefangenschaft gefallen). The reason was again financial. Firstly, returning men had to be issued with backpay. Secondly, from the Third Coalition War onwards, reciprocal wartime prisoner swaps (Cartel) were discontinued, but the system remained in place to ensure that mutual settlement of accounts between two belligerent armies could happen after the war. This is not the only reason why prisoners make the largest single category in our database. For much of the Revolutionary and Napoleonic period, entire Austrian army corps were forced to surrender (for instance in Ulm in 1805). This happened so often that musters from 1806 and 1811 sometimes blankly omitted case of POWs, based on the assumption that nearly every soldier fell prisoner in the previous war. Therefore, for regiments who fought in Germany and Austerlitz in 1805, and in Bavaria and Deutsch-Wagram in 1809, one must also consult the monthly tables.

4. M/MIA (Missing in Action) – Recorded as Vor Feind vermisst or vermisst for short, this category denotes men who were missing when the battle ended. Anything could have happened to them. Some were dead (see rubric one), but others were taken prisoner, were lost, or deserted. The army recorded such missing men for the same reason as prisoners of war – to settle their backpay in future if necessary.

The total for each category of casualties is given at the bottom of the table for every war fought by the Habsburg army from 1792 to 1815. At the right hand side of the table are the grand totals for each category marked in red. At the end of every personal record are fields showing what happened to the soldier after he became a casualty. Wounded could recover or perish in hospital, while the prisoners and the missing could return. The same soldier could appear in the database more than once as he could be taken prisoner, be wounded or go missing several times. Only for those killed in action could the record be closed. For those who survived, the final fate was noted where known: discharge (including sub category), invaliding, desertion, or non-combat death. Men still in service when last mentioned in the documents are noted as ‘serves’. Whether complete or not, a detailed service record for each soldier as as I could reconstruct it from the sources is available in the database Jewish Soldiers of the Habsburg Army (1788-1820).

About this dataset

Context

The dataset tabulates the population of War by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for War. The dataset can be utilized to understand the population distribution of War by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in War. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for War.

Key observations

Largest age group (population): Male # 15-19 years (61) | Female # 30-34 years (42). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the War population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the War is shown in the following column.
• Population (Female): The female population in the War is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in War for each age group.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for War Population by Gender. You can refer the same here

this graph was created in OurDataWorld and R :

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Since 1800, the specter of war has claimed the lives of over 37 million people worldwide. This staggering statistic represents only those who actively fought in wars. The true cost of war is far greater when we consider the countless civilian lives lost, the increased mortality from hunger and disease in war-torn regions, and the untold suffering of those displaced from their homes.

Beyond the loss of life, war leaves a trail of destruction in its wake:

Insecurity and instability: Wars create an environment of fear and uncertainty, making it difficult for people to live their lives in peace. Reduced living standards: War-torn countries often suffer from economic collapse, leading to widespread poverty and hunger. Environmental damage: Wars can cause extensive damage to the natural environment, with long-term consequences for both people and wildlife. The threat of nuclear annihilation: In the age of nuclear weapons, the potential for war to escalate into a global catastrophe is a constant threat to humanity. While each war is a tragedy in its own right, data suggests that fewer people have died in conflicts in recent decades than in most of the 20th century. Additionally, there has been a trend towards more peaceful relations between and within countries.

However, the future of these trends is uncertain. Recent conflicts in the Middle East, Africa, and Europe have seen a resurgence in war-related deaths, underscoring the need for continued vigilance and effort to prevent future conflicts.

On this page, you will find data, visualizations, and analysis on the prevalence of war and peace between and within countries, and how this has changed over time. This information is essential for understanding the true cost of war and the imperative for building a more peaceful world.

Join us in the fight for peace. By raising awareness of the human cost of war and supporting initiatives that promote peace and cooperation, we can help to create a future where war is no longer a reality.

Key Points:

Over 37 million people have died in wars since 1800. The true cost of war includes civilian deaths, increased mortality from hunger and disease, and displacement. War has devastating consequences for human society and the environment. There has been a trend towards fewer war-related deaths in recent decades, but the future of this trend is uncertain. We must continue to work towards a more peaceful world.

Version 2 (18 March 2025) includes a further 356 service itineraries. In addition, 41 entries from the previous version were updated or expanded. Currently the database covers a total of 1,858 Jewish soldiers, 421 wives and 83 children.

