Related papers: Metadata practices for simulation workflows
Reusable data/code and reproducible analyses are foundational to quality research. This aspect, however, is often overlooked when designing interactive stream analysis workflows for time-series data (e.g., eye-tracking data). A mechanism to…
This study demonstrates that the soft biological tissues of humans can be used as a type of soft body in physical reservoir computing. Soft biological tissues possess characteristics such as stress-strain nonlinearity and viscoelasticity…
Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, researchers often resort to simulation of the aggregated data…
Dataset distillation has emerged as a strategy to overcome the hurdles associated with large datasets by learning a compact set of synthetic data that retains essential information from the original dataset. While distilled data can be used…
Numerical algorithms and computational tools are instrumental in navigating and addressing complex simulation and data processing tasks. The exponential growth of metadata and parameter-driven simulations has led to an increasing demand for…
Process mining extends far beyond process discovery and conformance checking, and also provides techniques for bottleneck analysis and organizational mining. However, these techniques are mostly backward-looking. PMSD is a web application…
Knowledge is the most precious asset of humankind. People extract the experience from the data that provide for us the reality through the feelings. Generally speaking, it is possible to see the analogy of knowledge elaboration between…
AI-augmented data workflows introduce complex governance challenges, as both human and model-driven processes generate, transform, and consume data artifacts. These workflows blend heterogeneous tools, dynamic execution patterns, and opaque…
Monte Carlo simulation studies are at the core of the modern applied, computational, and theoretical statistical literature. Simulation is a broadly applicable research tool, used to collect data on the relative performance of methods or…
Software repositories is one of the sources of data in Empirical Software Engineering, primarily in the Mining Software Repositories field, aimed at extracting knowledge from the dynamics and practice of software projects. With the…
Workflows are critical for scientific discovery. However, the sophistication, heterogeneity, and scale of workflows make building, testing, and optimizing them increasingly challenging. Furthermore, their complexity and heterogeneity make…
Over the last two decades, the science has come a long way from relying on only physical experiments and observations to experimentation using computer simulators. This chapter focusses on the modelling and analysis of data arising from…
GitHub natively supports workflow automation through GitHub Actions. Yet, workflow maintenance is often considered a burden for software developers, who frequently face difficulties in writing, testing, debugging, and maintaining workflows.…
For large-scale scientific simulations, it is expensive to store raw simulation results to perform post-analysis. To minimize expensive I/O, "in-situ" analysis is often used, where analysis applications are tightly coupled with scientific…
Writing generic data processing pipelines requires that the algorithmic code does not ever have to know about data formats of files, or the locations of those files. At LSST we have a software system known as "the Data Butler," that…
Most real-world document collections involve various types of metadata, such as author, source, and date, and yet the most commonly-used approaches to modeling text corpora ignore this information. While specialized models have been…
Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world systems, saving on time and costs. Despite significant…
Data preparation, especially data cleaning, is very important to ensure data quality and to improve the output of automated decision systems. Since there is no single tool that covers all steps required, a combination of tools -- namely a…
Machine learning techniques are powerful tools for construction of emulators for complex systems. We explore different machine learning methods and conceptual methodologies, ranging from functional approximations to dynamical…
Knowledge distillation between machine learning models has opened many new avenues for parameter count reduction, performance improvements, or amortizing training time when changing architectures between the teacher and student network. In…