Escience researchers often invest significant time and effort in projects that can have unexpected outcomes. When results are obtained that do not fit the established molds expected by publishing venues, these results can be lost. The ERROR Workshop invites research results from computing and computational projects that encounter unexpected or negative results that would be difficult to report on elsewhere.

Novel hardware technologies and machine learning approaches (among others) are rapidly changing approaches, methods, and scale applied in escience domains. Researchers must deal with this novelty in multiple dimensions, many of which are beyond their control. Consequently, it is likely that some of the obtained results will not be of the expected form: they are negative (deviating from initial hypothesis), abnormal (anomalous to results from similar studies), or otherwise not useful in the established sense.

Under normal circumstances, such negative results and why they were obtained are seldom discussed, analyzed and published. Useful lessons are thus lost to the scientific community. Yet ignoring such results and the process by which they were obtained poses a risk of repetition. The fact that other researchers likely face the same situations and the same pitfalls further increases the cost of research, a cost that would have been avoided if the negative results were brought forward and discussed in-depth within and across communities.