ORIGINAL VERSION 1 (18 September 2024)

With more than 1,500 individual entries, this is the inaugural instalment of my research database collated in the framework of the Project Forgotten Soldiers: Jewish Military Experience in the Habsburg Monarchy. This is an open access database, and everyone is welcome to use it according to their own scholarly and personal interests. In 1,189 cases we have official documented records confirming the soldiers were Jewish. In another 313 entries I was able to identify likely Jewish soldiers based on circumstantial evidence cross-referencing names and places of birth, with the presence of confirmed Jewish soldiers drafted into the same units as part of the same recruitment drive. This dataset further includes evidence for 156 spouses and 47 children. While military records do mentions these, their number suggests that the Habsburg army preferred to enlist unmarried men.

The database is structured in a similar way to an official individual entry in the Habsburg military records. These were arranged in tables, with soldiers listed by seniority. Name, place and land of birth are followed by age and religion. This latter rubric allows identifying the bulk of the Jewish soldiers. Also included in the record is marital status, profession (if any), number, names and ages of children (if any), followed by a short summary text of the soldier’s service itinerary. While not always consistent in detail, these texts mention enlistment dates, transfers between units, promotions, desertions, periods as prisoner of war and military awards (if any). I have taken the material from the personal records and added several additional parameters:

The soldiers are entered into the database according to their date of enlistment. This is followed by a colour-coded table showing their years of service. To see the meaning of the different colours employed, scroll to the legend at the end of the dataset.

Following the years of service, we see the date when the soldier left service (final year in service for incomplete service records). When known, the reason the soldier left the army is given (discharge/ death/ desertion etc).

Then come the three most important columns within the table: service record, primary sources and units. At first glance, these columns have only a few letters and numbers, but bring your mouse courser onto the relevant field marked with red triangles. An additional window will then open:

a. Service Record: Shows the entire service record of the soldier arranged by date. I use original German as it appears in the archival records. If you see spelling differences with modern German – they are there for a reason.

b. Primary Sources: Provides the information on all the archival records consulted to reconstruct the service itinerary. The number in the field denotes the number of the archival cartons consulted.

c. Units: Number of units in which a soldier serves. Bringing the cursor on to the field will open their list. Most Jewish soldiers served in the line infantry (IR) and the Military Transport Corps (MFWK or MFK). However, there were also Jewish sharpshooters, cavalrymen, gunners and even a few members of the nascent Austrian Navy.

The next two columns provide entries of the soldier’s conduct and medical condition, which in Habsburg military jargon was referred to rather callously as Defekten. I note the original medical diagnoses verbatim. When possible to identify, I note the modern medical term.

General database-wide parameters are then noted in the next part of the table. Among others, it provides information on enlistment type (conscript/ volunteer?), main branches of service (such as Infantry/ Cavalry/ Artillery), and roles within the military (such as non-commissioned officers/ drummers/ medics).

Concluding this part of the table are columns covering desertions, periods as prisoner of war and awards of the army cannon cross (for veterans of 1813-14) and other military awards.

The last column provides the original German outtake rubric as to how the soldier left service. In special cases, additional service notes are provides on the right.

How to use this dataset

This depends on what you are looking for. Firstly, download the dataset on to your computer via the link provided below. It is a simple Excel file which is easy to work with. If you wish to find out whether one of your ancestors served in the Habsburg army, use a simple keyword search. Please note that in our period there was no single accepted orthography meaning that some letters were used interchangeably (for instance B/P; D/T). There were also various patronymic suffices used in different parts of the monarchy (-witz in German/ -wicz in Polish/ -vits in Hungarian). Habsburg military clerks were mostly German speakers who often recorded the name phonetically. For instance, Jankel/ Jankl/ Jacob/ Jacobus all denote the same name. A Jewish teenager who identified himself as Moische when first reporting to duty, may have stayed so in the military records for decades, even if he was already a non-commissioned officer whose subordinates referred to as Herr Corporal.

If you study the history of concrete Jewish communities, use the keyword search and the filter option to find entries in the database where this locality is mentioned. Some places like Prague and Lublin could be identified effortlessly. In other cases (and see the above point on German-speaking clerks), place names were recorded phonetically. The military authority usually stuck to official Polish names in Galicia, and Hungarian in the Lands of the Crown of St. Stephan. In reality, a Jewish recruit from Transcarpathian Ruthenia could have his place of birth recorded in Hungarian, Romanian or Rusin. When I could not identify the place in question, I marked it with italics. Do you think you identified something I could not? Excellent! Then please write me, and I will correct the entry in the next instalment of this database.

