MDverse, shedding light on the dark matter of molecular dynamics simulations.

{"sci_version": "1.0", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39212001", "kind": "science-record", "revision": 1, "content_hash": "sha256:2960a62cd59c7b82029b769617af2113e512c2787cc843ecc57a4289b4673cea", "created_at": "2026-11-01T20:29:20Z", "updated_at": "2026-11-01T20:29:20Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "MDverse, shedding light on the dark matter of molecular dynamics simulations.", "doi": "10.7554/eLife.90061", "pmid": "39212001", "authors": ["Johanna K S Tiemann", "Magdalena Szczuka", "Lisa Bouarroudj", "Mohamed Oussaren", "Steven Garcia", "Rebecca J Howard", "Lucie Delemotte", "Erik Lindahl", "Marc Baaden", "Kresten Lindorff-Larsen", "Matthieu Chavent", "Pierre Poulain"], "journal": "eLife", "year": 2024, "abstract": "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 2000 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.", "claims": [{"id": "claim-data-access", "statement": "Data files produced from the data collection and processing are shared in Parquet format in Zenodo. They are freely available under the Creative Commons Attribution 4.0 International license (CC-BY). Python scripts to search and index MD files, and to download and parse .mdp and.gro files are open-source (under the AGPL-3.0 license), freely available on GitHub and archived in Software Heritage (Poulain et al., 2023). A detailed documentation is provided along the scripts to easily reproduce the data collection and processing. Jupyter notebooks used to analyze results and create the figures of this paper are open-source (under the BSD 3-Clause license), freely available on GitHub and archived in Software Heritage (Poulain, 2023). The code of the MDverse data explorer web application is open-source (under the BSD 3-Clause license), freely available on GitHub and archived in Software Heritage (Poulain and Oussaren, 2023).\nThe following dataset was generated:\nJohannaKST MathieuC PierreP 2023MDverse datasetsZenodo10.5281/zenodo.7856806", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39212001", "locator": "sections[35] \"Data availability\" paragraphs 1-4", "quote": "Data files produced from the data collection and processing are shared in Parquet format in Zenodo. They are freely available under the Creative Commons Attribution 4.0 International license (CC-BY). Python scripts to search and index MD files, and to download and parse .mdp and.gro files are open-source (under the AGPL-3.0 license), freely available on GitHub and archived in Software Heritage (Poulain et al., 2023). A detailed documentation is provided along the scripts to easily reproduce the data collection and processing. Jupyter notebooks used to analyze results and create the figures of this paper are open-source (under the BSD 3-Clause license), freely available on GitHub and archived in Software Heritage (Poulain, 2023). The code of the MDverse data explorer web application is open-source (under the BSD 3-Clause license), freely available on GitHub and archived in Software Heritage (Poulain and Oussaren, 2023).\nThe following dataset was generated:\nJohannaKST MathieuC PierreP 2023MDverse datasetsZenodo10.5281/zenodo.7856806"}], "state": "complete"}, {"id": "claim-target-result", "statement": "The authors found and indexed about 250,000 molecular dynamics files and 2000 datasets from Zenodo, Figshare and Open Science Framework.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39212001", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Leveraging an original search strategy, we found and indexed about 250,000 files and 2000 datasets from Zenodo, Figshare and Open Science Framework."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The authors found and indexed about 250,000 molecular dynamics files and 2000 datasets from Zenodo, Figshare and Open Science Framework.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:39212001"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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