What is ACLED?
The Armed Conflict Location & Event Data Project (ACLED) is a disaggregated conflict collection, analysis and crisis mapping project. ACLED collects the dates, actors, types of violence, locations, and fatalities of all reported political violence and protest events across Africa, South Asia, South East Asia, the Middle East, Europe, and Latin America. Political violence and protest includes events that occur within civil wars and periods of instability, public protest and regime breakdown. ACLED’s aim is to capture the forms, actors, dates and locations of political violence and protest as it occurs across states. The ACLED team conducts analysis to describe, explore and test conflict scenarios, and makes both data and analysis open to freely use by the public.
ACLED is a registered non-profit organization with 501c3 status in the United States.
Please contact email@example.com with comments or queries regarding the ACLED dataset.
For members of the media seeking information, please contact ACLED Communications at firstname.lastname@example.org.
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ACLED regularly provides support to users on ACLED methodology queries. ACLED also provides consultation to donors and partners on the application of ACLED data to specific use cases, bespoke analysis and visualization projects, as well as advising around establishing one’s own data structures and methodologies. If you are not a donor or partner, but would like to request support of this nature, please contact email@example.com to discuss becoming a partner/donor or hiring an ACLED representative for consultation.
Interested in working for ACLED? Visit our Careers page for information on opportunities.
ACLED is a registered non-profit organization with 501c3 status in the United States. ACLED receives financial support from the Bureau of Conflict and Stabilization Operations (CSO) at the U.S. Department of State, the Dutch Ministry of Foreign Affairs, the German Federal Foreign Office (FFO), the Tableau Foundation, the International Organization for Migration (IOM), and the University of Texas at Austin.
This project has also received funding from the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement n° 283755 from 2013 to 2017.