I should stress that, currently, the database is not statistically representative. I have worked chronologically, meaning that there are disproportionally more entries for Jewish soldiers from the Turkish War, the first two Coalition Wars, and the Wars of 1805 and 1809. If you look at some of my other databases (for instance, that of the 1st Line Infantry Regiment 'Kaiser'), you will find least as many Jews who served in the wars of 1813-15. I will cover these in due course. This said, using the filter option of the Excel sheet, you can already make some individual queries. For instance, did Jewish grenadiers meet the minimal height requirement to be eligible for transfer into the elite infantry? (Hint: they did not!) If you are interested in the historical study of nutritional standards, compare the height of the soldiers with their year and place of birth. In my other project, I made calculations of the average height of Habsburg soldiers and I can already reveal that Jewish conscripts were, on average, several centimetres smaller than their non-Jewish comrades drafted in the same annual intake. Whatever stereotypes said, most Jews in the Habsburg Monarchy around 1800 were very poor and the sad fact of malnutrition as a child is reflected in their height as adults.

I should stress that this is a cumulative database. ZENODO has an excellent feature allowing updated versions to supersede earlier files while retaining the same DOI (Digital Object Identifier) and metadata. As my research progresses, I plan to upload new versions of this database bi-annually. This includes not only adding new entries, but also expanding and correcting existing ones. It might well be that the service record of a soldier covered up to 1806 will be brought to a later date, possibly even to his discharge from the army. If you have not found whom you are looking for, or if you want to work with larger samples for your research, visit this page again in a few months’ time. And if you do use this database for scholarly research (by all means, please do), do not forget to cite it as you would cite any other item in your bibliography! If you are a museum professional and you want to employ material from your database to illustrate your exhibitions, you are welcome, but please cite this resource for others to learn. Links to this database will also be appreciated.

Where are you from; This is the most common question that Greeks ask foreign visitors. The question talks about the need to understand where someone comes from, to understand how it is connected to a place and at the same time to place it in a temporal and social context. And yet, about 4,000 Greeks can not give a clear answer to this question. International adoptions from Greece to the United States (and later to the Netherlands) from the 1950s onwards disrupted that sense of belonging. These people and their families have spent seventy years wondering what exactly happened and whether there is still a chance for them to discover their roots in a Greek family and in the Greek tradition. I hope my research provides some answers through the difficult paths taken by the adoptees during their lifetime, paths that all together compose an unknown chapter in the history of Greece and the United States during the Cold War.

Some Task Ideas:

Analyze Fatality Trends: Explore the dataset and track the trends in fatalities over time. Identify any significant changes, spikes, or declines in the number of fatalities. Demographic Analysis:Conduct a demographic analysis by examining the age, gender, and citizenship of the individuals killed. Determine if there are any notable patterns or disparities in the data. Geospatial Analysis: Utilize the event location, district, and region information to perform geospatial analysis. Visualize the distribution of fatalities on a map and identify areas that have experienced higher levels of violence. Hostilities Participation Analysis:Investigate the extent of individuals' participation in hostilities before their deaths. Analyze the relationship between participation and the circumstances surrounding each fatality. Injury Analysis: Examine the types of injuries inflicted on individuals. Identify the most common types of injuries and assess their severity. Weapons Used: Analyze the ammunition and means by which the individuals were killed. Determine the most frequently used weapons or methods and evaluate their impact. Victim Profiles: Create profiles of the victims based on the available data such as age, gender, citizenship, and place of residence. Identify common characteristics among the victims.

Overview

The Corpus of Resolution: UN Security Council (CR-UNSC) collects and presents for the first time in human and machine-readable form all resolutions, drafts, and meeting records of the UN Security Council, including detailed metadata, as published by the UN Digital Library and revised by the authors.

The United Nations Security Council (UNSC) is the most influential of the principal UN organs. Composed of five permanent and ten non-permanent members, its functioning is constrained by the political context in which it operates. During the Cold War, the complex political relationships between the permanent members and their veto powers significantly affected the capacity of the UNSC to address violations of international peace and security, with only 646 resolutions passed from 1946 to 1989. Since the 1990s, the activity of the UN Security Council has increased dramatically and produced 2721 resolutions up to the end of 2023. The length, complexity and thematic breadth of the resolutions has also increased, prompting calls to redefine it as a quasi-legislative body.

Under Articles 24 and 25 of the UN Charter, member states have conferred upon the UNSC the "primary responsibility for the maintenance of international peace and security" and have agreed "to accept and carry out" its decisions. The discharge of this function is carried out through the powers bestowed upon it under Chapter VI of the UN Charter, "Pacific Settlement of Disputes", Chapter VII, "Action with Respect to Threats to the Peace, Breaches of the Peace, and Acts of Aggression", Chapter VIII, "Regional Arrangements", and Chapter XII, "International Trusteeship System".

Under the peace and security mandate, its areas of activity cover disarmament, pacific settlement of disputes, enforcement, and, until 1994, strategic areas in a trusteeship agreement. Its functions also pertain to the correct working of the United Nations, covering issues of membership, the appointment of the Secretary General, the elections of judges of the International Court of Justice (ICJ), the calling of special and emergency sessions of the General Assembly, the amendment of the Charter and of the ICJ Statute.

