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Event datasets, such as those provided by the Uppsala Conflict Data Program (UCDP), provide high-quality data on conflict fatalities. However, such data are likely to suffer from an unknown extent of bias and uncertainties in the reports they are based on. Although a substantial literature documents reporting bias in conflict research, analyses that quantify this bias are mostly limited to single countries. Here, we combine a survey with UCDP coders and statistical modeling to derive a distribution of plausible number of fatalities given the number of battle-related deaths and the type of violence documented by the UCDP. We provide a generalizable, cross-national measure of uncertainty around UCDP reported fatalities that is more robust and realistic than UCDP’s documented low and high estimates, countering UCDP’s intrinsic tendency to under-estimate fatalities, and we make available a dataset and R package that can be applied to future releases of the UCDP data.

Peace agreements are a tool used to terminate conflict. Definitions of the concept span a broad spectrum; it can be defined broadly to include any understanding between actors related to a conflict or narrowly to only include signed agreements between the main warring parties in a conflict, solving the main conflict issues. Research works on peace agreements have, for example, concerned if peace agreements result in a more stable peace than victories, and how to make peace agreements more lasting by, for example, including provisions on peacekeeping, verification, and power-sharing.

This article reports on trends in organized violence, building on new data by the Uppsala Conflict Data Program (UCDP). The falling trend in fatalities stemming from organized violence in the world, observed for five consecutive years, broke upwards in 2020 and deaths in organized violence seem to have settled on a high plateau. UCDP registered more than 80,100 deaths in organized violence in 2020, compared to 76,300 in 2019. The decrease in violence in Afghanistan and Syria was countered by escalating conflicts in, for example, Artsakh (Nagorno-Karabakh), Azerbaijan and Tigray, Ethiopia. Moreover, the call for a global ceasefire following the outbreak of the COVID-19 pandemic failed to produce any results. In fact, the number of active state-based and non-state conflicts, as well as the number of actors carrying out one-sided violence against civilians, increased when compared to 2019. UCDP noted a record-high number of 56 state-based conflicts in 2020, including eight wars. Most of the conflicts occurred in Africa, as the region registered 30 state-based conflicts, including nine new or restarted ones.

The Uppsala Conflict Data Program’s (UCDP) Peace Agreement Dataset was first published in 2006. Its main goal was to provide the research community with a dataset on peace agreements that was not linked to conflict termination, i. e. included both successful and failed agreements. The latest update of the dataset includes 355 peace agreements concluded in the 1975–2018 period. A number of studies have been based on the dataset over the years. The dataset is unique in its strict connection to the UCDP conflict data and in its focus on the conflict dyad, actors, and the conflict incompatibility. The dataset’s focus on only those agreements that involve the dyadic relationship between primary warring parties – between governments and rebel groups or between two governments – has direct policy implications, as it is exactly these parties who need to change their stances on incompatibilities in order to solve a conflict. Also, the Peace Agreement Dataset’s focus on agreements that address the key incompatibilities contested by the parties allow it to distinguish peace agreements on other negotiated deals, including ceasefires, and to differentiate between full, partial and peace process agreements. Finally, the analysis of key trends in peace agreements is presented. It shows that in contrast to the previous historical peak in the number of armed conflicts back in the early 1990s that corresponded to the peak in annual numbers of peace agreements, the new peak in annual numbers of armed conflicts in the late 2010s was not matched by a similar rise in peace agreements.

This article presents a new, monthly updated dataset on organized violence—the Uppsala Conflict Data Program Candidate Events Dataset. It contains recent observations of candidate events, a majority of which are eventually included in the Uppsala Conflict Data Program Georeferenced Event Dataset as part of its annual update after a careful vetting process. We describe the definitions, sources and procedures employed to code the candidate events, and a set of issues that emerge when coding data on organized violence in near-real time. Together, the Uppsala Conflict Data Program Candidate and Georeferenced Event Datasets minimize an inherent trade-off between update speed and quality control. Having monthly updated conflict data is advantageous for users needing near-real time monitoring of violent situations and aiming to anticipate future developments. To demonstrate this, we show that including them in a conflict forecasting system yields distinct improvements in terms of predictive performance: Average precision increases by 20–40% relative to using the Uppsala Conflict Data Program Georeferenced Event Dataset only. We also show that to ensure quality and consistency, revisiting the initial coding making use of sources that become available later is absolutely necessary.

This article reports on trends in organized violence and peace agreements collected by the Uppsala Conflict Data Program (UCDP). The number of fatalities in organized violence decreased for the fourth consecutive year, to reach the lowest level since 2012. In 2018, UCDP recorded almost 76,000 deaths: a decrease of 20% compared to 2017, and 43% compared to the latest peak in 2014. State-based armed conflict drives this downward trend in organized violence, with Syria accounting for much of the change. The number of civilians killed in one-sided violence also dropped in 2018, reaching its lowest level since 2012. In contrast, non-state conflict remained on a high level. The general decline in fatalities from organized violence does not correspond with the trend in the number of active conflicts. In fact, the world has seen a new peak in the number of conflicts after 2014, matched only by the number of conflicts in the early 1990s. In 1991, the peak in the number of armed conflicts corresponded with a similar peak in the number of signed peace agreements. This was followed by a decrease in the number of conflicts in the late 1990s and early 2000s. However, the most recent rise in armed conflicts has not been matched by a similar rise in the number of peace agreements. Two circumstances that characterize the recent rise in conflicts have also been found to make conflicts harder to solve: explicit religious claims and high levels of internationalization.

This article presents ViEWS – a political violence early-warning system that seeks to be maximally transparent, publicly available, and have uniform coverage, and sketches the methodological innovations required to achieve these objectives. ViEWS produces monthly forecasts at the country and subnational level for 36 months into the future and all three UCDP types of organized violence: state-based conflict, non-state conflict, and one-sided violence in Africa. The article presents the methodology and data behind these forecasts, evaluates their predictive performance, provides selected forecasts for October 2018 through October 2021, and indicates future extensions. ViEWS is built as an ensemble of constituent models designed to optimize its predictions. Each of these represents a theme that the conflict research literature suggests is relevant, or implements a specific statistical/machine-learning approach. Current forecasts indicate a persistence of conflict in regions in Africa with a recent history of political violence but also alert to new conflicts such as in Southern Cameroon and Northern Mozambique. The subsequent evaluation additionally shows that ViEWS is able to accurately capture the long-term behavior of established political violence, as well as diffusion processes such as the spread of violence in Cameroon. The performance demonstrated here indicates that ViEWS can be a useful complement to non-public conflict-warning systems, and also serves as a reference against which future improvements can be evaluated.