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Sharing Data from Molecular Simulations.

Mark Abraham1, Rossen Apostolov2, Jonathan Barnoud3

  • 1Science for Life Laboratory, Department of Applied Physics , KTH Royal Institute of Technology , Box 1031, SE-171 21 Solna , Sweden.

Journal of Chemical Information and Modeling
|September 19, 2019
PubMed
Summary
This summary is machine-generated.

Researchers need better standards for sharing molecular dynamics (MD) simulation data and workflows. This overview discusses workshop outcomes aimed at promoting open, reproducible scientific output in the MD field.

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Area of Science:

  • Computational chemistry and biophysics
  • Molecular dynamics simulations
  • Scientific reproducibility

Background:

  • Lack of standardized practices for sharing molecular dynamics (MD) simulation data and workflows hinders scientific reproducibility.
  • Diverse MD software packages offer limited interoperability, dictating specific simulation and analysis workflows.
  • Variability in documenting workflows and analysis code impedes the reuse of research findings.

Purpose of the Study:

  • To address the challenges in sharing and reusing MD simulation data.
  • To discuss best practices for open and reproducible scientific output in molecular dynamics.
  • To foster community conversation towards improved data and workflow sharing.

Main Methods:

  • Organized a workshop in November 2018 focused on sharing data from molecular simulations.
  • Presented an overview of the workshop and key topics discussed.
  • Facilitated community dialogue on best practices in molecular dynamics research.

Main Results:

  • Identified critical issues in current MD data and workflow sharing practices.
  • Highlighted the need for increased interoperability between different MD software packages.
  • Acknowledged the growing motivation among researchers to share data, despite existing challenges.

Conclusions:

  • Emphasized the importance of establishing standards for open and reproducible scientific output in MD simulations.
  • Called for further community engagement to develop solutions for effective data and workflow sharing.
  • Aimed to inspire future efforts towards more interoperable and reusable MD research outputs.