Please refer to the Codebook for a detailed explanation of the dataset and instructions on how to make use of it.

Updates

The CR-UNSC will be updated at least once per year.

In case of serious errors an update will be provided at the earliest opportunity and a highlighted advisory issued on the Zenodo page of the current version. Minor errors will be documented in the GitHub issue tracker and fixed with the next scheduled release.

The CR-UNSC is versioned according to the day of the last run of the data pipeline, in the ISO format YYYY-MM-DD. Its initial release version is 2024-05-03.

Notifications regarding new and updated data sets will be published on my academic website at www.seanfobbe.com or on the Fediverse at @seanfobbe@fediscience.org

Changelog

• New variant: EN_TXT_BEST containing a write-out of the English resolution texts equivalent to the CSV file text variable
• New diagrams: bar charts of top M49 regions and sub-regions of countries mentioned in resolution texts
• Fixed naming mix-up of BIBTEX and GRAPHML zip archives
• Fixed whitespace character detection in citation extraction (adds ca. 10% more citations)
• Fixed improper merging of weights in citation network
• Fixed "cannot xtfrm data frames" warning
• Improve REGEX detection for certain geographic entities
• Improve Codebook (headings, citation network docs)

Key Metrics

Version: 2024-05-19

Scope: UNSC Resolutions from 1 (1946) up to and including 2722 (2024)

Tokens: 3,704,016 (English resolution texts)

Languages: English, French, Spanish, Arabic, Chinese, Russian

Features

• 82 Variables
• Resolution texts in all six official UN languages (English, French, Spanish, Arabic, Chinese, Russian)
• Draft texts of resolutions in English
• Meeting record texts in English
• URLs to draft texts in all other languages (French, Spanish, Arabic, Chinese, Russian)
• URLs to meeting record texts in all other languages (French, Spanish, Arabic, Chinese, Russian)
• Citation data as GraphML (UNSC-to-UNSC resolutions and UNSC-to-UNGA resolutions)
• Bibliographic database in BibTeX/OSCOLA format for e.g. Zotero, Endnote and Jabref
• Extensive Codebook to explain the uses of the dataset
• Compilation Report and Quality Assurance Report explain construction and validation of the data set
• Publication quality diagrams for teaching, research and all other purposes (PDF for printing, PNG for web)
• Open and platform independent file formats (CSV, PDF, TXT, GraphML)
• Software version controlled with Docker
• Publication of full data set (Open Data)
• Publication of full source code (Open Source)
• Data published under Public Domain waiver (CC Zero 1.0)
• Source Code is Free Software published under the GNU General Public License Version 3 (GNU GPL v3)
• Secure cryptographic signatures for all files in version of record (SHA2-256 and SHA3-512)

Recommended Variants

Traditional Scholars	ALL_PDF_Resolutions EN_TXT_BEST BIBTEX_OSCOLA
Quantitative Scholars	ALL_CSV_FULL EN_TXT_BEST CITATIONS_GRAPHML

Please refer to the Codebook regarding for details on each variant. The ZIP archives include texts in all languages, unless noted in the filename.

We strongly recommend using the CSV files for quantitative analysis, but if you find CSV hard to use and want to analyze only the text of resolutions, the EN_TXT_BEST variant is a mix of expert-revised OCR and born digital texts equivalent to the "text" variable in the CSV file.

Compilation Report and Quality Assurance Report

With every compilation of the full data set, an extensive Compilation Report and detailed Quality Assurance Report are created and published in PDF format.

The Compilation Report includes the source code for the pipeline architecture, comments and explanations of design decisions, relevant computational results, exact timestamps and a table of contents with clickable internal hyperlinks to each section.

The Quality Assurance Report contains a count of all hard tests and expectations, additional visualizations and documented test results for all soft tests that require further interpretation

The Compilation Report, Quality Assurance Report and Source Code are published under the following DOI: https://zenodo.org/doi/10.5281/zenodo.7319783

Attribution and Copyright

This data is derived from the United Nations Digital Library at https://digitallibrary.un.org. Records were accessed and downloaded on 13 and 26 March 2024, with additional work on revisions and corrections up to and including the date given as the version number.

Pursuant to UN Administrative Instruction ST/AI/189/Add.9/Rev.2 of 17 September 1987 all official records and United Nations Documents (including resolutions, compilations of resolutions, drafts and meeting records) are in the public domain. We wish to honor the letter and spirit of this UN policy. To ensure the widest possible distribution of official UN documents and to promote the international rule of law we waive any copyright that might have accrued by creating the dataset under a Creative Commons CC0 1.0 Universal (CC0 1.0) Public Domain Dedication.

