Work

Becksteinlab/zarrtraj: v0.2.3

Year: 2024

Type: software-paper

Abstract: Molecular dynamics (MD) simulations provide a microscope into the behavior of atomic-scale environments otherwise prohibitively difficult to observe. However, the resulting trajectory data are too often siloed in a single institutions' HPC environment, rendering it unusable by the broader scientific community. Additionally, it is increasingly common for trajectory data to be entirely stored in a cloud storage provider, rather than a traditional on-premise storage site. Zarrtraj enables these trajectories to be read directly from cloud storage providers like AWS, Google Cloud, and Microsoft Azure into MDAnalysis, a popular Python package for analyzing trajectory data, providing a method to open up access to trajectory data to anyone with an internet connection. Enabling cloud streaming for MD trajectories empowers easier replication of published analysis results, analyses of large, conglomerate datasets from different sources, and training machine learning models without downloading and storing trajectory data.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.13887976

Authors: Woods, Lawson, MacDermott-Opeskin, Hugo, Jakupovic, Edis, Zhuang, Yuxuan, Gowers, Richard, Beckstein, Oliver

Institutions: Arizona State University, Open Molecular Software Foundation, Stanford University

Language: English

Topic: Scientific Computing and Data Management

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

Open Access status: green

Funder: National Science Foundation

Award: Elements: Streaming Molecular Dynamics Simulation Trajectories for Direct Analysis: Applications to Sub-Picosecond Dynamics in Microsecond Simulations

OpenAlex ID: W6949560735

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