The 2nd Workshop on E-science ReseaRch leading tO negative Results (ERROR) will provide the e-Science community a dedicated and active forum for exchanging cross-discipline experiences on research leading to negative results and lessons learned. The workshop will cover both applications and systems areas, including topics in research methodology, reproducibility, the applications/systems interface, social problems in computational science, and other relevant areas.
Research Software Engineers (RSEs) bridge the gap between traditional software engineering and domain science research. These developers have a unique combination of skills to combine expertise in software development with a deep understanding of the scientific field for which software is being written. This workshop, organized by the US Research Software Engineer Association (US-RSE), will present RSE contributions to scientific research through a series of talks that explore scientific project needs, how access to RSEs made the project successful, and how this work can be applied to future scientific research efforts. The workshop will be of interest to existing RSEs, potential RSEs, domain scientists, and software developers who want to understand the lessons from both good and bad research software engineering experiences.
Emerging and future computational workloads are combining traditional HPC applications with tools and techniques from the scale out data analytics and machine learning community. Getting these technologies to coexist and interoperate to advance scientific discovery is a daunting task with few known good solutions. In general, constructing these workflows has the potential to create pitfalls and incompatibilities that limit adoption.
This workshop seeks to explore ideas and experiences on what kinds of infrastructure developments can improve upon the state of the art. Explorations of component packaging via containers and virtual machines, automation scripting, deployment, portability builds, and system support for these and other relevant activities are key infrastructure. Provenance collection, exploration, and tracking are key for a well documented scientific output. Using existing systems to achieve these goals via experiences are important for developing best practices that span application domains. Data privacy techniques such as multi-party encryption and differential privacy are important as well. Issues with managing large data sets and workflow intermediate data, particularly those intended to manage publicly accessed data for use and reuse are encouraged. New techniques and technologies that address reproducibility requirements are also requested. We seek work on all of these, and related, topics as well as position and experience papers looking to drive conversation for practitioners and researchers in these spaces.
The goal of this workshop is to provide a unique venue for the presentation of results and to facilitate interaction between software engineers and computational scientists, including those from the humanities, social sciences and engineering. To address this goal, we seek contributions from members of those communities that describe perspectives, research outcomes, and lessons learned (positive or negative) from the development of eScience software.