Disclaimer

This data set is an academic initiative and is not associated with or endorsed by the United Nations or any of its constituent organs and organizations.

Author Websites

Personal Website of Seán Fobbe

Personal Website of Lorenzo Gasbarri

Personal Website of Niccolò Ridi

Contact

Did you discover any errors? Do you have suggestions on how to improve the data set? You can either post these to the Issue Tracker on GitHub or contact Seán Fobbe via https://seanfobbe.com/contact/

Social media can be mirrors of human interaction, society, and world events. Their reach enables the global dissemination of information in the shortest possible time and thus the individual participation of people all over the world in global events in almost real-time. However, equally efficient, these platforms can be misused in the context of information warfare in order to manipulate human perception and opinion formation. The outbreak of war between Russia and Ukraine on February 24, 2022, demonstrated this in a striking manner.

Here we publish a dataset of raw tweets collected by using the Twitter Streaming API in the context of the onset of the war which Russia started on Ukraine on February 24, 2022. A distinctive feature of the dataset is that it covers the period from one week before to one week after Russia's invasion of Ukraine. We publish the IDs of all tweets we streamed during that time, the time we rehydrated them using Twitter's API as well as the result of the rehydration. If you use this dataset, please cite our related Paper:

Pohl, Janina Susanne and Seiler, Moritz Vinzent and Assenmacher, Dennis and Grimme, Christian, A Twitter Streaming Dataset collected before and after the Onset of the War between Russia and Ukraine in 2022 (March 25, 2022). Available at SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4066543

About this dataset

Context

The dataset tabulates the Battle Creek population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Battle Creek. The dataset can be utilized to understand the population distribution of Battle Creek by age. For example, using this dataset, we can identify the largest age group in Battle Creek.

Key observations

The largest age group in Battle Creek, MI was for the group of age 15 to 19 years years with a population of 3,876 (7.40%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Battle Creek, MI was the 85 years and over years with a population of 757 (1.44%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

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

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Battle Creek is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Battle Creek total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Battle Creek Population by Age. You can refer the same here

Introduction This dataset collection is created within the context of the digitisation project ‘First-Hand Accounts of War: War Letters (1935-1950) from NIOD Digitised’, that ran over a period of three years (2020-2023) and was funded by the Mondriaan Fund, the Dutch Ministry of Health, Welfare, and Sport, and the NIOD Institute for War, Holocaust, and Genocide Studies in Amsterdam. The aim of the project was to preserve, digitize, and transcribe the NIOD’s war letters collection and to enhance access to these historical records in various ways. Creator The NIOD Institute for War, Holocaust, and Genocide Studies was founded in 1945. The NIOD is a national and international archival institution and research institute. The NIOD-researchers conduct interdisciplinary academic research into the history of wars, mass violence and genocides. The institute holds over 400 archives and collections (2500 meters) about various topics related to World War II and mass violence in the 20th century. War letters The project ‘First-Hand Accounts of War: War Letters (1935-1950) from NIOD Digitised’ digitised NIOD’s paper archival letter collection, also known as ‘247 Collectie Correspondentie’. The collection contains personal correspondence written and received in the context of the German Occupation of the Netherlands (1940-1945) and the War of Independence in Indonesia (in the late 1940s). Many people have been donating personal correspondence to NIOD since 1945 and new documents are acquired on a regular basis. The vast majority of the letters are written in Dutch and originate from the period 1935-1950. The archival collection ‘247 Collectie Correspondentie’ currently measures 14,1 meters and is divided into different inventory numbers. The collection entails a wide variety of different kinds of personal correspondence from various letter-writers. Contents The dataset ‘First-Hand Accounts of War: War Letters (1935-1950) from NIOD Digitised’ contains machine-readable data in plain text and structured file formats. The data is aimed particularly at researchers interested in the (computational or quantitative) analysis of personal correspondence (‘egodocuments’) in bulk. The dataset consists of four different folders: 1. Handwritten Text Recognition (HTR) model ‘NIOD_WarLet_1935-1950’ The Handwritten Text Recognition (HTR) model ‘NIOD_WarLet_1935-1950’ is trained using READ COOP’s Transkribus software (PyLaia HTR). The computer model is based on ‘Ground Truth’ transcriptions of high-resolution (600 dpi) scans of handwritten correspondence. Contents: README (.txt) with URL to web page with trained public Handwritten Text Recognition (HTR)-model and online demo version (building on the READ COOP’s Transkribus server). 2. Ground Truth War Letters Transcriptions The folder ‘Ground Truth War Letters Transcriptions’ includes 966 manually generated and checked transcriptions of high-resolution (600 dpi) scans of handwritten wartime correspondence. Contents: README (.txt) Ground Truth Transcriptions in ALTO-XML, 966 files (.xml) 3. War Letters (1935-1950) Transcriptions The folder ‘War Letters (1935-1950) Transcriptions’ includes a large number of automatically generated transcriptions of historical handwritten correspondence. The dataset also includes the 966 manually transcribed and checked transcriptions in the Ground Truth War Letters Transcriptions Dataset. Contents: README (.txt) 3.1 War Letters (1935-1950) Transcriptions Dataset (per inventory number) Transcriptions in plain text format, 1480 files, (.txt) 4. War Letters (1935-1950) Metadata The folder ‘War Letters (1935-1950) Metadata’ contains a matrix with the metadata (inventory-number level). This metadataset contains additional information about the data, linked to identifiers (ISIL-codes) related to inventory numbers. This metadata scheme includes the following features: isil: International Standard Identifier for Libraries and Related Organizations (ISIL) code as used by the NIOD Institute for War-, Holocaust-, and Genocide Studies and additional information about the collection (247) and the inventory number of the sub-collection. Example: ‘NL-AsdNIOD_247_0310’. inv_no: The inventory number of the particular sub-collection. Example: ‘310’. no_of_scans: The number of scans (retrieved from the Transkribus-folder containing all scans of each inventory number). Example: ‘1’. pid: persistent identifier of the particular sub-collection. Example: ‘https://proxy.archieven.nl/0/21FE75B1551287C9E0538A77ABC22FE9’. description: A short description of the contents of the sub-collection, derived from the archival index of the NIOD. Example: ‘Brief van Dolly aan haar vader en moeder’. date_range: Date of writing/sending a letter, or date range in case of a correspondence. Example: ‘15 augustus 1943’. keyword_inventory_1: A keyword referring to the contents of the inventory number, derived from the archival inventory index of the NIOD. Some keyword terms...

