Project Evaluation
ACLED reflects many of the best practices emphasized in Digital Humanities, especially in documentation, methodological transparency, collaborative research, and public-facing access to data. At the same time, evaluating ACLED critically requires recognizing that even a strong dataset is shaped by decisions about scope, sourcing, and coding, which means its strengths are most convincing when paired with clear methodological explanation and acknowledgment of limits.
Section 1
Why evaluation matters
Evaluation is the final step in assessing a DH project because it asks whether the project’s methods, values, and results actually align with the standards established earlier in the analysis. In the case of ACLED, that means judging not only whether the dataset is useful, but also whether the project is documented, transparent, and methodologically defensible enough to support public and scholarly use.
Section 2
Strengths
ACLED’s clearest strength is its commitment to documentation and quality control. The codebook explains the project’s data structure, while the quality pages describe a multi-stage review process involving researchers, regional managers, and final reviewers. That workflow supports intra-coder reliability, inter-coder reliability, and methodological consistency across contexts.
The project is also strong in public access. ACLED presents itself as an independent, impartial conflict monitor that provides real-time data and analysis on violent conflict and protest around the world. Its platforms and downloadable data make the project usable for researchers, policymakers, journalists, and the wider public, which matches DH’s emphasis on sharing knowledge broadly.
Section 3
Critical limits
A serious evaluation also has to acknowledge ACLED’s limits. Conflict data depend on source availability, reporting practices, and coding decisions, so the dataset reflects not just events on the ground but also the structures that make certain events visible or invisible. ACLED’s own article on political instability argues that differences in scope, sourcing, and coding can obscure patterns and alter the conclusions users draw from the data.
This does not weaken ACLED’s value; instead, it clarifies the conditions under which the dataset should be interpreted. A good DH project is not one that claims perfect neutrality, but one that explains its procedures clearly enough that users can understand the trade-offs built into the data. ACLED does this better than many comparable conflict datasets.
Section 4
Final judgment
Overall, ACLED should be evaluated as a strong and credible DH project. It demonstrates best practices through transparent documentation, repeated quality review, accessible tools, and a clear public mission. Its limitations are real, but they are also the kind that careful methodological design can expose rather than hide.