
The rise of open science has led to the accumulation of molecular dynamics (MD) simulation data in generalist data repositories, constituting the "dark matter" of MD - data that is technically accessible but not indexed, curated, or easily searchable. This work presents a strategy to index and analyze this vast amount of publicly available MD data to shed light on current simulation practices and enable better data sharing and reuse.


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The rise of open science and the absence of a global dedicated data repository for molecular dynamics (MD) simulations has led to the accumulation of MD ﬁles in generalist data repositories, constituting the dark matter of MD — data that is technically accessible, but neither indexed, curated, or easily searchable. Leveraging an original search strategy, we found and indexed about 250,000 ﬁles and 2,000 datasets from Zenodo, Figshare and Open Science Framework. With a focus on ﬁles produced by the Gromacs MD software, we illustrate the potential offered by the mining of publicly available MD data. We identiﬁed systems with speciﬁc molecular composition and were able to characterize essential parameters of MD simulation such as temperature and simulation length, and could identify model resolution, such as all-atom and coarse-grain. Based on this analysis, we inferred metadata to propose a search engine prototype to explore the MD data. To continue in this direction, we call on the community to pursue the effort of sharing MD data, and to report and standardize metadata to reuse this valuable matter.

### Competing Interest Statement

The authors have declared no competing interest.

MDverse: Shedding Light on the Dark Matter of Molecular Dynamics Simulations

indexing-and-analyzing-the-dark-matter-of-molecular-dynamics-simulations-in-open-data-repositories


Indexing and Analyzing the Dark Matter of Molecular Dynamics Simulations in Open Data Repositories