Many consumer choices nowadays involve ethical considerations (e.g., when buying clothes consumers can often choose between a standard and an ethically produced collection). Previous work has shown that how people feel after such ethically connotated consumer choices is a central determinant of their future ethical consumer behavior. However, most of that research focuses on the effect of choice outcomes (ethical vs. unethical product) on emotional and behavioral reactions. Challenging this outcome-focused approach, the present paper proposes that the experience of conflict (e.g., between ethical goals vs. self-interest) during decision-making has a significant additional effect on emotional reactions and future behavior. Results from one fully powered, preregistered study (N = 383) showed that conflict strength predicted increased negative post-choice emotions and reduced choice satisfaction. Importantly, this pattern occurred for ethical as well as unethical choice outcomes. Conflict also increased the likelihood of making the opposite choice in a subsequent ethical consumer choice situation—again independent of participants' prior choice. Those results are an important extension of previous work as they highlight the importance of taking experienced conflict into account when predicting post-choice emotions and future ethical consumer behavior.

Since 1800, more than 37 million people worldwide have died while actively fighting in wars.

The number would be much higher still if it also considered the civilians who died due to the fighting, the increased number of deaths from hunger and disease resulting from these conflicts, and the deaths in smaller conflicts that are not considered wars.1

Wars are also terrible in many other ways: they make people’s lives insecure, lower their living standards, destroy the environment, and, if fought between countries armed with nuclear weapons, can be an existential threat to humanity.

Looking at the news alone, it can be difficult to understand whether more or less people are dying as a result of war than in the past. One has to rely on statistics that are carefully collected so that they can be compared over time.

While every war is a tragedy, the data suggests that fewer people died in conflicts in recent decades than in most of the 20th century. Countries have also built more peaceful relations between and within them.

How many wars are avoided, and whether the trend of fewer deaths in them continues, is up to our own actions. Conflict deaths recently increased in the Middle East, Africa, and Europe, stressing that the future of these trends is uncertain.

Original Data

Acknowlegement

Foto von UX Gun auf Unsplash

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Battle Creek. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Battle Creek, the median income for all workers aged 15 years and older, regardless of work hours, was $35,002 for males and $28,641 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 18% between the median incomes of males and females in Battle Creek. With women, regardless of work hours, earning 82 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Battle Creek.

- Full-time workers, aged 15 years and older: In Battle Creek, among full-time, year-round workers aged 15 years and older, males earned a median income of $53,310, while females earned $46,664, resulting in a 12% gender pay gap among full-time workers. This illustrates that women earn 88 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Battle Creek.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Battle Creek.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Battle Creek median household income by race. You can refer the same here

This Flash Eurobarometer survey shows large consensus among EU citizens in all EU Member States in favour of the EU’s response to Russia’s invasion of Ukraine. The majority of Europeans think that since the war started, the EU has shown solidarity (79%) and has been united (63%) and fast (58%) in its reaction. Respondents are widely in favour of the unwavering support to Ukraine and its people. In particular, more than nine out of ten respondents (93%) approve providing humanitarian support to the people affected by the war. 88% of Europeans approve the idea of welcoming in the EU people fleeing the war. 80% approve the financial support provided to Ukraine. 66% agree that ‘Ukraine should join the EU when it is ready’, 71% believe that Ukraine is part of the European family and 89% feel sympathy towards Ukrainians.

