Python in Science Conferences

The increasing volume of research data in fields such as astronomy, biology, and engineering necessitates efficient distributed data management. This paper presents the Librarian, a custom framework designed for data transfer in large academic collaborations, designed for the Simons Observatory.

Water column sonar data collected by echosounders are essential for fisheries and marine ecosystem research, enabling the detection, classification, and quantification of fish and zooplankton from many different ocean observing platforms. We introduce Echostack, a suite of open-source Python software packages that leverage existing distributed computing and cloud-interfacing libraries to support intuitive and scalable data access, processing, and interpretation.

In recent years, leveraging satellite imagery with deep learning architectures has become an effective approach for environmental monitoring tasks, including forest wildfire detection. This paper presents a Python-based methodology for gathering and using a labeled high-resolution satellite imagery dataset for forest wildfire detection.

With the influx of large data from multiple instruments and experiments, scientists are wrangling complex data pipelines that are context-dependent and non-reproducible. Echodataflow provides transparent reproducible pipelines that can be edited with text "recipes", scaled and monitored.