Processed data

Processed data files for the Eurobarometer surveys are published in .xlsx format.

• Volume A "Countries/EU" The file contains frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for each country or territory and for (weighted) EU results.
• Volume AP "Trends" The file compares to previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume A and for (weighted) results.
• Volume AA "Groups of countries" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of (weighted) replies for groups of countries specified by the managing unit on the part of the EC.
• Volume AAP "Trends of groups of countries" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each groups of countries foreseen in Volume AA and for (weighted) results.
• Volume B "EU/socio-demographics" The file contains (labelled) frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for the EU as a whole (weighted) and cross-tabulated by some 20 sociodemographic, socio-political or other variables, depending on the request from the managing unit on the part of the EC or the managing department of the other contracting authorities.
• Volume BP "Trends of EU/socio-demographics" The file contains shifts compared to the previous poll in (weighted) frequencies and means (or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies); shifts for each country or territory foreseen in Volume B above)and for (weighted) results.
• Volume C "Country/socio-demographics" The file contains (labelled) weighted frequencies and means or other synthetic indicators including elementary bivariate statistics describing distribution patterns of replies for each country or territory surveyed separately and cross-tabulated by some 20 socio-demographic, socio-political or other variables (including a regional breakdown).

For SPSS files and questionnaires, please contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

The data addresses the dynamics of coexistence and conflict in increasingly diverse cities from a human-centred perspective. It was collected as part of the EU-funded project Coexistence and Conflict in the Age of Complexity (EmergentCommunity) in nine European cities in Finland, France, and Sweden. The dataset comprises of two parts: EmergentCommunityEthno (qualitative data) and EmergentCommunityVR (quantitative and qualitative data) that were collected during the project. In addition to these, desk research was conducted and these files have been included in the metadata description.

EmergentCommunityEthno (dataset 1):

Across the nine cities, participants consisted of people above 15-years of age, living in the studied urban neighbourhoods or using their public spaces. In Finland, data were collected in the neighbourhoods of Peltolammi and Multisilta in Tampere, in Malmi in Helsinki, and in Martti and Paavola in Hyvinkää. In Tampere, part of the data (n=31 interviews) was collected in collaboration with the EKOS research project (this part of the data is described and archived in the Finnish Social Science Data Archive, DoI: https://doi.org/10.60686/t-fsd3816). The second part of the data was collected in Sweden. The data collection sites there were the neighborhoods of Möllevången and Nydala in Malmö, Farsta and Rågsved in Stockholm, and Fröslunda and Årby in Eskilstuna. The French data were collected in the La Plaine area in Marseille; in La-Chapelle-Saint-Luc, Saint-Andre-Les-Vergers and Les Chartreux in Troyes; and in Guillotière in Lyon.

Across these sites shared methods were used in data collection, consisting of thematic interviews, walking interviews, and observations. The dataset emphasizes the diversity of experiences and the manifestations of distinctions in diverse urban environments and examines the ways in which people form bonds in relation to each other, their neighborhoods, and the broader society.

The first set of participants were located through social media groups (Facebook), from the premises of associations organizing community activities in the areas, libraries, cafes, community events, and youth centers. After this, snowball sampling was used, in addition to which targeted recruitment was applied if a population group represented in the area was completely missing from the dataset. Ethnographic observations were conducted in public spaces, community centres, cafés, stations, and shopping centres that were selected as potentially interesting places based on extant scholarship on living with difference and urban encounters. Here, attention was paid at how people used these sites, who were there and who were absent, as well as how people moved in and across the sites. Notes were made of what kinds of encounters, patterns of behaviour, cooperations, and conflicts occurred. These observations were made at various times of the day, to capture potential temporal changes. This resulted in a rich collection of fieldnotes, sketches, photographs, and movement maps.

Relevant files: 1) EmergentCommunity ethnographic matrix.pdf, 2) EmergentCommunityEthno interview questions.docx, 3) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 4) EmergentCommunityEthno_metadata.csv (contains metadata only on desk research, ethnographic interviews and fieldnotes).

EmergentCommunityVR (dataset 2):

Data collection was conducted in Helsinki, Marseille, and Malmö. The data was collected using 360-degree videos based on the aforementioned ethnographic data as stimuli to which participants were exposed. A separate video was created for each city, using specifically the data collected therein. We put together a mobile laboratory set-up that travelled to each city and collaborated with local NGOs whose premises were used as our laboratory space. The equipment and software used are explained in the document "EmergentCommunity mobile laboratory.pdf".

The inclusion criteria for participation were: being a major, healthy, not having hearing or vision impairments, being a resident in the city that the video depicted, and knowledge of the local language in which the video was executed. During the viewing of the video stimulus, participants' physiological responses were measured and their eye movements were tracked. VR eye tracking was used as it enables the precise analysis of gaze behaviour – such as fixations and saccades – within immersive, ecologically valid environments. Regarding physiological signals, the focus was on the electrical activity of the heart using electrocardiography (ECG), the electrical activity of the facial muscles using facial electromyography (fEMG), and the electrical conductivity of the skin using galvanic skin response (GSR). To complement the physiological data, a multimodal setup was established to assess the affective content of the stimulus in terms of arousal/valence, avoidance/approach, and unpredictability. After viewing, the participants were asked to evaluate the intensity of their emotional experience and to name the emotional reactions elicited by the video using a questionnaire carried out with Gorilla Experiment Builder. The questionnaire also contained background questions, from basic participant information, such as age and gender, to aspects that relate to diversity and inequality in contemporary societies: language, income, housing, education, political activity, participation, as well as political opinions and social values. After completing the measurements and the questionnaire, participants were interviewed about their experience and the thoughts it provoked, and they were asked to share information regarding their daily lives.

The purpose of the dataset was to help understand the formation of emotional experiences and the significance and functioning of emotions in the everyday life of increasingly diverse and unequal cities. The call for participation was distributed in several thematic Facebook groups (related to e.g., urban development, multiculturalism, neighborhood, local NGOs and minority communities) and via Instagram, as well as through flyers/posters in libraries, local associations, shopping centers, cafes, and on the project's Facebook page and Instagram profile. In the case of Marseille and Malmö, local assistants were used to spread the invitation within their networks and distribute participation invitation leaflets on the streets. In each city, it was possible for already registered participants to invite additional participants as well. Overall, the goal was to ensure the representativeness of the data in terms of age, gender, and minority status.

Relevant files: 1) EmergentCommunity video stimuli.pdf, 2) EmergentCommunityVR interview questions.pdf, 3) EmergentCommunityVR Gorilla questionnaires.pdf, 4) EmergentCommunity mobile laboratory.pdf, 5) EmergentCommunity_metadata public.xlsx (contains all metadata from the project), 6) EmergentCommunityVR interviews.csv (contains metadata on interviews done after watching the 360-degree video), 7) EmergentCommunityVR physio.csv (contains metadata on physiological measuring and questionnaires).

Purpose of the data

The EmergentCommunity project aimed at producing knowledge about what community means and how it is formed in increasingly diverse societies, as well as the conflicts and tensions that everyday life brings out. The project empirically examined the concrete challenges that societal changes produce for cities and coexistence. The aim was to identify how peaceful coexistence could be supported and population relations promoted in urban everyday life. The project emphasized that community relations and everyday coexistence are affective, social, and spatial phenomena, which is why a wide range of research methods from ethnography and observation to psychophysiological measurements and interviews were applied. These approaches were brought into dialogue through virtual reality by utilizing ethnography-based 360-degree videos depicting everyday life in the latter part of the project (EmergentCommunityVR). Thus, the project created new understanding of emotions formed in everyday life and produced unique knowledge in the fields of psychological and sociological emotion research. Bringing these areas together enabled a critical examination of the concept of community and the identification of the practices and ways in which communities are produced in the everyday life of diverse and unequal cities (see CORDIS database for public description, results, and reporting).

Throughout the data collection, the research focused on everyday life and the forms, practices, and interpretations of everyday coexistence in public urban spaces in the selected research neighbourhoods. Participants were also asked to share their experiences, interpretations, and views on societal change and how the change has been visible in their own neighborhoods and what thoughts and feelings it evokes in them. The data was formed through non-probability sampling (self-formed sample).

The research sites were selected by examining statistics, policy reports, and available data on demographic changes and diversity, income inequality, trends of residential and ethnic segregation in different countries and cities (desk research). We chose the countries and cities so that they would complement each other and that changes were observable in each selected context, although their forms, emphases, and manifestations might vary. After this extensive background review, we focused on the city level, complementing the available statistical data with news articles and reports and analyses related to urban areas and development. This allowed us to identify pockets of diversity and inequality within each city. Finally, study neighborhoods were selected based on them having undergone urban development projects, being targeted with anti-segregation measures, their residents' socio-economic